Namespace(arch='densenet161', batch_size=160, data='/scratch/bzhou/places365_standard', epochs=90, evaluate=False, lr=0.1, momentum=0.9, num_classes=365, pretrained=True, print_freq=10, resume='', start_epoch=0, weight_decay=0.0001, workers=4) => creating model 'densenet161' DataParallel ( (module): DenseNet ( (features): Sequential ( (conv0): Conv2d(3, 96, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False) (norm0): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True) (relu0): ReLU (inplace) (pool0): MaxPool2d (size=(3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1)) (denseblock1): _DenseBlock ( (denselayer1): _DenseLayer ( (norm.1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(96, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer2): _DenseLayer ( (norm.1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(144, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer3): _DenseLayer ( (norm.1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer4): _DenseLayer ( (norm.1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(240, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer5): _DenseLayer ( (norm.1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(288, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer6): _DenseLayer ( (norm.1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(336, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (transition1): _Transition ( (norm): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True) (relu): ReLU (inplace) (conv): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (pool): AvgPool2d (size=2, stride=2, padding=0, ceil_mode=False, count_include_pad=True) ) (denseblock2): _DenseBlock ( (denselayer1): _DenseLayer ( (norm.1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer2): _DenseLayer ( (norm.1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(240, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer3): _DenseLayer ( (norm.1): BatchNorm2d(288, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(288, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer4): _DenseLayer ( (norm.1): BatchNorm2d(336, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(336, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer5): _DenseLayer ( (norm.1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer6): _DenseLayer ( (norm.1): BatchNorm2d(432, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(432, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer7): _DenseLayer ( (norm.1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer8): _DenseLayer ( (norm.1): BatchNorm2d(528, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(528, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer9): _DenseLayer ( (norm.1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer10): _DenseLayer ( (norm.1): BatchNorm2d(624, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(624, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer11): _DenseLayer ( (norm.1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer12): _DenseLayer ( (norm.1): BatchNorm2d(720, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(720, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (transition2): _Transition ( (norm): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True) (relu): ReLU (inplace) (conv): Conv2d(768, 384, kernel_size=(1, 1), stride=(1, 1), bias=False) (pool): AvgPool2d (size=2, stride=2, padding=0, ceil_mode=False, count_include_pad=True) ) (denseblock3): _DenseBlock ( (denselayer1): _DenseLayer ( (norm.1): BatchNorm2d(384, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer2): _DenseLayer ( (norm.1): BatchNorm2d(432, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(432, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer3): _DenseLayer ( (norm.1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(480, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer4): _DenseLayer ( (norm.1): BatchNorm2d(528, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(528, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer5): _DenseLayer ( (norm.1): BatchNorm2d(576, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(576, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer6): _DenseLayer ( (norm.1): BatchNorm2d(624, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(624, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer7): _DenseLayer ( (norm.1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer8): _DenseLayer ( (norm.1): BatchNorm2d(720, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(720, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer9): _DenseLayer ( (norm.1): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(768, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer10): _DenseLayer ( (norm.1): BatchNorm2d(816, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(816, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer11): _DenseLayer ( (norm.1): BatchNorm2d(864, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(864, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer12): _DenseLayer ( (norm.1): BatchNorm2d(912, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(912, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer13): _DenseLayer ( (norm.1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(960, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer14): _DenseLayer ( (norm.1): BatchNorm2d(1008, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1008, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer15): _DenseLayer ( (norm.1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1056, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer16): _DenseLayer ( (norm.1): BatchNorm2d(1104, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1104, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer17): _DenseLayer ( (norm.1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer18): _DenseLayer ( (norm.1): BatchNorm2d(1200, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1200, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer19): _DenseLayer ( (norm.1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1248, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer20): _DenseLayer ( (norm.1): BatchNorm2d(1296, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1296, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer21): _DenseLayer ( (norm.1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1344, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer22): _DenseLayer ( (norm.1): BatchNorm2d(1392, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1392, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer23): _DenseLayer ( (norm.1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1440, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer24): _DenseLayer ( (norm.1): BatchNorm2d(1488, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1488, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer25): _DenseLayer ( (norm.1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1536, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer26): _DenseLayer ( (norm.1): BatchNorm2d(1584, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1584, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer27): _DenseLayer ( (norm.1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1632, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer28): _DenseLayer ( (norm.1): BatchNorm2d(1680, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1680, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer29): _DenseLayer ( (norm.1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1728, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer30): _DenseLayer ( (norm.1): BatchNorm2d(1776, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1776, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer31): _DenseLayer ( (norm.1): BatchNorm2d(1824, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1824, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer32): _DenseLayer ( (norm.1): BatchNorm2d(1872, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1872, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer33): _DenseLayer ( (norm.1): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1920, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer34): _DenseLayer ( (norm.1): BatchNorm2d(1968, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1968, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer35): _DenseLayer ( (norm.1): BatchNorm2d(2016, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(2016, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer36): _DenseLayer ( (norm.1): BatchNorm2d(2064, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(2064, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (transition3): _Transition ( (norm): BatchNorm2d(2112, eps=1e-05, momentum=0.1, affine=True) (relu): ReLU (inplace) (conv): Conv2d(2112, 1056, kernel_size=(1, 1), stride=(1, 1), bias=False) (pool): AvgPool2d (size=2, stride=2, padding=0, ceil_mode=False, count_include_pad=True) ) (denseblock4): _DenseBlock ( (denselayer1): _DenseLayer ( (norm.1): BatchNorm2d(1056, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1056, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer2): _DenseLayer ( (norm.1): BatchNorm2d(1104, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1104, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer3): _DenseLayer ( (norm.1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer4): _DenseLayer ( (norm.1): BatchNorm2d(1200, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1200, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer5): _DenseLayer ( (norm.1): BatchNorm2d(1248, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1248, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer6): _DenseLayer ( (norm.1): BatchNorm2d(1296, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1296, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer7): _DenseLayer ( (norm.1): BatchNorm2d(1344, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1344, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer8): _DenseLayer ( (norm.1): BatchNorm2d(1392, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1392, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer9): _DenseLayer ( (norm.1): BatchNorm2d(1440, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1440, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer10): _DenseLayer ( (norm.1): BatchNorm2d(1488, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1488, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer11): _DenseLayer ( (norm.1): BatchNorm2d(1536, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1536, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer12): _DenseLayer ( (norm.1): BatchNorm2d(1584, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1584, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer13): _DenseLayer ( (norm.1): BatchNorm2d(1632, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1632, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer14): _DenseLayer ( (norm.1): BatchNorm2d(1680, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1680, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer15): _DenseLayer ( (norm.1): BatchNorm2d(1728, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1728, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer16): _DenseLayer ( (norm.1): BatchNorm2d(1776, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1776, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer17): _DenseLayer ( (norm.1): BatchNorm2d(1824, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1824, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer18): _DenseLayer ( (norm.1): BatchNorm2d(1872, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1872, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer19): _DenseLayer ( (norm.1): BatchNorm2d(1920, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1920, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer20): _DenseLayer ( (norm.1): BatchNorm2d(1968, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(1968, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer21): _DenseLayer ( (norm.1): BatchNorm2d(2016, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(2016, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer22): _DenseLayer ( (norm.1): BatchNorm2d(2064, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(2064, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer23): _DenseLayer ( (norm.1): BatchNorm2d(2112, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(2112, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) (denselayer24): _DenseLayer ( (norm.1): BatchNorm2d(2160, eps=1e-05, momentum=0.1, affine=True) (relu.1): ReLU (inplace) (conv.1): Conv2d(2160, 192, kernel_size=(1, 1), stride=(1, 1), bias=False) (norm.2): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True) (relu.2): ReLU (inplace) (conv.2): Conv2d(192, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (norm5): BatchNorm2d(2208, eps=1e-05, momentum=0.1, affine=True) ) (classifier): Linear (2208 -> 365) ) ) Epoch: [0][0/11272] Time 33.512 (33.512) Data 1.947 (1.947) Loss 5.9147 (5.9147) Prec@1 0.000 (0.000) Prec@5 0.625 (0.625) Epoch: [0][10/11272] Time 0.737 (3.903) Data 0.001 (0.178) Loss 6.0416 (5.9456) Prec@1 0.625 (0.455) Prec@5 1.875 (1.648) Epoch: [0][20/11272] Time 0.737 (2.430) Data 0.002 (0.094) Loss 5.8995 (5.9574) Prec@1 1.250 (0.387) Prec@5 4.375 (2.232) Epoch: [0][30/11272] Time 0.912 (1.913) Data 0.002 (0.064) Loss 5.9684 (5.9423) Prec@1 0.625 (0.544) Prec@5 1.250 (2.581) Epoch: [0][40/11272] Time 0.908 (1.647) Data 0.002 (0.049) Loss 5.8034 (5.9103) Prec@1 1.250 (0.655) Prec@5 7.500 (2.896) Epoch: [0][50/11272] Time 0.768 (1.484) Data 0.002 (0.040) Loss 5.7625 (5.8808) Prec@1 1.250 (0.735) Prec@5 5.625 (3.321) Epoch: [0][60/11272] Time 0.758 (1.374) Data 0.002 (0.034) Loss 5.6609 (5.8464) Prec@1 0.000 (0.809) Prec@5 4.375 (3.637) Epoch: [0][70/11272] Time 0.852 (1.299) Data 0.002 (0.029) Loss 5.6186 (5.8202) Prec@1 0.000 (0.863) Prec@5 3.750 (3.750) Epoch: [0][80/11272] Time 0.751 (1.237) Data 0.003 (0.026) Loss 5.5969 (5.7980) Prec@1 2.500 (0.918) Prec@5 8.125 (3.958) Epoch: [0][90/11272] Time 0.750 (1.192) Data 0.002 (0.023) Loss 5.5605 (5.7758) Prec@1 0.000 (0.934) Prec@5 5.000 (4.231) Epoch: [0][100/11272] Time 0.868 (1.155) Data 0.001 (0.021) Loss 5.5809 (5.7595) Prec@1 1.875 (0.965) Prec@5 4.375 (4.307) Epoch: [0][110/11272] Time 0.853 (1.124) Data 0.002 (0.019) Loss 5.4806 (5.7360) Prec@1 3.125 (1.030) Prec@5 8.750 (4.662) Epoch: [0][120/11272] Time 0.799 (1.099) Data 0.002 (0.018) Loss 5.6532 (5.7232) Prec@1 1.250 (1.064) Prec@5 4.375 (4.721) Epoch: [0][130/11272] Time 0.737 (1.078) Data 0.001 (0.017) Loss 5.6695 (5.7127) Prec@1 0.625 (1.093) Prec@5 4.375 (4.776) Epoch: [0][140/11272] Time 0.894 (1.062) Data 0.002 (0.015) Loss 5.5720 (5.6989) Prec@1 1.875 (1.113) Prec@5 6.875 (4.951) Epoch: [0][150/11272] Time 0.873 (1.046) Data 0.001 (0.015) Loss 5.5416 (5.6838) Prec@1 2.500 (1.147) Prec@5 9.375 (5.091) Epoch: [0][160/11272] Time 0.752 (1.032) Data 0.002 (0.014) Loss 5.5118 (5.6707) Prec@1 1.875 (1.188) Prec@5 7.500 (5.268) Epoch: [0][170/11272] Time 0.773 (1.020) Data 0.002 (0.013) Loss 5.4476 (5.6576) Prec@1 0.625 (1.246) Prec@5 4.375 (5.347) Epoch: [0][180/11272] Time 0.838 (1.009) Data 0.001 (0.012) Loss 5.3092 (5.6441) Prec@1 5.000 (1.302) Prec@5 11.250 (5.563) Epoch: [0][190/11272] Time 0.852 (0.999) Data 0.002 (0.012) Loss 5.3290 (5.6309) Prec@1 3.125 (1.342) Prec@5 12.500 (5.762) Epoch: [0][200/11272] Time 0.764 (0.990) Data 0.002 (0.011) Loss 5.3657 (5.6187) Prec@1 2.500 (1.365) Prec@5 9.375 (5.899) Epoch: [0][210/11272] Time 0.903 (0.983) Data 0.003 (0.011) Loss 5.4243 (5.6086) Prec@1 1.875 (1.431) Prec@5 8.125 (6.001) Epoch: [0][220/11272] Time 0.926 (0.977) Data 0.002 (0.010) Loss 5.3622 (5.5980) Prec@1 3.125 (1.490) Prec@5 7.500 (6.126) Epoch: [0][230/11272] Time 0.739 (0.970) Data 0.002 (0.010) Loss 5.3187 (5.5884) Prec@1 1.875 (1.537) Prec@5 9.375 (6.231) Epoch: [0][240/11272] Time 0.796 (0.965) Data 0.001 (0.010) Loss 5.2905 (5.5781) Prec@1 1.875 (1.533) Prec@5 10.625 (6.362) Epoch: [0][250/11272] Time 0.969 (0.960) Data 0.002 (0.009) Loss 5.3121 (5.5683) Prec@1 4.375 (1.619) Prec@5 13.750 (6.529) Epoch: [0][260/11272] Time 0.908 (0.955) Data 0.002 (0.009) Loss 5.2495 (5.5562) Prec@1 2.500 (1.667) Prec@5 11.250 (6.657) Epoch: [0][270/11272] Time 0.742 (0.950) Data 0.001 (0.009) Loss 5.2408 (5.5468) Prec@1 3.750 (1.681) Prec@5 11.250 (6.760) Epoch: [0][280/11272] Time 0.770 (0.945) Data 0.002 (0.009) Loss 5.3319 (5.5386) Prec@1 4.375 (1.717) Prec@5 10.000 (6.871) Epoch: [0][290/11272] Time 0.846 (0.942) Data 0.001 (0.008) Loss 5.4818 (5.5299) Prec@1 2.500 (1.759) Prec@5 5.625 (6.982) Epoch: [0][300/11272] Time 0.846 (0.937) Data 0.001 (0.008) Loss 5.2525 (5.5196) Prec@1 1.250 (1.802) Prec@5 9.375 (7.145) Epoch: [0][310/11272] Time 0.748 (0.933) Data 0.002 (0.008) Loss 5.2251 (5.5110) Prec@1 1.250 (1.831) Prec@5 7.500 (7.245) Epoch: [0][320/11272] Time 0.777 (0.930) Data 0.002 (0.008) Loss 5.3822 (5.5027) Prec@1 3.125 (1.859) Prec@5 13.125 (7.371) Epoch: [0][330/11272] Time 0.923 (0.927) Data 0.003 (0.008) Loss 5.3591 (5.4950) Prec@1 1.875 (1.888) Prec@5 10.000 (7.489) Epoch: [0][340/11272] Time 0.900 (0.924) Data 0.003 (0.007) Loss 5.2305 (5.4869) Prec@1 2.500 (1.932) Prec@5 11.250 (7.621) Epoch: [0][350/11272] Time 0.741 (0.921) Data 0.001 (0.007) Loss 5.3709 (5.4802) Prec@1 3.125 (1.962) Prec@5 11.250 (7.701) Epoch: [0][360/11272] Time 0.864 (0.918) Data 0.002 (0.007) Loss 5.1492 (5.4710) Prec@1 1.875 (1.998) Prec@5 13.125 (7.843) Epoch: [0][370/11272] Time 0.838 (0.915) Data 0.001 (0.007) Loss 5.1182 (5.4639) Prec@1 1.875 (2.035) Prec@5 11.250 (7.948) Epoch: [0][380/11272] Time 0.831 (0.913) Data 0.002 (0.007) Loss 5.1722 (5.4571) Prec@1 4.375 (2.069) Prec@5 12.500 (8.063) Epoch: [0][390/11272] Time 0.753 (0.911) Data 0.001 (0.007) Loss 5.2581 (5.4481) Prec@1 1.875 (2.120) Prec@5 11.250 (8.221) Epoch: [0][400/11272] Time 0.855 (0.909) Data 0.002 (0.007) Loss 5.1679 (5.4395) Prec@1 4.375 (2.166) Prec@5 11.875 (8.326) Epoch: [0][410/11272] Time 0.912 (0.907) Data 0.002 (0.006) Loss 5.1445 (5.4333) Prec@1 1.875 (2.197) Prec@5 7.500 (8.415) Epoch: [0][420/11272] Time 0.760 (0.905) Data 0.002 (0.006) Loss 5.3079 (5.4255) Prec@1 2.500 (2.230) Prec@5 11.250 (8.545) Epoch: [0][430/11272] Time 0.739 (0.903) Data 0.001 (0.006) Loss 5.1255 (5.4185) Prec@1 4.375 (2.256) Prec@5 13.750 (8.643) Epoch: [0][440/11272] Time 0.906 (0.902) Data 0.002 (0.006) Loss 4.9627 (5.4111) Prec@1 1.875 (2.303) Prec@5 12.500 (8.740) Epoch: [0][450/11272] Time 0.950 (0.901) Data 0.002 (0.006) Loss 5.0029 (5.4023) Prec@1 6.250 (2.361) Prec@5 18.125 (8.900) Epoch: [0][460/11272] Time 0.764 (0.899) Data 0.001 (0.006) Loss 5.1096 (5.3953) Prec@1 8.125 (2.400) Prec@5 13.125 (9.002) Epoch: [0][470/11272] Time 0.789 (0.898) Data 0.001 (0.006) Loss 5.1392 (5.3897) Prec@1 4.375 (2.428) Prec@5 15.625 (9.092) Epoch: [0][480/11272] Time 1.004 (0.897) Data 0.001 (0.006) Loss 5.0060 (5.3823) Prec@1 4.375 (2.469) Prec@5 14.375 (9.209) Epoch: [0][490/11272] Time 0.733 (0.895) Data 0.001 (0.006) Loss 4.9108 (5.3745) Prec@1 3.750 (2.501) Prec@5 19.375 (9.319) Epoch: [0][500/11272] Time 0.763 (0.894) Data 0.002 (0.006) Loss 5.0333 (5.3684) Prec@1 6.875 (2.547) Prec@5 16.875 (9.416) Epoch: [0][510/11272] Time 0.901 (0.893) Data 0.002 (0.005) Loss 5.0500 (5.3607) Prec@1 3.125 (2.591) Prec@5 13.125 (9.535) Epoch: [0][520/11272] Time 0.880 (0.891) Data 0.001 (0.005) Loss 5.0015 (5.3531) Prec@1 4.375 (2.624) Prec@5 12.500 (9.646) Epoch: [0][530/11272] Time 0.803 (0.891) Data 0.002 (0.005) Loss 5.0350 (5.3462) Prec@1 5.625 (2.661) Prec@5 14.375 (9.761) Epoch: [0][540/11272] Time 0.760 (0.890) Data 0.001 (0.005) Loss 5.0332 (5.3400) Prec@1 4.375 (2.680) Prec@5 16.875 (9.866) Epoch: [0][550/11272] Time 0.878 (0.889) Data 0.002 (0.005) Loss 5.0003 (5.3335) Prec@1 2.500 (2.723) Prec@5 13.750 (9.961) Epoch: [0][560/11272] Time 0.876 (0.887) Data 0.004 (0.005) Loss 4.9688 (5.3277) Prec@1 3.125 (2.757) Prec@5 15.625 (10.047) Epoch: [0][570/11272] Time 0.748 (0.886) Data 0.002 (0.005) Loss 4.9960 (5.3210) Prec@1 3.125 (2.793) Prec@5 15.000 (10.155) Epoch: [0][580/11272] Time 0.771 (0.885) Data 0.002 (0.005) Loss 4.9737 (5.3151) Prec@1 3.750 (2.813) Prec@5 16.875 (10.247) Epoch: [0][590/11272] Time 0.861 (0.884) Data 0.002 (0.005) Loss 4.9512 (5.3088) Prec@1 4.375 (2.850) Prec@5 15.625 (10.353) Epoch: [0][600/11272] Time 0.931 (0.883) Data 0.002 (0.005) Loss 4.7910 (5.3012) Prec@1 3.125 (2.891) Prec@5 21.250 (10.490) Epoch: [0][610/11272] Time 0.749 (0.882) Data 0.002 (0.005) Loss 4.9380 (5.2953) Prec@1 5.000 (2.918) Prec@5 15.625 (10.596) Epoch: [0][620/11272] Time 0.906 (0.882) Data 0.001 (0.005) Loss 4.7540 (5.2887) Prec@1 6.875 (2.944) Prec@5 18.750 (10.712) Epoch: [0][630/11272] Time 0.878 (0.881) Data 0.001 (0.005) Loss 5.0143 (5.2819) Prec@1 2.500 (2.974) Prec@5 13.125 (10.840) Epoch: [0][640/11272] Time 0.789 (0.880) Data 0.001 (0.005) Loss 4.7451 (5.2747) Prec@1 6.250 (3.017) Prec@5 20.625 (10.974) Epoch: [0][650/11272] Time 0.772 (0.879) Data 0.002 (0.005) Loss 4.8062 (5.2683) Prec@1 1.875 (3.046) Prec@5 16.250 (11.073) Epoch: [0][660/11272] Time 0.884 (0.879) Data 0.001 (0.005) Loss 4.9590 (5.2628) Prec@1 3.750 (3.083) Prec@5 15.000 (11.159) Epoch: [0][670/11272] Time 0.892 (0.878) Data 0.002 (0.005) Loss 4.9025 (5.2570) Prec@1 5.625 (3.126) Prec@5 21.875 (11.271) Epoch: [0][680/11272] Time 0.732 (0.877) Data 0.002 (0.005) Loss 4.8379 (5.2516) Prec@1 4.375 (3.151) Prec@5 20.625 (11.363) Epoch: [0][690/11272] Time 0.738 (0.876) Data 0.001 (0.005) Loss 4.7858 (5.2455) Prec@1 6.875 (3.205) Prec@5 21.250 (11.471) Epoch: [0][700/11272] Time 0.879 (0.875) Data 0.002 (0.004) Loss 4.8151 (5.2391) Prec@1 7.500 (3.248) Prec@5 18.750 (11.575) Epoch: [0][710/11272] Time 0.950 (0.875) Data 0.002 (0.004) Loss 4.7738 (5.2337) Prec@1 5.625 (3.292) Prec@5 17.500 (11.672) Epoch: [0][720/11272] Time 0.754 (0.874) Data 0.001 (0.004) Loss 4.7611 (5.2281) Prec@1 2.500 (3.305) Prec@5 18.125 (11.762) Epoch: [0][730/11272] Time 0.752 (0.873) Data 0.002 (0.004) Loss 4.6382 (5.2212) Prec@1 6.875 (3.335) Prec@5 18.125 (11.873) Epoch: [0][740/11272] Time 0.951 (0.873) Data 0.002 (0.004) Loss 4.8049 (5.2139) Prec@1 6.875 (3.390) Prec@5 20.625 (12.008) Epoch: [0][750/11272] Time 0.782 (0.872) Data 0.004 (0.004) Loss 4.8423 (5.2080) Prec@1 7.500 (3.427) Prec@5 20.625 (12.114) Epoch: [0][760/11272] Time 0.734 (0.871) Data 0.002 (0.004) Loss 4.8013 (5.2028) Prec@1 6.250 (3.450) Prec@5 21.875 (12.197) Epoch: [0][770/11272] Time 0.934 (0.870) Data 0.002 (0.004) Loss 4.7186 (5.1971) Prec@1 6.250 (3.490) Prec@5 20.000 (12.310) Epoch: [0][780/11272] Time 0.912 (0.870) Data 0.001 (0.004) Loss 4.8723 (5.1918) Prec@1 7.500 (3.526) Prec@5 23.750 (12.421) Epoch: [0][790/11272] Time 0.789 (0.870) Data 0.004 (0.004) Loss 4.6945 (5.1861) Prec@1 6.250 (3.567) Prec@5 23.750 (12.524) Epoch: [0][800/11272] Time 0.751 (0.869) Data 0.002 (0.004) Loss 4.7088 (5.1814) Prec@1 6.875 (3.596) Prec@5 18.125 (12.608) Epoch: [0][810/11272] Time 0.901 (0.869) Data 0.002 (0.004) Loss 4.5779 (5.1754) Prec@1 5.625 (3.624) Prec@5 25.000 (12.707) Epoch: [0][820/11272] Time 0.928 (0.869) Data 0.001 (0.004) Loss 4.5436 (5.1695) Prec@1 6.875 (3.651) Prec@5 25.625 (12.818) Epoch: [0][830/11272] Time 0.740 (0.868) Data 0.002 (0.004) Loss 4.7554 (5.1643) Prec@1 5.625 (3.686) Prec@5 20.625 (12.926) Epoch: [0][840/11272] Time 0.746 (0.867) Data 0.001 (0.004) Loss 4.5150 (5.1588) Prec@1 11.250 (3.727) Prec@5 26.875 (13.040) Epoch: [0][850/11272] Time 0.927 (0.867) Data 0.002 (0.004) Loss 4.6472 (5.1531) Prec@1 8.125 (3.755) Prec@5 23.125 (13.137) Epoch: [0][860/11272] Time 0.872 (0.866) Data 0.002 (0.004) Loss 4.7232 (5.1477) Prec@1 9.375 (3.799) Prec@5 18.750 (13.233) Epoch: [0][870/11272] Time 0.808 (0.866) Data 0.002 (0.004) Loss 4.7753 (5.1422) Prec@1 8.125 (3.835) Prec@5 21.875 (13.340) Epoch: [0][880/11272] Time 0.900 (0.865) Data 0.002 (0.004) Loss 4.6244 (5.1381) Prec@1 8.125 (3.859) Prec@5 24.375 (13.427) Epoch: [0][890/11272] Time 0.880 (0.865) Data 0.002 (0.004) Loss 4.6959 (5.1327) Prec@1 6.250 (3.893) Prec@5 23.125 (13.521) Epoch: [0][900/11272] Time 0.782 (0.865) Data 0.002 (0.004) Loss 4.6913 (5.1273) Prec@1 6.250 (3.935) Prec@5 20.000 (13.618) Epoch: [0][910/11272] Time 0.772 (0.864) Data 0.002 (0.004) Loss 4.5822 (5.1225) Prec@1 8.125 (3.965) Prec@5 22.500 (13.707) Epoch: [0][920/11272] Time 0.854 (0.864) Data 0.001 (0.004) Loss 4.6671 (5.1183) Prec@1 6.875 (3.996) Prec@5 24.375 (13.780) Epoch: [0][930/11272] Time 0.848 (0.864) Data 0.001 (0.004) Loss 4.8750 (5.1134) Prec@1 6.250 (4.035) Prec@5 19.375 (13.873) Epoch: [0][940/11272] Time 0.734 (0.863) Data 0.002 (0.004) Loss 4.6647 (5.1084) Prec@1 3.750 (4.065) Prec@5 18.125 (13.957) Epoch: [0][950/11272] Time 0.736 (0.863) Data 0.002 (0.004) Loss 4.7148 (5.1041) Prec@1 5.000 (4.093) Prec@5 16.250 (14.037) Epoch: [0][960/11272] Time 0.897 (0.863) Data 0.002 (0.004) Loss 4.5698 (5.0987) Prec@1 8.125 (4.139) Prec@5 23.750 (14.142) Epoch: [0][970/11272] Time 0.878 (0.862) Data 0.002 (0.004) Loss 4.6617 (5.0936) Prec@1 6.250 (4.175) Prec@5 15.625 (14.234) Epoch: [0][980/11272] Time 0.785 (0.862) Data 0.002 (0.004) Loss 4.5401 (5.0887) Prec@1 6.875 (4.204) Prec@5 21.875 (14.320) Epoch: [0][990/11272] Time 0.754 (0.862) Data 0.002 (0.004) Loss 4.5774 (5.0832) Prec@1 9.375 (4.249) Prec@5 24.375 (14.415) Epoch: [0][1000/11272] Time 0.879 (0.861) Data 0.002 (0.004) Loss 4.7544 (5.0785) Prec@1 3.125 (4.281) Prec@5 20.000 (14.500) Epoch: [0][1010/11272] Time 0.742 (0.861) Data 0.004 (0.004) Loss 4.5691 (5.0737) Prec@1 7.500 (4.323) Prec@5 22.500 (14.593) Epoch: [0][1020/11272] Time 0.777 (0.861) Data 0.002 (0.004) Loss 4.7449 (5.0693) Prec@1 7.500 (4.357) Prec@5 21.875 (14.676) Epoch: [0][1030/11272] Time 0.906 (0.860) Data 0.002 (0.004) Loss 4.5542 (5.0646) Prec@1 7.500 (4.390) Prec@5 23.750 (14.761) Epoch: [0][1040/11272] Time 0.862 (0.860) Data 0.002 (0.004) Loss 4.4883 (5.0597) Prec@1 7.500 (4.415) Prec@5 25.625 (14.852) Epoch: [0][1050/11272] Time 0.790 (0.860) Data 0.002 (0.004) Loss 4.6135 (5.0556) Prec@1 5.625 (4.449) Prec@5 22.500 (14.924) Epoch: [0][1060/11272] Time 0.778 (0.859) Data 0.002 (0.004) Loss 4.5209 (5.0514) Prec@1 6.875 (4.487) Prec@5 25.000 (15.021) Epoch: [0][1070/11272] Time 0.918 (0.859) Data 0.002 (0.004) Loss 4.4300 (5.0467) Prec@1 6.875 (4.519) Prec@5 23.750 (15.109) Epoch: [0][1080/11272] Time 0.879 (0.859) Data 0.002 (0.003) Loss 4.4134 (5.0418) Prec@1 8.750 (4.560) Prec@5 25.000 (15.204) Epoch: [0][1090/11272] Time 0.744 (0.858) Data 0.001 (0.003) Loss 4.4540 (5.0371) Prec@1 8.750 (4.599) Prec@5 26.875 (15.305) Epoch: [0][1100/11272] Time 0.759 (0.858) Data 0.001 (0.003) Loss 4.5926 (5.0329) Prec@1 8.750 (4.634) Prec@5 26.875 (15.401) Epoch: [0][1110/11272] Time 0.848 (0.858) Data 0.001 (0.003) Loss 4.5535 (5.0280) Prec@1 6.875 (4.663) Prec@5 22.500 (15.495) Epoch: [0][1120/11272] Time 0.897 (0.857) Data 0.001 (0.003) Loss 4.3353 (5.0239) Prec@1 9.375 (4.700) Prec@5 26.875 (15.576) Epoch: [0][1130/11272] Time 0.792 (0.857) Data 0.002 (0.003) Loss 4.4334 (5.0194) Prec@1 7.500 (4.724) Prec@5 28.125 (15.664) Epoch: [0][1140/11272] Time 0.894 (0.857) Data 0.002 (0.003) Loss 4.4634 (5.0150) Prec@1 9.375 (4.767) Prec@5 25.625 (15.755) Epoch: [0][1150/11272] Time 0.891 (0.856) Data 0.002 (0.003) Loss 4.5060 (5.0100) Prec@1 8.125 (4.814) Prec@5 23.125 (15.858) Epoch: [0][1160/11272] Time 0.718 (0.856) Data 0.001 (0.003) Loss 4.3633 (5.0052) Prec@1 14.375 (4.855) Prec@5 31.875 (15.960) Epoch: [0][1170/11272] Time 0.749 (0.856) Data 0.002 (0.003) Loss 4.4194 (5.0009) Prec@1 8.750 (4.877) Prec@5 21.875 (16.040) Epoch: [0][1180/11272] Time 0.880 (0.856) Data 0.001 (0.003) Loss 4.6783 (4.9967) Prec@1 5.625 (4.901) Prec@5 21.875 (16.118) Epoch: [0][1190/11272] Time 0.904 (0.855) Data 0.002 (0.003) Loss 4.5811 (4.9921) Prec@1 11.875 (4.934) Prec@5 28.125 (16.206) Epoch: [0][1200/11272] Time 0.736 (0.855) Data 0.001 (0.003) Loss 4.6588 (4.9879) Prec@1 6.250 (4.960) Prec@5 19.375 (16.293) Epoch: [0][1210/11272] Time 0.769 (0.855) Data 0.001 (0.003) Loss 4.4180 (4.9834) Prec@1 12.500 (4.998) Prec@5 29.375 (16.385) Epoch: [0][1220/11272] Time 0.886 (0.854) Data 0.002 (0.003) Loss 4.6519 (4.9797) Prec@1 6.250 (5.033) Prec@5 21.875 (16.458) Epoch: [0][1230/11272] Time 0.897 (0.854) Data 0.002 (0.003) Loss 4.4220 (4.9756) Prec@1 10.625 (5.062) Prec@5 28.750 (16.529) Epoch: [0][1240/11272] Time 0.771 (0.854) Data 0.001 (0.003) Loss 4.7558 (4.9713) Prec@1 5.000 (5.084) Prec@5 15.000 (16.603) Epoch: [0][1250/11272] Time 0.743 (0.854) Data 0.001 (0.003) Loss 4.4660 (4.9672) Prec@1 8.125 (5.114) Prec@5 28.125 (16.682) Epoch: [0][1260/11272] Time 0.874 (0.853) Data 0.002 (0.003) Loss 4.8222 (4.9634) Prec@1 6.250 (5.140) Prec@5 20.625 (16.755) Epoch: [0][1270/11272] Time 0.813 (0.853) Data 0.001 (0.003) Loss 4.3229 (4.9595) Prec@1 6.875 (5.165) Prec@5 28.125 (16.837) Epoch: [0][1280/11272] Time 0.814 (0.853) Data 0.002 (0.003) Loss 4.6252 (4.9556) Prec@1 6.875 (5.192) Prec@5 26.875 (16.905) Epoch: [0][1290/11272] Time 0.941 (0.853) Data 0.002 (0.003) Loss 4.4507 (4.9513) Prec@1 8.750 (5.228) Prec@5 29.375 (16.997) Epoch: [0][1300/11272] Time 0.926 (0.853) Data 0.002 (0.003) Loss 4.3571 (4.9474) Prec@1 13.125 (5.251) Prec@5 33.125 (17.078) Epoch: [0][1310/11272] Time 0.747 (0.852) Data 0.001 (0.003) Loss 4.4425 (4.9435) Prec@1 10.000 (5.279) Prec@5 26.250 (17.153) Epoch: [0][1320/11272] Time 0.803 (0.852) Data 0.001 (0.003) Loss 4.3183 (4.9390) Prec@1 8.750 (5.309) Prec@5 30.000 (17.234) Epoch: [0][1330/11272] Time 0.880 (0.852) Data 0.002 (0.003) Loss 4.3324 (4.9353) Prec@1 11.875 (5.331) Prec@5 29.375 (17.303) Epoch: [0][1340/11272] Time 0.859 (0.852) Data 0.002 (0.003) Loss 4.5487 (4.9311) Prec@1 10.000 (5.372) Prec@5 25.625 (17.394) Epoch: [0][1350/11272] Time 0.742 (0.851) Data 0.001 (0.003) Loss 4.4934 (4.9274) Prec@1 7.500 (5.397) Prec@5 26.875 (17.467) Epoch: [0][1360/11272] Time 0.743 (0.851) Data 0.001 (0.003) Loss 4.3465 (4.9234) Prec@1 17.500 (5.427) Prec@5 31.250 (17.551) Epoch: [0][1370/11272] Time 0.813 (0.851) Data 0.001 (0.003) Loss 4.5758 (4.9194) Prec@1 11.250 (5.471) Prec@5 27.500 (17.639) Epoch: [0][1380/11272] Time 0.880 (0.851) Data 0.001 (0.003) Loss 4.5531 (4.9158) Prec@1 10.000 (5.498) Prec@5 22.500 (17.707) Epoch: [0][1390/11272] Time 0.758 (0.851) Data 0.001 (0.003) Loss 4.5329 (4.9120) Prec@1 9.375 (5.533) Prec@5 26.875 (17.784) Epoch: [0][1400/11272] Time 0.723 (0.851) Data 0.001 (0.003) Loss 4.4098 (4.9081) Prec@1 8.750 (5.569) Prec@5 30.625 (17.873) Epoch: [0][1410/11272] Time 0.901 (0.850) Data 0.001 (0.003) Loss 4.3374 (4.9040) Prec@1 10.000 (5.607) Prec@5 25.625 (17.954) Epoch: [0][1420/11272] Time 0.739 (0.850) Data 0.001 (0.003) Loss 4.3233 (4.9001) Prec@1 10.625 (5.641) Prec@5 31.250 (18.041) Epoch: [0][1430/11272] Time 0.767 (0.850) Data 0.001 (0.003) Loss 4.3882 (4.8963) Prec@1 10.000 (5.681) Prec@5 27.500 (18.115) Epoch: [0][1440/11272] Time 0.910 (0.850) Data 0.002 (0.003) Loss 4.3685 (4.8923) Prec@1 8.750 (5.714) Prec@5 31.250 (18.206) Epoch: [0][1450/11272] Time 0.904 (0.850) Data 0.005 (0.003) Loss 4.3870 (4.8884) Prec@1 10.000 (5.750) Prec@5 31.250 (18.280) Epoch: [0][1460/11272] Time 0.755 (0.850) Data 0.001 (0.003) Loss 4.4925 (4.8847) Prec@1 13.750 (5.792) Prec@5 26.250 (18.358) Epoch: [0][1470/11272] Time 0.773 (0.850) Data 0.002 (0.003) Loss 4.2067 (4.8811) Prec@1 9.375 (5.818) Prec@5 32.500 (18.432) Epoch: [0][1480/11272] Time 0.955 (0.849) Data 0.001 (0.003) Loss 4.4069 (4.8778) Prec@1 5.625 (5.845) Prec@5 27.500 (18.503) Epoch: [0][1490/11272] Time 0.885 (0.849) Data 0.002 (0.003) Loss 4.2893 (4.8744) Prec@1 12.500 (5.873) Prec@5 30.000 (18.573) Epoch: [0][1500/11272] Time 0.757 (0.849) Data 0.001 (0.003) Loss 4.1832 (4.8710) Prec@1 11.875 (5.901) Prec@5 33.750 (18.639) Epoch: [0][1510/11272] Time 0.734 (0.849) Data 0.001 (0.003) Loss 4.2461 (4.8667) Prec@1 10.000 (5.931) Prec@5 33.125 (18.724) Epoch: [0][1520/11272] Time 0.932 (0.849) Data 0.001 (0.003) Loss 4.4293 (4.8631) Prec@1 11.875 (5.956) Prec@5 30.625 (18.799) Epoch: [0][1530/11272] Time 0.938 (0.849) Data 0.002 (0.003) Loss 4.3361 (4.8594) Prec@1 10.625 (5.986) Prec@5 30.625 (18.874) Epoch: [0][1540/11272] Time 0.796 (0.849) Data 0.002 (0.003) Loss 4.3984 (4.8558) Prec@1 10.625 (6.016) Prec@5 33.125 (18.950) Epoch: [0][1550/11272] Time 0.863 (0.849) Data 0.001 (0.003) Loss 4.2588 (4.8521) Prec@1 11.250 (6.050) Prec@5 34.375 (19.028) Epoch: [0][1560/11272] Time 0.841 (0.849) Data 0.001 (0.003) Loss 4.4184 (4.8486) Prec@1 6.875 (6.082) Prec@5 27.500 (19.102) Epoch: [0][1570/11272] Time 0.706 (0.848) Data 0.001 (0.003) Loss 4.3152 (4.8450) Prec@1 14.375 (6.118) Prec@5 29.375 (19.174) Epoch: [0][1580/11272] Time 0.827 (0.848) Data 0.002 (0.003) Loss 4.4798 (4.8412) Prec@1 7.500 (6.150) Prec@5 27.500 (19.245) Epoch: [0][1590/11272] Time 0.899 (0.848) Data 0.001 (0.003) Loss 4.2585 (4.8375) Prec@1 10.625 (6.186) Prec@5 35.000 (19.330) Epoch: [0][1600/11272] Time 0.884 (0.848) Data 0.002 (0.003) Loss 4.3806 (4.8338) Prec@1 8.750 (6.215) Prec@5 23.750 (19.398) Epoch: [0][1610/11272] Time 0.758 (0.848) Data 0.001 (0.003) Loss 4.1724 (4.8301) Prec@1 13.125 (6.255) Prec@5 36.875 (19.475) Epoch: [0][1620/11272] Time 0.780 (0.848) Data 0.002 (0.003) Loss 4.2464 (4.8263) Prec@1 10.000 (6.290) Prec@5 30.625 (19.556) Epoch: [0][1630/11272] Time 0.916 (0.848) Data 0.002 (0.003) Loss 4.3302 (4.8227) Prec@1 8.750 (6.332) Prec@5 26.250 (19.631) Epoch: [0][1640/11272] Time 0.837 (0.847) Data 0.002 (0.003) Loss 4.2303 (4.8192) Prec@1 14.375 (6.368) Prec@5 33.750 (19.709) Epoch: [0][1650/11272] Time 0.736 (0.847) Data 0.001 (0.003) Loss 4.1098 (4.8155) Prec@1 14.375 (6.402) Prec@5 34.375 (19.791) Epoch: [0][1660/11272] Time 0.732 (0.847) Data 0.001 (0.003) Loss 4.1362 (4.8119) Prec@1 13.125 (6.437) Prec@5 36.250 (19.868) Epoch: [0][1670/11272] Time 0.894 (0.847) Data 0.002 (0.003) Loss 4.1351 (4.8085) Prec@1 12.500 (6.467) Prec@5 38.750 (19.936) Epoch: [0][1680/11272] Time 0.752 (0.847) Data 0.003 (0.003) Loss 4.0202 (4.8050) Prec@1 13.750 (6.501) Prec@5 38.125 (20.015) Epoch: [0][1690/11272] Time 0.735 (0.847) Data 0.001 (0.003) Loss 4.0988 (4.8016) Prec@1 14.375 (6.533) Prec@5 36.250 (20.084) Epoch: [0][1700/11272] Time 0.908 (0.846) Data 0.002 (0.003) Loss 3.9541 (4.7975) Prec@1 10.625 (6.573) Prec@5 35.000 (20.167) Epoch: [0][1710/11272] Time 0.862 (0.846) Data 0.001 (0.003) Loss 4.0133 (4.7939) Prec@1 18.750 (6.614) Prec@5 35.000 (20.240) Epoch: [0][1720/11272] Time 0.791 (0.846) Data 0.002 (0.003) Loss 4.2816 (4.7902) Prec@1 11.250 (6.649) Prec@5 31.250 (20.320) Epoch: [0][1730/11272] Time 0.737 (0.846) Data 0.001 (0.003) Loss 4.1475 (4.7867) Prec@1 16.875 (6.692) Prec@5 35.000 (20.398) Epoch: [0][1740/11272] Time 0.894 (0.846) Data 0.002 (0.003) Loss 4.1818 (4.7830) Prec@1 8.750 (6.722) Prec@5 33.750 (20.475) Epoch: [0][1750/11272] Time 0.878 (0.846) Data 0.002 (0.003) Loss 4.1780 (4.7796) Prec@1 12.500 (6.753) Prec@5 36.875 (20.546) Epoch: [0][1760/11272] Time 0.730 (0.845) Data 0.001 (0.003) Loss 4.2118 (4.7764) Prec@1 9.375 (6.783) Prec@5 31.250 (20.619) Epoch: [0][1770/11272] Time 0.750 (0.845) Data 0.002 (0.003) Loss 4.0780 (4.7727) Prec@1 18.125 (6.826) Prec@5 37.500 (20.702) Epoch: [0][1780/11272] Time 0.926 (0.845) Data 0.002 (0.003) Loss 4.1570 (4.7695) Prec@1 10.000 (6.853) Prec@5 32.500 (20.770) Epoch: [0][1790/11272] Time 0.891 (0.845) Data 0.002 (0.003) Loss 4.2024 (4.7661) Prec@1 14.375 (6.889) Prec@5 38.125 (20.853) Epoch: [0][1800/11272] Time 0.741 (0.845) Data 0.002 (0.003) Loss 4.1741 (4.7628) Prec@1 10.000 (6.915) Prec@5 30.000 (20.913) Epoch: [0][1810/11272] Time 0.966 (0.845) Data 0.002 (0.003) Loss 4.2842 (4.7597) Prec@1 7.500 (6.938) Prec@5 31.875 (20.978) Epoch: [0][1820/11272] Time 0.877 (0.845) Data 0.001 (0.003) Loss 4.1971 (4.7565) Prec@1 11.875 (6.968) Prec@5 35.000 (21.044) Epoch: [0][1830/11272] Time 0.778 (0.845) Data 0.002 (0.003) Loss 4.0484 (4.7530) Prec@1 10.000 (6.997) Prec@5 33.750 (21.111) Epoch: [0][1840/11272] Time 0.735 (0.845) Data 0.001 (0.003) Loss 4.2198 (4.7498) Prec@1 8.750 (7.027) Prec@5 30.625 (21.178) Epoch: [0][1850/11272] Time 0.843 (0.845) Data 0.001 (0.003) Loss 4.1619 (4.7469) Prec@1 10.000 (7.053) Prec@5 37.500 (21.243) Epoch: [0][1860/11272] Time 0.869 (0.845) Data 0.002 (0.003) Loss 4.2880 (4.7433) Prec@1 13.125 (7.094) Prec@5 31.875 (21.320) Epoch: [0][1870/11272] Time 0.803 (0.845) Data 0.002 (0.003) Loss 3.8804 (4.7396) Prec@1 14.375 (7.126) Prec@5 38.750 (21.397) Epoch: [0][1880/11272] Time 0.785 (0.845) Data 0.001 (0.003) Loss 4.2683 (4.7362) Prec@1 13.750 (7.154) Prec@5 33.125 (21.473) Epoch: [0][1890/11272] Time 0.904 (0.845) Data 0.002 (0.003) Loss 4.0794 (4.7327) Prec@1 10.000 (7.186) Prec@5 36.875 (21.549) Epoch: [0][1900/11272] Time 0.918 (0.844) Data 0.001 (0.003) Loss 4.1131 (4.7296) Prec@1 12.500 (7.214) Prec@5 36.875 (21.619) Epoch: [0][1910/11272] Time 0.781 (0.844) Data 0.002 (0.003) Loss 4.2544 (4.7264) Prec@1 10.625 (7.243) Prec@5 31.250 (21.684) Epoch: [0][1920/11272] Time 0.757 (0.844) Data 0.002 (0.003) Loss 4.0849 (4.7230) Prec@1 16.875 (7.281) Prec@5 34.375 (21.757) Epoch: [0][1930/11272] Time 0.877 (0.844) Data 0.002 (0.003) Loss 4.1326 (4.7198) Prec@1 14.375 (7.316) Prec@5 33.125 (21.831) Epoch: [0][1940/11272] Time 0.765 (0.844) Data 0.005 (0.003) Loss 4.5039 (4.7165) Prec@1 10.625 (7.355) Prec@5 29.375 (21.901) Epoch: [0][1950/11272] Time 0.717 (0.844) Data 0.001 (0.003) Loss 4.2526 (4.7136) Prec@1 16.875 (7.397) Prec@5 30.000 (21.966) Epoch: [0][1960/11272] Time 0.879 (0.844) Data 0.002 (0.003) Loss 4.1450 (4.7108) Prec@1 16.875 (7.422) Prec@5 33.750 (22.028) Epoch: [0][1970/11272] Time 0.887 (0.844) Data 0.001 (0.003) Loss 4.1183 (4.7080) Prec@1 11.875 (7.451) Prec@5 31.875 (22.093) Epoch: [0][1980/11272] Time 0.791 (0.844) Data 0.002 (0.003) Loss 4.1414 (4.7051) Prec@1 10.625 (7.479) Prec@5 31.875 (22.149) Epoch: [0][1990/11272] Time 0.749 (0.844) Data 0.002 (0.003) Loss 4.2020 (4.7018) Prec@1 20.000 (7.513) Prec@5 33.750 (22.221) Epoch: [0][2000/11272] Time 0.909 (0.844) Data 0.001 (0.003) Loss 4.1734 (4.6992) Prec@1 13.750 (7.542) Prec@5 29.375 (22.274) Epoch: [0][2010/11272] Time 0.920 (0.844) Data 0.001 (0.003) Loss 3.9459 (4.6961) Prec@1 12.500 (7.574) Prec@5 41.875 (22.341) Epoch: [0][2020/11272] Time 0.794 (0.844) Data 0.002 (0.003) Loss 4.1618 (4.6932) Prec@1 15.625 (7.601) Prec@5 34.375 (22.405) Epoch: [0][2030/11272] Time 0.736 (0.844) Data 0.001 (0.003) Loss 3.8260 (4.6902) Prec@1 14.375 (7.632) Prec@5 40.000 (22.472) Epoch: [0][2040/11272] Time 0.933 (0.843) Data 0.002 (0.003) Loss 4.1832 (4.6873) Prec@1 13.125 (7.666) Prec@5 36.250 (22.535) Epoch: [0][2050/11272] Time 0.853 (0.843) Data 0.002 (0.003) Loss 4.2296 (4.6844) Prec@1 10.000 (7.693) Prec@5 30.000 (22.601) Epoch: [0][2060/11272] Time 0.767 (0.843) Data 0.002 (0.003) Loss 4.1954 (4.6816) Prec@1 9.375 (7.717) Prec@5 30.000 (22.665) Epoch: [0][2070/11272] Time 0.857 (0.843) Data 0.001 (0.003) Loss 4.1260 (4.6791) Prec@1 15.625 (7.746) Prec@5 30.625 (22.723) Epoch: [0][2080/11272] Time 0.952 (0.843) Data 0.002 (0.003) Loss 4.0343 (4.6761) Prec@1 16.250 (7.778) Prec@5 38.125 (22.790) Epoch: [0][2090/11272] Time 0.749 (0.843) Data 0.002 (0.003) Loss 3.9672 (4.6730) Prec@1 16.875 (7.809) Prec@5 37.500 (22.853) Epoch: [0][2100/11272] Time 0.751 (0.843) Data 0.001 (0.003) Loss 4.0149 (4.6700) Prec@1 13.750 (7.839) Prec@5 35.000 (22.924) Epoch: [0][2110/11272] Time 0.883 (0.843) Data 0.002 (0.003) Loss 3.9759 (4.6672) Prec@1 16.250 (7.870) Prec@5 31.875 (22.974) Epoch: [0][2120/11272] Time 0.872 (0.843) Data 0.002 (0.003) Loss 3.6598 (4.6641) Prec@1 18.125 (7.904) Prec@5 44.375 (23.038) Epoch: [0][2130/11272] Time 0.768 (0.843) Data 0.001 (0.003) Loss 4.2593 (4.6614) Prec@1 11.250 (7.931) Prec@5 28.750 (23.092) Epoch: [0][2140/11272] Time 0.733 (0.842) Data 0.002 (0.003) Loss 4.0201 (4.6587) Prec@1 15.000 (7.958) Prec@5 38.125 (23.153) Epoch: [0][2150/11272] Time 0.884 (0.842) Data 0.002 (0.003) Loss 4.1351 (4.6560) Prec@1 11.250 (7.988) Prec@5 35.625 (23.211) Epoch: [0][2160/11272] Time 0.870 (0.842) Data 0.002 (0.003) Loss 3.9016 (4.6531) Prec@1 17.500 (8.018) Prec@5 35.000 (23.268) Epoch: [0][2170/11272] Time 0.753 (0.842) Data 0.002 (0.003) Loss 4.0127 (4.6502) Prec@1 16.250 (8.051) Prec@5 39.375 (23.334) Epoch: [0][2180/11272] Time 0.738 (0.842) Data 0.001 (0.003) Loss 4.0538 (4.6476) Prec@1 13.750 (8.078) Prec@5 35.625 (23.389) Epoch: [0][2190/11272] Time 0.872 (0.842) Data 0.002 (0.003) Loss 4.0907 (4.6446) Prec@1 12.500 (8.111) Prec@5 36.250 (23.461) Epoch: [0][2200/11272] Time 0.872 (0.842) Data 0.002 (0.003) Loss 3.8579 (4.6415) Prec@1 16.250 (8.142) Prec@5 40.625 (23.530) Epoch: [0][2210/11272] Time 0.760 (0.842) Data 0.002 (0.003) Loss 3.8791 (4.6388) Prec@1 18.750 (8.169) Prec@5 37.500 (23.586) Epoch: [0][2220/11272] Time 0.944 (0.842) Data 0.002 (0.003) Loss 3.8933 (4.6360) Prec@1 13.125 (8.189) Prec@5 38.750 (23.651) Epoch: [0][2230/11272] Time 0.879 (0.842) Data 0.001 (0.003) Loss 3.9735 (4.6334) Prec@1 13.125 (8.214) Prec@5 38.750 (23.709) Epoch: [0][2240/11272] Time 0.762 (0.842) Data 0.002 (0.003) Loss 4.0038 (4.6310) Prec@1 16.250 (8.239) Prec@5 35.625 (23.759) Epoch: [0][2250/11272] Time 0.735 (0.842) Data 0.002 (0.003) Loss 4.1568 (4.6286) Prec@1 11.250 (8.260) Prec@5 34.375 (23.809) Epoch: [0][2260/11272] Time 0.895 (0.842) Data 0.002 (0.003) Loss 4.1351 (4.6259) Prec@1 12.500 (8.288) Prec@5 31.875 (23.863) Epoch: [0][2270/11272] Time 0.884 (0.842) Data 0.001 (0.003) Loss 3.8750 (4.6232) Prec@1 15.000 (8.322) Prec@5 35.625 (23.919) Epoch: [0][2280/11272] Time 0.764 (0.842) Data 0.002 (0.003) Loss 3.9352 (4.6201) Prec@1 11.250 (8.352) Prec@5 36.875 (23.985) Epoch: [0][2290/11272] Time 0.774 (0.842) Data 0.001 (0.003) Loss 3.6997 (4.6170) Prec@1 17.500 (8.384) Prec@5 44.375 (24.058) Epoch: [0][2300/11272] Time 0.876 (0.842) Data 0.001 (0.003) Loss 3.8474 (4.6140) Prec@1 20.000 (8.412) Prec@5 40.625 (24.120) Epoch: [0][2310/11272] Time 0.874 (0.842) Data 0.002 (0.003) Loss 4.0282 (4.6111) Prec@1 15.000 (8.443) Prec@5 36.875 (24.187) Epoch: [0][2320/11272] Time 0.783 (0.842) Data 0.001 (0.003) Loss 3.9392 (4.6083) Prec@1 16.250 (8.475) Prec@5 36.875 (24.248) Epoch: [0][2330/11272] Time 0.793 (0.842) Data 0.002 (0.003) Loss 3.8082 (4.6058) Prec@1 17.500 (8.502) Prec@5 41.875 (24.303) Epoch: [0][2340/11272] Time 0.903 (0.842) Data 0.002 (0.002) Loss 3.6810 (4.6031) Prec@1 20.625 (8.536) Prec@5 42.500 (24.359) Epoch: [0][2350/11272] Time 0.786 (0.842) Data 0.001 (0.002) Loss 3.8009 (4.6002) Prec@1 16.875 (8.566) Prec@5 42.500 (24.423) Epoch: [0][2360/11272] Time 0.767 (0.842) Data 0.002 (0.002) Loss 3.9680 (4.5973) Prec@1 16.875 (8.594) Prec@5 40.000 (24.485) Epoch: [0][2370/11272] Time 0.892 (0.842) Data 0.001 (0.002) Loss 3.9598 (4.5946) Prec@1 13.125 (8.623) Prec@5 38.750 (24.546) Epoch: [0][2380/11272] Time 0.965 (0.842) Data 0.002 (0.002) Loss 3.8554 (4.5918) Prec@1 16.250 (8.650) Prec@5 39.375 (24.609) Epoch: [0][2390/11272] Time 0.740 (0.842) Data 0.002 (0.002) Loss 3.9902 (4.5892) Prec@1 17.500 (8.678) Prec@5 38.125 (24.666) Epoch: [0][2400/11272] Time 0.754 (0.842) Data 0.002 (0.002) Loss 3.9119 (4.5867) Prec@1 16.875 (8.709) Prec@5 38.125 (24.721) Epoch: [0][2410/11272] Time 0.902 (0.842) Data 0.002 (0.002) Loss 4.0562 (4.5843) Prec@1 13.125 (8.734) Prec@5 35.000 (24.775) Epoch: [0][2420/11272] Time 0.905 (0.842) Data 0.002 (0.002) Loss 3.6628 (4.5816) Prec@1 17.500 (8.758) Prec@5 42.500 (24.831) Epoch: [0][2430/11272] Time 0.765 (0.841) Data 0.002 (0.002) Loss 3.7177 (4.5789) Prec@1 15.625 (8.783) Prec@5 44.375 (24.892) Epoch: [0][2440/11272] Time 0.769 (0.841) Data 0.002 (0.002) Loss 3.9507 (4.5762) Prec@1 16.875 (8.812) Prec@5 38.750 (24.947) Epoch: [0][2450/11272] Time 0.885 (0.841) Data 0.001 (0.002) Loss 4.0967 (4.5734) Prec@1 11.875 (8.844) Prec@5 36.250 (25.004) Epoch: [0][2460/11272] Time 0.846 (0.841) Data 0.001 (0.002) Loss 4.0184 (4.5708) Prec@1 10.625 (8.871) Prec@5 35.625 (25.053) Epoch: [0][2470/11272] Time 0.788 (0.841) Data 0.001 (0.002) Loss 3.9039 (4.5682) Prec@1 18.750 (8.900) Prec@5 41.250 (25.109) Epoch: [0][2480/11272] Time 0.876 (0.841) Data 0.001 (0.002) Loss 3.9230 (4.5655) Prec@1 18.750 (8.930) Prec@5 38.125 (25.172) Epoch: [0][2490/11272] Time 0.904 (0.841) Data 0.002 (0.002) Loss 4.1122 (4.5632) Prec@1 13.125 (8.951) Prec@5 36.875 (25.224) Epoch: [0][2500/11272] Time 0.775 (0.841) Data 0.001 (0.002) Loss 3.9898 (4.5607) Prec@1 13.750 (8.976) Prec@5 40.625 (25.282) Epoch: [0][2510/11272] Time 0.770 (0.841) Data 0.002 (0.002) Loss 3.9287 (4.5581) Prec@1 15.000 (9.003) Prec@5 36.250 (25.337) Epoch: [0][2520/11272] Time 0.873 (0.841) Data 0.002 (0.002) Loss 3.8650 (4.5556) Prec@1 16.875 (9.027) Prec@5 41.875 (25.389) Epoch: [0][2530/11272] Time 0.849 (0.841) Data 0.002 (0.002) Loss 3.8824 (4.5531) Prec@1 18.750 (9.054) Prec@5 36.875 (25.439) Epoch: [0][2540/11272] Time 0.759 (0.841) Data 0.001 (0.002) Loss 4.0173 (4.5509) Prec@1 14.375 (9.081) Prec@5 40.625 (25.495) Epoch: [0][2550/11272] Time 0.734 (0.841) Data 0.002 (0.002) Loss 3.8111 (4.5485) Prec@1 17.500 (9.105) Prec@5 41.875 (25.550) Epoch: [0][2560/11272] Time 0.856 (0.841) Data 0.001 (0.002) Loss 3.8927 (4.5459) Prec@1 16.250 (9.136) Prec@5 40.000 (25.603) Epoch: [0][2570/11272] Time 0.855 (0.841) Data 0.002 (0.002) Loss 4.1093 (4.5434) Prec@1 10.625 (9.160) Prec@5 37.500 (25.656) Epoch: [0][2580/11272] Time 0.793 (0.841) Data 0.001 (0.002) Loss 3.8186 (4.5409) Prec@1 13.750 (9.189) Prec@5 38.125 (25.709) Epoch: [0][2590/11272] Time 0.739 (0.841) Data 0.002 (0.002) Loss 3.8270 (4.5386) Prec@1 16.250 (9.213) Prec@5 40.625 (25.761) Epoch: [0][2600/11272] Time 0.864 (0.841) Data 0.001 (0.002) Loss 3.8167 (4.5361) Prec@1 20.000 (9.242) Prec@5 40.000 (25.813) Epoch: [0][2610/11272] Time 0.765 (0.841) Data 0.005 (0.002) Loss 3.8302 (4.5332) Prec@1 16.250 (9.277) Prec@5 38.750 (25.877) Epoch: [0][2620/11272] Time 0.803 (0.841) Data 0.002 (0.002) Loss 3.8356 (4.5309) Prec@1 16.250 (9.304) Prec@5 40.000 (25.926) Epoch: [0][2630/11272] Time 0.953 (0.841) Data 0.002 (0.002) Loss 3.6661 (4.5287) Prec@1 17.500 (9.326) Prec@5 40.000 (25.973) Epoch: [0][2640/11272] Time 0.872 (0.841) Data 0.002 (0.002) Loss 4.0556 (4.5262) Prec@1 14.375 (9.352) Prec@5 41.250 (26.029) Epoch: [0][2650/11272] Time 0.764 (0.840) Data 0.001 (0.002) Loss 3.9204 (4.5240) Prec@1 16.875 (9.378) Prec@5 42.500 (26.076) Epoch: [0][2660/11272] Time 0.800 (0.841) Data 0.002 (0.002) Loss 3.9622 (4.5216) Prec@1 15.000 (9.409) Prec@5 35.000 (26.125) Epoch: [0][2670/11272] Time 0.981 (0.841) Data 0.002 (0.002) Loss 3.7693 (4.5189) Prec@1 16.250 (9.438) Prec@5 43.125 (26.186) Epoch: [0][2680/11272] Time 0.897 (0.841) Data 0.001 (0.002) Loss 3.7975 (4.5163) Prec@1 17.500 (9.470) Prec@5 46.875 (26.249) Epoch: [0][2690/11272] Time 0.730 (0.841) Data 0.001 (0.002) Loss 3.9493 (4.5138) Prec@1 14.375 (9.496) Prec@5 38.125 (26.298) Epoch: [0][2700/11272] Time 0.833 (0.841) Data 0.002 (0.002) Loss 4.0809 (4.5113) Prec@1 15.625 (9.523) Prec@5 35.000 (26.351) Epoch: [0][2710/11272] Time 0.881 (0.841) Data 0.001 (0.002) Loss 3.8251 (4.5090) Prec@1 14.375 (9.546) Prec@5 40.000 (26.405) Epoch: [0][2720/11272] Time 0.923 (0.841) Data 0.002 (0.002) Loss 4.0105 (4.5070) Prec@1 14.375 (9.568) Prec@5 40.000 (26.449) Epoch: [0][2730/11272] Time 0.744 (0.841) Data 0.001 (0.002) Loss 3.9470 (4.5044) Prec@1 16.875 (9.593) Prec@5 42.500 (26.506) Epoch: [0][2740/11272] Time 0.865 (0.841) Data 0.002 (0.002) Loss 3.7186 (4.5021) Prec@1 16.875 (9.614) Prec@5 40.000 (26.552) Epoch: [0][2750/11272] Time 0.854 (0.840) Data 0.002 (0.002) Loss 3.6571 (4.4998) Prec@1 16.875 (9.638) Prec@5 44.375 (26.600) Epoch: [0][2760/11272] Time 0.725 (0.840) Data 0.001 (0.002) Loss 3.8503 (4.4976) Prec@1 18.125 (9.661) Prec@5 46.250 (26.653) Epoch: [0][2770/11272] Time 0.766 (0.840) Data 0.001 (0.002) Loss 3.7960 (4.4954) Prec@1 13.750 (9.684) Prec@5 46.875 (26.699) Epoch: [0][2780/11272] Time 0.895 (0.840) Data 0.001 (0.002) Loss 3.7708 (4.4932) Prec@1 16.250 (9.705) Prec@5 41.875 (26.743) Epoch: [0][2790/11272] Time 0.857 (0.840) Data 0.001 (0.002) Loss 3.8535 (4.4908) Prec@1 16.250 (9.730) Prec@5 43.750 (26.798) Epoch: [0][2800/11272] Time 0.743 (0.840) Data 0.001 (0.002) Loss 3.8753 (4.4886) Prec@1 15.000 (9.752) Prec@5 44.375 (26.848) Epoch: [0][2810/11272] Time 0.757 (0.840) Data 0.002 (0.002) Loss 3.5729 (4.4861) Prec@1 20.625 (9.779) Prec@5 46.250 (26.902) Epoch: [0][2820/11272] Time 0.860 (0.840) Data 0.001 (0.002) Loss 3.6125 (4.4839) Prec@1 24.375 (9.806) Prec@5 45.000 (26.950) Epoch: [0][2830/11272] Time 0.930 (0.840) Data 0.002 (0.002) Loss 3.7726 (4.4815) Prec@1 20.000 (9.837) Prec@5 42.500 (27.009) Epoch: [0][2840/11272] Time 0.808 (0.840) Data 0.002 (0.002) Loss 3.7302 (4.4793) Prec@1 15.625 (9.861) Prec@5 47.500 (27.054) Epoch: [0][2850/11272] Time 0.802 (0.840) Data 0.002 (0.002) Loss 4.0282 (4.4772) Prec@1 19.375 (9.885) Prec@5 38.125 (27.101) Epoch: [0][2860/11272] Time 0.886 (0.840) Data 0.001 (0.002) Loss 3.6749 (4.4748) Prec@1 21.875 (9.908) Prec@5 50.000 (27.152) Epoch: [0][2870/11272] Time 0.782 (0.840) Data 0.003 (0.002) Loss 3.7582 (4.4727) Prec@1 18.750 (9.936) Prec@5 43.125 (27.201) Epoch: [0][2880/11272] Time 0.817 (0.840) Data 0.002 (0.002) Loss 3.9765 (4.4705) Prec@1 15.625 (9.962) Prec@5 38.750 (27.250) Epoch: [0][2890/11272] Time 0.931 (0.840) Data 0.001 (0.002) Loss 3.8575 (4.4682) Prec@1 16.250 (9.986) Prec@5 40.625 (27.298) Epoch: [0][2900/11272] Time 0.930 (0.840) Data 0.001 (0.002) Loss 3.8049 (4.4661) Prec@1 16.875 (10.015) Prec@5 45.625 (27.346) Epoch: [0][2910/11272] Time 0.755 (0.840) Data 0.001 (0.002) Loss 3.7322 (4.4640) Prec@1 21.250 (10.038) Prec@5 43.750 (27.392) Epoch: [0][2920/11272] Time 0.782 (0.840) Data 0.002 (0.002) Loss 3.8896 (4.4620) Prec@1 15.000 (10.058) Prec@5 41.875 (27.436) Epoch: [0][2930/11272] Time 0.892 (0.840) Data 0.001 (0.002) Loss 3.7483 (4.4598) Prec@1 19.375 (10.083) Prec@5 42.500 (27.484) Epoch: [0][2940/11272] Time 0.832 (0.840) Data 0.002 (0.002) Loss 3.6813 (4.4574) Prec@1 20.625 (10.108) Prec@5 41.250 (27.535) Epoch: [0][2950/11272] Time 0.776 (0.840) Data 0.002 (0.002) Loss 3.7518 (4.4553) Prec@1 14.375 (10.128) Prec@5 50.000 (27.583) Epoch: [0][2960/11272] Time 0.741 (0.840) Data 0.002 (0.002) Loss 3.8885 (4.4532) Prec@1 17.500 (10.155) Prec@5 45.625 (27.628) Epoch: [0][2970/11272] Time 0.877 (0.840) Data 0.002 (0.002) Loss 3.9585 (4.4510) Prec@1 11.875 (10.176) Prec@5 36.875 (27.674) Epoch: [0][2980/11272] Time 0.907 (0.839) Data 0.002 (0.002) Loss 3.8850 (4.4486) Prec@1 16.875 (10.207) Prec@5 38.125 (27.726) Epoch: [0][2990/11272] Time 0.779 (0.839) Data 0.002 (0.002) Loss 3.8659 (4.4466) Prec@1 20.000 (10.230) Prec@5 38.125 (27.770) Epoch: [0][3000/11272] Time 0.880 (0.839) Data 0.002 (0.002) Loss 3.6704 (4.4443) Prec@1 16.875 (10.257) Prec@5 42.500 (27.819) Epoch: [0][3010/11272] Time 0.903 (0.839) Data 0.002 (0.002) Loss 3.9087 (4.4419) Prec@1 16.875 (10.289) Prec@5 38.750 (27.870) Epoch: [0][3020/11272] Time 0.764 (0.839) Data 0.002 (0.002) Loss 3.7313 (4.4394) Prec@1 16.250 (10.317) Prec@5 45.625 (27.924) Epoch: [0][3030/11272] Time 0.756 (0.839) Data 0.002 (0.002) Loss 3.5787 (4.4373) Prec@1 22.500 (10.338) Prec@5 42.500 (27.967) Epoch: [0][3040/11272] Time 0.862 (0.839) Data 0.001 (0.002) Loss 3.7837 (4.4350) Prec@1 19.375 (10.370) Prec@5 42.500 (28.019) Epoch: [0][3050/11272] Time 0.923 (0.839) Data 0.002 (0.002) Loss 3.7085 (4.4326) Prec@1 16.250 (10.398) Prec@5 45.000 (28.073) Epoch: [0][3060/11272] Time 0.753 (0.839) Data 0.004 (0.002) Loss 3.9537 (4.4307) Prec@1 16.250 (10.419) Prec@5 41.875 (28.121) Epoch: [0][3070/11272] Time 0.755 (0.839) Data 0.001 (0.002) Loss 3.8395 (4.4286) Prec@1 20.625 (10.444) Prec@5 43.750 (28.165) Epoch: [0][3080/11272] Time 0.889 (0.839) Data 0.001 (0.002) Loss 3.8070 (4.4265) Prec@1 16.875 (10.470) Prec@5 42.500 (28.212) Epoch: [0][3090/11272] Time 0.908 (0.839) Data 0.002 (0.002) Loss 3.4918 (4.4243) Prec@1 23.125 (10.500) Prec@5 49.375 (28.266) Epoch: [0][3100/11272] Time 0.766 (0.839) Data 0.002 (0.002) Loss 3.7611 (4.4223) Prec@1 17.500 (10.525) Prec@5 45.625 (28.311) Epoch: [0][3110/11272] Time 0.774 (0.839) Data 0.002 (0.002) Loss 3.7346 (4.4201) Prec@1 18.125 (10.550) Prec@5 43.750 (28.357) Epoch: [0][3120/11272] Time 0.886 (0.839) Data 0.001 (0.002) Loss 3.7442 (4.4181) Prec@1 14.375 (10.573) Prec@5 39.375 (28.395) Epoch: [0][3130/11272] Time 0.882 (0.839) Data 0.002 (0.002) Loss 3.5243 (4.4161) Prec@1 23.750 (10.597) Prec@5 53.125 (28.437) Epoch: [0][3140/11272] Time 0.795 (0.839) Data 0.002 (0.002) Loss 3.5121 (4.4138) Prec@1 24.375 (10.630) Prec@5 50.000 (28.488) Epoch: [0][3150/11272] Time 0.925 (0.839) Data 0.002 (0.002) Loss 3.7803 (4.4116) Prec@1 19.375 (10.656) Prec@5 41.250 (28.534) Epoch: [0][3160/11272] Time 0.923 (0.839) Data 0.002 (0.002) Loss 3.4699 (4.4093) Prec@1 23.750 (10.683) Prec@5 46.875 (28.580) Epoch: [0][3170/11272] Time 0.763 (0.839) Data 0.002 (0.002) Loss 3.5713 (4.4072) Prec@1 30.000 (10.710) Prec@5 49.375 (28.625) Epoch: [0][3180/11272] Time 0.752 (0.839) Data 0.001 (0.002) Loss 3.6633 (4.4052) Prec@1 21.250 (10.736) Prec@5 46.250 (28.668) Epoch: [0][3190/11272] Time 0.926 (0.839) Data 0.002 (0.002) Loss 3.4976 (4.4031) Prec@1 18.750 (10.759) Prec@5 49.375 (28.711) Epoch: [0][3200/11272] Time 0.850 (0.839) Data 0.001 (0.002) Loss 3.9998 (4.4013) Prec@1 11.875 (10.778) Prec@5 40.000 (28.753) Epoch: [0][3210/11272] Time 0.743 (0.839) Data 0.001 (0.002) Loss 3.6742 (4.3990) Prec@1 18.125 (10.803) Prec@5 46.875 (28.803) Epoch: [0][3220/11272] Time 0.735 (0.839) Data 0.001 (0.002) Loss 3.8110 (4.3973) Prec@1 16.250 (10.823) Prec@5 41.875 (28.843) Epoch: [0][3230/11272] Time 0.919 (0.839) Data 0.001 (0.002) Loss 3.6738 (4.3951) Prec@1 13.750 (10.845) Prec@5 43.750 (28.891) Epoch: [0][3240/11272] Time 0.871 (0.839) Data 0.001 (0.002) Loss 3.6508 (4.3931) Prec@1 20.625 (10.870) Prec@5 44.375 (28.934) Epoch: [0][3250/11272] Time 0.734 (0.839) Data 0.001 (0.002) Loss 3.5921 (4.3908) Prec@1 23.125 (10.896) Prec@5 49.375 (28.986) Epoch: [0][3260/11272] Time 0.764 (0.839) Data 0.002 (0.002) Loss 3.7042 (4.3888) Prec@1 18.750 (10.921) Prec@5 46.250 (29.031) Epoch: [0][3270/11272] Time 0.845 (0.839) Data 0.001 (0.002) Loss 3.6783 (4.3868) Prec@1 21.250 (10.940) Prec@5 45.625 (29.076) Epoch: [0][3280/11272] Time 0.727 (0.839) Data 0.002 (0.002) Loss 3.8881 (4.3850) Prec@1 19.375 (10.961) Prec@5 43.125 (29.113) Epoch: [0][3290/11272] Time 0.748 (0.839) Data 0.002 (0.002) Loss 3.7530 (4.3830) Prec@1 21.875 (10.988) Prec@5 43.750 (29.158) Epoch: [0][3300/11272] Time 0.891 (0.839) Data 0.002 (0.002) Loss 3.7915 (4.3810) Prec@1 16.250 (11.010) Prec@5 39.375 (29.199) Epoch: [0][3310/11272] Time 0.882 (0.839) Data 0.001 (0.002) Loss 3.4970 (4.3788) Prec@1 21.250 (11.034) Prec@5 49.375 (29.245) Epoch: [0][3320/11272] Time 0.767 (0.839) Data 0.002 (0.002) Loss 3.8129 (4.3769) Prec@1 18.750 (11.053) Prec@5 41.250 (29.289) Epoch: [0][3330/11272] Time 0.730 (0.838) Data 0.001 (0.002) Loss 3.4623 (4.3750) Prec@1 25.000 (11.071) Prec@5 49.375 (29.330) Epoch: [0][3340/11272] Time 0.909 (0.838) Data 0.002 (0.002) Loss 3.8482 (4.3731) Prec@1 11.875 (11.091) Prec@5 40.000 (29.374) Epoch: [0][3350/11272] Time 0.809 (0.838) Data 0.002 (0.002) Loss 3.6087 (4.3711) Prec@1 16.875 (11.113) Prec@5 46.250 (29.419) Epoch: [0][3360/11272] Time 0.747 (0.838) Data 0.002 (0.002) Loss 3.6110 (4.3690) Prec@1 20.000 (11.137) Prec@5 43.750 (29.467) Epoch: [0][3370/11272] Time 0.784 (0.838) Data 0.002 (0.002) Loss 3.3404 (4.3670) Prec@1 26.250 (11.160) Prec@5 47.500 (29.505) Epoch: [0][3380/11272] Time 0.862 (0.838) Data 0.002 (0.002) Loss 3.6356 (4.3648) Prec@1 19.375 (11.185) Prec@5 43.125 (29.550) Epoch: [0][3390/11272] Time 0.852 (0.838) Data 0.001 (0.002) Loss 3.6309 (4.3630) Prec@1 20.000 (11.206) Prec@5 51.875 (29.592) Epoch: [0][3400/11272] Time 0.742 (0.838) Data 0.002 (0.002) Loss 4.0474 (4.3608) Prec@1 11.250 (11.231) Prec@5 36.250 (29.642) Epoch: [0][3410/11272] Time 0.838 (0.838) Data 0.001 (0.002) Loss 4.0174 (4.3590) Prec@1 16.250 (11.253) Prec@5 37.500 (29.684) Epoch: [0][3420/11272] Time 0.877 (0.838) Data 0.002 (0.002) Loss 3.8162 (4.3572) Prec@1 15.000 (11.272) Prec@5 46.875 (29.725) Epoch: [0][3430/11272] Time 0.789 (0.838) Data 0.001 (0.002) Loss 3.6259 (4.3553) Prec@1 16.250 (11.293) Prec@5 45.000 (29.766) Epoch: [0][3440/11272] Time 0.775 (0.838) Data 0.002 (0.002) Loss 3.8967 (4.3537) Prec@1 15.625 (11.312) Prec@5 37.500 (29.800) Epoch: [0][3450/11272] Time 0.944 (0.838) Data 0.003 (0.002) Loss 3.6317 (4.3518) Prec@1 16.250 (11.332) Prec@5 44.375 (29.839) Epoch: [0][3460/11272] Time 0.934 (0.838) Data 0.001 (0.002) Loss 3.8408 (4.3498) Prec@1 13.750 (11.357) Prec@5 41.250 (29.882) Epoch: [0][3470/11272] Time 0.763 (0.838) Data 0.002 (0.002) Loss 3.5003 (4.3480) Prec@1 20.000 (11.374) Prec@5 48.125 (29.919) Epoch: [0][3480/11272] Time 0.753 (0.838) Data 0.001 (0.002) Loss 3.6771 (4.3462) Prec@1 17.500 (11.393) Prec@5 44.375 (29.960) Epoch: [0][3490/11272] Time 0.944 (0.838) Data 0.002 (0.002) Loss 3.8908 (4.3442) Prec@1 15.000 (11.413) Prec@5 37.500 (30.003) Epoch: [0][3500/11272] Time 0.901 (0.838) Data 0.002 (0.002) Loss 3.6725 (4.3425) Prec@1 21.250 (11.437) Prec@5 46.250 (30.043) Epoch: [0][3510/11272] Time 0.746 (0.838) Data 0.002 (0.002) Loss 3.6246 (4.3404) Prec@1 20.000 (11.463) Prec@5 43.125 (30.085) Epoch: [0][3520/11272] Time 0.754 (0.838) Data 0.002 (0.002) Loss 3.7064 (4.3386) Prec@1 20.625 (11.483) Prec@5 40.625 (30.123) Epoch: [0][3530/11272] Time 0.904 (0.838) Data 0.001 (0.002) Loss 3.6810 (4.3366) Prec@1 23.125 (11.510) Prec@5 42.500 (30.164) Epoch: [0][3540/11272] Time 0.806 (0.838) Data 0.004 (0.002) Loss 3.6319 (4.3345) Prec@1 21.250 (11.539) Prec@5 45.000 (30.211) Epoch: [0][3550/11272] Time 0.814 (0.838) Data 0.002 (0.002) Loss 3.5636 (4.3326) Prec@1 20.625 (11.564) Prec@5 48.750 (30.258) Epoch: [0][3560/11272] Time 0.908 (0.838) Data 0.001 (0.002) Loss 3.6653 (4.3304) Prec@1 19.375 (11.590) Prec@5 42.500 (30.305) Epoch: [0][3570/11272] Time 0.958 (0.838) Data 0.001 (0.002) Loss 3.7883 (4.3285) Prec@1 16.875 (11.609) Prec@5 46.875 (30.344) Epoch: [0][3580/11272] Time 0.827 (0.838) Data 0.002 (0.002) Loss 3.6712 (4.3265) Prec@1 18.125 (11.633) Prec@5 41.875 (30.386) Epoch: [0][3590/11272] Time 0.738 (0.838) Data 0.002 (0.002) Loss 3.6368 (4.3247) Prec@1 17.500 (11.652) Prec@5 46.875 (30.428) Epoch: [0][3600/11272] Time 0.881 (0.838) Data 0.002 (0.002) Loss 3.6936 (4.3228) Prec@1 16.250 (11.675) Prec@5 44.375 (30.472) Epoch: [0][3610/11272] Time 0.881 (0.838) Data 0.002 (0.002) Loss 3.7928 (4.3210) Prec@1 17.500 (11.697) Prec@5 43.750 (30.511) Epoch: [0][3620/11272] Time 0.773 (0.838) Data 0.002 (0.002) Loss 3.8379 (4.3193) Prec@1 18.750 (11.715) Prec@5 45.625 (30.546) Epoch: [0][3630/11272] Time 0.792 (0.838) Data 0.002 (0.002) Loss 3.8347 (4.3174) Prec@1 16.875 (11.738) Prec@5 41.875 (30.586) Epoch: [0][3640/11272] Time 0.889 (0.838) Data 0.002 (0.002) Loss 3.6978 (4.3157) Prec@1 20.625 (11.762) Prec@5 45.000 (30.628) Epoch: [0][3650/11272] Time 0.914 (0.838) Data 0.002 (0.002) Loss 3.9084 (4.3140) Prec@1 20.625 (11.782) Prec@5 41.250 (30.665) Epoch: [0][3660/11272] Time 0.749 (0.838) Data 0.001 (0.002) Loss 3.6140 (4.3121) Prec@1 21.250 (11.807) Prec@5 46.875 (30.705) Epoch: [0][3670/11272] Time 0.888 (0.838) Data 0.002 (0.002) Loss 3.8833 (4.3104) Prec@1 12.500 (11.827) Prec@5 41.250 (30.746) Epoch: [0][3680/11272] Time 0.884 (0.838) Data 0.002 (0.002) Loss 3.8224 (4.3086) Prec@1 18.125 (11.851) Prec@5 42.500 (30.785) Epoch: [0][3690/11272] Time 0.830 (0.838) Data 0.002 (0.002) Loss 3.5916 (4.3068) Prec@1 20.625 (11.867) Prec@5 46.250 (30.825) Epoch: [0][3700/11272] Time 0.736 (0.838) Data 0.002 (0.002) Loss 3.6603 (4.3051) Prec@1 19.375 (11.887) Prec@5 48.125 (30.866) Epoch: [0][3710/11272] Time 0.914 (0.838) Data 0.001 (0.002) Loss 3.6842 (4.3032) Prec@1 16.250 (11.909) Prec@5 45.000 (30.909) Epoch: [0][3720/11272] Time 0.847 (0.838) Data 0.001 (0.002) Loss 3.6099 (4.3014) Prec@1 18.750 (11.931) Prec@5 50.000 (30.949) Epoch: [0][3730/11272] Time 0.764 (0.838) Data 0.002 (0.002) Loss 3.4228 (4.2997) Prec@1 23.125 (11.951) Prec@5 46.250 (30.987) Epoch: [0][3740/11272] Time 0.720 (0.838) Data 0.001 (0.002) Loss 3.3926 (4.2977) Prec@1 24.375 (11.978) Prec@5 51.875 (31.035) Epoch: [0][3750/11272] Time 0.965 (0.838) Data 0.002 (0.002) Loss 3.4101 (4.2958) Prec@1 32.500 (12.002) Prec@5 51.250 (31.075) Epoch: [0][3760/11272] Time 0.965 (0.838) Data 0.002 (0.002) Loss 3.4729 (4.2942) Prec@1 23.125 (12.021) Prec@5 50.000 (31.112) Epoch: [0][3770/11272] Time 0.737 (0.838) Data 0.001 (0.002) Loss 3.7835 (4.2923) Prec@1 17.500 (12.043) Prec@5 38.750 (31.153) Epoch: [0][3780/11272] Time 0.740 (0.837) Data 0.002 (0.002) Loss 3.7326 (4.2905) Prec@1 17.500 (12.064) Prec@5 42.500 (31.189) Epoch: [0][3790/11272] Time 0.920 (0.838) Data 0.002 (0.002) Loss 3.7010 (4.2889) Prec@1 17.500 (12.082) Prec@5 46.250 (31.224) Epoch: [0][3800/11272] Time 0.796 (0.838) Data 0.004 (0.002) Loss 3.5450 (4.2872) Prec@1 21.250 (12.106) Prec@5 50.000 (31.261) Epoch: [0][3810/11272] Time 0.750 (0.837) Data 0.002 (0.002) Loss 3.7079 (4.2858) Prec@1 21.875 (12.123) Prec@5 46.250 (31.292) Epoch: [0][3820/11272] Time 0.830 (0.837) Data 0.001 (0.002) Loss 3.4936 (4.2839) Prec@1 20.625 (12.143) Prec@5 50.000 (31.333) Epoch: [0][3830/11272] Time 0.870 (0.837) Data 0.001 (0.002) Loss 3.6871 (4.2821) Prec@1 20.000 (12.163) Prec@5 45.000 (31.374) Epoch: [0][3840/11272] Time 0.761 (0.837) Data 0.001 (0.002) Loss 3.6133 (4.2803) Prec@1 18.125 (12.181) Prec@5 48.125 (31.413) Epoch: [0][3850/11272] Time 0.737 (0.837) Data 0.001 (0.002) Loss 3.5166 (4.2786) Prec@1 21.875 (12.202) Prec@5 46.875 (31.452) Epoch: [0][3860/11272] Time 1.040 (0.837) Data 0.002 (0.002) Loss 3.6802 (4.2770) Prec@1 18.125 (12.224) Prec@5 46.250 (31.487) Epoch: [0][3870/11272] Time 0.932 (0.837) Data 0.002 (0.002) Loss 3.6342 (4.2753) Prec@1 18.750 (12.242) Prec@5 44.375 (31.525) Epoch: [0][3880/11272] Time 0.761 (0.837) Data 0.001 (0.002) Loss 3.8123 (4.2735) Prec@1 11.875 (12.264) Prec@5 40.000 (31.567) Epoch: [0][3890/11272] Time 0.763 (0.837) Data 0.002 (0.002) Loss 3.3537 (4.2717) Prec@1 21.250 (12.285) Prec@5 50.000 (31.603) Epoch: [0][3900/11272] Time 0.853 (0.837) Data 0.001 (0.002) Loss 3.6147 (4.2703) Prec@1 18.750 (12.301) Prec@5 45.000 (31.632) Epoch: [0][3910/11272] Time 0.924 (0.837) Data 0.002 (0.002) Loss 3.5072 (4.2687) Prec@1 25.625 (12.322) Prec@5 48.125 (31.668) Epoch: [0][3920/11272] Time 0.795 (0.837) Data 0.002 (0.002) Loss 3.6979 (4.2673) Prec@1 15.000 (12.342) Prec@5 43.750 (31.701) Epoch: [0][3930/11272] Time 0.994 (0.837) Data 0.001 (0.002) Loss 3.7872 (4.2655) Prec@1 18.750 (12.365) Prec@5 43.750 (31.739) Epoch: [0][3940/11272] Time 0.915 (0.837) Data 0.001 (0.002) Loss 3.8868 (4.2640) Prec@1 15.000 (12.382) Prec@5 36.250 (31.771) Epoch: [0][3950/11272] Time 0.733 (0.837) Data 0.001 (0.002) Loss 3.7151 (4.2622) Prec@1 18.750 (12.402) Prec@5 46.875 (31.810) Epoch: [0][3960/11272] Time 0.769 (0.837) Data 0.001 (0.002) Loss 3.4243 (4.2605) Prec@1 20.000 (12.421) Prec@5 55.625 (31.851) Epoch: [0][3970/11272] Time 0.896 (0.837) Data 0.002 (0.002) Loss 3.5507 (4.2589) Prec@1 23.750 (12.444) Prec@5 46.875 (31.885) Epoch: [0][3980/11272] Time 0.883 (0.837) Data 0.002 (0.002) Loss 3.6665 (4.2571) Prec@1 18.750 (12.470) Prec@5 46.875 (31.925) Epoch: [0][3990/11272] Time 0.735 (0.837) Data 0.002 (0.002) Loss 3.6491 (4.2556) Prec@1 20.000 (12.491) Prec@5 48.750 (31.958) Epoch: [0][4000/11272] Time 0.805 (0.837) Data 0.002 (0.002) Loss 3.7983 (4.2541) Prec@1 14.375 (12.509) Prec@5 41.250 (31.993) Epoch: [0][4010/11272] Time 0.860 (0.837) Data 0.001 (0.002) Loss 3.5803 (4.2524) Prec@1 17.500 (12.529) Prec@5 53.125 (32.034) Epoch: [0][4020/11272] Time 0.841 (0.837) Data 0.002 (0.002) Loss 3.5434 (4.2509) Prec@1 20.000 (12.545) Prec@5 48.125 (32.066) Epoch: [0][4030/11272] Time 0.778 (0.837) Data 0.002 (0.002) Loss 3.6714 (4.2492) Prec@1 21.875 (12.567) Prec@5 48.750 (32.108) Epoch: [0][4040/11272] Time 0.742 (0.837) Data 0.001 (0.002) Loss 3.3594 (4.2475) Prec@1 22.500 (12.587) Prec@5 50.625 (32.145) Epoch: [0][4050/11272] Time 0.888 (0.837) Data 0.002 (0.002) Loss 3.3935 (4.2457) Prec@1 23.750 (12.611) Prec@5 50.625 (32.182) Epoch: [0][4060/11272] Time 0.881 (0.837) Data 0.002 (0.002) Loss 3.5751 (4.2443) Prec@1 22.500 (12.631) Prec@5 45.625 (32.217) Epoch: [0][4070/11272] Time 0.760 (0.837) Data 0.002 (0.002) Loss 3.4881 (4.2427) Prec@1 25.000 (12.650) Prec@5 52.500 (32.249) Epoch: [0][4080/11272] Time 0.955 (0.837) Data 0.002 (0.002) Loss 3.5746 (4.2409) Prec@1 18.750 (12.675) Prec@5 44.375 (32.284) Epoch: [0][4090/11272] Time 0.895 (0.837) Data 0.002 (0.002) Loss 3.5930 (4.2393) Prec@1 18.750 (12.695) Prec@5 46.250 (32.319) Epoch: [0][4100/11272] Time 0.814 (0.837) Data 0.001 (0.002) Loss 3.4985 (4.2376) Prec@1 19.375 (12.715) Prec@5 50.000 (32.355) Epoch: [0][4110/11272] Time 0.787 (0.837) Data 0.002 (0.002) Loss 3.5889 (4.2359) Prec@1 20.000 (12.732) Prec@5 43.750 (32.393) Epoch: [0][4120/11272] Time 0.970 (0.837) Data 0.002 (0.002) Loss 3.5944 (4.2343) Prec@1 19.375 (12.757) Prec@5 45.000 (32.433) Epoch: [0][4130/11272] Time 0.924 (0.837) Data 0.001 (0.002) Loss 3.2934 (4.2324) Prec@1 23.125 (12.780) Prec@5 58.750 (32.478) Epoch: [0][4140/11272] Time 0.799 (0.837) Data 0.002 (0.002) Loss 3.6999 (4.2308) Prec@1 17.500 (12.802) Prec@5 41.875 (32.517) Epoch: [0][4150/11272] Time 0.730 (0.837) Data 0.002 (0.002) Loss 3.3920 (4.2291) Prec@1 21.250 (12.823) Prec@5 46.250 (32.554) Epoch: [0][4160/11272] Time 0.956 (0.837) Data 0.002 (0.002) Loss 3.3041 (4.2273) Prec@1 23.750 (12.848) Prec@5 55.000 (32.594) Epoch: [0][4170/11272] Time 0.910 (0.837) Data 0.002 (0.002) Loss 3.5072 (4.2257) Prec@1 21.875 (12.870) Prec@5 48.125 (32.632) Epoch: [0][4180/11272] Time 0.745 (0.837) Data 0.001 (0.002) Loss 3.5463 (4.2242) Prec@1 21.250 (12.887) Prec@5 47.500 (32.662) Epoch: [0][4190/11272] Time 0.728 (0.837) Data 0.001 (0.002) Loss 3.7491 (4.2228) Prec@1 14.375 (12.904) Prec@5 47.500 (32.695) Epoch: [0][4200/11272] Time 0.862 (0.837) Data 0.003 (0.002) Loss 3.4748 (4.2212) Prec@1 21.875 (12.924) Prec@5 48.125 (32.730) Epoch: [0][4210/11272] Time 0.765 (0.837) Data 0.001 (0.002) Loss 3.5107 (4.2197) Prec@1 21.250 (12.942) Prec@5 47.500 (32.762) Epoch: [0][4220/11272] Time 0.772 (0.837) Data 0.002 (0.002) Loss 3.4320 (4.2181) Prec@1 23.125 (12.964) Prec@5 46.875 (32.799) Epoch: [0][4230/11272] Time 0.955 (0.837) Data 0.002 (0.002) Loss 3.4594 (4.2165) Prec@1 21.875 (12.983) Prec@5 45.000 (32.831) Epoch: [0][4240/11272] Time 0.873 (0.837) Data 0.002 (0.002) Loss 3.4918 (4.2150) Prec@1 23.750 (13.003) Prec@5 53.125 (32.871) Epoch: [0][4250/11272] Time 0.741 (0.837) Data 0.002 (0.002) Loss 3.4082 (4.2134) Prec@1 24.375 (13.025) Prec@5 53.125 (32.909) Epoch: [0][4260/11272] Time 0.755 (0.837) Data 0.001 (0.002) Loss 3.4175 (4.2118) Prec@1 20.000 (13.044) Prec@5 46.875 (32.941) Epoch: [0][4270/11272] Time 0.892 (0.836) Data 0.001 (0.002) Loss 3.4297 (4.2101) Prec@1 20.000 (13.063) Prec@5 47.500 (32.977) Epoch: [0][4280/11272] Time 0.869 (0.836) Data 0.001 (0.002) Loss 3.6594 (4.2085) Prec@1 19.375 (13.086) Prec@5 43.125 (33.016) Epoch: [0][4290/11272] Time 0.789 (0.836) Data 0.002 (0.002) Loss 3.3589 (4.2067) Prec@1 18.750 (13.108) Prec@5 48.125 (33.051) Epoch: [0][4300/11272] Time 0.769 (0.836) Data 0.002 (0.002) Loss 3.5304 (4.2053) Prec@1 21.250 (13.121) Prec@5 47.500 (33.086) Epoch: [0][4310/11272] Time 0.937 (0.836) Data 0.002 (0.002) Loss 3.3688 (4.2036) Prec@1 23.750 (13.141) Prec@5 50.625 (33.123) Epoch: [0][4320/11272] Time 0.932 (0.836) Data 0.001 (0.002) Loss 3.7507 (4.2019) Prec@1 20.625 (13.163) Prec@5 50.000 (33.162) Epoch: [0][4330/11272] Time 0.773 (0.836) Data 0.002 (0.002) Loss 3.6782 (4.2006) Prec@1 19.375 (13.182) Prec@5 43.750 (33.190) Epoch: [0][4340/11272] Time 0.901 (0.836) Data 0.002 (0.002) Loss 3.5269 (4.1992) Prec@1 21.250 (13.198) Prec@5 51.875 (33.221) Epoch: [0][4350/11272] Time 0.886 (0.836) Data 0.002 (0.002) Loss 3.6347 (4.1978) Prec@1 21.875 (13.214) Prec@5 45.625 (33.252) Epoch: [0][4360/11272] Time 0.751 (0.836) Data 0.001 (0.002) Loss 3.4189 (4.1962) Prec@1 20.000 (13.232) Prec@5 49.375 (33.289) Epoch: [0][4370/11272] Time 0.781 (0.836) Data 0.002 (0.002) Loss 3.3098 (4.1947) Prec@1 21.875 (13.249) Prec@5 53.750 (33.325) Epoch: [0][4380/11272] Time 0.885 (0.836) Data 0.001 (0.002) Loss 3.5020 (4.1931) Prec@1 22.500 (13.266) Prec@5 45.000 (33.361) Epoch: [0][4390/11272] Time 0.885 (0.836) Data 0.002 (0.002) Loss 3.6745 (4.1915) Prec@1 15.625 (13.288) Prec@5 43.750 (33.395) Epoch: [0][4400/11272] Time 0.747 (0.836) Data 0.001 (0.002) Loss 3.6690 (4.1899) Prec@1 17.500 (13.307) Prec@5 40.625 (33.429) Epoch: [0][4410/11272] Time 0.747 (0.836) Data 0.002 (0.002) Loss 3.3994 (4.1884) Prec@1 21.875 (13.324) Prec@5 48.750 (33.462) Epoch: [0][4420/11272] Time 0.931 (0.836) Data 0.001 (0.002) Loss 3.6063 (4.1869) Prec@1 19.375 (13.341) Prec@5 50.625 (33.500) Epoch: [0][4430/11272] Time 0.938 (0.836) Data 0.002 (0.002) Loss 3.7646 (4.1855) Prec@1 18.750 (13.354) Prec@5 42.500 (33.530) Epoch: [0][4440/11272] Time 0.765 (0.836) Data 0.002 (0.002) Loss 3.4557 (4.1841) Prec@1 21.875 (13.372) Prec@5 46.875 (33.560) Epoch: [0][4450/11272] Time 0.751 (0.836) Data 0.001 (0.002) Loss 3.2023 (4.1825) Prec@1 26.875 (13.394) Prec@5 51.875 (33.598) Epoch: [0][4460/11272] Time 0.865 (0.836) Data 0.001 (0.002) Loss 3.4352 (4.1810) Prec@1 23.125 (13.412) Prec@5 53.750 (33.633) Epoch: [0][4470/11272] Time 0.757 (0.836) Data 0.003 (0.002) Loss 3.5056 (4.1797) Prec@1 24.375 (13.429) Prec@5 47.500 (33.662) Epoch: [0][4480/11272] Time 0.734 (0.836) Data 0.001 (0.002) Loss 3.5906 (4.1783) Prec@1 21.250 (13.444) Prec@5 48.750 (33.695) Epoch: [0][4490/11272] Time 0.880 (0.836) Data 0.002 (0.002) Loss 3.4241 (4.1766) Prec@1 28.125 (13.466) Prec@5 49.375 (33.730) Epoch: [0][4500/11272] Time 0.980 (0.836) Data 0.002 (0.002) Loss 3.5746 (4.1751) Prec@1 21.875 (13.487) Prec@5 45.000 (33.762) Epoch: [0][4510/11272] Time 0.774 (0.836) Data 0.002 (0.002) Loss 3.5283 (4.1736) Prec@1 20.625 (13.507) Prec@5 51.875 (33.795) Epoch: [0][4520/11272] Time 0.748 (0.836) Data 0.001 (0.002) Loss 3.6125 (4.1723) Prec@1 22.500 (13.523) Prec@5 45.000 (33.822) Epoch: [0][4530/11272] Time 0.946 (0.836) Data 0.002 (0.002) Loss 3.4525 (4.1708) Prec@1 23.125 (13.545) Prec@5 49.375 (33.856) Epoch: [0][4540/11272] Time 0.887 (0.836) Data 0.001 (0.002) Loss 3.4649 (4.1693) Prec@1 23.750 (13.562) Prec@5 51.250 (33.890) Epoch: [0][4550/11272] Time 0.757 (0.836) Data 0.002 (0.002) Loss 3.9169 (4.1677) Prec@1 16.875 (13.584) Prec@5 44.375 (33.925) Epoch: [0][4560/11272] Time 0.736 (0.836) Data 0.002 (0.002) Loss 3.3477 (4.1664) Prec@1 30.625 (13.604) Prec@5 56.250 (33.960) Epoch: [0][4570/11272] Time 0.895 (0.836) Data 0.002 (0.002) Loss 3.2134 (4.1650) Prec@1 27.500 (13.624) Prec@5 55.625 (33.992) Epoch: [0][4580/11272] Time 0.950 (0.836) Data 0.002 (0.002) Loss 3.3056 (4.1636) Prec@1 24.375 (13.639) Prec@5 53.125 (34.022) Epoch: [0][4590/11272] Time 0.778 (0.836) Data 0.002 (0.002) Loss 3.3166 (4.1622) Prec@1 22.500 (13.656) Prec@5 54.375 (34.052) Epoch: [0][4600/11272] Time 0.908 (0.836) Data 0.002 (0.002) Loss 3.5447 (4.1608) Prec@1 23.125 (13.674) Prec@5 48.750 (34.084) Epoch: [0][4610/11272] Time 0.968 (0.836) Data 0.002 (0.002) Loss 3.6679 (4.1591) Prec@1 23.750 (13.697) Prec@5 47.500 (34.123) Epoch: [0][4620/11272] Time 0.738 (0.836) Data 0.001 (0.002) Loss 3.3840 (4.1578) Prec@1 26.250 (13.713) Prec@5 49.375 (34.155) Epoch: [0][4630/11272] Time 0.792 (0.836) Data 0.002 (0.002) Loss 3.6571 (4.1565) Prec@1 18.750 (13.729) Prec@5 41.875 (34.181) Epoch: [0][4640/11272] Time 0.925 (0.836) Data 0.002 (0.002) Loss 3.4656 (4.1551) Prec@1 26.250 (13.747) Prec@5 55.000 (34.211) Epoch: [0][4650/11272] Time 0.861 (0.836) Data 0.001 (0.002) Loss 3.3833 (4.1535) Prec@1 23.125 (13.767) Prec@5 51.250 (34.249) Epoch: [0][4660/11272] Time 0.752 (0.836) Data 0.002 (0.002) Loss 3.3474 (4.1522) Prec@1 25.625 (13.783) Prec@5 58.750 (34.282) Epoch: [0][4670/11272] Time 0.749 (0.836) Data 0.001 (0.002) Loss 3.4760 (4.1507) Prec@1 23.125 (13.803) Prec@5 46.250 (34.316) Epoch: [0][4680/11272] Time 0.873 (0.836) Data 0.002 (0.002) Loss 3.6791 (4.1494) Prec@1 25.625 (13.818) Prec@5 45.625 (34.345) Epoch: [0][4690/11272] Time 0.864 (0.836) Data 0.001 (0.002) Loss 3.6410 (4.1479) Prec@1 21.250 (13.837) Prec@5 46.875 (34.382) Epoch: [0][4700/11272] Time 0.741 (0.836) Data 0.002 (0.002) Loss 3.3653 (4.1467) Prec@1 21.875 (13.853) Prec@5 50.625 (34.408) Epoch: [0][4710/11272] Time 0.748 (0.836) Data 0.001 (0.002) Loss 3.5611 (4.1452) Prec@1 22.500 (13.874) Prec@5 45.000 (34.441) Epoch: [0][4720/11272] Time 0.882 (0.836) Data 0.001 (0.002) Loss 3.5530 (4.1438) Prec@1 18.125 (13.892) Prec@5 48.750 (34.473) Epoch: [0][4730/11272] Time 0.793 (0.836) Data 0.003 (0.002) Loss 3.4476 (4.1424) Prec@1 21.875 (13.913) Prec@5 50.000 (34.505) Epoch: [0][4740/11272] Time 0.726 (0.835) Data 0.001 (0.002) Loss 3.4366 (4.1408) Prec@1 25.000 (13.933) Prec@5 51.250 (34.539) Epoch: [0][4750/11272] Time 0.952 (0.835) Data 0.001 (0.002) Loss 3.6396 (4.1394) Prec@1 23.750 (13.954) Prec@5 48.125 (34.572) Epoch: [0][4760/11272] Time 0.920 (0.835) Data 0.001 (0.002) Loss 3.4514 (4.1380) Prec@1 21.875 (13.971) Prec@5 52.500 (34.605) Epoch: [0][4770/11272] Time 0.749 (0.835) Data 0.001 (0.002) Loss 3.5393 (4.1364) Prec@1 26.875 (13.993) Prec@5 47.500 (34.642) Epoch: [0][4780/11272] Time 0.744 (0.835) Data 0.001 (0.002) Loss 3.3735 (4.1351) Prec@1 23.750 (14.010) Prec@5 51.250 (34.671) Epoch: [0][4790/11272] Time 0.891 (0.835) Data 0.002 (0.002) Loss 3.4487 (4.1339) Prec@1 23.750 (14.024) Prec@5 48.125 (34.698) Epoch: [0][4800/11272] Time 0.885 (0.835) Data 0.001 (0.002) Loss 3.4703 (4.1325) Prec@1 27.500 (14.043) Prec@5 48.125 (34.732) Epoch: [0][4810/11272] Time 0.719 (0.835) Data 0.001 (0.002) Loss 3.4641 (4.1312) Prec@1 23.750 (14.060) Prec@5 48.750 (34.761) Epoch: [0][4820/11272] Time 0.756 (0.835) Data 0.001 (0.002) Loss 3.3814 (4.1297) Prec@1 24.375 (14.078) Prec@5 50.625 (34.794) Epoch: [0][4830/11272] Time 0.978 (0.835) Data 0.002 (0.002) Loss 3.4729 (4.1283) Prec@1 23.125 (14.096) Prec@5 51.250 (34.826) Epoch: [0][4840/11272] Time 0.861 (0.835) Data 0.001 (0.002) Loss 3.2887 (4.1269) Prec@1 22.500 (14.113) Prec@5 52.500 (34.858) Epoch: [0][4850/11272] Time 0.838 (0.835) Data 0.001 (0.002) Loss 3.4616 (4.1256) Prec@1 25.625 (14.130) Prec@5 52.500 (34.888) Epoch: [0][4860/11272] Time 0.954 (0.835) Data 0.002 (0.002) Loss 3.4195 (4.1241) Prec@1 19.375 (14.150) Prec@5 53.750 (34.920) Epoch: [0][4870/11272] Time 0.929 (0.835) Data 0.002 (0.002) Loss 3.4317 (4.1227) Prec@1 21.250 (14.170) Prec@5 49.375 (34.949) Epoch: [0][4880/11272] Time 0.754 (0.835) Data 0.002 (0.002) Loss 3.2748 (4.1212) Prec@1 29.375 (14.188) Prec@5 51.250 (34.981) Epoch: [0][4890/11272] Time 0.732 (0.835) Data 0.001 (0.002) Loss 3.3960 (4.1199) Prec@1 24.375 (14.202) Prec@5 50.625 (35.010) Epoch: [0][4900/11272] Time 0.976 (0.835) Data 0.001 (0.002) Loss 3.5641 (4.1187) Prec@1 16.875 (14.219) Prec@5 42.500 (35.038) Epoch: [0][4910/11272] Time 0.885 (0.835) Data 0.001 (0.002) Loss 3.8278 (4.1173) Prec@1 16.250 (14.234) Prec@5 40.000 (35.070) Epoch: [0][4920/11272] Time 0.738 (0.835) Data 0.001 (0.002) Loss 3.6373 (4.1159) Prec@1 18.750 (14.250) Prec@5 48.750 (35.101) Epoch: [0][4930/11272] Time 0.772 (0.835) Data 0.002 (0.002) Loss 3.7370 (4.1147) Prec@1 20.000 (14.268) Prec@5 42.500 (35.129) Epoch: [0][4940/11272] Time 0.881 (0.835) Data 0.001 (0.002) Loss 3.6427 (4.1134) Prec@1 17.500 (14.282) Prec@5 45.000 (35.156) Epoch: [0][4950/11272] Time 0.907 (0.835) Data 0.002 (0.002) Loss 3.2259 (4.1119) Prec@1 24.375 (14.301) Prec@5 54.375 (35.187) Epoch: [0][4960/11272] Time 0.771 (0.835) Data 0.002 (0.002) Loss 3.1619 (4.1105) Prec@1 28.125 (14.319) Prec@5 55.000 (35.218) Epoch: [0][4970/11272] Time 0.731 (0.835) Data 0.002 (0.002) Loss 3.4081 (4.1093) Prec@1 23.750 (14.335) Prec@5 53.750 (35.246) Epoch: [0][4980/11272] Time 0.854 (0.835) Data 0.002 (0.002) Loss 3.4711 (4.1077) Prec@1 23.125 (14.356) Prec@5 51.875 (35.282) Epoch: [0][4990/11272] Time 0.848 (0.835) Data 0.001 (0.002) Loss 3.4181 (4.1063) Prec@1 21.250 (14.374) Prec@5 46.250 (35.313) Epoch: [0][5000/11272] Time 0.751 (0.835) Data 0.001 (0.002) Loss 3.3920 (4.1049) Prec@1 23.750 (14.392) Prec@5 52.500 (35.345) Epoch: [0][5010/11272] Time 0.863 (0.835) Data 0.001 (0.002) Loss 3.1731 (4.1037) Prec@1 30.625 (14.409) Prec@5 57.500 (35.371) Epoch: [0][5020/11272] Time 0.941 (0.835) Data 0.002 (0.002) Loss 3.5570 (4.1025) Prec@1 19.375 (14.426) Prec@5 50.000 (35.401) Epoch: [0][5030/11272] Time 0.764 (0.835) Data 0.001 (0.002) Loss 3.4146 (4.1012) Prec@1 25.000 (14.444) Prec@5 48.125 (35.430) Epoch: [0][5040/11272] Time 0.775 (0.835) Data 0.002 (0.002) Loss 3.3669 (4.1000) Prec@1 23.750 (14.458) Prec@5 50.000 (35.455) Epoch: [0][5050/11272] Time 0.867 (0.835) Data 0.001 (0.002) Loss 3.5303 (4.0988) Prec@1 21.875 (14.473) Prec@5 47.500 (35.478) Epoch: [0][5060/11272] Time 0.925 (0.835) Data 0.002 (0.002) Loss 3.6410 (4.0977) Prec@1 17.500 (14.488) Prec@5 44.375 (35.504) Epoch: [0][5070/11272] Time 0.753 (0.835) Data 0.001 (0.002) Loss 3.5400 (4.0963) Prec@1 23.750 (14.508) Prec@5 45.625 (35.533) Epoch: [0][5080/11272] Time 0.797 (0.835) Data 0.001 (0.002) Loss 3.4900 (4.0950) Prec@1 21.875 (14.523) Prec@5 48.125 (35.557) Epoch: [0][5090/11272] Time 0.940 (0.835) Data 0.002 (0.002) Loss 3.3142 (4.0937) Prec@1 24.375 (14.538) Prec@5 55.625 (35.585) Epoch: [0][5100/11272] Time 0.849 (0.835) Data 0.001 (0.002) Loss 3.3675 (4.0924) Prec@1 20.625 (14.552) Prec@5 56.875 (35.616) Epoch: [0][5110/11272] Time 0.792 (0.835) Data 0.002 (0.002) Loss 3.4615 (4.0908) Prec@1 27.500 (14.575) Prec@5 47.500 (35.649) Epoch: [0][5120/11272] Time 0.742 (0.835) Data 0.002 (0.002) Loss 3.3636 (4.0895) Prec@1 25.625 (14.593) Prec@5 50.625 (35.681) Epoch: [0][5130/11272] Time 0.895 (0.835) Data 0.002 (0.002) Loss 3.6338 (4.0883) Prec@1 22.500 (14.609) Prec@5 47.500 (35.710) Epoch: [0][5140/11272] Time 0.761 (0.835) Data 0.001 (0.002) Loss 3.3098 (4.0871) Prec@1 23.750 (14.624) Prec@5 50.625 (35.736) Epoch: [0][5150/11272] Time 0.750 (0.835) Data 0.002 (0.002) Loss 3.2082 (4.0858) Prec@1 25.625 (14.642) Prec@5 55.625 (35.768) Epoch: [0][5160/11272] Time 0.884 (0.835) Data 0.002 (0.002) Loss 3.4279 (4.0845) Prec@1 25.625 (14.659) Prec@5 48.125 (35.795) Epoch: [0][5170/11272] Time 0.882 (0.835) Data 0.002 (0.002) Loss 3.4027 (4.0833) Prec@1 25.625 (14.675) Prec@5 51.875 (35.822) Epoch: [0][5180/11272] Time 0.741 (0.835) Data 0.002 (0.002) Loss 3.2198 (4.0818) Prec@1 26.875 (14.697) Prec@5 51.875 (35.856) Epoch: [0][5190/11272] Time 0.757 (0.835) Data 0.001 (0.002) Loss 3.3445 (4.0806) Prec@1 22.500 (14.712) Prec@5 50.625 (35.882) Epoch: [0][5200/11272] Time 0.902 (0.835) Data 0.002 (0.002) Loss 3.6833 (4.0793) Prec@1 21.875 (14.728) Prec@5 45.000 (35.914) Epoch: [0][5210/11272] Time 0.958 (0.835) Data 0.002 (0.002) Loss 3.4520 (4.0781) Prec@1 21.875 (14.743) Prec@5 48.125 (35.938) Epoch: [0][5220/11272] Time 0.761 (0.835) Data 0.002 (0.002) Loss 3.3509 (4.0770) Prec@1 23.750 (14.758) Prec@5 52.500 (35.964) Epoch: [0][5230/11272] Time 0.765 (0.835) Data 0.002 (0.002) Loss 3.3157 (4.0757) Prec@1 23.750 (14.775) Prec@5 48.125 (35.993) Epoch: [0][5240/11272] Time 0.851 (0.835) Data 0.001 (0.002) Loss 3.4010 (4.0745) Prec@1 30.000 (14.789) Prec@5 55.000 (36.018) Epoch: [0][5250/11272] Time 0.905 (0.835) Data 0.002 (0.002) Loss 3.3538 (4.0734) Prec@1 22.500 (14.807) Prec@5 53.125 (36.043) Epoch: [0][5260/11272] Time 0.810 (0.835) Data 0.001 (0.002) Loss 3.3650 (4.0722) Prec@1 18.125 (14.821) Prec@5 54.375 (36.068) Epoch: [0][5270/11272] Time 0.872 (0.835) Data 0.001 (0.002) Loss 3.4466 (4.0710) Prec@1 25.000 (14.836) Prec@5 53.125 (36.096) Epoch: [0][5280/11272] Time 0.892 (0.835) Data 0.002 (0.002) Loss 3.2937 (4.0696) Prec@1 28.750 (14.854) Prec@5 51.250 (36.127) Epoch: [0][5290/11272] Time 0.729 (0.835) Data 0.001 (0.002) Loss 3.4137 (4.0684) Prec@1 22.500 (14.867) Prec@5 53.750 (36.154) Epoch: [0][5300/11272] Time 0.787 (0.835) Data 0.001 (0.002) Loss 3.5921 (4.0671) Prec@1 20.000 (14.884) Prec@5 47.500 (36.181) Epoch: [0][5310/11272] Time 0.886 (0.835) Data 0.002 (0.002) Loss 3.5022 (4.0659) Prec@1 23.125 (14.900) Prec@5 51.875 (36.207) Epoch: [0][5320/11272] Time 0.923 (0.835) Data 0.002 (0.002) Loss 3.7778 (4.0649) Prec@1 16.250 (14.910) Prec@5 43.125 (36.231) Epoch: [0][5330/11272] Time 0.809 (0.835) Data 0.001 (0.002) Loss 3.1802 (4.0635) Prec@1 27.500 (14.926) Prec@5 53.750 (36.263) Epoch: [0][5340/11272] Time 0.752 (0.835) Data 0.002 (0.002) Loss 3.1143 (4.0621) Prec@1 30.000 (14.944) Prec@5 58.750 (36.295) Epoch: [0][5350/11272] Time 0.843 (0.835) Data 0.001 (0.002) Loss 3.6190 (4.0607) Prec@1 19.375 (14.961) Prec@5 48.750 (36.328) Epoch: [0][5360/11272] Time 0.849 (0.835) Data 0.001 (0.002) Loss 3.3760 (4.0595) Prec@1 23.750 (14.975) Prec@5 53.125 (36.355) Epoch: [0][5370/11272] Time 0.746 (0.835) Data 0.001 (0.002) Loss 3.5178 (4.0582) Prec@1 19.375 (14.988) Prec@5 46.875 (36.381) Epoch: [0][5380/11272] Time 0.736 (0.835) Data 0.001 (0.002) Loss 3.5452 (4.0570) Prec@1 20.625 (15.002) Prec@5 48.125 (36.408) Epoch: [0][5390/11272] Time 0.940 (0.835) Data 0.001 (0.002) Loss 3.3222 (4.0559) Prec@1 25.625 (15.018) Prec@5 53.750 (36.433) Epoch: [0][5400/11272] Time 0.743 (0.834) Data 0.003 (0.002) Loss 3.2101 (4.0546) Prec@1 23.750 (15.032) Prec@5 53.125 (36.461) Epoch: [0][5410/11272] Time 0.749 (0.834) Data 0.001 (0.002) Loss 3.4318 (4.0537) Prec@1 22.500 (15.044) Prec@5 50.000 (36.482) Epoch: [0][5420/11272] Time 0.858 (0.834) Data 0.001 (0.002) Loss 3.3164 (4.0525) Prec@1 21.875 (15.059) Prec@5 48.125 (36.507) Epoch: [0][5430/11272] Time 0.905 (0.834) Data 0.003 (0.002) Loss 3.2722 (4.0512) Prec@1 22.500 (15.075) Prec@5 48.125 (36.536) Epoch: [0][5440/11272] Time 0.778 (0.834) Data 0.001 (0.002) Loss 3.2396 (4.0500) Prec@1 25.000 (15.092) Prec@5 52.500 (36.560) Epoch: [0][5450/11272] Time 0.757 (0.834) Data 0.001 (0.002) Loss 3.2899 (4.0487) Prec@1 26.250 (15.109) Prec@5 53.125 (36.586) Epoch: [0][5460/11272] Time 0.920 (0.834) Data 0.002 (0.002) Loss 3.3009 (4.0476) Prec@1 25.625 (15.124) Prec@5 53.750 (36.613) Epoch: [0][5470/11272] Time 0.901 (0.834) Data 0.002 (0.002) Loss 3.6387 (4.0465) Prec@1 21.250 (15.136) Prec@5 51.875 (36.639) Epoch: [0][5480/11272] Time 0.807 (0.834) Data 0.002 (0.002) Loss 3.3902 (4.0455) Prec@1 21.875 (15.149) Prec@5 46.250 (36.661) Epoch: [0][5490/11272] Time 0.731 (0.834) Data 0.002 (0.002) Loss 3.3135 (4.0443) Prec@1 23.750 (15.161) Prec@5 54.375 (36.686) Epoch: [0][5500/11272] Time 0.902 (0.834) Data 0.002 (0.002) Loss 3.4951 (4.0432) Prec@1 22.500 (15.174) Prec@5 49.375 (36.710) Epoch: [0][5510/11272] Time 0.843 (0.834) Data 0.001 (0.002) Loss 3.3572 (4.0420) Prec@1 20.000 (15.187) Prec@5 53.125 (36.738) Epoch: [0][5520/11272] Time 0.748 (0.834) Data 0.001 (0.002) Loss 3.4640 (4.0409) Prec@1 23.750 (15.201) Prec@5 48.125 (36.760) Epoch: [0][5530/11272] Time 0.861 (0.834) Data 0.001 (0.002) Loss 3.2958 (4.0397) Prec@1 26.250 (15.218) Prec@5 55.625 (36.790) Epoch: [0][5540/11272] Time 0.878 (0.834) Data 0.001 (0.002) Loss 3.4012 (4.0384) Prec@1 22.500 (15.232) Prec@5 48.750 (36.818) Epoch: [0][5550/11272] Time 0.772 (0.834) Data 0.002 (0.002) Loss 3.4284 (4.0373) Prec@1 22.500 (15.247) Prec@5 51.875 (36.842) Epoch: [0][5560/11272] Time 0.758 (0.834) Data 0.002 (0.002) Loss 3.3425 (4.0360) Prec@1 23.750 (15.265) Prec@5 53.125 (36.870) Epoch: [0][5570/11272] Time 0.884 (0.834) Data 0.002 (0.002) Loss 3.1369 (4.0348) Prec@1 25.000 (15.281) Prec@5 56.250 (36.897) Epoch: [0][5580/11272] Time 0.921 (0.834) Data 0.001 (0.002) Loss 3.3983 (4.0336) Prec@1 23.125 (15.297) Prec@5 49.375 (36.923) Epoch: [0][5590/11272] Time 0.762 (0.834) Data 0.002 (0.002) Loss 3.4758 (4.0323) Prec@1 25.000 (15.316) Prec@5 45.625 (36.952) Epoch: [0][5600/11272] Time 0.755 (0.834) Data 0.002 (0.002) Loss 3.4482 (4.0311) Prec@1 20.625 (15.331) Prec@5 53.125 (36.977) Epoch: [0][5610/11272] Time 0.863 (0.834) Data 0.002 (0.002) Loss 3.4759 (4.0302) Prec@1 23.750 (15.341) Prec@5 48.125 (36.999) Epoch: [0][5620/11272] Time 0.880 (0.834) Data 0.002 (0.002) Loss 3.7276 (4.0290) Prec@1 16.250 (15.356) Prec@5 44.375 (37.027) Epoch: [0][5630/11272] Time 0.731 (0.834) Data 0.001 (0.002) Loss 3.3489 (4.0278) Prec@1 23.750 (15.370) Prec@5 55.625 (37.055) Epoch: [0][5640/11272] Time 0.727 (0.834) Data 0.001 (0.002) Loss 3.3357 (4.0266) Prec@1 18.750 (15.382) Prec@5 51.875 (37.081) Epoch: [0][5650/11272] Time 0.914 (0.834) Data 0.003 (0.002) Loss 3.2125 (4.0253) Prec@1 24.375 (15.399) Prec@5 57.500 (37.112) Epoch: [0][5660/11272] Time 0.787 (0.834) Data 0.004 (0.002) Loss 3.3091 (4.0241) Prec@1 24.375 (15.416) Prec@5 52.500 (37.138) Epoch: [0][5670/11272] Time 0.736 (0.834) Data 0.002 (0.002) Loss 3.6144 (4.0230) Prec@1 21.250 (15.429) Prec@5 44.375 (37.162) Epoch: [0][5680/11272] Time 0.903 (0.834) Data 0.002 (0.002) Loss 3.4700 (4.0220) Prec@1 21.250 (15.442) Prec@5 50.625 (37.186) Epoch: [0][5690/11272] Time 0.897 (0.834) Data 0.002 (0.002) Loss 3.5687 (4.0209) Prec@1 25.000 (15.457) Prec@5 48.125 (37.208) Epoch: [0][5700/11272] Time 0.766 (0.834) Data 0.002 (0.002) Loss 3.3218 (4.0198) Prec@1 25.625 (15.471) Prec@5 53.125 (37.235) Epoch: [0][5710/11272] Time 0.728 (0.834) Data 0.001 (0.002) Loss 3.4275 (4.0187) Prec@1 23.125 (15.488) Prec@5 47.500 (37.260) Epoch: [0][5720/11272] Time 0.929 (0.834) Data 0.002 (0.002) Loss 3.3822 (4.0175) Prec@1 27.500 (15.504) Prec@5 53.125 (37.285) Epoch: [0][5730/11272] Time 0.888 (0.834) Data 0.002 (0.002) Loss 3.3880 (4.0164) Prec@1 22.500 (15.520) Prec@5 45.625 (37.310) Epoch: [0][5740/11272] Time 0.768 (0.834) Data 0.002 (0.002) Loss 3.3039 (4.0153) Prec@1 27.500 (15.535) Prec@5 51.875 (37.335) Epoch: [0][5750/11272] Time 0.745 (0.834) Data 0.002 (0.002) Loss 3.2499 (4.0142) Prec@1 27.500 (15.549) Prec@5 55.625 (37.363) Epoch: [0][5760/11272] Time 0.897 (0.834) Data 0.002 (0.002) Loss 3.5291 (4.0131) Prec@1 22.500 (15.561) Prec@5 49.375 (37.385) Epoch: [0][5770/11272] Time 0.931 (0.834) Data 0.002 (0.002) Loss 3.2792 (4.0121) Prec@1 26.250 (15.575) Prec@5 47.500 (37.409) Epoch: [0][5780/11272] Time 0.848 (0.834) Data 0.002 (0.002) Loss 3.3695 (4.0108) Prec@1 20.000 (15.591) Prec@5 48.750 (37.435) Epoch: [0][5790/11272] Time 0.924 (0.834) Data 0.001 (0.002) Loss 3.3604 (4.0097) Prec@1 21.875 (15.605) Prec@5 51.250 (37.463) Epoch: [0][5800/11272] Time 0.892 (0.834) Data 0.002 (0.002) Loss 3.4498 (4.0085) Prec@1 21.875 (15.621) Prec@5 54.375 (37.490) Epoch: [0][5810/11272] Time 0.744 (0.834) Data 0.001 (0.002) Loss 3.4003 (4.0075) Prec@1 28.125 (15.633) Prec@5 51.250 (37.512) Epoch: [0][5820/11272] Time 0.752 (0.834) Data 0.001 (0.002) Loss 3.4731 (4.0064) Prec@1 25.000 (15.646) Prec@5 49.375 (37.535) Epoch: [0][5830/11272] Time 0.901 (0.834) Data 0.002 (0.002) Loss 3.1615 (4.0052) Prec@1 27.500 (15.663) Prec@5 58.125 (37.560) Epoch: [0][5840/11272] Time 0.895 (0.834) Data 0.001 (0.002) Loss 3.5864 (4.0042) Prec@1 20.000 (15.679) Prec@5 44.375 (37.583) Epoch: [0][5850/11272] Time 0.775 (0.834) Data 0.001 (0.002) Loss 3.5918 (4.0032) Prec@1 22.500 (15.692) Prec@5 45.000 (37.606) Epoch: [0][5860/11272] Time 0.759 (0.834) Data 0.001 (0.002) Loss 3.3065 (4.0024) Prec@1 24.375 (15.705) Prec@5 54.375 (37.623) Epoch: [0][5870/11272] Time 0.913 (0.834) Data 0.002 (0.002) Loss 3.3982 (4.0013) Prec@1 23.750 (15.719) Prec@5 51.250 (37.648) Epoch: [0][5880/11272] Time 0.872 (0.834) Data 0.001 (0.002) Loss 3.3796 (4.0002) Prec@1 26.250 (15.733) Prec@5 57.500 (37.673) Epoch: [0][5890/11272] Time 0.752 (0.834) Data 0.001 (0.002) Loss 3.4534 (3.9990) Prec@1 21.250 (15.747) Prec@5 45.625 (37.697) Epoch: [0][5900/11272] Time 0.746 (0.834) Data 0.002 (0.002) Loss 3.4937 (3.9979) Prec@1 22.500 (15.763) Prec@5 50.625 (37.721) Epoch: [0][5910/11272] Time 0.886 (0.834) Data 0.002 (0.002) Loss 3.6354 (3.9970) Prec@1 21.875 (15.776) Prec@5 40.625 (37.743) Epoch: [0][5920/11272] Time 0.884 (0.834) Data 0.002 (0.002) Loss 3.2388 (3.9959) Prec@1 28.125 (15.789) Prec@5 55.000 (37.768) Epoch: [0][5930/11272] Time 0.773 (0.834) Data 0.002 (0.002) Loss 3.3002 (3.9948) Prec@1 25.000 (15.804) Prec@5 51.875 (37.793) Epoch: [0][5940/11272] Time 0.952 (0.834) Data 0.002 (0.002) Loss 3.4709 (3.9938) Prec@1 23.125 (15.817) Prec@5 50.000 (37.816) Epoch: [0][5950/11272] Time 0.901 (0.834) Data 0.002 (0.002) Loss 3.2653 (3.9928) Prec@1 29.375 (15.831) Prec@5 56.250 (37.837) Epoch: [0][5960/11272] Time 0.733 (0.834) Data 0.002 (0.002) Loss 3.4578 (3.9917) Prec@1 24.375 (15.846) Prec@5 50.000 (37.858) Epoch: [0][5970/11272] Time 0.813 (0.834) Data 0.003 (0.002) Loss 3.3114 (3.9906) Prec@1 25.625 (15.862) Prec@5 51.250 (37.884) Epoch: [0][5980/11272] Time 0.959 (0.834) Data 0.002 (0.002) Loss 3.3332 (3.9895) Prec@1 26.875 (15.878) Prec@5 51.875 (37.910) Epoch: [0][5990/11272] Time 0.920 (0.834) Data 0.002 (0.002) Loss 3.3044 (3.9885) Prec@1 26.250 (15.891) Prec@5 51.875 (37.933) Epoch: [0][6000/11272] Time 0.737 (0.834) Data 0.002 (0.002) Loss 3.3510 (3.9873) Prec@1 20.625 (15.903) Prec@5 51.250 (37.958) Epoch: [0][6010/11272] Time 0.805 (0.834) Data 0.002 (0.002) Loss 3.2377 (3.9863) Prec@1 26.875 (15.920) Prec@5 55.000 (37.983) Epoch: [0][6020/11272] Time 0.857 (0.834) Data 0.002 (0.002) Loss 3.2859 (3.9851) Prec@1 22.500 (15.936) Prec@5 48.750 (38.006) Epoch: [0][6030/11272] Time 0.904 (0.834) Data 0.002 (0.002) Loss 3.4991 (3.9840) Prec@1 18.750 (15.948) Prec@5 50.625 (38.030) Epoch: [0][6040/11272] Time 0.805 (0.834) Data 0.002 (0.002) Loss 3.2426 (3.9829) Prec@1 24.375 (15.963) Prec@5 53.125 (38.056) Epoch: [0][6050/11272] Time 0.776 (0.834) Data 0.002 (0.002) Loss 3.2932 (3.9819) Prec@1 25.000 (15.974) Prec@5 55.625 (38.077) Epoch: [0][6060/11272] Time 0.883 (0.834) Data 0.006 (0.002) Loss 3.0873 (3.9808) Prec@1 31.250 (15.989) Prec@5 56.250 (38.097) Epoch: [0][6070/11272] Time 0.761 (0.834) Data 0.001 (0.002) Loss 3.0642 (3.9797) Prec@1 28.750 (16.005) Prec@5 62.500 (38.123) Epoch: [0][6080/11272] Time 0.765 (0.834) Data 0.002 (0.002) Loss 3.2449 (3.9785) Prec@1 23.750 (16.019) Prec@5 55.625 (38.149) Epoch: [0][6090/11272] Time 0.889 (0.834) Data 0.002 (0.002) Loss 3.6164 (3.9774) Prec@1 21.250 (16.034) Prec@5 51.250 (38.172) Epoch: [0][6100/11272] Time 0.906 (0.834) Data 0.001 (0.002) Loss 3.3475 (3.9764) Prec@1 28.750 (16.051) Prec@5 52.500 (38.197) Epoch: [0][6110/11272] Time 0.775 (0.834) Data 0.002 (0.002) Loss 3.3340 (3.9752) Prec@1 25.625 (16.068) Prec@5 52.500 (38.222) Epoch: [0][6120/11272] Time 0.757 (0.834) Data 0.002 (0.002) Loss 3.3696 (3.9742) Prec@1 23.750 (16.082) Prec@5 53.750 (38.245) Epoch: [0][6130/11272] Time 0.914 (0.834) Data 0.002 (0.002) Loss 3.6466 (3.9732) Prec@1 20.625 (16.097) Prec@5 49.375 (38.269) Epoch: [0][6140/11272] Time 0.896 (0.834) Data 0.003 (0.002) Loss 3.3370 (3.9721) Prec@1 22.500 (16.110) Prec@5 55.625 (38.292) Epoch: [0][6150/11272] Time 0.709 (0.834) Data 0.001 (0.002) Loss 3.4633 (3.9711) Prec@1 22.500 (16.123) Prec@5 55.000 (38.315) Epoch: [0][6160/11272] Time 0.764 (0.834) Data 0.002 (0.002) Loss 3.2676 (3.9701) Prec@1 20.625 (16.138) Prec@5 53.125 (38.340) Epoch: [0][6170/11272] Time 0.902 (0.834) Data 0.002 (0.002) Loss 3.2966 (3.9691) Prec@1 24.375 (16.152) Prec@5 51.250 (38.363) Epoch: [0][6180/11272] Time 0.896 (0.834) Data 0.001 (0.002) Loss 3.2412 (3.9680) Prec@1 28.750 (16.169) Prec@5 52.500 (38.387) Epoch: [0][6190/11272] Time 0.807 (0.834) Data 0.002 (0.002) Loss 3.4816 (3.9671) Prec@1 21.250 (16.181) Prec@5 46.250 (38.409) Epoch: [0][6200/11272] Time 0.910 (0.834) Data 0.001 (0.002) Loss 3.1056 (3.9659) Prec@1 25.625 (16.198) Prec@5 58.750 (38.434) Epoch: [0][6210/11272] Time 0.845 (0.834) Data 0.001 (0.002) Loss 3.2634 (3.9649) Prec@1 26.250 (16.212) Prec@5 51.250 (38.457) Epoch: [0][6220/11272] Time 0.745 (0.833) Data 0.002 (0.002) Loss 3.6051 (3.9639) Prec@1 22.500 (16.227) Prec@5 46.250 (38.479) Epoch: [0][6230/11272] Time 0.737 (0.833) Data 0.001 (0.002) Loss 3.2652 (3.9628) Prec@1 18.750 (16.240) Prec@5 56.875 (38.502) Epoch: [0][6240/11272] Time 0.917 (0.833) Data 0.002 (0.002) Loss 3.2573 (3.9617) Prec@1 23.125 (16.256) Prec@5 51.875 (38.526) Epoch: [0][6250/11272] Time 0.905 (0.833) Data 0.001 (0.002) Loss 3.2349 (3.9609) Prec@1 33.750 (16.269) Prec@5 53.125 (38.545) Epoch: [0][6260/11272] Time 0.756 (0.833) Data 0.002 (0.002) Loss 3.4308 (3.9599) Prec@1 22.500 (16.283) Prec@5 51.250 (38.565) Epoch: [0][6270/11272] Time 0.814 (0.833) Data 0.001 (0.002) Loss 3.2773 (3.9588) Prec@1 26.250 (16.300) Prec@5 48.125 (38.588) Epoch: [0][6280/11272] Time 0.876 (0.833) Data 0.001 (0.002) Loss 3.3197 (3.9578) Prec@1 25.000 (16.313) Prec@5 53.750 (38.613) Epoch: [0][6290/11272] Time 0.872 (0.833) Data 0.002 (0.002) Loss 3.5036 (3.9569) Prec@1 19.375 (16.324) Prec@5 48.125 (38.631) Epoch: [0][6300/11272] Time 0.758 (0.833) Data 0.003 (0.002) Loss 3.3698 (3.9558) Prec@1 25.625 (16.340) Prec@5 52.500 (38.654) Epoch: [0][6310/11272] Time 0.746 (0.833) Data 0.001 (0.002) Loss 3.3078 (3.9548) Prec@1 21.875 (16.352) Prec@5 53.750 (38.674) Epoch: [0][6320/11272] Time 0.865 (0.833) Data 0.001 (0.002) Loss 3.2235 (3.9537) Prec@1 23.750 (16.370) Prec@5 50.000 (38.699) Epoch: [0][6330/11272] Time 0.792 (0.833) Data 0.005 (0.002) Loss 3.2133 (3.9527) Prec@1 26.250 (16.382) Prec@5 57.500 (38.720) Epoch: [0][6340/11272] Time 0.744 (0.833) Data 0.002 (0.002) Loss 3.3065 (3.9518) Prec@1 21.250 (16.394) Prec@5 52.500 (38.739) Epoch: [0][6350/11272] Time 0.894 (0.833) Data 0.002 (0.002) Loss 3.2521 (3.9509) Prec@1 23.125 (16.407) Prec@5 51.875 (38.762) Epoch: [0][6360/11272] Time 0.843 (0.833) Data 0.001 (0.002) Loss 3.4466 (3.9499) Prec@1 24.375 (16.420) Prec@5 48.750 (38.783) Epoch: [0][6370/11272] Time 0.747 (0.833) Data 0.002 (0.002) Loss 3.2649 (3.9487) Prec@1 22.500 (16.438) Prec@5 56.875 (38.811) Epoch: [0][6380/11272] Time 0.741 (0.833) Data 0.001 (0.002) Loss 3.3550 (3.9477) Prec@1 25.625 (16.453) Prec@5 53.750 (38.836) Epoch: [0][6390/11272] Time 0.852 (0.833) Data 0.002 (0.002) Loss 3.2349 (3.9467) Prec@1 25.000 (16.464) Prec@5 51.250 (38.860) Epoch: [0][6400/11272] Time 0.873 (0.833) Data 0.002 (0.002) Loss 3.2542 (3.9456) Prec@1 25.000 (16.478) Prec@5 51.875 (38.882) Epoch: [0][6410/11272] Time 0.749 (0.833) Data 0.001 (0.002) Loss 3.4204 (3.9446) Prec@1 21.250 (16.491) Prec@5 53.750 (38.906) Epoch: [0][6420/11272] Time 0.729 (0.833) Data 0.001 (0.002) Loss 3.3004 (3.9436) Prec@1 19.375 (16.503) Prec@5 57.500 (38.928) Epoch: [0][6430/11272] Time 0.873 (0.833) Data 0.001 (0.002) Loss 3.1821 (3.9426) Prec@1 27.500 (16.517) Prec@5 53.125 (38.948) Epoch: [0][6440/11272] Time 0.867 (0.833) Data 0.002 (0.002) Loss 3.1174 (3.9415) Prec@1 34.375 (16.533) Prec@5 53.125 (38.971) Epoch: [0][6450/11272] Time 0.741 (0.833) Data 0.001 (0.002) Loss 3.4288 (3.9406) Prec@1 25.000 (16.545) Prec@5 50.625 (38.989) Epoch: [0][6460/11272] Time 0.889 (0.833) Data 0.002 (0.002) Loss 3.3970 (3.9396) Prec@1 23.750 (16.561) Prec@5 50.000 (39.012) Epoch: [0][6470/11272] Time 0.872 (0.833) Data 0.002 (0.002) Loss 3.3469 (3.9388) Prec@1 30.000 (16.572) Prec@5 53.125 (39.030) Epoch: [0][6480/11272] Time 0.758 (0.833) Data 0.005 (0.002) Loss 3.3795 (3.9379) Prec@1 28.125 (16.583) Prec@5 53.125 (39.050) Epoch: [0][6490/11272] Time 0.752 (0.833) Data 0.002 (0.002) Loss 3.4348 (3.9370) Prec@1 19.375 (16.595) Prec@5 52.500 (39.070) Epoch: [0][6500/11272] Time 0.915 (0.833) Data 0.001 (0.002) Loss 3.2254 (3.9360) Prec@1 29.375 (16.612) Prec@5 56.250 (39.092) Epoch: [0][6510/11272] Time 0.915 (0.833) Data 0.001 (0.002) Loss 3.5157 (3.9351) Prec@1 20.625 (16.622) Prec@5 50.625 (39.112) Epoch: [0][6520/11272] Time 0.766 (0.833) Data 0.001 (0.002) Loss 3.3753 (3.9341) Prec@1 26.875 (16.636) Prec@5 48.750 (39.131) Epoch: [0][6530/11272] Time 0.760 (0.833) Data 0.001 (0.002) Loss 3.2820 (3.9333) Prec@1 25.625 (16.650) Prec@5 51.250 (39.150) Epoch: [0][6540/11272] Time 0.872 (0.833) Data 0.001 (0.002) Loss 3.3373 (3.9324) Prec@1 26.250 (16.661) Prec@5 55.000 (39.171) Epoch: [0][6550/11272] Time 0.854 (0.833) Data 0.001 (0.002) Loss 3.2024 (3.9316) Prec@1 28.125 (16.672) Prec@5 56.875 (39.190) Epoch: [0][6560/11272] Time 0.735 (0.833) Data 0.001 (0.002) Loss 3.6457 (3.9307) Prec@1 15.625 (16.683) Prec@5 43.750 (39.207) Epoch: [0][6570/11272] Time 0.756 (0.833) Data 0.002 (0.002) Loss 3.4573 (3.9297) Prec@1 19.375 (16.695) Prec@5 48.750 (39.227) Epoch: [0][6580/11272] Time 0.898 (0.833) Data 0.002 (0.002) Loss 3.4369 (3.9287) Prec@1 25.625 (16.711) Prec@5 46.250 (39.249) Epoch: [0][6590/11272] Time 0.760 (0.833) Data 0.003 (0.002) Loss 3.3127 (3.9277) Prec@1 25.625 (16.726) Prec@5 50.625 (39.272) Epoch: [0][6600/11272] Time 0.755 (0.833) Data 0.002 (0.002) Loss 3.3574 (3.9267) Prec@1 26.875 (16.739) Prec@5 58.125 (39.297) Epoch: [0][6610/11272] Time 0.910 (0.833) Data 0.001 (0.002) Loss 3.3297 (3.9257) Prec@1 26.875 (16.751) Prec@5 49.375 (39.316) Epoch: [0][6620/11272] Time 0.921 (0.833) Data 0.002 (0.002) Loss 3.4038 (3.9246) Prec@1 22.500 (16.767) Prec@5 50.625 (39.341) Epoch: [0][6630/11272] Time 0.764 (0.833) Data 0.002 (0.002) Loss 3.0417 (3.9237) Prec@1 32.500 (16.780) Prec@5 61.875 (39.362) Epoch: [0][6640/11272] Time 0.736 (0.833) Data 0.002 (0.002) Loss 3.2888 (3.9227) Prec@1 23.750 (16.795) Prec@5 53.125 (39.384) Epoch: [0][6650/11272] Time 0.912 (0.833) Data 0.001 (0.002) Loss 3.3429 (3.9218) Prec@1 24.375 (16.808) Prec@5 53.125 (39.405) Epoch: [0][6660/11272] Time 0.882 (0.833) Data 0.002 (0.002) Loss 3.2254 (3.9207) Prec@1 29.375 (16.826) Prec@5 56.875 (39.428) Epoch: [0][6670/11272] Time 0.779 (0.833) Data 0.001 (0.002) Loss 3.7481 (3.9199) Prec@1 19.375 (16.838) Prec@5 41.250 (39.448) Epoch: [0][6680/11272] Time 0.773 (0.833) Data 0.002 (0.002) Loss 3.3905 (3.9188) Prec@1 23.750 (16.852) Prec@5 50.000 (39.471) Epoch: [0][6690/11272] Time 0.894 (0.833) Data 0.002 (0.002) Loss 3.3239 (3.9180) Prec@1 25.000 (16.863) Prec@5 53.125 (39.491) Epoch: [0][6700/11272] Time 0.846 (0.833) Data 0.002 (0.002) Loss 3.2299 (3.9171) Prec@1 26.250 (16.873) Prec@5 55.000 (39.511) Epoch: [0][6710/11272] Time 0.790 (0.833) Data 0.002 (0.002) Loss 3.1120 (3.9161) Prec@1 28.125 (16.887) Prec@5 56.250 (39.532) Epoch: [0][6720/11272] Time 0.872 (0.833) Data 0.002 (0.002) Loss 3.1851 (3.9150) Prec@1 28.125 (16.902) Prec@5 50.625 (39.556) Epoch: [0][6730/11272] Time 0.902 (0.833) Data 0.002 (0.002) Loss 3.4322 (3.9139) Prec@1 16.250 (16.917) Prec@5 51.875 (39.579) Epoch: [0][6740/11272] Time 0.739 (0.833) Data 0.001 (0.002) Loss 3.4135 (3.9129) Prec@1 26.875 (16.932) Prec@5 48.125 (39.602) Epoch: [0][6750/11272] Time 0.751 (0.833) Data 0.001 (0.002) Loss 3.5250 (3.9120) Prec@1 20.625 (16.945) Prec@5 47.500 (39.621) Epoch: [0][6760/11272] Time 0.858 (0.833) Data 0.001 (0.002) Loss 3.3628 (3.9110) Prec@1 21.250 (16.956) Prec@5 52.500 (39.642) Epoch: [0][6770/11272] Time 0.973 (0.833) Data 0.002 (0.002) Loss 3.5486 (3.9102) Prec@1 22.500 (16.966) Prec@5 50.000 (39.661) Epoch: [0][6780/11272] Time 0.784 (0.833) Data 0.002 (0.002) Loss 3.1471 (3.9092) Prec@1 27.500 (16.980) Prec@5 56.875 (39.684) Epoch: [0][6790/11272] Time 0.746 (0.833) Data 0.001 (0.002) Loss 3.0781 (3.9083) Prec@1 25.000 (16.990) Prec@5 57.500 (39.704) Epoch: [0][6800/11272] Time 0.909 (0.833) Data 0.002 (0.002) Loss 3.3602 (3.9073) Prec@1 23.750 (17.004) Prec@5 54.375 (39.727) Epoch: [0][6810/11272] Time 0.847 (0.833) Data 0.002 (0.002) Loss 3.3600 (3.9063) Prec@1 25.625 (17.020) Prec@5 55.625 (39.750) Epoch: [0][6820/11272] Time 0.745 (0.833) Data 0.001 (0.002) Loss 3.1147 (3.9054) Prec@1 28.125 (17.032) Prec@5 53.750 (39.769) Epoch: [0][6830/11272] Time 0.739 (0.833) Data 0.002 (0.002) Loss 3.3598 (3.9045) Prec@1 25.000 (17.044) Prec@5 55.000 (39.789) Epoch: [0][6840/11272] Time 0.918 (0.833) Data 0.002 (0.002) Loss 3.4565 (3.9036) Prec@1 23.750 (17.055) Prec@5 48.750 (39.809) Epoch: [0][6850/11272] Time 0.890 (0.833) Data 0.002 (0.002) Loss 3.0362 (3.9028) Prec@1 25.625 (17.065) Prec@5 59.375 (39.826) Epoch: [0][6860/11272] Time 0.751 (0.833) Data 0.002 (0.002) Loss 3.4738 (3.9020) Prec@1 24.375 (17.078) Prec@5 48.750 (39.844) Epoch: [0][6870/11272] Time 0.872 (0.833) Data 0.002 (0.002) Loss 3.1271 (3.9010) Prec@1 31.875 (17.092) Prec@5 58.750 (39.866) Epoch: [0][6880/11272] Time 0.879 (0.833) Data 0.002 (0.002) Loss 3.5629 (3.9002) Prec@1 21.250 (17.102) Prec@5 49.375 (39.885) Epoch: [0][6890/11272] Time 0.768 (0.833) Data 0.001 (0.002) Loss 3.1977 (3.8992) Prec@1 24.375 (17.115) Prec@5 55.625 (39.906) Epoch: [0][6900/11272] Time 0.756 (0.833) Data 0.002 (0.002) Loss 3.2798 (3.8983) Prec@1 23.125 (17.127) Prec@5 53.750 (39.928) Epoch: [0][6910/11272] Time 0.859 (0.833) Data 0.002 (0.002) Loss 3.1249 (3.8973) Prec@1 31.875 (17.141) Prec@5 56.875 (39.950) Epoch: [0][6920/11272] Time 0.872 (0.833) Data 0.002 (0.002) Loss 3.4510 (3.8965) Prec@1 23.750 (17.152) Prec@5 48.750 (39.970) Epoch: [0][6930/11272] Time 0.748 (0.833) Data 0.002 (0.002) Loss 3.0563 (3.8955) Prec@1 28.750 (17.164) Prec@5 59.375 (39.991) Epoch: [0][6940/11272] Time 0.746 (0.833) Data 0.001 (0.002) Loss 3.4243 (3.8946) Prec@1 23.125 (17.176) Prec@5 50.625 (40.010) Epoch: [0][6950/11272] Time 0.875 (0.833) Data 0.002 (0.002) Loss 3.2819 (3.8937) Prec@1 21.875 (17.186) Prec@5 55.000 (40.029) Epoch: [0][6960/11272] Time 0.889 (0.833) Data 0.002 (0.002) Loss 3.1735 (3.8928) Prec@1 26.875 (17.198) Prec@5 51.250 (40.048) Epoch: [0][6970/11272] Time 0.731 (0.833) Data 0.002 (0.002) Loss 3.1964 (3.8920) Prec@1 30.000 (17.210) Prec@5 58.125 (40.069) Epoch: [0][6980/11272] Time 0.768 (0.833) Data 0.002 (0.002) Loss 3.2801 (3.8910) Prec@1 24.375 (17.224) Prec@5 55.625 (40.093) Epoch: [0][6990/11272] Time 0.891 (0.833) Data 0.001 (0.002) Loss 3.0177 (3.8901) Prec@1 28.750 (17.238) Prec@5 59.375 (40.112) Epoch: [0][7000/11272] Time 0.806 (0.833) Data 0.002 (0.002) Loss 3.2469 (3.8892) Prec@1 25.000 (17.249) Prec@5 56.250 (40.131) Epoch: [0][7010/11272] Time 0.769 (0.833) Data 0.002 (0.002) Loss 3.4246 (3.8883) Prec@1 22.500 (17.261) Prec@5 48.750 (40.150) Epoch: [0][7020/11272] Time 0.912 (0.833) Data 0.001 (0.002) Loss 3.2165 (3.8875) Prec@1 28.125 (17.272) Prec@5 53.125 (40.169) Epoch: [0][7030/11272] Time 0.941 (0.833) Data 0.002 (0.002) Loss 2.8938 (3.8866) Prec@1 33.125 (17.285) Prec@5 61.250 (40.189) Epoch: [0][7040/11272] Time 0.817 (0.833) Data 0.002 (0.002) Loss 3.1955 (3.8856) Prec@1 22.500 (17.297) Prec@5 56.875 (40.210) Epoch: [0][7050/11272] Time 0.729 (0.833) Data 0.002 (0.002) Loss 3.3796 (3.8847) Prec@1 25.000 (17.311) Prec@5 47.500 (40.228) Epoch: [0][7060/11272] Time 0.875 (0.833) Data 0.002 (0.002) Loss 3.2633 (3.8838) Prec@1 26.250 (17.325) Prec@5 55.000 (40.252) Epoch: [0][7070/11272] Time 0.966 (0.833) Data 0.003 (0.002) Loss 3.3006 (3.8829) Prec@1 24.375 (17.335) Prec@5 48.750 (40.270) Epoch: [0][7080/11272] Time 0.772 (0.833) Data 0.002 (0.002) Loss 3.5414 (3.8821) Prec@1 23.125 (17.345) Prec@5 45.000 (40.288) Epoch: [0][7090/11272] Time 0.764 (0.833) Data 0.002 (0.002) Loss 3.3568 (3.8812) Prec@1 25.000 (17.359) Prec@5 53.125 (40.310) Epoch: [0][7100/11272] Time 0.913 (0.833) Data 0.002 (0.002) Loss 2.9696 (3.8803) Prec@1 31.875 (17.369) Prec@5 56.875 (40.330) Epoch: [0][7110/11272] Time 0.930 (0.833) Data 0.001 (0.002) Loss 3.2138 (3.8794) Prec@1 26.250 (17.379) Prec@5 51.250 (40.347) Epoch: [0][7120/11272] Time 0.776 (0.833) Data 0.002 (0.002) Loss 3.2663 (3.8785) Prec@1 24.375 (17.391) Prec@5 54.375 (40.370) Epoch: [0][7130/11272] Time 0.868 (0.833) Data 0.002 (0.002) Loss 3.3644 (3.8775) Prec@1 24.375 (17.403) Prec@5 48.750 (40.391) Epoch: [0][7140/11272] Time 0.878 (0.833) Data 0.001 (0.002) Loss 3.1428 (3.8766) Prec@1 33.125 (17.415) Prec@5 55.625 (40.411) Epoch: [0][7150/11272] Time 0.732 (0.833) Data 0.002 (0.002) Loss 3.2872 (3.8758) Prec@1 25.000 (17.425) Prec@5 55.000 (40.429) Epoch: [0][7160/11272] Time 0.775 (0.833) Data 0.002 (0.002) Loss 3.2253 (3.8751) Prec@1 23.750 (17.434) Prec@5 55.625 (40.446) Epoch: [0][7170/11272] Time 0.896 (0.833) Data 0.001 (0.002) Loss 3.1713 (3.8743) Prec@1 27.500 (17.446) Prec@5 56.875 (40.464) Epoch: [0][7180/11272] Time 0.941 (0.833) Data 0.002 (0.002) Loss 3.2623 (3.8734) Prec@1 26.875 (17.457) Prec@5 55.000 (40.484) Epoch: [0][7190/11272] Time 0.764 (0.833) Data 0.001 (0.002) Loss 3.2411 (3.8726) Prec@1 26.250 (17.466) Prec@5 54.375 (40.500) Epoch: [0][7200/11272] Time 0.733 (0.833) Data 0.001 (0.002) Loss 3.4495 (3.8718) Prec@1 25.000 (17.476) Prec@5 51.875 (40.519) Epoch: [0][7210/11272] Time 0.931 (0.833) Data 0.002 (0.002) Loss 3.2306 (3.8708) Prec@1 25.625 (17.487) Prec@5 52.500 (40.541) Epoch: [0][7220/11272] Time 0.867 (0.833) Data 0.002 (0.002) Loss 3.0485 (3.8700) Prec@1 30.000 (17.497) Prec@5 62.500 (40.560) Epoch: [0][7230/11272] Time 0.736 (0.833) Data 0.001 (0.002) Loss 3.0335 (3.8691) Prec@1 28.125 (17.508) Prec@5 64.375 (40.580) Epoch: [0][7240/11272] Time 0.830 (0.833) Data 0.002 (0.002) Loss 3.4734 (3.8682) Prec@1 22.500 (17.522) Prec@5 52.500 (40.601) Epoch: [0][7250/11272] Time 0.885 (0.833) Data 0.001 (0.002) Loss 3.4274 (3.8675) Prec@1 24.375 (17.532) Prec@5 46.875 (40.617) Epoch: [0][7260/11272] Time 0.760 (0.833) Data 0.003 (0.002) Loss 3.2813 (3.8665) Prec@1 25.625 (17.545) Prec@5 55.000 (40.637) Epoch: [0][7270/11272] Time 0.727 (0.833) Data 0.001 (0.002) Loss 3.2305 (3.8656) Prec@1 30.000 (17.559) Prec@5 58.750 (40.659) Epoch: [0][7280/11272] Time 0.910 (0.833) Data 0.002 (0.002) Loss 3.3190 (3.8648) Prec@1 28.125 (17.569) Prec@5 55.625 (40.676) Epoch: [0][7290/11272] Time 0.965 (0.833) Data 0.002 (0.002) Loss 3.1472 (3.8638) Prec@1 23.750 (17.582) Prec@5 58.125 (40.697) Epoch: [0][7300/11272] Time 0.804 (0.833) Data 0.002 (0.002) Loss 3.1886 (3.8631) Prec@1 26.250 (17.592) Prec@5 56.250 (40.714) Epoch: [0][7310/11272] Time 0.803 (0.833) Data 0.001 (0.002) Loss 3.3696 (3.8623) Prec@1 21.875 (17.604) Prec@5 50.625 (40.732) Epoch: [0][7320/11272] Time 0.969 (0.833) Data 0.002 (0.002) Loss 3.2403 (3.8613) Prec@1 21.875 (17.616) Prec@5 55.000 (40.752) Epoch: [0][7330/11272] Time 0.891 (0.833) Data 0.001 (0.002) Loss 3.4806 (3.8605) Prec@1 24.375 (17.629) Prec@5 50.625 (40.771) Epoch: [0][7340/11272] Time 0.755 (0.833) Data 0.002 (0.002) Loss 2.9872 (3.8597) Prec@1 30.000 (17.639) Prec@5 60.625 (40.788) Epoch: [0][7350/11272] Time 0.793 (0.833) Data 0.002 (0.002) Loss 3.4025 (3.8589) Prec@1 18.750 (17.648) Prec@5 48.750 (40.804) Epoch: [0][7360/11272] Time 0.896 (0.833) Data 0.001 (0.002) Loss 3.3481 (3.8581) Prec@1 20.000 (17.660) Prec@5 48.125 (40.822) Epoch: [0][7370/11272] Time 0.941 (0.833) Data 0.002 (0.002) Loss 3.2239 (3.8572) Prec@1 26.250 (17.672) Prec@5 53.750 (40.842) Epoch: [0][7380/11272] Time 0.771 (0.833) Data 0.002 (0.002) Loss 3.0856 (3.8563) Prec@1 30.625 (17.683) Prec@5 58.125 (40.861) Epoch: [0][7390/11272] Time 0.903 (0.833) Data 0.001 (0.002) Loss 3.2386 (3.8555) Prec@1 22.500 (17.695) Prec@5 51.875 (40.880) Epoch: [0][7400/11272] Time 0.918 (0.833) Data 0.001 (0.002) Loss 3.0165 (3.8545) Prec@1 29.375 (17.708) Prec@5 56.875 (40.902) Epoch: [0][7410/11272] Time 0.733 (0.833) Data 0.001 (0.002) Loss 3.2858 (3.8537) Prec@1 24.375 (17.718) Prec@5 53.125 (40.920) Epoch: [0][7420/11272] Time 0.787 (0.833) Data 0.002 (0.002) Loss 3.2200 (3.8530) Prec@1 23.750 (17.728) Prec@5 54.375 (40.935) Epoch: [0][7430/11272] Time 0.851 (0.833) Data 0.002 (0.002) Loss 3.2573 (3.8521) Prec@1 19.375 (17.741) Prec@5 51.250 (40.956) Epoch: [0][7440/11272] Time 0.870 (0.833) Data 0.002 (0.002) Loss 3.4393 (3.8513) Prec@1 25.000 (17.753) Prec@5 52.500 (40.972) Epoch: [0][7450/11272] Time 0.772 (0.833) Data 0.002 (0.002) Loss 3.3590 (3.8505) Prec@1 18.125 (17.767) Prec@5 51.875 (40.991) Epoch: [0][7460/11272] Time 0.732 (0.833) Data 0.001 (0.002) Loss 3.2521 (3.8496) Prec@1 25.000 (17.781) Prec@5 60.625 (41.013) Epoch: [0][7470/11272] Time 0.949 (0.833) Data 0.002 (0.002) Loss 3.0984 (3.8488) Prec@1 30.625 (17.792) Prec@5 61.250 (41.030) Epoch: [0][7480/11272] Time 0.886 (0.833) Data 0.002 (0.002) Loss 3.0729 (3.8480) Prec@1 26.250 (17.802) Prec@5 57.500 (41.048) Epoch: [0][7490/11272] Time 0.760 (0.833) Data 0.002 (0.002) Loss 3.1065 (3.8472) Prec@1 28.750 (17.812) Prec@5 51.875 (41.065) Epoch: [0][7500/11272] Time 0.774 (0.833) Data 0.002 (0.002) Loss 2.9976 (3.8464) Prec@1 29.375 (17.822) Prec@5 63.125 (41.083) Epoch: [0][7510/11272] Time 0.872 (0.833) Data 0.002 (0.002) Loss 3.1005 (3.8456) Prec@1 34.375 (17.834) Prec@5 56.250 (41.099) Epoch: [0][7520/11272] Time 0.771 (0.833) Data 0.004 (0.002) Loss 3.3268 (3.8448) Prec@1 20.625 (17.846) Prec@5 50.000 (41.116) Epoch: [0][7530/11272] Time 0.750 (0.833) Data 0.002 (0.002) Loss 3.2007 (3.8440) Prec@1 26.250 (17.855) Prec@5 53.125 (41.134) Epoch: [0][7540/11272] Time 0.899 (0.833) Data 0.002 (0.002) Loss 3.2142 (3.8432) Prec@1 27.500 (17.867) Prec@5 54.375 (41.150) Epoch: [0][7550/11272] Time 0.943 (0.833) Data 0.001 (0.002) Loss 3.2265 (3.8423) Prec@1 26.250 (17.878) Prec@5 55.000 (41.170) Epoch: [0][7560/11272] Time 0.729 (0.833) Data 0.002 (0.002) Loss 3.3366 (3.8416) Prec@1 24.375 (17.888) Prec@5 53.125 (41.183) Epoch: [0][7570/11272] Time 0.736 (0.833) Data 0.002 (0.002) Loss 2.9586 (3.8406) Prec@1 30.625 (17.901) Prec@5 65.000 (41.205) Epoch: [0][7580/11272] Time 0.891 (0.833) Data 0.002 (0.002) Loss 3.0928 (3.8399) Prec@1 26.250 (17.911) Prec@5 55.000 (41.221) Epoch: [0][7590/11272] Time 0.884 (0.833) Data 0.001 (0.002) Loss 3.3795 (3.8390) Prec@1 21.875 (17.920) Prec@5 53.125 (41.240) Epoch: [0][7600/11272] Time 0.765 (0.833) Data 0.002 (0.002) Loss 3.4078 (3.8382) Prec@1 25.000 (17.931) Prec@5 55.000 (41.257) Epoch: [0][7610/11272] Time 0.730 (0.833) Data 0.001 (0.002) Loss 3.3430 (3.8375) Prec@1 24.375 (17.941) Prec@5 51.250 (41.273) Epoch: [0][7620/11272] Time 0.890 (0.833) Data 0.002 (0.002) Loss 3.1322 (3.8366) Prec@1 30.000 (17.952) Prec@5 54.375 (41.291) Epoch: [0][7630/11272] Time 0.920 (0.833) Data 0.002 (0.002) Loss 2.9827 (3.8358) Prec@1 30.625 (17.963) Prec@5 60.000 (41.309) Epoch: [0][7640/11272] Time 0.807 (0.833) Data 0.004 (0.002) Loss 3.1807 (3.8349) Prec@1 28.125 (17.976) Prec@5 55.625 (41.330) Epoch: [0][7650/11272] Time 0.968 (0.833) Data 0.002 (0.002) Loss 3.3496 (3.8341) Prec@1 20.625 (17.987) Prec@5 54.375 (41.349) Epoch: [0][7660/11272] Time 0.887 (0.833) Data 0.001 (0.002) Loss 3.1296 (3.8333) Prec@1 28.750 (17.999) Prec@5 58.125 (41.367) Epoch: [0][7670/11272] Time 0.763 (0.833) Data 0.002 (0.002) Loss 3.2243 (3.8325) Prec@1 28.750 (18.010) Prec@5 55.000 (41.385) Epoch: [0][7680/11272] Time 0.784 (0.833) Data 0.001 (0.002) Loss 3.4193 (3.8316) Prec@1 18.125 (18.019) Prec@5 49.375 (41.403) Epoch: [0][7690/11272] Time 0.890 (0.833) Data 0.002 (0.002) Loss 3.4342 (3.8308) Prec@1 23.125 (18.032) Prec@5 45.625 (41.421) Epoch: [0][7700/11272] Time 0.850 (0.833) Data 0.002 (0.002) Loss 3.2205 (3.8299) Prec@1 30.000 (18.045) Prec@5 57.500 (41.440) Epoch: [0][7710/11272] Time 0.797 (0.833) Data 0.002 (0.002) Loss 2.9981 (3.8292) Prec@1 27.500 (18.055) Prec@5 58.125 (41.455) Epoch: [0][7720/11272] Time 0.742 (0.833) Data 0.002 (0.002) Loss 3.2500 (3.8284) Prec@1 26.875 (18.067) Prec@5 51.250 (41.471) Epoch: [0][7730/11272] Time 0.962 (0.833) Data 0.002 (0.002) Loss 3.2979 (3.8277) Prec@1 25.000 (18.079) Prec@5 53.750 (41.488) Epoch: [0][7740/11272] Time 0.909 (0.833) Data 0.002 (0.002) Loss 3.3607 (3.8269) Prec@1 24.375 (18.091) Prec@5 50.000 (41.507) Epoch: [0][7750/11272] Time 0.772 (0.833) Data 0.002 (0.002) Loss 3.1452 (3.8262) Prec@1 33.125 (18.103) Prec@5 53.750 (41.523) Epoch: [0][7760/11272] Time 0.798 (0.833) Data 0.002 (0.002) Loss 3.3385 (3.8254) Prec@1 18.750 (18.113) Prec@5 52.500 (41.539) Epoch: [0][7770/11272] Time 0.893 (0.833) Data 0.002 (0.002) Loss 3.4724 (3.8247) Prec@1 25.000 (18.124) Prec@5 48.750 (41.556) Epoch: [0][7780/11272] Time 0.868 (0.833) Data 0.002 (0.002) Loss 3.2370 (3.8239) Prec@1 25.000 (18.133) Prec@5 51.875 (41.573) Epoch: [0][7790/11272] Time 0.783 (0.833) Data 0.001 (0.002) Loss 3.2109 (3.8231) Prec@1 25.000 (18.144) Prec@5 55.000 (41.592) Epoch: [0][7800/11272] Time 0.931 (0.833) Data 0.002 (0.002) Loss 3.2344 (3.8224) Prec@1 24.375 (18.155) Prec@5 55.000 (41.607) Epoch: [0][7810/11272] Time 0.931 (0.833) Data 0.001 (0.002) Loss 3.3164 (3.8216) Prec@1 24.375 (18.166) Prec@5 53.125 (41.624) Epoch: [0][7820/11272] Time 0.765 (0.833) Data 0.002 (0.002) Loss 3.4208 (3.8208) Prec@1 23.750 (18.178) Prec@5 54.375 (41.641) Epoch: [0][7830/11272] Time 0.787 (0.833) Data 0.001 (0.002) Loss 3.4385 (3.8201) Prec@1 21.875 (18.187) Prec@5 49.375 (41.657) Epoch: [0][7840/11272] Time 0.890 (0.833) Data 0.002 (0.002) Loss 3.3702 (3.8193) Prec@1 28.750 (18.199) Prec@5 52.500 (41.675) Epoch: [0][7850/11272] Time 0.820 (0.833) Data 0.002 (0.002) Loss 3.2007 (3.8184) Prec@1 30.000 (18.211) Prec@5 55.000 (41.693) Epoch: [0][7860/11272] Time 0.784 (0.833) Data 0.002 (0.002) Loss 3.2108 (3.8178) Prec@1 25.625 (18.220) Prec@5 56.250 (41.708) Epoch: [0][7870/11272] Time 0.762 (0.833) Data 0.002 (0.002) Loss 3.1824 (3.8169) Prec@1 21.250 (18.231) Prec@5 53.125 (41.725) Epoch: [0][7880/11272] Time 0.923 (0.833) Data 0.001 (0.002) Loss 3.1818 (3.8161) Prec@1 26.250 (18.243) Prec@5 53.125 (41.744) Epoch: [0][7890/11272] Time 0.875 (0.833) Data 0.002 (0.002) Loss 3.4047 (3.8154) Prec@1 26.250 (18.252) Prec@5 56.250 (41.760) Epoch: [0][7900/11272] Time 0.823 (0.833) Data 0.002 (0.002) Loss 3.1473 (3.8147) Prec@1 28.125 (18.263) Prec@5 56.875 (41.777) Epoch: [0][7910/11272] Time 0.760 (0.833) Data 0.001 (0.002) Loss 3.3890 (3.8139) Prec@1 25.000 (18.273) Prec@5 48.750 (41.793) Epoch: [0][7920/11272] Time 0.871 (0.833) Data 0.002 (0.002) Loss 3.2414 (3.8132) Prec@1 28.125 (18.283) Prec@5 56.875 (41.809) Epoch: [0][7930/11272] Time 0.756 (0.833) Data 0.002 (0.002) Loss 3.2325 (3.8124) Prec@1 21.875 (18.292) Prec@5 53.125 (41.825) Epoch: [0][7940/11272] Time 0.758 (0.833) Data 0.002 (0.002) Loss 2.8624 (3.8116) Prec@1 30.625 (18.304) Prec@5 63.125 (41.843) Epoch: [0][7950/11272] Time 0.892 (0.833) Data 0.001 (0.002) Loss 3.1433 (3.8109) Prec@1 25.000 (18.313) Prec@5 54.375 (41.859) Epoch: [0][7960/11272] Time 0.886 (0.833) Data 0.002 (0.002) Loss 3.4316 (3.8101) Prec@1 23.125 (18.324) Prec@5 50.625 (41.876) Epoch: [0][7970/11272] Time 0.731 (0.833) Data 0.002 (0.002) Loss 3.2858 (3.8093) Prec@1 30.000 (18.336) Prec@5 51.250 (41.893) Epoch: [0][7980/11272] Time 0.753 (0.833) Data 0.001 (0.002) Loss 3.2560 (3.8085) Prec@1 21.875 (18.348) Prec@5 50.625 (41.910) Epoch: [0][7990/11272] Time 0.873 (0.833) Data 0.002 (0.002) Loss 3.1597 (3.8077) Prec@1 28.125 (18.359) Prec@5 55.625 (41.927) Epoch: [0][8000/11272] Time 0.954 (0.833) Data 0.002 (0.002) Loss 2.9125 (3.8070) Prec@1 28.125 (18.368) Prec@5 63.750 (41.944) Epoch: [0][8010/11272] Time 0.766 (0.833) Data 0.002 (0.002) Loss 3.0566 (3.8062) Prec@1 30.000 (18.381) Prec@5 60.625 (41.960) Epoch: [0][8020/11272] Time 0.775 (0.833) Data 0.002 (0.002) Loss 3.2505 (3.8055) Prec@1 30.000 (18.391) Prec@5 55.000 (41.976) Epoch: [0][8030/11272] Time 0.915 (0.833) Data 0.001 (0.002) Loss 3.1514 (3.8048) Prec@1 26.250 (18.400) Prec@5 57.500 (41.993) Epoch: [0][8040/11272] Time 0.943 (0.833) Data 0.002 (0.002) Loss 3.6172 (3.8041) Prec@1 17.500 (18.409) Prec@5 41.875 (42.007) Epoch: [0][8050/11272] Time 0.794 (0.833) Data 0.003 (0.002) Loss 3.1727 (3.8033) Prec@1 28.125 (18.419) Prec@5 58.125 (42.023) Epoch: [0][8060/11272] Time 0.932 (0.833) Data 0.002 (0.002) Loss 3.1718 (3.8026) Prec@1 22.500 (18.429) Prec@5 55.625 (42.039) Epoch: [0][8070/11272] Time 0.920 (0.833) Data 0.001 (0.002) Loss 3.3114 (3.8019) Prec@1 30.000 (18.438) Prec@5 51.250 (42.053) Epoch: [0][8080/11272] Time 0.788 (0.833) Data 0.001 (0.002) Loss 3.4264 (3.8012) Prec@1 28.750 (18.447) Prec@5 56.875 (42.069) Epoch: [0][8090/11272] Time 0.736 (0.833) Data 0.002 (0.002) Loss 3.2174 (3.8005) Prec@1 28.750 (18.458) Prec@5 51.250 (42.084) Epoch: [0][8100/11272] Time 0.920 (0.833) Data 0.005 (0.002) Loss 3.1052 (3.7997) Prec@1 31.875 (18.470) Prec@5 54.375 (42.102) Epoch: [0][8110/11272] Time 0.925 (0.833) Data 0.001 (0.002) Loss 3.4278 (3.7989) Prec@1 23.125 (18.482) Prec@5 42.500 (42.118) Epoch: [0][8120/11272] Time 0.789 (0.833) Data 0.002 (0.002) Loss 3.4120 (3.7982) Prec@1 21.250 (18.491) Prec@5 51.250 (42.134) Epoch: [0][8130/11272] Time 0.734 (0.833) Data 0.001 (0.002) Loss 2.9222 (3.7974) Prec@1 33.125 (18.502) Prec@5 62.500 (42.152) Epoch: [0][8140/11272] Time 0.888 (0.833) Data 0.002 (0.002) Loss 3.3908 (3.7966) Prec@1 25.000 (18.512) Prec@5 51.875 (42.169) Epoch: [0][8150/11272] Time 0.891 (0.833) Data 0.002 (0.002) Loss 3.0736 (3.7959) Prec@1 29.375 (18.521) Prec@5 56.875 (42.186) Epoch: [0][8160/11272] Time 0.740 (0.833) Data 0.002 (0.002) Loss 3.2490 (3.7950) Prec@1 21.250 (18.533) Prec@5 59.375 (42.204) Epoch: [0][8170/11272] Time 0.745 (0.833) Data 0.001 (0.002) Loss 3.1479 (3.7943) Prec@1 26.875 (18.543) Prec@5 58.125 (42.221) Epoch: [0][8180/11272] Time 0.920 (0.833) Data 0.002 (0.002) Loss 3.2754 (3.7936) Prec@1 25.625 (18.553) Prec@5 52.500 (42.238) Epoch: [0][8190/11272] Time 0.776 (0.833) Data 0.003 (0.002) Loss 3.3685 (3.7929) Prec@1 30.000 (18.562) Prec@5 50.625 (42.253) Epoch: [0][8200/11272] Time 0.771 (0.833) Data 0.002 (0.002) Loss 3.2076 (3.7923) Prec@1 31.250 (18.572) Prec@5 53.750 (42.267) Epoch: [0][8210/11272] Time 0.880 (0.833) Data 0.002 (0.002) Loss 3.3415 (3.7916) Prec@1 20.000 (18.580) Prec@5 54.375 (42.282) Epoch: [0][8220/11272] Time 0.850 (0.833) Data 0.002 (0.002) Loss 3.3921 (3.7908) Prec@1 28.125 (18.591) Prec@5 53.750 (42.297) Epoch: [0][8230/11272] Time 0.744 (0.833) Data 0.002 (0.002) Loss 3.2871 (3.7902) Prec@1 23.750 (18.598) Prec@5 51.875 (42.311) Epoch: [0][8240/11272] Time 0.765 (0.833) Data 0.002 (0.002) Loss 3.4335 (3.7895) Prec@1 23.750 (18.608) Prec@5 49.375 (42.326) Epoch: [0][8250/11272] Time 0.922 (0.833) Data 0.002 (0.002) Loss 3.3972 (3.7889) Prec@1 23.750 (18.615) Prec@5 53.125 (42.340) Epoch: [0][8260/11272] Time 0.879 (0.833) Data 0.001 (0.002) Loss 3.2149 (3.7882) Prec@1 27.500 (18.624) Prec@5 57.500 (42.355) Epoch: [0][8270/11272] Time 0.785 (0.833) Data 0.002 (0.002) Loss 3.3531 (3.7876) Prec@1 26.875 (18.633) Prec@5 53.750 (42.369) Epoch: [0][8280/11272] Time 0.751 (0.833) Data 0.001 (0.002) Loss 3.3963 (3.7868) Prec@1 21.875 (18.643) Prec@5 48.750 (42.386) Epoch: [0][8290/11272] Time 0.946 (0.833) Data 0.002 (0.002) Loss 3.4274 (3.7862) Prec@1 25.000 (18.651) Prec@5 49.375 (42.400) Epoch: [0][8300/11272] Time 0.840 (0.833) Data 0.001 (0.002) Loss 3.2377 (3.7854) Prec@1 25.625 (18.662) Prec@5 56.250 (42.416) Epoch: [0][8310/11272] Time 0.766 (0.833) Data 0.003 (0.002) Loss 2.9973 (3.7846) Prec@1 26.875 (18.673) Prec@5 60.625 (42.431) Epoch: [0][8320/11272] Time 0.863 (0.833) Data 0.003 (0.002) Loss 3.0272 (3.7840) Prec@1 33.750 (18.681) Prec@5 60.625 (42.445) Epoch: [0][8330/11272] Time 0.874 (0.833) Data 0.001 (0.002) Loss 2.7898 (3.7831) Prec@1 34.375 (18.692) Prec@5 61.875 (42.461) Epoch: [0][8340/11272] Time 0.742 (0.833) Data 0.001 (0.002) Loss 3.1901 (3.7824) Prec@1 27.500 (18.701) Prec@5 51.250 (42.476) Epoch: [0][8350/11272] Time 0.765 (0.833) Data 0.002 (0.002) Loss 3.1088 (3.7817) Prec@1 25.000 (18.709) Prec@5 59.375 (42.492) Epoch: [0][8360/11272] Time 0.872 (0.833) Data 0.002 (0.002) Loss 3.0334 (3.7809) Prec@1 33.125 (18.719) Prec@5 63.125 (42.509) Epoch: [0][8370/11272] Time 0.913 (0.833) Data 0.002 (0.002) Loss 3.3458 (3.7801) Prec@1 23.750 (18.730) Prec@5 47.500 (42.525) Epoch: [0][8380/11272] Time 0.743 (0.833) Data 0.003 (0.002) Loss 3.1636 (3.7794) Prec@1 26.875 (18.740) Prec@5 56.875 (42.542) Epoch: [0][8390/11272] Time 0.743 (0.833) Data 0.002 (0.002) Loss 3.1616 (3.7786) Prec@1 28.125 (18.751) Prec@5 58.125 (42.560) Epoch: [0][8400/11272] Time 0.894 (0.833) Data 0.002 (0.002) Loss 2.8722 (3.7778) Prec@1 28.750 (18.762) Prec@5 61.875 (42.575) Epoch: [0][8410/11272] Time 0.895 (0.833) Data 0.002 (0.002) Loss 3.0920 (3.7770) Prec@1 30.625 (18.774) Prec@5 61.250 (42.593) Epoch: [0][8420/11272] Time 0.748 (0.833) Data 0.001 (0.002) Loss 3.2913 (3.7763) Prec@1 26.250 (18.785) Prec@5 53.125 (42.609) Epoch: [0][8430/11272] Time 0.782 (0.833) Data 0.001 (0.002) Loss 3.0284 (3.7755) Prec@1 33.125 (18.798) Prec@5 56.875 (42.627) Epoch: [0][8440/11272] Time 0.857 (0.833) Data 0.001 (0.002) Loss 3.0644 (3.7747) Prec@1 26.875 (18.809) Prec@5 58.125 (42.644) Epoch: [0][8450/11272] Time 0.763 (0.833) Data 0.003 (0.002) Loss 3.0570 (3.7739) Prec@1 31.250 (18.822) Prec@5 58.125 (42.661) Epoch: [0][8460/11272] Time 0.772 (0.833) Data 0.002 (0.002) Loss 3.1750 (3.7730) Prec@1 26.875 (18.834) Prec@5 53.125 (42.679) Epoch: [0][8470/11272] Time 0.877 (0.833) Data 0.002 (0.002) Loss 3.2388 (3.7724) Prec@1 28.125 (18.843) Prec@5 52.500 (42.693) Epoch: [0][8480/11272] Time 0.925 (0.833) Data 0.002 (0.002) Loss 3.0361 (3.7717) Prec@1 26.875 (18.851) Prec@5 59.375 (42.709) Epoch: [0][8490/11272] Time 0.752 (0.833) Data 0.001 (0.002) Loss 3.1913 (3.7710) Prec@1 30.625 (18.860) Prec@5 57.500 (42.722) Epoch: [0][8500/11272] Time 0.790 (0.833) Data 0.001 (0.002) Loss 3.1367 (3.7702) Prec@1 26.875 (18.870) Prec@5 61.250 (42.740) Epoch: [0][8510/11272] Time 0.896 (0.833) Data 0.001 (0.002) Loss 2.8078 (3.7695) Prec@1 33.125 (18.881) Prec@5 60.625 (42.756) Epoch: [0][8520/11272] Time 0.936 (0.833) Data 0.002 (0.002) Loss 3.3112 (3.7688) Prec@1 21.875 (18.890) Prec@5 55.000 (42.770) Epoch: [0][8530/11272] Time 0.820 (0.833) Data 0.002 (0.002) Loss 3.1608 (3.7681) Prec@1 26.875 (18.901) Prec@5 58.125 (42.787) Epoch: [0][8540/11272] Time 0.773 (0.833) Data 0.002 (0.002) Loss 3.2436 (3.7674) Prec@1 31.250 (18.911) Prec@5 55.625 (42.802) Epoch: [0][8550/11272] Time 0.935 (0.833) Data 0.002 (0.002) Loss 3.0862 (3.7666) Prec@1 30.000 (18.922) Prec@5 58.125 (42.819) Epoch: [0][8560/11272] Time 0.891 (0.833) Data 0.001 (0.002) Loss 3.1934 (3.7660) Prec@1 24.375 (18.930) Prec@5 55.000 (42.833) Epoch: [0][8570/11272] Time 0.782 (0.833) Data 0.002 (0.002) Loss 3.0235 (3.7652) Prec@1 30.625 (18.943) Prec@5 57.500 (42.850) Epoch: [0][8580/11272] Time 0.938 (0.833) Data 0.001 (0.002) Loss 3.2030 (3.7645) Prec@1 30.625 (18.953) Prec@5 57.500 (42.867) Epoch: [0][8590/11272] Time 0.846 (0.833) Data 0.001 (0.002) Loss 3.0882 (3.7638) Prec@1 31.875 (18.963) Prec@5 53.125 (42.883) Epoch: [0][8600/11272] Time 0.796 (0.833) Data 0.001 (0.002) Loss 3.2917 (3.7631) Prec@1 27.500 (18.973) Prec@5 51.250 (42.898) Epoch: [0][8610/11272] Time 0.790 (0.833) Data 0.002 (0.002) Loss 3.4015 (3.7624) Prec@1 24.375 (18.982) Prec@5 51.250 (42.913) Epoch: [0][8620/11272] Time 0.985 (0.833) Data 0.002 (0.002) Loss 3.3969 (3.7618) Prec@1 24.375 (18.990) Prec@5 53.125 (42.927) Epoch: [0][8630/11272] Time 0.857 (0.833) Data 0.001 (0.002) Loss 3.1213 (3.7611) Prec@1 28.125 (19.002) Prec@5 60.625 (42.945) Epoch: [0][8640/11272] Time 0.750 (0.833) Data 0.002 (0.002) Loss 3.2413 (3.7604) Prec@1 23.125 (19.011) Prec@5 50.000 (42.960) Epoch: [0][8650/11272] Time 0.790 (0.833) Data 0.002 (0.002) Loss 3.0708 (3.7598) Prec@1 26.875 (19.020) Prec@5 58.125 (42.976) Epoch: [0][8660/11272] Time 0.886 (0.833) Data 0.002 (0.002) Loss 2.9764 (3.7590) Prec@1 34.375 (19.031) Prec@5 60.000 (42.991) Epoch: [0][8670/11272] Time 0.976 (0.833) Data 0.002 (0.002) Loss 3.1194 (3.7583) Prec@1 23.750 (19.040) Prec@5 55.625 (43.006) Epoch: [0][8680/11272] Time 0.784 (0.833) Data 0.002 (0.002) Loss 3.1004 (3.7575) Prec@1 29.375 (19.054) Prec@5 56.250 (43.024) Epoch: [0][8690/11272] Time 0.764 (0.833) Data 0.002 (0.002) Loss 3.3162 (3.7568) Prec@1 26.875 (19.064) Prec@5 50.625 (43.037) Epoch: [0][8700/11272] Time 0.971 (0.833) Data 0.002 (0.002) Loss 3.0124 (3.7562) Prec@1 28.750 (19.071) Prec@5 55.625 (43.049) Epoch: [0][8710/11272] Time 0.895 (0.833) Data 0.001 (0.002) Loss 3.3401 (3.7555) Prec@1 24.375 (19.081) Prec@5 51.250 (43.064) Epoch: [0][8720/11272] Time 0.751 (0.833) Data 0.002 (0.002) Loss 3.0866 (3.7549) Prec@1 30.000 (19.089) Prec@5 56.875 (43.077) Epoch: [0][8730/11272] Time 0.863 (0.833) Data 0.001 (0.002) Loss 3.1608 (3.7542) Prec@1 29.375 (19.099) Prec@5 58.125 (43.092) Epoch: [0][8740/11272] Time 0.876 (0.833) Data 0.001 (0.002) Loss 2.8248 (3.7535) Prec@1 31.250 (19.109) Prec@5 64.375 (43.107) Epoch: [0][8750/11272] Time 0.769 (0.833) Data 0.002 (0.002) Loss 3.1575 (3.7529) Prec@1 26.875 (19.118) Prec@5 53.750 (43.122) Epoch: [0][8760/11272] Time 0.748 (0.833) Data 0.001 (0.002) Loss 3.5382 (3.7522) Prec@1 18.750 (19.129) Prec@5 47.500 (43.138) Epoch: [0][8770/11272] Time 0.969 (0.833) Data 0.002 (0.002) Loss 3.0436 (3.7514) Prec@1 25.000 (19.138) Prec@5 56.875 (43.154) Epoch: [0][8780/11272] Time 0.876 (0.833) Data 0.002 (0.002) Loss 3.0045 (3.7508) Prec@1 33.125 (19.148) Prec@5 63.125 (43.169) Epoch: [0][8790/11272] Time 0.760 (0.833) Data 0.001 (0.002) Loss 3.3118 (3.7502) Prec@1 25.000 (19.158) Prec@5 48.125 (43.183) Epoch: [0][8800/11272] Time 0.752 (0.833) Data 0.002 (0.002) Loss 3.2396 (3.7495) Prec@1 23.125 (19.166) Prec@5 56.250 (43.198) Epoch: [0][8810/11272] Time 0.911 (0.833) Data 0.002 (0.002) Loss 3.1395 (3.7488) Prec@1 28.750 (19.178) Prec@5 53.750 (43.214) Epoch: [0][8820/11272] Time 0.879 (0.833) Data 0.001 (0.002) Loss 3.0599 (3.7482) Prec@1 23.750 (19.185) Prec@5 53.125 (43.227) Epoch: [0][8830/11272] Time 0.821 (0.833) Data 0.001 (0.002) Loss 3.3445 (3.7475) Prec@1 22.500 (19.194) Prec@5 52.500 (43.242) Epoch: [0][8840/11272] Time 0.752 (0.833) Data 0.002 (0.002) Loss 2.9639 (3.7466) Prec@1 31.875 (19.208) Prec@5 62.500 (43.259) Epoch: [0][8850/11272] Time 0.897 (0.833) Data 0.002 (0.002) Loss 3.1078 (3.7461) Prec@1 28.125 (19.215) Prec@5 60.000 (43.270) Epoch: [0][8860/11272] Time 0.744 (0.833) Data 0.002 (0.002) Loss 3.0196 (3.7455) Prec@1 27.500 (19.224) Prec@5 60.625 (43.284) Epoch: [0][8870/11272] Time 0.810 (0.833) Data 0.001 (0.002) Loss 3.3992 (3.7448) Prec@1 23.750 (19.235) Prec@5 50.000 (43.300) Epoch: [0][8880/11272] Time 0.878 (0.833) Data 0.001 (0.002) Loss 2.9901 (3.7441) Prec@1 25.625 (19.244) Prec@5 60.625 (43.316) Epoch: [0][8890/11272] Time 0.828 (0.833) Data 0.002 (0.002) Loss 3.3163 (3.7435) Prec@1 22.500 (19.253) Prec@5 50.000 (43.329) Epoch: [0][8900/11272] Time 0.737 (0.833) Data 0.002 (0.002) Loss 3.1312 (3.7429) Prec@1 28.125 (19.261) Prec@5 53.125 (43.342) Epoch: [0][8910/11272] Time 0.730 (0.833) Data 0.002 (0.002) Loss 3.2272 (3.7422) Prec@1 26.875 (19.270) Prec@5 54.375 (43.355) Epoch: [0][8920/11272] Time 0.850 (0.833) Data 0.001 (0.002) Loss 3.0978 (3.7415) Prec@1 26.875 (19.281) Prec@5 54.375 (43.371) Epoch: [0][8930/11272] Time 0.926 (0.833) Data 0.002 (0.002) Loss 3.4152 (3.7408) Prec@1 25.000 (19.289) Prec@5 49.375 (43.383) Epoch: [0][8940/11272] Time 0.756 (0.833) Data 0.001 (0.002) Loss 3.2733 (3.7401) Prec@1 23.750 (19.299) Prec@5 51.250 (43.400) Epoch: [0][8950/11272] Time 0.733 (0.833) Data 0.001 (0.002) Loss 3.1291 (3.7395) Prec@1 21.875 (19.308) Prec@5 61.250 (43.414) Epoch: [0][8960/11272] Time 0.864 (0.833) Data 0.002 (0.002) Loss 3.0332 (3.7388) Prec@1 29.375 (19.318) Prec@5 60.625 (43.430) Epoch: [0][8970/11272] Time 0.924 (0.833) Data 0.002 (0.002) Loss 3.2521 (3.7382) Prec@1 26.250 (19.326) Prec@5 53.750 (43.444) Epoch: [0][8980/11272] Time 0.789 (0.833) Data 0.001 (0.002) Loss 3.2132 (3.7375) Prec@1 25.625 (19.335) Prec@5 50.625 (43.457) Epoch: [0][8990/11272] Time 0.877 (0.833) Data 0.001 (0.002) Loss 3.2505 (3.7369) Prec@1 30.000 (19.345) Prec@5 56.250 (43.473) Epoch: [0][9000/11272] Time 0.905 (0.833) Data 0.002 (0.002) Loss 2.9473 (3.7362) Prec@1 33.750 (19.356) Prec@5 61.250 (43.488) Epoch: [0][9010/11272] Time 0.737 (0.833) Data 0.001 (0.002) Loss 3.4479 (3.7355) Prec@1 22.500 (19.366) Prec@5 52.500 (43.503) Epoch: [0][9020/11272] Time 0.835 (0.833) Data 0.002 (0.002) Loss 3.2527 (3.7349) Prec@1 27.500 (19.373) Prec@5 50.625 (43.515) Epoch: [0][9030/11272] Time 0.884 (0.833) Data 0.001 (0.002) Loss 3.2021 (3.7342) Prec@1 26.875 (19.382) Prec@5 59.375 (43.529) Epoch: [0][9040/11272] Time 0.936 (0.833) Data 0.001 (0.002) Loss 3.0232 (3.7336) Prec@1 28.750 (19.392) Prec@5 57.500 (43.544) Epoch: [0][9050/11272] Time 0.748 (0.833) Data 0.002 (0.002) Loss 3.0273 (3.7328) Prec@1 30.625 (19.403) Prec@5 58.750 (43.560) Epoch: [0][9060/11272] Time 0.761 (0.833) Data 0.002 (0.002) Loss 2.9520 (3.7321) Prec@1 24.375 (19.414) Prec@5 64.375 (43.576) Epoch: [0][9070/11272] Time 0.883 (0.833) Data 0.002 (0.002) Loss 3.2324 (3.7315) Prec@1 29.375 (19.423) Prec@5 56.875 (43.591) Epoch: [0][9080/11272] Time 0.906 (0.833) Data 0.002 (0.002) Loss 3.1630 (3.7309) Prec@1 26.250 (19.431) Prec@5 56.875 (43.605) Epoch: [0][9090/11272] Time 0.767 (0.833) Data 0.002 (0.002) Loss 3.1084 (3.7302) Prec@1 23.125 (19.441) Prec@5 55.000 (43.620) Epoch: [0][9100/11272] Time 0.795 (0.833) Data 0.002 (0.002) Loss 3.0134 (3.7294) Prec@1 30.000 (19.451) Prec@5 60.000 (43.636) Epoch: [0][9110/11272] Time 0.874 (0.833) Data 0.002 (0.002) Loss 3.4102 (3.7288) Prec@1 20.000 (19.459) Prec@5 50.625 (43.652) Epoch: [0][9120/11272] Time 0.792 (0.833) Data 0.003 (0.002) Loss 3.2146 (3.7282) Prec@1 28.125 (19.469) Prec@5 55.000 (43.666) Epoch: [0][9130/11272] Time 0.781 (0.833) Data 0.002 (0.002) Loss 3.0325 (3.7275) Prec@1 30.625 (19.478) Prec@5 61.875 (43.681) Epoch: [0][9140/11272] Time 0.889 (0.833) Data 0.002 (0.002) Loss 2.9814 (3.7270) Prec@1 32.500 (19.487) Prec@5 61.250 (43.694) Epoch: [0][9150/11272] Time 0.886 (0.833) Data 0.001 (0.002) Loss 2.9217 (3.7264) Prec@1 28.125 (19.496) Prec@5 60.000 (43.707) Epoch: [0][9160/11272] Time 0.767 (0.833) Data 0.001 (0.002) Loss 3.0960 (3.7257) Prec@1 30.000 (19.506) Prec@5 55.625 (43.722) Epoch: [0][9170/11272] Time 0.796 (0.833) Data 0.002 (0.002) Loss 3.2799 (3.7251) Prec@1 26.875 (19.512) Prec@5 53.750 (43.735) Epoch: [0][9180/11272] Time 0.842 (0.833) Data 0.002 (0.002) Loss 3.4085 (3.7246) Prec@1 19.375 (19.520) Prec@5 50.000 (43.747) Epoch: [0][9190/11272] Time 0.887 (0.833) Data 0.002 (0.002) Loss 3.2800 (3.7240) Prec@1 28.750 (19.528) Prec@5 55.000 (43.761) Epoch: [0][9200/11272] Time 0.801 (0.833) Data 0.001 (0.002) Loss 3.1409 (3.7233) Prec@1 25.625 (19.540) Prec@5 56.250 (43.775) Epoch: [0][9210/11272] Time 0.766 (0.833) Data 0.001 (0.002) Loss 3.1490 (3.7226) Prec@1 29.375 (19.550) Prec@5 60.000 (43.792) Epoch: [0][9220/11272] Time 0.875 (0.833) Data 0.002 (0.002) Loss 3.2542 (3.7219) Prec@1 28.750 (19.561) Prec@5 57.500 (43.807) Epoch: [0][9230/11272] Time 0.864 (0.833) Data 0.002 (0.002) Loss 3.0742 (3.7211) Prec@1 26.875 (19.570) Prec@5 55.000 (43.823) Epoch: [0][9240/11272] Time 0.761 (0.833) Data 0.002 (0.002) Loss 2.8924 (3.7205) Prec@1 34.375 (19.578) Prec@5 60.000 (43.837) Epoch: [0][9250/11272] Time 0.868 (0.833) Data 0.002 (0.002) Loss 3.0909 (3.7198) Prec@1 26.250 (19.586) Prec@5 60.625 (43.850) Epoch: [0][9260/11272] Time 0.863 (0.833) Data 0.002 (0.002) Loss 3.2908 (3.7192) Prec@1 26.875 (19.597) Prec@5 51.875 (43.864) Epoch: [0][9270/11272] Time 0.763 (0.833) Data 0.001 (0.002) Loss 3.2961 (3.7186) Prec@1 19.375 (19.605) Prec@5 52.500 (43.877) Epoch: [0][9280/11272] Time 0.746 (0.833) Data 0.001 (0.002) Loss 2.9624 (3.7180) Prec@1 33.750 (19.614) Prec@5 60.000 (43.889) Epoch: [0][9290/11272] Time 0.927 (0.833) Data 0.002 (0.002) Loss 3.0789 (3.7174) Prec@1 25.625 (19.622) Prec@5 59.375 (43.902) Epoch: [0][9300/11272] Time 0.937 (0.833) Data 0.002 (0.002) Loss 2.8679 (3.7167) Prec@1 34.375 (19.633) Prec@5 66.250 (43.917) Epoch: [0][9310/11272] Time 0.729 (0.833) Data 0.001 (0.002) Loss 2.7859 (3.7161) Prec@1 35.625 (19.641) Prec@5 63.125 (43.930) Epoch: [0][9320/11272] Time 0.736 (0.833) Data 0.002 (0.002) Loss 3.1948 (3.7155) Prec@1 25.000 (19.650) Prec@5 53.750 (43.944) Epoch: [0][9330/11272] Time 0.951 (0.833) Data 0.002 (0.002) Loss 3.1087 (3.7149) Prec@1 29.375 (19.659) Prec@5 55.625 (43.957) Epoch: [0][9340/11272] Time 0.896 (0.833) Data 0.002 (0.002) Loss 3.1707 (3.7142) Prec@1 28.125 (19.670) Prec@5 56.875 (43.973) Epoch: [0][9350/11272] Time 0.766 (0.833) Data 0.002 (0.002) Loss 3.3420 (3.7135) Prec@1 20.625 (19.680) Prec@5 54.375 (43.988) Epoch: [0][9360/11272] Time 0.731 (0.833) Data 0.001 (0.002) Loss 3.1959 (3.7128) Prec@1 31.250 (19.691) Prec@5 56.875 (44.004) Epoch: [0][9370/11272] Time 0.915 (0.833) Data 0.002 (0.002) Loss 3.1770 (3.7122) Prec@1 25.625 (19.700) Prec@5 56.250 (44.017) Epoch: [0][9380/11272] Time 0.783 (0.833) Data 0.004 (0.002) Loss 2.9518 (3.7116) Prec@1 28.750 (19.708) Prec@5 60.000 (44.030) Epoch: [0][9390/11272] Time 0.789 (0.833) Data 0.002 (0.002) Loss 2.8707 (3.7110) Prec@1 33.125 (19.717) Prec@5 66.875 (44.044) Epoch: [0][9400/11272] Time 0.880 (0.833) Data 0.002 (0.002) Loss 3.3067 (3.7104) Prec@1 24.375 (19.725) Prec@5 53.750 (44.057) Epoch: [0][9410/11272] Time 0.868 (0.833) Data 0.001 (0.002) Loss 2.9474 (3.7098) Prec@1 29.375 (19.734) Prec@5 61.875 (44.070) Epoch: [0][9420/11272] Time 0.748 (0.833) Data 0.001 (0.002) Loss 3.0456 (3.7091) Prec@1 31.875 (19.743) Prec@5 59.375 (44.084) Epoch: [0][9430/11272] Time 0.802 (0.833) Data 0.002 (0.002) Loss 3.1930 (3.7085) Prec@1 28.125 (19.754) Prec@5 53.125 (44.098) Epoch: [0][9440/11272] Time 0.915 (0.833) Data 0.002 (0.002) Loss 3.3471 (3.7080) Prec@1 23.125 (19.761) Prec@5 48.750 (44.108) Epoch: [0][9450/11272] Time 0.858 (0.833) Data 0.002 (0.002) Loss 3.3874 (3.7075) Prec@1 29.375 (19.769) Prec@5 55.625 (44.120) Epoch: [0][9460/11272] Time 0.764 (0.833) Data 0.002 (0.002) Loss 3.1504 (3.7069) Prec@1 28.125 (19.777) Prec@5 55.625 (44.134) Epoch: [0][9470/11272] Time 0.760 (0.833) Data 0.002 (0.002) Loss 3.1165 (3.7062) Prec@1 24.375 (19.787) Prec@5 58.125 (44.149) Epoch: [0][9480/11272] Time 0.923 (0.833) Data 0.002 (0.002) Loss 3.0172 (3.7056) Prec@1 27.500 (19.796) Prec@5 61.250 (44.162) Epoch: [0][9490/11272] Time 0.903 (0.832) Data 0.001 (0.002) Loss 3.3097 (3.7050) Prec@1 23.125 (19.802) Prec@5 52.500 (44.173) Epoch: [0][9500/11272] Time 0.743 (0.832) Data 0.001 (0.002) Loss 3.3844 (3.7044) Prec@1 24.375 (19.811) Prec@5 56.250 (44.189) Epoch: [0][9510/11272] Time 0.850 (0.832) Data 0.001 (0.002) Loss 3.0058 (3.7038) Prec@1 30.000 (19.818) Prec@5 62.500 (44.203) Epoch: [0][9520/11272] Time 0.883 (0.832) Data 0.002 (0.002) Loss 3.0399 (3.7030) Prec@1 29.375 (19.829) Prec@5 55.625 (44.220) Epoch: [0][9530/11272] Time 0.823 (0.832) Data 0.001 (0.002) Loss 3.1086 (3.7025) Prec@1 28.125 (19.838) Prec@5 56.875 (44.232) Epoch: [0][9540/11272] Time 0.734 (0.832) Data 0.001 (0.002) Loss 2.9577 (3.7018) Prec@1 30.625 (19.850) Prec@5 61.250 (44.247) Epoch: [0][9550/11272] Time 0.950 (0.832) Data 0.001 (0.002) Loss 3.2420 (3.7012) Prec@1 28.750 (19.859) Prec@5 54.375 (44.257) Epoch: [0][9560/11272] Time 0.877 (0.832) Data 0.002 (0.002) Loss 3.3804 (3.7006) Prec@1 25.625 (19.868) Prec@5 56.250 (44.272) Epoch: [0][9570/11272] Time 0.746 (0.832) Data 0.001 (0.002) Loss 3.1452 (3.7000) Prec@1 28.125 (19.879) Prec@5 53.750 (44.285) Epoch: [0][9580/11272] Time 0.794 (0.832) Data 0.001 (0.002) Loss 3.0659 (3.6994) Prec@1 26.875 (19.888) Prec@5 58.125 (44.298) Epoch: [0][9590/11272] Time 0.914 (0.832) Data 0.001 (0.002) Loss 3.1925 (3.6987) Prec@1 22.500 (19.897) Prec@5 55.000 (44.311) Epoch: [0][9600/11272] Time 0.859 (0.832) Data 0.002 (0.002) Loss 2.9762 (3.6982) Prec@1 31.250 (19.906) Prec@5 59.375 (44.324) Epoch: [0][9610/11272] Time 0.746 (0.832) Data 0.001 (0.002) Loss 3.3024 (3.6976) Prec@1 27.500 (19.912) Prec@5 54.375 (44.335) Epoch: [0][9620/11272] Time 0.746 (0.832) Data 0.002 (0.002) Loss 3.1438 (3.6970) Prec@1 26.250 (19.921) Prec@5 56.875 (44.349) Epoch: [0][9630/11272] Time 0.871 (0.832) Data 0.001 (0.002) Loss 3.1321 (3.6964) Prec@1 26.875 (19.927) Prec@5 58.125 (44.362) Epoch: [0][9640/11272] Time 0.853 (0.832) Data 0.002 (0.002) Loss 2.9292 (3.6958) Prec@1 31.875 (19.934) Prec@5 60.625 (44.376) Epoch: [0][9650/11272] Time 0.776 (0.832) Data 0.002 (0.002) Loss 3.1101 (3.6953) Prec@1 26.250 (19.942) Prec@5 57.500 (44.389) Epoch: [0][9660/11272] Time 0.939 (0.832) Data 0.002 (0.002) Loss 3.2796 (3.6947) Prec@1 23.750 (19.950) Prec@5 56.250 (44.400) Epoch: [0][9670/11272] Time 0.891 (0.832) Data 0.002 (0.002) Loss 3.0993 (3.6941) Prec@1 33.750 (19.959) Prec@5 57.500 (44.414) Epoch: [0][9680/11272] Time 0.799 (0.832) Data 0.002 (0.002) Loss 3.2365 (3.6935) Prec@1 26.250 (19.967) Prec@5 58.125 (44.427) Epoch: [0][9690/11272] Time 0.741 (0.832) Data 0.002 (0.002) Loss 2.9762 (3.6929) Prec@1 24.375 (19.974) Prec@5 61.875 (44.440) Epoch: [0][9700/11272] Time 0.919 (0.832) Data 0.002 (0.002) Loss 3.2183 (3.6924) Prec@1 27.500 (19.983) Prec@5 51.250 (44.452) Epoch: [0][9710/11272] Time 0.854 (0.832) Data 0.001 (0.002) Loss 3.3596 (3.6918) Prec@1 24.375 (19.993) Prec@5 51.250 (44.465) Epoch: [0][9720/11272] Time 0.792 (0.832) Data 0.002 (0.002) Loss 3.3803 (3.6912) Prec@1 24.375 (20.000) Prec@5 50.000 (44.478) Epoch: [0][9730/11272] Time 0.735 (0.832) Data 0.001 (0.002) Loss 3.0216 (3.6906) Prec@1 28.750 (20.009) Prec@5 60.000 (44.490) Epoch: [0][9740/11272] Time 0.884 (0.832) Data 0.002 (0.002) Loss 3.0782 (3.6900) Prec@1 29.375 (20.017) Prec@5 55.000 (44.503) Epoch: [0][9750/11272] Time 0.921 (0.832) Data 0.002 (0.002) Loss 3.0450 (3.6894) Prec@1 30.000 (20.025) Prec@5 63.125 (44.517) Epoch: [0][9760/11272] Time 0.801 (0.832) Data 0.002 (0.002) Loss 3.2108 (3.6888) Prec@1 22.500 (20.035) Prec@5 54.375 (44.530) Epoch: [0][9770/11272] Time 0.775 (0.832) Data 0.002 (0.002) Loss 3.1744 (3.6883) Prec@1 30.000 (20.043) Prec@5 57.500 (44.543) Epoch: [0][9780/11272] Time 0.881 (0.832) Data 0.001 (0.002) Loss 3.2067 (3.6877) Prec@1 26.250 (20.050) Prec@5 54.375 (44.555) Epoch: [0][9790/11272] Time 0.742 (0.832) Data 0.002 (0.002) Loss 3.2259 (3.6871) Prec@1 26.250 (20.059) Prec@5 58.750 (44.569) Epoch: [0][9800/11272] Time 0.813 (0.832) Data 0.002 (0.002) Loss 3.2943 (3.6866) Prec@1 25.000 (20.065) Prec@5 53.125 (44.580) Epoch: [0][9810/11272] Time 0.860 (0.832) Data 0.001 (0.002) Loss 3.0614 (3.6860) Prec@1 33.125 (20.074) Prec@5 61.250 (44.594) Epoch: [0][9820/11272] Time 0.878 (0.832) Data 0.002 (0.002) Loss 2.9890 (3.6853) Prec@1 38.125 (20.083) Prec@5 55.625 (44.608) Epoch: [0][9830/11272] Time 0.737 (0.832) Data 0.001 (0.002) Loss 3.1266 (3.6847) Prec@1 27.500 (20.093) Prec@5 60.625 (44.622) Epoch: [0][9840/11272] Time 0.773 (0.832) Data 0.002 (0.002) Loss 3.0394 (3.6841) Prec@1 35.625 (20.103) Prec@5 58.750 (44.635) Epoch: [0][9850/11272] Time 0.913 (0.832) Data 0.002 (0.002) Loss 2.9959 (3.6835) Prec@1 30.625 (20.111) Prec@5 60.625 (44.647) Epoch: [0][9860/11272] Time 0.890 (0.832) Data 0.001 (0.002) Loss 3.1644 (3.6830) Prec@1 27.500 (20.117) Prec@5 53.125 (44.659) Epoch: [0][9870/11272] Time 0.763 (0.832) Data 0.002 (0.002) Loss 2.8479 (3.6823) Prec@1 35.625 (20.128) Prec@5 63.750 (44.674) Epoch: [0][9880/11272] Time 0.748 (0.832) Data 0.001 (0.002) Loss 3.1449 (3.6817) Prec@1 26.875 (20.135) Prec@5 58.125 (44.687) Epoch: [0][9890/11272] Time 0.895 (0.832) Data 0.002 (0.002) Loss 3.0730 (3.6811) Prec@1 28.750 (20.144) Prec@5 58.750 (44.698) Epoch: [0][9900/11272] Time 0.873 (0.832) Data 0.001 (0.002) Loss 2.9144 (3.6805) Prec@1 32.500 (20.150) Prec@5 60.000 (44.711) Epoch: [0][9910/11272] Time 0.763 (0.832) Data 0.002 (0.002) Loss 3.2188 (3.6799) Prec@1 29.375 (20.161) Prec@5 57.500 (44.727) Epoch: [0][9920/11272] Time 0.833 (0.832) Data 0.001 (0.002) Loss 3.0367 (3.6793) Prec@1 26.875 (20.169) Prec@5 54.375 (44.737) Epoch: [0][9930/11272] Time 0.903 (0.832) Data 0.002 (0.002) Loss 2.9877 (3.6788) Prec@1 26.875 (20.176) Prec@5 57.500 (44.749) Epoch: [0][9940/11272] Time 0.768 (0.832) Data 0.002 (0.002) Loss 3.0724 (3.6782) Prec@1 28.125 (20.185) Prec@5 55.625 (44.763) Epoch: [0][9950/11272] Time 0.770 (0.832) Data 0.002 (0.002) Loss 3.3132 (3.6776) Prec@1 23.750 (20.191) Prec@5 53.750 (44.775) Epoch: [0][9960/11272] Time 0.955 (0.832) Data 0.002 (0.002) Loss 3.1073 (3.6770) Prec@1 28.750 (20.200) Prec@5 56.875 (44.787) Epoch: [0][9970/11272] Time 0.856 (0.832) Data 0.001 (0.002) Loss 3.2791 (3.6764) Prec@1 21.250 (20.209) Prec@5 50.625 (44.800) Epoch: [0][9980/11272] Time 0.771 (0.832) Data 0.002 (0.002) Loss 3.2700 (3.6759) Prec@1 28.125 (20.217) Prec@5 52.500 (44.812) Epoch: [0][9990/11272] Time 0.764 (0.832) Data 0.002 (0.002) Loss 2.8028 (3.6753) Prec@1 32.500 (20.227) Prec@5 68.125 (44.825) Epoch: [0][10000/11272] Time 0.961 (0.832) Data 0.002 (0.002) Loss 3.1296 (3.6746) Prec@1 28.750 (20.235) Prec@5 55.000 (44.839) Epoch: [0][10010/11272] Time 0.883 (0.832) Data 0.002 (0.002) Loss 3.0209 (3.6739) Prec@1 26.250 (20.245) Prec@5 60.625 (44.854) Epoch: [0][10020/11272] Time 0.757 (0.832) Data 0.001 (0.002) Loss 3.1278 (3.6734) Prec@1 26.875 (20.254) Prec@5 57.500 (44.866) Epoch: [0][10030/11272] Time 0.754 (0.832) Data 0.002 (0.002) Loss 3.0473 (3.6727) Prec@1 30.000 (20.263) Prec@5 62.500 (44.881) Epoch: [0][10040/11272] Time 0.916 (0.832) Data 0.002 (0.002) Loss 3.2600 (3.6722) Prec@1 25.625 (20.270) Prec@5 58.125 (44.893) Epoch: [0][10050/11272] Time 0.795 (0.832) Data 0.004 (0.002) Loss 3.1836 (3.6717) Prec@1 28.750 (20.277) Prec@5 60.000 (44.904) Epoch: [0][10060/11272] Time 0.757 (0.832) Data 0.002 (0.002) Loss 3.1319 (3.6712) Prec@1 25.000 (20.284) Prec@5 51.875 (44.914) Epoch: [0][10070/11272] Time 0.892 (0.832) Data 0.001 (0.002) Loss 3.0250 (3.6706) Prec@1 29.375 (20.291) Prec@5 60.000 (44.926) Epoch: [0][10080/11272] Time 0.926 (0.832) Data 0.002 (0.002) Loss 3.1185 (3.6700) Prec@1 29.375 (20.300) Prec@5 56.250 (44.939) Epoch: [0][10090/11272] Time 0.784 (0.832) Data 0.002 (0.002) Loss 3.0758 (3.6695) Prec@1 28.750 (20.308) Prec@5 56.250 (44.951) Epoch: [0][10100/11272] Time 0.735 (0.832) Data 0.002 (0.002) Loss 3.1606 (3.6689) Prec@1 28.125 (20.317) Prec@5 58.125 (44.964) Epoch: [0][10110/11272] Time 0.861 (0.832) Data 0.001 (0.002) Loss 2.6925 (3.6683) Prec@1 36.875 (20.326) Prec@5 63.125 (44.977) Epoch: [0][10120/11272] Time 0.924 (0.832) Data 0.002 (0.002) Loss 3.1231 (3.6677) Prec@1 26.875 (20.334) Prec@5 60.000 (44.991) Epoch: [0][10130/11272] Time 0.750 (0.832) Data 0.001 (0.002) Loss 3.1073 (3.6672) Prec@1 30.000 (20.341) Prec@5 55.625 (45.002) Epoch: [0][10140/11272] Time 0.787 (0.832) Data 0.002 (0.002) Loss 3.2262 (3.6666) Prec@1 26.250 (20.348) Prec@5 54.375 (45.014) Epoch: [0][10150/11272] Time 0.965 (0.832) Data 0.002 (0.002) Loss 3.0745 (3.6660) Prec@1 26.250 (20.356) Prec@5 53.125 (45.027) Epoch: [0][10160/11272] Time 0.878 (0.832) Data 0.002 (0.002) Loss 3.1951 (3.6655) Prec@1 28.125 (20.363) Prec@5 59.375 (45.040) Epoch: [0][10170/11272] Time 0.783 (0.832) Data 0.002 (0.002) Loss 2.9350 (3.6649) Prec@1 32.500 (20.372) Prec@5 61.250 (45.051) Epoch: [0][10180/11272] Time 0.903 (0.832) Data 0.002 (0.002) Loss 3.2240 (3.6643) Prec@1 30.625 (20.378) Prec@5 56.250 (45.062) Epoch: [0][10190/11272] Time 0.878 (0.832) Data 0.002 (0.002) Loss 3.0630 (3.6637) Prec@1 35.000 (20.387) Prec@5 58.125 (45.076) Epoch: [0][10200/11272] Time 0.741 (0.832) Data 0.002 (0.002) Loss 3.1076 (3.6632) Prec@1 28.750 (20.394) Prec@5 58.750 (45.087) Epoch: [0][10210/11272] Time 0.793 (0.832) Data 0.002 (0.002) Loss 2.9491 (3.6627) Prec@1 30.000 (20.403) Prec@5 63.750 (45.099) Epoch: [0][10220/11272] Time 0.823 (0.832) Data 0.001 (0.002) Loss 3.5313 (3.6622) Prec@1 28.750 (20.411) Prec@5 49.375 (45.109) Epoch: [0][10230/11272] Time 0.862 (0.832) Data 0.002 (0.002) Loss 2.9891 (3.6616) Prec@1 28.750 (20.418) Prec@5 60.000 (45.123) Epoch: [0][10240/11272] Time 0.742 (0.832) Data 0.001 (0.002) Loss 3.0814 (3.6610) Prec@1 28.750 (20.426) Prec@5 55.000 (45.135) Epoch: [0][10250/11272] Time 0.756 (0.832) Data 0.001 (0.002) Loss 3.4701 (3.6606) Prec@1 23.750 (20.432) Prec@5 50.625 (45.144) Epoch: [0][10260/11272] Time 0.932 (0.832) Data 0.002 (0.002) Loss 2.7616 (3.6599) Prec@1 33.750 (20.441) Prec@5 62.500 (45.158) Epoch: [0][10270/11272] Time 0.883 (0.832) Data 0.002 (0.002) Loss 3.0779 (3.6594) Prec@1 30.625 (20.449) Prec@5 57.500 (45.170) Epoch: [0][10280/11272] Time 0.738 (0.832) Data 0.001 (0.002) Loss 2.9949 (3.6588) Prec@1 25.625 (20.456) Prec@5 61.875 (45.182) Epoch: [0][10290/11272] Time 0.738 (0.832) Data 0.002 (0.002) Loss 3.2512 (3.6582) Prec@1 25.625 (20.463) Prec@5 55.625 (45.194) Epoch: [0][10300/11272] Time 0.911 (0.832) Data 0.002 (0.002) Loss 3.1010 (3.6577) Prec@1 28.125 (20.472) Prec@5 56.250 (45.207) Epoch: [0][10310/11272] Time 0.772 (0.832) Data 0.004 (0.002) Loss 3.2807 (3.6572) Prec@1 21.250 (20.479) Prec@5 53.750 (45.218) Epoch: [0][10320/11272] Time 0.750 (0.832) Data 0.002 (0.002) Loss 3.0401 (3.6567) Prec@1 30.000 (20.486) Prec@5 58.125 (45.229) Epoch: [0][10330/11272] Time 0.896 (0.832) Data 0.002 (0.002) Loss 3.0480 (3.6562) Prec@1 28.750 (20.492) Prec@5 57.500 (45.239) Epoch: [0][10340/11272] Time 0.887 (0.832) Data 0.001 (0.002) Loss 2.8740 (3.6556) Prec@1 30.625 (20.499) Prec@5 62.500 (45.252) Epoch: [0][10350/11272] Time 0.746 (0.832) Data 0.001 (0.002) Loss 2.9693 (3.6551) Prec@1 28.125 (20.507) Prec@5 58.750 (45.265) Epoch: [0][10360/11272] Time 0.793 (0.832) Data 0.002 (0.002) Loss 3.1063 (3.6545) Prec@1 31.875 (20.518) Prec@5 60.000 (45.278) Epoch: [0][10370/11272] Time 0.920 (0.832) Data 0.002 (0.002) Loss 3.4188 (3.6539) Prec@1 23.125 (20.527) Prec@5 50.625 (45.290) Epoch: [0][10380/11272] Time 0.906 (0.832) Data 0.005 (0.002) Loss 2.9885 (3.6534) Prec@1 30.625 (20.533) Prec@5 61.250 (45.301) Epoch: [0][10390/11272] Time 0.768 (0.832) Data 0.002 (0.002) Loss 2.8929 (3.6528) Prec@1 27.500 (20.543) Prec@5 61.250 (45.315) Epoch: [0][10400/11272] Time 0.778 (0.832) Data 0.002 (0.002) Loss 3.1413 (3.6522) Prec@1 30.000 (20.553) Prec@5 58.750 (45.327) Epoch: [0][10410/11272] Time 0.928 (0.832) Data 0.002 (0.002) Loss 2.9531 (3.6518) Prec@1 34.375 (20.559) Prec@5 59.375 (45.338) Epoch: [0][10420/11272] Time 0.852 (0.832) Data 0.001 (0.002) Loss 2.9702 (3.6512) Prec@1 31.250 (20.568) Prec@5 57.500 (45.351) Epoch: [0][10430/11272] Time 0.759 (0.832) Data 0.002 (0.002) Loss 3.1821 (3.6507) Prec@1 28.750 (20.574) Prec@5 54.375 (45.362) Epoch: [0][10440/11272] Time 0.957 (0.832) Data 0.002 (0.002) Loss 2.8052 (3.6501) Prec@1 31.250 (20.581) Prec@5 64.375 (45.374) Epoch: [0][10450/11272] Time 0.935 (0.832) Data 0.001 (0.002) Loss 3.1176 (3.6496) Prec@1 24.375 (20.589) Prec@5 56.875 (45.384) Epoch: [0][10460/11272] Time 0.754 (0.832) Data 0.001 (0.002) Loss 3.0406 (3.6490) Prec@1 27.500 (20.597) Prec@5 57.500 (45.396) Epoch: [0][10470/11272] Time 0.758 (0.832) Data 0.002 (0.002) Loss 3.0236 (3.6485) Prec@1 26.250 (20.603) Prec@5 58.750 (45.406) Epoch: [0][10480/11272] Time 0.852 (0.832) Data 0.001 (0.002) Loss 3.1284 (3.6480) Prec@1 25.625 (20.609) Prec@5 55.000 (45.417) Epoch: [0][10490/11272] Time 0.849 (0.832) Data 0.001 (0.002) Loss 3.0588 (3.6475) Prec@1 31.250 (20.615) Prec@5 60.625 (45.428) Epoch: [0][10500/11272] Time 0.785 (0.832) Data 0.002 (0.002) Loss 3.1612 (3.6470) Prec@1 25.000 (20.623) Prec@5 55.000 (45.439) Epoch: [0][10510/11272] Time 0.788 (0.832) Data 0.002 (0.002) Loss 3.0583 (3.6465) Prec@1 32.500 (20.631) Prec@5 59.375 (45.451) Epoch: [0][10520/11272] Time 0.889 (0.832) Data 0.001 (0.002) Loss 2.9361 (3.6459) Prec@1 27.500 (20.639) Prec@5 64.375 (45.464) Epoch: [0][10530/11272] Time 0.881 (0.832) Data 0.002 (0.002) Loss 2.8803 (3.6453) Prec@1 28.750 (20.648) Prec@5 60.000 (45.475) Epoch: [0][10540/11272] Time 0.748 (0.832) Data 0.001 (0.002) Loss 2.9807 (3.6448) Prec@1 31.250 (20.656) Prec@5 58.125 (45.486) Epoch: [0][10550/11272] Time 0.754 (0.832) Data 0.002 (0.002) Loss 2.8019 (3.6442) Prec@1 30.000 (20.664) Prec@5 63.750 (45.498) Epoch: [0][10560/11272] Time 0.884 (0.832) Data 0.002 (0.002) Loss 2.9357 (3.6436) Prec@1 32.500 (20.672) Prec@5 62.500 (45.512) Epoch: [0][10570/11272] Time 0.832 (0.832) Data 0.001 (0.002) Loss 2.9843 (3.6431) Prec@1 33.750 (20.681) Prec@5 58.750 (45.525) Epoch: [0][10580/11272] Time 0.784 (0.832) Data 0.001 (0.002) Loss 3.1564 (3.6425) Prec@1 23.750 (20.688) Prec@5 55.625 (45.536) Epoch: [0][10590/11272] Time 0.867 (0.832) Data 0.002 (0.002) Loss 2.8810 (3.6421) Prec@1 30.625 (20.696) Prec@5 63.750 (45.548) Epoch: [0][10600/11272] Time 0.888 (0.832) Data 0.002 (0.002) Loss 3.0352 (3.6415) Prec@1 27.500 (20.705) Prec@5 61.250 (45.559) Epoch: [0][10610/11272] Time 0.790 (0.832) Data 0.002 (0.002) Loss 3.0510 (3.6410) Prec@1 35.625 (20.715) Prec@5 56.875 (45.570) Epoch: [0][10620/11272] Time 0.806 (0.832) Data 0.002 (0.002) Loss 3.1743 (3.6406) Prec@1 24.375 (20.723) Prec@5 57.500 (45.580) Epoch: [0][10630/11272] Time 0.877 (0.832) Data 0.002 (0.002) Loss 3.0473 (3.6400) Prec@1 27.500 (20.730) Prec@5 58.125 (45.591) Epoch: [0][10640/11272] Time 0.973 (0.832) Data 0.002 (0.002) Loss 3.1016 (3.6395) Prec@1 25.625 (20.737) Prec@5 58.750 (45.602) Epoch: [0][10650/11272] Time 0.751 (0.832) Data 0.002 (0.002) Loss 3.1993 (3.6390) Prec@1 27.500 (20.745) Prec@5 53.125 (45.614) Epoch: [0][10660/11272] Time 0.741 (0.832) Data 0.002 (0.002) Loss 2.9875 (3.6384) Prec@1 28.750 (20.752) Prec@5 65.000 (45.626) Epoch: [0][10670/11272] Time 0.922 (0.832) Data 0.002 (0.002) Loss 3.0110 (3.6379) Prec@1 25.000 (20.759) Prec@5 59.375 (45.637) Epoch: [0][10680/11272] Time 0.909 (0.832) Data 0.001 (0.002) Loss 2.8638 (3.6374) Prec@1 31.250 (20.765) Prec@5 62.500 (45.650) Epoch: [0][10690/11272] Time 0.760 (0.832) Data 0.002 (0.002) Loss 2.8669 (3.6367) Prec@1 35.000 (20.775) Prec@5 62.500 (45.664) Epoch: [0][10700/11272] Time 0.743 (0.832) Data 0.002 (0.002) Loss 3.2971 (3.6363) Prec@1 29.375 (20.780) Prec@5 58.125 (45.674) Epoch: [0][10710/11272] Time 0.932 (0.832) Data 0.002 (0.002) Loss 3.1359 (3.6357) Prec@1 25.625 (20.788) Prec@5 53.750 (45.687) Epoch: [0][10720/11272] Time 0.753 (0.832) Data 0.003 (0.002) Loss 3.0941 (3.6352) Prec@1 28.750 (20.795) Prec@5 58.750 (45.699) Epoch: [0][10730/11272] Time 0.772 (0.832) Data 0.002 (0.002) Loss 2.8892 (3.6346) Prec@1 34.375 (20.805) Prec@5 65.000 (45.714) Epoch: [0][10740/11272] Time 0.871 (0.832) Data 0.002 (0.002) Loss 3.1046 (3.6340) Prec@1 26.250 (20.812) Prec@5 56.250 (45.726) Epoch: [0][10750/11272] Time 0.854 (0.832) Data 0.002 (0.002) Loss 3.0827 (3.6336) Prec@1 25.625 (20.819) Prec@5 59.375 (45.737) Epoch: [0][10760/11272] Time 0.750 (0.832) Data 0.002 (0.002) Loss 3.0742 (3.6331) Prec@1 25.000 (20.824) Prec@5 56.250 (45.748) Epoch: [0][10770/11272] Time 0.772 (0.832) Data 0.002 (0.002) Loss 3.1954 (3.6325) Prec@1 25.000 (20.831) Prec@5 54.375 (45.759) Epoch: [0][10780/11272] Time 0.866 (0.832) Data 0.002 (0.002) Loss 3.3753 (3.6321) Prec@1 20.000 (20.837) Prec@5 50.000 (45.769) Epoch: [0][10790/11272] Time 0.863 (0.832) Data 0.001 (0.002) Loss 2.8898 (3.6315) Prec@1 32.500 (20.845) Prec@5 66.875 (45.781) Epoch: [0][10800/11272] Time 0.769 (0.832) Data 0.002 (0.002) Loss 3.3296 (3.6309) Prec@1 19.375 (20.854) Prec@5 50.000 (45.793) Epoch: [0][10810/11272] Time 0.796 (0.832) Data 0.002 (0.002) Loss 3.1312 (3.6305) Prec@1 31.250 (20.860) Prec@5 56.250 (45.803) Epoch: [0][10820/11272] Time 0.941 (0.832) Data 0.002 (0.002) Loss 3.3728 (3.6300) Prec@1 24.375 (20.867) Prec@5 53.125 (45.814) Epoch: [0][10830/11272] Time 0.848 (0.832) Data 0.002 (0.002) Loss 2.9224 (3.6294) Prec@1 30.625 (20.874) Prec@5 58.125 (45.826) Epoch: [0][10840/11272] Time 0.769 (0.832) Data 0.001 (0.002) Loss 3.0619 (3.6289) Prec@1 23.750 (20.882) Prec@5 59.375 (45.839) Epoch: [0][10850/11272] Time 0.941 (0.832) Data 0.002 (0.002) Loss 3.1879 (3.6284) Prec@1 32.500 (20.889) Prec@5 56.250 (45.850) Epoch: [0][10860/11272] Time 0.836 (0.832) Data 0.001 (0.002) Loss 3.1607 (3.6279) Prec@1 26.250 (20.896) Prec@5 60.000 (45.860) Epoch: [0][10870/11272] Time 0.822 (0.832) Data 0.001 (0.002) Loss 2.9687 (3.6274) Prec@1 31.250 (20.903) Prec@5 64.375 (45.870) Epoch: [0][10880/11272] Time 0.730 (0.832) Data 0.001 (0.002) Loss 3.0748 (3.6270) Prec@1 29.375 (20.910) Prec@5 55.625 (45.881) Epoch: [0][10890/11272] Time 0.904 (0.832) Data 0.001 (0.002) Loss 2.8688 (3.6264) Prec@1 30.625 (20.919) Prec@5 60.625 (45.894) Epoch: [0][10900/11272] Time 0.943 (0.832) Data 0.001 (0.002) Loss 3.0875 (3.6258) Prec@1 27.500 (20.928) Prec@5 55.000 (45.907) Epoch: [0][10910/11272] Time 0.760 (0.832) Data 0.001 (0.002) Loss 2.9817 (3.6253) Prec@1 32.500 (20.935) Prec@5 64.375 (45.918) Epoch: [0][10920/11272] Time 0.740 (0.832) Data 0.001 (0.002) Loss 3.1375 (3.6248) Prec@1 29.375 (20.943) Prec@5 56.250 (45.928) Epoch: [0][10930/11272] Time 0.936 (0.832) Data 0.001 (0.002) Loss 3.3257 (3.6243) Prec@1 24.375 (20.949) Prec@5 50.625 (45.938) Epoch: [0][10940/11272] Time 0.879 (0.832) Data 0.002 (0.002) Loss 3.3468 (3.6238) Prec@1 25.000 (20.957) Prec@5 51.250 (45.949) Epoch: [0][10950/11272] Time 0.746 (0.832) Data 0.001 (0.002) Loss 3.1360 (3.6234) Prec@1 27.500 (20.961) Prec@5 54.375 (45.958) Epoch: [0][10960/11272] Time 0.767 (0.832) Data 0.002 (0.002) Loss 3.1650 (3.6228) Prec@1 25.625 (20.969) Prec@5 56.875 (45.969) Epoch: [0][10970/11272] Time 0.879 (0.832) Data 0.002 (0.002) Loss 2.8779 (3.6223) Prec@1 31.875 (20.977) Prec@5 65.000 (45.981) Epoch: [0][10980/11272] Time 0.743 (0.832) Data 0.004 (0.002) Loss 2.7257 (3.6218) Prec@1 35.000 (20.985) Prec@5 63.125 (45.992) Epoch: [0][10990/11272] Time 0.778 (0.832) Data 0.002 (0.002) Loss 3.2559 (3.6213) Prec@1 32.500 (20.993) Prec@5 51.875 (46.004) Epoch: [0][11000/11272] Time 0.968 (0.832) Data 0.001 (0.002) Loss 3.1274 (3.6208) Prec@1 27.500 (21.001) Prec@5 55.625 (46.014) Epoch: [0][11010/11272] Time 0.886 (0.832) Data 0.002 (0.002) Loss 3.0794 (3.6203) Prec@1 28.750 (21.008) Prec@5 59.375 (46.025) Epoch: [0][11020/11272] Time 0.749 (0.832) Data 0.001 (0.002) Loss 3.1515 (3.6199) Prec@1 28.125 (21.013) Prec@5 58.750 (46.036) Epoch: [0][11030/11272] Time 0.790 (0.832) Data 0.002 (0.002) Loss 2.9389 (3.6193) Prec@1 33.750 (21.021) Prec@5 61.875 (46.048) Epoch: [0][11040/11272] Time 0.929 (0.832) Data 0.002 (0.002) Loss 2.8782 (3.6188) Prec@1 30.000 (21.028) Prec@5 60.000 (46.059) Epoch: [0][11050/11272] Time 0.849 (0.832) Data 0.001 (0.002) Loss 2.8827 (3.6182) Prec@1 31.875 (21.035) Prec@5 63.750 (46.072) Epoch: [0][11060/11272] Time 0.747 (0.832) Data 0.002 (0.002) Loss 3.3190 (3.6178) Prec@1 25.625 (21.042) Prec@5 53.125 (46.081) Epoch: [0][11070/11272] Time 0.734 (0.832) Data 0.002 (0.002) Loss 3.0274 (3.6173) Prec@1 32.500 (21.050) Prec@5 60.625 (46.091) Epoch: [0][11080/11272] Time 0.917 (0.832) Data 0.001 (0.002) Loss 2.7009 (3.6168) Prec@1 40.000 (21.057) Prec@5 68.125 (46.102) Epoch: [0][11090/11272] Time 0.878 (0.832) Data 0.002 (0.002) Loss 3.0928 (3.6163) Prec@1 29.375 (21.064) Prec@5 58.125 (46.114) Epoch: [0][11100/11272] Time 0.779 (0.832) Data 0.001 (0.002) Loss 3.0568 (3.6157) Prec@1 27.500 (21.072) Prec@5 58.125 (46.127) Epoch: [0][11110/11272] Time 0.960 (0.832) Data 0.002 (0.002) Loss 3.3908 (3.6152) Prec@1 26.875 (21.079) Prec@5 51.250 (46.137) Epoch: [0][11120/11272] Time 0.908 (0.832) Data 0.002 (0.002) Loss 3.0958 (3.6147) Prec@1 21.875 (21.087) Prec@5 53.125 (46.148) Epoch: [0][11130/11272] Time 0.737 (0.832) Data 0.001 (0.002) Loss 3.1686 (3.6143) Prec@1 28.125 (21.092) Prec@5 58.750 (46.158) Epoch: [0][11140/11272] Time 0.760 (0.832) Data 0.002 (0.002) Loss 2.9777 (3.6137) Prec@1 27.500 (21.101) Prec@5 62.500 (46.171) Epoch: [0][11150/11272] Time 0.889 (0.832) Data 0.001 (0.002) Loss 3.1757 (3.6132) Prec@1 25.625 (21.108) Prec@5 53.125 (46.180) Epoch: [0][11160/11272] Time 0.918 (0.832) Data 0.002 (0.002) Loss 2.8727 (3.6127) Prec@1 31.250 (21.116) Prec@5 59.375 (46.191) Epoch: [0][11170/11272] Time 0.763 (0.832) Data 0.001 (0.002) Loss 2.9633 (3.6123) Prec@1 30.000 (21.122) Prec@5 60.000 (46.201) Epoch: [0][11180/11272] Time 0.786 (0.832) Data 0.002 (0.002) Loss 3.1378 (3.6118) Prec@1 28.750 (21.128) Prec@5 58.750 (46.212) Epoch: [0][11190/11272] Time 0.874 (0.832) Data 0.001 (0.002) Loss 3.2151 (3.6113) Prec@1 25.000 (21.136) Prec@5 55.000 (46.223) Epoch: [0][11200/11272] Time 0.876 (0.832) Data 0.003 (0.002) Loss 2.8836 (3.6108) Prec@1 30.625 (21.141) Prec@5 61.250 (46.235) Epoch: [0][11210/11272] Time 0.756 (0.832) Data 0.002 (0.002) Loss 2.9344 (3.6102) Prec@1 28.750 (21.150) Prec@5 60.625 (46.244) Epoch: [0][11220/11272] Time 0.775 (0.832) Data 0.002 (0.002) Loss 3.1345 (3.6097) Prec@1 24.375 (21.156) Prec@5 59.375 (46.255) Epoch: [0][11230/11272] Time 0.938 (0.832) Data 0.002 (0.002) Loss 3.1320 (3.6092) Prec@1 28.750 (21.164) Prec@5 56.875 (46.267) Epoch: [0][11240/11272] Time 0.791 (0.832) Data 0.004 (0.002) Loss 3.4799 (3.6087) Prec@1 19.375 (21.172) Prec@5 49.375 (46.278) Epoch: [0][11250/11272] Time 0.751 (0.832) Data 0.001 (0.002) Loss 3.1830 (3.6082) Prec@1 28.125 (21.178) Prec@5 57.500 (46.290) Epoch: [0][11260/11272] Time 0.867 (0.832) Data 0.001 (0.002) Loss 2.9150 (3.6077) Prec@1 31.875 (21.186) Prec@5 61.250 (46.302) Epoch: [0][11270/11272] Time 0.823 (0.832) Data 0.000 (0.002) Loss 3.4469 (3.6073) Prec@1 20.625 (21.189) Prec@5 48.125 (46.310) Test: [0/229] Time 1.922 (1.922) Loss 1.6252 (1.6252) Prec@1 59.375 (59.375) Prec@5 89.375 (89.375) Test: [10/229] Time 0.384 (0.525) Loss 1.6489 (2.5384) Prec@1 56.250 (39.830) Prec@5 90.625 (70.170) Test: [20/229] Time 0.390 (0.468) Loss 3.4298 (2.7060) Prec@1 23.750 (35.923) Prec@5 50.000 (65.417) Test: [30/229] Time 0.418 (0.442) Loss 2.5734 (2.5714) Prec@1 36.875 (38.871) Prec@5 66.250 (67.903) Test: [40/229] Time 0.388 (0.435) Loss 1.8809 (2.6254) Prec@1 58.750 (37.348) Prec@5 75.000 (67.271) Test: [50/229] Time 0.442 (0.429) Loss 3.3316 (2.6266) Prec@1 18.750 (37.390) Prec@5 51.875 (66.875) Test: [60/229] Time 0.359 (0.421) Loss 3.4129 (2.6679) Prec@1 15.625 (36.383) Prec@5 48.750 (65.768) Test: [70/229] Time 0.459 (0.417) Loss 2.5715 (2.7094) Prec@1 40.000 (35.079) Prec@5 63.750 (64.604) Test: [80/229] Time 0.331 (0.415) Loss 2.6036 (2.7144) Prec@1 25.000 (34.614) Prec@5 70.625 (64.846) Test: [90/229] Time 0.359 (0.412) Loss 2.5498 (2.6897) Prec@1 38.125 (34.904) Prec@5 70.625 (65.508) Test: [100/229] Time 0.448 (0.412) Loss 3.0724 (2.6852) Prec@1 24.375 (35.074) Prec@5 51.875 (65.631) Test: [110/229] Time 0.377 (0.409) Loss 2.8028 (2.6727) Prec@1 21.250 (34.837) Prec@5 66.875 (66.081) Test: [120/229] Time 0.408 (0.407) Loss 3.3436 (2.6765) Prec@1 20.625 (34.463) Prec@5 58.125 (66.353) Test: [130/229] Time 0.319 (0.405) Loss 2.7982 (2.6747) Prec@1 31.875 (34.509) Prec@5 65.000 (66.422) Test: [140/229] Time 0.436 (0.403) Loss 3.2156 (2.7109) Prec@1 22.500 (33.537) Prec@5 51.875 (65.660) Test: [150/229] Time 0.448 (0.402) Loss 1.9960 (2.7243) Prec@1 53.125 (33.448) Prec@5 76.875 (65.443) Test: [160/229] Time 0.356 (0.400) Loss 1.8875 (2.7149) Prec@1 57.500 (33.832) Prec@5 83.125 (65.609) Test: [170/229] Time 0.486 (0.399) Loss 2.5981 (2.7104) Prec@1 33.750 (33.816) Prec@5 75.000 (65.749) Test: [180/229] Time 0.310 (0.398) Loss 3.7283 (2.7210) Prec@1 16.875 (33.778) Prec@5 38.125 (65.383) Test: [190/229] Time 0.506 (0.398) Loss 1.6604 (2.7221) Prec@1 57.500 (33.848) Prec@5 89.375 (65.393) Test: [200/229] Time 0.372 (0.397) Loss 3.0375 (2.7085) Prec@1 18.750 (33.856) Prec@5 55.000 (65.771) Test: [210/229] Time 0.363 (0.396) Loss 2.2660 (2.6979) Prec@1 30.625 (34.138) Prec@5 74.375 (65.983) Test: [220/229] Time 0.446 (0.397) Loss 2.5241 (2.6882) Prec@1 33.750 (34.423) Prec@5 71.250 (66.120) * Prec@1 34.706 Prec@5 66.278 Epoch: [1][0/11272] Time 4.619 (4.619) Data 2.019 (2.019) Loss 3.0906 (3.0906) Prec@1 25.625 (25.625) Prec@5 61.875 (61.875) Epoch: [1][10/11272] Time 0.873 (1.169) Data 0.002 (0.185) Loss 3.2021 (3.0307) Prec@1 26.875 (28.239) Prec@5 55.625 (58.750) Epoch: [1][20/11272] Time 0.755 (0.995) Data 0.002 (0.098) Loss 2.9748 (3.0718) Prec@1 26.875 (28.095) Prec@5 57.500 (57.500) Epoch: [1][30/11272] Time 0.742 (0.939) Data 0.002 (0.067) Loss 2.8782 (3.0625) Prec@1 34.375 (27.883) Prec@5 63.750 (57.762) Epoch: [1][40/11272] Time 0.931 (0.908) Data 0.001 (0.051) Loss 3.2411 (3.0468) Prec@1 24.375 (28.521) Prec@5 52.500 (58.277) Epoch: [1][50/11272] Time 0.780 (0.894) Data 0.001 (0.041) Loss 3.3187 (3.0465) Prec@1 21.250 (28.468) Prec@5 55.625 (58.382) Epoch: [1][60/11272] Time 0.766 (0.885) Data 0.001 (0.035) Loss 3.1435 (3.0495) Prec@1 28.125 (28.586) Prec@5 58.750 (58.330) Epoch: [1][70/11272] Time 0.951 (0.879) Data 0.001 (0.030) Loss 2.9324 (3.0426) Prec@1 33.125 (28.653) Prec@5 59.375 (58.460) Epoch: [1][80/11272] Time 0.860 (0.873) Data 0.002 (0.027) Loss 3.1558 (3.0364) Prec@1 30.000 (28.850) Prec@5 56.250 (58.627) Epoch: [1][90/11272] Time 0.741 (0.866) Data 0.001 (0.024) Loss 3.2099 (3.0343) Prec@1 25.625 (28.894) Prec@5 55.000 (58.729) Epoch: [1][100/11272] Time 0.777 (0.862) Data 0.001 (0.022) Loss 3.1059 (3.0417) Prec@1 29.375 (28.812) Prec@5 58.750 (58.521) Epoch: [1][110/11272] Time 0.949 (0.861) Data 0.001 (0.020) Loss 3.0442 (3.0411) Prec@1 33.125 (28.941) Prec@5 58.750 (58.474) Epoch: [1][120/11272] Time 0.892 (0.858) Data 0.001 (0.018) Loss 3.4718 (3.0476) Prec@1 23.125 (28.843) Prec@5 52.500 (58.357) Epoch: [1][130/11272] Time 0.750 (0.855) Data 0.002 (0.017) Loss 2.9955 (3.0467) Prec@1 26.875 (28.989) Prec@5 60.625 (58.459) Epoch: [1][140/11272] Time 0.774 (0.852) Data 0.001 (0.016) Loss 3.0191 (3.0430) Prec@1 26.875 (29.051) Prec@5 61.250 (58.537) Epoch: [1][150/11272] Time 0.844 (0.851) Data 0.001 (0.015) Loss 3.0541 (3.0416) Prec@1 28.750 (29.172) Prec@5 53.750 (58.539) Epoch: [1][160/11272] Time 0.868 (0.849) Data 0.002 (0.014) Loss 2.9796 (3.0471) Prec@1 28.125 (29.123) Prec@5 64.375 (58.459) Epoch: [1][170/11272] Time 0.745 (0.847) Data 0.001 (0.013) Loss 3.1979 (3.0494) Prec@1 23.125 (29.061) Prec@5 51.875 (58.410) Epoch: [1][180/11272] Time 0.905 (0.847) Data 0.001 (0.013) Loss 2.9114 (3.0491) Prec@1 29.375 (29.050) Prec@5 63.125 (58.346) Epoch: [1][190/11272] Time 0.904 (0.846) Data 0.002 (0.012) Loss 2.7538 (3.0440) Prec@1 33.750 (29.136) Prec@5 66.875 (58.491) Epoch: [1][200/11272] Time 0.747 (0.844) Data 0.001 (0.012) Loss 3.1879 (3.0431) Prec@1 28.750 (29.080) Prec@5 58.750 (58.458) Epoch: [1][210/11272] Time 0.743 (0.843) Data 0.002 (0.011) Loss 3.2189 (3.0404) Prec@1 24.375 (29.180) Prec@5 55.625 (58.510) Epoch: [1][220/11272] Time 0.917 (0.842) Data 0.001 (0.011) Loss 2.8471 (3.0412) Prec@1 31.875 (29.191) Prec@5 63.125 (58.527) Epoch: [1][230/11272] Time 0.921 (0.842) Data 0.001 (0.010) Loss 2.8601 (3.0408) Prec@1 33.125 (29.183) Prec@5 61.250 (58.552) Epoch: [1][240/11272] Time 0.764 (0.841) Data 0.001 (0.010) Loss 3.0041 (3.0431) Prec@1 29.375 (29.092) Prec@5 61.875 (58.555) Epoch: [1][250/11272] Time 0.753 (0.840) Data 0.001 (0.010) Loss 3.1781 (3.0460) Prec@1 25.625 (29.079) Prec@5 56.875 (58.506) Epoch: [1][260/11272] Time 0.978 (0.840) Data 0.001 (0.009) Loss 3.0798 (3.0473) Prec@1 27.500 (29.047) Prec@5 56.250 (58.506) Epoch: [1][270/11272] Time 0.888 (0.839) Data 0.001 (0.009) Loss 3.0770 (3.0474) Prec@1 26.250 (29.064) Prec@5 56.875 (58.496) Epoch: [1][280/11272] Time 0.759 (0.839) Data 0.001 (0.009) Loss 2.9372 (3.0488) Prec@1 31.875 (29.081) Prec@5 62.500 (58.465) Epoch: [1][290/11272] Time 0.732 (0.839) Data 0.002 (0.009) Loss 3.3032 (3.0499) Prec@1 21.250 (29.059) Prec@5 59.375 (58.479) Epoch: [1][300/11272] Time 0.916 (0.839) Data 0.002 (0.008) Loss 3.1945 (3.0499) Prec@1 31.250 (29.103) Prec@5 57.500 (58.524) Epoch: [1][310/11272] Time 0.742 (0.837) Data 0.003 (0.008) Loss 3.1470 (3.0521) Prec@1 28.125 (29.070) Prec@5 53.750 (58.465) Epoch: [1][320/11272] Time 0.809 (0.837) Data 0.002 (0.008) Loss 2.8572 (3.0516) Prec@1 31.875 (29.102) Prec@5 64.375 (58.516) Epoch: [1][330/11272] Time 0.897 (0.837) Data 0.001 (0.008) Loss 2.8890 (3.0489) Prec@1 25.625 (29.111) Prec@5 60.625 (58.578) Epoch: [1][340/11272] Time 0.980 (0.837) Data 0.002 (0.008) Loss 3.1227 (3.0519) Prec@1 29.375 (29.084) Prec@5 55.000 (58.521) Epoch: [1][350/11272] Time 0.778 (0.837) Data 0.004 (0.007) Loss 2.8738 (3.0520) Prec@1 30.000 (29.079) Prec@5 61.250 (58.552) Epoch: [1][360/11272] Time 0.745 (0.837) Data 0.002 (0.007) Loss 3.1443 (3.0511) Prec@1 29.375 (29.105) Prec@5 55.000 (58.558) Epoch: [1][370/11272] Time 0.890 (0.837) Data 0.003 (0.007) Loss 3.1867 (3.0529) Prec@1 26.250 (29.092) Prec@5 55.000 (58.489) Epoch: [1][380/11272] Time 0.888 (0.837) Data 0.001 (0.007) Loss 3.1487 (3.0547) Prec@1 28.125 (29.101) Prec@5 56.250 (58.399) Epoch: [1][390/11272] Time 0.737 (0.836) Data 0.001 (0.007) Loss 3.2586 (3.0545) Prec@1 29.375 (29.097) Prec@5 52.500 (58.378) Epoch: [1][400/11272] Time 0.758 (0.836) Data 0.001 (0.007) Loss 2.9301 (3.0532) Prec@1 29.375 (29.133) Prec@5 60.000 (58.392) Epoch: [1][410/11272] Time 0.865 (0.836) Data 0.002 (0.007) Loss 3.1989 (3.0539) Prec@1 28.125 (29.127) Prec@5 55.625 (58.365) Epoch: [1][420/11272] Time 0.957 (0.836) Data 0.003 (0.006) Loss 3.1520 (3.0535) Prec@1 30.625 (29.132) Prec@5 56.250 (58.365) Epoch: [1][430/11272] Time 0.802 (0.836) Data 0.001 (0.006) Loss 3.0215 (3.0528) Prec@1 33.750 (29.136) Prec@5 55.625 (58.398) Epoch: [1][440/11272] Time 0.863 (0.835) Data 0.002 (0.006) Loss 3.0250 (3.0519) Prec@1 30.625 (29.137) Prec@5 58.125 (58.413) Epoch: [1][450/11272] Time 0.865 (0.835) Data 0.001 (0.006) Loss 3.0732 (3.0512) Prec@1 33.125 (29.146) Prec@5 60.000 (58.433) Epoch: [1][460/11272] Time 0.750 (0.835) Data 0.001 (0.006) Loss 3.1034 (3.0512) Prec@1 31.250 (29.132) Prec@5 57.500 (58.429) Epoch: [1][470/11272] Time 0.731 (0.835) Data 0.001 (0.006) Loss 2.7629 (3.0499) Prec@1 34.375 (29.185) Prec@5 66.250 (58.433) Epoch: [1][480/11272] Time 0.898 (0.835) Data 0.002 (0.006) Loss 3.1627 (3.0520) Prec@1 25.000 (29.181) Prec@5 55.000 (58.386) Epoch: [1][490/11272] Time 0.993 (0.835) Data 0.002 (0.006) Loss 3.1926 (3.0496) Prec@1 29.375 (29.234) Prec@5 53.125 (58.423) Epoch: [1][500/11272] Time 0.770 (0.835) Data 0.002 (0.006) Loss 2.8801 (3.0477) Prec@1 28.125 (29.259) Prec@5 63.750 (58.482) Epoch: [1][510/11272] Time 0.747 (0.834) Data 0.001 (0.006) Loss 2.8578 (3.0470) Prec@1 29.375 (29.275) Prec@5 60.000 (58.491) Epoch: [1][520/11272] Time 0.885 (0.835) Data 0.002 (0.006) Loss 2.9637 (3.0464) Prec@1 35.000 (29.292) Prec@5 57.500 (58.504) Epoch: [1][530/11272] Time 0.961 (0.835) Data 0.001 (0.005) Loss 3.0802 (3.0466) Prec@1 33.125 (29.298) Prec@5 58.125 (58.480) Epoch: [1][540/11272] Time 0.763 (0.835) Data 0.002 (0.005) Loss 3.1373 (3.0463) Prec@1 25.625 (29.290) Prec@5 56.875 (58.481) Epoch: [1][550/11272] Time 0.772 (0.835) Data 0.002 (0.005) Loss 3.1179 (3.0475) Prec@1 30.625 (29.285) Prec@5 55.000 (58.474) Epoch: [1][560/11272] Time 0.897 (0.835) Data 0.002 (0.005) Loss 3.1275 (3.0476) Prec@1 26.250 (29.283) Prec@5 59.375 (58.502) Epoch: [1][570/11272] Time 0.754 (0.835) Data 0.003 (0.005) Loss 3.2150 (3.0459) Prec@1 25.625 (29.285) Prec@5 53.750 (58.549) Epoch: [1][580/11272] Time 0.779 (0.835) Data 0.001 (0.005) Loss 3.1329 (3.0457) Prec@1 31.875 (29.326) Prec@5 54.375 (58.539) Epoch: [1][590/11272] Time 0.944 (0.835) Data 0.001 (0.005) Loss 3.0047 (3.0459) Prec@1 24.375 (29.289) Prec@5 56.875 (58.542) Epoch: [1][600/11272] Time 0.961 (0.835) Data 0.002 (0.005) Loss 3.0355 (3.0452) Prec@1 25.000 (29.301) Prec@5 56.875 (58.557) Epoch: [1][610/11272] Time 0.788 (0.835) Data 0.002 (0.005) Loss 3.0291 (3.0448) Prec@1 28.750 (29.317) Prec@5 59.375 (58.562) Epoch: [1][620/11272] Time 0.747 (0.834) Data 0.001 (0.005) Loss 3.0156 (3.0447) Prec@1 32.500 (29.312) Prec@5 53.750 (58.544) Epoch: [1][630/11272] Time 0.910 (0.835) Data 0.001 (0.005) Loss 3.4038 (3.0446) Prec@1 23.750 (29.328) Prec@5 53.750 (58.576) Epoch: [1][640/11272] Time 0.950 (0.835) Data 0.001 (0.005) Loss 3.1047 (3.0443) Prec@1 30.625 (29.343) Prec@5 59.375 (58.563) Epoch: [1][650/11272] Time 0.755 (0.835) Data 0.002 (0.005) Loss 2.9707 (3.0446) Prec@1 31.875 (29.334) Prec@5 60.625 (58.568) Epoch: [1][660/11272] Time 0.745 (0.834) Data 0.001 (0.005) Loss 3.0458 (3.0436) Prec@1 26.875 (29.344) Prec@5 61.250 (58.600) Epoch: [1][670/11272] Time 0.901 (0.834) Data 0.001 (0.005) Loss 3.1832 (3.0432) Prec@1 28.125 (29.335) Prec@5 55.000 (58.604) Epoch: [1][680/11272] Time 0.856 (0.834) Data 0.001 (0.005) Loss 2.8822 (3.0425) Prec@1 31.250 (29.343) Prec@5 64.375 (58.629) Epoch: [1][690/11272] Time 0.759 (0.834) Data 0.001 (0.005) Loss 3.0765 (3.0427) Prec@1 25.625 (29.320) Prec@5 56.875 (58.604) Epoch: [1][700/11272] Time 0.859 (0.834) Data 0.001 (0.005) Loss 3.1676 (3.0426) Prec@1 25.000 (29.321) Prec@5 59.375 (58.606) Epoch: [1][710/11272] Time 0.880 (0.834) Data 0.002 (0.004) Loss 2.8992 (3.0416) Prec@1 31.250 (29.319) Prec@5 61.875 (58.645) Epoch: [1][720/11272] Time 0.759 (0.834) Data 0.001 (0.004) Loss 3.2235 (3.0416) Prec@1 28.750 (29.333) Prec@5 56.875 (58.643) Epoch: [1][730/11272] Time 0.762 (0.833) Data 0.001 (0.004) Loss 3.0537 (3.0410) Prec@1 28.750 (29.351) Prec@5 58.125 (58.654) Epoch: [1][740/11272] Time 0.972 (0.833) Data 0.002 (0.004) Loss 3.3189 (3.0417) Prec@1 29.375 (29.340) Prec@5 54.375 (58.652) Epoch: [1][750/11272] Time 0.897 (0.833) Data 0.002 (0.004) Loss 3.0606 (3.0413) Prec@1 29.375 (29.351) Prec@5 54.375 (58.670) Epoch: [1][760/11272] Time 0.777 (0.833) Data 0.002 (0.004) Loss 2.7392 (3.0409) Prec@1 38.125 (29.354) Prec@5 68.125 (58.674) Epoch: [1][770/11272] Time 0.730 (0.833) Data 0.001 (0.004) Loss 3.0247 (3.0408) Prec@1 28.125 (29.339) Prec@5 58.125 (58.681) Epoch: [1][780/11272] Time 0.962 (0.833) Data 0.002 (0.004) Loss 2.8854 (3.0401) Prec@1 29.375 (29.362) Prec@5 64.375 (58.701) Epoch: [1][790/11272] Time 0.950 (0.833) Data 0.002 (0.004) Loss 3.1378 (3.0393) Prec@1 31.875 (29.378) Prec@5 58.125 (58.710) Epoch: [1][800/11272] Time 0.767 (0.833) Data 0.002 (0.004) Loss 2.9691 (3.0381) Prec@1 34.375 (29.391) Prec@5 58.750 (58.737) Epoch: [1][810/11272] Time 0.737 (0.833) Data 0.002 (0.004) Loss 3.2931 (3.0374) Prec@1 27.500 (29.401) Prec@5 55.625 (58.753) Epoch: [1][820/11272] Time 0.866 (0.834) Data 0.002 (0.004) Loss 2.8557 (3.0376) Prec@1 34.375 (29.399) Prec@5 65.000 (58.754) Epoch: [1][830/11272] Time 0.860 (0.834) Data 0.002 (0.004) Loss 2.9627 (3.0371) Prec@1 29.375 (29.398) Prec@5 63.750 (58.777) Epoch: [1][840/11272] Time 0.740 (0.833) Data 0.001 (0.004) Loss 2.9672 (3.0372) Prec@1 31.875 (29.400) Prec@5 55.625 (58.763) Epoch: [1][850/11272] Time 0.889 (0.833) Data 0.002 (0.004) Loss 3.1431 (3.0364) Prec@1 33.125 (29.423) Prec@5 54.375 (58.754) Epoch: [1][860/11272] Time 0.935 (0.833) Data 0.002 (0.004) Loss 3.0484 (3.0368) Prec@1 26.250 (29.406) Prec@5 60.000 (58.749) Epoch: [1][870/11272] Time 0.736 (0.833) Data 0.001 (0.004) Loss 3.0981 (3.0365) Prec@1 29.375 (29.425) Prec@5 55.000 (58.745) Epoch: [1][880/11272] Time 0.808 (0.833) Data 0.003 (0.004) Loss 2.8665 (3.0355) Prec@1 27.500 (29.430) Prec@5 63.125 (58.755) Epoch: [1][890/11272] Time 0.873 (0.833) Data 0.001 (0.004) Loss 3.2413 (3.0369) Prec@1 23.125 (29.402) Prec@5 49.375 (58.725) Epoch: [1][900/11272] Time 0.887 (0.833) Data 0.001 (0.004) Loss 2.9637 (3.0372) Prec@1 34.375 (29.417) Prec@5 60.625 (58.727) Epoch: [1][910/11272] Time 0.794 (0.833) Data 0.001 (0.004) Loss 3.0455 (3.0364) Prec@1 25.625 (29.425) Prec@5 60.000 (58.738) Epoch: [1][920/11272] Time 0.738 (0.833) Data 0.002 (0.004) Loss 2.5521 (3.0351) Prec@1 33.125 (29.443) Prec@5 74.375 (58.770) Epoch: [1][930/11272] Time 0.919 (0.833) Data 0.001 (0.004) Loss 3.2311 (3.0354) Prec@1 26.250 (29.431) Prec@5 53.750 (58.761) Epoch: [1][940/11272] Time 0.957 (0.833) Data 0.002 (0.004) Loss 2.8504 (3.0353) Prec@1 30.000 (29.433) Prec@5 58.750 (58.761) Epoch: [1][950/11272] Time 0.772 (0.833) Data 0.002 (0.004) Loss 2.9794 (3.0352) Prec@1 26.875 (29.427) Prec@5 61.875 (58.771) Epoch: [1][960/11272] Time 0.778 (0.833) Data 0.002 (0.004) Loss 3.0595 (3.0352) Prec@1 30.000 (29.444) Prec@5 60.000 (58.772) Epoch: [1][970/11272] Time 0.914 (0.833) Data 0.002 (0.004) Loss 3.5371 (3.0361) Prec@1 23.125 (29.432) Prec@5 51.250 (58.751) Epoch: [1][980/11272] Time 0.752 (0.833) Data 0.002 (0.004) Loss 2.9731 (3.0356) Prec@1 27.500 (29.431) Prec@5 58.750 (58.761) Epoch: [1][990/11272] Time 0.744 (0.833) Data 0.001 (0.004) Loss 3.2391 (3.0361) Prec@1 28.125 (29.422) Prec@5 53.125 (58.753) Epoch: [1][1000/11272] Time 0.946 (0.833) Data 0.001 (0.004) Loss 2.9423 (3.0361) Prec@1 31.250 (29.443) Prec@5 60.000 (58.751) Epoch: [1][1010/11272] Time 0.924 (0.833) Data 0.002 (0.004) Loss 3.0033 (3.0359) Prec@1 32.500 (29.454) Prec@5 60.000 (58.748) Epoch: [1][1020/11272] Time 0.784 (0.833) Data 0.002 (0.004) Loss 2.8257 (3.0355) Prec@1 31.875 (29.461) Prec@5 69.375 (58.762) Epoch: [1][1030/11272] Time 0.734 (0.833) Data 0.002 (0.004) Loss 3.1053 (3.0346) Prec@1 28.125 (29.471) Prec@5 56.875 (58.785) Epoch: [1][1040/11272] Time 0.953 (0.833) Data 0.002 (0.004) Loss 3.0176 (3.0345) Prec@1 28.750 (29.470) Prec@5 56.250 (58.786) Epoch: [1][1050/11272] Time 0.915 (0.834) Data 0.001 (0.004) Loss 3.3829 (3.0352) Prec@1 28.125 (29.470) Prec@5 52.500 (58.768) Epoch: [1][1060/11272] Time 0.737 (0.833) Data 0.001 (0.004) Loss 2.9938 (3.0354) Prec@1 28.750 (29.466) Prec@5 60.625 (58.761) Epoch: [1][1070/11272] Time 0.791 (0.833) Data 0.002 (0.004) Loss 3.1560 (3.0351) Prec@1 23.750 (29.470) Prec@5 55.000 (58.758) Epoch: [1][1080/11272] Time 0.901 (0.833) Data 0.001 (0.004) Loss 2.8771 (3.0351) Prec@1 33.125 (29.464) Prec@5 63.125 (58.765) Epoch: [1][1090/11272] Time 0.888 (0.833) Data 0.002 (0.003) Loss 2.8506 (3.0349) Prec@1 31.875 (29.460) Prec@5 61.250 (58.763) Epoch: [1][1100/11272] Time 0.760 (0.833) Data 0.002 (0.003) Loss 3.1179 (3.0350) Prec@1 27.500 (29.454) Prec@5 58.125 (58.755) Epoch: [1][1110/11272] Time 0.859 (0.834) Data 0.001 (0.003) Loss 2.7640 (3.0346) Prec@1 30.625 (29.459) Prec@5 65.625 (58.761) Epoch: [1][1120/11272] Time 0.911 (0.834) Data 0.001 (0.003) Loss 2.9902 (3.0340) Prec@1 30.625 (29.475) Prec@5 58.125 (58.767) Epoch: [1][1130/11272] Time 0.758 (0.833) Data 0.001 (0.003) Loss 3.0965 (3.0338) Prec@1 28.750 (29.471) Prec@5 59.375 (58.775) Epoch: [1][1140/11272] Time 0.757 (0.833) Data 0.001 (0.003) Loss 2.7934 (3.0339) Prec@1 35.000 (29.469) Prec@5 68.750 (58.770) Epoch: [1][1150/11272] Time 0.893 (0.834) Data 0.001 (0.003) Loss 3.0730 (3.0346) Prec@1 29.375 (29.461) Prec@5 61.250 (58.762) Epoch: [1][1160/11272] Time 0.898 (0.834) Data 0.002 (0.003) Loss 2.8735 (3.0348) Prec@1 28.125 (29.455) Prec@5 65.000 (58.765) Epoch: [1][1170/11272] Time 0.748 (0.833) Data 0.001 (0.003) Loss 3.0457 (3.0352) Prec@1 29.375 (29.435) Prec@5 58.750 (58.758) Epoch: [1][1180/11272] Time 0.749 (0.833) Data 0.001 (0.003) Loss 2.9903 (3.0356) Prec@1 30.000 (29.417) Prec@5 55.000 (58.749) Epoch: [1][1190/11272] Time 0.938 (0.834) Data 0.002 (0.003) Loss 3.1913 (3.0356) Prec@1 27.500 (29.422) Prec@5 55.000 (58.755) Epoch: [1][1200/11272] Time 0.909 (0.834) Data 0.002 (0.003) Loss 2.9592 (3.0357) Prec@1 30.625 (29.441) Prec@5 56.875 (58.757) Epoch: [1][1210/11272] Time 0.739 (0.834) Data 0.001 (0.003) Loss 3.1700 (3.0351) Prec@1 26.875 (29.454) Prec@5 56.250 (58.767) Epoch: [1][1220/11272] Time 0.741 (0.834) Data 0.001 (0.003) Loss 2.7040 (3.0346) Prec@1 37.500 (29.462) Prec@5 62.500 (58.774) Epoch: [1][1230/11272] Time 0.919 (0.834) Data 0.002 (0.003) Loss 2.8854 (3.0345) Prec@1 29.375 (29.468) Prec@5 63.750 (58.786) Epoch: [1][1240/11272] Time 0.828 (0.834) Data 0.004 (0.003) Loss 2.8889 (3.0342) Prec@1 30.625 (29.479) Prec@5 58.750 (58.788) Epoch: [1][1250/11272] Time 0.745 (0.834) Data 0.001 (0.003) Loss 2.9787 (3.0343) Prec@1 31.875 (29.468) Prec@5 63.750 (58.793) Epoch: [1][1260/11272] Time 0.933 (0.834) Data 0.002 (0.003) Loss 3.0731 (3.0348) Prec@1 27.500 (29.455) Prec@5 59.375 (58.780) Epoch: [1][1270/11272] Time 0.905 (0.834) Data 0.002 (0.003) Loss 3.0384 (3.0346) Prec@1 27.500 (29.446) Prec@5 58.125 (58.788) Epoch: [1][1280/11272] Time 0.774 (0.834) Data 0.002 (0.003) Loss 2.7796 (3.0342) Prec@1 36.250 (29.457) Prec@5 65.000 (58.803) Epoch: [1][1290/11272] Time 0.737 (0.834) Data 0.002 (0.003) Loss 2.7501 (3.0333) Prec@1 35.000 (29.478) Prec@5 65.000 (58.816) Epoch: [1][1300/11272] Time 0.945 (0.834) Data 0.002 (0.003) Loss 2.8715 (3.0333) Prec@1 30.000 (29.480) Prec@5 61.875 (58.820) Epoch: [1][1310/11272] Time 0.879 (0.834) Data 0.001 (0.003) Loss 2.7213 (3.0326) Prec@1 28.125 (29.497) Prec@5 70.000 (58.831) Epoch: [1][1320/11272] Time 0.764 (0.834) Data 0.001 (0.003) Loss 2.7425 (3.0319) Prec@1 33.750 (29.502) Prec@5 65.000 (58.849) Epoch: [1][1330/11272] Time 0.744 (0.834) Data 0.001 (0.003) Loss 2.6970 (3.0308) Prec@1 33.750 (29.515) Prec@5 71.875 (58.875) Epoch: [1][1340/11272] Time 0.933 (0.834) Data 0.002 (0.003) Loss 3.1457 (3.0305) Prec@1 24.375 (29.518) Prec@5 53.750 (58.880) Epoch: [1][1350/11272] Time 0.881 (0.834) Data 0.002 (0.003) Loss 2.9468 (3.0300) Prec@1 32.500 (29.532) Prec@5 60.000 (58.888) Epoch: [1][1360/11272] Time 0.742 (0.834) Data 0.001 (0.003) Loss 2.9629 (3.0292) Prec@1 32.500 (29.556) Prec@5 60.000 (58.905) Epoch: [1][1370/11272] Time 0.858 (0.834) Data 0.002 (0.003) Loss 2.9377 (3.0289) Prec@1 31.250 (29.571) Prec@5 62.500 (58.908) Epoch: [1][1380/11272] Time 0.938 (0.834) Data 0.002 (0.003) Loss 2.9644 (3.0289) Prec@1 32.500 (29.575) Prec@5 60.000 (58.906) Epoch: [1][1390/11272] Time 0.757 (0.834) Data 0.001 (0.003) Loss 2.8940 (3.0288) Prec@1 28.750 (29.577) Prec@5 61.250 (58.912) Epoch: [1][1400/11272] Time 0.761 (0.834) Data 0.002 (0.003) Loss 3.0395 (3.0290) Prec@1 30.625 (29.572) Prec@5 58.750 (58.911) Epoch: [1][1410/11272] Time 0.863 (0.834) Data 0.001 (0.003) Loss 2.8019 (3.0286) Prec@1 30.000 (29.581) Prec@5 58.750 (58.912) Epoch: [1][1420/11272] Time 0.961 (0.834) Data 0.004 (0.003) Loss 3.0102 (3.0286) Prec@1 35.000 (29.580) Prec@5 55.000 (58.903) Epoch: [1][1430/11272] Time 0.742 (0.834) Data 0.001 (0.003) Loss 2.8614 (3.0282) Prec@1 32.500 (29.572) Prec@5 63.750 (58.912) Epoch: [1][1440/11272] Time 0.752 (0.834) Data 0.001 (0.003) Loss 3.0086 (3.0280) Prec@1 23.125 (29.574) Prec@5 58.750 (58.918) Epoch: [1][1450/11272] Time 0.888 (0.834) Data 0.001 (0.003) Loss 2.9958 (3.0274) Prec@1 34.375 (29.584) Prec@5 60.625 (58.928) Epoch: [1][1460/11272] Time 0.898 (0.834) Data 0.002 (0.003) Loss 2.8521 (3.0272) Prec@1 28.750 (29.592) Prec@5 62.500 (58.938) Epoch: [1][1470/11272] Time 0.729 (0.834) Data 0.001 (0.003) Loss 3.2262 (3.0269) Prec@1 23.750 (29.593) Prec@5 57.500 (58.935) Epoch: [1][1480/11272] Time 0.820 (0.834) Data 0.002 (0.003) Loss 3.0050 (3.0265) Prec@1 26.875 (29.601) Prec@5 58.125 (58.937) Epoch: [1][1490/11272] Time 0.856 (0.834) Data 0.001 (0.003) Loss 2.8447 (3.0262) Prec@1 30.625 (29.599) Prec@5 61.250 (58.933) Epoch: [1][1500/11272] Time 0.804 (0.834) Data 0.003 (0.003) Loss 3.0133 (3.0258) Prec@1 29.375 (29.601) Prec@5 60.000 (58.944) Epoch: [1][1510/11272] Time 0.741 (0.834) Data 0.001 (0.003) Loss 2.9278 (3.0252) Prec@1 32.500 (29.617) Prec@5 62.500 (58.956) Epoch: [1][1520/11272] Time 0.933 (0.834) Data 0.001 (0.003) Loss 2.8643 (3.0253) Prec@1 29.375 (29.610) Prec@5 57.500 (58.940) Epoch: [1][1530/11272] Time 0.893 (0.834) Data 0.002 (0.003) Loss 3.0876 (3.0253) Prec@1 28.750 (29.614) Prec@5 57.500 (58.944) Epoch: [1][1540/11272] Time 0.771 (0.834) Data 0.002 (0.003) Loss 2.7482 (3.0251) Prec@1 36.875 (29.625) Prec@5 66.875 (58.952) Epoch: [1][1550/11272] Time 0.729 (0.834) Data 0.002 (0.003) Loss 3.0389 (3.0249) Prec@1 30.625 (29.634) Prec@5 56.250 (58.955) Epoch: [1][1560/11272] Time 0.943 (0.834) Data 0.001 (0.003) Loss 2.7954 (3.0246) Prec@1 33.750 (29.646) Prec@5 58.750 (58.960) Epoch: [1][1570/11272] Time 0.954 (0.834) Data 0.001 (0.003) Loss 3.0839 (3.0245) Prec@1 25.625 (29.644) Prec@5 59.375 (58.959) Epoch: [1][1580/11272] Time 0.787 (0.834) Data 0.002 (0.003) Loss 3.1135 (3.0244) Prec@1 23.750 (29.633) Prec@5 59.375 (58.958) Epoch: [1][1590/11272] Time 0.707 (0.834) Data 0.001 (0.003) Loss 3.1322 (3.0245) Prec@1 29.375 (29.635) Prec@5 53.750 (58.948) Epoch: [1][1600/11272] Time 0.887 (0.834) Data 0.001 (0.003) Loss 3.0451 (3.0246) Prec@1 26.875 (29.629) Prec@5 58.750 (58.942) Epoch: [1][1610/11272] Time 0.934 (0.834) Data 0.001 (0.003) Loss 3.0501 (3.0244) Prec@1 29.375 (29.630) Prec@5 60.625 (58.949) Epoch: [1][1620/11272] Time 0.750 (0.834) Data 0.001 (0.003) Loss 3.0343 (3.0243) Prec@1 29.375 (29.623) Prec@5 58.125 (58.955) Epoch: [1][1630/11272] Time 0.858 (0.834) Data 0.002 (0.003) Loss 3.0533 (3.0247) Prec@1 29.375 (29.608) Prec@5 57.500 (58.948) Epoch: [1][1640/11272] Time 0.932 (0.834) Data 0.002 (0.003) Loss 2.9942 (3.0250) Prec@1 30.000 (29.609) Prec@5 63.125 (58.942) Epoch: [1][1650/11272] Time 0.754 (0.834) Data 0.001 (0.003) Loss 2.9554 (3.0248) Prec@1 33.750 (29.611) Prec@5 65.000 (58.945) Epoch: [1][1660/11272] Time 0.736 (0.834) Data 0.001 (0.003) Loss 2.7744 (3.0243) Prec@1 31.250 (29.618) Prec@5 65.625 (58.963) Epoch: [1][1670/11272] Time 0.898 (0.834) Data 0.001 (0.003) Loss 2.9185 (3.0243) Prec@1 31.250 (29.621) Prec@5 61.250 (58.964) Epoch: [1][1680/11272] Time 0.898 (0.834) Data 0.001 (0.003) Loss 3.2141 (3.0246) Prec@1 27.500 (29.615) Prec@5 53.750 (58.961) Epoch: [1][1690/11272] Time 0.792 (0.834) Data 0.002 (0.003) Loss 2.9956 (3.0245) Prec@1 26.875 (29.610) Prec@5 58.750 (58.960) Epoch: [1][1700/11272] Time 0.773 (0.834) Data 0.001 (0.003) Loss 3.0539 (3.0247) Prec@1 27.500 (29.608) Prec@5 59.375 (58.956) Epoch: [1][1710/11272] Time 0.844 (0.834) Data 0.002 (0.003) Loss 3.2349 (3.0245) Prec@1 26.250 (29.622) Prec@5 55.000 (58.959) Epoch: [1][1720/11272] Time 0.895 (0.834) Data 0.001 (0.003) Loss 3.0397 (3.0242) Prec@1 35.000 (29.631) Prec@5 57.500 (58.970) Epoch: [1][1730/11272] Time 0.765 (0.834) Data 0.002 (0.003) Loss 2.9106 (3.0239) Prec@1 37.500 (29.643) Prec@5 63.750 (58.974) Epoch: [1][1740/11272] Time 0.781 (0.834) Data 0.002 (0.003) Loss 2.7485 (3.0237) Prec@1 38.125 (29.655) Prec@5 63.750 (58.975) Epoch: [1][1750/11272] Time 0.861 (0.834) Data 0.001 (0.003) Loss 3.0834 (3.0235) Prec@1 30.000 (29.658) Prec@5 58.125 (58.981) Epoch: [1][1760/11272] Time 0.896 (0.834) Data 0.002 (0.003) Loss 2.8887 (3.0232) Prec@1 33.125 (29.662) Prec@5 57.500 (58.980) Epoch: [1][1770/11272] Time 0.752 (0.834) Data 0.002 (0.003) Loss 2.9916 (3.0234) Prec@1 27.500 (29.659) Prec@5 59.375 (58.981) Epoch: [1][1780/11272] Time 0.953 (0.834) Data 0.002 (0.003) Loss 3.0046 (3.0236) Prec@1 31.875 (29.660) Prec@5 63.750 (58.976) Epoch: [1][1790/11272] Time 0.893 (0.834) Data 0.002 (0.003) Loss 2.7891 (3.0235) Prec@1 33.750 (29.666) Prec@5 64.375 (58.973) Epoch: [1][1800/11272] Time 0.789 (0.834) Data 0.002 (0.003) Loss 2.9628 (3.0235) Prec@1 28.125 (29.661) Prec@5 61.250 (58.974) Epoch: [1][1810/11272] Time 0.789 (0.834) Data 0.002 (0.003) Loss 2.8534 (3.0236) Prec@1 31.875 (29.659) Prec@5 59.375 (58.962) Epoch: [1][1820/11272] Time 0.852 (0.834) Data 0.001 (0.003) Loss 3.1728 (3.0234) Prec@1 25.000 (29.660) Prec@5 57.500 (58.969) Epoch: [1][1830/11272] Time 0.928 (0.833) Data 0.002 (0.003) Loss 2.8681 (3.0230) Prec@1 30.625 (29.664) Prec@5 61.250 (58.985) Epoch: [1][1840/11272] Time 0.769 (0.833) Data 0.001 (0.003) Loss 2.8011 (3.0232) Prec@1 37.500 (29.662) Prec@5 58.750 (58.981) Epoch: [1][1850/11272] Time 0.814 (0.833) Data 0.002 (0.003) Loss 3.2724 (3.0236) Prec@1 23.125 (29.650) Prec@5 56.250 (58.980) Epoch: [1][1860/11272] Time 0.915 (0.833) Data 0.002 (0.003) Loss 3.0995 (3.0237) Prec@1 28.750 (29.655) Prec@5 59.375 (58.977) Epoch: [1][1870/11272] Time 0.876 (0.833) Data 0.001 (0.003) Loss 2.9035 (3.0235) Prec@1 30.625 (29.660) Prec@5 60.625 (58.978) Epoch: [1][1880/11272] Time 0.748 (0.833) Data 0.001 (0.003) Loss 3.0199 (3.0229) Prec@1 27.500 (29.673) Prec@5 58.750 (58.987) Epoch: [1][1890/11272] Time 0.757 (0.833) Data 0.002 (0.003) Loss 3.1208 (3.0225) Prec@1 25.625 (29.682) Prec@5 58.125 (59.001) Epoch: [1][1900/11272] Time 0.913 (0.833) Data 0.001 (0.003) Loss 2.8708 (3.0219) Prec@1 31.875 (29.688) Prec@5 60.000 (59.012) Epoch: [1][1910/11272] Time 0.779 (0.833) Data 0.002 (0.003) Loss 2.9571 (3.0219) Prec@1 35.000 (29.700) Prec@5 63.750 (59.007) Epoch: [1][1920/11272] Time 0.749 (0.833) Data 0.001 (0.003) Loss 3.1251 (3.0217) Prec@1 28.125 (29.703) Prec@5 58.750 (59.011) Epoch: [1][1930/11272] Time 0.872 (0.833) Data 0.001 (0.003) Loss 3.0500 (3.0212) Prec@1 33.125 (29.710) Prec@5 54.375 (59.024) Epoch: [1][1940/11272] Time 0.848 (0.833) Data 0.002 (0.003) Loss 2.8580 (3.0212) Prec@1 31.250 (29.711) Prec@5 61.875 (59.028) Epoch: [1][1950/11272] Time 0.777 (0.833) Data 0.001 (0.003) Loss 2.8929 (3.0210) Prec@1 33.125 (29.715) Prec@5 58.750 (59.031) Epoch: [1][1960/11272] Time 0.743 (0.833) Data 0.002 (0.003) Loss 2.9297 (3.0202) Prec@1 26.875 (29.727) Prec@5 61.250 (59.050) Epoch: [1][1970/11272] Time 0.883 (0.833) Data 0.002 (0.003) Loss 2.9633 (3.0203) Prec@1 31.250 (29.730) Prec@5 59.375 (59.050) Epoch: [1][1980/11272] Time 0.923 (0.833) Data 0.002 (0.003) Loss 3.0571 (3.0203) Prec@1 23.750 (29.719) Prec@5 56.250 (59.044) Epoch: [1][1990/11272] Time 0.747 (0.833) Data 0.002 (0.003) Loss 3.0203 (3.0205) Prec@1 31.250 (29.717) Prec@5 55.000 (59.029) Epoch: [1][2000/11272] Time 0.747 (0.833) Data 0.002 (0.003) Loss 2.7013 (3.0199) Prec@1 35.000 (29.722) Prec@5 68.125 (59.040) Epoch: [1][2010/11272] Time 0.885 (0.833) Data 0.002 (0.003) Loss 3.0376 (3.0197) Prec@1 31.875 (29.731) Prec@5 60.625 (59.044) Epoch: [1][2020/11272] Time 0.927 (0.833) Data 0.002 (0.003) Loss 2.9117 (3.0197) Prec@1 32.500 (29.729) Prec@5 61.250 (59.048) Epoch: [1][2030/11272] Time 0.743 (0.833) Data 0.002 (0.003) Loss 2.8529 (3.0200) Prec@1 30.625 (29.728) Prec@5 64.375 (59.043) Epoch: [1][2040/11272] Time 0.926 (0.833) Data 0.002 (0.003) Loss 2.9980 (3.0204) Prec@1 26.875 (29.715) Prec@5 59.375 (59.037) Epoch: [1][2050/11272] Time 0.859 (0.833) Data 0.002 (0.003) Loss 2.9938 (3.0203) Prec@1 25.625 (29.714) Prec@5 59.375 (59.040) Epoch: [1][2060/11272] Time 0.754 (0.833) Data 0.002 (0.003) Loss 3.2167 (3.0204) Prec@1 22.500 (29.706) Prec@5 58.125 (59.044) Epoch: [1][2070/11272] Time 0.741 (0.833) Data 0.001 (0.003) Loss 3.0338 (3.0202) Prec@1 23.750 (29.706) Prec@5 54.375 (59.042) Epoch: [1][2080/11272] Time 0.882 (0.833) Data 0.002 (0.003) Loss 2.9726 (3.0199) Prec@1 34.375 (29.719) Prec@5 58.750 (59.049) Epoch: [1][2090/11272] Time 0.852 (0.833) Data 0.002 (0.003) Loss 2.9085 (3.0198) Prec@1 31.250 (29.720) Prec@5 61.250 (59.052) Epoch: [1][2100/11272] Time 0.735 (0.833) Data 0.001 (0.003) Loss 2.9959 (3.0198) Prec@1 29.375 (29.724) Prec@5 59.375 (59.059) Epoch: [1][2110/11272] Time 0.734 (0.833) Data 0.001 (0.003) Loss 3.3022 (3.0194) Prec@1 26.250 (29.727) Prec@5 51.875 (59.059) Epoch: [1][2120/11272] Time 0.985 (0.833) Data 0.001 (0.003) Loss 3.1215 (3.0195) Prec@1 25.625 (29.723) Prec@5 58.125 (59.057) Epoch: [1][2130/11272] Time 0.932 (0.833) Data 0.002 (0.003) Loss 2.6391 (3.0194) Prec@1 36.875 (29.725) Prec@5 62.500 (59.055) Epoch: [1][2140/11272] Time 0.798 (0.833) Data 0.002 (0.003) Loss 3.0471 (3.0193) Prec@1 30.625 (29.730) Prec@5 55.625 (59.055) Epoch: [1][2150/11272] Time 0.742 (0.833) Data 0.002 (0.003) Loss 3.3462 (3.0193) Prec@1 25.000 (29.724) Prec@5 51.875 (59.059) Epoch: [1][2160/11272] Time 0.867 (0.833) Data 0.001 (0.003) Loss 2.9077 (3.0195) Prec@1 28.750 (29.719) Prec@5 63.750 (59.058) Epoch: [1][2170/11272] Time 0.744 (0.832) Data 0.003 (0.003) Loss 3.1902 (3.0193) Prec@1 26.875 (29.726) Prec@5 49.375 (59.053) Epoch: [1][2180/11272] Time 0.755 (0.832) Data 0.001 (0.003) Loss 2.8635 (3.0195) Prec@1 28.125 (29.725) Prec@5 61.875 (59.048) Epoch: [1][2190/11272] Time 0.866 (0.832) Data 0.002 (0.003) Loss 2.7917 (3.0191) Prec@1 36.875 (29.728) Prec@5 61.250 (59.058) Epoch: [1][2200/11272] Time 0.866 (0.832) Data 0.001 (0.003) Loss 2.8834 (3.0186) Prec@1 32.500 (29.740) Prec@5 63.125 (59.064) Epoch: [1][2210/11272] Time 0.741 (0.832) Data 0.002 (0.003) Loss 2.9729 (3.0183) Prec@1 30.000 (29.744) Prec@5 62.500 (59.073) Epoch: [1][2220/11272] Time 0.749 (0.832) Data 0.001 (0.003) Loss 3.4097 (3.0183) Prec@1 26.250 (29.743) Prec@5 50.625 (59.071) Epoch: [1][2230/11272] Time 0.887 (0.832) Data 0.002 (0.003) Loss 2.9423 (3.0181) Prec@1 31.875 (29.744) Prec@5 63.125 (59.077) Epoch: [1][2240/11272] Time 0.931 (0.833) Data 0.001 (0.003) Loss 2.8629 (3.0181) Prec@1 30.625 (29.742) Prec@5 62.500 (59.075) Epoch: [1][2250/11272] Time 0.746 (0.832) Data 0.001 (0.003) Loss 3.0304 (3.0179) Prec@1 31.875 (29.745) Prec@5 58.125 (59.085) Epoch: [1][2260/11272] Time 0.760 (0.832) Data 0.001 (0.003) Loss 3.2268 (3.0181) Prec@1 25.000 (29.742) Prec@5 51.250 (59.081) Epoch: [1][2270/11272] Time 0.906 (0.832) Data 0.002 (0.003) Loss 2.9545 (3.0181) Prec@1 33.750 (29.741) Prec@5 60.625 (59.078) Epoch: [1][2280/11272] Time 0.894 (0.832) Data 0.002 (0.003) Loss 3.2346 (3.0180) Prec@1 21.875 (29.733) Prec@5 54.375 (59.077) Epoch: [1][2290/11272] Time 0.770 (0.832) Data 0.002 (0.003) Loss 2.8463 (3.0176) Prec@1 31.875 (29.732) Prec@5 67.500 (59.085) Epoch: [1][2300/11272] Time 0.955 (0.832) Data 0.001 (0.003) Loss 2.9047 (3.0173) Prec@1 31.250 (29.733) Prec@5 61.250 (59.088) Epoch: [1][2310/11272] Time 0.878 (0.832) Data 0.001 (0.003) Loss 2.9904 (3.0171) Prec@1 32.500 (29.736) Prec@5 59.375 (59.093) Epoch: [1][2320/11272] Time 0.743 (0.832) Data 0.001 (0.003) Loss 3.1618 (3.0170) Prec@1 29.375 (29.734) Prec@5 58.125 (59.095) Epoch: [1][2330/11272] Time 0.741 (0.832) Data 0.002 (0.003) Loss 3.0113 (3.0168) Prec@1 26.875 (29.743) Prec@5 61.250 (59.100) Epoch: [1][2340/11272] Time 0.927 (0.832) Data 0.002 (0.002) Loss 2.9459 (3.0167) Prec@1 33.750 (29.739) Prec@5 56.250 (59.096) Epoch: [1][2350/11272] Time 0.923 (0.832) Data 0.002 (0.002) Loss 3.0074 (3.0167) Prec@1 30.000 (29.743) Prec@5 58.125 (59.090) Epoch: [1][2360/11272] Time 0.804 (0.832) Data 0.001 (0.002) Loss 3.1476 (3.0168) Prec@1 28.750 (29.741) Prec@5 58.750 (59.087) Epoch: [1][2370/11272] Time 0.747 (0.832) Data 0.001 (0.002) Loss 2.8202 (3.0169) Prec@1 30.000 (29.740) Prec@5 66.250 (59.085) Epoch: [1][2380/11272] Time 0.861 (0.832) Data 0.001 (0.002) Loss 2.8177 (3.0169) Prec@1 31.250 (29.737) Prec@5 64.375 (59.088) Epoch: [1][2390/11272] Time 0.920 (0.832) Data 0.002 (0.002) Loss 2.8818 (3.0168) Prec@1 30.000 (29.741) Prec@5 61.250 (59.087) Epoch: [1][2400/11272] Time 0.751 (0.832) Data 0.001 (0.002) Loss 3.0754 (3.0169) Prec@1 27.500 (29.738) Prec@5 56.875 (59.083) Epoch: [1][2410/11272] Time 0.782 (0.832) Data 0.002 (0.002) Loss 2.7154 (3.0167) Prec@1 36.250 (29.739) Prec@5 65.000 (59.093) Epoch: [1][2420/11272] Time 0.912 (0.832) Data 0.001 (0.002) Loss 2.8765 (3.0164) Prec@1 29.375 (29.745) Prec@5 60.000 (59.095) Epoch: [1][2430/11272] Time 0.761 (0.832) Data 0.004 (0.002) Loss 2.8746 (3.0164) Prec@1 38.125 (29.752) Prec@5 65.000 (59.098) Epoch: [1][2440/11272] Time 0.798 (0.832) Data 0.001 (0.002) Loss 3.1196 (3.0163) Prec@1 31.250 (29.756) Prec@5 56.875 (59.098) Epoch: [1][2450/11272] Time 0.854 (0.832) Data 0.001 (0.002) Loss 3.0019 (3.0162) Prec@1 29.375 (29.755) Prec@5 58.750 (59.103) Epoch: [1][2460/11272] Time 0.919 (0.832) Data 0.002 (0.002) Loss 3.1337 (3.0160) Prec@1 27.500 (29.758) Prec@5 54.375 (59.108) Epoch: [1][2470/11272] Time 0.733 (0.832) Data 0.002 (0.002) Loss 2.9326 (3.0160) Prec@1 33.750 (29.760) Prec@5 59.375 (59.105) Epoch: [1][2480/11272] Time 0.780 (0.832) Data 0.002 (0.002) Loss 2.9794 (3.0157) Prec@1 30.625 (29.764) Prec@5 56.875 (59.110) Epoch: [1][2490/11272] Time 0.868 (0.832) Data 0.001 (0.002) Loss 2.9917 (3.0155) Prec@1 33.125 (29.769) Prec@5 60.000 (59.112) Epoch: [1][2500/11272] Time 0.922 (0.832) Data 0.001 (0.002) Loss 2.9374 (3.0153) Prec@1 28.125 (29.774) Prec@5 63.125 (59.120) Epoch: [1][2510/11272] Time 0.784 (0.832) Data 0.001 (0.002) Loss 2.6091 (3.0150) Prec@1 37.500 (29.783) Prec@5 65.625 (59.125) Epoch: [1][2520/11272] Time 0.765 (0.832) Data 0.001 (0.002) Loss 2.9740 (3.0151) Prec@1 25.000 (29.790) Prec@5 59.375 (59.128) Epoch: [1][2530/11272] Time 0.882 (0.832) Data 0.001 (0.002) Loss 2.8462 (3.0150) Prec@1 30.625 (29.786) Prec@5 62.500 (59.125) Epoch: [1][2540/11272] Time 0.921 (0.832) Data 0.001 (0.002) Loss 2.7181 (3.0149) Prec@1 36.875 (29.786) Prec@5 64.375 (59.124) Epoch: [1][2550/11272] Time 0.732 (0.832) Data 0.001 (0.002) Loss 2.9866 (3.0149) Prec@1 28.750 (29.793) Prec@5 59.375 (59.127) Epoch: [1][2560/11272] Time 0.882 (0.832) Data 0.001 (0.002) Loss 2.9673 (3.0151) Prec@1 31.875 (29.792) Prec@5 61.875 (59.121) Epoch: [1][2570/11272] Time 0.869 (0.832) Data 0.001 (0.002) Loss 2.9668 (3.0149) Prec@1 32.500 (29.796) Prec@5 58.750 (59.128) Epoch: [1][2580/11272] Time 0.758 (0.832) Data 0.001 (0.002) Loss 2.9467 (3.0145) Prec@1 28.750 (29.801) Prec@5 60.000 (59.140) Epoch: [1][2590/11272] Time 0.824 (0.832) Data 0.004 (0.002) Loss 3.1591 (3.0147) Prec@1 34.375 (29.798) Prec@5 57.500 (59.137) Epoch: [1][2600/11272] Time 0.914 (0.832) Data 0.002 (0.002) Loss 2.7887 (3.0147) Prec@1 29.375 (29.795) Prec@5 59.375 (59.138) Epoch: [1][2610/11272] Time 0.881 (0.832) Data 0.001 (0.002) Loss 2.9543 (3.0145) Prec@1 26.875 (29.794) Prec@5 61.875 (59.140) Epoch: [1][2620/11272] Time 0.778 (0.832) Data 0.001 (0.002) Loss 2.9569 (3.0147) Prec@1 23.125 (29.793) Prec@5 58.750 (59.140) Epoch: [1][2630/11272] Time 0.759 (0.832) Data 0.001 (0.002) Loss 2.8392 (3.0144) Prec@1 35.000 (29.797) Prec@5 63.125 (59.148) Epoch: [1][2640/11272] Time 0.889 (0.832) Data 0.002 (0.002) Loss 2.9999 (3.0140) Prec@1 30.625 (29.801) Prec@5 55.000 (59.157) Epoch: [1][2650/11272] Time 0.885 (0.832) Data 0.001 (0.002) Loss 2.7784 (3.0135) Prec@1 31.250 (29.804) Prec@5 61.250 (59.171) Epoch: [1][2660/11272] Time 0.744 (0.832) Data 0.002 (0.002) Loss 3.1710 (3.0137) Prec@1 29.375 (29.804) Prec@5 55.000 (59.170) Epoch: [1][2670/11272] Time 0.735 (0.832) Data 0.001 (0.002) Loss 3.0415 (3.0137) Prec@1 26.250 (29.809) Prec@5 54.375 (59.171) Epoch: [1][2680/11272] Time 0.865 (0.832) Data 0.002 (0.002) Loss 3.2327 (3.0137) Prec@1 26.250 (29.814) Prec@5 56.250 (59.173) Epoch: [1][2690/11272] Time 0.933 (0.832) Data 0.005 (0.002) Loss 2.9570 (3.0136) Prec@1 30.625 (29.812) Prec@5 61.875 (59.169) Epoch: [1][2700/11272] Time 0.772 (0.832) Data 0.002 (0.002) Loss 3.0878 (3.0134) Prec@1 27.500 (29.817) Prec@5 62.500 (59.176) Epoch: [1][2710/11272] Time 0.924 (0.832) Data 0.001 (0.002) Loss 3.2004 (3.0134) Prec@1 30.625 (29.813) Prec@5 56.250 (59.174) Epoch: [1][2720/11272] Time 0.952 (0.832) Data 0.002 (0.002) Loss 3.1117 (3.0132) Prec@1 29.375 (29.818) Prec@5 56.250 (59.181) Epoch: [1][2730/11272] Time 0.738 (0.832) Data 0.001 (0.002) Loss 3.1042 (3.0132) Prec@1 29.375 (29.817) Prec@5 56.875 (59.189) Epoch: [1][2740/11272] Time 0.748 (0.832) Data 0.001 (0.002) Loss 3.0446 (3.0131) Prec@1 32.500 (29.821) Prec@5 59.375 (59.196) Epoch: [1][2750/11272] Time 0.847 (0.832) Data 0.002 (0.002) Loss 2.9092 (3.0131) Prec@1 28.750 (29.825) Prec@5 66.875 (59.196) Epoch: [1][2760/11272] Time 0.928 (0.832) Data 0.001 (0.002) Loss 3.2290 (3.0131) Prec@1 22.500 (29.820) Prec@5 53.750 (59.195) Epoch: [1][2770/11272] Time 0.745 (0.832) Data 0.001 (0.002) Loss 3.1834 (3.0130) Prec@1 28.125 (29.821) Prec@5 60.625 (59.204) Epoch: [1][2780/11272] Time 0.780 (0.832) Data 0.001 (0.002) Loss 2.8937 (3.0127) Prec@1 33.750 (29.827) Prec@5 63.125 (59.211) Epoch: [1][2790/11272] Time 0.962 (0.832) Data 0.002 (0.002) Loss 2.7094 (3.0124) Prec@1 33.125 (29.833) Prec@5 63.125 (59.218) Epoch: [1][2800/11272] Time 0.915 (0.832) Data 0.002 (0.002) Loss 3.0079 (3.0124) Prec@1 35.000 (29.836) Prec@5 61.250 (59.215) Epoch: [1][2810/11272] Time 0.749 (0.832) Data 0.002 (0.002) Loss 3.1737 (3.0124) Prec@1 29.375 (29.833) Prec@5 56.250 (59.215) Epoch: [1][2820/11272] Time 0.742 (0.832) Data 0.001 (0.002) Loss 2.7752 (3.0122) Prec@1 34.375 (29.835) Prec@5 64.375 (59.220) Epoch: [1][2830/11272] Time 0.910 (0.832) Data 0.001 (0.002) Loss 2.8891 (3.0122) Prec@1 35.625 (29.840) Prec@5 61.250 (59.220) Epoch: [1][2840/11272] Time 0.752 (0.832) Data 0.001 (0.002) Loss 3.2632 (3.0118) Prec@1 25.625 (29.844) Prec@5 49.375 (59.227) Epoch: [1][2850/11272] Time 0.742 (0.832) Data 0.001 (0.002) Loss 3.0890 (3.0117) Prec@1 33.750 (29.853) Prec@5 60.000 (59.231) Epoch: [1][2860/11272] Time 0.936 (0.832) Data 0.002 (0.002) Loss 2.9084 (3.0114) Prec@1 31.875 (29.852) Prec@5 63.750 (59.239) Epoch: [1][2870/11272] Time 0.974 (0.832) Data 0.001 (0.002) Loss 3.1350 (3.0111) Prec@1 26.875 (29.857) Prec@5 57.500 (59.246) Epoch: [1][2880/11272] Time 0.808 (0.832) Data 0.001 (0.002) Loss 2.8899 (3.0107) Prec@1 33.125 (29.859) Prec@5 56.250 (59.254) Epoch: [1][2890/11272] Time 0.744 (0.832) Data 0.002 (0.002) Loss 2.8507 (3.0106) Prec@1 36.875 (29.868) Prec@5 65.000 (59.255) Epoch: [1][2900/11272] Time 0.887 (0.832) Data 0.001 (0.002) Loss 2.8800 (3.0104) Prec@1 30.000 (29.868) Prec@5 57.500 (59.260) Epoch: [1][2910/11272] Time 0.841 (0.832) Data 0.001 (0.002) Loss 3.2179 (3.0104) Prec@1 27.500 (29.872) Prec@5 55.625 (59.262) Epoch: [1][2920/11272] Time 0.773 (0.832) Data 0.001 (0.002) Loss 3.1236 (3.0103) Prec@1 26.250 (29.875) Prec@5 58.125 (59.266) Epoch: [1][2930/11272] Time 0.738 (0.832) Data 0.001 (0.002) Loss 2.8510 (3.0102) Prec@1 27.500 (29.876) Prec@5 61.250 (59.266) Epoch: [1][2940/11272] Time 0.912 (0.832) Data 0.001 (0.002) Loss 2.9208 (3.0100) Prec@1 30.000 (29.879) Prec@5 62.500 (59.272) Epoch: [1][2950/11272] Time 0.914 (0.832) Data 0.001 (0.002) Loss 2.6503 (3.0098) Prec@1 35.625 (29.886) Prec@5 68.750 (59.275) Epoch: [1][2960/11272] Time 0.752 (0.832) Data 0.001 (0.002) Loss 2.9374 (3.0098) Prec@1 28.750 (29.890) Prec@5 60.625 (59.274) Epoch: [1][2970/11272] Time 0.887 (0.832) Data 0.001 (0.002) Loss 3.1140 (3.0096) Prec@1 29.375 (29.893) Prec@5 51.250 (59.278) Epoch: [1][2980/11272] Time 0.930 (0.832) Data 0.001 (0.002) Loss 3.1833 (3.0092) Prec@1 26.875 (29.894) Prec@5 59.375 (59.285) Epoch: [1][2990/11272] Time 0.737 (0.832) Data 0.001 (0.002) Loss 3.1733 (3.0092) Prec@1 28.125 (29.894) Prec@5 57.500 (59.289) Epoch: [1][3000/11272] Time 0.741 (0.832) Data 0.001 (0.002) Loss 3.1921 (3.0094) Prec@1 26.875 (29.894) Prec@5 58.750 (59.288) Epoch: [1][3010/11272] Time 0.914 (0.832) Data 0.002 (0.002) Loss 3.0027 (3.0093) Prec@1 28.750 (29.894) Prec@5 57.500 (59.297) Epoch: [1][3020/11272] Time 0.952 (0.832) Data 0.001 (0.002) Loss 2.6382 (3.0091) Prec@1 33.750 (29.893) Prec@5 66.875 (59.303) Epoch: [1][3030/11272] Time 0.744 (0.832) Data 0.002 (0.002) Loss 2.9236 (3.0090) Prec@1 29.375 (29.895) Prec@5 60.625 (59.301) Epoch: [1][3040/11272] Time 0.749 (0.832) Data 0.002 (0.002) Loss 2.9317 (3.0089) Prec@1 31.250 (29.899) Prec@5 58.750 (59.304) Epoch: [1][3050/11272] Time 0.948 (0.832) Data 0.002 (0.002) Loss 3.2243 (3.0087) Prec@1 28.125 (29.899) Prec@5 54.375 (59.310) Epoch: [1][3060/11272] Time 0.894 (0.832) Data 0.001 (0.002) Loss 2.8827 (3.0084) Prec@1 33.125 (29.905) Prec@5 60.625 (59.318) Epoch: [1][3070/11272] Time 0.737 (0.832) Data 0.001 (0.002) Loss 3.0171 (3.0083) Prec@1 31.250 (29.911) Prec@5 58.750 (59.320) Epoch: [1][3080/11272] Time 0.747 (0.832) Data 0.001 (0.002) Loss 2.9132 (3.0082) Prec@1 28.125 (29.906) Prec@5 65.000 (59.321) Epoch: [1][3090/11272] Time 0.909 (0.832) Data 0.001 (0.002) Loss 3.0141 (3.0081) Prec@1 28.125 (29.905) Prec@5 55.000 (59.322) Epoch: [1][3100/11272] Time 0.782 (0.832) Data 0.004 (0.002) Loss 2.9881 (3.0082) Prec@1 31.250 (29.907) Prec@5 62.500 (59.322) Epoch: [1][3110/11272] Time 0.770 (0.832) Data 0.002 (0.002) Loss 3.0143 (3.0080) Prec@1 31.875 (29.911) Prec@5 63.125 (59.327) Epoch: [1][3120/11272] Time 1.020 (0.832) Data 0.001 (0.002) Loss 3.1284 (3.0078) Prec@1 23.125 (29.912) Prec@5 58.125 (59.336) Epoch: [1][3130/11272] Time 0.883 (0.832) Data 0.002 (0.002) Loss 2.8249 (3.0074) Prec@1 35.625 (29.924) Prec@5 65.000 (59.344) Epoch: [1][3140/11272] Time 0.759 (0.832) Data 0.002 (0.002) Loss 2.8571 (3.0072) Prec@1 33.125 (29.927) Prec@5 62.500 (59.348) Epoch: [1][3150/11272] Time 0.755 (0.832) Data 0.001 (0.002) Loss 2.8923 (3.0072) Prec@1 28.750 (29.925) Prec@5 63.125 (59.348) Epoch: [1][3160/11272] Time 0.937 (0.832) Data 0.002 (0.002) Loss 3.0160 (3.0070) Prec@1 31.875 (29.926) Prec@5 57.500 (59.348) Epoch: [1][3170/11272] Time 0.902 (0.832) Data 0.002 (0.002) Loss 3.1456 (3.0070) Prec@1 30.625 (29.925) Prec@5 55.000 (59.348) Epoch: [1][3180/11272] Time 0.764 (0.832) Data 0.001 (0.002) Loss 3.0508 (3.0070) Prec@1 27.500 (29.923) Prec@5 58.750 (59.346) Epoch: [1][3190/11272] Time 0.733 (0.832) Data 0.001 (0.002) Loss 2.7648 (3.0069) Prec@1 30.000 (29.930) Prec@5 65.625 (59.351) Epoch: [1][3200/11272] Time 0.873 (0.832) Data 0.001 (0.002) Loss 2.7755 (3.0067) Prec@1 32.500 (29.928) Prec@5 62.500 (59.355) Epoch: [1][3210/11272] Time 0.905 (0.832) Data 0.001 (0.002) Loss 2.8187 (3.0065) Prec@1 30.000 (29.933) Prec@5 66.250 (59.360) Epoch: [1][3220/11272] Time 0.751 (0.832) Data 0.002 (0.002) Loss 2.7607 (3.0062) Prec@1 33.750 (29.939) Prec@5 65.625 (59.370) Epoch: [1][3230/11272] Time 0.842 (0.832) Data 0.001 (0.002) Loss 2.8898 (3.0062) Prec@1 30.000 (29.939) Prec@5 64.375 (59.372) Epoch: [1][3240/11272] Time 0.886 (0.832) Data 0.001 (0.002) Loss 3.1106 (3.0061) Prec@1 27.500 (29.940) Prec@5 55.625 (59.374) Epoch: [1][3250/11272] Time 0.737 (0.832) Data 0.001 (0.002) Loss 2.8750 (3.0057) Prec@1 26.875 (29.947) Prec@5 56.250 (59.378) Epoch: [1][3260/11272] Time 0.828 (0.832) Data 0.001 (0.002) Loss 3.3036 (3.0056) Prec@1 26.250 (29.947) Prec@5 52.500 (59.380) Epoch: [1][3270/11272] Time 0.957 (0.832) Data 0.001 (0.002) Loss 3.0925 (3.0056) Prec@1 26.875 (29.949) Prec@5 60.000 (59.382) Epoch: [1][3280/11272] Time 0.920 (0.832) Data 0.002 (0.002) Loss 3.0167 (3.0055) Prec@1 29.375 (29.948) Prec@5 56.250 (59.382) Epoch: [1][3290/11272] Time 0.742 (0.832) Data 0.001 (0.002) Loss 3.1443 (3.0055) Prec@1 25.625 (29.953) Prec@5 58.125 (59.381) Epoch: [1][3300/11272] Time 0.749 (0.832) Data 0.001 (0.002) Loss 3.3252 (3.0056) Prec@1 23.750 (29.952) Prec@5 48.125 (59.380) Epoch: [1][3310/11272] Time 0.901 (0.832) Data 0.002 (0.002) Loss 3.0987 (3.0056) Prec@1 29.375 (29.953) Prec@5 58.125 (59.384) Epoch: [1][3320/11272] Time 0.921 (0.832) Data 0.001 (0.002) Loss 3.0851 (3.0056) Prec@1 25.625 (29.953) Prec@5 58.750 (59.382) Epoch: [1][3330/11272] Time 0.735 (0.832) Data 0.002 (0.002) Loss 2.9784 (3.0054) Prec@1 30.000 (29.960) Prec@5 57.500 (59.381) Epoch: [1][3340/11272] Time 0.737 (0.832) Data 0.001 (0.002) Loss 3.0540 (3.0054) Prec@1 33.750 (29.963) Prec@5 59.375 (59.384) Epoch: [1][3350/11272] Time 0.938 (0.832) Data 0.002 (0.002) Loss 3.1247 (3.0053) Prec@1 21.250 (29.958) Prec@5 54.375 (59.387) Epoch: [1][3360/11272] Time 0.763 (0.832) Data 0.005 (0.002) Loss 2.8909 (3.0051) Prec@1 26.875 (29.959) Prec@5 58.125 (59.392) Epoch: [1][3370/11272] Time 0.739 (0.832) Data 0.002 (0.002) Loss 2.9298 (3.0050) Prec@1 36.875 (29.964) Prec@5 58.750 (59.395) Epoch: [1][3380/11272] Time 0.896 (0.832) Data 0.001 (0.002) Loss 3.1907 (3.0052) Prec@1 23.750 (29.965) Prec@5 56.875 (59.390) Epoch: [1][3390/11272] Time 0.892 (0.832) Data 0.002 (0.002) Loss 2.7662 (3.0048) Prec@1 33.750 (29.973) Prec@5 60.625 (59.398) Epoch: [1][3400/11272] Time 0.734 (0.832) Data 0.002 (0.002) Loss 2.6840 (3.0048) Prec@1 38.125 (29.977) Prec@5 65.625 (59.399) Epoch: [1][3410/11272] Time 0.736 (0.832) Data 0.001 (0.002) Loss 2.9457 (3.0047) Prec@1 33.750 (29.976) Prec@5 62.500 (59.401) Epoch: [1][3420/11272] Time 0.951 (0.832) Data 0.002 (0.002) Loss 2.8409 (3.0045) Prec@1 32.500 (29.984) Prec@5 61.250 (59.404) Epoch: [1][3430/11272] Time 0.900 (0.832) Data 0.001 (0.002) Loss 2.9941 (3.0045) Prec@1 27.500 (29.979) Prec@5 60.000 (59.401) Epoch: [1][3440/11272] Time 0.750 (0.832) Data 0.001 (0.002) Loss 3.0120 (3.0044) Prec@1 28.750 (29.980) Prec@5 61.250 (59.406) Epoch: [1][3450/11272] Time 0.729 (0.832) Data 0.002 (0.002) Loss 3.0262 (3.0043) Prec@1 28.125 (29.981) Prec@5 59.375 (59.406) Epoch: [1][3460/11272] Time 0.960 (0.832) Data 0.001 (0.002) Loss 3.1476 (3.0041) Prec@1 22.500 (29.983) Prec@5 59.375 (59.411) Epoch: [1][3470/11272] Time 0.851 (0.832) Data 0.001 (0.002) Loss 2.9627 (3.0041) Prec@1 29.375 (29.985) Prec@5 61.250 (59.414) Epoch: [1][3480/11272] Time 0.739 (0.832) Data 0.002 (0.002) Loss 3.0334 (3.0040) Prec@1 30.000 (29.986) Prec@5 58.125 (59.417) Epoch: [1][3490/11272] Time 0.812 (0.832) Data 0.001 (0.002) Loss 3.0704 (3.0037) Prec@1 30.625 (29.996) Prec@5 60.625 (59.422) Epoch: [1][3500/11272] Time 0.899 (0.832) Data 0.002 (0.002) Loss 2.7320 (3.0034) Prec@1 36.250 (30.006) Prec@5 65.000 (59.425) Epoch: [1][3510/11272] Time 0.802 (0.832) Data 0.001 (0.002) Loss 2.5713 (3.0033) Prec@1 41.875 (30.012) Prec@5 65.000 (59.427) Epoch: [1][3520/11272] Time 0.776 (0.832) Data 0.001 (0.002) Loss 2.8983 (3.0031) Prec@1 28.750 (30.017) Prec@5 64.375 (59.431) Epoch: [1][3530/11272] Time 0.910 (0.832) Data 0.002 (0.002) Loss 3.1520 (3.0031) Prec@1 27.500 (30.015) Prec@5 55.625 (59.430) Epoch: [1][3540/11272] Time 0.909 (0.832) Data 0.001 (0.002) Loss 2.9344 (3.0030) Prec@1 31.875 (30.015) Prec@5 60.000 (59.431) Epoch: [1][3550/11272] Time 0.742 (0.832) Data 0.001 (0.002) Loss 3.0366 (3.0029) Prec@1 28.125 (30.016) Prec@5 59.375 (59.429) Epoch: [1][3560/11272] Time 0.747 (0.832) Data 0.001 (0.002) Loss 2.5291 (3.0028) Prec@1 36.875 (30.018) Prec@5 67.500 (59.427) Epoch: [1][3570/11272] Time 0.950 (0.832) Data 0.001 (0.002) Loss 2.9068 (3.0025) Prec@1 26.875 (30.022) Prec@5 61.250 (59.436) Epoch: [1][3580/11272] Time 0.876 (0.832) Data 0.001 (0.002) Loss 3.0695 (3.0024) Prec@1 31.250 (30.025) Prec@5 56.875 (59.432) Epoch: [1][3590/11272] Time 0.736 (0.832) Data 0.001 (0.002) Loss 3.0754 (3.0024) Prec@1 28.125 (30.027) Prec@5 57.500 (59.432) Epoch: [1][3600/11272] Time 0.803 (0.832) Data 0.002 (0.002) Loss 3.0434 (3.0022) Prec@1 30.000 (30.031) Prec@5 56.875 (59.438) Epoch: [1][3610/11272] Time 0.882 (0.832) Data 0.001 (0.002) Loss 3.1171 (3.0021) Prec@1 26.875 (30.030) Prec@5 60.000 (59.440) Epoch: [1][3620/11272] Time 0.910 (0.832) Data 0.003 (0.002) Loss 3.1004 (3.0020) Prec@1 28.750 (30.032) Prec@5 58.125 (59.441) Epoch: [1][3630/11272] Time 0.803 (0.832) Data 0.001 (0.002) Loss 3.0454 (3.0017) Prec@1 31.250 (30.033) Prec@5 56.875 (59.445) Epoch: [1][3640/11272] Time 0.923 (0.832) Data 0.002 (0.002) Loss 2.8704 (3.0018) Prec@1 35.000 (30.036) Prec@5 64.375 (59.444) Epoch: [1][3650/11272] Time 0.845 (0.832) Data 0.002 (0.002) Loss 2.9343 (3.0017) Prec@1 26.250 (30.036) Prec@5 61.250 (59.446) Epoch: [1][3660/11272] Time 0.759 (0.832) Data 0.001 (0.002) Loss 3.1087 (3.0018) Prec@1 23.125 (30.034) Prec@5 55.625 (59.447) Epoch: [1][3670/11272] Time 0.745 (0.832) Data 0.002 (0.002) Loss 2.5058 (3.0017) Prec@1 38.125 (30.033) Prec@5 70.625 (59.445) Epoch: [1][3680/11272] Time 0.944 (0.832) Data 0.002 (0.002) Loss 3.0061 (3.0018) Prec@1 28.125 (30.033) Prec@5 58.750 (59.444) Epoch: [1][3690/11272] Time 0.880 (0.832) Data 0.002 (0.002) Loss 2.8904 (3.0016) Prec@1 31.250 (30.034) Prec@5 58.125 (59.449) Epoch: [1][3700/11272] Time 0.766 (0.832) Data 0.001 (0.002) Loss 2.8193 (3.0014) Prec@1 38.125 (30.042) Prec@5 64.375 (59.455) Epoch: [1][3710/11272] Time 0.744 (0.832) Data 0.001 (0.002) Loss 2.7701 (3.0014) Prec@1 40.625 (30.042) Prec@5 63.750 (59.452) Epoch: [1][3720/11272] Time 0.951 (0.832) Data 0.002 (0.002) Loss 2.8484 (3.0011) Prec@1 26.875 (30.045) Prec@5 63.125 (59.456) Epoch: [1][3730/11272] Time 0.845 (0.832) Data 0.001 (0.002) Loss 2.9422 (3.0007) Prec@1 32.500 (30.049) Prec@5 63.750 (59.463) Epoch: [1][3740/11272] Time 0.784 (0.832) Data 0.002 (0.002) Loss 2.6667 (3.0004) Prec@1 33.125 (30.055) Prec@5 66.875 (59.470) Epoch: [1][3750/11272] Time 0.747 (0.832) Data 0.001 (0.002) Loss 2.8115 (3.0003) Prec@1 35.000 (30.056) Prec@5 60.000 (59.470) Epoch: [1][3760/11272] Time 0.946 (0.832) Data 0.001 (0.002) Loss 2.7523 (3.0001) Prec@1 30.625 (30.059) Prec@5 62.500 (59.476) Epoch: [1][3770/11272] Time 0.748 (0.832) Data 0.001 (0.002) Loss 2.7864 (3.0002) Prec@1 27.500 (30.055) Prec@5 60.000 (59.475) Epoch: [1][3780/11272] Time 0.741 (0.832) Data 0.001 (0.002) Loss 2.8716 (3.0001) Prec@1 33.125 (30.056) Prec@5 66.875 (59.477) Epoch: [1][3790/11272] Time 0.862 (0.832) Data 0.002 (0.002) Loss 3.2472 (3.0001) Prec@1 26.250 (30.054) Prec@5 58.125 (59.474) Epoch: [1][3800/11272] Time 0.922 (0.832) Data 0.002 (0.002) Loss 2.8732 (2.9998) Prec@1 34.375 (30.060) Prec@5 63.750 (59.484) Epoch: [1][3810/11272] Time 0.754 (0.832) Data 0.001 (0.002) Loss 2.7927 (2.9997) Prec@1 33.125 (30.061) Prec@5 68.750 (59.489) Epoch: [1][3820/11272] Time 0.749 (0.832) Data 0.002 (0.002) Loss 2.9013 (2.9996) Prec@1 35.000 (30.064) Prec@5 61.875 (59.493) Epoch: [1][3830/11272] Time 0.891 (0.832) Data 0.002 (0.002) Loss 2.9469 (2.9995) Prec@1 33.125 (30.065) Prec@5 61.875 (59.496) Epoch: [1][3840/11272] Time 0.881 (0.832) Data 0.002 (0.002) Loss 3.0064 (2.9995) Prec@1 30.000 (30.065) Prec@5 62.500 (59.498) Epoch: [1][3850/11272] Time 0.752 (0.832) Data 0.001 (0.002) Loss 3.1870 (2.9995) Prec@1 29.375 (30.064) Prec@5 55.000 (59.499) Epoch: [1][3860/11272] Time 0.753 (0.832) Data 0.001 (0.002) Loss 2.8907 (2.9995) Prec@1 31.250 (30.064) Prec@5 59.375 (59.498) Epoch: [1][3870/11272] Time 0.924 (0.832) Data 0.001 (0.002) Loss 2.9043 (2.9994) Prec@1 25.625 (30.066) Prec@5 60.000 (59.503) Epoch: [1][3880/11272] Time 0.872 (0.832) Data 0.001 (0.002) Loss 3.1227 (2.9994) Prec@1 26.250 (30.065) Prec@5 53.750 (59.500) Epoch: [1][3890/11272] Time 0.747 (0.832) Data 0.001 (0.002) Loss 2.9342 (2.9992) Prec@1 31.875 (30.066) Prec@5 61.875 (59.504) Epoch: [1][3900/11272] Time 0.942 (0.832) Data 0.001 (0.002) Loss 3.1400 (2.9992) Prec@1 29.375 (30.065) Prec@5 53.750 (59.502) Epoch: [1][3910/11272] Time 0.918 (0.832) Data 0.002 (0.002) Loss 2.9694 (2.9991) Prec@1 29.375 (30.067) Prec@5 60.000 (59.504) Epoch: [1][3920/11272] Time 0.757 (0.832) Data 0.001 (0.002) Loss 3.0443 (2.9990) Prec@1 28.125 (30.068) Prec@5 56.875 (59.506) Epoch: [1][3930/11272] Time 0.755 (0.832) Data 0.001 (0.002) Loss 2.7299 (2.9988) Prec@1 30.625 (30.071) Prec@5 66.875 (59.510) Epoch: [1][3940/11272] Time 0.864 (0.832) Data 0.001 (0.002) Loss 3.0097 (2.9988) Prec@1 29.375 (30.073) Prec@5 57.500 (59.510) Epoch: [1][3950/11272] Time 0.947 (0.832) Data 0.002 (0.002) Loss 3.1218 (2.9987) Prec@1 31.250 (30.080) Prec@5 60.000 (59.514) Epoch: [1][3960/11272] Time 0.738 (0.832) Data 0.001 (0.002) Loss 3.1437 (2.9985) Prec@1 29.375 (30.089) Prec@5 56.875 (59.519) Epoch: [1][3970/11272] Time 0.778 (0.832) Data 0.002 (0.002) Loss 2.9646 (2.9983) Prec@1 25.000 (30.090) Prec@5 61.875 (59.522) Epoch: [1][3980/11272] Time 0.922 (0.832) Data 0.001 (0.002) Loss 2.8931 (2.9982) Prec@1 28.750 (30.093) Prec@5 56.875 (59.523) Epoch: [1][3990/11272] Time 0.884 (0.832) Data 0.002 (0.002) Loss 3.0769 (2.9984) Prec@1 23.125 (30.089) Prec@5 58.125 (59.520) Epoch: [1][4000/11272] Time 0.780 (0.832) Data 0.001 (0.002) Loss 2.9321 (2.9983) Prec@1 31.250 (30.089) Prec@5 63.125 (59.523) Epoch: [1][4010/11272] Time 0.722 (0.832) Data 0.001 (0.002) Loss 3.4234 (2.9984) Prec@1 26.250 (30.088) Prec@5 55.000 (59.520) Epoch: [1][4020/11272] Time 0.933 (0.832) Data 0.002 (0.002) Loss 2.6686 (2.9980) Prec@1 40.625 (30.093) Prec@5 64.375 (59.528) Epoch: [1][4030/11272] Time 0.793 (0.832) Data 0.003 (0.002) Loss 3.1530 (2.9979) Prec@1 28.125 (30.096) Prec@5 59.375 (59.531) Epoch: [1][4040/11272] Time 0.768 (0.832) Data 0.001 (0.002) Loss 2.8765 (2.9977) Prec@1 30.000 (30.096) Prec@5 62.500 (59.536) Epoch: [1][4050/11272] Time 0.922 (0.832) Data 0.002 (0.002) Loss 3.1522 (2.9976) Prec@1 26.875 (30.103) Prec@5 56.875 (59.533) Epoch: [1][4060/11272] Time 0.904 (0.832) Data 0.002 (0.002) Loss 3.1481 (2.9978) Prec@1 30.625 (30.103) Prec@5 55.625 (59.528) Epoch: [1][4070/11272] Time 0.740 (0.832) Data 0.001 (0.002) Loss 2.9088 (2.9976) Prec@1 34.375 (30.107) Prec@5 61.875 (59.529) Epoch: [1][4080/11272] Time 0.739 (0.832) Data 0.001 (0.002) Loss 3.2201 (2.9975) Prec@1 30.625 (30.110) Prec@5 58.125 (59.532) Epoch: [1][4090/11272] Time 0.907 (0.832) Data 0.001 (0.002) Loss 2.8352 (2.9976) Prec@1 36.875 (30.112) Prec@5 63.750 (59.532) Epoch: [1][4100/11272] Time 0.902 (0.832) Data 0.002 (0.002) Loss 2.9080 (2.9975) Prec@1 31.250 (30.114) Prec@5 59.375 (59.535) Epoch: [1][4110/11272] Time 0.740 (0.832) Data 0.001 (0.002) Loss 3.0362 (2.9974) Prec@1 27.500 (30.115) Prec@5 55.625 (59.537) Epoch: [1][4120/11272] Time 0.750 (0.832) Data 0.001 (0.002) Loss 3.2696 (2.9975) Prec@1 22.500 (30.115) Prec@5 51.250 (59.535) Epoch: [1][4130/11272] Time 0.875 (0.832) Data 0.001 (0.002) Loss 2.9606 (2.9974) Prec@1 30.625 (30.112) Prec@5 58.750 (59.537) Epoch: [1][4140/11272] Time 0.894 (0.832) Data 0.002 (0.002) Loss 2.6438 (2.9972) Prec@1 33.750 (30.116) Prec@5 67.500 (59.540) Epoch: [1][4150/11272] Time 0.777 (0.832) Data 0.001 (0.002) Loss 2.8897 (2.9970) Prec@1 31.250 (30.120) Prec@5 61.250 (59.542) Epoch: [1][4160/11272] Time 0.950 (0.832) Data 0.002 (0.002) Loss 3.2523 (2.9968) Prec@1 21.875 (30.123) Prec@5 53.750 (59.546) Epoch: [1][4170/11272] Time 0.967 (0.832) Data 0.003 (0.002) Loss 2.9621 (2.9967) Prec@1 30.625 (30.123) Prec@5 62.500 (59.551) Epoch: [1][4180/11272] Time 0.714 (0.832) Data 0.001 (0.002) Loss 2.8391 (2.9965) Prec@1 32.500 (30.129) Prec@5 64.375 (59.555) Epoch: [1][4190/11272] Time 0.753 (0.832) Data 0.001 (0.002) Loss 2.8700 (2.9965) Prec@1 33.125 (30.131) Prec@5 60.625 (59.558) Epoch: [1][4200/11272] Time 0.961 (0.832) Data 0.001 (0.002) Loss 2.8907 (2.9962) Prec@1 31.250 (30.136) Prec@5 62.500 (59.563) Epoch: [1][4210/11272] Time 0.899 (0.832) Data 0.002 (0.002) Loss 3.0157 (2.9962) Prec@1 28.750 (30.134) Prec@5 58.125 (59.562) Epoch: [1][4220/11272] Time 0.740 (0.832) Data 0.001 (0.002) Loss 2.8184 (2.9959) Prec@1 33.750 (30.136) Prec@5 61.875 (59.570) Epoch: [1][4230/11272] Time 0.753 (0.832) Data 0.002 (0.002) Loss 2.5957 (2.9957) Prec@1 37.500 (30.142) Prec@5 72.500 (59.577) Epoch: [1][4240/11272] Time 0.902 (0.832) Data 0.001 (0.002) Loss 3.0735 (2.9956) Prec@1 32.500 (30.143) Prec@5 58.750 (59.577) Epoch: [1][4250/11272] Time 0.922 (0.832) Data 0.002 (0.002) Loss 2.8324 (2.9955) Prec@1 35.000 (30.143) Prec@5 60.625 (59.579) Epoch: [1][4260/11272] Time 0.753 (0.832) Data 0.001 (0.002) Loss 2.9090 (2.9951) Prec@1 28.750 (30.148) Prec@5 63.750 (59.586) Epoch: [1][4270/11272] Time 0.754 (0.832) Data 0.001 (0.002) Loss 2.7990 (2.9950) Prec@1 38.125 (30.150) Prec@5 66.875 (59.590) Epoch: [1][4280/11272] Time 0.878 (0.832) Data 0.001 (0.002) Loss 2.8926 (2.9949) Prec@1 31.250 (30.156) Prec@5 58.125 (59.595) Epoch: [1][4290/11272] Time 0.776 (0.832) Data 0.003 (0.002) Loss 2.8803 (2.9948) Prec@1 33.750 (30.157) Prec@5 66.250 (59.595) Epoch: [1][4300/11272] Time 0.759 (0.832) Data 0.002 (0.002) Loss 2.8328 (2.9948) Prec@1 31.250 (30.157) Prec@5 62.500 (59.597) Epoch: [1][4310/11272] Time 0.867 (0.832) Data 0.001 (0.002) Loss 3.0724 (2.9948) Prec@1 31.250 (30.156) Prec@5 55.625 (59.597) Epoch: [1][4320/11272] Time 0.976 (0.832) Data 0.001 (0.002) Loss 2.8229 (2.9947) Prec@1 35.625 (30.155) Prec@5 67.500 (59.602) Epoch: [1][4330/11272] Time 0.739 (0.832) Data 0.002 (0.002) Loss 2.8757 (2.9947) Prec@1 31.875 (30.153) Prec@5 60.000 (59.600) Epoch: [1][4340/11272] Time 0.751 (0.832) Data 0.001 (0.002) Loss 3.0532 (2.9946) Prec@1 33.750 (30.156) Prec@5 53.125 (59.601) Epoch: [1][4350/11272] Time 0.888 (0.832) Data 0.002 (0.002) Loss 3.0078 (2.9946) Prec@1 30.000 (30.156) Prec@5 61.875 (59.603) Epoch: [1][4360/11272] Time 0.871 (0.832) Data 0.002 (0.002) Loss 3.2261 (2.9947) Prec@1 27.500 (30.158) Prec@5 56.250 (59.604) Epoch: [1][4370/11272] Time 0.739 (0.832) Data 0.002 (0.002) Loss 2.8271 (2.9945) Prec@1 31.250 (30.160) Prec@5 65.000 (59.610) Epoch: [1][4380/11272] Time 0.808 (0.832) Data 0.002 (0.002) Loss 3.1086 (2.9944) Prec@1 29.375 (30.162) Prec@5 55.625 (59.612) Epoch: [1][4390/11272] Time 0.870 (0.832) Data 0.001 (0.002) Loss 2.7494 (2.9943) Prec@1 32.500 (30.166) Prec@5 66.875 (59.615) Epoch: [1][4400/11272] Time 0.940 (0.832) Data 0.001 (0.002) Loss 3.0961 (2.9941) Prec@1 32.500 (30.168) Prec@5 59.375 (59.622) Epoch: [1][4410/11272] Time 0.739 (0.832) Data 0.001 (0.002) Loss 3.1491 (2.9939) Prec@1 26.250 (30.166) Prec@5 53.125 (59.625) Epoch: [1][4420/11272] Time 0.891 (0.832) Data 0.001 (0.002) Loss 3.1129 (2.9939) Prec@1 27.500 (30.166) Prec@5 60.625 (59.622) Epoch: [1][4430/11272] Time 0.874 (0.832) Data 0.001 (0.002) Loss 3.1946 (2.9938) Prec@1 27.500 (30.171) Prec@5 53.750 (59.624) Epoch: [1][4440/11272] Time 0.758 (0.832) Data 0.002 (0.002) Loss 2.8352 (2.9936) Prec@1 38.125 (30.176) Prec@5 59.375 (59.627) Epoch: [1][4450/11272] Time 0.823 (0.832) Data 0.002 (0.002) Loss 2.9503 (2.9936) Prec@1 27.500 (30.179) Prec@5 65.000 (59.631) Epoch: [1][4460/11272] Time 0.887 (0.832) Data 0.002 (0.002) Loss 3.0083 (2.9934) Prec@1 31.250 (30.182) Prec@5 66.250 (59.640) Epoch: [1][4470/11272] Time 0.930 (0.832) Data 0.001 (0.002) Loss 3.1166 (2.9935) Prec@1 31.250 (30.180) Prec@5 61.250 (59.640) Epoch: [1][4480/11272] Time 0.735 (0.832) Data 0.001 (0.002) Loss 3.0090 (2.9933) Prec@1 32.500 (30.181) Prec@5 55.000 (59.643) Epoch: [1][4490/11272] Time 0.770 (0.832) Data 0.002 (0.002) Loss 2.9721 (2.9933) Prec@1 28.125 (30.183) Prec@5 58.750 (59.642) Epoch: [1][4500/11272] Time 0.912 (0.832) Data 0.002 (0.002) Loss 2.8977 (2.9931) Prec@1 33.125 (30.187) Prec@5 63.750 (59.646) Epoch: [1][4510/11272] Time 0.901 (0.832) Data 0.002 (0.002) Loss 2.7899 (2.9931) Prec@1 33.750 (30.186) Prec@5 61.250 (59.645) Epoch: [1][4520/11272] Time 0.749 (0.832) Data 0.002 (0.002) Loss 2.8031 (2.9930) Prec@1 36.250 (30.190) Prec@5 67.500 (59.650) Epoch: [1][4530/11272] Time 0.731 (0.832) Data 0.001 (0.002) Loss 2.6339 (2.9927) Prec@1 31.875 (30.193) Prec@5 67.500 (59.656) Epoch: [1][4540/11272] Time 0.928 (0.832) Data 0.001 (0.002) Loss 2.9322 (2.9926) Prec@1 31.875 (30.201) Prec@5 59.375 (59.660) Epoch: [1][4550/11272] Time 0.847 (0.832) Data 0.008 (0.002) Loss 2.9266 (2.9925) Prec@1 33.750 (30.202) Prec@5 60.000 (59.662) Epoch: [1][4560/11272] Time 0.750 (0.832) Data 0.001 (0.002) Loss 2.7397 (2.9923) Prec@1 32.500 (30.205) Prec@5 66.250 (59.665) Epoch: [1][4570/11272] Time 0.964 (0.832) Data 0.001 (0.002) Loss 2.7613 (2.9922) Prec@1 30.000 (30.207) Prec@5 68.750 (59.669) Epoch: [1][4580/11272] Time 0.916 (0.832) Data 0.002 (0.002) Loss 2.9568 (2.9921) Prec@1 28.750 (30.208) Prec@5 58.750 (59.671) Epoch: [1][4590/11272] Time 0.738 (0.832) Data 0.001 (0.002) Loss 2.9783 (2.9920) Prec@1 30.000 (30.206) Prec@5 60.000 (59.673) Epoch: [1][4600/11272] Time 0.746 (0.832) Data 0.002 (0.002) Loss 3.0804 (2.9921) Prec@1 23.125 (30.204) Prec@5 58.125 (59.670) Epoch: [1][4610/11272] Time 0.934 (0.832) Data 0.002 (0.002) Loss 2.8694 (2.9919) Prec@1 36.250 (30.209) Prec@5 63.750 (59.672) Epoch: [1][4620/11272] Time 0.942 (0.832) Data 0.002 (0.002) Loss 3.0757 (2.9918) Prec@1 30.625 (30.212) Prec@5 51.875 (59.672) Epoch: [1][4630/11272] Time 0.759 (0.832) Data 0.002 (0.002) Loss 3.2107 (2.9915) Prec@1 28.750 (30.220) Prec@5 55.625 (59.676) Epoch: [1][4640/11272] Time 0.793 (0.832) Data 0.002 (0.002) Loss 2.8906 (2.9915) Prec@1 31.875 (30.220) Prec@5 61.250 (59.680) Epoch: [1][4650/11272] Time 0.883 (0.832) Data 0.001 (0.002) Loss 2.7545 (2.9914) Prec@1 33.750 (30.223) Prec@5 66.875 (59.684) Epoch: [1][4660/11272] Time 0.896 (0.832) Data 0.001 (0.002) Loss 2.9762 (2.9912) Prec@1 29.375 (30.223) Prec@5 59.375 (59.686) Epoch: [1][4670/11272] Time 0.713 (0.832) Data 0.001 (0.002) Loss 2.7323 (2.9910) Prec@1 34.375 (30.227) Prec@5 64.375 (59.689) Epoch: [1][4680/11272] Time 0.759 (0.832) Data 0.001 (0.002) Loss 3.1040 (2.9908) Prec@1 27.500 (30.228) Prec@5 60.000 (59.694) Epoch: [1][4690/11272] Time 0.890 (0.832) Data 0.001 (0.002) Loss 2.9388 (2.9907) Prec@1 28.750 (30.229) Prec@5 59.375 (59.696) Epoch: [1][4700/11272] Time 0.751 (0.832) Data 0.001 (0.002) Loss 2.8399 (2.9905) Prec@1 36.250 (30.231) Prec@5 62.500 (59.698) Epoch: [1][4710/11272] Time 0.734 (0.832) Data 0.001 (0.002) Loss 2.9331 (2.9904) Prec@1 29.375 (30.233) Prec@5 59.375 (59.694) Epoch: [1][4720/11272] Time 0.896 (0.832) Data 0.002 (0.002) Loss 3.1640 (2.9903) Prec@1 25.625 (30.234) Prec@5 57.500 (59.697) Epoch: [1][4730/11272] Time 0.885 (0.832) Data 0.002 (0.002) Loss 3.2053 (2.9902) Prec@1 27.500 (30.233) Prec@5 53.750 (59.700) Epoch: [1][4740/11272] Time 0.739 (0.832) Data 0.001 (0.002) Loss 3.0926 (2.9902) Prec@1 30.625 (30.231) Prec@5 52.500 (59.698) Epoch: [1][4750/11272] Time 0.739 (0.832) Data 0.001 (0.002) Loss 3.1803 (2.9902) Prec@1 23.125 (30.233) Prec@5 56.875 (59.701) Epoch: [1][4760/11272] Time 0.942 (0.832) Data 0.001 (0.002) Loss 2.8733 (2.9901) Prec@1 31.875 (30.238) Prec@5 62.500 (59.705) Epoch: [1][4770/11272] Time 0.923 (0.832) Data 0.002 (0.002) Loss 3.1143 (2.9899) Prec@1 26.250 (30.238) Prec@5 55.625 (59.708) Epoch: [1][4780/11272] Time 0.743 (0.832) Data 0.002 (0.002) Loss 2.8781 (2.9898) Prec@1 31.875 (30.238) Prec@5 60.000 (59.708) Epoch: [1][4790/11272] Time 0.742 (0.832) Data 0.001 (0.002) Loss 2.8722 (2.9900) Prec@1 38.125 (30.238) Prec@5 63.125 (59.706) Epoch: [1][4800/11272] Time 0.885 (0.832) Data 0.002 (0.002) Loss 2.8676 (2.9899) Prec@1 26.875 (30.239) Prec@5 58.125 (59.706) Epoch: [1][4810/11272] Time 0.905 (0.832) Data 0.001 (0.002) Loss 3.1681 (2.9897) Prec@1 26.250 (30.243) Prec@5 61.875 (59.713) Epoch: [1][4820/11272] Time 0.742 (0.832) Data 0.001 (0.002) Loss 2.9377 (2.9896) Prec@1 28.125 (30.242) Prec@5 57.500 (59.710) Epoch: [1][4830/11272] Time 0.832 (0.832) Data 0.001 (0.002) Loss 2.6488 (2.9895) Prec@1 39.375 (30.249) Prec@5 65.000 (59.711) Epoch: [1][4840/11272] Time 0.918 (0.832) Data 0.001 (0.002) Loss 3.0868 (2.9894) Prec@1 24.375 (30.247) Prec@5 55.625 (59.711) Epoch: [1][4850/11272] Time 0.742 (0.832) Data 0.001 (0.002) Loss 3.0922 (2.9895) Prec@1 28.125 (30.246) Prec@5 53.125 (59.708) Epoch: [1][4860/11272] Time 0.765 (0.832) Data 0.001 (0.002) Loss 2.7999 (2.9895) Prec@1 34.375 (30.246) Prec@5 66.250 (59.707) Epoch: [1][4870/11272] Time 0.868 (0.832) Data 0.002 (0.002) Loss 3.0613 (2.9893) Prec@1 26.875 (30.249) Prec@5 61.250 (59.714) Epoch: [1][4880/11272] Time 0.956 (0.832) Data 0.001 (0.002) Loss 2.7693 (2.9891) Prec@1 30.625 (30.251) Prec@5 64.375 (59.716) Epoch: [1][4890/11272] Time 0.760 (0.832) Data 0.001 (0.002) Loss 2.8030 (2.9892) Prec@1 31.250 (30.251) Prec@5 61.875 (59.715) Epoch: [1][4900/11272] Time 0.758 (0.832) Data 0.001 (0.002) Loss 3.0262 (2.9889) Prec@1 30.625 (30.256) Prec@5 58.750 (59.722) Epoch: [1][4910/11272] Time 0.867 (0.832) Data 0.001 (0.002) Loss 3.0979 (2.9887) Prec@1 28.750 (30.256) Prec@5 59.375 (59.724) Epoch: [1][4920/11272] Time 0.917 (0.832) Data 0.001 (0.002) Loss 2.6343 (2.9885) Prec@1 40.625 (30.263) Prec@5 65.625 (59.727) Epoch: [1][4930/11272] Time 0.801 (0.832) Data 0.001 (0.002) Loss 2.9830 (2.9884) Prec@1 31.250 (30.264) Prec@5 56.250 (59.727) Epoch: [1][4940/11272] Time 0.759 (0.832) Data 0.001 (0.002) Loss 2.7846 (2.9882) Prec@1 30.625 (30.268) Prec@5 65.625 (59.735) Epoch: [1][4950/11272] Time 0.924 (0.832) Data 0.001 (0.002) Loss 2.9747 (2.9881) Prec@1 30.000 (30.269) Prec@5 58.125 (59.737) Epoch: [1][4960/11272] Time 0.831 (0.832) Data 0.004 (0.002) Loss 3.5310 (2.9881) Prec@1 14.375 (30.265) Prec@5 48.125 (59.735) Epoch: [1][4970/11272] Time 0.789 (0.832) Data 0.001 (0.002) Loss 2.9555 (2.9881) Prec@1 26.875 (30.265) Prec@5 61.875 (59.734) Epoch: [1][4980/11272] Time 0.888 (0.832) Data 0.001 (0.002) Loss 2.7745 (2.9879) Prec@1 40.000 (30.270) Prec@5 64.375 (59.737) Epoch: [1][4990/11272] Time 0.917 (0.832) Data 0.002 (0.002) Loss 2.9592 (2.9879) Prec@1 32.500 (30.271) Prec@5 58.750 (59.736) Epoch: [1][5000/11272] Time 0.775 (0.832) Data 0.001 (0.002) Loss 2.7086 (2.9877) Prec@1 35.625 (30.276) Prec@5 70.000 (59.741) Epoch: [1][5010/11272] Time 0.759 (0.832) Data 0.001 (0.002) Loss 2.6489 (2.9876) Prec@1 36.250 (30.278) Prec@5 68.125 (59.741) Epoch: [1][5020/11272] Time 0.889 (0.832) Data 0.002 (0.002) Loss 3.0129 (2.9874) Prec@1 24.375 (30.280) Prec@5 58.750 (59.742) Epoch: [1][5030/11272] Time 0.880 (0.832) Data 0.001 (0.002) Loss 2.8630 (2.9873) Prec@1 30.625 (30.283) Prec@5 60.000 (59.745) Epoch: [1][5040/11272] Time 0.742 (0.832) Data 0.002 (0.002) Loss 3.0814 (2.9870) Prec@1 32.500 (30.286) Prec@5 56.250 (59.747) Epoch: [1][5050/11272] Time 0.763 (0.832) Data 0.002 (0.002) Loss 2.9381 (2.9870) Prec@1 28.750 (30.283) Prec@5 63.125 (59.749) Epoch: [1][5060/11272] Time 0.923 (0.832) Data 0.002 (0.002) Loss 2.6180 (2.9869) Prec@1 39.375 (30.284) Prec@5 66.250 (59.752) Epoch: [1][5070/11272] Time 0.934 (0.832) Data 0.002 (0.002) Loss 2.8212 (2.9868) Prec@1 30.000 (30.288) Prec@5 67.500 (59.755) Epoch: [1][5080/11272] Time 0.753 (0.832) Data 0.002 (0.002) Loss 2.9808 (2.9868) Prec@1 34.375 (30.287) Prec@5 66.250 (59.756) Epoch: [1][5090/11272] Time 0.892 (0.832) Data 0.001 (0.002) Loss 2.9588 (2.9867) Prec@1 32.500 (30.289) Prec@5 58.750 (59.756) Epoch: [1][5100/11272] Time 0.946 (0.832) Data 0.001 (0.002) Loss 2.8988 (2.9864) Prec@1 30.000 (30.294) Prec@5 60.625 (59.760) Epoch: [1][5110/11272] Time 0.736 (0.832) Data 0.001 (0.002) Loss 3.0186 (2.9863) Prec@1 26.875 (30.292) Prec@5 60.000 (59.762) Epoch: [1][5120/11272] Time 0.762 (0.832) Data 0.002 (0.002) Loss 3.0003 (2.9861) Prec@1 31.875 (30.296) Prec@5 61.875 (59.768) Epoch: [1][5130/11272] Time 0.874 (0.832) Data 0.002 (0.002) Loss 2.9962 (2.9859) Prec@1 27.500 (30.298) Prec@5 57.500 (59.771) Epoch: [1][5140/11272] Time 0.915 (0.832) Data 0.002 (0.002) Loss 2.8191 (2.9858) Prec@1 32.500 (30.299) Prec@5 62.500 (59.773) Epoch: [1][5150/11272] Time 0.738 (0.832) Data 0.001 (0.002) Loss 2.7757 (2.9856) Prec@1 36.250 (30.303) Prec@5 60.625 (59.776) Epoch: [1][5160/11272] Time 0.753 (0.832) Data 0.002 (0.002) Loss 3.3020 (2.9855) Prec@1 28.125 (30.306) Prec@5 53.125 (59.781) Epoch: [1][5170/11272] Time 0.949 (0.832) Data 0.001 (0.002) Loss 2.8201 (2.9853) Prec@1 33.125 (30.311) Prec@5 61.875 (59.785) Epoch: [1][5180/11272] Time 0.920 (0.832) Data 0.001 (0.002) Loss 3.0898 (2.9852) Prec@1 32.500 (30.314) Prec@5 59.375 (59.789) Epoch: [1][5190/11272] Time 0.743 (0.832) Data 0.001 (0.002) Loss 3.1488 (2.9851) Prec@1 28.125 (30.316) Prec@5 57.500 (59.791) Epoch: [1][5200/11272] Time 0.784 (0.832) Data 0.002 (0.002) Loss 2.8667 (2.9850) Prec@1 30.625 (30.314) Prec@5 65.000 (59.793) Epoch: [1][5210/11272] Time 0.995 (0.832) Data 0.001 (0.002) Loss 2.9051 (2.9850) Prec@1 27.500 (30.313) Prec@5 61.250 (59.792) Epoch: [1][5220/11272] Time 0.819 (0.832) Data 0.003 (0.002) Loss 2.9306 (2.9851) Prec@1 28.125 (30.309) Prec@5 63.125 (59.790) Epoch: [1][5230/11272] Time 0.736 (0.832) Data 0.001 (0.002) Loss 3.0160 (2.9851) Prec@1 30.000 (30.308) Prec@5 60.000 (59.790) Epoch: [1][5240/11272] Time 0.936 (0.832) Data 0.002 (0.002) Loss 3.2395 (2.9850) Prec@1 27.500 (30.311) Prec@5 56.250 (59.792) Epoch: [1][5250/11272] Time 0.894 (0.832) Data 0.001 (0.002) Loss 2.8941 (2.9849) Prec@1 30.000 (30.315) Prec@5 58.750 (59.794) Epoch: [1][5260/11272] Time 0.777 (0.832) Data 0.002 (0.002) Loss 2.6406 (2.9847) Prec@1 38.750 (30.320) Prec@5 64.375 (59.799) Epoch: [1][5270/11272] Time 0.763 (0.832) Data 0.002 (0.002) Loss 2.8001 (2.9845) Prec@1 31.250 (30.321) Prec@5 60.000 (59.800) Epoch: [1][5280/11272] Time 0.894 (0.832) Data 0.002 (0.002) Loss 3.1310 (2.9845) Prec@1 31.250 (30.321) Prec@5 56.250 (59.800) Epoch: [1][5290/11272] Time 0.863 (0.832) Data 0.001 (0.002) Loss 2.9119 (2.9845) Prec@1 31.875 (30.323) Prec@5 56.875 (59.797) Epoch: [1][5300/11272] Time 0.780 (0.832) Data 0.002 (0.002) Loss 3.2379 (2.9845) Prec@1 28.125 (30.324) Prec@5 56.250 (59.797) Epoch: [1][5310/11272] Time 0.730 (0.832) Data 0.001 (0.002) Loss 2.5541 (2.9844) Prec@1 36.875 (30.327) Prec@5 68.125 (59.800) Epoch: [1][5320/11272] Time 0.960 (0.832) Data 0.002 (0.002) Loss 2.6927 (2.9842) Prec@1 35.000 (30.327) Prec@5 60.000 (59.804) Epoch: [1][5330/11272] Time 0.877 (0.832) Data 0.001 (0.002) Loss 2.8785 (2.9841) Prec@1 28.125 (30.328) Prec@5 64.375 (59.806) Epoch: [1][5340/11272] Time 0.793 (0.832) Data 0.002 (0.002) Loss 2.9627 (2.9840) Prec@1 27.500 (30.331) Prec@5 61.250 (59.809) Epoch: [1][5350/11272] Time 0.874 (0.832) Data 0.001 (0.002) Loss 3.0263 (2.9839) Prec@1 27.500 (30.333) Prec@5 61.875 (59.812) Epoch: [1][5360/11272] Time 0.916 (0.832) Data 0.002 (0.002) Loss 2.8290 (2.9837) Prec@1 30.625 (30.336) Prec@5 61.875 (59.814) Epoch: [1][5370/11272] Time 0.728 (0.832) Data 0.001 (0.002) Loss 3.1588 (2.9838) Prec@1 25.625 (30.332) Prec@5 53.125 (59.812) Epoch: [1][5380/11272] Time 0.736 (0.832) Data 0.001 (0.002) Loss 3.3304 (2.9838) Prec@1 23.125 (30.330) Prec@5 54.375 (59.812) Epoch: [1][5390/11272] Time 0.907 (0.832) Data 0.001 (0.002) Loss 2.9210 (2.9837) Prec@1 32.500 (30.331) Prec@5 63.125 (59.816) Epoch: [1][5400/11272] Time 0.869 (0.832) Data 0.001 (0.002) Loss 2.9955 (2.9835) Prec@1 29.375 (30.334) Prec@5 59.375 (59.819) Epoch: [1][5410/11272] Time 0.737 (0.832) Data 0.002 (0.002) Loss 2.9187 (2.9833) Prec@1 29.375 (30.335) Prec@5 59.375 (59.822) Epoch: [1][5420/11272] Time 0.767 (0.832) Data 0.001 (0.002) Loss 2.7474 (2.9834) Prec@1 31.875 (30.332) Prec@5 64.375 (59.822) Epoch: [1][5430/11272] Time 0.883 (0.832) Data 0.001 (0.002) Loss 3.0744 (2.9832) Prec@1 25.000 (30.332) Prec@5 58.750 (59.827) Epoch: [1][5440/11272] Time 0.905 (0.832) Data 0.001 (0.002) Loss 2.8937 (2.9831) Prec@1 28.750 (30.331) Prec@5 65.625 (59.831) Epoch: [1][5450/11272] Time 0.750 (0.832) Data 0.001 (0.002) Loss 2.7777 (2.9831) Prec@1 33.750 (30.331) Prec@5 61.875 (59.828) Epoch: [1][5460/11272] Time 0.736 (0.832) Data 0.001 (0.002) Loss 2.8028 (2.9831) Prec@1 31.250 (30.330) Prec@5 64.375 (59.830) Epoch: [1][5470/11272] Time 0.854 (0.832) Data 0.001 (0.002) Loss 2.7588 (2.9830) Prec@1 27.500 (30.328) Prec@5 66.250 (59.832) Epoch: [1][5480/11272] Time 0.947 (0.832) Data 0.001 (0.002) Loss 2.8669 (2.9829) Prec@1 31.250 (30.330) Prec@5 65.000 (59.834) Epoch: [1][5490/11272] Time 0.737 (0.832) Data 0.001 (0.002) Loss 2.8192 (2.9827) Prec@1 33.750 (30.330) Prec@5 64.375 (59.837) Epoch: [1][5500/11272] Time 0.921 (0.832) Data 0.001 (0.002) Loss 2.8100 (2.9825) Prec@1 31.875 (30.334) Prec@5 62.500 (59.841) Epoch: [1][5510/11272] Time 0.843 (0.832) Data 0.002 (0.002) Loss 2.8268 (2.9825) Prec@1 35.000 (30.334) Prec@5 62.500 (59.840) Epoch: [1][5520/11272] Time 0.752 (0.832) Data 0.001 (0.002) Loss 3.1882 (2.9824) Prec@1 29.375 (30.336) Prec@5 58.125 (59.838) Epoch: [1][5530/11272] Time 0.744 (0.832) Data 0.002 (0.002) Loss 3.1244 (2.9824) Prec@1 28.750 (30.336) Prec@5 54.375 (59.840) Epoch: [1][5540/11272] Time 0.878 (0.832) Data 0.002 (0.002) Loss 2.9751 (2.9822) Prec@1 28.750 (30.338) Prec@5 63.125 (59.843) Epoch: [1][5550/11272] Time 0.885 (0.832) Data 0.001 (0.002) Loss 2.7635 (2.9820) Prec@1 31.250 (30.344) Prec@5 63.125 (59.847) Epoch: [1][5560/11272] Time 0.772 (0.832) Data 0.002 (0.002) Loss 3.0268 (2.9819) Prec@1 27.500 (30.344) Prec@5 61.250 (59.849) Epoch: [1][5570/11272] Time 0.741 (0.832) Data 0.001 (0.002) Loss 3.1196 (2.9819) Prec@1 27.500 (30.345) Prec@5 56.250 (59.850) Epoch: [1][5580/11272] Time 0.950 (0.832) Data 0.002 (0.002) Loss 2.9575 (2.9819) Prec@1 33.125 (30.344) Prec@5 61.875 (59.850) Epoch: [1][5590/11272] Time 0.872 (0.832) Data 0.002 (0.002) Loss 2.8706 (2.9818) Prec@1 33.125 (30.344) Prec@5 61.875 (59.851) Epoch: [1][5600/11272] Time 0.783 (0.832) Data 0.001 (0.002) Loss 3.4402 (2.9817) Prec@1 21.875 (30.345) Prec@5 50.625 (59.853) Epoch: [1][5610/11272] Time 0.767 (0.832) Data 0.002 (0.002) Loss 2.8764 (2.9817) Prec@1 33.750 (30.347) Prec@5 68.750 (59.858) Epoch: [1][5620/11272] Time 0.871 (0.832) Data 0.001 (0.002) Loss 2.8676 (2.9815) Prec@1 32.500 (30.350) Prec@5 61.875 (59.860) Epoch: [1][5630/11272] Time 0.730 (0.832) Data 0.002 (0.002) Loss 2.8945 (2.9812) Prec@1 28.750 (30.352) Prec@5 63.125 (59.868) Epoch: [1][5640/11272] Time 0.801 (0.832) Data 0.001 (0.002) Loss 2.9649 (2.9812) Prec@1 28.125 (30.351) Prec@5 58.750 (59.867) Epoch: [1][5650/11272] Time 0.891 (0.832) Data 0.001 (0.002) Loss 3.0579 (2.9812) Prec@1 28.125 (30.352) Prec@5 56.875 (59.867) Epoch: [1][5660/11272] Time 0.881 (0.832) Data 0.002 (0.002) Loss 3.3582 (2.9813) Prec@1 20.625 (30.350) Prec@5 50.625 (59.867) Epoch: [1][5670/11272] Time 0.765 (0.832) Data 0.001 (0.002) Loss 3.0762 (2.9811) Prec@1 29.375 (30.351) Prec@5 56.250 (59.872) Epoch: [1][5680/11272] Time 0.779 (0.832) Data 0.002 (0.002) Loss 3.0993 (2.9809) Prec@1 31.250 (30.358) Prec@5 56.875 (59.880) Epoch: [1][5690/11272] Time 0.938 (0.832) Data 0.002 (0.002) Loss 2.9801 (2.9808) Prec@1 28.125 (30.356) Prec@5 61.875 (59.880) Epoch: [1][5700/11272] Time 0.954 (0.832) Data 0.001 (0.002) Loss 2.7308 (2.9806) Prec@1 37.500 (30.361) Prec@5 63.125 (59.884) Epoch: [1][5710/11272] Time 0.745 (0.832) Data 0.001 (0.002) Loss 2.8067 (2.9806) Prec@1 31.875 (30.363) Prec@5 62.500 (59.886) Epoch: [1][5720/11272] Time 0.777 (0.832) Data 0.001 (0.002) Loss 3.0700 (2.9807) Prec@1 26.250 (30.361) Prec@5 56.875 (59.885) Epoch: [1][5730/11272] Time 0.901 (0.832) Data 0.001 (0.002) Loss 2.8566 (2.9806) Prec@1 32.500 (30.361) Prec@5 64.375 (59.886) Epoch: [1][5740/11272] Time 0.924 (0.832) Data 0.002 (0.002) Loss 2.9992 (2.9804) Prec@1 31.250 (30.364) Prec@5 58.125 (59.890) Epoch: [1][5750/11272] Time 0.737 (0.832) Data 0.001 (0.002) Loss 2.8819 (2.9802) Prec@1 35.000 (30.368) Prec@5 60.625 (59.894) Epoch: [1][5760/11272] Time 0.901 (0.832) Data 0.001 (0.002) Loss 2.9549 (2.9802) Prec@1 34.375 (30.368) Prec@5 62.500 (59.898) Epoch: [1][5770/11272] Time 0.874 (0.832) Data 0.002 (0.002) Loss 2.8693 (2.9802) Prec@1 35.000 (30.368) Prec@5 65.000 (59.897) Epoch: [1][5780/11272] Time 0.780 (0.832) Data 0.002 (0.002) Loss 2.8759 (2.9801) Prec@1 31.875 (30.370) Prec@5 57.500 (59.899) Epoch: [1][5790/11272] Time 0.751 (0.832) Data 0.002 (0.002) Loss 2.9022 (2.9800) Prec@1 30.000 (30.370) Prec@5 66.250 (59.903) Epoch: [1][5800/11272] Time 0.983 (0.832) Data 0.002 (0.002) Loss 3.3387 (2.9800) Prec@1 23.125 (30.368) Prec@5 51.250 (59.901) Epoch: [1][5810/11272] Time 0.908 (0.832) Data 0.001 (0.002) Loss 3.2795 (2.9799) Prec@1 24.375 (30.369) Prec@5 50.000 (59.901) Epoch: [1][5820/11272] Time 0.771 (0.832) Data 0.001 (0.002) Loss 2.7610 (2.9799) Prec@1 32.500 (30.372) Prec@5 68.750 (59.905) Epoch: [1][5830/11272] Time 0.807 (0.832) Data 0.002 (0.002) Loss 3.0390 (2.9797) Prec@1 28.125 (30.376) Prec@5 60.625 (59.909) Epoch: [1][5840/11272] Time 0.869 (0.832) Data 0.001 (0.002) Loss 2.9531 (2.9795) Prec@1 30.000 (30.380) Prec@5 56.875 (59.912) Epoch: [1][5850/11272] Time 0.930 (0.832) Data 0.001 (0.002) Loss 3.1607 (2.9794) Prec@1 28.125 (30.381) Prec@5 51.875 (59.915) Epoch: [1][5860/11272] Time 0.756 (0.832) Data 0.001 (0.002) Loss 3.1823 (2.9793) Prec@1 28.750 (30.381) Prec@5 56.250 (59.918) Epoch: [1][5870/11272] Time 0.748 (0.831) Data 0.001 (0.002) Loss 2.9954 (2.9792) Prec@1 22.500 (30.383) Prec@5 58.750 (59.921) Epoch: [1][5880/11272] Time 0.945 (0.831) Data 0.001 (0.002) Loss 3.0033 (2.9791) Prec@1 34.375 (30.386) Prec@5 58.125 (59.923) Epoch: [1][5890/11272] Time 0.774 (0.831) Data 0.003 (0.002) Loss 2.9069 (2.9790) Prec@1 32.500 (30.387) Prec@5 63.125 (59.925) Epoch: [1][5900/11272] Time 0.773 (0.831) Data 0.002 (0.002) Loss 2.7850 (2.9790) Prec@1 34.375 (30.386) Prec@5 63.125 (59.925) Epoch: [1][5910/11272] Time 0.915 (0.831) Data 0.001 (0.002) Loss 3.0478 (2.9790) Prec@1 26.250 (30.387) Prec@5 61.250 (59.928) Epoch: [1][5920/11272] Time 0.905 (0.831) Data 0.002 (0.002) Loss 3.0532 (2.9788) Prec@1 29.375 (30.391) Prec@5 60.000 (59.929) Epoch: [1][5930/11272] Time 0.741 (0.831) Data 0.002 (0.002) Loss 2.9782 (2.9787) Prec@1 33.125 (30.395) Prec@5 59.375 (59.931) Epoch: [1][5940/11272] Time 0.792 (0.831) Data 0.002 (0.002) Loss 2.9290 (2.9786) Prec@1 33.750 (30.398) Prec@5 62.500 (59.931) Epoch: [1][5950/11272] Time 0.880 (0.832) Data 0.002 (0.002) Loss 3.0401 (2.9785) Prec@1 30.000 (30.399) Prec@5 62.500 (59.933) Epoch: [1][5960/11272] Time 0.928 (0.831) Data 0.001 (0.002) Loss 3.0918 (2.9785) Prec@1 23.750 (30.398) Prec@5 56.875 (59.933) Epoch: [1][5970/11272] Time 0.748 (0.831) Data 0.002 (0.002) Loss 2.8328 (2.9785) Prec@1 31.250 (30.399) Prec@5 65.625 (59.934) Epoch: [1][5980/11272] Time 0.735 (0.831) Data 0.001 (0.002) Loss 3.0415 (2.9784) Prec@1 28.750 (30.401) Prec@5 60.000 (59.935) Epoch: [1][5990/11272] Time 0.906 (0.831) Data 0.001 (0.002) Loss 3.0677 (2.9783) Prec@1 28.750 (30.404) Prec@5 56.875 (59.935) Epoch: [1][6000/11272] Time 0.894 (0.831) Data 0.002 (0.002) Loss 3.0013 (2.9782) Prec@1 36.250 (30.410) Prec@5 57.500 (59.938) Epoch: [1][6010/11272] Time 0.738 (0.831) Data 0.001 (0.002) Loss 3.0837 (2.9780) Prec@1 26.875 (30.413) Prec@5 58.125 (59.939) Epoch: [1][6020/11272] Time 0.927 (0.832) Data 0.001 (0.002) Loss 3.0211 (2.9779) Prec@1 26.250 (30.413) Prec@5 58.750 (59.940) Epoch: [1][6030/11272] Time 0.932 (0.832) Data 0.002 (0.002) Loss 2.9054 (2.9777) Prec@1 31.250 (30.416) Prec@5 61.250 (59.944) Epoch: [1][6040/11272] Time 0.753 (0.832) Data 0.001 (0.002) Loss 2.7889 (2.9777) Prec@1 31.250 (30.416) Prec@5 68.125 (59.945) Epoch: [1][6050/11272] Time 0.739 (0.832) Data 0.001 (0.002) Loss 2.7759 (2.9776) Prec@1 34.375 (30.420) Prec@5 62.500 (59.945) Epoch: [1][6060/11272] Time 0.894 (0.832) Data 0.001 (0.002) Loss 2.8078 (2.9774) Prec@1 38.125 (30.424) Prec@5 64.375 (59.946) Epoch: [1][6070/11272] Time 0.883 (0.832) Data 0.002 (0.002) Loss 2.7036 (2.9773) Prec@1 35.625 (30.426) Prec@5 63.750 (59.950) Epoch: [1][6080/11272] Time 0.744 (0.832) Data 0.002 (0.002) Loss 2.9084 (2.9772) Prec@1 33.125 (30.428) Prec@5 63.750 (59.951) Epoch: [1][6090/11272] Time 0.759 (0.832) Data 0.001 (0.002) Loss 2.8492 (2.9770) Prec@1 31.250 (30.429) Prec@5 66.250 (59.955) Epoch: [1][6100/11272] Time 0.905 (0.832) Data 0.001 (0.002) Loss 2.8805 (2.9768) Prec@1 28.125 (30.434) Prec@5 58.750 (59.960) Epoch: [1][6110/11272] Time 0.956 (0.832) Data 0.001 (0.002) Loss 3.0664 (2.9768) Prec@1 32.500 (30.435) Prec@5 58.125 (59.961) Epoch: [1][6120/11272] Time 0.755 (0.831) Data 0.001 (0.002) Loss 2.8160 (2.9767) Prec@1 30.625 (30.437) Prec@5 64.375 (59.965) Epoch: [1][6130/11272] Time 0.751 (0.831) Data 0.001 (0.002) Loss 3.1235 (2.9766) Prec@1 28.750 (30.437) Prec@5 55.625 (59.966) Epoch: [1][6140/11272] Time 0.893 (0.832) Data 0.002 (0.002) Loss 2.8275 (2.9764) Prec@1 36.250 (30.441) Prec@5 60.625 (59.969) Epoch: [1][6150/11272] Time 0.787 (0.831) Data 0.003 (0.002) Loss 3.0352 (2.9763) Prec@1 26.250 (30.443) Prec@5 63.125 (59.971) Epoch: [1][6160/11272] Time 0.755 (0.831) Data 0.001 (0.002) Loss 2.9518 (2.9762) Prec@1 30.000 (30.444) Prec@5 61.875 (59.974) Epoch: [1][6170/11272] Time 0.854 (0.831) Data 0.001 (0.002) Loss 2.7986 (2.9762) Prec@1 33.125 (30.443) Prec@5 66.250 (59.975) Epoch: [1][6180/11272] Time 0.960 (0.831) Data 0.001 (0.002) Loss 2.7837 (2.9760) Prec@1 28.750 (30.445) Prec@5 62.500 (59.977) Epoch: [1][6190/11272] Time 0.740 (0.831) Data 0.001 (0.002) Loss 3.0431 (2.9759) Prec@1 30.625 (30.447) Prec@5 58.125 (59.979) Epoch: [1][6200/11272] Time 0.753 (0.831) Data 0.001 (0.002) Loss 2.8028 (2.9758) Prec@1 31.250 (30.449) Prec@5 66.250 (59.978) Epoch: [1][6210/11272] Time 0.943 (0.831) Data 0.001 (0.002) Loss 3.0559 (2.9757) Prec@1 29.375 (30.452) Prec@5 56.250 (59.978) Epoch: [1][6220/11272] Time 0.834 (0.831) Data 0.002 (0.002) Loss 2.9055 (2.9758) Prec@1 30.000 (30.449) Prec@5 56.875 (59.977) Epoch: [1][6230/11272] Time 0.760 (0.831) Data 0.001 (0.002) Loss 2.7611 (2.9756) Prec@1 29.375 (30.452) Prec@5 58.125 (59.981) Epoch: [1][6240/11272] Time 0.753 (0.831) Data 0.001 (0.002) Loss 2.8473 (2.9756) Prec@1 31.875 (30.455) Prec@5 65.625 (59.982) Epoch: [1][6250/11272] Time 0.875 (0.831) Data 0.001 (0.002) Loss 2.8963 (2.9754) Prec@1 32.500 (30.457) Prec@5 62.500 (59.984) Epoch: [1][6260/11272] Time 0.897 (0.831) Data 0.001 (0.002) Loss 2.9019 (2.9753) Prec@1 33.750 (30.460) Prec@5 65.625 (59.987) Epoch: [1][6270/11272] Time 0.735 (0.831) Data 0.001 (0.002) Loss 3.1327 (2.9752) Prec@1 21.875 (30.458) Prec@5 58.750 (59.990) Epoch: [1][6280/11272] Time 0.895 (0.831) Data 0.001 (0.002) Loss 2.6317 (2.9749) Prec@1 39.375 (30.467) Prec@5 63.125 (59.994) Epoch: [1][6290/11272] Time 0.861 (0.831) Data 0.001 (0.002) Loss 2.9916 (2.9749) Prec@1 34.375 (30.468) Prec@5 56.875 (59.993) Epoch: [1][6300/11272] Time 0.758 (0.831) Data 0.001 (0.002) Loss 2.6807 (2.9749) Prec@1 35.000 (30.467) Prec@5 72.500 (59.997) Epoch: [1][6310/11272] Time 0.772 (0.831) Data 0.001 (0.002) Loss 2.7041 (2.9747) Prec@1 31.875 (30.471) Prec@5 66.875 (60.001) Epoch: [1][6320/11272] Time 0.950 (0.831) Data 0.002 (0.002) Loss 3.0201 (2.9747) Prec@1 33.125 (30.473) Prec@5 54.375 (60.000) Epoch: [1][6330/11272] Time 0.917 (0.831) Data 0.001 (0.002) Loss 2.8156 (2.9746) Prec@1 33.125 (30.476) Prec@5 63.750 (60.002) Epoch: [1][6340/11272] Time 0.800 (0.831) Data 0.001 (0.002) Loss 2.9137 (2.9745) Prec@1 33.750 (30.479) Prec@5 63.125 (60.006) Epoch: [1][6350/11272] Time 0.798 (0.831) Data 0.002 (0.002) Loss 2.7644 (2.9743) Prec@1 34.375 (30.480) Prec@5 66.250 (60.009) Epoch: [1][6360/11272] Time 0.977 (0.831) Data 0.001 (0.002) Loss 2.7798 (2.9742) Prec@1 31.875 (30.481) Prec@5 65.000 (60.012) Epoch: [1][6370/11272] Time 0.858 (0.831) Data 0.002 (0.002) Loss 2.6233 (2.9740) Prec@1 39.375 (30.484) Prec@5 66.875 (60.016) Epoch: [1][6380/11272] Time 0.747 (0.831) Data 0.001 (0.002) Loss 2.6450 (2.9739) Prec@1 33.750 (30.484) Prec@5 72.500 (60.020) Epoch: [1][6390/11272] Time 0.757 (0.831) Data 0.002 (0.002) Loss 2.9774 (2.9739) Prec@1 26.250 (30.485) Prec@5 61.250 (60.021) Epoch: [1][6400/11272] Time 0.914 (0.831) Data 0.002 (0.002) Loss 2.6657 (2.9738) Prec@1 32.500 (30.487) Prec@5 70.000 (60.022) Epoch: [1][6410/11272] Time 0.873 (0.831) Data 0.002 (0.002) Loss 3.1760 (2.9738) Prec@1 28.750 (30.489) Prec@5 55.000 (60.021) Epoch: [1][6420/11272] Time 0.743 (0.831) Data 0.001 (0.002) Loss 2.9795 (2.9737) Prec@1 28.750 (30.491) Prec@5 58.750 (60.024) Epoch: [1][6430/11272] Time 0.914 (0.831) Data 0.002 (0.002) Loss 2.8040 (2.9735) Prec@1 38.125 (30.495) Prec@5 63.750 (60.028) Epoch: [1][6440/11272] Time 0.886 (0.831) Data 0.001 (0.002) Loss 3.0617 (2.9735) Prec@1 28.125 (30.497) Prec@5 60.625 (60.030) Epoch: [1][6450/11272] Time 0.734 (0.831) Data 0.001 (0.002) Loss 2.7813 (2.9734) Prec@1 33.125 (30.498) Prec@5 64.375 (60.034) Epoch: [1][6460/11272] Time 0.752 (0.831) Data 0.002 (0.002) Loss 3.2321 (2.9733) Prec@1 30.000 (30.499) Prec@5 55.625 (60.036) Epoch: [1][6470/11272] Time 0.993 (0.831) Data 0.002 (0.002) Loss 2.6692 (2.9732) Prec@1 34.375 (30.499) Prec@5 70.000 (60.039) Epoch: [1][6480/11272] Time 0.960 (0.831) Data 0.001 (0.002) Loss 2.7598 (2.9731) Prec@1 33.750 (30.502) Prec@5 62.500 (60.039) Epoch: [1][6490/11272] Time 0.737 (0.831) Data 0.001 (0.002) Loss 2.7278 (2.9731) Prec@1 28.125 (30.502) Prec@5 66.250 (60.041) Epoch: [1][6500/11272] Time 0.765 (0.831) Data 0.001 (0.002) Loss 2.7809 (2.9730) Prec@1 36.250 (30.506) Prec@5 66.250 (60.042) Epoch: [1][6510/11272] Time 0.883 (0.831) Data 0.001 (0.002) Loss 2.6032 (2.9728) Prec@1 34.375 (30.511) Prec@5 66.250 (60.046) Epoch: [1][6520/11272] Time 0.886 (0.831) Data 0.002 (0.002) Loss 2.8468 (2.9727) Prec@1 30.000 (30.514) Prec@5 61.250 (60.049) Epoch: [1][6530/11272] Time 0.740 (0.831) Data 0.002 (0.002) Loss 2.9597 (2.9726) Prec@1 31.875 (30.513) Prec@5 63.750 (60.051) Epoch: [1][6540/11272] Time 0.743 (0.831) Data 0.002 (0.002) Loss 2.7437 (2.9725) Prec@1 31.250 (30.514) Prec@5 68.125 (60.052) Epoch: [1][6550/11272] Time 0.943 (0.831) Data 0.001 (0.002) Loss 2.9099 (2.9725) Prec@1 31.875 (30.516) Prec@5 61.875 (60.054) Epoch: [1][6560/11272] Time 0.750 (0.831) Data 0.002 (0.002) Loss 2.8123 (2.9722) Prec@1 37.500 (30.520) Prec@5 64.375 (60.059) Epoch: [1][6570/11272] Time 0.748 (0.831) Data 0.002 (0.002) Loss 2.6783 (2.9721) Prec@1 32.500 (30.521) Prec@5 66.250 (60.062) Epoch: [1][6580/11272] Time 0.889 (0.831) Data 0.002 (0.002) Loss 3.0993 (2.9720) Prec@1 26.250 (30.524) Prec@5 56.875 (60.065) Epoch: [1][6590/11272] Time 0.910 (0.831) Data 0.002 (0.002) Loss 3.0873 (2.9721) Prec@1 27.500 (30.522) Prec@5 55.000 (60.063) Epoch: [1][6600/11272] Time 0.756 (0.831) Data 0.002 (0.002) Loss 3.2149 (2.9721) Prec@1 30.000 (30.520) Prec@5 56.250 (60.062) Epoch: [1][6610/11272] Time 0.755 (0.831) Data 0.001 (0.002) Loss 2.9363 (2.9721) Prec@1 27.500 (30.522) Prec@5 61.250 (60.064) Epoch: [1][6620/11272] Time 0.989 (0.831) Data 0.002 (0.002) Loss 2.7873 (2.9720) Prec@1 33.750 (30.524) Prec@5 65.000 (60.065) Epoch: [1][6630/11272] Time 0.883 (0.831) Data 0.002 (0.002) Loss 2.9503 (2.9719) Prec@1 28.750 (30.525) Prec@5 65.000 (60.069) Epoch: [1][6640/11272] Time 0.742 (0.831) Data 0.001 (0.002) Loss 3.1149 (2.9718) Prec@1 26.250 (30.528) Prec@5 59.375 (60.070) Epoch: [1][6650/11272] Time 0.740 (0.831) Data 0.001 (0.002) Loss 3.0335 (2.9717) Prec@1 26.250 (30.531) Prec@5 55.000 (60.070) Epoch: [1][6660/11272] Time 0.872 (0.831) Data 0.001 (0.002) Loss 3.0123 (2.9716) Prec@1 33.750 (30.532) Prec@5 61.875 (60.071) Epoch: [1][6670/11272] Time 0.889 (0.831) Data 0.002 (0.002) Loss 2.7498 (2.9714) Prec@1 36.875 (30.534) Prec@5 66.250 (60.074) Epoch: [1][6680/11272] Time 0.757 (0.831) Data 0.002 (0.002) Loss 2.7844 (2.9713) Prec@1 33.125 (30.538) Prec@5 65.000 (60.077) Epoch: [1][6690/11272] Time 0.868 (0.831) Data 0.002 (0.002) Loss 2.8533 (2.9711) Prec@1 34.375 (30.539) Prec@5 59.375 (60.079) Epoch: [1][6700/11272] Time 0.910 (0.831) Data 0.001 (0.002) Loss 2.6054 (2.9709) Prec@1 38.750 (30.543) Prec@5 65.625 (60.083) Epoch: [1][6710/11272] Time 0.757 (0.831) Data 0.002 (0.002) Loss 3.0337 (2.9708) Prec@1 24.375 (30.546) Prec@5 59.375 (60.085) Epoch: [1][6720/11272] Time 0.757 (0.831) Data 0.002 (0.002) Loss 2.9746 (2.9706) Prec@1 28.125 (30.549) Prec@5 58.750 (60.088) Epoch: [1][6730/11272] Time 0.926 (0.831) Data 0.001 (0.002) Loss 3.0442 (2.9705) Prec@1 28.750 (30.551) Prec@5 58.125 (60.090) Epoch: [1][6740/11272] Time 0.889 (0.831) Data 0.001 (0.002) Loss 3.2962 (2.9704) Prec@1 27.500 (30.552) Prec@5 55.000 (60.093) Epoch: [1][6750/11272] Time 0.740 (0.831) Data 0.001 (0.002) Loss 3.0394 (2.9703) Prec@1 31.875 (30.555) Prec@5 57.500 (60.095) Epoch: [1][6760/11272] Time 0.765 (0.831) Data 0.001 (0.002) Loss 2.9517 (2.9704) Prec@1 31.875 (30.555) Prec@5 63.125 (60.093) Epoch: [1][6770/11272] Time 0.877 (0.831) Data 0.001 (0.002) Loss 2.8334 (2.9702) Prec@1 31.250 (30.556) Prec@5 64.375 (60.096) Epoch: [1][6780/11272] Time 0.871 (0.831) Data 0.001 (0.002) Loss 2.9664 (2.9701) Prec@1 35.000 (30.556) Prec@5 61.250 (60.099) Epoch: [1][6790/11272] Time 0.779 (0.831) Data 0.001 (0.002) Loss 2.9241 (2.9699) Prec@1 31.250 (30.561) Prec@5 56.250 (60.103) Epoch: [1][6800/11272] Time 0.767 (0.831) Data 0.001 (0.002) Loss 2.9452 (2.9697) Prec@1 32.500 (30.565) Prec@5 58.750 (60.106) Epoch: [1][6810/11272] Time 0.887 (0.831) Data 0.002 (0.002) Loss 3.0960 (2.9696) Prec@1 27.500 (30.567) Prec@5 54.375 (60.109) Epoch: [1][6820/11272] Time 0.776 (0.831) Data 0.004 (0.002) Loss 2.7076 (2.9694) Prec@1 37.500 (30.569) Prec@5 67.500 (60.112) Epoch: [1][6830/11272] Time 0.741 (0.831) Data 0.002 (0.002) Loss 3.0709 (2.9694) Prec@1 23.125 (30.571) Prec@5 57.500 (60.112) Epoch: [1][6840/11272] Time 0.952 (0.831) Data 0.001 (0.002) Loss 2.8652 (2.9693) Prec@1 34.375 (30.571) Prec@5 62.500 (60.117) Epoch: [1][6850/11272] Time 0.863 (0.831) Data 0.002 (0.002) Loss 2.7176 (2.9690) Prec@1 38.750 (30.576) Prec@5 67.500 (60.122) Epoch: [1][6860/11272] Time 0.734 (0.831) Data 0.002 (0.002) Loss 2.8517 (2.9690) Prec@1 30.625 (30.576) Prec@5 64.375 (60.122) Epoch: [1][6870/11272] Time 0.757 (0.831) Data 0.002 (0.002) Loss 2.8914 (2.9688) Prec@1 33.750 (30.582) Prec@5 61.875 (60.127) Epoch: [1][6880/11272] Time 0.869 (0.831) Data 0.001 (0.002) Loss 2.9068 (2.9690) Prec@1 32.500 (30.582) Prec@5 58.750 (60.124) Epoch: [1][6890/11272] Time 0.880 (0.831) Data 0.001 (0.002) Loss 2.9749 (2.9688) Prec@1 29.375 (30.584) Prec@5 62.500 (60.127) Epoch: [1][6900/11272] Time 0.741 (0.831) Data 0.001 (0.002) Loss 2.9661 (2.9687) Prec@1 33.750 (30.586) Prec@5 60.625 (60.129) Epoch: [1][6910/11272] Time 0.724 (0.831) Data 0.002 (0.002) Loss 2.9592 (2.9686) Prec@1 28.125 (30.589) Prec@5 59.375 (60.134) Epoch: [1][6920/11272] Time 0.891 (0.831) Data 0.001 (0.002) Loss 3.1038 (2.9686) Prec@1 26.250 (30.588) Prec@5 62.500 (60.134) Epoch: [1][6930/11272] Time 0.857 (0.831) Data 0.001 (0.002) Loss 2.7945 (2.9685) Prec@1 37.500 (30.591) Prec@5 59.375 (60.135) Epoch: [1][6940/11272] Time 0.776 (0.831) Data 0.002 (0.002) Loss 2.9691 (2.9684) Prec@1 31.875 (30.594) Prec@5 57.500 (60.138) Epoch: [1][6950/11272] Time 0.859 (0.831) Data 0.001 (0.002) Loss 2.9289 (2.9683) Prec@1 33.750 (30.597) Prec@5 60.000 (60.139) Epoch: [1][6960/11272] Time 0.905 (0.831) Data 0.002 (0.002) Loss 2.9060 (2.9683) Prec@1 33.125 (30.599) Prec@5 62.500 (60.139) Epoch: [1][6970/11272] Time 0.739 (0.831) Data 0.001 (0.002) Loss 2.9324 (2.9682) Prec@1 31.875 (30.602) Prec@5 57.500 (60.141) Epoch: [1][6980/11272] Time 0.795 (0.831) Data 0.002 (0.002) Loss 2.8668 (2.9681) Prec@1 38.750 (30.604) Prec@5 65.000 (60.144) Epoch: [1][6990/11272] Time 0.901 (0.831) Data 0.001 (0.002) Loss 3.3584 (2.9681) Prec@1 25.000 (30.602) Prec@5 55.625 (60.144) Epoch: [1][7000/11272] Time 0.883 (0.831) Data 0.001 (0.002) Loss 2.9566 (2.9680) Prec@1 28.125 (30.605) Prec@5 60.000 (60.147) Epoch: [1][7010/11272] Time 0.761 (0.831) Data 0.001 (0.002) Loss 2.7232 (2.9679) Prec@1 30.625 (30.605) Prec@5 68.750 (60.149) Epoch: [1][7020/11272] Time 0.793 (0.831) Data 0.002 (0.002) Loss 3.0582 (2.9679) Prec@1 28.750 (30.606) Prec@5 57.500 (60.150) Epoch: [1][7030/11272] Time 0.921 (0.831) Data 0.001 (0.002) Loss 2.8435 (2.9678) Prec@1 32.500 (30.608) Prec@5 65.625 (60.152) Epoch: [1][7040/11272] Time 0.952 (0.831) Data 0.004 (0.002) Loss 2.6597 (2.9677) Prec@1 31.875 (30.610) Prec@5 68.125 (60.154) Epoch: [1][7050/11272] Time 0.733 (0.831) Data 0.001 (0.002) Loss 2.8410 (2.9676) Prec@1 31.875 (30.613) Prec@5 66.250 (60.157) Epoch: [1][7060/11272] Time 0.734 (0.831) Data 0.001 (0.002) Loss 2.8367 (2.9676) Prec@1 33.750 (30.613) Prec@5 64.375 (60.158) Epoch: [1][7070/11272] Time 0.842 (0.831) Data 0.002 (0.002) Loss 2.8276 (2.9675) Prec@1 34.375 (30.617) Prec@5 61.875 (60.160) Epoch: [1][7080/11272] Time 0.823 (0.831) Data 0.004 (0.002) Loss 3.1132 (2.9674) Prec@1 25.000 (30.620) Prec@5 56.875 (60.163) Epoch: [1][7090/11272] Time 0.788 (0.831) Data 0.002 (0.002) Loss 2.9246 (2.9673) Prec@1 30.000 (30.622) Prec@5 58.125 (60.166) Epoch: [1][7100/11272] Time 0.979 (0.831) Data 0.001 (0.002) Loss 2.9322 (2.9673) Prec@1 32.500 (30.622) Prec@5 63.125 (60.167) Epoch: [1][7110/11272] Time 0.892 (0.831) Data 0.001 (0.002) Loss 2.9145 (2.9672) Prec@1 31.875 (30.624) Prec@5 61.250 (60.168) Epoch: [1][7120/11272] Time 0.769 (0.831) Data 0.001 (0.002) Loss 2.8965 (2.9670) Prec@1 30.000 (30.627) Prec@5 63.750 (60.171) Epoch: [1][7130/11272] Time 0.739 (0.831) Data 0.001 (0.002) Loss 3.0855 (2.9668) Prec@1 28.125 (30.631) Prec@5 60.000 (60.177) Epoch: [1][7140/11272] Time 0.882 (0.831) Data 0.002 (0.002) Loss 2.7560 (2.9667) Prec@1 38.750 (30.633) Prec@5 64.375 (60.179) Epoch: [1][7150/11272] Time 0.920 (0.831) Data 0.002 (0.002) Loss 2.9066 (2.9667) Prec@1 33.125 (30.635) Prec@5 63.125 (60.178) Epoch: [1][7160/11272] Time 0.774 (0.831) Data 0.001 (0.002) Loss 2.9096 (2.9666) Prec@1 32.500 (30.635) Prec@5 61.875 (60.180) Epoch: [1][7170/11272] Time 0.743 (0.831) Data 0.001 (0.002) Loss 2.9791 (2.9666) Prec@1 28.750 (30.635) Prec@5 62.500 (60.182) Epoch: [1][7180/11272] Time 0.958 (0.831) Data 0.002 (0.002) Loss 2.9891 (2.9665) Prec@1 27.500 (30.637) Prec@5 60.625 (60.183) Epoch: [1][7190/11272] Time 0.896 (0.831) Data 0.001 (0.002) Loss 2.8886 (2.9664) Prec@1 36.250 (30.640) Prec@5 62.500 (60.186) Epoch: [1][7200/11272] Time 0.776 (0.831) Data 0.001 (0.002) Loss 3.0613 (2.9663) Prec@1 26.250 (30.642) Prec@5 58.125 (60.189) Epoch: [1][7210/11272] Time 0.829 (0.831) Data 0.001 (0.002) Loss 2.8148 (2.9662) Prec@1 31.250 (30.643) Prec@5 62.500 (60.192) Epoch: [1][7220/11272] Time 0.872 (0.831) Data 0.001 (0.002) Loss 3.0531 (2.9661) Prec@1 29.375 (30.644) Prec@5 52.500 (60.192) Epoch: [1][7230/11272] Time 0.744 (0.831) Data 0.001 (0.002) Loss 2.8425 (2.9660) Prec@1 33.125 (30.645) Prec@5 58.750 (60.194) Epoch: [1][7240/11272] Time 0.773 (0.831) Data 0.001 (0.002) Loss 2.8037 (2.9659) Prec@1 33.125 (30.648) Prec@5 66.250 (60.195) Epoch: [1][7250/11272] Time 0.956 (0.831) Data 0.001 (0.002) Loss 2.9481 (2.9658) Prec@1 28.750 (30.649) Prec@5 65.000 (60.197) Epoch: [1][7260/11272] Time 0.889 (0.831) Data 0.002 (0.002) Loss 3.0473 (2.9658) Prec@1 25.625 (30.646) Prec@5 55.000 (60.197) Epoch: [1][7270/11272] Time 0.754 (0.831) Data 0.002 (0.002) Loss 2.6741 (2.9656) Prec@1 40.000 (30.649) Prec@5 65.000 (60.198) Epoch: [1][7280/11272] Time 0.754 (0.831) Data 0.001 (0.002) Loss 2.9425 (2.9656) Prec@1 27.500 (30.649) Prec@5 61.250 (60.198) Epoch: [1][7290/11272] Time 0.904 (0.831) Data 0.001 (0.002) Loss 2.8717 (2.9654) Prec@1 33.125 (30.654) Prec@5 65.625 (60.202) Epoch: [1][7300/11272] Time 0.925 (0.831) Data 0.002 (0.002) Loss 3.2661 (2.9653) Prec@1 29.375 (30.654) Prec@5 55.000 (60.203) Epoch: [1][7310/11272] Time 0.739 (0.831) Data 0.001 (0.002) Loss 2.9926 (2.9652) Prec@1 28.750 (30.655) Prec@5 61.250 (60.203) Epoch: [1][7320/11272] Time 0.754 (0.831) Data 0.001 (0.002) Loss 3.0356 (2.9651) Prec@1 32.500 (30.657) Prec@5 58.125 (60.206) Epoch: [1][7330/11272] Time 0.874 (0.831) Data 0.002 (0.002) Loss 2.8998 (2.9650) Prec@1 35.625 (30.657) Prec@5 57.500 (60.206) Epoch: [1][7340/11272] Time 0.881 (0.831) Data 0.001 (0.002) Loss 2.9012 (2.9649) Prec@1 33.750 (30.657) Prec@5 60.625 (60.207) Epoch: [1][7350/11272] Time 0.736 (0.831) Data 0.001 (0.002) Loss 3.1697 (2.9649) Prec@1 26.875 (30.659) Prec@5 53.750 (60.208) Epoch: [1][7360/11272] Time 0.916 (0.831) Data 0.001 (0.002) Loss 3.0651 (2.9649) Prec@1 28.750 (30.658) Prec@5 55.000 (60.207) Epoch: [1][7370/11272] Time 0.897 (0.831) Data 0.001 (0.002) Loss 3.2195 (2.9649) Prec@1 31.875 (30.661) Prec@5 56.875 (60.208) Epoch: [1][7380/11272] Time 0.745 (0.831) Data 0.001 (0.002) Loss 2.6333 (2.9648) Prec@1 36.875 (30.661) Prec@5 68.750 (60.210) Epoch: [1][7390/11272] Time 0.742 (0.831) Data 0.001 (0.002) Loss 2.9612 (2.9647) Prec@1 33.125 (30.662) Prec@5 62.500 (60.212) Epoch: [1][7400/11272] Time 0.891 (0.831) Data 0.001 (0.002) Loss 2.9826 (2.9646) Prec@1 31.875 (30.664) Prec@5 60.625 (60.212) Epoch: [1][7410/11272] Time 0.886 (0.831) Data 0.001 (0.002) Loss 2.9652 (2.9646) Prec@1 28.750 (30.664) Prec@5 58.750 (60.214) Epoch: [1][7420/11272] Time 0.795 (0.831) Data 0.002 (0.002) Loss 2.9474 (2.9644) Prec@1 31.250 (30.666) Prec@5 55.625 (60.217) Epoch: [1][7430/11272] Time 0.770 (0.831) Data 0.002 (0.002) Loss 3.0574 (2.9643) Prec@1 31.250 (30.666) Prec@5 58.125 (60.220) Epoch: [1][7440/11272] Time 0.889 (0.831) Data 0.001 (0.002) Loss 3.0434 (2.9642) Prec@1 29.375 (30.666) Prec@5 61.250 (60.221) Epoch: [1][7450/11272] Time 0.841 (0.831) Data 0.001 (0.002) Loss 3.0660 (2.9642) Prec@1 32.500 (30.666) Prec@5 58.125 (60.222) Epoch: [1][7460/11272] Time 0.729 (0.831) Data 0.001 (0.002) Loss 2.7445 (2.9640) Prec@1 37.500 (30.671) Prec@5 68.125 (60.228) Epoch: [1][7470/11272] Time 0.772 (0.831) Data 0.001 (0.002) Loss 2.8175 (2.9638) Prec@1 32.500 (30.673) Prec@5 67.500 (60.232) Epoch: [1][7480/11272] Time 0.885 (0.831) Data 0.001 (0.002) Loss 3.1136 (2.9638) Prec@1 26.250 (30.673) Prec@5 60.625 (60.233) Epoch: [1][7490/11272] Time 0.770 (0.831) Data 0.001 (0.002) Loss 2.9828 (2.9637) Prec@1 26.250 (30.673) Prec@5 63.125 (60.234) Epoch: [1][7500/11272] Time 0.804 (0.831) Data 0.002 (0.002) Loss 2.9225 (2.9636) Prec@1 28.750 (30.672) Prec@5 62.500 (60.235) Epoch: [1][7510/11272] Time 0.853 (0.831) Data 0.002 (0.002) Loss 2.7359 (2.9636) Prec@1 30.000 (30.673) Prec@5 61.250 (60.235) Epoch: [1][7520/11272] Time 0.879 (0.831) Data 0.002 (0.002) Loss 3.0900 (2.9636) Prec@1 30.625 (30.676) Prec@5 58.125 (60.235) Epoch: [1][7530/11272] Time 0.738 (0.831) Data 0.002 (0.002) Loss 2.9282 (2.9634) Prec@1 32.500 (30.679) Prec@5 59.375 (60.238) Epoch: [1][7540/11272] Time 0.743 (0.831) Data 0.001 (0.002) Loss 2.7184 (2.9633) Prec@1 32.500 (30.682) Prec@5 68.750 (60.239) Epoch: [1][7550/11272] Time 0.877 (0.831) Data 0.001 (0.002) Loss 3.0974 (2.9632) Prec@1 27.500 (30.683) Prec@5 58.125 (60.241) Epoch: [1][7560/11272] Time 0.923 (0.831) Data 0.001 (0.002) Loss 2.7519 (2.9632) Prec@1 30.000 (30.682) Prec@5 63.750 (60.242) Epoch: [1][7570/11272] Time 0.737 (0.831) Data 0.002 (0.002) Loss 2.9754 (2.9631) Prec@1 33.750 (30.684) Prec@5 60.000 (60.242) Epoch: [1][7580/11272] Time 0.737 (0.831) Data 0.001 (0.002) Loss 2.6468 (2.9630) Prec@1 36.250 (30.687) Prec@5 68.750 (60.244) Epoch: [1][7590/11272] Time 0.933 (0.831) Data 0.002 (0.002) Loss 2.8534 (2.9630) Prec@1 32.500 (30.685) Prec@5 65.000 (60.244) Epoch: [1][7600/11272] Time 0.891 (0.831) Data 0.002 (0.002) Loss 2.8738 (2.9630) Prec@1 30.625 (30.685) Prec@5 62.500 (60.245) Epoch: [1][7610/11272] Time 0.744 (0.831) Data 0.001 (0.002) Loss 2.8777 (2.9629) Prec@1 26.875 (30.686) Prec@5 66.250 (60.247) Epoch: [1][7620/11272] Time 0.924 (0.831) Data 0.001 (0.002) Loss 3.1293 (2.9628) Prec@1 29.375 (30.687) Prec@5 60.000 (60.248) Epoch: [1][7630/11272] Time 0.863 (0.831) Data 0.001 (0.002) Loss 2.9234 (2.9627) Prec@1 32.500 (30.691) Prec@5 58.750 (60.252) Epoch: [1][7640/11272] Time 0.781 (0.831) Data 0.002 (0.002) Loss 2.7998 (2.9626) Prec@1 31.875 (30.692) Prec@5 58.125 (60.253) Epoch: [1][7650/11272] Time 0.749 (0.831) Data 0.001 (0.002) Loss 3.0274 (2.9626) Prec@1 28.125 (30.693) Prec@5 62.500 (60.253) Epoch: [1][7660/11272] Time 0.917 (0.831) Data 0.001 (0.002) Loss 2.8400 (2.9624) Prec@1 29.375 (30.695) Prec@5 61.875 (60.257) Epoch: [1][7670/11272] Time 0.953 (0.831) Data 0.002 (0.002) Loss 2.7708 (2.9624) Prec@1 30.625 (30.696) Prec@5 64.375 (60.258) Epoch: [1][7680/11272] Time 0.768 (0.831) Data 0.001 (0.002) Loss 3.0861 (2.9622) Prec@1 32.500 (30.700) Prec@5 54.375 (60.263) Epoch: [1][7690/11272] Time 0.742 (0.831) Data 0.002 (0.002) Loss 2.5048 (2.9621) Prec@1 34.375 (30.700) Prec@5 73.750 (60.266) Epoch: [1][7700/11272] Time 1.014 (0.831) Data 0.002 (0.002) Loss 2.7550 (2.9621) Prec@1 33.750 (30.701) Prec@5 65.000 (60.267) Epoch: [1][7710/11272] Time 0.919 (0.831) Data 0.002 (0.002) Loss 2.8457 (2.9619) Prec@1 32.500 (30.704) Prec@5 61.250 (60.269) Epoch: [1][7720/11272] Time 0.775 (0.831) Data 0.002 (0.002) Loss 3.0020 (2.9618) Prec@1 31.875 (30.704) Prec@5 58.125 (60.270) Epoch: [1][7730/11272] Time 0.729 (0.831) Data 0.001 (0.002) Loss 2.8752 (2.9617) Prec@1 34.375 (30.706) Prec@5 61.875 (60.272) Epoch: [1][7740/11272] Time 0.914 (0.831) Data 0.001 (0.002) Loss 2.9524 (2.9617) Prec@1 26.875 (30.704) Prec@5 59.375 (60.271) Epoch: [1][7750/11272] Time 0.741 (0.831) Data 0.001 (0.002) Loss 2.8980 (2.9617) Prec@1 26.250 (30.704) Prec@5 65.625 (60.272) Epoch: [1][7760/11272] Time 0.742 (0.831) Data 0.001 (0.002) Loss 2.8786 (2.9616) Prec@1 30.000 (30.705) Prec@5 62.500 (60.275) Epoch: [1][7770/11272] Time 0.896 (0.831) Data 0.001 (0.002) Loss 3.0756 (2.9614) Prec@1 25.625 (30.707) Prec@5 58.125 (60.278) Epoch: [1][7780/11272] Time 0.972 (0.831) Data 0.002 (0.002) Loss 2.9570 (2.9614) Prec@1 28.750 (30.707) Prec@5 61.875 (60.279) Epoch: [1][7790/11272] Time 0.736 (0.831) Data 0.001 (0.002) Loss 2.6679 (2.9613) Prec@1 36.250 (30.708) Prec@5 67.500 (60.280) Epoch: [1][7800/11272] Time 0.749 (0.831) Data 0.002 (0.002) Loss 2.8635 (2.9613) Prec@1 34.375 (30.709) Prec@5 62.500 (60.282) Epoch: [1][7810/11272] Time 0.988 (0.831) Data 0.001 (0.002) Loss 2.9455 (2.9611) Prec@1 33.125 (30.712) Prec@5 60.000 (60.286) Epoch: [1][7820/11272] Time 0.906 (0.831) Data 0.001 (0.002) Loss 3.0280 (2.9611) Prec@1 28.125 (30.712) Prec@5 62.500 (60.288) Epoch: [1][7830/11272] Time 0.773 (0.831) Data 0.002 (0.002) Loss 2.6776 (2.9610) Prec@1 33.125 (30.713) Prec@5 66.250 (60.292) Epoch: [1][7840/11272] Time 0.737 (0.831) Data 0.001 (0.002) Loss 2.6966 (2.9609) Prec@1 31.250 (30.714) Prec@5 67.500 (60.293) Epoch: [1][7850/11272] Time 0.882 (0.831) Data 0.001 (0.002) Loss 3.0644 (2.9607) Prec@1 30.000 (30.717) Prec@5 59.375 (60.297) Epoch: [1][7860/11272] Time 0.899 (0.831) Data 0.002 (0.002) Loss 3.0303 (2.9606) Prec@1 28.750 (30.720) Prec@5 56.875 (60.298) Epoch: [1][7870/11272] Time 0.743 (0.831) Data 0.001 (0.002) Loss 3.0062 (2.9606) Prec@1 26.875 (30.718) Prec@5 61.250 (60.301) Epoch: [1][7880/11272] Time 0.942 (0.831) Data 0.002 (0.002) Loss 2.7785 (2.9606) Prec@1 35.625 (30.719) Prec@5 70.000 (60.302) Epoch: [1][7890/11272] Time 0.873 (0.831) Data 0.002 (0.002) Loss 3.1855 (2.9606) Prec@1 23.750 (30.718) Prec@5 53.750 (60.303) Epoch: [1][7900/11272] Time 0.791 (0.831) Data 0.001 (0.002) Loss 3.0280 (2.9604) Prec@1 28.750 (30.721) Prec@5 58.750 (60.308) Epoch: [1][7910/11272] Time 0.745 (0.831) Data 0.001 (0.002) Loss 2.8608 (2.9603) Prec@1 33.125 (30.721) Prec@5 58.750 (60.310) Epoch: [1][7920/11272] Time 0.900 (0.831) Data 0.002 (0.002) Loss 2.9396 (2.9602) Prec@1 32.500 (30.722) Prec@5 58.125 (60.312) Epoch: [1][7930/11272] Time 0.855 (0.831) Data 0.002 (0.002) Loss 2.9418 (2.9601) Prec@1 30.625 (30.725) Prec@5 60.625 (60.313) Epoch: [1][7940/11272] Time 0.780 (0.831) Data 0.001 (0.002) Loss 2.6613 (2.9600) Prec@1 33.750 (30.727) Prec@5 69.375 (60.318) Epoch: [1][7950/11272] Time 0.736 (0.831) Data 0.001 (0.002) Loss 2.6362 (2.9598) Prec@1 35.000 (30.730) Prec@5 64.375 (60.322) Epoch: [1][7960/11272] Time 0.895 (0.831) Data 0.001 (0.002) Loss 3.1072 (2.9598) Prec@1 31.875 (30.731) Prec@5 55.625 (60.324) Epoch: [1][7970/11272] Time 0.900 (0.831) Data 0.001 (0.002) Loss 2.6350 (2.9596) Prec@1 35.625 (30.735) Prec@5 66.250 (60.327) Epoch: [1][7980/11272] Time 0.778 (0.831) Data 0.001 (0.002) Loss 2.6867 (2.9595) Prec@1 33.750 (30.734) Prec@5 67.500 (60.329) Epoch: [1][7990/11272] Time 0.732 (0.831) Data 0.002 (0.002) Loss 2.8709 (2.9594) Prec@1 36.250 (30.733) Prec@5 62.500 (60.330) Epoch: [1][8000/11272] Time 0.870 (0.831) Data 0.001 (0.002) Loss 2.9831 (2.9594) Prec@1 32.500 (30.734) Prec@5 58.125 (60.330) Epoch: [1][8010/11272] Time 0.761 (0.831) Data 0.003 (0.002) Loss 3.0292 (2.9594) Prec@1 31.875 (30.734) Prec@5 58.750 (60.330) Epoch: [1][8020/11272] Time 0.759 (0.831) Data 0.001 (0.002) Loss 3.0028 (2.9594) Prec@1 34.375 (30.734) Prec@5 62.500 (60.331) Epoch: [1][8030/11272] Time 0.939 (0.831) Data 0.002 (0.002) Loss 2.8001 (2.9593) Prec@1 34.375 (30.738) Prec@5 60.000 (60.334) Epoch: [1][8040/11272] Time 0.913 (0.831) Data 0.002 (0.002) Loss 2.9927 (2.9592) Prec@1 26.875 (30.740) Prec@5 58.125 (60.333) Epoch: [1][8050/11272] Time 0.739 (0.831) Data 0.003 (0.002) Loss 2.9837 (2.9592) Prec@1 29.375 (30.739) Prec@5 61.250 (60.331) Epoch: [1][8060/11272] Time 0.744 (0.831) Data 0.001 (0.002) Loss 3.0931 (2.9591) Prec@1 27.500 (30.738) Prec@5 55.625 (60.332) Epoch: [1][8070/11272] Time 0.885 (0.831) Data 0.001 (0.002) Loss 2.8640 (2.9592) Prec@1 31.875 (30.739) Prec@5 60.000 (60.331) Epoch: [1][8080/11272] Time 0.913 (0.831) Data 0.001 (0.002) Loss 2.7914 (2.9591) Prec@1 35.000 (30.740) Prec@5 65.625 (60.334) Epoch: [1][8090/11272] Time 0.750 (0.831) Data 0.001 (0.002) Loss 2.9515 (2.9591) Prec@1 36.875 (30.741) Prec@5 59.375 (60.336) Epoch: [1][8100/11272] Time 0.762 (0.831) Data 0.001 (0.002) Loss 2.8702 (2.9591) Prec@1 30.000 (30.740) Prec@5 63.125 (60.335) Epoch: [1][8110/11272] Time 0.903 (0.831) Data 0.002 (0.002) Loss 3.0930 (2.9591) Prec@1 33.125 (30.741) Prec@5 56.875 (60.337) Epoch: [1][8120/11272] Time 0.884 (0.831) Data 0.001 (0.002) Loss 2.8120 (2.9590) Prec@1 33.750 (30.741) Prec@5 60.625 (60.338) Epoch: [1][8130/11272] Time 0.745 (0.831) Data 0.001 (0.002) Loss 2.7685 (2.9589) Prec@1 33.750 (30.743) Prec@5 61.875 (60.340) Epoch: [1][8140/11272] Time 0.907 (0.831) Data 0.002 (0.002) Loss 2.7885 (2.9588) Prec@1 31.250 (30.744) Prec@5 63.750 (60.343) Epoch: [1][8150/11272] Time 0.851 (0.831) Data 0.002 (0.002) Loss 3.0775 (2.9587) Prec@1 30.625 (30.745) Prec@5 61.875 (60.346) Epoch: [1][8160/11272] Time 0.741 (0.831) Data 0.001 (0.002) Loss 2.8936 (2.9585) Prec@1 33.750 (30.747) Prec@5 60.625 (60.349) Epoch: [1][8170/11272] Time 0.735 (0.831) Data 0.001 (0.002) Loss 2.8754 (2.9583) Prec@1 31.875 (30.750) Prec@5 57.500 (60.352) Epoch: [1][8180/11272] Time 0.860 (0.831) Data 0.001 (0.002) Loss 2.7657 (2.9583) Prec@1 35.625 (30.753) Prec@5 64.375 (60.353) Epoch: [1][8190/11272] Time 0.880 (0.831) Data 0.002 (0.002) Loss 2.6983 (2.9582) Prec@1 33.750 (30.753) Prec@5 61.250 (60.354) Epoch: [1][8200/11272] Time 0.740 (0.831) Data 0.001 (0.002) Loss 2.8485 (2.9581) Prec@1 33.750 (30.756) Prec@5 58.750 (60.355) Epoch: [1][8210/11272] Time 0.742 (0.831) Data 0.001 (0.002) Loss 3.0479 (2.9580) Prec@1 30.625 (30.757) Prec@5 56.250 (60.358) Epoch: [1][8220/11272] Time 0.943 (0.831) Data 0.001 (0.002) Loss 2.9899 (2.9578) Prec@1 30.000 (30.759) Prec@5 61.250 (60.361) Epoch: [1][8230/11272] Time 0.887 (0.831) Data 0.002 (0.002) Loss 2.8541 (2.9578) Prec@1 27.500 (30.757) Prec@5 63.125 (60.361) Epoch: [1][8240/11272] Time 0.742 (0.831) Data 0.001 (0.002) Loss 2.7899 (2.9577) Prec@1 29.375 (30.759) Prec@5 67.500 (60.364) Epoch: [1][8250/11272] Time 0.755 (0.831) Data 0.002 (0.002) Loss 2.8311 (2.9578) Prec@1 30.625 (30.758) Prec@5 62.500 (60.363) Epoch: [1][8260/11272] Time 0.973 (0.831) Data 0.001 (0.002) Loss 2.7867 (2.9578) Prec@1 35.000 (30.759) Prec@5 60.000 (60.364) Epoch: [1][8270/11272] Time 0.869 (0.831) Data 0.002 (0.002) Loss 2.8719 (2.9578) Prec@1 33.750 (30.758) Prec@5 60.000 (60.361) Epoch: [1][8280/11272] Time 0.742 (0.831) Data 0.001 (0.002) Loss 3.0506 (2.9577) Prec@1 29.375 (30.760) Prec@5 56.875 (60.363) Epoch: [1][8290/11272] Time 0.881 (0.831) Data 0.002 (0.002) Loss 2.6759 (2.9577) Prec@1 35.000 (30.760) Prec@5 70.000 (60.363) Epoch: [1][8300/11272] Time 1.003 (0.831) Data 0.001 (0.002) Loss 2.8358 (2.9576) Prec@1 33.750 (30.759) Prec@5 63.750 (60.364) Epoch: [1][8310/11272] Time 0.744 (0.831) Data 0.001 (0.002) Loss 3.0239 (2.9575) Prec@1 28.750 (30.760) Prec@5 61.250 (60.367) Epoch: [1][8320/11272] Time 0.782 (0.831) Data 0.001 (0.002) Loss 2.9363 (2.9576) Prec@1 28.750 (30.758) Prec@5 63.125 (60.366) Epoch: [1][8330/11272] Time 0.879 (0.831) Data 0.001 (0.002) Loss 3.1545 (2.9574) Prec@1 30.625 (30.760) Prec@5 56.250 (60.369) Epoch: [1][8340/11272] Time 0.922 (0.831) Data 0.002 (0.002) Loss 2.9933 (2.9574) Prec@1 25.625 (30.759) Prec@5 59.375 (60.372) Epoch: [1][8350/11272] Time 0.750 (0.831) Data 0.002 (0.002) Loss 2.8118 (2.9574) Prec@1 31.875 (30.760) Prec@5 61.875 (60.373) Epoch: [1][8360/11272] Time 0.778 (0.831) Data 0.001 (0.002) Loss 2.8332 (2.9572) Prec@1 30.625 (30.764) Prec@5 65.625 (60.376) Epoch: [1][8370/11272] Time 0.951 (0.831) Data 0.001 (0.002) Loss 2.7028 (2.9571) Prec@1 36.875 (30.768) Prec@5 67.500 (60.380) Epoch: [1][8380/11272] Time 0.882 (0.831) Data 0.002 (0.002) Loss 2.7662 (2.9569) Prec@1 38.125 (30.770) Prec@5 63.750 (60.383) Epoch: [1][8390/11272] Time 0.783 (0.831) Data 0.001 (0.002) Loss 3.0126 (2.9568) Prec@1 25.625 (30.771) Prec@5 58.750 (60.386) Epoch: [1][8400/11272] Time 0.770 (0.831) Data 0.002 (0.002) Loss 2.6109 (2.9566) Prec@1 35.000 (30.774) Prec@5 70.625 (60.390) Epoch: [1][8410/11272] Time 0.872 (0.831) Data 0.001 (0.002) Loss 3.1446 (2.9565) Prec@1 25.000 (30.776) Prec@5 57.500 (60.393) Epoch: [1][8420/11272] Time 0.763 (0.831) Data 0.001 (0.002) Loss 2.6646 (2.9564) Prec@1 33.750 (30.776) Prec@5 64.375 (60.394) Epoch: [1][8430/11272] Time 0.733 (0.831) Data 0.002 (0.002) Loss 2.5616 (2.9562) Prec@1 36.250 (30.780) Prec@5 68.125 (60.397) Epoch: [1][8440/11272] Time 0.908 (0.831) Data 0.001 (0.002) Loss 2.9926 (2.9562) Prec@1 30.000 (30.780) Prec@5 59.375 (60.399) Epoch: [1][8450/11272] Time 0.940 (0.831) Data 0.002 (0.002) Loss 2.9025 (2.9561) Prec@1 31.250 (30.781) Prec@5 63.750 (60.400) Epoch: [1][8460/11272] Time 0.776 (0.831) Data 0.002 (0.002) Loss 2.8427 (2.9561) Prec@1 35.625 (30.781) Prec@5 60.625 (60.401) Epoch: [1][8470/11272] Time 0.742 (0.831) Data 0.001 (0.002) Loss 3.0546 (2.9560) Prec@1 27.500 (30.781) Prec@5 62.500 (60.402) Epoch: [1][8480/11272] Time 0.968 (0.831) Data 0.001 (0.002) Loss 3.0851 (2.9559) Prec@1 27.500 (30.782) Prec@5 59.375 (60.404) Epoch: [1][8490/11272] Time 0.893 (0.831) Data 0.002 (0.002) Loss 2.8386 (2.9558) Prec@1 29.375 (30.784) Prec@5 63.125 (60.406) Epoch: [1][8500/11272] Time 0.752 (0.831) Data 0.001 (0.002) Loss 3.2558 (2.9557) Prec@1 28.750 (30.787) Prec@5 53.125 (60.409) Epoch: [1][8510/11272] Time 0.746 (0.831) Data 0.001 (0.002) Loss 2.7457 (2.9556) Prec@1 40.000 (30.788) Prec@5 65.000 (60.411) Epoch: [1][8520/11272] Time 0.893 (0.831) Data 0.001 (0.002) Loss 3.1325 (2.9555) Prec@1 30.000 (30.789) Prec@5 62.500 (60.414) Epoch: [1][8530/11272] Time 0.857 (0.831) Data 0.001 (0.002) Loss 2.8499 (2.9554) Prec@1 36.250 (30.793) Prec@5 63.125 (60.417) Epoch: [1][8540/11272] Time 0.797 (0.831) Data 0.002 (0.002) Loss 3.0432 (2.9553) Prec@1 31.875 (30.794) Prec@5 57.500 (60.417) Epoch: [1][8550/11272] Time 0.830 (0.831) Data 0.001 (0.002) Loss 2.9362 (2.9552) Prec@1 27.500 (30.795) Prec@5 63.125 (60.420) Epoch: [1][8560/11272] Time 0.949 (0.831) Data 0.001 (0.002) Loss 2.8155 (2.9552) Prec@1 35.625 (30.795) Prec@5 64.375 (60.421) Epoch: [1][8570/11272] Time 0.732 (0.831) Data 0.002 (0.002) Loss 2.8405 (2.9552) Prec@1 26.875 (30.797) Prec@5 66.875 (60.422) Epoch: [1][8580/11272] Time 0.765 (0.831) Data 0.001 (0.002) Loss 2.9761 (2.9552) Prec@1 28.125 (30.798) Prec@5 59.375 (60.422) Epoch: [1][8590/11272] Time 0.935 (0.831) Data 0.002 (0.002) Loss 3.0757 (2.9551) Prec@1 26.250 (30.801) Prec@5 60.000 (60.424) Epoch: [1][8600/11272] Time 0.896 (0.831) Data 0.002 (0.002) Loss 2.8877 (2.9550) Prec@1 31.875 (30.803) Prec@5 60.625 (60.424) Epoch: [1][8610/11272] Time 0.743 (0.831) Data 0.001 (0.002) Loss 2.6506 (2.9549) Prec@1 42.500 (30.804) Prec@5 66.250 (60.426) Epoch: [1][8620/11272] Time 0.755 (0.831) Data 0.001 (0.002) Loss 2.7534 (2.9549) Prec@1 33.750 (30.806) Prec@5 62.500 (60.428) Epoch: [1][8630/11272] Time 0.914 (0.831) Data 0.001 (0.002) Loss 2.8602 (2.9548) Prec@1 34.375 (30.807) Prec@5 61.875 (60.429) Epoch: [1][8640/11272] Time 0.895 (0.831) Data 0.001 (0.002) Loss 2.8694 (2.9548) Prec@1 31.250 (30.806) Prec@5 66.250 (60.430) Epoch: [1][8650/11272] Time 0.711 (0.831) Data 0.001 (0.002) Loss 2.7163 (2.9547) Prec@1 33.750 (30.809) Prec@5 67.500 (60.433) Epoch: [1][8660/11272] Time 0.734 (0.831) Data 0.001 (0.002) Loss 3.0259 (2.9545) Prec@1 30.000 (30.809) Prec@5 61.875 (60.436) Epoch: [1][8670/11272] Time 0.901 (0.831) Data 0.002 (0.002) Loss 2.9162 (2.9544) Prec@1 28.750 (30.812) Prec@5 60.000 (60.438) Epoch: [1][8680/11272] Time 0.778 (0.831) Data 0.003 (0.002) Loss 2.7381 (2.9544) Prec@1 34.375 (30.812) Prec@5 61.250 (60.439) Epoch: [1][8690/11272] Time 0.738 (0.831) Data 0.002 (0.002) Loss 2.9870 (2.9542) Prec@1 31.875 (30.812) Prec@5 60.000 (60.442) Epoch: [1][8700/11272] Time 0.893 (0.831) Data 0.002 (0.002) Loss 2.6310 (2.9542) Prec@1 38.750 (30.813) Prec@5 66.250 (60.445) Epoch: [1][8710/11272] Time 0.877 (0.831) Data 0.002 (0.002) Loss 2.7315 (2.9541) Prec@1 38.125 (30.815) Prec@5 63.125 (60.448) Epoch: [1][8720/11272] Time 0.744 (0.831) Data 0.002 (0.002) Loss 2.6478 (2.9540) Prec@1 36.875 (30.818) Prec@5 69.375 (60.451) Epoch: [1][8730/11272] Time 0.737 (0.831) Data 0.001 (0.002) Loss 2.8921 (2.9539) Prec@1 26.250 (30.818) Prec@5 61.875 (60.452) Epoch: [1][8740/11272] Time 0.886 (0.831) Data 0.001 (0.002) Loss 2.9175 (2.9539) Prec@1 31.250 (30.818) Prec@5 63.750 (60.454) Epoch: [1][8750/11272] Time 0.913 (0.831) Data 0.001 (0.002) Loss 2.9586 (2.9540) Prec@1 30.000 (30.817) Prec@5 61.875 (60.454) Epoch: [1][8760/11272] Time 0.771 (0.831) Data 0.002 (0.002) Loss 2.8118 (2.9539) Prec@1 34.375 (30.820) Prec@5 65.625 (60.457) Epoch: [1][8770/11272] Time 0.740 (0.831) Data 0.001 (0.002) Loss 3.1112 (2.9537) Prec@1 25.000 (30.821) Prec@5 57.500 (60.461) Epoch: [1][8780/11272] Time 0.985 (0.831) Data 0.002 (0.002) Loss 2.9340 (2.9536) Prec@1 31.250 (30.822) Prec@5 58.125 (60.462) Epoch: [1][8790/11272] Time 0.886 (0.831) Data 0.002 (0.002) Loss 2.6865 (2.9535) Prec@1 35.000 (30.823) Prec@5 63.750 (60.463) Epoch: [1][8800/11272] Time 0.748 (0.831) Data 0.002 (0.002) Loss 2.8026 (2.9535) Prec@1 34.375 (30.824) Prec@5 62.500 (60.464) Epoch: [1][8810/11272] Time 0.885 (0.831) Data 0.002 (0.002) Loss 2.7661 (2.9534) Prec@1 35.625 (30.825) Prec@5 65.000 (60.467) Epoch: [1][8820/11272] Time 0.881 (0.831) Data 0.001 (0.002) Loss 2.6050 (2.9533) Prec@1 39.375 (30.826) Prec@5 67.500 (60.470) Epoch: [1][8830/11272] Time 0.786 (0.831) Data 0.002 (0.002) Loss 2.9059 (2.9532) Prec@1 31.875 (30.827) Prec@5 61.875 (60.471) Epoch: [1][8840/11272] Time 0.787 (0.831) Data 0.003 (0.002) Loss 2.8831 (2.9532) Prec@1 30.625 (30.827) Prec@5 60.000 (60.471) Epoch: [1][8850/11272] Time 0.891 (0.831) Data 0.002 (0.002) Loss 2.8818 (2.9531) Prec@1 36.875 (30.829) Prec@5 65.625 (60.474) Epoch: [1][8860/11272] Time 0.855 (0.831) Data 0.002 (0.002) Loss 3.1199 (2.9530) Prec@1 30.625 (30.831) Prec@5 58.125 (60.475) Epoch: [1][8870/11272] Time 0.752 (0.831) Data 0.001 (0.002) Loss 2.5843 (2.9528) Prec@1 40.000 (30.836) Prec@5 70.000 (60.480) Epoch: [1][8880/11272] Time 0.738 (0.831) Data 0.002 (0.002) Loss 2.9136 (2.9527) Prec@1 35.625 (30.837) Prec@5 63.750 (60.483) Epoch: [1][8890/11272] Time 0.869 (0.831) Data 0.001 (0.002) Loss 2.6977 (2.9526) Prec@1 35.625 (30.839) Prec@5 67.500 (60.484) Epoch: [1][8900/11272] Time 0.918 (0.831) Data 0.002 (0.002) Loss 3.0866 (2.9526) Prec@1 31.250 (30.838) Prec@5 58.750 (60.484) Epoch: [1][8910/11272] Time 0.728 (0.831) Data 0.001 (0.002) Loss 3.1311 (2.9525) Prec@1 27.500 (30.839) Prec@5 56.875 (60.486) Epoch: [1][8920/11272] Time 0.768 (0.831) Data 0.001 (0.002) Loss 2.9394 (2.9526) Prec@1 33.750 (30.838) Prec@5 61.250 (60.484) Epoch: [1][8930/11272] Time 0.895 (0.831) Data 0.001 (0.002) Loss 2.7822 (2.9525) Prec@1 31.875 (30.841) Prec@5 65.625 (60.485) Epoch: [1][8940/11272] Time 0.809 (0.831) Data 0.003 (0.002) Loss 2.7235 (2.9524) Prec@1 32.500 (30.842) Prec@5 66.875 (60.489) Epoch: [1][8950/11272] Time 0.752 (0.831) Data 0.001 (0.002) Loss 3.0745 (2.9523) Prec@1 32.500 (30.841) Prec@5 58.125 (60.490) Epoch: [1][8960/11272] Time 0.972 (0.831) Data 0.001 (0.002) Loss 2.8733 (2.9523) Prec@1 29.375 (30.842) Prec@5 60.625 (60.489) Epoch: [1][8970/11272] Time 0.912 (0.831) Data 0.001 (0.002) Loss 2.7319 (2.9523) Prec@1 33.125 (30.843) Prec@5 63.750 (60.489) Epoch: [1][8980/11272] Time 0.754 (0.831) Data 0.001 (0.002) Loss 2.8593 (2.9522) Prec@1 35.000 (30.845) Prec@5 62.500 (60.492) Epoch: [1][8990/11272] Time 0.737 (0.831) Data 0.001 (0.002) Loss 2.8096 (2.9520) Prec@1 31.250 (30.847) Prec@5 66.250 (60.495) Epoch: [1][9000/11272] Time 0.961 (0.831) Data 0.002 (0.002) Loss 2.9107 (2.9520) Prec@1 32.500 (30.846) Prec@5 61.250 (60.495) Epoch: [1][9010/11272] Time 0.963 (0.831) Data 0.002 (0.002) Loss 2.9545 (2.9520) Prec@1 33.750 (30.847) Prec@5 60.625 (60.495) Epoch: [1][9020/11272] Time 0.743 (0.831) Data 0.001 (0.002) Loss 2.7928 (2.9518) Prec@1 30.000 (30.848) Prec@5 61.250 (60.499) Epoch: [1][9030/11272] Time 0.776 (0.831) Data 0.002 (0.002) Loss 2.8739 (2.9517) Prec@1 34.375 (30.851) Prec@5 63.125 (60.502) Epoch: [1][9040/11272] Time 0.920 (0.831) Data 0.002 (0.002) Loss 2.9367 (2.9516) Prec@1 31.250 (30.852) Prec@5 61.250 (60.505) Epoch: [1][9050/11272] Time 0.874 (0.831) Data 0.001 (0.002) Loss 3.0427 (2.9515) Prec@1 23.750 (30.854) Prec@5 56.875 (60.506) Epoch: [1][9060/11272] Time 0.740 (0.831) Data 0.001 (0.002) Loss 2.7153 (2.9514) Prec@1 36.875 (30.855) Prec@5 66.875 (60.508) Epoch: [1][9070/11272] Time 0.922 (0.831) Data 0.001 (0.002) Loss 3.1227 (2.9513) Prec@1 32.500 (30.856) Prec@5 57.500 (60.508) Epoch: [1][9080/11272] Time 0.930 (0.831) Data 0.002 (0.002) Loss 2.8215 (2.9513) Prec@1 37.500 (30.858) Prec@5 61.875 (60.510) Epoch: [1][9090/11272] Time 0.801 (0.831) Data 0.001 (0.002) Loss 2.9630 (2.9512) Prec@1 31.875 (30.860) Prec@5 59.375 (60.511) Epoch: [1][9100/11272] Time 0.773 (0.831) Data 0.002 (0.002) Loss 2.6895 (2.9511) Prec@1 30.000 (30.862) Prec@5 64.375 (60.514) Epoch: [1][9110/11272] Time 0.984 (0.831) Data 0.002 (0.002) Loss 2.6703 (2.9510) Prec@1 35.000 (30.860) Prec@5 64.375 (60.515) Epoch: [1][9120/11272] Time 0.909 (0.831) Data 0.002 (0.002) Loss 2.7969 (2.9510) Prec@1 38.750 (30.862) Prec@5 63.125 (60.514) Epoch: [1][9130/11272] Time 0.780 (0.831) Data 0.002 (0.002) Loss 2.7746 (2.9508) Prec@1 32.500 (30.865) Prec@5 61.875 (60.518) Epoch: [1][9140/11272] Time 0.785 (0.831) Data 0.002 (0.002) Loss 2.9844 (2.9508) Prec@1 26.875 (30.865) Prec@5 64.375 (60.519) Epoch: [1][9150/11272] Time 0.844 (0.831) Data 0.001 (0.002) Loss 2.9932 (2.9506) Prec@1 32.500 (30.868) Prec@5 56.875 (60.522) Epoch: [1][9160/11272] Time 0.892 (0.831) Data 0.002 (0.002) Loss 3.2069 (2.9506) Prec@1 21.875 (30.866) Prec@5 50.000 (60.523) Epoch: [1][9170/11272] Time 0.760 (0.831) Data 0.002 (0.002) Loss 2.9168 (2.9505) Prec@1 30.625 (30.866) Prec@5 64.375 (60.525) Epoch: [1][9180/11272] Time 0.756 (0.831) Data 0.002 (0.002) Loss 2.9721 (2.9504) Prec@1 36.875 (30.868) Prec@5 59.375 (60.526) Epoch: [1][9190/11272] Time 0.897 (0.831) Data 0.001 (0.002) Loss 2.9214 (2.9503) Prec@1 31.250 (30.870) Prec@5 63.125 (60.529) Epoch: [1][9200/11272] Time 0.893 (0.831) Data 0.002 (0.002) Loss 3.0262 (2.9502) Prec@1 27.500 (30.871) Prec@5 58.750 (60.531) Epoch: [1][9210/11272] Time 0.739 (0.831) Data 0.001 (0.002) Loss 2.9328 (2.9501) Prec@1 30.000 (30.872) Prec@5 59.375 (60.531) Epoch: [1][9220/11272] Time 0.896 (0.831) Data 0.001 (0.002) Loss 2.8133 (2.9501) Prec@1 28.125 (30.872) Prec@5 63.750 (60.531) Epoch: [1][9230/11272] Time 0.870 (0.831) Data 0.001 (0.002) Loss 3.1826 (2.9501) Prec@1 28.750 (30.874) Prec@5 57.500 (60.532) Epoch: [1][9240/11272] Time 0.772 (0.831) Data 0.002 (0.002) Loss 2.7537 (2.9500) Prec@1 33.750 (30.876) Prec@5 66.250 (60.533) Epoch: [1][9250/11272] Time 0.738 (0.831) Data 0.001 (0.002) Loss 2.6485 (2.9499) Prec@1 36.250 (30.876) Prec@5 66.250 (60.537) Epoch: [1][9260/11272] Time 0.895 (0.831) Data 0.001 (0.002) Loss 2.8243 (2.9499) Prec@1 33.125 (30.876) Prec@5 66.875 (60.538) Epoch: [1][9270/11272] Time 0.890 (0.831) Data 0.001 (0.002) Loss 3.0690 (2.9499) Prec@1 29.375 (30.877) Prec@5 61.250 (60.540) Epoch: [1][9280/11272] Time 0.714 (0.831) Data 0.002 (0.002) Loss 2.8666 (2.9498) Prec@1 34.375 (30.877) Prec@5 60.625 (60.542) Epoch: [1][9290/11272] Time 0.741 (0.831) Data 0.001 (0.002) Loss 2.9437 (2.9498) Prec@1 29.375 (30.877) Prec@5 59.375 (60.542) Epoch: [1][9300/11272] Time 0.880 (0.831) Data 0.001 (0.002) Loss 2.8898 (2.9497) Prec@1 30.000 (30.879) Prec@5 61.250 (60.543) Epoch: [1][9310/11272] Time 0.874 (0.831) Data 0.002 (0.002) Loss 2.9744 (2.9497) Prec@1 31.250 (30.881) Prec@5 62.500 (60.546) Epoch: [1][9320/11272] Time 0.767 (0.831) Data 0.004 (0.002) Loss 2.7887 (2.9497) Prec@1 31.250 (30.882) Prec@5 66.875 (60.547) Epoch: [1][9330/11272] Time 0.754 (0.831) Data 0.001 (0.002) Loss 3.0039 (2.9495) Prec@1 32.500 (30.882) Prec@5 58.125 (60.550) Epoch: [1][9340/11272] Time 0.870 (0.831) Data 0.001 (0.002) Loss 3.0092 (2.9494) Prec@1 32.500 (30.886) Prec@5 57.500 (60.553) Epoch: [1][9350/11272] Time 0.795 (0.831) Data 0.002 (0.002) Loss 2.8854 (2.9494) Prec@1 33.750 (30.887) Prec@5 64.375 (60.555) Epoch: [1][9360/11272] Time 0.784 (0.831) Data 0.002 (0.002) Loss 2.8679 (2.9493) Prec@1 34.375 (30.888) Prec@5 63.750 (60.557) Epoch: [1][9370/11272] Time 0.912 (0.831) Data 0.001 (0.002) Loss 3.0805 (2.9492) Prec@1 34.375 (30.890) Prec@5 59.375 (60.560) Epoch: [1][9380/11272] Time 0.878 (0.831) Data 0.002 (0.002) Loss 2.8740 (2.9492) Prec@1 35.625 (30.889) Prec@5 63.125 (60.561) Epoch: [1][9390/11272] Time 0.752 (0.831) Data 0.002 (0.002) Loss 2.6648 (2.9490) Prec@1 39.375 (30.892) Prec@5 65.625 (60.564) Epoch: [1][9400/11272] Time 0.759 (0.831) Data 0.001 (0.002) Loss 2.9279 (2.9489) Prec@1 33.125 (30.893) Prec@5 61.250 (60.567) Epoch: [1][9410/11272] Time 0.900 (0.831) Data 0.002 (0.002) Loss 2.7291 (2.9488) Prec@1 37.500 (30.896) Prec@5 63.125 (60.570) Epoch: [1][9420/11272] Time 0.914 (0.831) Data 0.002 (0.002) Loss 2.8150 (2.9487) Prec@1 30.625 (30.897) Prec@5 61.875 (60.571) Epoch: [1][9430/11272] Time 0.765 (0.830) Data 0.002 (0.002) Loss 2.8828 (2.9486) Prec@1 28.750 (30.898) Prec@5 64.375 (60.572) Epoch: [1][9440/11272] Time 0.747 (0.830) Data 0.001 (0.002) Loss 2.7040 (2.9486) Prec@1 35.000 (30.899) Prec@5 65.625 (60.574) Epoch: [1][9450/11272] Time 0.860 (0.830) Data 0.001 (0.002) Loss 2.7942 (2.9485) Prec@1 35.000 (30.899) Prec@5 60.000 (60.576) Epoch: [1][9460/11272] Time 0.960 (0.830) Data 0.002 (0.002) Loss 2.9412 (2.9483) Prec@1 30.625 (30.901) Prec@5 61.250 (60.579) Epoch: [1][9470/11272] Time 0.768 (0.830) Data 0.001 (0.002) Loss 2.8006 (2.9483) Prec@1 32.500 (30.904) Prec@5 66.250 (60.581) Epoch: [1][9480/11272] Time 0.892 (0.830) Data 0.002 (0.002) Loss 2.8556 (2.9482) Prec@1 34.375 (30.905) Prec@5 63.125 (60.582) Epoch: [1][9490/11272] Time 0.909 (0.830) Data 0.001 (0.002) Loss 2.6445 (2.9481) Prec@1 35.625 (30.906) Prec@5 65.625 (60.584) Epoch: [1][9500/11272] Time 0.764 (0.830) Data 0.001 (0.002) Loss 3.2344 (2.9480) Prec@1 24.375 (30.905) Prec@5 52.500 (60.584) Epoch: [1][9510/11272] Time 0.743 (0.830) Data 0.001 (0.002) Loss 2.8996 (2.9480) Prec@1 28.125 (30.906) Prec@5 58.750 (60.585) Epoch: [1][9520/11272] Time 0.917 (0.830) Data 0.001 (0.002) Loss 2.8576 (2.9480) Prec@1 32.500 (30.906) Prec@5 66.250 (60.584) Epoch: [1][9530/11272] Time 0.883 (0.830) Data 0.001 (0.002) Loss 2.7031 (2.9481) Prec@1 39.375 (30.906) Prec@5 64.375 (60.582) Epoch: [1][9540/11272] Time 0.756 (0.830) Data 0.001 (0.002) Loss 2.9835 (2.9480) Prec@1 34.375 (30.906) Prec@5 65.000 (60.584) Epoch: [1][9550/11272] Time 0.735 (0.830) Data 0.001 (0.002) Loss 3.0454 (2.9481) Prec@1 28.750 (30.904) Prec@5 58.125 (60.582) Epoch: [1][9560/11272] Time 0.869 (0.830) Data 0.001 (0.002) Loss 2.7251 (2.9480) Prec@1 38.125 (30.907) Prec@5 62.500 (60.586) Epoch: [1][9570/11272] Time 0.891 (0.830) Data 0.002 (0.002) Loss 2.7503 (2.9479) Prec@1 35.000 (30.910) Prec@5 63.750 (60.587) Epoch: [1][9580/11272] Time 0.765 (0.830) Data 0.002 (0.002) Loss 2.8713 (2.9477) Prec@1 36.875 (30.913) Prec@5 63.125 (60.591) Epoch: [1][9590/11272] Time 0.737 (0.830) Data 0.002 (0.002) Loss 2.9129 (2.9476) Prec@1 31.875 (30.913) Prec@5 60.000 (60.593) Epoch: [1][9600/11272] Time 0.901 (0.830) Data 0.002 (0.002) Loss 3.1098 (2.9475) Prec@1 29.375 (30.916) Prec@5 55.625 (60.596) Epoch: [1][9610/11272] Time 0.797 (0.830) Data 0.004 (0.002) Loss 2.7005 (2.9474) Prec@1 35.625 (30.917) Prec@5 69.375 (60.598) Epoch: [1][9620/11272] Time 0.759 (0.830) Data 0.001 (0.002) Loss 2.7994 (2.9473) Prec@1 33.125 (30.918) Prec@5 60.625 (60.601) Epoch: [1][9630/11272] Time 0.881 (0.830) Data 0.001 (0.002) Loss 3.0228 (2.9472) Prec@1 27.500 (30.920) Prec@5 57.500 (60.603) Epoch: [1][9640/11272] Time 0.903 (0.830) Data 0.002 (0.002) Loss 2.7683 (2.9471) Prec@1 30.625 (30.920) Prec@5 63.125 (60.605) Epoch: [1][9650/11272] Time 0.749 (0.830) Data 0.001 (0.002) Loss 3.0235 (2.9470) Prec@1 30.625 (30.922) Prec@5 56.250 (60.606) Epoch: [1][9660/11272] Time 0.747 (0.830) Data 0.002 (0.002) Loss 2.8141 (2.9468) Prec@1 31.875 (30.924) Prec@5 63.125 (60.609) Epoch: [1][9670/11272] Time 0.881 (0.830) Data 0.001 (0.002) Loss 2.9779 (2.9467) Prec@1 31.875 (30.926) Prec@5 58.125 (60.611) Epoch: [1][9680/11272] Time 0.893 (0.830) Data 0.001 (0.002) Loss 2.8761 (2.9467) Prec@1 29.375 (30.927) Prec@5 58.750 (60.611) Epoch: [1][9690/11272] Time 0.731 (0.830) Data 0.002 (0.002) Loss 2.9663 (2.9466) Prec@1 28.125 (30.927) Prec@5 58.750 (60.613) Epoch: [1][9700/11272] Time 0.780 (0.830) Data 0.001 (0.002) Loss 2.7311 (2.9465) Prec@1 35.625 (30.927) Prec@5 66.250 (60.614) Epoch: [1][9710/11272] Time 0.909 (0.830) Data 0.002 (0.002) Loss 3.2445 (2.9466) Prec@1 25.625 (30.928) Prec@5 55.625 (60.614) Epoch: [1][9720/11272] Time 0.877 (0.830) Data 0.001 (0.002) Loss 2.8759 (2.9465) Prec@1 35.625 (30.930) Prec@5 62.500 (60.615) Epoch: [1][9730/11272] Time 0.744 (0.830) Data 0.001 (0.002) Loss 2.6906 (2.9464) Prec@1 36.250 (30.932) Prec@5 62.500 (60.617) Epoch: [1][9740/11272] Time 0.904 (0.830) Data 0.001 (0.002) Loss 3.0771 (2.9464) Prec@1 27.500 (30.932) Prec@5 59.375 (60.619) Epoch: [1][9750/11272] Time 0.861 (0.830) Data 0.001 (0.002) Loss 3.3241 (2.9464) Prec@1 19.375 (30.930) Prec@5 53.125 (60.618) Epoch: [1][9760/11272] Time 0.766 (0.830) Data 0.001 (0.002) Loss 3.1102 (2.9464) Prec@1 30.000 (30.932) Prec@5 60.000 (60.620) Epoch: [1][9770/11272] Time 0.734 (0.830) Data 0.001 (0.002) Loss 2.7733 (2.9463) Prec@1 36.250 (30.933) Prec@5 65.625 (60.623) Epoch: [1][9780/11272] Time 0.911 (0.830) Data 0.001 (0.002) Loss 2.4570 (2.9461) Prec@1 42.500 (30.936) Prec@5 70.000 (60.627) Epoch: [1][9790/11272] Time 0.875 (0.830) Data 0.001 (0.002) Loss 2.7107 (2.9459) Prec@1 39.375 (30.942) Prec@5 64.375 (60.631) Epoch: [1][9800/11272] Time 0.747 (0.830) Data 0.002 (0.002) Loss 2.5218 (2.9458) Prec@1 36.250 (30.941) Prec@5 68.125 (60.631) Epoch: [1][9810/11272] Time 0.729 (0.830) Data 0.001 (0.002) Loss 2.7299 (2.9457) Prec@1 34.375 (30.944) Prec@5 70.000 (60.635) Epoch: [1][9820/11272] Time 0.952 (0.830) Data 0.002 (0.002) Loss 2.9862 (2.9456) Prec@1 31.250 (30.947) Prec@5 61.250 (60.636) Epoch: [1][9830/11272] Time 0.981 (0.830) Data 0.002 (0.002) Loss 2.9013 (2.9455) Prec@1 31.250 (30.948) Prec@5 61.250 (60.638) Epoch: [1][9840/11272] Time 0.739 (0.830) Data 0.001 (0.002) Loss 2.6554 (2.9454) Prec@1 35.000 (30.948) Prec@5 67.500 (60.641) Epoch: [1][9850/11272] Time 0.732 (0.830) Data 0.001 (0.002) Loss 2.8465 (2.9453) Prec@1 33.750 (30.953) Prec@5 63.750 (60.644) Epoch: [1][9860/11272] Time 0.886 (0.830) Data 0.002 (0.002) Loss 2.9781 (2.9452) Prec@1 29.375 (30.954) Prec@5 62.500 (60.645) Epoch: [1][9870/11272] Time 0.738 (0.830) Data 0.003 (0.002) Loss 2.9340 (2.9451) Prec@1 26.250 (30.956) Prec@5 58.125 (60.645) Epoch: [1][9880/11272] Time 0.754 (0.830) Data 0.001 (0.002) Loss 2.9710 (2.9450) Prec@1 36.250 (30.958) Prec@5 58.125 (60.646) Epoch: [1][9890/11272] Time 0.930 (0.830) Data 0.002 (0.002) Loss 2.8914 (2.9450) Prec@1 29.375 (30.959) Prec@5 61.875 (60.647) Epoch: [1][9900/11272] Time 0.913 (0.830) Data 0.002 (0.002) Loss 2.6409 (2.9449) Prec@1 37.500 (30.960) Prec@5 70.000 (60.650) Epoch: [1][9910/11272] Time 0.733 (0.830) Data 0.002 (0.002) Loss 3.2038 (2.9449) Prec@1 26.875 (30.960) Prec@5 54.375 (60.651) Epoch: [1][9920/11272] Time 0.780 (0.830) Data 0.002 (0.002) Loss 2.7113 (2.9448) Prec@1 38.750 (30.961) Prec@5 66.875 (60.652) Epoch: [1][9930/11272] Time 0.864 (0.830) Data 0.002 (0.002) Loss 2.6102 (2.9446) Prec@1 34.375 (30.963) Prec@5 68.125 (60.656) Epoch: [1][9940/11272] Time 0.918 (0.830) Data 0.002 (0.002) Loss 2.9495 (2.9446) Prec@1 30.625 (30.963) Prec@5 55.000 (60.656) Epoch: [1][9950/11272] Time 0.736 (0.830) Data 0.002 (0.002) Loss 2.8408 (2.9445) Prec@1 33.750 (30.965) Prec@5 63.125 (60.657) Epoch: [1][9960/11272] Time 0.751 (0.830) Data 0.001 (0.002) Loss 3.0349 (2.9445) Prec@1 31.875 (30.966) Prec@5 56.875 (60.656) Epoch: [1][9970/11272] Time 0.884 (0.830) Data 0.001 (0.002) Loss 2.8953 (2.9444) Prec@1 30.625 (30.968) Prec@5 57.500 (60.658) Epoch: [1][9980/11272] Time 0.868 (0.830) Data 0.002 (0.002) Loss 2.9721 (2.9444) Prec@1 30.000 (30.970) Prec@5 58.750 (60.659) Epoch: [1][9990/11272] Time 0.754 (0.830) Data 0.002 (0.002) Loss 2.8712 (2.9443) Prec@1 35.625 (30.971) Prec@5 64.375 (60.661) Epoch: [1][10000/11272] Time 0.898 (0.830) Data 0.001 (0.002) Loss 2.9975 (2.9443) Prec@1 29.375 (30.969) Prec@5 60.000 (60.660) Epoch: [1][10010/11272] Time 0.883 (0.830) Data 0.001 (0.002) Loss 2.9741 (2.9442) Prec@1 31.875 (30.971) Prec@5 59.375 (60.663) Epoch: [1][10020/11272] Time 0.742 (0.830) Data 0.001 (0.002) Loss 2.7497 (2.9442) Prec@1 35.000 (30.972) Prec@5 63.750 (60.664) Epoch: [1][10030/11272] Time 0.737 (0.830) Data 0.001 (0.002) Loss 3.0472 (2.9441) Prec@1 28.750 (30.973) Prec@5 57.500 (60.665) Epoch: [1][10040/11272] Time 0.944 (0.830) Data 0.001 (0.002) Loss 2.6864 (2.9440) Prec@1 38.125 (30.974) Prec@5 63.125 (60.667) Epoch: [1][10050/11272] Time 0.909 (0.830) Data 0.001 (0.002) Loss 2.9257 (2.9439) Prec@1 33.750 (30.976) Prec@5 61.250 (60.670) Epoch: [1][10060/11272] Time 0.744 (0.830) Data 0.002 (0.002) Loss 2.7666 (2.9438) Prec@1 36.875 (30.979) Prec@5 61.250 (60.673) Epoch: [1][10070/11272] Time 0.743 (0.830) Data 0.001 (0.002) Loss 2.8591 (2.9437) Prec@1 33.125 (30.979) Prec@5 61.875 (60.673) Epoch: [1][10080/11272] Time 0.932 (0.830) Data 0.002 (0.002) Loss 3.1670 (2.9437) Prec@1 24.375 (30.979) Prec@5 56.250 (60.674) Epoch: [1][10090/11272] Time 0.923 (0.830) Data 0.001 (0.002) Loss 2.5960 (2.9437) Prec@1 38.125 (30.980) Prec@5 68.125 (60.676) Epoch: [1][10100/11272] Time 0.733 (0.830) Data 0.001 (0.002) Loss 2.8097 (2.9435) Prec@1 29.375 (30.982) Prec@5 63.125 (60.679) Epoch: [1][10110/11272] Time 0.763 (0.830) Data 0.002 (0.002) Loss 2.7474 (2.9435) Prec@1 35.625 (30.982) Prec@5 65.000 (60.679) Epoch: [1][10120/11272] Time 0.896 (0.830) Data 0.002 (0.002) Loss 2.7024 (2.9433) Prec@1 33.750 (30.985) Prec@5 63.125 (60.681) Epoch: [1][10130/11272] Time 0.867 (0.830) Data 0.002 (0.002) Loss 2.9308 (2.9433) Prec@1 26.875 (30.986) Prec@5 62.500 (60.682) Epoch: [1][10140/11272] Time 0.748 (0.830) Data 0.001 (0.002) Loss 2.7899 (2.9432) Prec@1 30.625 (30.987) Prec@5 67.500 (60.684) Epoch: [1][10150/11272] Time 0.894 (0.830) Data 0.002 (0.002) Loss 2.9053 (2.9431) Prec@1 33.750 (30.990) Prec@5 60.625 (60.684) Epoch: [1][10160/11272] Time 0.942 (0.830) Data 0.001 (0.002) Loss 2.8132 (2.9430) Prec@1 33.750 (30.990) Prec@5 63.750 (60.687) Epoch: [1][10170/11272] Time 0.732 (0.830) Data 0.001 (0.002) Loss 2.9871 (2.9429) Prec@1 34.375 (30.993) Prec@5 55.000 (60.689) Epoch: [1][10180/11272] Time 0.780 (0.830) Data 0.001 (0.002) Loss 2.7645 (2.9427) Prec@1 35.000 (30.995) Prec@5 65.625 (60.694) Epoch: [1][10190/11272] Time 0.871 (0.830) Data 0.001 (0.002) Loss 2.7394 (2.9426) Prec@1 37.500 (30.999) Prec@5 63.750 (60.696) Epoch: [1][10200/11272] Time 0.936 (0.830) Data 0.002 (0.002) Loss 3.0404 (2.9426) Prec@1 30.625 (31.000) Prec@5 54.375 (60.695) Epoch: [1][10210/11272] Time 0.762 (0.830) Data 0.001 (0.002) Loss 2.9446 (2.9426) Prec@1 28.125 (31.001) Prec@5 62.500 (60.697) Epoch: [1][10220/11272] Time 0.758 (0.830) Data 0.001 (0.002) Loss 2.5277 (2.9425) Prec@1 41.250 (31.002) Prec@5 69.375 (60.698) Epoch: [1][10230/11272] Time 0.862 (0.830) Data 0.001 (0.002) Loss 3.0854 (2.9424) Prec@1 28.750 (31.004) Prec@5 58.750 (60.701) Epoch: [1][10240/11272] Time 0.922 (0.830) Data 0.002 (0.002) Loss 2.8431 (2.9424) Prec@1 31.875 (31.005) Prec@5 62.500 (60.701) Epoch: [1][10250/11272] Time 0.735 (0.830) Data 0.001 (0.002) Loss 2.8312 (2.9423) Prec@1 30.625 (31.006) Prec@5 61.250 (60.703) Epoch: [1][10260/11272] Time 0.754 (0.830) Data 0.001 (0.002) Loss 3.0767 (2.9422) Prec@1 30.000 (31.008) Prec@5 57.500 (60.704) Epoch: [1][10270/11272] Time 0.872 (0.830) Data 0.001 (0.002) Loss 3.0845 (2.9421) Prec@1 26.875 (31.010) Prec@5 60.000 (60.703) Epoch: [1][10280/11272] Time 0.767 (0.830) Data 0.001 (0.002) Loss 2.9458 (2.9421) Prec@1 26.250 (31.011) Prec@5 59.375 (60.704) Epoch: [1][10290/11272] Time 0.749 (0.830) Data 0.001 (0.002) Loss 2.4933 (2.9420) Prec@1 36.875 (31.013) Prec@5 71.250 (60.705) Epoch: [1][10300/11272] Time 0.956 (0.830) Data 0.001 (0.002) Loss 2.9008 (2.9419) Prec@1 30.000 (31.015) Prec@5 64.375 (60.706) Epoch: [1][10310/11272] Time 0.872 (0.830) Data 0.002 (0.002) Loss 2.7751 (2.9419) Prec@1 33.125 (31.016) Prec@5 63.125 (60.706) Epoch: [1][10320/11272] Time 0.755 (0.830) Data 0.002 (0.002) Loss 2.7855 (2.9418) Prec@1 36.250 (31.018) Prec@5 65.000 (60.707) Epoch: [1][10330/11272] Time 0.756 (0.830) Data 0.001 (0.002) Loss 3.0838 (2.9417) Prec@1 25.000 (31.019) Prec@5 59.375 (60.709) Epoch: [1][10340/11272] Time 0.888 (0.830) Data 0.002 (0.002) Loss 3.1936 (2.9417) Prec@1 26.875 (31.019) Prec@5 55.000 (60.710) Epoch: [1][10350/11272] Time 0.920 (0.830) Data 0.002 (0.002) Loss 2.9158 (2.9416) Prec@1 30.000 (31.020) Prec@5 60.000 (60.713) Epoch: [1][10360/11272] Time 0.738 (0.830) Data 0.002 (0.002) Loss 2.7956 (2.9414) Prec@1 34.375 (31.023) Prec@5 66.250 (60.716) Epoch: [1][10370/11272] Time 0.746 (0.830) Data 0.001 (0.002) Loss 2.9349 (2.9414) Prec@1 35.000 (31.024) Prec@5 58.125 (60.717) Epoch: [1][10380/11272] Time 0.927 (0.830) Data 0.001 (0.002) Loss 2.8251 (2.9412) Prec@1 30.625 (31.026) Prec@5 60.000 (60.721) Epoch: [1][10390/11272] Time 0.899 (0.830) Data 0.001 (0.002) Loss 2.7605 (2.9411) Prec@1 34.375 (31.027) Prec@5 63.750 (60.723) Epoch: [1][10400/11272] Time 0.722 (0.830) Data 0.001 (0.002) Loss 2.8736 (2.9411) Prec@1 32.500 (31.027) Prec@5 59.375 (60.723) Epoch: [1][10410/11272] Time 0.846 (0.830) Data 0.002 (0.002) Loss 2.7935 (2.9412) Prec@1 33.750 (31.026) Prec@5 67.500 (60.724) Epoch: [1][10420/11272] Time 0.911 (0.830) Data 0.001 (0.002) Loss 2.9752 (2.9411) Prec@1 33.125 (31.026) Prec@5 61.875 (60.725) Epoch: [1][10430/11272] Time 0.747 (0.830) Data 0.002 (0.002) Loss 2.9894 (2.9410) Prec@1 33.125 (31.028) Prec@5 57.500 (60.727) Epoch: [1][10440/11272] Time 0.790 (0.830) Data 0.001 (0.002) Loss 2.9995 (2.9410) Prec@1 33.125 (31.029) Prec@5 65.000 (60.729) Epoch: [1][10450/11272] Time 0.970 (0.830) Data 0.001 (0.002) Loss 2.8711 (2.9410) Prec@1 33.750 (31.032) Prec@5 60.625 (60.729) Epoch: [1][10460/11272] Time 0.906 (0.830) Data 0.001 (0.002) Loss 2.8869 (2.9409) Prec@1 39.375 (31.036) Prec@5 61.250 (60.732) Epoch: [1][10470/11272] Time 0.748 (0.830) Data 0.002 (0.002) Loss 3.0363 (2.9408) Prec@1 25.625 (31.036) Prec@5 57.500 (60.733) Epoch: [1][10480/11272] Time 0.761 (0.830) Data 0.001 (0.002) Loss 2.7525 (2.9407) Prec@1 35.000 (31.039) Prec@5 68.125 (60.736) Epoch: [1][10490/11272] Time 0.919 (0.830) Data 0.002 (0.002) Loss 2.6587 (2.9406) Prec@1 40.000 (31.040) Prec@5 68.750 (60.739) Epoch: [1][10500/11272] Time 0.908 (0.830) Data 0.001 (0.002) Loss 2.6933 (2.9405) Prec@1 36.250 (31.041) Prec@5 62.500 (60.739) Epoch: [1][10510/11272] Time 0.738 (0.830) Data 0.002 (0.002) Loss 2.4315 (2.9404) Prec@1 40.625 (31.044) Prec@5 76.875 (60.742) Epoch: [1][10520/11272] Time 0.757 (0.830) Data 0.001 (0.002) Loss 2.7089 (2.9404) Prec@1 36.250 (31.047) Prec@5 68.125 (60.743) Epoch: [1][10530/11272] Time 0.843 (0.830) Data 0.001 (0.002) Loss 2.8334 (2.9403) Prec@1 36.250 (31.048) Prec@5 60.000 (60.745) Epoch: [1][10540/11272] Time 0.797 (0.830) Data 0.005 (0.002) Loss 2.7866 (2.9402) Prec@1 35.000 (31.050) Prec@5 68.125 (60.747) Epoch: [1][10550/11272] Time 0.737 (0.830) Data 0.002 (0.002) Loss 2.7120 (2.9402) Prec@1 38.750 (31.053) Prec@5 65.625 (60.749) Epoch: [1][10560/11272] Time 0.899 (0.830) Data 0.002 (0.002) Loss 2.6087 (2.9401) Prec@1 32.500 (31.054) Prec@5 68.750 (60.752) Epoch: [1][10570/11272] Time 0.876 (0.830) Data 0.002 (0.002) Loss 2.7986 (2.9400) Prec@1 28.750 (31.056) Prec@5 66.250 (60.754) Epoch: [1][10580/11272] Time 0.772 (0.830) Data 0.001 (0.002) Loss 2.5934 (2.9399) Prec@1 35.000 (31.057) Prec@5 69.375 (60.757) Epoch: [1][10590/11272] Time 0.754 (0.830) Data 0.002 (0.002) Loss 3.0052 (2.9398) Prec@1 31.250 (31.058) Prec@5 64.375 (60.759) Epoch: [1][10600/11272] Time 0.881 (0.830) Data 0.001 (0.002) Loss 2.8972 (2.9398) Prec@1 28.125 (31.057) Prec@5 58.750 (60.759) Epoch: [1][10610/11272] Time 0.904 (0.830) Data 0.001 (0.002) Loss 2.4318 (2.9397) Prec@1 41.250 (31.060) Prec@5 65.000 (60.760) Epoch: [1][10620/11272] Time 0.736 (0.830) Data 0.001 (0.002) Loss 2.5362 (2.9396) Prec@1 35.000 (31.059) Prec@5 67.500 (60.760) Epoch: [1][10630/11272] Time 0.726 (0.830) Data 0.001 (0.002) Loss 3.0465 (2.9396) Prec@1 27.500 (31.060) Prec@5 60.625 (60.761) Epoch: [1][10640/11272] Time 0.901 (0.830) Data 0.002 (0.002) Loss 2.6562 (2.9395) Prec@1 35.000 (31.062) Prec@5 63.750 (60.762) Epoch: [1][10650/11272] Time 0.902 (0.830) Data 0.002 (0.002) Loss 2.6803 (2.9394) Prec@1 31.250 (31.063) Prec@5 66.250 (60.764) Epoch: [1][10660/11272] Time 0.758 (0.830) Data 0.001 (0.002) Loss 2.7342 (2.9393) Prec@1 31.250 (31.064) Prec@5 66.250 (60.765) Epoch: [1][10670/11272] Time 0.892 (0.830) Data 0.001 (0.002) Loss 2.7566 (2.9392) Prec@1 32.500 (31.066) Prec@5 61.875 (60.767) Epoch: [1][10680/11272] Time 0.906 (0.830) Data 0.002 (0.002) Loss 2.8606 (2.9392) Prec@1 33.125 (31.067) Prec@5 63.125 (60.768) Epoch: [1][10690/11272] Time 0.735 (0.830) Data 0.002 (0.002) Loss 2.9807 (2.9392) Prec@1 30.000 (31.068) Prec@5 59.375 (60.768) Epoch: [1][10700/11272] Time 0.785 (0.830) Data 0.002 (0.002) Loss 2.9604 (2.9392) Prec@1 32.500 (31.067) Prec@5 61.250 (60.769) Epoch: [1][10710/11272] Time 0.894 (0.830) Data 0.002 (0.002) Loss 2.8543 (2.9391) Prec@1 28.750 (31.069) Prec@5 61.875 (60.771) Epoch: [1][10720/11272] Time 0.917 (0.830) Data 0.002 (0.002) Loss 2.6406 (2.9390) Prec@1 33.750 (31.069) Prec@5 69.375 (60.773) Epoch: [1][10730/11272] Time 0.751 (0.830) Data 0.002 (0.002) Loss 2.8097 (2.9390) Prec@1 33.125 (31.071) Prec@5 62.500 (60.772) Epoch: [1][10740/11272] Time 0.762 (0.830) Data 0.001 (0.002) Loss 3.1445 (2.9390) Prec@1 33.125 (31.072) Prec@5 53.750 (60.772) Epoch: [1][10750/11272] Time 0.890 (0.830) Data 0.002 (0.002) Loss 2.6815 (2.9389) Prec@1 36.875 (31.073) Prec@5 66.250 (60.774) Epoch: [1][10760/11272] Time 0.907 (0.830) Data 0.001 (0.002) Loss 3.0034 (2.9388) Prec@1 28.125 (31.074) Prec@5 61.250 (60.775) Epoch: [1][10770/11272] Time 0.776 (0.830) Data 0.001 (0.002) Loss 2.8393 (2.9387) Prec@1 28.750 (31.073) Prec@5 60.625 (60.776) Epoch: [1][10780/11272] Time 0.751 (0.830) Data 0.001 (0.002) Loss 3.0361 (2.9386) Prec@1 26.250 (31.074) Prec@5 58.125 (60.778) Epoch: [1][10790/11272] Time 0.953 (0.830) Data 0.002 (0.002) Loss 2.6926 (2.9386) Prec@1 37.500 (31.074) Prec@5 66.250 (60.780) Epoch: [1][10800/11272] Time 0.810 (0.830) Data 0.003 (0.002) Loss 2.7831 (2.9385) Prec@1 31.250 (31.077) Prec@5 61.875 (60.783) Epoch: [1][10810/11272] Time 0.781 (0.830) Data 0.002 (0.002) Loss 2.7593 (2.9384) Prec@1 34.375 (31.078) Prec@5 64.375 (60.786) Epoch: [1][10820/11272] Time 0.935 (0.830) Data 0.001 (0.002) Loss 2.7225 (2.9383) Prec@1 35.625 (31.081) Prec@5 61.875 (60.788) Epoch: [1][10830/11272] Time 0.941 (0.830) Data 0.001 (0.002) Loss 2.7324 (2.9382) Prec@1 31.250 (31.083) Prec@5 63.750 (60.790) Epoch: [1][10840/11272] Time 0.757 (0.830) Data 0.002 (0.002) Loss 2.9176 (2.9382) Prec@1 30.000 (31.084) Prec@5 59.375 (60.791) Epoch: [1][10850/11272] Time 0.768 (0.830) Data 0.002 (0.002) Loss 2.9381 (2.9381) Prec@1 28.125 (31.084) Prec@5 61.250 (60.793) Epoch: [1][10860/11272] Time 0.897 (0.830) Data 0.001 (0.002) Loss 2.9340 (2.9380) Prec@1 31.875 (31.085) Prec@5 60.000 (60.795) Epoch: [1][10870/11272] Time 0.934 (0.830) Data 0.002 (0.002) Loss 3.0507 (2.9380) Prec@1 25.000 (31.086) Prec@5 59.375 (60.796) Epoch: [1][10880/11272] Time 0.802 (0.830) Data 0.002 (0.002) Loss 2.8433 (2.9380) Prec@1 37.500 (31.086) Prec@5 63.125 (60.795) Epoch: [1][10890/11272] Time 0.742 (0.830) Data 0.001 (0.002) Loss 2.5685 (2.9379) Prec@1 35.625 (31.087) Prec@5 71.250 (60.798) Epoch: [1][10900/11272] Time 0.930 (0.830) Data 0.001 (0.002) Loss 2.8146 (2.9378) Prec@1 33.125 (31.088) Prec@5 61.250 (60.799) Epoch: [1][10910/11272] Time 0.875 (0.830) Data 0.002 (0.002) Loss 2.8934 (2.9378) Prec@1 36.250 (31.090) Prec@5 62.500 (60.800) Epoch: [1][10920/11272] Time 0.766 (0.830) Data 0.004 (0.002) Loss 2.7042 (2.9377) Prec@1 39.375 (31.091) Prec@5 65.000 (60.801) Epoch: [1][10930/11272] Time 0.876 (0.830) Data 0.002 (0.002) Loss 2.8156 (2.9376) Prec@1 32.500 (31.093) Prec@5 62.500 (60.803) Epoch: [1][10940/11272] Time 0.939 (0.830) Data 0.001 (0.002) Loss 3.0282 (2.9375) Prec@1 31.250 (31.096) Prec@5 56.875 (60.804) Epoch: [1][10950/11272] Time 0.775 (0.830) Data 0.001 (0.002) Loss 2.7954 (2.9374) Prec@1 33.750 (31.097) Prec@5 61.875 (60.806) Epoch: [1][10960/11272] Time 0.755 (0.830) Data 0.001 (0.002) Loss 2.6682 (2.9373) Prec@1 33.750 (31.097) Prec@5 65.000 (60.807) Epoch: [1][10970/11272] Time 0.964 (0.830) Data 0.001 (0.002) Loss 2.9447 (2.9374) Prec@1 28.125 (31.096) Prec@5 60.000 (60.808) Epoch: [1][10980/11272] Time 0.910 (0.830) Data 0.001 (0.002) Loss 2.8083 (2.9373) Prec@1 30.000 (31.097) Prec@5 65.000 (60.810) Epoch: [1][10990/11272] Time 0.742 (0.830) Data 0.001 (0.002) Loss 2.7807 (2.9372) Prec@1 30.000 (31.097) Prec@5 66.250 (60.812) Epoch: [1][11000/11272] Time 0.743 (0.830) Data 0.001 (0.002) Loss 3.1213 (2.9371) Prec@1 27.500 (31.098) Prec@5 53.750 (60.812) Epoch: [1][11010/11272] Time 0.949 (0.830) Data 0.002 (0.002) Loss 3.0279 (2.9371) Prec@1 33.125 (31.097) Prec@5 61.250 (60.813) Epoch: [1][11020/11272] Time 0.925 (0.830) Data 0.001 (0.002) Loss 3.1817 (2.9371) Prec@1 30.625 (31.099) Prec@5 56.250 (60.814) Epoch: [1][11030/11272] Time 0.738 (0.830) Data 0.002 (0.002) Loss 2.8029 (2.9370) Prec@1 37.500 (31.098) Prec@5 63.750 (60.815) Epoch: [1][11040/11272] Time 0.747 (0.830) Data 0.006 (0.002) Loss 3.1988 (2.9370) Prec@1 26.250 (31.099) Prec@5 56.875 (60.816) Epoch: [1][11050/11272] Time 0.895 (0.830) Data 0.002 (0.002) Loss 2.6332 (2.9369) Prec@1 38.125 (31.100) Prec@5 67.500 (60.816) Epoch: [1][11060/11272] Time 0.882 (0.830) Data 0.002 (0.002) Loss 2.6996 (2.9368) Prec@1 34.375 (31.100) Prec@5 66.250 (60.818) Epoch: [1][11070/11272] Time 0.750 (0.830) Data 0.001 (0.002) Loss 2.5873 (2.9368) Prec@1 37.500 (31.099) Prec@5 66.250 (60.819) Epoch: [1][11080/11272] Time 0.915 (0.830) Data 0.001 (0.002) Loss 2.6379 (2.9367) Prec@1 36.875 (31.100) Prec@5 67.500 (60.820) Epoch: [1][11090/11272] Time 0.884 (0.830) Data 0.001 (0.002) Loss 2.7953 (2.9367) Prec@1 36.875 (31.100) Prec@5 65.000 (60.819) Epoch: [1][11100/11272] Time 0.760 (0.830) Data 0.002 (0.002) Loss 2.9902 (2.9367) Prec@1 32.500 (31.101) Prec@5 59.375 (60.820) Epoch: [1][11110/11272] Time 0.739 (0.830) Data 0.002 (0.002) Loss 2.9042 (2.9366) Prec@1 32.500 (31.103) Prec@5 62.500 (60.824) Epoch: [1][11120/11272] Time 0.918 (0.830) Data 0.002 (0.002) Loss 2.6806 (2.9364) Prec@1 40.000 (31.106) Prec@5 69.375 (60.827) Epoch: [1][11130/11272] Time 0.887 (0.830) Data 0.001 (0.002) Loss 2.8284 (2.9364) Prec@1 40.000 (31.108) Prec@5 66.250 (60.827) Epoch: [1][11140/11272] Time 0.746 (0.830) Data 0.001 (0.002) Loss 2.9108 (2.9364) Prec@1 36.250 (31.109) Prec@5 57.500 (60.826) Epoch: [1][11150/11272] Time 0.767 (0.830) Data 0.001 (0.002) Loss 2.6805 (2.9363) Prec@1 36.875 (31.111) Prec@5 63.750 (60.829) Epoch: [1][11160/11272] Time 0.880 (0.830) Data 0.002 (0.002) Loss 2.8969 (2.9362) Prec@1 31.875 (31.112) Prec@5 60.000 (60.829) Epoch: [1][11170/11272] Time 0.890 (0.830) Data 0.002 (0.002) Loss 2.8617 (2.9362) Prec@1 35.625 (31.112) Prec@5 61.875 (60.831) Epoch: [1][11180/11272] Time 0.779 (0.830) Data 0.002 (0.002) Loss 2.7263 (2.9361) Prec@1 33.125 (31.113) Prec@5 66.250 (60.832) Epoch: [1][11190/11272] Time 0.750 (0.830) Data 0.001 (0.002) Loss 2.8037 (2.9360) Prec@1 33.125 (31.115) Prec@5 63.750 (60.835) Epoch: [1][11200/11272] Time 0.897 (0.830) Data 0.001 (0.002) Loss 2.4985 (2.9359) Prec@1 41.250 (31.117) Prec@5 66.875 (60.836) Epoch: [1][11210/11272] Time 0.744 (0.830) Data 0.001 (0.002) Loss 2.9300 (2.9359) Prec@1 27.500 (31.116) Prec@5 60.000 (60.836) Epoch: [1][11220/11272] Time 0.737 (0.830) Data 0.002 (0.002) Loss 3.0071 (2.9358) Prec@1 33.750 (31.117) Prec@5 60.000 (60.838) Epoch: [1][11230/11272] Time 0.868 (0.830) Data 0.001 (0.002) Loss 2.9312 (2.9357) Prec@1 33.750 (31.118) Prec@5 59.375 (60.839) Epoch: [1][11240/11272] Time 0.882 (0.830) Data 0.001 (0.002) Loss 3.1063 (2.9356) Prec@1 35.000 (31.121) Prec@5 57.500 (60.841) Epoch: [1][11250/11272] Time 0.748 (0.830) Data 0.002 (0.002) Loss 3.0180 (2.9355) Prec@1 28.750 (31.122) Prec@5 58.750 (60.843) Epoch: [1][11260/11272] Time 0.790 (0.830) Data 0.002 (0.002) Loss 2.8985 (2.9355) Prec@1 33.125 (31.123) Prec@5 63.750 (60.843) Epoch: [1][11270/11272] Time 0.827 (0.830) Data 0.000 (0.002) Loss 3.0799 (2.9354) Prec@1 26.875 (31.125) Prec@5 59.375 (60.845) Test: [0/229] Time 1.804 (1.804) Loss 1.9758 (1.9758) Prec@1 43.750 (43.750) Prec@5 87.500 (87.500) Test: [10/229] Time 0.407 (0.520) Loss 1.5621 (2.4147) Prec@1 58.125 (38.864) Prec@5 90.000 (75.227) Test: [20/229] Time 0.508 (0.473) Loss 2.2688 (2.5377) Prec@1 47.500 (37.321) Prec@5 75.625 (70.893) Test: [30/229] Time 0.370 (0.441) Loss 2.3588 (2.4207) Prec@1 35.000 (40.565) Prec@5 76.875 (72.077) Test: [40/229] Time 0.380 (0.430) Loss 1.6854 (2.4364) Prec@1 65.000 (39.832) Prec@5 80.000 (72.210) Test: [50/229] Time 0.443 (0.427) Loss 2.9086 (2.4819) Prec@1 31.250 (39.559) Prec@5 63.750 (70.919) Test: [60/229] Time 0.348 (0.424) Loss 3.6878 (2.5093) Prec@1 13.750 (39.016) Prec@5 45.625 (70.215) Test: [70/229] Time 0.508 (0.422) Loss 2.1839 (2.5299) Prec@1 45.625 (38.415) Prec@5 74.375 (69.551) Test: [80/229] Time 0.384 (0.419) Loss 2.6106 (2.5384) Prec@1 36.875 (38.063) Prec@5 66.250 (69.514) Test: [90/229] Time 0.444 (0.418) Loss 2.0199 (2.5146) Prec@1 56.250 (38.413) Prec@5 73.125 (70.000) Test: [100/229] Time 0.306 (0.414) Loss 3.4378 (2.5316) Prec@1 18.750 (38.094) Prec@5 52.500 (69.684) Test: [110/229] Time 0.370 (0.412) Loss 2.0434 (2.4955) Prec@1 43.125 (38.784) Prec@5 78.125 (70.265) Test: [120/229] Time 0.450 (0.411) Loss 3.2946 (2.5080) Prec@1 11.875 (38.146) Prec@5 56.875 (70.165) Test: [130/229] Time 0.348 (0.409) Loss 2.4438 (2.5129) Prec@1 41.875 (38.049) Prec@5 75.625 (70.119) Test: [140/229] Time 0.517 (0.409) Loss 3.4444 (2.5217) Prec@1 23.750 (37.806) Prec@5 48.125 (69.947) Test: [150/229] Time 0.353 (0.409) Loss 1.6532 (2.5375) Prec@1 63.750 (37.546) Prec@5 83.750 (69.801) Test: [160/229] Time 0.463 (0.408) Loss 2.6858 (2.5296) Prec@1 39.375 (37.760) Prec@5 71.875 (69.868) Test: [170/229] Time 0.375 (0.408) Loss 2.7249 (2.5547) Prec@1 26.875 (37.145) Prec@5 69.375 (69.371) Test: [180/229] Time 0.406 (0.408) Loss 3.7601 (2.5724) Prec@1 24.375 (37.010) Prec@5 41.250 (68.961) Test: [190/229] Time 0.455 (0.407) Loss 2.2381 (2.5541) Prec@1 36.250 (37.376) Prec@5 81.875 (69.319) Test: [200/229] Time 0.361 (0.406) Loss 2.4527 (2.5495) Prec@1 31.250 (37.167) Prec@5 68.750 (69.524) Test: [210/229] Time 0.470 (0.407) Loss 1.8013 (2.5462) Prec@1 41.875 (37.213) Prec@5 83.125 (69.639) Test: [220/229] Time 0.322 (0.406) Loss 2.2472 (2.5315) Prec@1 34.375 (37.627) Prec@5 78.125 (69.910) * Prec@1 37.916 Prec@5 70.049 Epoch: [2][0/11272] Time 3.207 (3.207) Data 2.291 (2.291) Loss 2.8957 (2.8957) Prec@1 32.500 (32.500) Prec@5 56.875 (56.875) Epoch: [2][10/11272] Time 0.886 (1.047) Data 0.002 (0.210) Loss 2.7785 (2.8365) Prec@1 33.125 (31.364) Prec@5 63.125 (63.068) Epoch: [2][20/11272] Time 0.735 (0.943) Data 0.001 (0.111) Loss 2.9886 (2.8483) Prec@1 30.000 (31.905) Prec@5 63.125 (62.619) Epoch: [2][30/11272] Time 0.758 (0.903) Data 0.001 (0.076) Loss 3.0903 (2.8513) Prec@1 23.125 (31.835) Prec@5 61.250 (62.802) Epoch: [2][40/11272] Time 0.895 (0.887) Data 0.002 (0.057) Loss 2.9548 (2.8330) Prec@1 32.500 (32.530) Prec@5 62.500 (63.003) Epoch: [2][50/11272] Time 0.862 (0.874) Data 0.001 (0.047) Loss 2.9687 (2.8397) Prec@1 30.000 (32.586) Prec@5 58.750 (62.684) Epoch: [2][60/11272] Time 0.769 (0.865) Data 0.002 (0.039) Loss 2.6064 (2.8239) Prec@1 37.500 (32.982) Prec@5 70.625 (63.084) Epoch: [2][70/11272] Time 0.741 (0.859) Data 0.001 (0.034) Loss 2.9149 (2.8383) Prec@1 33.125 (32.570) Prec@5 61.250 (62.905) Epoch: [2][80/11272] Time 0.914 (0.856) Data 0.002 (0.030) Loss 2.7551 (2.8332) Prec@1 33.750 (32.724) Prec@5 63.750 (62.917) Epoch: [2][90/11272] Time 0.951 (0.854) Data 0.002 (0.027) Loss 2.9685 (2.8367) Prec@1 29.375 (32.658) Prec@5 65.000 (62.967) Epoch: [2][100/11272] Time 0.726 (0.851) Data 0.001 (0.024) Loss 2.8473 (2.8311) Prec@1 31.250 (32.890) Prec@5 60.625 (62.958) Epoch: [2][110/11272] Time 0.742 (0.849) Data 0.002 (0.022) Loss 3.0781 (2.8456) Prec@1 25.625 (32.765) Prec@5 56.875 (62.646) Epoch: [2][120/11272] Time 0.941 (0.849) Data 0.002 (0.021) Loss 3.1558 (2.8525) Prec@1 25.000 (32.608) Prec@5 55.000 (62.526) Epoch: [2][130/11272] Time 0.868 (0.847) Data 0.001 (0.019) Loss 2.7196 (2.8499) Prec@1 36.875 (32.586) Prec@5 65.625 (62.638) Epoch: [2][140/11272] Time 0.735 (0.845) Data 0.001 (0.018) Loss 2.8371 (2.8486) Prec@1 38.750 (32.682) Prec@5 64.375 (62.660) Epoch: [2][150/11272] Time 0.856 (0.843) Data 0.002 (0.017) Loss 2.8267 (2.8492) Prec@1 36.875 (32.695) Prec@5 59.375 (62.686) Epoch: [2][160/11272] Time 0.927 (0.843) Data 0.002 (0.016) Loss 2.7977 (2.8490) Prec@1 30.000 (32.667) Prec@5 62.500 (62.655) Epoch: [2][170/11272] Time 0.749 (0.842) Data 0.001 (0.015) Loss 2.9934 (2.8448) Prec@1 33.750 (32.767) Prec@5 59.375 (62.796) Epoch: [2][180/11272] Time 0.735 (0.842) Data 0.002 (0.014) Loss 3.1242 (2.8428) Prec@1 26.875 (32.797) Prec@5 56.250 (62.866) Epoch: [2][190/11272] Time 0.929 (0.841) Data 0.002 (0.014) Loss 2.8473 (2.8408) Prec@1 30.625 (32.817) Prec@5 63.125 (62.929) Epoch: [2][200/11272] Time 0.920 (0.841) Data 0.002 (0.013) Loss 2.7937 (2.8370) Prec@1 33.750 (32.854) Prec@5 63.750 (62.976) Epoch: [2][210/11272] Time 0.769 (0.841) Data 0.002 (0.012) Loss 2.8309 (2.8407) Prec@1 29.375 (32.793) Prec@5 61.250 (62.867) Epoch: [2][220/11272] Time 0.759 (0.840) Data 0.001 (0.012) Loss 2.8892 (2.8438) Prec@1 27.500 (32.755) Prec@5 61.250 (62.822) Epoch: [2][230/11272] Time 0.925 (0.841) Data 0.001 (0.012) Loss 2.8733 (2.8435) Prec@1 33.750 (32.787) Prec@5 63.125 (62.860) Epoch: [2][240/11272] Time 0.916 (0.840) Data 0.002 (0.011) Loss 2.7529 (2.8459) Prec@1 40.000 (32.759) Prec@5 61.250 (62.793) Epoch: [2][250/11272] Time 0.742 (0.840) Data 0.001 (0.011) Loss 2.8302 (2.8488) Prec@1 33.750 (32.722) Prec@5 61.250 (62.744) Epoch: [2][260/11272] Time 0.754 (0.839) Data 0.002 (0.010) Loss 2.9722 (2.8489) Prec@1 30.625 (32.723) Prec@5 56.250 (62.739) Epoch: [2][270/11272] Time 0.908 (0.839) Data 0.001 (0.010) Loss 2.8288 (2.8466) Prec@1 33.125 (32.751) Prec@5 64.375 (62.800) Epoch: [2][280/11272] Time 0.811 (0.838) Data 0.005 (0.010) Loss 2.6574 (2.8468) Prec@1 38.750 (32.718) Prec@5 66.250 (62.831) Epoch: [2][290/11272] Time 0.747 (0.838) Data 0.002 (0.010) Loss 2.9638 (2.8492) Prec@1 28.750 (32.676) Prec@5 65.000 (62.826) Epoch: [2][300/11272] Time 0.936 (0.838) Data 0.002 (0.009) Loss 2.9524 (2.8479) Prec@1 31.250 (32.693) Prec@5 58.125 (62.818) Epoch: [2][310/11272] Time 0.889 (0.838) Data 0.002 (0.009) Loss 2.8928 (2.8454) Prec@1 33.125 (32.745) Prec@5 58.125 (62.886) Epoch: [2][320/11272] Time 0.754 (0.838) Data 0.002 (0.009) Loss 2.8831 (2.8453) Prec@1 27.500 (32.697) Prec@5 61.250 (62.901) Epoch: [2][330/11272] Time 0.737 (0.838) Data 0.001 (0.009) Loss 2.9486 (2.8455) Prec@1 30.000 (32.696) Prec@5 60.625 (62.900) Epoch: [2][340/11272] Time 0.912 (0.838) Data 0.002 (0.008) Loss 2.5895 (2.8445) Prec@1 33.125 (32.703) Prec@5 71.250 (62.909) Epoch: [2][350/11272] Time 0.939 (0.838) Data 0.001 (0.008) Loss 2.8444 (2.8434) Prec@1 28.750 (32.698) Prec@5 58.125 (62.927) Epoch: [2][360/11272] Time 0.757 (0.837) Data 0.002 (0.008) Loss 2.8415 (2.8438) Prec@1 35.000 (32.704) Prec@5 64.375 (62.933) Epoch: [2][370/11272] Time 0.729 (0.837) Data 0.001 (0.008) Loss 2.6915 (2.8424) Prec@1 40.000 (32.736) Prec@5 69.375 (62.957) Epoch: [2][380/11272] Time 0.893 (0.837) Data 0.002 (0.008) Loss 2.7253 (2.8415) Prec@1 29.375 (32.748) Prec@5 65.625 (62.990) Epoch: [2][390/11272] Time 0.904 (0.837) Data 0.001 (0.007) Loss 2.7373 (2.8422) Prec@1 32.500 (32.738) Prec@5 66.875 (62.999) Epoch: [2][400/11272] Time 0.755 (0.837) Data 0.002 (0.007) Loss 2.8362 (2.8449) Prec@1 32.500 (32.696) Prec@5 63.750 (62.941) Epoch: [2][410/11272] Time 0.859 (0.837) Data 0.002 (0.007) Loss 2.8882 (2.8457) Prec@1 32.500 (32.687) Prec@5 60.625 (62.923) Epoch: [2][420/11272] Time 0.909 (0.836) Data 0.002 (0.007) Loss 2.6739 (2.8464) Prec@1 36.875 (32.674) Prec@5 68.125 (62.913) Epoch: [2][430/11272] Time 0.739 (0.836) Data 0.001 (0.007) Loss 2.8372 (2.8466) Prec@1 25.625 (32.648) Prec@5 67.500 (62.926) Epoch: [2][440/11272] Time 0.789 (0.836) Data 0.002 (0.007) Loss 3.0203 (2.8443) Prec@1 28.125 (32.657) Prec@5 60.000 (62.965) Epoch: [2][450/11272] Time 0.926 (0.836) Data 0.002 (0.007) Loss 2.5938 (2.8438) Prec@1 36.875 (32.669) Prec@5 69.375 (62.941) Epoch: [2][460/11272] Time 0.900 (0.836) Data 0.002 (0.007) Loss 3.0064 (2.8427) Prec@1 28.750 (32.687) Prec@5 60.625 (62.951) Epoch: [2][470/11272] Time 0.740 (0.836) Data 0.001 (0.006) Loss 2.7095 (2.8416) Prec@1 33.750 (32.708) Prec@5 67.500 (62.975) Epoch: [2][480/11272] Time 0.768 (0.836) Data 0.002 (0.006) Loss 2.6966 (2.8426) Prec@1 32.500 (32.683) Prec@5 65.000 (62.953) Epoch: [2][490/11272] Time 0.861 (0.836) Data 0.002 (0.006) Loss 2.7032 (2.8427) Prec@1 30.000 (32.674) Prec@5 66.250 (62.960) Epoch: [2][500/11272] Time 0.908 (0.836) Data 0.002 (0.006) Loss 2.8053 (2.8421) Prec@1 33.125 (32.685) Prec@5 63.125 (62.963) Epoch: [2][510/11272] Time 0.732 (0.836) Data 0.001 (0.006) Loss 2.8021 (2.8432) Prec@1 33.125 (32.663) Prec@5 63.125 (62.955) Epoch: [2][520/11272] Time 0.749 (0.835) Data 0.002 (0.006) Loss 2.7806 (2.8446) Prec@1 36.875 (32.625) Prec@5 61.875 (62.951) Epoch: [2][530/11272] Time 0.931 (0.835) Data 0.002 (0.006) Loss 2.7393 (2.8471) Prec@1 35.625 (32.587) Prec@5 65.625 (62.891) Epoch: [2][540/11272] Time 0.792 (0.835) Data 0.004 (0.006) Loss 2.4686 (2.8473) Prec@1 42.500 (32.577) Prec@5 68.750 (62.889) Epoch: [2][550/11272] Time 0.752 (0.835) Data 0.002 (0.006) Loss 2.7439 (2.8468) Prec@1 38.125 (32.617) Prec@5 65.000 (62.914) Epoch: [2][560/11272] Time 0.892 (0.835) Data 0.002 (0.006) Loss 2.7778 (2.8457) Prec@1 36.875 (32.636) Prec@5 63.750 (62.960) Epoch: [2][570/11272] Time 0.904 (0.835) Data 0.001 (0.006) Loss 2.5990 (2.8447) Prec@1 36.875 (32.653) Prec@5 66.875 (62.961) Epoch: [2][580/11272] Time 0.750 (0.835) Data 0.002 (0.006) Loss 2.9181 (2.8453) Prec@1 30.000 (32.656) Prec@5 60.000 (62.932) Epoch: [2][590/11272] Time 0.764 (0.835) Data 0.002 (0.005) Loss 2.8277 (2.8450) Prec@1 27.500 (32.650) Prec@5 61.250 (62.927) Epoch: [2][600/11272] Time 0.916 (0.835) Data 0.002 (0.005) Loss 2.9931 (2.8457) Prec@1 31.250 (32.631) Prec@5 57.500 (62.926) Epoch: [2][610/11272] Time 0.884 (0.835) Data 0.002 (0.005) Loss 2.9692 (2.8468) Prec@1 28.125 (32.600) Prec@5 57.500 (62.860) Epoch: [2][620/11272] Time 0.779 (0.835) Data 0.002 (0.005) Loss 3.1511 (2.8472) Prec@1 29.375 (32.591) Prec@5 55.625 (62.847) Epoch: [2][630/11272] Time 0.834 (0.836) Data 0.002 (0.005) Loss 2.8935 (2.8468) Prec@1 30.000 (32.579) Prec@5 56.875 (62.827) Epoch: [2][640/11272] Time 0.918 (0.836) Data 0.002 (0.005) Loss 2.9830 (2.8462) Prec@1 30.625 (32.578) Prec@5 59.375 (62.851) Epoch: [2][650/11272] Time 0.984 (0.836) Data 0.002 (0.005) Loss 2.6321 (2.8474) Prec@1 35.625 (32.581) Prec@5 72.500 (62.855) Epoch: [2][660/11272] Time 0.728 (0.836) Data 0.001 (0.005) Loss 3.1678 (2.8472) Prec@1 25.000 (32.583) Prec@5 59.375 (62.872) Epoch: [2][670/11272] Time 0.832 (0.836) Data 0.002 (0.005) Loss 2.8280 (2.8464) Prec@1 30.625 (32.597) Prec@5 63.750 (62.875) Epoch: [2][680/11272] Time 0.980 (0.836) Data 0.002 (0.005) Loss 2.6446 (2.8464) Prec@1 36.875 (32.593) Prec@5 66.875 (62.865) Epoch: [2][690/11272] Time 0.774 (0.836) Data 0.002 (0.005) Loss 3.0050 (2.8464) Prec@1 29.375 (32.619) Prec@5 61.250 (62.865) Epoch: [2][700/11272] Time 0.735 (0.836) Data 0.001 (0.005) Loss 2.5268 (2.8476) Prec@1 37.500 (32.590) Prec@5 68.750 (62.850) Epoch: [2][710/11272] Time 0.897 (0.836) Data 0.001 (0.005) Loss 2.9141 (2.8471) Prec@1 31.875 (32.579) Prec@5 64.375 (62.845) Epoch: [2][720/11272] Time 0.910 (0.836) Data 0.002 (0.005) Loss 2.7422 (2.8471) Prec@1 35.625 (32.575) Prec@5 66.875 (62.835) Epoch: [2][730/11272] Time 0.736 (0.835) Data 0.001 (0.005) Loss 2.9379 (2.8466) Prec@1 30.000 (32.581) Prec@5 58.125 (62.835) Epoch: [2][740/11272] Time 0.776 (0.835) Data 0.002 (0.005) Loss 2.6869 (2.8447) Prec@1 35.000 (32.617) Prec@5 65.625 (62.864) Epoch: [2][750/11272] Time 0.862 (0.835) Data 0.001 (0.005) Loss 3.1424 (2.8457) Prec@1 33.125 (32.603) Prec@5 55.000 (62.845) Epoch: [2][760/11272] Time 0.903 (0.835) Data 0.002 (0.005) Loss 2.9298 (2.8460) Prec@1 35.000 (32.606) Prec@5 64.375 (62.838) Epoch: [2][770/11272] Time 0.763 (0.835) Data 0.001 (0.005) Loss 2.9158 (2.8457) Prec@1 31.250 (32.599) Prec@5 60.625 (62.848) Epoch: [2][780/11272] Time 0.768 (0.835) Data 0.002 (0.005) Loss 2.9966 (2.8456) Prec@1 28.750 (32.599) Prec@5 65.000 (62.858) Epoch: [2][790/11272] Time 0.948 (0.835) Data 0.002 (0.005) Loss 2.6806 (2.8456) Prec@1 31.875 (32.599) Prec@5 67.500 (62.859) Epoch: [2][800/11272] Time 0.867 (0.835) Data 0.002 (0.004) Loss 2.6065 (2.8464) Prec@1 36.250 (32.574) Prec@5 67.500 (62.836) Epoch: [2][810/11272] Time 0.740 (0.835) Data 0.001 (0.004) Loss 2.8084 (2.8455) Prec@1 32.500 (32.593) Prec@5 61.250 (62.848) Epoch: [2][820/11272] Time 0.872 (0.835) Data 0.002 (0.004) Loss 2.5966 (2.8450) Prec@1 37.500 (32.599) Prec@5 63.750 (62.840) Epoch: [2][830/11272] Time 0.942 (0.835) Data 0.001 (0.004) Loss 2.8661 (2.8449) Prec@1 33.125 (32.608) Prec@5 59.375 (62.832) Epoch: [2][840/11272] Time 0.775 (0.835) Data 0.002 (0.004) Loss 2.9223 (2.8446) Prec@1 33.750 (32.596) Prec@5 55.000 (62.849) Epoch: [2][850/11272] Time 0.728 (0.835) Data 0.001 (0.004) Loss 2.8564 (2.8441) Prec@1 34.375 (32.601) Prec@5 60.000 (62.857) Epoch: [2][860/11272] Time 0.894 (0.835) Data 0.002 (0.004) Loss 2.7571 (2.8441) Prec@1 35.625 (32.597) Prec@5 65.000 (62.857) Epoch: [2][870/11272] Time 0.894 (0.835) Data 0.002 (0.004) Loss 2.6832 (2.8433) Prec@1 33.750 (32.618) Prec@5 61.875 (62.877) Epoch: [2][880/11272] Time 0.743 (0.835) Data 0.001 (0.004) Loss 2.9716 (2.8428) Prec@1 30.000 (32.605) Prec@5 56.250 (62.886) Epoch: [2][890/11272] Time 0.748 (0.835) Data 0.001 (0.004) Loss 2.6969 (2.8425) Prec@1 35.000 (32.619) Prec@5 64.375 (62.895) Epoch: [2][900/11272] Time 0.864 (0.835) Data 0.001 (0.004) Loss 2.8011 (2.8427) Prec@1 26.875 (32.613) Prec@5 65.625 (62.901) Epoch: [2][910/11272] Time 0.906 (0.834) Data 0.002 (0.004) Loss 2.7407 (2.8430) Prec@1 33.750 (32.609) Prec@5 62.500 (62.898) Epoch: [2][920/11272] Time 0.783 (0.834) Data 0.002 (0.004) Loss 2.9292 (2.8432) Prec@1 26.250 (32.598) Prec@5 62.500 (62.884) Epoch: [2][930/11272] Time 0.773 (0.834) Data 0.002 (0.004) Loss 2.6323 (2.8435) Prec@1 36.875 (32.588) Prec@5 63.750 (62.869) Epoch: [2][940/11272] Time 0.948 (0.834) Data 0.002 (0.004) Loss 2.6540 (2.8431) Prec@1 36.875 (32.602) Prec@5 68.125 (62.883) Epoch: [2][950/11272] Time 0.739 (0.834) Data 0.001 (0.004) Loss 2.9530 (2.8431) Prec@1 28.125 (32.603) Prec@5 61.250 (62.871) Epoch: [2][960/11272] Time 0.738 (0.834) Data 0.002 (0.004) Loss 2.4386 (2.8431) Prec@1 40.625 (32.619) Prec@5 71.250 (62.871) Epoch: [2][970/11272] Time 0.924 (0.834) Data 0.002 (0.004) Loss 2.7416 (2.8427) Prec@1 37.500 (32.620) Prec@5 65.625 (62.866) Epoch: [2][980/11272] Time 0.880 (0.834) Data 0.002 (0.004) Loss 2.7296 (2.8433) Prec@1 38.750 (32.622) Prec@5 65.000 (62.852) Epoch: [2][990/11272] Time 0.766 (0.834) Data 0.002 (0.004) Loss 2.8518 (2.8421) Prec@1 31.250 (32.626) Prec@5 62.500 (62.870) Epoch: [2][1000/11272] Time 0.734 (0.834) Data 0.002 (0.004) Loss 2.7703 (2.8418) Prec@1 34.375 (32.649) Prec@5 63.750 (62.875) Epoch: [2][1010/11272] Time 0.874 (0.834) Data 0.002 (0.004) Loss 2.9787 (2.8420) Prec@1 23.750 (32.644) Prec@5 58.750 (62.857) Epoch: [2][1020/11272] Time 0.932 (0.834) Data 0.002 (0.004) Loss 2.9242 (2.8417) Prec@1 34.375 (32.668) Prec@5 59.375 (62.864) Epoch: [2][1030/11272] Time 0.814 (0.834) Data 0.002 (0.004) Loss 2.9030 (2.8414) Prec@1 34.375 (32.685) Prec@5 62.500 (62.865) Epoch: [2][1040/11272] Time 0.776 (0.834) Data 0.003 (0.004) Loss 2.8819 (2.8412) Prec@1 32.500 (32.693) Prec@5 63.750 (62.878) Epoch: [2][1050/11272] Time 0.951 (0.834) Data 0.002 (0.004) Loss 2.9288 (2.8407) Prec@1 32.500 (32.699) Prec@5 62.500 (62.881) Epoch: [2][1060/11272] Time 0.941 (0.834) Data 0.002 (0.004) Loss 2.8490 (2.8398) Prec@1 35.000 (32.713) Prec@5 63.125 (62.900) Epoch: [2][1070/11272] Time 0.776 (0.834) Data 0.001 (0.004) Loss 2.6394 (2.8400) Prec@1 35.000 (32.721) Prec@5 64.375 (62.892) Epoch: [2][1080/11272] Time 0.933 (0.834) Data 0.002 (0.004) Loss 2.7403 (2.8399) Prec@1 33.750 (32.715) Prec@5 70.000 (62.893) Epoch: [2][1090/11272] Time 0.885 (0.834) Data 0.001 (0.004) Loss 3.0240 (2.8406) Prec@1 30.625 (32.708) Prec@5 60.625 (62.873) Epoch: [2][1100/11272] Time 0.754 (0.834) Data 0.002 (0.004) Loss 3.1845 (2.8407) Prec@1 30.000 (32.718) Prec@5 52.500 (62.863) Epoch: [2][1110/11272] Time 0.740 (0.834) Data 0.001 (0.004) Loss 2.8626 (2.8410) Prec@1 31.875 (32.712) Prec@5 60.000 (62.863) Epoch: [2][1120/11272] Time 0.912 (0.833) Data 0.002 (0.004) Loss 2.7050 (2.8406) Prec@1 36.250 (32.734) Prec@5 66.250 (62.874) Epoch: [2][1130/11272] Time 0.933 (0.833) Data 0.002 (0.004) Loss 2.9367 (2.8409) Prec@1 35.625 (32.738) Prec@5 58.750 (62.871) Epoch: [2][1140/11272] Time 0.773 (0.833) Data 0.002 (0.004) Loss 2.9722 (2.8405) Prec@1 30.625 (32.738) Prec@5 55.000 (62.891) Epoch: [2][1150/11272] Time 0.740 (0.833) Data 0.001 (0.004) Loss 2.6566 (2.8398) Prec@1 35.000 (32.745) Prec@5 63.125 (62.902) Epoch: [2][1160/11272] Time 0.894 (0.833) Data 0.002 (0.004) Loss 2.6680 (2.8396) Prec@1 33.750 (32.745) Prec@5 67.500 (62.901) Epoch: [2][1170/11272] Time 0.884 (0.833) Data 0.001 (0.004) Loss 2.9012 (2.8395) Prec@1 36.875 (32.752) Prec@5 64.375 (62.901) Epoch: [2][1180/11272] Time 0.754 (0.833) Data 0.002 (0.004) Loss 2.8959 (2.8397) Prec@1 31.875 (32.749) Prec@5 61.250 (62.897) Epoch: [2][1190/11272] Time 0.760 (0.833) Data 0.001 (0.004) Loss 3.0091 (2.8403) Prec@1 31.875 (32.740) Prec@5 57.500 (62.882) Epoch: [2][1200/11272] Time 0.932 (0.833) Data 0.002 (0.004) Loss 2.6401 (2.8403) Prec@1 37.500 (32.738) Prec@5 68.125 (62.887) Epoch: [2][1210/11272] Time 0.761 (0.833) Data 0.003 (0.004) Loss 2.8055 (2.8400) Prec@1 35.000 (32.738) Prec@5 64.375 (62.891) Epoch: [2][1220/11272] Time 0.755 (0.833) Data 0.002 (0.004) Loss 2.8089 (2.8402) Prec@1 33.125 (32.744) Prec@5 65.000 (62.886) Epoch: [2][1230/11272] Time 0.983 (0.834) Data 0.001 (0.003) Loss 2.9346 (2.8407) Prec@1 33.125 (32.735) Prec@5 62.500 (62.870) Epoch: [2][1240/11272] Time 0.961 (0.834) Data 0.002 (0.003) Loss 3.0068 (2.8406) Prec@1 28.125 (32.738) Prec@5 61.875 (62.872) Epoch: [2][1250/11272] Time 0.751 (0.833) Data 0.001 (0.003) Loss 2.6994 (2.8407) Prec@1 32.500 (32.728) Prec@5 66.875 (62.867) Epoch: [2][1260/11272] Time 0.789 (0.833) Data 0.002 (0.003) Loss 2.9452 (2.8410) Prec@1 28.125 (32.732) Prec@5 62.500 (62.860) Epoch: [2][1270/11272] Time 0.941 (0.833) Data 0.002 (0.003) Loss 2.9058 (2.8411) Prec@1 31.250 (32.736) Prec@5 61.875 (62.860) Epoch: [2][1280/11272] Time 0.897 (0.833) Data 0.002 (0.003) Loss 2.7506 (2.8406) Prec@1 36.250 (32.733) Prec@5 64.375 (62.869) Epoch: [2][1290/11272] Time 0.733 (0.833) Data 0.002 (0.003) Loss 2.7090 (2.8404) Prec@1 36.250 (32.740) Prec@5 63.125 (62.873) Epoch: [2][1300/11272] Time 0.749 (0.833) Data 0.001 (0.003) Loss 2.7669 (2.8406) Prec@1 33.750 (32.733) Prec@5 64.375 (62.867) Epoch: [2][1310/11272] Time 0.973 (0.833) Data 0.002 (0.003) Loss 2.9598 (2.8412) Prec@1 31.250 (32.725) Prec@5 61.250 (62.858) Epoch: [2][1320/11272] Time 0.871 (0.833) Data 0.001 (0.003) Loss 3.1611 (2.8412) Prec@1 25.625 (32.721) Prec@5 55.000 (62.854) Epoch: [2][1330/11272] Time 0.764 (0.833) Data 0.002 (0.003) Loss 2.9860 (2.8412) Prec@1 33.125 (32.724) Prec@5 61.875 (62.852) Epoch: [2][1340/11272] Time 0.854 (0.833) Data 0.002 (0.003) Loss 2.9605 (2.8424) Prec@1 28.125 (32.713) Prec@5 61.875 (62.827) Epoch: [2][1350/11272] Time 0.950 (0.833) Data 0.002 (0.003) Loss 2.8290 (2.8419) Prec@1 31.250 (32.713) Prec@5 58.750 (62.822) Epoch: [2][1360/11272] Time 0.764 (0.833) Data 0.002 (0.003) Loss 2.6900 (2.8418) Prec@1 36.875 (32.717) Prec@5 63.125 (62.821) Epoch: [2][1370/11272] Time 0.774 (0.833) Data 0.002 (0.003) Loss 3.0080 (2.8413) Prec@1 30.000 (32.736) Prec@5 58.750 (62.829) Epoch: [2][1380/11272] Time 0.892 (0.833) Data 0.002 (0.003) Loss 2.6243 (2.8418) Prec@1 35.625 (32.736) Prec@5 68.125 (62.813) Epoch: [2][1390/11272] Time 0.868 (0.833) Data 0.002 (0.003) Loss 3.0105 (2.8420) Prec@1 28.750 (32.736) Prec@5 59.375 (62.811) Epoch: [2][1400/11272] Time 0.731 (0.833) Data 0.002 (0.003) Loss 3.0412 (2.8424) Prec@1 31.875 (32.735) Prec@5 58.750 (62.804) Epoch: [2][1410/11272] Time 0.753 (0.833) Data 0.002 (0.003) Loss 2.7596 (2.8427) Prec@1 34.375 (32.729) Prec@5 65.625 (62.799) Epoch: [2][1420/11272] Time 0.934 (0.833) Data 0.002 (0.003) Loss 3.2095 (2.8433) Prec@1 30.000 (32.725) Prec@5 60.000 (62.785) Epoch: [2][1430/11272] Time 0.880 (0.833) Data 0.002 (0.003) Loss 2.8693 (2.8430) Prec@1 33.750 (32.733) Prec@5 65.000 (62.787) Epoch: [2][1440/11272] Time 0.748 (0.833) Data 0.002 (0.003) Loss 2.9502 (2.8424) Prec@1 34.375 (32.749) Prec@5 59.375 (62.790) Epoch: [2][1450/11272] Time 0.775 (0.833) Data 0.002 (0.003) Loss 2.8540 (2.8416) Prec@1 28.750 (32.764) Prec@5 63.125 (62.808) Epoch: [2][1460/11272] Time 0.851 (0.833) Data 0.002 (0.003) Loss 2.8148 (2.8415) Prec@1 31.250 (32.770) Prec@5 63.750 (62.813) Epoch: [2][1470/11272] Time 0.794 (0.833) Data 0.004 (0.003) Loss 2.9594 (2.8415) Prec@1 29.375 (32.765) Prec@5 60.000 (62.813) Epoch: [2][1480/11272] Time 0.733 (0.833) Data 0.001 (0.003) Loss 2.7358 (2.8413) Prec@1 32.500 (32.766) Prec@5 66.875 (62.815) Epoch: [2][1490/11272] Time 0.900 (0.833) Data 0.002 (0.003) Loss 2.7760 (2.8411) Prec@1 31.875 (32.762) Prec@5 63.750 (62.819) Epoch: [2][1500/11272] Time 0.918 (0.833) Data 0.002 (0.003) Loss 2.9939 (2.8417) Prec@1 28.125 (32.755) Prec@5 65.625 (62.814) Epoch: [2][1510/11272] Time 0.763 (0.833) Data 0.002 (0.003) Loss 2.7336 (2.8415) Prec@1 34.375 (32.756) Prec@5 65.000 (62.822) Epoch: [2][1520/11272] Time 0.808 (0.833) Data 0.002 (0.003) Loss 2.8967 (2.8414) Prec@1 35.625 (32.772) Prec@5 63.750 (62.826) Epoch: [2][1530/11272] Time 0.904 (0.833) Data 0.002 (0.003) Loss 2.9577 (2.8414) Prec@1 30.625 (32.767) Prec@5 57.500 (62.824) Epoch: [2][1540/11272] Time 0.905 (0.833) Data 0.001 (0.003) Loss 2.9427 (2.8412) Prec@1 29.375 (32.770) Prec@5 61.250 (62.828) Epoch: [2][1550/11272] Time 0.767 (0.833) Data 0.002 (0.003) Loss 2.6881 (2.8411) Prec@1 35.000 (32.771) Prec@5 66.875 (62.837) Epoch: [2][1560/11272] Time 0.759 (0.833) Data 0.002 (0.003) Loss 2.8989 (2.8415) Prec@1 31.250 (32.754) Prec@5 58.125 (62.825) Epoch: [2][1570/11272] Time 0.917 (0.833) Data 0.003 (0.003) Loss 3.0274 (2.8414) Prec@1 29.375 (32.755) Prec@5 58.750 (62.843) Epoch: [2][1580/11272] Time 0.912 (0.833) Data 0.002 (0.003) Loss 2.8707 (2.8418) Prec@1 34.375 (32.754) Prec@5 57.500 (62.831) Epoch: [2][1590/11272] Time 0.745 (0.833) Data 0.002 (0.003) Loss 2.8974 (2.8412) Prec@1 33.125 (32.765) Prec@5 61.250 (62.837) Epoch: [2][1600/11272] Time 0.875 (0.833) Data 0.002 (0.003) Loss 2.7733 (2.8413) Prec@1 34.375 (32.768) Prec@5 65.000 (62.840) Epoch: [2][1610/11272] Time 0.864 (0.833) Data 0.002 (0.003) Loss 2.6241 (2.8411) Prec@1 37.500 (32.778) Prec@5 66.250 (62.844) Epoch: [2][1620/11272] Time 0.719 (0.833) Data 0.001 (0.003) Loss 2.7473 (2.8404) Prec@1 34.375 (32.796) Prec@5 62.500 (62.849) Epoch: [2][1630/11272] Time 0.781 (0.833) Data 0.002 (0.003) Loss 2.7971 (2.8404) Prec@1 36.250 (32.790) Prec@5 65.625 (62.855) Epoch: [2][1640/11272] Time 0.939 (0.833) Data 0.001 (0.003) Loss 2.7050 (2.8405) Prec@1 37.500 (32.786) Prec@5 65.000 (62.851) Epoch: [2][1650/11272] Time 0.912 (0.833) Data 0.002 (0.003) Loss 2.8255 (2.8405) Prec@1 28.750 (32.785) Prec@5 61.250 (62.844) Epoch: [2][1660/11272] Time 0.727 (0.833) Data 0.001 (0.003) Loss 2.8263 (2.8408) Prec@1 37.500 (32.787) Prec@5 61.875 (62.836) Epoch: [2][1670/11272] Time 0.747 (0.833) Data 0.001 (0.003) Loss 2.8347 (2.8410) Prec@1 35.000 (32.791) Prec@5 60.000 (62.825) Epoch: [2][1680/11272] Time 0.916 (0.833) Data 0.001 (0.003) Loss 2.8573 (2.8409) Prec@1 30.625 (32.798) Prec@5 65.625 (62.831) Epoch: [2][1690/11272] Time 0.887 (0.833) Data 0.002 (0.003) Loss 2.6728 (2.8412) Prec@1 37.500 (32.800) Prec@5 64.375 (62.819) Epoch: [2][1700/11272] Time 0.749 (0.833) Data 0.001 (0.003) Loss 2.8102 (2.8409) Prec@1 33.125 (32.804) Prec@5 65.625 (62.829) Epoch: [2][1710/11272] Time 0.806 (0.833) Data 0.002 (0.003) Loss 2.8303 (2.8409) Prec@1 31.875 (32.808) Prec@5 58.125 (62.825) Epoch: [2][1720/11272] Time 0.938 (0.833) Data 0.002 (0.003) Loss 2.9660 (2.8410) Prec@1 31.875 (32.816) Prec@5 61.250 (62.824) Epoch: [2][1730/11272] Time 0.920 (0.833) Data 0.002 (0.003) Loss 2.8745 (2.8407) Prec@1 30.000 (32.822) Prec@5 59.375 (62.825) Epoch: [2][1740/11272] Time 0.739 (0.833) Data 0.001 (0.003) Loss 3.0336 (2.8405) Prec@1 29.375 (32.820) Prec@5 55.000 (62.826) Epoch: [2][1750/11272] Time 0.899 (0.833) Data 0.002 (0.003) Loss 2.7498 (2.8401) Prec@1 35.625 (32.828) Prec@5 63.750 (62.834) Epoch: [2][1760/11272] Time 0.922 (0.833) Data 0.001 (0.003) Loss 3.0381 (2.8404) Prec@1 26.250 (32.819) Prec@5 54.375 (62.831) Epoch: [2][1770/11272] Time 0.772 (0.833) Data 0.002 (0.003) Loss 3.0308 (2.8408) Prec@1 32.500 (32.810) Prec@5 60.625 (62.828) Epoch: [2][1780/11272] Time 0.738 (0.833) Data 0.002 (0.003) Loss 2.6305 (2.8405) Prec@1 38.125 (32.817) Prec@5 69.375 (62.831) Epoch: [2][1790/11272] Time 0.922 (0.833) Data 0.002 (0.003) Loss 2.6048 (2.8399) Prec@1 41.250 (32.817) Prec@5 69.375 (62.847) Epoch: [2][1800/11272] Time 0.952 (0.833) Data 0.002 (0.003) Loss 2.8708 (2.8398) Prec@1 33.125 (32.820) Prec@5 61.875 (62.851) Epoch: [2][1810/11272] Time 0.766 (0.833) Data 0.002 (0.003) Loss 2.7639 (2.8398) Prec@1 36.875 (32.810) Prec@5 63.125 (62.853) Epoch: [2][1820/11272] Time 0.761 (0.833) Data 0.002 (0.003) Loss 2.8471 (2.8392) Prec@1 33.125 (32.816) Prec@5 65.625 (62.868) Epoch: [2][1830/11272] Time 0.910 (0.833) Data 0.001 (0.003) Loss 2.9055 (2.8388) Prec@1 28.125 (32.816) Prec@5 61.250 (62.873) Epoch: [2][1840/11272] Time 0.953 (0.833) Data 0.005 (0.003) Loss 2.6386 (2.8386) Prec@1 34.375 (32.818) Prec@5 65.000 (62.877) Epoch: [2][1850/11272] Time 0.733 (0.833) Data 0.001 (0.003) Loss 2.6878 (2.8385) Prec@1 34.375 (32.824) Prec@5 66.875 (62.880) Epoch: [2][1860/11272] Time 0.782 (0.833) Data 0.002 (0.003) Loss 2.7537 (2.8383) Prec@1 30.000 (32.823) Prec@5 65.625 (62.884) Epoch: [2][1870/11272] Time 0.887 (0.833) Data 0.002 (0.003) Loss 2.7934 (2.8383) Prec@1 35.625 (32.830) Prec@5 65.625 (62.883) Epoch: [2][1880/11272] Time 0.741 (0.833) Data 0.001 (0.003) Loss 2.6588 (2.8380) Prec@1 36.250 (32.835) Prec@5 61.875 (62.885) Epoch: [2][1890/11272] Time 0.745 (0.833) Data 0.001 (0.003) Loss 2.8588 (2.8382) Prec@1 32.500 (32.828) Prec@5 59.375 (62.879) Epoch: [2][1900/11272] Time 0.894 (0.833) Data 0.001 (0.003) Loss 3.1249 (2.8382) Prec@1 28.750 (32.833) Prec@5 55.625 (62.880) Epoch: [2][1910/11272] Time 0.891 (0.833) Data 0.002 (0.003) Loss 2.9386 (2.8377) Prec@1 28.750 (32.838) Prec@5 59.375 (62.892) Epoch: [2][1920/11272] Time 0.737 (0.833) Data 0.001 (0.003) Loss 2.7400 (2.8376) Prec@1 35.625 (32.840) Prec@5 61.250 (62.891) Epoch: [2][1930/11272] Time 0.727 (0.833) Data 0.002 (0.003) Loss 2.7608 (2.8375) Prec@1 37.500 (32.848) Prec@5 64.375 (62.894) Epoch: [2][1940/11272] Time 0.985 (0.833) Data 0.001 (0.003) Loss 2.5930 (2.8370) Prec@1 40.000 (32.858) Prec@5 71.250 (62.896) Epoch: [2][1950/11272] Time 0.934 (0.833) Data 0.001 (0.003) Loss 2.9360 (2.8369) Prec@1 29.375 (32.856) Prec@5 60.000 (62.896) Epoch: [2][1960/11272] Time 0.717 (0.833) Data 0.001 (0.003) Loss 2.8330 (2.8371) Prec@1 33.750 (32.847) Prec@5 60.000 (62.887) Epoch: [2][1970/11272] Time 0.726 (0.833) Data 0.001 (0.003) Loss 2.7749 (2.8371) Prec@1 35.000 (32.850) Prec@5 66.875 (62.890) Epoch: [2][1980/11272] Time 0.936 (0.833) Data 0.001 (0.003) Loss 2.7172 (2.8370) Prec@1 36.250 (32.851) Prec@5 65.000 (62.893) Epoch: [2][1990/11272] Time 0.863 (0.833) Data 0.002 (0.003) Loss 2.4672 (2.8368) Prec@1 35.000 (32.855) Prec@5 67.500 (62.900) Epoch: [2][2000/11272] Time 0.735 (0.833) Data 0.001 (0.003) Loss 2.9677 (2.8368) Prec@1 30.000 (32.856) Prec@5 63.125 (62.907) Epoch: [2][2010/11272] Time 0.887 (0.833) Data 0.002 (0.003) Loss 2.8003 (2.8367) Prec@1 38.125 (32.858) Prec@5 62.500 (62.914) Epoch: [2][2020/11272] Time 0.911 (0.833) Data 0.001 (0.003) Loss 3.0069 (2.8367) Prec@1 30.000 (32.862) Prec@5 61.250 (62.913) Epoch: [2][2030/11272] Time 0.777 (0.833) Data 0.002 (0.003) Loss 2.6390 (2.8362) Prec@1 27.500 (32.869) Prec@5 68.125 (62.923) Epoch: [2][2040/11272] Time 0.824 (0.833) Data 0.001 (0.003) Loss 3.0277 (2.8363) Prec@1 30.625 (32.869) Prec@5 59.375 (62.922) Epoch: [2][2050/11272] Time 0.976 (0.833) Data 0.002 (0.003) Loss 2.9329 (2.8364) Prec@1 32.500 (32.873) Prec@5 58.125 (62.918) Epoch: [2][2060/11272] Time 0.946 (0.833) Data 0.001 (0.003) Loss 2.7849 (2.8364) Prec@1 28.750 (32.873) Prec@5 66.250 (62.918) Epoch: [2][2070/11272] Time 0.751 (0.833) Data 0.002 (0.003) Loss 2.6792 (2.8363) Prec@1 35.625 (32.872) Prec@5 58.125 (62.916) Epoch: [2][2080/11272] Time 0.750 (0.833) Data 0.001 (0.003) Loss 2.5635 (2.8360) Prec@1 36.250 (32.881) Prec@5 64.375 (62.917) Epoch: [2][2090/11272] Time 0.974 (0.833) Data 0.002 (0.003) Loss 2.9889 (2.8361) Prec@1 26.250 (32.880) Prec@5 60.000 (62.923) Epoch: [2][2100/11272] Time 0.907 (0.833) Data 0.002 (0.003) Loss 3.0136 (2.8358) Prec@1 35.625 (32.886) Prec@5 57.500 (62.929) Epoch: [2][2110/11272] Time 0.742 (0.833) Data 0.001 (0.003) Loss 3.1937 (2.8359) Prec@1 27.500 (32.886) Prec@5 54.375 (62.924) Epoch: [2][2120/11272] Time 0.775 (0.833) Data 0.002 (0.003) Loss 2.9000 (2.8359) Prec@1 33.125 (32.884) Prec@5 60.625 (62.919) Epoch: [2][2130/11272] Time 0.913 (0.833) Data 0.002 (0.003) Loss 2.7221 (2.8361) Prec@1 30.000 (32.881) Prec@5 65.000 (62.919) Epoch: [2][2140/11272] Time 0.816 (0.833) Data 0.004 (0.003) Loss 2.6484 (2.8354) Prec@1 35.000 (32.891) Prec@5 71.250 (62.928) Epoch: [2][2150/11272] Time 0.764 (0.833) Data 0.001 (0.003) Loss 2.9089 (2.8350) Prec@1 33.750 (32.900) Prec@5 61.875 (62.934) Epoch: [2][2160/11272] Time 0.915 (0.833) Data 0.002 (0.003) Loss 2.7624 (2.8344) Prec@1 33.750 (32.912) Prec@5 66.250 (62.949) Epoch: [2][2170/11272] Time 0.870 (0.833) Data 0.002 (0.003) Loss 2.9149 (2.8348) Prec@1 31.250 (32.911) Prec@5 61.250 (62.941) Epoch: [2][2180/11272] Time 0.773 (0.833) Data 0.001 (0.003) Loss 2.9552 (2.8349) Prec@1 27.500 (32.900) Prec@5 65.625 (62.939) Epoch: [2][2190/11272] Time 0.789 (0.833) Data 0.002 (0.003) Loss 2.8053 (2.8347) Prec@1 37.500 (32.899) Prec@5 66.250 (62.948) Epoch: [2][2200/11272] Time 0.896 (0.833) Data 0.001 (0.003) Loss 2.8092 (2.8345) Prec@1 33.750 (32.906) Prec@5 63.750 (62.953) Epoch: [2][2210/11272] Time 0.944 (0.833) Data 0.002 (0.003) Loss 2.8544 (2.8346) Prec@1 34.375 (32.907) Prec@5 61.250 (62.952) Epoch: [2][2220/11272] Time 0.768 (0.833) Data 0.002 (0.003) Loss 2.4751 (2.8347) Prec@1 35.000 (32.904) Prec@5 74.375 (62.950) Epoch: [2][2230/11272] Time 0.739 (0.833) Data 0.001 (0.003) Loss 2.7996 (2.8347) Prec@1 34.375 (32.905) Prec@5 68.125 (62.957) Epoch: [2][2240/11272] Time 0.914 (0.833) Data 0.001 (0.003) Loss 2.7934 (2.8349) Prec@1 33.750 (32.894) Prec@5 63.750 (62.946) Epoch: [2][2250/11272] Time 0.900 (0.833) Data 0.002 (0.003) Loss 2.9000 (2.8351) Prec@1 29.375 (32.890) Prec@5 57.500 (62.938) Epoch: [2][2260/11272] Time 0.803 (0.833) Data 0.002 (0.003) Loss 2.6547 (2.8351) Prec@1 30.625 (32.884) Prec@5 71.875 (62.937) Epoch: [2][2270/11272] Time 0.849 (0.833) Data 0.002 (0.003) Loss 2.8747 (2.8354) Prec@1 31.875 (32.879) Prec@5 65.000 (62.931) Epoch: [2][2280/11272] Time 0.883 (0.833) Data 0.002 (0.003) Loss 2.7509 (2.8353) Prec@1 33.750 (32.877) Prec@5 63.750 (62.935) Epoch: [2][2290/11272] Time 0.714 (0.833) Data 0.002 (0.003) Loss 2.8480 (2.8354) Prec@1 35.000 (32.868) Prec@5 62.500 (62.932) Epoch: [2][2300/11272] Time 0.748 (0.833) Data 0.002 (0.003) Loss 2.6991 (2.8354) Prec@1 36.250 (32.863) Prec@5 63.750 (62.934) Epoch: [2][2310/11272] Time 0.903 (0.833) Data 0.002 (0.003) Loss 2.7416 (2.8352) Prec@1 31.250 (32.859) Prec@5 60.000 (62.939) Epoch: [2][2320/11272] Time 0.920 (0.833) Data 0.001 (0.003) Loss 2.9213 (2.8353) Prec@1 34.375 (32.869) Prec@5 63.125 (62.940) Epoch: [2][2330/11272] Time 0.735 (0.833) Data 0.002 (0.003) Loss 2.9530 (2.8354) Prec@1 31.250 (32.865) Prec@5 60.000 (62.937) Epoch: [2][2340/11272] Time 0.768 (0.833) Data 0.001 (0.003) Loss 2.8555 (2.8353) Prec@1 33.750 (32.866) Prec@5 62.500 (62.943) Epoch: [2][2350/11272] Time 0.941 (0.833) Data 0.002 (0.003) Loss 2.9639 (2.8354) Prec@1 34.375 (32.866) Prec@5 60.000 (62.937) Epoch: [2][2360/11272] Time 0.921 (0.833) Data 0.002 (0.003) Loss 2.9305 (2.8357) Prec@1 34.375 (32.865) Prec@5 63.125 (62.930) Epoch: [2][2370/11272] Time 0.762 (0.833) Data 0.002 (0.003) Loss 2.7837 (2.8356) Prec@1 31.875 (32.861) Prec@5 64.375 (62.937) Epoch: [2][2380/11272] Time 0.746 (0.833) Data 0.001 (0.003) Loss 2.5469 (2.8356) Prec@1 38.750 (32.858) Prec@5 70.625 (62.934) Epoch: [2][2390/11272] Time 0.933 (0.833) Data 0.002 (0.003) Loss 2.7602 (2.8357) Prec@1 38.750 (32.858) Prec@5 62.500 (62.930) Epoch: [2][2400/11272] Time 0.756 (0.833) Data 0.003 (0.003) Loss 2.8273 (2.8353) Prec@1 36.875 (32.865) Prec@5 61.250 (62.932) Epoch: [2][2410/11272] Time 0.730 (0.833) Data 0.002 (0.003) Loss 2.8581 (2.8350) Prec@1 32.500 (32.869) Prec@5 62.500 (62.937) Epoch: [2][2420/11272] Time 0.898 (0.833) Data 0.001 (0.003) Loss 2.6793 (2.8351) Prec@1 31.250 (32.867) Prec@5 65.000 (62.937) Epoch: [2][2430/11272] Time 0.877 (0.833) Data 0.002 (0.003) Loss 2.6802 (2.8350) Prec@1 35.000 (32.868) Prec@5 66.875 (62.946) Epoch: [2][2440/11272] Time 0.741 (0.833) Data 0.002 (0.003) Loss 2.8195 (2.8352) Prec@1 36.875 (32.869) Prec@5 61.250 (62.940) Epoch: [2][2450/11272] Time 0.756 (0.833) Data 0.002 (0.003) Loss 2.8179 (2.8351) Prec@1 33.125 (32.869) Prec@5 60.625 (62.941) Epoch: [2][2460/11272] Time 0.972 (0.833) Data 0.001 (0.003) Loss 2.8209 (2.8350) Prec@1 31.875 (32.871) Prec@5 61.250 (62.940) Epoch: [2][2470/11272] Time 0.946 (0.833) Data 0.002 (0.003) Loss 2.8360 (2.8348) Prec@1 38.125 (32.880) Prec@5 59.375 (62.943) Epoch: [2][2480/11272] Time 0.765 (0.833) Data 0.001 (0.003) Loss 3.0177 (2.8349) Prec@1 31.250 (32.883) Prec@5 59.375 (62.940) Epoch: [2][2490/11272] Time 0.728 (0.833) Data 0.001 (0.003) Loss 2.6632 (2.8348) Prec@1 34.375 (32.886) Prec@5 65.625 (62.942) Epoch: [2][2500/11272] Time 0.826 (0.833) Data 0.001 (0.003) Loss 2.8125 (2.8347) Prec@1 36.250 (32.888) Prec@5 65.000 (62.942) Epoch: [2][2510/11272] Time 0.929 (0.833) Data 0.002 (0.003) Loss 2.8085 (2.8344) Prec@1 34.375 (32.892) Prec@5 61.875 (62.946) Epoch: [2][2520/11272] Time 0.804 (0.833) Data 0.002 (0.003) Loss 2.7433 (2.8342) Prec@1 33.750 (32.897) Prec@5 64.375 (62.950) Epoch: [2][2530/11272] Time 0.812 (0.833) Data 0.001 (0.003) Loss 2.9605 (2.8342) Prec@1 32.500 (32.897) Prec@5 59.375 (62.948) Epoch: [2][2540/11272] Time 0.877 (0.833) Data 0.001 (0.003) Loss 2.7640 (2.8343) Prec@1 37.500 (32.893) Prec@5 63.125 (62.945) Epoch: [2][2550/11272] Time 0.757 (0.833) Data 0.002 (0.003) Loss 2.8833 (2.8344) Prec@1 29.375 (32.891) Prec@5 60.000 (62.942) Epoch: [2][2560/11272] Time 0.718 (0.833) Data 0.001 (0.003) Loss 2.9585 (2.8344) Prec@1 32.500 (32.894) Prec@5 58.125 (62.941) Epoch: [2][2570/11272] Time 0.868 (0.833) Data 0.002 (0.003) Loss 2.8174 (2.8344) Prec@1 31.875 (32.889) Prec@5 64.375 (62.941) Epoch: [2][2580/11272] Time 0.874 (0.834) Data 0.001 (0.003) Loss 3.0961 (2.8345) Prec@1 25.000 (32.882) Prec@5 57.500 (62.936) Epoch: [2][2590/11272] Time 0.777 (0.833) Data 0.002 (0.003) Loss 2.7319 (2.8344) Prec@1 38.125 (32.883) Prec@5 68.750 (62.938) Epoch: [2][2600/11272] Time 0.741 (0.833) Data 0.001 (0.003) Loss 2.6471 (2.8341) Prec@1 37.500 (32.890) Prec@5 68.750 (62.943) Epoch: [2][2610/11272] Time 0.876 (0.833) Data 0.002 (0.003) Loss 2.7583 (2.8339) Prec@1 36.250 (32.888) Prec@5 62.500 (62.939) Epoch: [2][2620/11272] Time 0.872 (0.834) Data 0.001 (0.003) Loss 2.8494 (2.8339) Prec@1 30.625 (32.893) Prec@5 58.750 (62.936) Epoch: [2][2630/11272] Time 0.762 (0.834) Data 0.002 (0.003) Loss 2.9713 (2.8340) Prec@1 29.375 (32.889) Prec@5 61.250 (62.931) Epoch: [2][2640/11272] Time 0.756 (0.834) Data 0.002 (0.003) Loss 2.9720 (2.8338) Prec@1 32.500 (32.885) Prec@5 62.500 (62.937) Epoch: [2][2650/11272] Time 0.879 (0.834) Data 0.002 (0.003) Loss 2.9033 (2.8339) Prec@1 30.625 (32.880) Prec@5 62.500 (62.935) Epoch: [2][2660/11272] Time 0.871 (0.834) Data 0.002 (0.003) Loss 3.0112 (2.8340) Prec@1 33.125 (32.884) Prec@5 62.500 (62.934) Epoch: [2][2670/11272] Time 0.761 (0.834) Data 0.002 (0.003) Loss 2.6477 (2.8340) Prec@1 40.625 (32.888) Prec@5 68.125 (62.935) Epoch: [2][2680/11272] Time 0.898 (0.834) Data 0.002 (0.003) Loss 2.9592 (2.8340) Prec@1 29.375 (32.891) Prec@5 61.875 (62.934) Epoch: [2][2690/11272] Time 0.996 (0.834) Data 0.002 (0.002) Loss 2.7025 (2.8337) Prec@1 33.750 (32.894) Prec@5 68.125 (62.940) Epoch: [2][2700/11272] Time 0.746 (0.834) Data 0.001 (0.002) Loss 3.1046 (2.8337) Prec@1 33.750 (32.896) Prec@5 57.500 (62.938) Epoch: [2][2710/11272] Time 0.752 (0.834) Data 0.002 (0.002) Loss 2.5154 (2.8333) Prec@1 40.625 (32.901) Prec@5 65.000 (62.947) Epoch: [2][2720/11272] Time 0.901 (0.834) Data 0.001 (0.002) Loss 2.8234 (2.8333) Prec@1 27.500 (32.892) Prec@5 62.500 (62.945) Epoch: [2][2730/11272] Time 0.882 (0.834) Data 0.002 (0.002) Loss 2.7599 (2.8332) Prec@1 31.250 (32.895) Prec@5 67.500 (62.954) Epoch: [2][2740/11272] Time 0.761 (0.834) Data 0.002 (0.002) Loss 2.5092 (2.8330) Prec@1 41.250 (32.899) Prec@5 68.750 (62.954) Epoch: [2][2750/11272] Time 0.731 (0.834) Data 0.002 (0.002) Loss 2.9333 (2.8331) Prec@1 34.375 (32.899) Prec@5 59.375 (62.954) Epoch: [2][2760/11272] Time 0.923 (0.834) Data 0.001 (0.002) Loss 2.8664 (2.8329) Prec@1 33.125 (32.900) Prec@5 65.625 (62.955) Epoch: [2][2770/11272] Time 0.927 (0.834) Data 0.002 (0.002) Loss 2.7877 (2.8327) Prec@1 31.875 (32.899) Prec@5 65.625 (62.961) Epoch: [2][2780/11272] Time 0.737 (0.834) Data 0.001 (0.002) Loss 2.8399 (2.8327) Prec@1 36.250 (32.901) Prec@5 60.625 (62.960) Epoch: [2][2790/11272] Time 0.779 (0.834) Data 0.002 (0.002) Loss 2.6964 (2.8325) Prec@1 33.125 (32.903) Prec@5 66.250 (62.964) Epoch: [2][2800/11272] Time 0.928 (0.834) Data 0.002 (0.002) Loss 2.7828 (2.8323) Prec@1 35.000 (32.904) Prec@5 67.500 (62.967) Epoch: [2][2810/11272] Time 0.781 (0.834) Data 0.001 (0.002) Loss 2.5633 (2.8323) Prec@1 37.500 (32.902) Prec@5 70.000 (62.971) Epoch: [2][2820/11272] Time 0.734 (0.834) Data 0.001 (0.002) Loss 3.0117 (2.8324) Prec@1 31.250 (32.897) Prec@5 57.500 (62.969) Epoch: [2][2830/11272] Time 0.942 (0.834) Data 0.002 (0.002) Loss 2.5874 (2.8322) Prec@1 36.875 (32.901) Prec@5 66.875 (62.971) Epoch: [2][2840/11272] Time 0.929 (0.834) Data 0.002 (0.002) Loss 2.8684 (2.8324) Prec@1 33.125 (32.904) Prec@5 64.375 (62.971) Epoch: [2][2850/11272] Time 0.756 (0.834) Data 0.002 (0.002) Loss 2.8838 (2.8324) Prec@1 27.500 (32.905) Prec@5 60.000 (62.970) Epoch: [2][2860/11272] Time 0.771 (0.834) Data 0.002 (0.002) Loss 3.0625 (2.8322) Prec@1 28.125 (32.911) Prec@5 61.250 (62.974) Epoch: [2][2870/11272] Time 0.914 (0.834) Data 0.002 (0.002) Loss 2.6814 (2.8321) Prec@1 35.000 (32.905) Prec@5 67.500 (62.976) Epoch: [2][2880/11272] Time 0.903 (0.834) Data 0.002 (0.002) Loss 2.7760 (2.8322) Prec@1 28.125 (32.903) Prec@5 62.500 (62.975) Epoch: [2][2890/11272] Time 0.735 (0.834) Data 0.002 (0.002) Loss 2.9510 (2.8325) Prec@1 36.875 (32.896) Prec@5 61.875 (62.973) Epoch: [2][2900/11272] Time 0.750 (0.834) Data 0.001 (0.002) Loss 2.9712 (2.8323) Prec@1 27.500 (32.898) Prec@5 55.625 (62.980) Epoch: [2][2910/11272] Time 0.975 (0.834) Data 0.002 (0.002) Loss 2.8446 (2.8320) Prec@1 29.375 (32.905) Prec@5 60.000 (62.983) Epoch: [2][2920/11272] Time 0.894 (0.834) Data 0.002 (0.002) Loss 3.0766 (2.8319) Prec@1 24.375 (32.897) Prec@5 59.375 (62.988) Epoch: [2][2930/11272] Time 0.750 (0.834) Data 0.002 (0.002) Loss 2.7594 (2.8316) Prec@1 33.125 (32.899) Prec@5 65.625 (62.992) Epoch: [2][2940/11272] Time 0.982 (0.834) Data 0.001 (0.002) Loss 3.0399 (2.8315) Prec@1 31.250 (32.899) Prec@5 58.750 (62.990) Epoch: [2][2950/11272] Time 0.894 (0.834) Data 0.002 (0.002) Loss 2.8445 (2.8314) Prec@1 31.250 (32.904) Prec@5 57.500 (62.992) Epoch: [2][2960/11272] Time 0.791 (0.834) Data 0.002 (0.002) Loss 2.8754 (2.8313) Prec@1 30.000 (32.908) Prec@5 60.625 (62.993) Epoch: [2][2970/11272] Time 0.750 (0.834) Data 0.002 (0.002) Loss 2.6710 (2.8311) Prec@1 38.750 (32.912) Prec@5 67.500 (62.997) Epoch: [2][2980/11272] Time 0.877 (0.834) Data 0.002 (0.002) Loss 2.6375 (2.8311) Prec@1 35.625 (32.914) Prec@5 70.000 (62.995) Epoch: [2][2990/11272] Time 0.852 (0.834) Data 0.001 (0.002) Loss 2.8939 (2.8308) Prec@1 32.500 (32.920) Prec@5 59.375 (62.998) Epoch: [2][3000/11272] Time 0.755 (0.834) Data 0.001 (0.002) Loss 2.6868 (2.8310) Prec@1 31.875 (32.920) Prec@5 66.875 (62.993) Epoch: [2][3010/11272] Time 0.773 (0.834) Data 0.004 (0.002) Loss 2.6424 (2.8309) Prec@1 35.625 (32.917) Prec@5 68.750 (62.993) Epoch: [2][3020/11272] Time 0.889 (0.834) Data 0.002 (0.002) Loss 3.0059 (2.8308) Prec@1 29.375 (32.922) Prec@5 55.625 (62.990) Epoch: [2][3030/11272] Time 0.927 (0.834) Data 0.002 (0.002) Loss 3.1874 (2.8311) Prec@1 28.750 (32.916) Prec@5 57.500 (62.988) Epoch: [2][3040/11272] Time 0.757 (0.834) Data 0.003 (0.002) Loss 2.8479 (2.8314) Prec@1 36.250 (32.909) Prec@5 62.500 (62.981) Epoch: [2][3050/11272] Time 0.781 (0.834) Data 0.002 (0.002) Loss 2.8246 (2.8312) Prec@1 29.375 (32.909) Prec@5 61.875 (62.986) Epoch: [2][3060/11272] Time 0.873 (0.834) Data 0.001 (0.002) Loss 2.8323 (2.8314) Prec@1 29.375 (32.906) Prec@5 61.250 (62.983) Epoch: [2][3070/11272] Time 0.776 (0.834) Data 0.005 (0.002) Loss 2.8614 (2.8314) Prec@1 30.625 (32.904) Prec@5 63.750 (62.984) Epoch: [2][3080/11272] Time 0.781 (0.834) Data 0.001 (0.002) Loss 2.8454 (2.8312) Prec@1 29.375 (32.903) Prec@5 60.625 (62.987) Epoch: [2][3090/11272] Time 0.872 (0.834) Data 0.003 (0.002) Loss 2.8484 (2.8313) Prec@1 35.625 (32.904) Prec@5 65.000 (62.985) Epoch: [2][3100/11272] Time 0.937 (0.834) Data 0.001 (0.002) Loss 2.8158 (2.8314) Prec@1 32.500 (32.900) Prec@5 61.875 (62.980) Epoch: [2][3110/11272] Time 0.759 (0.834) Data 0.002 (0.002) Loss 3.0481 (2.8314) Prec@1 35.625 (32.899) Prec@5 59.375 (62.984) Epoch: [2][3120/11272] Time 0.792 (0.834) Data 0.002 (0.002) Loss 2.7969 (2.8313) Prec@1 30.000 (32.902) Prec@5 63.125 (62.982) Epoch: [2][3130/11272] Time 0.923 (0.834) Data 0.002 (0.002) Loss 2.6986 (2.8313) Prec@1 35.000 (32.903) Prec@5 68.750 (62.986) Epoch: [2][3140/11272] Time 0.877 (0.834) Data 0.001 (0.002) Loss 2.4865 (2.8309) Prec@1 35.000 (32.913) Prec@5 70.625 (62.995) Epoch: [2][3150/11272] Time 0.741 (0.834) Data 0.002 (0.002) Loss 2.6469 (2.8309) Prec@1 35.000 (32.910) Prec@5 65.000 (62.998) Epoch: [2][3160/11272] Time 0.743 (0.834) Data 0.001 (0.002) Loss 2.7339 (2.8308) Prec@1 34.375 (32.908) Prec@5 70.625 (63.000) Epoch: [2][3170/11272] Time 0.916 (0.834) Data 0.002 (0.002) Loss 2.5801 (2.8306) Prec@1 37.500 (32.916) Prec@5 68.125 (63.003) Epoch: [2][3180/11272] Time 0.870 (0.834) Data 0.001 (0.002) Loss 3.0768 (2.8307) Prec@1 27.500 (32.915) Prec@5 60.000 (63.003) Epoch: [2][3190/11272] Time 0.737 (0.834) Data 0.001 (0.002) Loss 3.0315 (2.8307) Prec@1 26.875 (32.914) Prec@5 56.250 (63.002) Epoch: [2][3200/11272] Time 0.879 (0.834) Data 0.002 (0.002) Loss 2.8295 (2.8308) Prec@1 33.750 (32.911) Prec@5 60.625 (63.003) Epoch: [2][3210/11272] Time 0.879 (0.834) Data 0.002 (0.002) Loss 2.5947 (2.8307) Prec@1 37.500 (32.914) Prec@5 71.250 (63.003) Epoch: [2][3220/11272] Time 0.765 (0.834) Data 0.001 (0.002) Loss 2.6883 (2.8309) Prec@1 35.625 (32.915) Prec@5 66.875 (63.001) Epoch: [2][3230/11272] Time 0.759 (0.834) Data 0.002 (0.002) Loss 2.8948 (2.8308) Prec@1 31.875 (32.922) Prec@5 60.000 (63.003) Epoch: [2][3240/11272] Time 0.894 (0.834) Data 0.001 (0.002) Loss 2.9660 (2.8307) Prec@1 28.750 (32.922) Prec@5 63.750 (63.001) Epoch: [2][3250/11272] Time 0.882 (0.834) Data 0.001 (0.002) Loss 2.7824 (2.8308) Prec@1 30.625 (32.919) Prec@5 64.375 (62.999) Epoch: [2][3260/11272] Time 0.740 (0.834) Data 0.001 (0.002) Loss 3.0137 (2.8306) Prec@1 31.875 (32.921) Prec@5 61.250 (63.001) Epoch: [2][3270/11272] Time 0.755 (0.834) Data 0.002 (0.002) Loss 2.9018 (2.8306) Prec@1 29.375 (32.921) Prec@5 66.250 (63.002) Epoch: [2][3280/11272] Time 0.952 (0.834) Data 0.002 (0.002) Loss 2.7759 (2.8307) Prec@1 37.500 (32.924) Prec@5 65.000 (62.997) Epoch: [2][3290/11272] Time 0.911 (0.834) Data 0.002 (0.002) Loss 2.6902 (2.8308) Prec@1 35.625 (32.923) Prec@5 64.375 (62.995) Epoch: [2][3300/11272] Time 0.738 (0.834) Data 0.001 (0.002) Loss 2.8644 (2.8309) Prec@1 28.125 (32.918) Prec@5 60.000 (62.992) Epoch: [2][3310/11272] Time 0.753 (0.834) Data 0.001 (0.002) Loss 2.7459 (2.8309) Prec@1 34.375 (32.921) Prec@5 66.250 (62.992) Epoch: [2][3320/11272] Time 0.929 (0.834) Data 0.001 (0.002) Loss 2.7948 (2.8311) Prec@1 29.375 (32.917) Prec@5 64.375 (62.993) Epoch: [2][3330/11272] Time 0.809 (0.834) Data 0.004 (0.002) Loss 2.7474 (2.8311) Prec@1 39.375 (32.919) Prec@5 67.500 (62.992) Epoch: [2][3340/11272] Time 0.741 (0.834) Data 0.001 (0.002) Loss 2.8540 (2.8313) Prec@1 33.750 (32.920) Prec@5 60.000 (62.985) Epoch: [2][3350/11272] Time 0.925 (0.834) Data 0.002 (0.002) Loss 2.6449 (2.8314) Prec@1 36.250 (32.919) Prec@5 65.625 (62.980) Epoch: [2][3360/11272] Time 0.894 (0.834) Data 0.001 (0.002) Loss 2.9751 (2.8314) Prec@1 34.375 (32.919) Prec@5 62.500 (62.980) Epoch: [2][3370/11272] Time 0.752 (0.834) Data 0.002 (0.002) Loss 2.7564 (2.8315) Prec@1 36.250 (32.918) Prec@5 65.625 (62.979) Epoch: [2][3380/11272] Time 0.769 (0.834) Data 0.001 (0.002) Loss 3.1018 (2.8315) Prec@1 31.250 (32.920) Prec@5 56.875 (62.977) Epoch: [2][3390/11272] Time 0.897 (0.834) Data 0.001 (0.002) Loss 2.6636 (2.8317) Prec@1 38.125 (32.915) Prec@5 68.125 (62.977) Epoch: [2][3400/11272] Time 0.917 (0.834) Data 0.002 (0.002) Loss 2.9541 (2.8313) Prec@1 31.250 (32.923) Prec@5 61.250 (62.984) Epoch: [2][3410/11272] Time 0.780 (0.834) Data 0.002 (0.002) Loss 2.6284 (2.8311) Prec@1 31.250 (32.926) Prec@5 68.750 (62.990) Epoch: [2][3420/11272] Time 0.748 (0.834) Data 0.002 (0.002) Loss 3.0877 (2.8312) Prec@1 28.750 (32.923) Prec@5 60.625 (62.985) Epoch: [2][3430/11272] Time 0.921 (0.834) Data 0.002 (0.002) Loss 2.5552 (2.8314) Prec@1 38.125 (32.923) Prec@5 70.625 (62.981) Epoch: [2][3440/11272] Time 0.874 (0.834) Data 0.001 (0.002) Loss 2.9311 (2.8316) Prec@1 31.875 (32.921) Prec@5 63.750 (62.977) Epoch: [2][3450/11272] Time 0.729 (0.834) Data 0.002 (0.002) Loss 2.7026 (2.8315) Prec@1 38.125 (32.919) Prec@5 66.875 (62.978) Epoch: [2][3460/11272] Time 0.869 (0.834) Data 0.002 (0.002) Loss 3.0198 (2.8316) Prec@1 28.750 (32.915) Prec@5 57.500 (62.973) Epoch: [2][3470/11272] Time 0.922 (0.834) Data 0.002 (0.002) Loss 2.9208 (2.8316) Prec@1 30.000 (32.915) Prec@5 62.500 (62.971) Epoch: [2][3480/11272] Time 0.756 (0.834) Data 0.001 (0.002) Loss 2.6267 (2.8314) Prec@1 34.375 (32.914) Prec@5 66.875 (62.976) Epoch: [2][3490/11272] Time 0.738 (0.834) Data 0.001 (0.002) Loss 2.6640 (2.8310) Prec@1 30.625 (32.919) Prec@5 68.750 (62.982) Epoch: [2][3500/11272] Time 0.934 (0.834) Data 0.002 (0.002) Loss 2.7846 (2.8312) Prec@1 36.875 (32.918) Prec@5 64.375 (62.982) Epoch: [2][3510/11272] Time 0.844 (0.834) Data 0.001 (0.002) Loss 2.8507 (2.8313) Prec@1 31.875 (32.916) Prec@5 58.750 (62.978) Epoch: [2][3520/11272] Time 0.753 (0.834) Data 0.002 (0.002) Loss 2.6836 (2.8311) Prec@1 40.000 (32.923) Prec@5 63.125 (62.980) Epoch: [2][3530/11272] Time 0.769 (0.834) Data 0.002 (0.002) Loss 2.6842 (2.8311) Prec@1 38.750 (32.923) Prec@5 68.750 (62.982) Epoch: [2][3540/11272] Time 0.976 (0.834) Data 0.001 (0.002) Loss 2.8458 (2.8309) Prec@1 36.875 (32.929) Prec@5 60.625 (62.987) Epoch: [2][3550/11272] Time 0.928 (0.834) Data 0.002 (0.002) Loss 2.6762 (2.8310) Prec@1 33.125 (32.926) Prec@5 65.000 (62.985) Epoch: [2][3560/11272] Time 0.741 (0.834) Data 0.001 (0.002) Loss 2.9306 (2.8310) Prec@1 31.250 (32.925) Prec@5 59.375 (62.988) Epoch: [2][3570/11272] Time 0.764 (0.834) Data 0.001 (0.002) Loss 2.9265 (2.8312) Prec@1 25.625 (32.919) Prec@5 63.125 (62.985) Epoch: [2][3580/11272] Time 0.953 (0.834) Data 0.002 (0.002) Loss 2.7079 (2.8311) Prec@1 34.375 (32.920) Prec@5 68.125 (62.990) Epoch: [2][3590/11272] Time 0.943 (0.834) Data 0.002 (0.002) Loss 3.1089 (2.8311) Prec@1 23.750 (32.918) Prec@5 54.375 (62.988) Epoch: [2][3600/11272] Time 0.748 (0.833) Data 0.001 (0.002) Loss 2.6108 (2.8311) Prec@1 38.125 (32.917) Prec@5 66.250 (62.985) Epoch: [2][3610/11272] Time 0.879 (0.833) Data 0.002 (0.002) Loss 2.7887 (2.8310) Prec@1 36.250 (32.917) Prec@5 63.125 (62.990) Epoch: [2][3620/11272] Time 0.886 (0.833) Data 0.002 (0.002) Loss 2.8565 (2.8309) Prec@1 28.125 (32.917) Prec@5 63.125 (62.989) Epoch: [2][3630/11272] Time 0.739 (0.833) Data 0.001 (0.002) Loss 2.7699 (2.8310) Prec@1 38.125 (32.918) Prec@5 61.250 (62.989) Epoch: [2][3640/11272] Time 0.747 (0.833) Data 0.001 (0.002) Loss 3.0886 (2.8310) Prec@1 33.750 (32.919) Prec@5 55.625 (62.990) Epoch: [2][3650/11272] Time 0.995 (0.833) Data 0.002 (0.002) Loss 2.8468 (2.8311) Prec@1 31.875 (32.918) Prec@5 61.250 (62.988) Epoch: [2][3660/11272] Time 0.883 (0.833) Data 0.001 (0.002) Loss 2.5327 (2.8311) Prec@1 41.250 (32.920) Prec@5 70.000 (62.987) Epoch: [2][3670/11272] Time 0.748 (0.833) Data 0.001 (0.002) Loss 2.9819 (2.8311) Prec@1 28.750 (32.918) Prec@5 56.250 (62.988) Epoch: [2][3680/11272] Time 0.766 (0.833) Data 0.002 (0.002) Loss 2.8471 (2.8311) Prec@1 31.250 (32.915) Prec@5 66.250 (62.991) Epoch: [2][3690/11272] Time 0.899 (0.833) Data 0.002 (0.002) Loss 2.9497 (2.8311) Prec@1 29.375 (32.914) Prec@5 58.750 (62.992) Epoch: [2][3700/11272] Time 0.916 (0.833) Data 0.001 (0.002) Loss 2.8583 (2.8309) Prec@1 28.125 (32.915) Prec@5 65.625 (62.998) Epoch: [2][3710/11272] Time 0.746 (0.833) Data 0.002 (0.002) Loss 2.5456 (2.8308) Prec@1 39.375 (32.917) Prec@5 65.625 (63.000) Epoch: [2][3720/11272] Time 0.758 (0.833) Data 0.001 (0.002) Loss 2.9467 (2.8303) Prec@1 30.625 (32.923) Prec@5 56.250 (63.009) Epoch: [2][3730/11272] Time 0.880 (0.833) Data 0.002 (0.002) Loss 2.8411 (2.8304) Prec@1 29.375 (32.917) Prec@5 61.250 (63.007) Epoch: [2][3740/11272] Time 0.744 (0.833) Data 0.001 (0.002) Loss 2.8195 (2.8304) Prec@1 36.875 (32.921) Prec@5 63.125 (63.010) Epoch: [2][3750/11272] Time 0.766 (0.833) Data 0.002 (0.002) Loss 2.8514 (2.8304) Prec@1 35.000 (32.923) Prec@5 62.500 (63.010) Epoch: [2][3760/11272] Time 0.874 (0.833) Data 0.001 (0.002) Loss 2.7921 (2.8303) Prec@1 30.625 (32.919) Prec@5 61.875 (63.012) Epoch: [2][3770/11272] Time 0.952 (0.833) Data 0.002 (0.002) Loss 3.1176 (2.8301) Prec@1 26.875 (32.919) Prec@5 60.625 (63.016) Epoch: [2][3780/11272] Time 0.742 (0.833) Data 0.001 (0.002) Loss 2.9915 (2.8302) Prec@1 35.000 (32.923) Prec@5 58.125 (63.013) Epoch: [2][3790/11272] Time 0.743 (0.833) Data 0.002 (0.002) Loss 2.7280 (2.8301) Prec@1 33.750 (32.925) Prec@5 69.375 (63.016) Epoch: [2][3800/11272] Time 0.919 (0.833) Data 0.001 (0.002) Loss 2.8807 (2.8304) Prec@1 29.375 (32.915) Prec@5 61.875 (63.011) Epoch: [2][3810/11272] Time 0.891 (0.833) Data 0.002 (0.002) Loss 2.9140 (2.8300) Prec@1 35.000 (32.925) Prec@5 59.375 (63.018) Epoch: [2][3820/11272] Time 0.743 (0.833) Data 0.001 (0.002) Loss 2.7740 (2.8298) Prec@1 35.625 (32.930) Prec@5 64.375 (63.022) Epoch: [2][3830/11272] Time 0.701 (0.833) Data 0.001 (0.002) Loss 2.7995 (2.8299) Prec@1 33.750 (32.928) Prec@5 63.750 (63.021) Epoch: [2][3840/11272] Time 0.896 (0.833) Data 0.002 (0.002) Loss 2.7351 (2.8298) Prec@1 38.750 (32.932) Prec@5 63.125 (63.021) Epoch: [2][3850/11272] Time 0.891 (0.833) Data 0.002 (0.002) Loss 2.5130 (2.8297) Prec@1 36.875 (32.933) Prec@5 68.750 (63.024) Epoch: [2][3860/11272] Time 0.755 (0.833) Data 0.001 (0.002) Loss 2.7959 (2.8294) Prec@1 30.000 (32.936) Prec@5 63.750 (63.031) Epoch: [2][3870/11272] Time 0.883 (0.833) Data 0.002 (0.002) Loss 2.8000 (2.8292) Prec@1 31.875 (32.942) Prec@5 60.000 (63.033) Epoch: [2][3880/11272] Time 0.944 (0.833) Data 0.001 (0.002) Loss 2.7110 (2.8291) Prec@1 35.000 (32.947) Prec@5 65.000 (63.039) Epoch: [2][3890/11272] Time 0.743 (0.833) Data 0.002 (0.002) Loss 2.8877 (2.8291) Prec@1 36.250 (32.947) Prec@5 60.625 (63.036) Epoch: [2][3900/11272] Time 0.741 (0.833) Data 0.001 (0.002) Loss 2.8925 (2.8291) Prec@1 31.875 (32.948) Prec@5 57.500 (63.033) Epoch: [2][3910/11272] Time 0.895 (0.833) Data 0.002 (0.002) Loss 2.8196 (2.8291) Prec@1 28.125 (32.947) Prec@5 63.125 (63.033) Epoch: [2][3920/11272] Time 0.879 (0.833) Data 0.001 (0.002) Loss 2.9050 (2.8290) Prec@1 34.375 (32.948) Prec@5 60.000 (63.034) Epoch: [2][3930/11272] Time 0.813 (0.833) Data 0.002 (0.002) Loss 2.8300 (2.8288) Prec@1 30.000 (32.951) Prec@5 63.750 (63.037) Epoch: [2][3940/11272] Time 0.747 (0.833) Data 0.002 (0.002) Loss 2.9490 (2.8286) Prec@1 30.625 (32.954) Prec@5 60.625 (63.039) Epoch: [2][3950/11272] Time 0.970 (0.833) Data 0.002 (0.002) Loss 2.9147 (2.8287) Prec@1 35.000 (32.950) Prec@5 55.625 (63.035) Epoch: [2][3960/11272] Time 0.918 (0.833) Data 0.002 (0.002) Loss 3.0644 (2.8288) Prec@1 25.625 (32.944) Prec@5 57.500 (63.030) Epoch: [2][3970/11272] Time 0.744 (0.833) Data 0.002 (0.002) Loss 2.7087 (2.8287) Prec@1 33.125 (32.945) Prec@5 65.000 (63.031) Epoch: [2][3980/11272] Time 0.736 (0.833) Data 0.001 (0.002) Loss 2.8919 (2.8289) Prec@1 34.375 (32.941) Prec@5 64.375 (63.026) Epoch: [2][3990/11272] Time 0.868 (0.833) Data 0.002 (0.002) Loss 2.8115 (2.8288) Prec@1 36.250 (32.940) Prec@5 61.875 (63.027) Epoch: [2][4000/11272] Time 0.815 (0.833) Data 0.005 (0.002) Loss 2.9529 (2.8289) Prec@1 29.375 (32.941) Prec@5 62.500 (63.025) Epoch: [2][4010/11272] Time 0.749 (0.833) Data 0.001 (0.002) Loss 2.8190 (2.8287) Prec@1 34.375 (32.941) Prec@5 65.000 (63.029) Epoch: [2][4020/11272] Time 0.853 (0.833) Data 0.002 (0.002) Loss 2.7940 (2.8286) Prec@1 38.125 (32.945) Prec@5 65.000 (63.032) Epoch: [2][4030/11272] Time 0.868 (0.833) Data 0.002 (0.002) Loss 2.5483 (2.8284) Prec@1 40.000 (32.948) Prec@5 67.500 (63.037) Epoch: [2][4040/11272] Time 0.741 (0.833) Data 0.002 (0.002) Loss 2.8057 (2.8281) Prec@1 34.375 (32.953) Prec@5 64.375 (63.042) Epoch: [2][4050/11272] Time 0.739 (0.833) Data 0.001 (0.002) Loss 2.7787 (2.8282) Prec@1 37.500 (32.953) Prec@5 62.500 (63.039) Epoch: [2][4060/11272] Time 0.908 (0.833) Data 0.001 (0.002) Loss 2.8055 (2.8282) Prec@1 29.375 (32.953) Prec@5 61.250 (63.041) Epoch: [2][4070/11272] Time 0.879 (0.833) Data 0.002 (0.002) Loss 2.7198 (2.8281) Prec@1 31.250 (32.958) Prec@5 68.750 (63.046) Epoch: [2][4080/11272] Time 0.764 (0.833) Data 0.002 (0.002) Loss 3.0040 (2.8281) Prec@1 25.625 (32.957) Prec@5 58.750 (63.046) Epoch: [2][4090/11272] Time 0.763 (0.833) Data 0.002 (0.002) Loss 2.8937 (2.8280) Prec@1 36.250 (32.960) Prec@5 65.625 (63.049) Epoch: [2][4100/11272] Time 0.939 (0.833) Data 0.001 (0.002) Loss 2.4918 (2.8278) Prec@1 40.000 (32.966) Prec@5 70.000 (63.052) Epoch: [2][4110/11272] Time 0.937 (0.833) Data 0.002 (0.002) Loss 2.8454 (2.8279) Prec@1 36.250 (32.968) Prec@5 63.750 (63.051) Epoch: [2][4120/11272] Time 0.746 (0.833) Data 0.001 (0.002) Loss 2.9811 (2.8278) Prec@1 28.750 (32.968) Prec@5 61.250 (63.054) Epoch: [2][4130/11272] Time 0.879 (0.833) Data 0.002 (0.002) Loss 2.8554 (2.8277) Prec@1 33.125 (32.971) Prec@5 63.125 (63.055) Epoch: [2][4140/11272] Time 0.945 (0.833) Data 0.001 (0.002) Loss 2.6127 (2.8275) Prec@1 36.250 (32.979) Prec@5 70.000 (63.062) Epoch: [2][4150/11272] Time 0.738 (0.833) Data 0.002 (0.002) Loss 2.7409 (2.8275) Prec@1 34.375 (32.976) Prec@5 64.375 (63.063) Epoch: [2][4160/11272] Time 0.783 (0.833) Data 0.002 (0.002) Loss 2.8240 (2.8274) Prec@1 28.125 (32.974) Prec@5 65.625 (63.065) Epoch: [2][4170/11272] Time 0.886 (0.833) Data 0.002 (0.002) Loss 2.5738 (2.8272) Prec@1 40.000 (32.979) Prec@5 68.750 (63.069) Epoch: [2][4180/11272] Time 0.981 (0.833) Data 0.002 (0.002) Loss 2.7552 (2.8270) Prec@1 33.750 (32.983) Prec@5 63.125 (63.074) Epoch: [2][4190/11272] Time 0.774 (0.833) Data 0.002 (0.002) Loss 2.8194 (2.8271) Prec@1 34.375 (32.984) Prec@5 62.500 (63.073) Epoch: [2][4200/11272] Time 0.747 (0.833) Data 0.002 (0.002) Loss 2.9231 (2.8269) Prec@1 26.250 (32.985) Prec@5 61.250 (63.078) Epoch: [2][4210/11272] Time 0.892 (0.833) Data 0.002 (0.002) Loss 2.9196 (2.8269) Prec@1 30.625 (32.988) Prec@5 63.750 (63.081) Epoch: [2][4220/11272] Time 0.867 (0.833) Data 0.001 (0.002) Loss 2.9108 (2.8270) Prec@1 32.500 (32.983) Prec@5 60.625 (63.076) Epoch: [2][4230/11272] Time 0.772 (0.833) Data 0.002 (0.002) Loss 2.9810 (2.8271) Prec@1 35.625 (32.981) Prec@5 60.000 (63.073) Epoch: [2][4240/11272] Time 0.751 (0.833) Data 0.002 (0.002) Loss 2.7744 (2.8270) Prec@1 35.000 (32.986) Prec@5 63.750 (63.075) Epoch: [2][4250/11272] Time 0.868 (0.833) Data 0.002 (0.002) Loss 2.5318 (2.8268) Prec@1 40.000 (32.989) Prec@5 71.250 (63.080) Epoch: [2][4260/11272] Time 0.789 (0.833) Data 0.003 (0.002) Loss 2.7935 (2.8268) Prec@1 30.625 (32.990) Prec@5 58.125 (63.078) Epoch: [2][4270/11272] Time 0.749 (0.833) Data 0.001 (0.002) Loss 2.9034 (2.8266) Prec@1 30.000 (32.994) Prec@5 55.625 (63.081) Epoch: [2][4280/11272] Time 0.878 (0.833) Data 0.002 (0.002) Loss 2.7287 (2.8267) Prec@1 33.125 (32.994) Prec@5 65.000 (63.081) Epoch: [2][4290/11272] Time 0.921 (0.833) Data 0.002 (0.002) Loss 2.6031 (2.8265) Prec@1 35.625 (32.993) Prec@5 65.625 (63.085) Epoch: [2][4300/11272] Time 0.825 (0.833) Data 0.002 (0.002) Loss 2.6723 (2.8265) Prec@1 34.375 (32.993) Prec@5 68.750 (63.083) Epoch: [2][4310/11272] Time 0.745 (0.833) Data 0.002 (0.002) Loss 2.6613 (2.8262) Prec@1 31.250 (32.998) Prec@5 68.750 (63.092) Epoch: [2][4320/11272] Time 0.901 (0.833) Data 0.002 (0.002) Loss 2.7061 (2.8262) Prec@1 34.375 (32.997) Prec@5 66.250 (63.091) Epoch: [2][4330/11272] Time 0.888 (0.833) Data 0.002 (0.002) Loss 2.8003 (2.8262) Prec@1 29.375 (32.997) Prec@5 61.875 (63.089) Epoch: [2][4340/11272] Time 0.739 (0.833) Data 0.001 (0.002) Loss 2.7495 (2.8262) Prec@1 38.125 (32.999) Prec@5 62.500 (63.088) Epoch: [2][4350/11272] Time 0.772 (0.833) Data 0.002 (0.002) Loss 2.8141 (2.8265) Prec@1 31.250 (32.994) Prec@5 63.750 (63.083) Epoch: [2][4360/11272] Time 0.871 (0.833) Data 0.001 (0.002) Loss 2.8229 (2.8265) Prec@1 33.125 (32.996) Prec@5 62.500 (63.084) Epoch: [2][4370/11272] Time 0.865 (0.833) Data 0.002 (0.002) Loss 2.6286 (2.8263) Prec@1 34.375 (32.999) Prec@5 64.375 (63.085) Epoch: [2][4380/11272] Time 0.750 (0.833) Data 0.002 (0.002) Loss 2.7994 (2.8264) Prec@1 26.875 (32.995) Prec@5 65.625 (63.086) Epoch: [2][4390/11272] Time 0.940 (0.833) Data 0.002 (0.002) Loss 2.8465 (2.8263) Prec@1 34.375 (32.993) Prec@5 62.500 (63.087) Epoch: [2][4400/11272] Time 0.881 (0.833) Data 0.002 (0.002) Loss 2.7666 (2.8264) Prec@1 38.750 (32.994) Prec@5 61.250 (63.085) Epoch: [2][4410/11272] Time 0.768 (0.833) Data 0.002 (0.002) Loss 2.8243 (2.8261) Prec@1 37.500 (33.000) Prec@5 60.000 (63.089) Epoch: [2][4420/11272] Time 0.728 (0.833) Data 0.001 (0.002) Loss 2.7919 (2.8261) Prec@1 30.000 (32.997) Prec@5 63.125 (63.089) Epoch: [2][4430/11272] Time 0.936 (0.833) Data 0.002 (0.002) Loss 2.8750 (2.8263) Prec@1 33.750 (32.996) Prec@5 61.875 (63.080) Epoch: [2][4440/11272] Time 0.906 (0.833) Data 0.002 (0.002) Loss 2.5059 (2.8261) Prec@1 36.875 (33.001) Prec@5 69.375 (63.089) Epoch: [2][4450/11272] Time 0.813 (0.833) Data 0.002 (0.002) Loss 3.0068 (2.8261) Prec@1 31.875 (32.999) Prec@5 60.625 (63.090) Epoch: [2][4460/11272] Time 0.737 (0.833) Data 0.002 (0.002) Loss 2.7705 (2.8262) Prec@1 35.000 (32.996) Prec@5 65.000 (63.088) Epoch: [2][4470/11272] Time 0.963 (0.833) Data 0.002 (0.002) Loss 2.5538 (2.8259) Prec@1 41.875 (33.002) Prec@5 66.875 (63.091) Epoch: [2][4480/11272] Time 0.889 (0.833) Data 0.002 (0.002) Loss 2.8720 (2.8259) Prec@1 30.000 (32.998) Prec@5 61.250 (63.091) Epoch: [2][4490/11272] Time 0.750 (0.833) Data 0.002 (0.002) Loss 2.7275 (2.8258) Prec@1 31.250 (33.002) Prec@5 66.250 (63.096) Epoch: [2][4500/11272] Time 0.768 (0.833) Data 0.001 (0.002) Loss 2.8605 (2.8257) Prec@1 31.250 (33.004) Prec@5 63.125 (63.100) Epoch: [2][4510/11272] Time 0.963 (0.833) Data 0.002 (0.002) Loss 2.8569 (2.8257) Prec@1 33.125 (33.002) Prec@5 62.500 (63.099) Epoch: [2][4520/11272] Time 0.858 (0.833) Data 0.001 (0.002) Loss 2.6347 (2.8255) Prec@1 35.625 (33.006) Prec@5 66.250 (63.102) Epoch: [2][4530/11272] Time 0.749 (0.833) Data 0.001 (0.002) Loss 2.9064 (2.8254) Prec@1 31.250 (33.007) Prec@5 65.000 (63.104) Epoch: [2][4540/11272] Time 0.915 (0.833) Data 0.002 (0.002) Loss 3.1316 (2.8254) Prec@1 28.750 (33.007) Prec@5 53.750 (63.105) Epoch: [2][4550/11272] Time 0.881 (0.833) Data 0.002 (0.002) Loss 2.7436 (2.8253) Prec@1 36.250 (33.009) Prec@5 64.375 (63.108) Epoch: [2][4560/11272] Time 0.754 (0.833) Data 0.002 (0.002) Loss 2.9945 (2.8254) Prec@1 29.375 (33.009) Prec@5 59.375 (63.107) Epoch: [2][4570/11272] Time 0.783 (0.833) Data 0.002 (0.002) Loss 3.0478 (2.8254) Prec@1 26.875 (33.007) Prec@5 60.625 (63.108) Epoch: [2][4580/11272] Time 0.895 (0.833) Data 0.001 (0.002) Loss 2.5823 (2.8254) Prec@1 36.250 (33.008) Prec@5 69.375 (63.110) Epoch: [2][4590/11272] Time 0.902 (0.833) Data 0.002 (0.002) Loss 2.5288 (2.8253) Prec@1 36.250 (33.012) Prec@5 69.375 (63.110) Epoch: [2][4600/11272] Time 0.786 (0.833) Data 0.002 (0.002) Loss 2.7541 (2.8252) Prec@1 40.625 (33.017) Prec@5 63.125 (63.114) Epoch: [2][4610/11272] Time 0.759 (0.833) Data 0.002 (0.002) Loss 2.9215 (2.8254) Prec@1 31.250 (33.010) Prec@5 61.250 (63.111) Epoch: [2][4620/11272] Time 0.892 (0.833) Data 0.002 (0.002) Loss 2.8424 (2.8253) Prec@1 32.500 (33.017) Prec@5 60.000 (63.111) Epoch: [2][4630/11272] Time 0.832 (0.833) Data 0.002 (0.002) Loss 2.6350 (2.8251) Prec@1 33.750 (33.019) Prec@5 65.000 (63.114) Epoch: [2][4640/11272] Time 0.782 (0.833) Data 0.002 (0.002) Loss 2.7743 (2.8250) Prec@1 33.125 (33.022) Prec@5 60.000 (63.118) Epoch: [2][4650/11272] Time 0.761 (0.833) Data 0.002 (0.002) Loss 2.8715 (2.8249) Prec@1 35.000 (33.026) Prec@5 62.500 (63.118) Epoch: [2][4660/11272] Time 0.913 (0.833) Data 0.001 (0.002) Loss 2.6163 (2.8248) Prec@1 36.875 (33.026) Prec@5 68.750 (63.117) Epoch: [2][4670/11272] Time 0.745 (0.833) Data 0.001 (0.002) Loss 2.7257 (2.8247) Prec@1 35.625 (33.028) Prec@5 68.750 (63.121) Epoch: [2][4680/11272] Time 0.738 (0.833) Data 0.002 (0.002) Loss 2.7984 (2.8247) Prec@1 34.375 (33.026) Prec@5 64.375 (63.120) Epoch: [2][4690/11272] Time 0.892 (0.833) Data 0.002 (0.002) Loss 2.5091 (2.8244) Prec@1 41.875 (33.031) Prec@5 70.000 (63.125) Epoch: [2][4700/11272] Time 0.914 (0.833) Data 0.002 (0.002) Loss 2.5168 (2.8244) Prec@1 39.375 (33.032) Prec@5 68.750 (63.126) Epoch: [2][4710/11272] Time 0.738 (0.833) Data 0.002 (0.002) Loss 3.0377 (2.8244) Prec@1 31.875 (33.030) Prec@5 58.125 (63.125) Epoch: [2][4720/11272] Time 0.734 (0.833) Data 0.001 (0.002) Loss 2.7147 (2.8244) Prec@1 40.000 (33.029) Prec@5 70.000 (63.125) Epoch: [2][4730/11272] Time 0.883 (0.833) Data 0.002 (0.002) Loss 2.6742 (2.8243) Prec@1 35.000 (33.030) Prec@5 63.750 (63.127) Epoch: [2][4740/11272] Time 0.921 (0.833) Data 0.002 (0.002) Loss 2.9441 (2.8244) Prec@1 34.375 (33.028) Prec@5 62.500 (63.122) Epoch: [2][4750/11272] Time 0.739 (0.833) Data 0.002 (0.002) Loss 2.7139 (2.8243) Prec@1 36.875 (33.029) Prec@5 62.500 (63.121) Epoch: [2][4760/11272] Time 0.751 (0.833) Data 0.001 (0.002) Loss 2.7844 (2.8243) Prec@1 33.125 (33.029) Prec@5 61.875 (63.118) Epoch: [2][4770/11272] Time 0.910 (0.833) Data 0.002 (0.002) Loss 2.5702 (2.8242) Prec@1 32.500 (33.032) Prec@5 69.375 (63.117) Epoch: [2][4780/11272] Time 0.889 (0.833) Data 0.001 (0.002) Loss 2.8728 (2.8244) Prec@1 32.500 (33.033) Prec@5 61.250 (63.118) Epoch: [2][4790/11272] Time 0.740 (0.833) Data 0.002 (0.002) Loss 2.6880 (2.8243) Prec@1 39.375 (33.032) Prec@5 63.125 (63.118) Epoch: [2][4800/11272] Time 0.883 (0.833) Data 0.001 (0.002) Loss 3.0799 (2.8243) Prec@1 30.625 (33.033) Prec@5 61.250 (63.119) Epoch: [2][4810/11272] Time 0.882 (0.833) Data 0.002 (0.002) Loss 2.8186 (2.8243) Prec@1 39.375 (33.035) Prec@5 63.750 (63.118) Epoch: [2][4820/11272] Time 0.781 (0.833) Data 0.002 (0.002) Loss 2.8210 (2.8243) Prec@1 32.500 (33.036) Prec@5 65.625 (63.122) Epoch: [2][4830/11272] Time 0.770 (0.833) Data 0.005 (0.002) Loss 2.6804 (2.8243) Prec@1 40.625 (33.034) Prec@5 67.500 (63.121) Epoch: [2][4840/11272] Time 0.948 (0.833) Data 0.002 (0.002) Loss 2.6994 (2.8241) Prec@1 37.500 (33.037) Prec@5 63.125 (63.123) Epoch: [2][4850/11272] Time 0.933 (0.833) Data 0.002 (0.002) Loss 2.7386 (2.8240) Prec@1 32.500 (33.040) Prec@5 65.000 (63.125) Epoch: [2][4860/11272] Time 0.777 (0.833) Data 0.002 (0.002) Loss 2.8248 (2.8238) Prec@1 30.000 (33.041) Prec@5 59.375 (63.126) Epoch: [2][4870/11272] Time 0.811 (0.833) Data 0.002 (0.002) Loss 2.8446 (2.8239) Prec@1 35.000 (33.038) Prec@5 61.875 (63.125) Epoch: [2][4880/11272] Time 0.856 (0.833) Data 0.002 (0.002) Loss 2.8814 (2.8240) Prec@1 30.625 (33.038) Prec@5 61.875 (63.123) Epoch: [2][4890/11272] Time 0.877 (0.833) Data 0.002 (0.002) Loss 2.8216 (2.8238) Prec@1 31.250 (33.042) Prec@5 65.000 (63.123) Epoch: [2][4900/11272] Time 0.739 (0.833) Data 0.002 (0.002) Loss 2.7000 (2.8239) Prec@1 33.125 (33.041) Prec@5 65.000 (63.119) Epoch: [2][4910/11272] Time 0.759 (0.833) Data 0.002 (0.002) Loss 2.8454 (2.8238) Prec@1 35.625 (33.042) Prec@5 66.875 (63.124) Epoch: [2][4920/11272] Time 0.919 (0.833) Data 0.001 (0.002) Loss 2.6354 (2.8240) Prec@1 38.125 (33.040) Prec@5 66.875 (63.120) Epoch: [2][4930/11272] Time 0.766 (0.833) Data 0.002 (0.002) Loss 2.8292 (2.8238) Prec@1 32.500 (33.043) Prec@5 59.375 (63.122) Epoch: [2][4940/11272] Time 0.734 (0.833) Data 0.001 (0.002) Loss 2.7004 (2.8239) Prec@1 35.000 (33.042) Prec@5 66.875 (63.122) Epoch: [2][4950/11272] Time 0.933 (0.833) Data 0.002 (0.002) Loss 2.6941 (2.8238) Prec@1 36.250 (33.042) Prec@5 62.500 (63.122) Epoch: [2][4960/11272] Time 0.883 (0.833) Data 0.002 (0.002) Loss 2.8812 (2.8236) Prec@1 28.125 (33.046) Prec@5 58.750 (63.122) Epoch: [2][4970/11272] Time 0.781 (0.833) Data 0.002 (0.002) Loss 2.7790 (2.8236) Prec@1 36.250 (33.044) Prec@5 61.875 (63.122) Epoch: [2][4980/11272] Time 0.788 (0.833) Data 0.002 (0.002) Loss 2.8420 (2.8235) Prec@1 31.875 (33.044) Prec@5 59.375 (63.124) Epoch: [2][4990/11272] Time 0.933 (0.833) Data 0.002 (0.002) Loss 2.8356 (2.8235) Prec@1 31.875 (33.044) Prec@5 61.250 (63.127) Epoch: [2][5000/11272] Time 0.861 (0.833) Data 0.001 (0.002) Loss 3.0303 (2.8234) Prec@1 30.625 (33.046) Prec@5 61.875 (63.131) Epoch: [2][5010/11272] Time 0.747 (0.833) Data 0.002 (0.002) Loss 2.9722 (2.8235) Prec@1 30.000 (33.041) Prec@5 65.000 (63.132) Epoch: [2][5020/11272] Time 0.753 (0.833) Data 0.002 (0.002) Loss 3.1125 (2.8235) Prec@1 29.375 (33.042) Prec@5 51.250 (63.129) Epoch: [2][5030/11272] Time 0.928 (0.833) Data 0.002 (0.002) Loss 2.9939 (2.8234) Prec@1 25.625 (33.044) Prec@5 61.875 (63.131) Epoch: [2][5040/11272] Time 0.898 (0.833) Data 0.001 (0.002) Loss 2.9514 (2.8235) Prec@1 30.625 (33.041) Prec@5 60.000 (63.129) Epoch: [2][5050/11272] Time 0.761 (0.833) Data 0.002 (0.002) Loss 2.8575 (2.8235) Prec@1 34.375 (33.041) Prec@5 60.000 (63.129) Epoch: [2][5060/11272] Time 0.891 (0.833) Data 0.002 (0.002) Loss 2.8898 (2.8235) Prec@1 31.250 (33.042) Prec@5 61.875 (63.129) Epoch: [2][5070/11272] Time 0.879 (0.833) Data 0.001 (0.002) Loss 2.8525 (2.8234) Prec@1 28.125 (33.043) Prec@5 65.000 (63.132) Epoch: [2][5080/11272] Time 0.810 (0.833) Data 0.001 (0.002) Loss 2.7851 (2.8233) Prec@1 31.250 (33.048) Prec@5 61.250 (63.132) Epoch: [2][5090/11272] Time 0.739 (0.833) Data 0.002 (0.002) Loss 2.7175 (2.8232) Prec@1 36.250 (33.050) Prec@5 66.250 (63.134) Epoch: [2][5100/11272] Time 0.901 (0.833) Data 0.002 (0.002) Loss 2.7728 (2.8231) Prec@1 30.000 (33.051) Prec@5 59.375 (63.133) Epoch: [2][5110/11272] Time 0.911 (0.833) Data 0.002 (0.002) Loss 2.7097 (2.8231) Prec@1 35.625 (33.053) Prec@5 67.500 (63.136) Epoch: [2][5120/11272] Time 0.756 (0.833) Data 0.001 (0.002) Loss 2.7774 (2.8230) Prec@1 32.500 (33.056) Prec@5 60.625 (63.135) Epoch: [2][5130/11272] Time 0.776 (0.833) Data 0.002 (0.002) Loss 2.7996 (2.8230) Prec@1 30.000 (33.055) Prec@5 62.500 (63.137) Epoch: [2][5140/11272] Time 0.929 (0.833) Data 0.002 (0.002) Loss 2.6683 (2.8230) Prec@1 33.750 (33.058) Prec@5 68.750 (63.139) Epoch: [2][5150/11272] Time 0.934 (0.833) Data 0.002 (0.002) Loss 2.8620 (2.8230) Prec@1 35.000 (33.062) Prec@5 63.125 (63.139) Epoch: [2][5160/11272] Time 0.753 (0.833) Data 0.002 (0.002) Loss 2.7405 (2.8230) Prec@1 35.000 (33.061) Prec@5 63.750 (63.136) Epoch: [2][5170/11272] Time 0.739 (0.833) Data 0.001 (0.002) Loss 2.6731 (2.8229) Prec@1 35.625 (33.062) Prec@5 65.000 (63.139) Epoch: [2][5180/11272] Time 0.833 (0.833) Data 0.001 (0.002) Loss 2.6936 (2.8228) Prec@1 35.000 (33.064) Prec@5 63.125 (63.143) Epoch: [2][5190/11272] Time 0.773 (0.833) Data 0.004 (0.002) Loss 2.7996 (2.8228) Prec@1 33.125 (33.065) Prec@5 67.500 (63.142) Epoch: [2][5200/11272] Time 0.726 (0.833) Data 0.001 (0.002) Loss 3.0618 (2.8229) Prec@1 30.000 (33.066) Prec@5 57.500 (63.139) Epoch: [2][5210/11272] Time 0.861 (0.833) Data 0.002 (0.002) Loss 2.5144 (2.8228) Prec@1 39.375 (33.069) Prec@5 68.750 (63.141) Epoch: [2][5220/11272] Time 0.880 (0.833) Data 0.001 (0.002) Loss 2.6749 (2.8226) Prec@1 34.375 (33.072) Prec@5 69.375 (63.143) Epoch: [2][5230/11272] Time 0.731 (0.833) Data 0.001 (0.002) Loss 2.7817 (2.8225) Prec@1 36.875 (33.074) Prec@5 66.250 (63.148) Epoch: [2][5240/11272] Time 0.759 (0.833) Data 0.002 (0.002) Loss 3.0011 (2.8225) Prec@1 30.625 (33.075) Prec@5 61.250 (63.146) Epoch: [2][5250/11272] Time 0.905 (0.833) Data 0.002 (0.002) Loss 2.6717 (2.8225) Prec@1 38.125 (33.074) Prec@5 66.250 (63.146) Epoch: [2][5260/11272] Time 0.893 (0.833) Data 0.001 (0.002) Loss 3.1180 (2.8226) Prec@1 25.000 (33.072) Prec@5 56.250 (63.144) Epoch: [2][5270/11272] Time 0.781 (0.833) Data 0.002 (0.002) Loss 2.6370 (2.8225) Prec@1 36.250 (33.074) Prec@5 70.000 (63.146) Epoch: [2][5280/11272] Time 0.755 (0.833) Data 0.001 (0.002) Loss 2.6125 (2.8223) Prec@1 40.625 (33.076) Prec@5 63.125 (63.148) Epoch: [2][5290/11272] Time 0.949 (0.833) Data 0.002 (0.002) Loss 3.0898 (2.8224) Prec@1 30.625 (33.075) Prec@5 58.750 (63.145) Epoch: [2][5300/11272] Time 0.896 (0.833) Data 0.001 (0.002) Loss 2.7025 (2.8222) Prec@1 33.125 (33.076) Prec@5 68.125 (63.149) Epoch: [2][5310/11272] Time 0.742 (0.833) Data 0.002 (0.002) Loss 2.8787 (2.8222) Prec@1 29.375 (33.081) Prec@5 64.375 (63.151) Epoch: [2][5320/11272] Time 0.936 (0.833) Data 0.001 (0.002) Loss 3.1501 (2.8223) Prec@1 26.250 (33.079) Prec@5 56.250 (63.149) Epoch: [2][5330/11272] Time 0.858 (0.833) Data 0.002 (0.002) Loss 3.0768 (2.8223) Prec@1 26.875 (33.078) Prec@5 53.750 (63.149) Epoch: [2][5340/11272] Time 0.780 (0.833) Data 0.001 (0.002) Loss 2.9080 (2.8222) Prec@1 33.125 (33.080) Prec@5 58.750 (63.151) Epoch: [2][5350/11272] Time 0.742 (0.833) Data 0.002 (0.002) Loss 2.9879 (2.8223) Prec@1 30.000 (33.077) Prec@5 62.500 (63.148) Epoch: [2][5360/11272] Time 0.911 (0.833) Data 0.002 (0.002) Loss 2.7596 (2.8223) Prec@1 31.250 (33.077) Prec@5 65.000 (63.150) Epoch: [2][5370/11272] Time 0.874 (0.833) Data 0.002 (0.002) Loss 2.8260 (2.8223) Prec@1 31.875 (33.076) Prec@5 59.375 (63.150) Epoch: [2][5380/11272] Time 0.732 (0.833) Data 0.002 (0.002) Loss 2.7521 (2.8223) Prec@1 38.125 (33.078) Prec@5 60.000 (63.150) Epoch: [2][5390/11272] Time 0.728 (0.833) Data 0.002 (0.002) Loss 2.8870 (2.8223) Prec@1 29.375 (33.077) Prec@5 62.500 (63.152) Epoch: [2][5400/11272] Time 0.857 (0.833) Data 0.001 (0.002) Loss 2.6901 (2.8223) Prec@1 36.875 (33.076) Prec@5 67.500 (63.151) Epoch: [2][5410/11272] Time 0.880 (0.833) Data 0.002 (0.002) Loss 2.9405 (2.8223) Prec@1 34.375 (33.074) Prec@5 60.000 (63.150) Epoch: [2][5420/11272] Time 0.795 (0.833) Data 0.001 (0.002) Loss 2.8393 (2.8225) Prec@1 33.125 (33.072) Prec@5 61.875 (63.147) Epoch: [2][5430/11272] Time 0.704 (0.833) Data 0.001 (0.002) Loss 2.7549 (2.8226) Prec@1 35.625 (33.071) Prec@5 61.250 (63.142) Epoch: [2][5440/11272] Time 0.887 (0.833) Data 0.001 (0.002) Loss 3.0207 (2.8227) Prec@1 28.750 (33.066) Prec@5 58.125 (63.139) Epoch: [2][5450/11272] Time 0.838 (0.833) Data 0.002 (0.002) Loss 2.6880 (2.8226) Prec@1 32.500 (33.067) Prec@5 68.125 (63.144) Epoch: [2][5460/11272] Time 0.745 (0.833) Data 0.002 (0.002) Loss 2.8367 (2.8225) Prec@1 35.000 (33.067) Prec@5 63.750 (63.144) Epoch: [2][5470/11272] Time 0.864 (0.833) Data 0.002 (0.002) Loss 2.9315 (2.8225) Prec@1 33.125 (33.068) Prec@5 62.500 (63.144) Epoch: [2][5480/11272] Time 0.853 (0.832) Data 0.001 (0.002) Loss 2.7579 (2.8225) Prec@1 36.250 (33.067) Prec@5 65.000 (63.144) Epoch: [2][5490/11272] Time 0.741 (0.832) Data 0.002 (0.002) Loss 2.7835 (2.8224) Prec@1 32.500 (33.067) Prec@5 62.500 (63.148) Epoch: [2][5500/11272] Time 0.752 (0.832) Data 0.002 (0.002) Loss 2.8297 (2.8224) Prec@1 32.500 (33.070) Prec@5 66.875 (63.148) Epoch: [2][5510/11272] Time 0.876 (0.832) Data 0.002 (0.002) Loss 2.8506 (2.8223) Prec@1 31.250 (33.070) Prec@5 63.750 (63.152) Epoch: [2][5520/11272] Time 0.909 (0.832) Data 0.001 (0.002) Loss 2.7648 (2.8222) Prec@1 35.000 (33.073) Prec@5 63.125 (63.155) Epoch: [2][5530/11272] Time 0.738 (0.832) Data 0.002 (0.002) Loss 2.7105 (2.8221) Prec@1 33.750 (33.073) Prec@5 65.000 (63.158) Epoch: [2][5540/11272] Time 0.736 (0.832) Data 0.002 (0.002) Loss 2.7823 (2.8220) Prec@1 30.625 (33.076) Prec@5 68.750 (63.159) Epoch: [2][5550/11272] Time 0.908 (0.832) Data 0.002 (0.002) Loss 2.5404 (2.8219) Prec@1 36.875 (33.075) Prec@5 67.500 (63.161) Epoch: [2][5560/11272] Time 0.856 (0.832) Data 0.001 (0.002) Loss 2.9297 (2.8218) Prec@1 29.375 (33.075) Prec@5 54.375 (63.164) Epoch: [2][5570/11272] Time 0.738 (0.832) Data 0.002 (0.002) Loss 2.7282 (2.8217) Prec@1 35.000 (33.075) Prec@5 64.375 (63.164) Epoch: [2][5580/11272] Time 0.724 (0.832) Data 0.001 (0.002) Loss 2.6126 (2.8216) Prec@1 36.250 (33.076) Prec@5 65.000 (63.166) Epoch: [2][5590/11272] Time 0.895 (0.832) Data 0.002 (0.002) Loss 3.0339 (2.8216) Prec@1 30.625 (33.074) Prec@5 60.000 (63.166) Epoch: [2][5600/11272] Time 0.743 (0.832) Data 0.001 (0.002) Loss 2.7226 (2.8215) Prec@1 37.500 (33.078) Prec@5 61.250 (63.168) Epoch: [2][5610/11272] Time 0.767 (0.832) Data 0.001 (0.002) Loss 2.6707 (2.8216) Prec@1 36.875 (33.075) Prec@5 67.500 (63.164) Epoch: [2][5620/11272] Time 0.858 (0.832) Data 0.002 (0.002) Loss 2.7640 (2.8216) Prec@1 33.750 (33.073) Prec@5 63.750 (63.163) Epoch: [2][5630/11272] Time 0.912 (0.832) Data 0.003 (0.002) Loss 2.7275 (2.8218) Prec@1 39.375 (33.069) Prec@5 63.750 (63.159) Epoch: [2][5640/11272] Time 0.755 (0.832) Data 0.002 (0.002) Loss 3.0304 (2.8218) Prec@1 25.625 (33.067) Prec@5 58.125 (63.160) Epoch: [2][5650/11272] Time 0.732 (0.832) Data 0.002 (0.002) Loss 2.9793 (2.8219) Prec@1 28.750 (33.066) Prec@5 60.625 (63.159) Epoch: [2][5660/11272] Time 0.897 (0.832) Data 0.001 (0.002) Loss 2.9397 (2.8220) Prec@1 33.750 (33.066) Prec@5 62.500 (63.159) Epoch: [2][5670/11272] Time 0.924 (0.832) Data 0.002 (0.002) Loss 2.6380 (2.8219) Prec@1 35.625 (33.066) Prec@5 66.875 (63.159) Epoch: [2][5680/11272] Time 0.768 (0.832) Data 0.001 (0.002) Loss 2.7651 (2.8218) Prec@1 33.125 (33.066) Prec@5 60.625 (63.160) Epoch: [2][5690/11272] Time 0.784 (0.832) Data 0.003 (0.002) Loss 3.0993 (2.8219) Prec@1 27.500 (33.065) Prec@5 61.250 (63.158) Epoch: [2][5700/11272] Time 0.847 (0.832) Data 0.002 (0.002) Loss 3.0237 (2.8220) Prec@1 29.375 (33.063) Prec@5 58.125 (63.154) Epoch: [2][5710/11272] Time 0.846 (0.832) Data 0.001 (0.002) Loss 2.8482 (2.8219) Prec@1 27.500 (33.064) Prec@5 65.000 (63.156) Epoch: [2][5720/11272] Time 0.744 (0.832) Data 0.002 (0.002) Loss 2.6290 (2.8219) Prec@1 40.625 (33.067) Prec@5 61.875 (63.156) Epoch: [2][5730/11272] Time 0.840 (0.832) Data 0.002 (0.002) Loss 2.7439 (2.8218) Prec@1 35.625 (33.071) Prec@5 57.500 (63.157) Epoch: [2][5740/11272] Time 0.884 (0.832) Data 0.002 (0.002) Loss 2.5393 (2.8215) Prec@1 35.000 (33.077) Prec@5 68.750 (63.165) Epoch: [2][5750/11272] Time 0.739 (0.832) Data 0.002 (0.002) Loss 2.6788 (2.8216) Prec@1 36.875 (33.076) Prec@5 70.625 (63.163) Epoch: [2][5760/11272] Time 0.725 (0.832) Data 0.001 (0.002) Loss 3.0258 (2.8217) Prec@1 30.000 (33.074) Prec@5 61.875 (63.165) Epoch: [2][5770/11272] Time 0.971 (0.832) Data 0.002 (0.002) Loss 2.9141 (2.8218) Prec@1 26.250 (33.073) Prec@5 66.250 (63.163) Epoch: [2][5780/11272] Time 0.877 (0.832) Data 0.002 (0.002) Loss 2.9430 (2.8218) Prec@1 34.375 (33.072) Prec@5 62.500 (63.163) Epoch: [2][5790/11272] Time 0.734 (0.832) Data 0.001 (0.002) Loss 2.6164 (2.8218) Prec@1 38.750 (33.074) Prec@5 62.500 (63.164) Epoch: [2][5800/11272] Time 0.759 (0.832) Data 0.002 (0.002) Loss 2.6800 (2.8219) Prec@1 39.375 (33.074) Prec@5 62.500 (63.159) Epoch: [2][5810/11272] Time 0.950 (0.832) Data 0.002 (0.002) Loss 2.8352 (2.8218) Prec@1 33.750 (33.077) Prec@5 65.000 (63.161) Epoch: [2][5820/11272] Time 0.864 (0.832) Data 0.002 (0.002) Loss 2.8524 (2.8217) Prec@1 32.500 (33.079) Prec@5 60.625 (63.161) Epoch: [2][5830/11272] Time 0.749 (0.832) Data 0.002 (0.002) Loss 2.6589 (2.8217) Prec@1 33.750 (33.080) Prec@5 63.750 (63.159) Epoch: [2][5840/11272] Time 0.757 (0.832) Data 0.002 (0.002) Loss 2.6981 (2.8217) Prec@1 35.000 (33.082) Prec@5 66.875 (63.161) Epoch: [2][5850/11272] Time 0.902 (0.832) Data 0.002 (0.002) Loss 3.1981 (2.8218) Prec@1 28.125 (33.080) Prec@5 55.625 (63.157) Epoch: [2][5860/11272] Time 0.747 (0.832) Data 0.003 (0.002) Loss 2.7647 (2.8218) Prec@1 31.875 (33.080) Prec@5 61.875 (63.153) Epoch: [2][5870/11272] Time 0.747 (0.832) Data 0.002 (0.002) Loss 2.7198 (2.8218) Prec@1 33.750 (33.082) Prec@5 65.625 (63.155) Epoch: [2][5880/11272] Time 0.875 (0.832) Data 0.002 (0.002) Loss 2.8105 (2.8217) Prec@1 38.125 (33.084) Prec@5 62.500 (63.156) Epoch: [2][5890/11272] Time 0.870 (0.832) Data 0.002 (0.002) Loss 2.5706 (2.8216) Prec@1 39.375 (33.088) Prec@5 67.500 (63.158) Epoch: [2][5900/11272] Time 0.751 (0.832) Data 0.002 (0.002) Loss 2.7897 (2.8215) Prec@1 32.500 (33.088) Prec@5 62.500 (63.160) Epoch: [2][5910/11272] Time 0.743 (0.832) Data 0.002 (0.002) Loss 2.9914 (2.8215) Prec@1 29.375 (33.089) Prec@5 63.125 (63.161) Epoch: [2][5920/11272] Time 0.952 (0.832) Data 0.002 (0.002) Loss 2.7021 (2.8216) Prec@1 35.625 (33.085) Prec@5 63.750 (63.159) Epoch: [2][5930/11272] Time 0.864 (0.832) Data 0.001 (0.002) Loss 2.7226 (2.8215) Prec@1 30.000 (33.085) Prec@5 65.625 (63.162) Epoch: [2][5940/11272] Time 0.814 (0.832) Data 0.002 (0.002) Loss 2.7166 (2.8213) Prec@1 33.750 (33.088) Prec@5 65.000 (63.167) Epoch: [2][5950/11272] Time 0.746 (0.832) Data 0.002 (0.002) Loss 2.6907 (2.8213) Prec@1 33.750 (33.085) Prec@5 68.125 (63.167) Epoch: [2][5960/11272] Time 0.903 (0.832) Data 0.001 (0.002) Loss 2.6899 (2.8212) Prec@1 33.750 (33.084) Prec@5 66.875 (63.170) Epoch: [2][5970/11272] Time 0.889 (0.832) Data 0.002 (0.002) Loss 2.9096 (2.8212) Prec@1 30.625 (33.084) Prec@5 61.875 (63.171) Epoch: [2][5980/11272] Time 0.727 (0.832) Data 0.001 (0.002) Loss 2.8123 (2.8212) Prec@1 33.125 (33.083) Prec@5 61.250 (63.171) Epoch: [2][5990/11272] Time 0.852 (0.832) Data 0.001 (0.002) Loss 2.5436 (2.8211) Prec@1 33.750 (33.085) Prec@5 66.875 (63.173) Epoch: [2][6000/11272] Time 0.876 (0.832) Data 0.001 (0.002) Loss 3.0990 (2.8211) Prec@1 29.375 (33.087) Prec@5 58.125 (63.173) Epoch: [2][6010/11272] Time 0.740 (0.832) Data 0.001 (0.002) Loss 2.8027 (2.8211) Prec@1 28.125 (33.085) Prec@5 63.750 (63.171) Epoch: [2][6020/11272] Time 0.768 (0.832) Data 0.002 (0.002) Loss 2.9353 (2.8211) Prec@1 27.500 (33.086) Prec@5 62.500 (63.173) Epoch: [2][6030/11272] Time 0.955 (0.832) Data 0.002 (0.002) Loss 2.8276 (2.8211) Prec@1 35.000 (33.086) Prec@5 63.125 (63.171) Epoch: [2][6040/11272] Time 0.885 (0.832) Data 0.001 (0.002) Loss 2.8663 (2.8210) Prec@1 35.000 (33.088) Prec@5 58.125 (63.172) Epoch: [2][6050/11272] Time 0.752 (0.832) Data 0.002 (0.002) Loss 2.7152 (2.8209) Prec@1 35.625 (33.091) Prec@5 65.625 (63.177) Epoch: [2][6060/11272] Time 0.742 (0.831) Data 0.001 (0.002) Loss 2.8764 (2.8209) Prec@1 26.250 (33.090) Prec@5 67.500 (63.180) Epoch: [2][6070/11272] Time 0.890 (0.831) Data 0.002 (0.002) Loss 2.7503 (2.8208) Prec@1 32.500 (33.092) Prec@5 65.625 (63.182) Epoch: [2][6080/11272] Time 0.910 (0.831) Data 0.001 (0.002) Loss 2.7085 (2.8207) Prec@1 32.500 (33.095) Prec@5 64.375 (63.184) Epoch: [2][6090/11272] Time 0.733 (0.831) Data 0.001 (0.002) Loss 2.6509 (2.8206) Prec@1 31.250 (33.092) Prec@5 66.250 (63.187) Epoch: [2][6100/11272] Time 0.759 (0.831) Data 0.002 (0.002) Loss 2.7109 (2.8206) Prec@1 35.000 (33.094) Prec@5 68.125 (63.189) Epoch: [2][6110/11272] Time 0.857 (0.831) Data 0.002 (0.002) Loss 2.9877 (2.8206) Prec@1 24.375 (33.093) Prec@5 63.125 (63.191) Epoch: [2][6120/11272] Time 0.750 (0.831) Data 0.003 (0.002) Loss 2.4578 (2.8206) Prec@1 40.625 (33.091) Prec@5 70.000 (63.188) Epoch: [2][6130/11272] Time 0.736 (0.831) Data 0.002 (0.002) Loss 2.7212 (2.8206) Prec@1 33.750 (33.089) Prec@5 70.625 (63.187) Epoch: [2][6140/11272] Time 0.912 (0.831) Data 0.002 (0.002) Loss 2.6858 (2.8206) Prec@1 33.750 (33.089) Prec@5 63.750 (63.187) Epoch: [2][6150/11272] Time 0.865 (0.831) Data 0.002 (0.002) Loss 2.6076 (2.8205) Prec@1 37.500 (33.093) Prec@5 67.500 (63.187) Epoch: [2][6160/11272] Time 0.779 (0.831) Data 0.002 (0.002) Loss 2.8411 (2.8205) Prec@1 32.500 (33.093) Prec@5 63.125 (63.188) Epoch: [2][6170/11272] Time 0.735 (0.831) Data 0.001 (0.002) Loss 2.8615 (2.8205) Prec@1 28.750 (33.093) Prec@5 64.375 (63.186) Epoch: [2][6180/11272] Time 0.891 (0.831) Data 0.001 (0.002) Loss 2.8106 (2.8204) Prec@1 35.625 (33.095) Prec@5 62.500 (63.189) Epoch: [2][6190/11272] Time 0.879 (0.831) Data 0.002 (0.002) Loss 2.7424 (2.8204) Prec@1 33.125 (33.096) Prec@5 65.000 (63.191) Epoch: [2][6200/11272] Time 0.767 (0.831) Data 0.002 (0.002) Loss 2.8868 (2.8204) Prec@1 31.875 (33.098) Prec@5 65.000 (63.193) Epoch: [2][6210/11272] Time 0.745 (0.831) Data 0.002 (0.002) Loss 3.0090 (2.8204) Prec@1 36.250 (33.102) Prec@5 60.625 (63.193) Epoch: [2][6220/11272] Time 0.884 (0.831) Data 0.001 (0.002) Loss 2.6593 (2.8203) Prec@1 35.000 (33.104) Prec@5 66.250 (63.193) Epoch: [2][6230/11272] Time 0.959 (0.831) Data 0.002 (0.002) Loss 2.7196 (2.8204) Prec@1 32.500 (33.100) Prec@5 68.125 (63.193) Epoch: [2][6240/11272] Time 0.732 (0.831) Data 0.001 (0.002) Loss 2.6507 (2.8204) Prec@1 33.125 (33.098) Prec@5 66.250 (63.193) Epoch: [2][6250/11272] Time 0.851 (0.831) Data 0.002 (0.002) Loss 2.5570 (2.8205) Prec@1 40.625 (33.096) Prec@5 73.750 (63.190) Epoch: [2][6260/11272] Time 0.877 (0.831) Data 0.001 (0.002) Loss 2.5170 (2.8204) Prec@1 35.625 (33.096) Prec@5 70.625 (63.193) Epoch: [2][6270/11272] Time 0.752 (0.831) Data 0.002 (0.002) Loss 3.0109 (2.8204) Prec@1 31.250 (33.095) Prec@5 57.500 (63.192) Epoch: [2][6280/11272] Time 0.749 (0.831) Data 0.001 (0.002) Loss 3.0012 (2.8205) Prec@1 32.500 (33.093) Prec@5 56.875 (63.192) Epoch: [2][6290/11272] Time 0.906 (0.831) Data 0.002 (0.002) Loss 2.6894 (2.8204) Prec@1 34.375 (33.094) Prec@5 70.625 (63.194) Epoch: [2][6300/11272] Time 0.876 (0.831) Data 0.001 (0.002) Loss 2.6021 (2.8203) Prec@1 31.250 (33.099) Prec@5 71.875 (63.198) Epoch: [2][6310/11272] Time 0.745 (0.831) Data 0.002 (0.002) Loss 2.7652 (2.8204) Prec@1 30.625 (33.095) Prec@5 63.125 (63.196) Epoch: [2][6320/11272] Time 0.737 (0.831) Data 0.001 (0.002) Loss 3.0426 (2.8204) Prec@1 29.375 (33.096) Prec@5 55.000 (63.196) Epoch: [2][6330/11272] Time 0.912 (0.831) Data 0.002 (0.002) Loss 2.8920 (2.8204) Prec@1 33.125 (33.096) Prec@5 58.125 (63.198) Epoch: [2][6340/11272] Time 0.870 (0.831) Data 0.001 (0.002) Loss 2.7730 (2.8204) Prec@1 35.000 (33.096) Prec@5 68.125 (63.198) Epoch: [2][6350/11272] Time 0.734 (0.831) Data 0.002 (0.002) Loss 2.9328 (2.8203) Prec@1 33.125 (33.098) Prec@5 59.375 (63.201) Epoch: [2][6360/11272] Time 0.712 (0.831) Data 0.001 (0.002) Loss 2.5851 (2.8203) Prec@1 33.125 (33.098) Prec@5 71.875 (63.202) Epoch: [2][6370/11272] Time 0.921 (0.831) Data 0.002 (0.002) Loss 2.7951 (2.8202) Prec@1 34.375 (33.099) Prec@5 58.750 (63.203) Epoch: [2][6380/11272] Time 0.907 (0.831) Data 0.002 (0.002) Loss 2.7450 (2.8201) Prec@1 33.750 (33.102) Prec@5 63.750 (63.205) Epoch: [2][6390/11272] Time 0.759 (0.831) Data 0.002 (0.002) Loss 2.7251 (2.8201) Prec@1 35.000 (33.100) Prec@5 63.750 (63.206) Epoch: [2][6400/11272] Time 0.902 (0.831) Data 0.002 (0.002) Loss 3.0209 (2.8199) Prec@1 25.625 (33.102) Prec@5 58.125 (63.210) Epoch: [2][6410/11272] Time 0.878 (0.831) Data 0.002 (0.002) Loss 2.6380 (2.8199) Prec@1 37.500 (33.103) Prec@5 63.750 (63.210) Epoch: [2][6420/11272] Time 0.767 (0.831) Data 0.002 (0.002) Loss 2.8553 (2.8199) Prec@1 33.750 (33.104) Prec@5 64.375 (63.211) Epoch: [2][6430/11272] Time 0.796 (0.831) Data 0.002 (0.002) Loss 2.7624 (2.8197) Prec@1 31.875 (33.107) Prec@5 65.000 (63.215) Epoch: [2][6440/11272] Time 0.863 (0.831) Data 0.001 (0.002) Loss 2.6722 (2.8196) Prec@1 35.625 (33.107) Prec@5 66.250 (63.215) Epoch: [2][6450/11272] Time 0.872 (0.831) Data 0.002 (0.002) Loss 2.7282 (2.8195) Prec@1 36.250 (33.109) Prec@5 62.500 (63.217) Epoch: [2][6460/11272] Time 0.767 (0.831) Data 0.001 (0.002) Loss 2.9262 (2.8195) Prec@1 30.000 (33.110) Prec@5 63.750 (63.217) Epoch: [2][6470/11272] Time 0.756 (0.831) Data 0.005 (0.002) Loss 2.6286 (2.8194) Prec@1 33.125 (33.113) Prec@5 65.625 (63.220) Epoch: [2][6480/11272] Time 0.871 (0.831) Data 0.001 (0.002) Loss 2.5834 (2.8193) Prec@1 41.875 (33.112) Prec@5 68.750 (63.220) Epoch: [2][6490/11272] Time 0.863 (0.831) Data 0.002 (0.002) Loss 2.6513 (2.8193) Prec@1 33.750 (33.111) Prec@5 71.250 (63.221) Epoch: [2][6500/11272] Time 0.764 (0.831) Data 0.001 (0.002) Loss 2.7130 (2.8193) Prec@1 32.500 (33.111) Prec@5 70.000 (63.220) Epoch: [2][6510/11272] Time 0.750 (0.831) Data 0.002 (0.002) Loss 2.8001 (2.8193) Prec@1 33.750 (33.110) Prec@5 58.750 (63.221) Epoch: [2][6520/11272] Time 0.860 (0.831) Data 0.002 (0.002) Loss 2.8449 (2.8192) Prec@1 35.625 (33.113) Prec@5 64.375 (63.224) Epoch: [2][6530/11272] Time 0.737 (0.831) Data 0.001 (0.002) Loss 2.6549 (2.8192) Prec@1 33.125 (33.112) Prec@5 67.500 (63.225) Epoch: [2][6540/11272] Time 0.734 (0.831) Data 0.002 (0.002) Loss 3.1763 (2.8193) Prec@1 25.000 (33.109) Prec@5 61.250 (63.224) Epoch: [2][6550/11272] Time 0.911 (0.831) Data 0.002 (0.002) Loss 3.0211 (2.8193) Prec@1 30.625 (33.108) Prec@5 56.875 (63.223) Epoch: [2][6560/11272] Time 0.866 (0.831) Data 0.002 (0.002) Loss 2.8403 (2.8194) Prec@1 34.375 (33.109) Prec@5 63.125 (63.222) Epoch: [2][6570/11272] Time 0.737 (0.831) Data 0.002 (0.002) Loss 3.0344 (2.8194) Prec@1 26.875 (33.109) Prec@5 58.750 (63.221) Epoch: [2][6580/11272] Time 0.750 (0.831) Data 0.002 (0.002) Loss 2.9923 (2.8194) Prec@1 31.875 (33.109) Prec@5 58.125 (63.223) Epoch: [2][6590/11272] Time 0.861 (0.831) Data 0.002 (0.002) Loss 2.6150 (2.8192) Prec@1 33.750 (33.112) Prec@5 67.500 (63.226) Epoch: [2][6600/11272] Time 0.860 (0.831) Data 0.001 (0.002) Loss 2.9554 (2.8192) Prec@1 33.750 (33.112) Prec@5 61.250 (63.225) Epoch: [2][6610/11272] Time 0.760 (0.831) Data 0.002 (0.002) Loss 2.8823 (2.8192) Prec@1 35.000 (33.110) Prec@5 60.000 (63.224) Epoch: [2][6620/11272] Time 0.735 (0.831) Data 0.002 (0.002) Loss 2.7804 (2.8193) Prec@1 32.500 (33.112) Prec@5 61.250 (63.224) Epoch: [2][6630/11272] Time 0.909 (0.831) Data 0.002 (0.002) Loss 2.8046 (2.8192) Prec@1 28.125 (33.112) Prec@5 61.875 (63.225) Epoch: [2][6640/11272] Time 0.854 (0.831) Data 0.002 (0.002) Loss 2.8544 (2.8192) Prec@1 31.875 (33.112) Prec@5 64.375 (63.225) Epoch: [2][6650/11272] Time 0.741 (0.831) Data 0.001 (0.002) Loss 2.8184 (2.8192) Prec@1 30.000 (33.113) Prec@5 63.125 (63.225) Epoch: [2][6660/11272] Time 0.845 (0.831) Data 0.002 (0.002) Loss 2.9185 (2.8192) Prec@1 30.000 (33.114) Prec@5 60.000 (63.226) Epoch: [2][6670/11272] Time 0.898 (0.831) Data 0.002 (0.002) Loss 3.0755 (2.8193) Prec@1 28.750 (33.111) Prec@5 55.625 (63.224) Epoch: [2][6680/11272] Time 0.773 (0.831) Data 0.001 (0.002) Loss 2.5767 (2.8191) Prec@1 41.875 (33.115) Prec@5 66.875 (63.229) Epoch: [2][6690/11272] Time 0.789 (0.831) Data 0.002 (0.002) Loss 2.7716 (2.8192) Prec@1 36.875 (33.111) Prec@5 65.000 (63.228) Epoch: [2][6700/11272] Time 0.889 (0.831) Data 0.001 (0.002) Loss 2.6020 (2.8190) Prec@1 39.375 (33.113) Prec@5 66.250 (63.231) Epoch: [2][6710/11272] Time 0.902 (0.831) Data 0.002 (0.002) Loss 2.6569 (2.8189) Prec@1 35.000 (33.114) Prec@5 68.125 (63.232) Epoch: [2][6720/11272] Time 0.744 (0.831) Data 0.002 (0.002) Loss 2.7092 (2.8188) Prec@1 36.875 (33.115) Prec@5 68.750 (63.237) Epoch: [2][6730/11272] Time 0.738 (0.831) Data 0.001 (0.002) Loss 3.0014 (2.8188) Prec@1 28.750 (33.115) Prec@5 59.375 (63.238) Epoch: [2][6740/11272] Time 0.916 (0.831) Data 0.001 (0.002) Loss 2.8019 (2.8187) Prec@1 31.250 (33.116) Prec@5 61.875 (63.240) Epoch: [2][6750/11272] Time 0.890 (0.831) Data 0.002 (0.002) Loss 2.9438 (2.8187) Prec@1 31.875 (33.115) Prec@5 58.750 (63.239) Epoch: [2][6760/11272] Time 0.736 (0.830) Data 0.001 (0.002) Loss 2.6786 (2.8186) Prec@1 33.750 (33.116) Prec@5 65.625 (63.241) Epoch: [2][6770/11272] Time 0.764 (0.830) Data 0.002 (0.002) Loss 2.3763 (2.8184) Prec@1 41.250 (33.119) Prec@5 74.375 (63.244) Epoch: [2][6780/11272] Time 0.889 (0.830) Data 0.002 (0.002) Loss 2.7138 (2.8184) Prec@1 35.625 (33.120) Prec@5 63.125 (63.244) Epoch: [2][6790/11272] Time 0.760 (0.830) Data 0.003 (0.002) Loss 2.5300 (2.8184) Prec@1 35.000 (33.119) Prec@5 69.375 (63.245) Epoch: [2][6800/11272] Time 0.736 (0.830) Data 0.001 (0.002) Loss 2.6525 (2.8184) Prec@1 31.875 (33.119) Prec@5 60.625 (63.244) Epoch: [2][6810/11272] Time 0.879 (0.830) Data 0.001 (0.002) Loss 2.9915 (2.8185) Prec@1 33.750 (33.119) Prec@5 58.125 (63.243) Epoch: [2][6820/11272] Time 0.863 (0.830) Data 0.001 (0.002) Loss 2.5332 (2.8184) Prec@1 43.750 (33.121) Prec@5 66.875 (63.244) Epoch: [2][6830/11272] Time 0.733 (0.830) Data 0.001 (0.002) Loss 3.0077 (2.8183) Prec@1 30.000 (33.119) Prec@5 56.250 (63.244) Epoch: [2][6840/11272] Time 0.730 (0.830) Data 0.002 (0.002) Loss 3.0808 (2.8182) Prec@1 28.750 (33.120) Prec@5 56.875 (63.246) Epoch: [2][6850/11272] Time 0.914 (0.830) Data 0.002 (0.002) Loss 2.7801 (2.8181) Prec@1 35.000 (33.120) Prec@5 64.375 (63.249) Epoch: [2][6860/11272] Time 0.911 (0.830) Data 0.002 (0.002) Loss 3.0401 (2.8183) Prec@1 27.500 (33.115) Prec@5 60.625 (63.245) Epoch: [2][6870/11272] Time 0.741 (0.830) Data 0.002 (0.002) Loss 2.9238 (2.8183) Prec@1 34.375 (33.113) Prec@5 60.000 (63.245) Epoch: [2][6880/11272] Time 0.737 (0.830) Data 0.002 (0.002) Loss 2.9415 (2.8184) Prec@1 27.500 (33.111) Prec@5 58.125 (63.242) Epoch: [2][6890/11272] Time 0.925 (0.830) Data 0.002 (0.002) Loss 2.6308 (2.8184) Prec@1 35.000 (33.109) Prec@5 69.375 (63.241) Epoch: [2][6900/11272] Time 0.871 (0.830) Data 0.001 (0.002) Loss 2.7679 (2.8183) Prec@1 36.875 (33.112) Prec@5 63.125 (63.242) Epoch: [2][6910/11272] Time 0.738 (0.830) Data 0.002 (0.002) Loss 2.8603 (2.8183) Prec@1 33.125 (33.113) Prec@5 59.375 (63.244) Epoch: [2][6920/11272] Time 0.891 (0.830) Data 0.001 (0.002) Loss 2.5567 (2.8182) Prec@1 36.250 (33.111) Prec@5 65.625 (63.243) Epoch: [2][6930/11272] Time 0.919 (0.830) Data 0.002 (0.002) Loss 2.8153 (2.8181) Prec@1 35.000 (33.113) Prec@5 61.250 (63.244) Epoch: [2][6940/11272] Time 0.791 (0.830) Data 0.001 (0.002) Loss 2.5801 (2.8181) Prec@1 38.125 (33.115) Prec@5 68.750 (63.245) Epoch: [2][6950/11272] Time 0.796 (0.830) Data 0.002 (0.002) Loss 2.5778 (2.8180) Prec@1 39.375 (33.115) Prec@5 65.625 (63.246) Epoch: [2][6960/11272] Time 0.864 (0.830) Data 0.001 (0.002) Loss 2.5885 (2.8179) Prec@1 31.250 (33.117) Prec@5 71.250 (63.249) Epoch: [2][6970/11272] Time 0.848 (0.830) Data 0.001 (0.002) Loss 3.1219 (2.8178) Prec@1 26.250 (33.120) Prec@5 58.125 (63.252) Epoch: [2][6980/11272] Time 0.721 (0.830) Data 0.001 (0.002) Loss 2.5697 (2.8178) Prec@1 39.375 (33.121) Prec@5 66.250 (63.252) Epoch: [2][6990/11272] Time 0.774 (0.830) Data 0.002 (0.002) Loss 2.8524 (2.8179) Prec@1 34.375 (33.119) Prec@5 61.250 (63.248) Epoch: [2][7000/11272] Time 0.892 (0.830) Data 0.002 (0.002) Loss 2.8147 (2.8177) Prec@1 32.500 (33.122) Prec@5 69.375 (63.253) Epoch: [2][7010/11272] Time 0.871 (0.830) Data 0.002 (0.002) Loss 2.7356 (2.8177) Prec@1 33.750 (33.121) Prec@5 64.375 (63.252) Epoch: [2][7020/11272] Time 0.759 (0.830) Data 0.002 (0.002) Loss 2.7722 (2.8177) Prec@1 30.625 (33.122) Prec@5 65.625 (63.253) Epoch: [2][7030/11272] Time 0.771 (0.830) Data 0.002 (0.002) Loss 2.4951 (2.8176) Prec@1 37.500 (33.124) Prec@5 68.750 (63.255) Epoch: [2][7040/11272] Time 0.844 (0.830) Data 0.001 (0.002) Loss 2.7826 (2.8176) Prec@1 32.500 (33.121) Prec@5 64.375 (63.256) Epoch: [2][7050/11272] Time 0.757 (0.830) Data 0.003 (0.002) Loss 2.4556 (2.8175) Prec@1 40.000 (33.125) Prec@5 71.250 (63.261) Epoch: [2][7060/11272] Time 0.792 (0.830) Data 0.002 (0.002) Loss 2.4857 (2.8174) Prec@1 35.625 (33.128) Prec@5 68.125 (63.261) Epoch: [2][7070/11272] Time 0.886 (0.830) Data 0.002 (0.002) Loss 2.9945 (2.8174) Prec@1 31.875 (33.127) Prec@5 60.625 (63.262) Epoch: [2][7080/11272] Time 0.860 (0.830) Data 0.001 (0.002) Loss 2.6867 (2.8173) Prec@1 35.625 (33.128) Prec@5 66.250 (63.262) Epoch: [2][7090/11272] Time 0.777 (0.830) Data 0.002 (0.002) Loss 2.9749 (2.8173) Prec@1 32.500 (33.127) Prec@5 58.125 (63.263) Epoch: [2][7100/11272] Time 0.732 (0.830) Data 0.002 (0.002) Loss 2.7797 (2.8173) Prec@1 32.500 (33.126) Prec@5 66.250 (63.264) Epoch: [2][7110/11272] Time 0.890 (0.830) Data 0.002 (0.002) Loss 2.7009 (2.8172) Prec@1 35.000 (33.128) Prec@5 65.000 (63.267) Epoch: [2][7120/11272] Time 0.861 (0.830) Data 0.001 (0.002) Loss 2.6342 (2.8172) Prec@1 41.875 (33.129) Prec@5 61.875 (63.265) Epoch: [2][7130/11272] Time 0.760 (0.830) Data 0.002 (0.002) Loss 3.0586 (2.8172) Prec@1 30.000 (33.130) Prec@5 61.250 (63.266) Epoch: [2][7140/11272] Time 0.748 (0.830) Data 0.002 (0.002) Loss 2.7637 (2.8171) Prec@1 40.000 (33.131) Prec@5 65.000 (63.268) Epoch: [2][7150/11272] Time 0.856 (0.830) Data 0.002 (0.002) Loss 2.6363 (2.8171) Prec@1 37.500 (33.130) Prec@5 66.875 (63.268) Epoch: [2][7160/11272] Time 0.852 (0.830) Data 0.002 (0.002) Loss 2.6890 (2.8170) Prec@1 34.375 (33.131) Prec@5 66.875 (63.269) Epoch: [2][7170/11272] Time 0.754 (0.830) Data 0.001 (0.002) Loss 2.6257 (2.8170) Prec@1 35.625 (33.132) Prec@5 65.000 (63.270) Epoch: [2][7180/11272] Time 0.943 (0.830) Data 0.001 (0.002) Loss 2.8475 (2.8169) Prec@1 32.500 (33.132) Prec@5 64.375 (63.273) Epoch: [2][7190/11272] Time 0.912 (0.830) Data 0.002 (0.002) Loss 3.0164 (2.8169) Prec@1 28.750 (33.134) Prec@5 60.000 (63.276) Epoch: [2][7200/11272] Time 0.740 (0.830) Data 0.001 (0.002) Loss 2.8659 (2.8169) Prec@1 33.125 (33.135) Prec@5 61.875 (63.275) Epoch: [2][7210/11272] Time 0.765 (0.830) Data 0.002 (0.002) Loss 2.8885 (2.8168) Prec@1 33.125 (33.136) Prec@5 60.625 (63.275) Epoch: [2][7220/11272] Time 0.853 (0.830) Data 0.001 (0.002) Loss 2.6974 (2.8167) Prec@1 30.625 (33.137) Prec@5 69.375 (63.278) Epoch: [2][7230/11272] Time 0.833 (0.830) Data 0.001 (0.002) Loss 2.5827 (2.8166) Prec@1 44.375 (33.140) Prec@5 71.250 (63.282) Epoch: [2][7240/11272] Time 0.735 (0.830) Data 0.002 (0.002) Loss 2.8761 (2.8166) Prec@1 34.375 (33.140) Prec@5 60.000 (63.281) Epoch: [2][7250/11272] Time 0.774 (0.830) Data 0.001 (0.002) Loss 2.5850 (2.8165) Prec@1 35.625 (33.141) Prec@5 67.500 (63.283) Epoch: [2][7260/11272] Time 0.864 (0.830) Data 0.001 (0.002) Loss 2.7127 (2.8164) Prec@1 35.000 (33.143) Prec@5 61.875 (63.283) Epoch: [2][7270/11272] Time 0.881 (0.830) Data 0.002 (0.002) Loss 2.6372 (2.8163) Prec@1 34.375 (33.145) Prec@5 67.500 (63.284) Epoch: [2][7280/11272] Time 0.738 (0.830) Data 0.001 (0.002) Loss 2.5699 (2.8163) Prec@1 36.250 (33.144) Prec@5 68.125 (63.284) Epoch: [2][7290/11272] Time 0.750 (0.830) Data 0.002 (0.002) Loss 2.8409 (2.8163) Prec@1 34.375 (33.146) Prec@5 61.250 (63.284) Epoch: [2][7300/11272] Time 0.851 (0.830) Data 0.002 (0.002) Loss 2.8873 (2.8162) Prec@1 30.625 (33.148) Prec@5 63.125 (63.286) Epoch: [2][7310/11272] Time 0.887 (0.830) Data 0.002 (0.002) Loss 2.8246 (2.8162) Prec@1 33.750 (33.149) Prec@5 60.625 (63.287) Epoch: [2][7320/11272] Time 0.741 (0.829) Data 0.002 (0.002) Loss 2.6341 (2.8162) Prec@1 35.625 (33.149) Prec@5 66.875 (63.290) Epoch: [2][7330/11272] Time 0.874 (0.829) Data 0.001 (0.002) Loss 3.0288 (2.8161) Prec@1 34.375 (33.152) Prec@5 60.625 (63.291) Epoch: [2][7340/11272] Time 0.862 (0.829) Data 0.001 (0.002) Loss 2.7219 (2.8161) Prec@1 26.250 (33.151) Prec@5 66.875 (63.291) Epoch: [2][7350/11272] Time 0.764 (0.829) Data 0.001 (0.002) Loss 2.8735 (2.8162) Prec@1 37.500 (33.149) Prec@5 60.000 (63.290) Epoch: [2][7360/11272] Time 0.747 (0.829) Data 0.001 (0.002) Loss 2.6734 (2.8162) Prec@1 40.625 (33.149) Prec@5 65.000 (63.289) Epoch: [2][7370/11272] Time 0.898 (0.829) Data 0.002 (0.002) Loss 2.6784 (2.8161) Prec@1 35.625 (33.152) Prec@5 66.875 (63.291) Epoch: [2][7380/11272] Time 0.856 (0.829) Data 0.002 (0.002) Loss 2.6740 (2.8161) Prec@1 38.750 (33.153) Prec@5 66.250 (63.291) Epoch: [2][7390/11272] Time 0.797 (0.829) Data 0.002 (0.002) Loss 2.6459 (2.8161) Prec@1 31.875 (33.152) Prec@5 64.375 (63.290) Epoch: [2][7400/11272] Time 0.753 (0.829) Data 0.001 (0.002) Loss 2.9560 (2.8160) Prec@1 27.500 (33.154) Prec@5 58.125 (63.292) Epoch: [2][7410/11272] Time 0.884 (0.829) Data 0.002 (0.002) Loss 2.9843 (2.8159) Prec@1 29.375 (33.157) Prec@5 60.000 (63.294) Epoch: [2][7420/11272] Time 0.845 (0.829) Data 0.001 (0.002) Loss 2.7653 (2.8159) Prec@1 31.875 (33.157) Prec@5 63.750 (63.294) Epoch: [2][7430/11272] Time 0.749 (0.829) Data 0.002 (0.002) Loss 2.7956 (2.8159) Prec@1 36.875 (33.155) Prec@5 65.000 (63.295) Epoch: [2][7440/11272] Time 0.735 (0.829) Data 0.001 (0.002) Loss 2.6167 (2.8159) Prec@1 32.500 (33.155) Prec@5 71.250 (63.298) Epoch: [2][7450/11272] Time 0.869 (0.829) Data 0.001 (0.002) Loss 2.7336 (2.8158) Prec@1 39.375 (33.155) Prec@5 66.250 (63.299) Epoch: [2][7460/11272] Time 0.781 (0.829) Data 0.003 (0.002) Loss 2.7238 (2.8158) Prec@1 33.750 (33.152) Prec@5 68.125 (63.300) Epoch: [2][7470/11272] Time 0.717 (0.829) Data 0.001 (0.002) Loss 2.9855 (2.8157) Prec@1 28.750 (33.153) Prec@5 60.625 (63.301) Epoch: [2][7480/11272] Time 0.879 (0.829) Data 0.002 (0.002) Loss 2.7964 (2.8156) Prec@1 35.000 (33.156) Prec@5 64.375 (63.303) Epoch: [2][7490/11272] Time 0.854 (0.829) Data 0.002 (0.002) Loss 2.7710 (2.8155) Prec@1 34.375 (33.158) Prec@5 62.500 (63.307) Epoch: [2][7500/11272] Time 0.788 (0.829) Data 0.001 (0.002) Loss 2.6442 (2.8155) Prec@1 37.500 (33.158) Prec@5 68.125 (63.308) Epoch: [2][7510/11272] Time 0.768 (0.829) Data 0.002 (0.002) Loss 2.8004 (2.8155) Prec@1 35.625 (33.158) Prec@5 63.125 (63.306) Epoch: [2][7520/11272] Time 0.904 (0.829) Data 0.002 (0.002) Loss 2.5567 (2.8154) Prec@1 37.500 (33.162) Prec@5 68.750 (63.308) Epoch: [2][7530/11272] Time 0.860 (0.829) Data 0.002 (0.002) Loss 2.8987 (2.8155) Prec@1 31.250 (33.160) Prec@5 61.250 (63.309) Epoch: [2][7540/11272] Time 0.784 (0.829) Data 0.002 (0.002) Loss 2.7028 (2.8154) Prec@1 34.375 (33.161) Prec@5 70.000 (63.311) Epoch: [2][7550/11272] Time 0.764 (0.829) Data 0.002 (0.002) Loss 2.8120 (2.8153) Prec@1 35.625 (33.159) Prec@5 66.250 (63.313) Epoch: [2][7560/11272] Time 0.903 (0.829) Data 0.001 (0.002) Loss 2.9333 (2.8153) Prec@1 31.875 (33.159) Prec@5 56.875 (63.313) Epoch: [2][7570/11272] Time 0.847 (0.829) Data 0.002 (0.002) Loss 2.7723 (2.8153) Prec@1 31.250 (33.159) Prec@5 61.875 (63.313) Epoch: [2][7580/11272] Time 0.809 (0.829) Data 0.001 (0.002) Loss 2.7601 (2.8153) Prec@1 31.250 (33.159) Prec@5 65.625 (63.312) Epoch: [2][7590/11272] Time 0.904 (0.829) Data 0.002 (0.002) Loss 3.0838 (2.8154) Prec@1 28.125 (33.160) Prec@5 58.750 (63.310) Epoch: [2][7600/11272] Time 0.855 (0.829) Data 0.002 (0.002) Loss 2.7353 (2.8155) Prec@1 36.250 (33.158) Prec@5 62.500 (63.309) Epoch: [2][7610/11272] Time 0.734 (0.829) Data 0.001 (0.002) Loss 2.5291 (2.8154) Prec@1 39.375 (33.159) Prec@5 66.875 (63.310) Epoch: [2][7620/11272] Time 0.763 (0.829) Data 0.001 (0.002) Loss 2.9767 (2.8154) Prec@1 28.125 (33.159) Prec@5 58.750 (63.310) Epoch: [2][7630/11272] Time 0.865 (0.829) Data 0.002 (0.002) Loss 2.9661 (2.8155) Prec@1 31.250 (33.158) Prec@5 62.500 (63.308) Epoch: [2][7640/11272] Time 0.854 (0.829) Data 0.001 (0.002) Loss 2.7745 (2.8154) Prec@1 34.375 (33.158) Prec@5 65.625 (63.309) Epoch: [2][7650/11272] Time 0.787 (0.829) Data 0.002 (0.002) Loss 2.6926 (2.8153) Prec@1 35.625 (33.161) Prec@5 63.125 (63.311) Epoch: [2][7660/11272] Time 0.748 (0.829) Data 0.001 (0.002) Loss 2.8435 (2.8152) Prec@1 36.250 (33.163) Prec@5 63.750 (63.312) Epoch: [2][7670/11272] Time 0.873 (0.829) Data 0.001 (0.002) Loss 2.8693 (2.8152) Prec@1 30.625 (33.162) Prec@5 64.375 (63.312) Epoch: [2][7680/11272] Time 0.849 (0.829) Data 0.002 (0.002) Loss 2.8777 (2.8152) Prec@1 33.750 (33.162) Prec@5 63.125 (63.312) Epoch: [2][7690/11272] Time 0.753 (0.829) Data 0.002 (0.002) Loss 2.6476 (2.8152) Prec@1 38.750 (33.164) Prec@5 66.875 (63.313) Epoch: [2][7700/11272] Time 0.735 (0.829) Data 0.001 (0.002) Loss 2.7000 (2.8151) Prec@1 38.750 (33.165) Prec@5 69.375 (63.313) Epoch: [2][7710/11272] Time 0.858 (0.829) Data 0.002 (0.002) Loss 2.5333 (2.8150) Prec@1 39.375 (33.170) Prec@5 66.250 (63.315) Epoch: [2][7720/11272] Time 0.738 (0.829) Data 0.003 (0.002) Loss 2.9538 (2.8150) Prec@1 30.625 (33.173) Prec@5 56.875 (63.315) Epoch: [2][7730/11272] Time 0.762 (0.829) Data 0.002 (0.002) Loss 3.1202 (2.8151) Prec@1 30.625 (33.171) Prec@5 54.375 (63.314) Epoch: [2][7740/11272] Time 0.872 (0.829) Data 0.001 (0.002) Loss 2.8559 (2.8151) Prec@1 33.125 (33.170) Prec@5 59.375 (63.313) Epoch: [2][7750/11272] Time 0.859 (0.828) Data 0.002 (0.002) Loss 2.8309 (2.8150) Prec@1 30.000 (33.172) Prec@5 61.875 (63.315) Epoch: [2][7760/11272] Time 0.736 (0.828) Data 0.001 (0.002) Loss 2.9492 (2.8149) Prec@1 30.000 (33.173) Prec@5 60.625 (63.317) Epoch: [2][7770/11272] Time 0.729 (0.828) Data 0.002 (0.002) Loss 2.7382 (2.8149) Prec@1 33.125 (33.173) Prec@5 68.125 (63.319) Epoch: [2][7780/11272] Time 0.852 (0.828) Data 0.002 (0.002) Loss 2.7049 (2.8148) Prec@1 33.125 (33.174) Prec@5 62.500 (63.319) Epoch: [2][7790/11272] Time 0.823 (0.828) Data 0.001 (0.002) Loss 2.7254 (2.8147) Prec@1 31.250 (33.176) Prec@5 64.375 (63.321) Epoch: [2][7800/11272] Time 0.800 (0.828) Data 0.002 (0.002) Loss 2.8489 (2.8146) Prec@1 30.625 (33.176) Prec@5 61.250 (63.322) Epoch: [2][7810/11272] Time 0.738 (0.828) Data 0.001 (0.002) Loss 2.9041 (2.8147) Prec@1 26.250 (33.174) Prec@5 62.500 (63.319) Epoch: [2][7820/11272] Time 0.866 (0.828) Data 0.001 (0.002) Loss 2.9083 (2.8148) Prec@1 35.625 (33.172) Prec@5 61.250 (63.318) Epoch: [2][7830/11272] Time 0.867 (0.828) Data 0.001 (0.002) Loss 2.8916 (2.8148) Prec@1 32.500 (33.172) Prec@5 60.000 (63.317) Epoch: [2][7840/11272] Time 0.786 (0.828) Data 0.002 (0.002) Loss 3.0003 (2.8149) Prec@1 30.000 (33.170) Prec@5 60.000 (63.316) Epoch: [2][7850/11272] Time 0.836 (0.828) Data 0.001 (0.002) Loss 2.5197 (2.8148) Prec@1 41.250 (33.171) Prec@5 67.500 (63.317) Epoch: [2][7860/11272] Time 0.906 (0.828) Data 0.002 (0.002) Loss 2.7355 (2.8148) Prec@1 34.375 (33.174) Prec@5 61.875 (63.317) Epoch: [2][7870/11272] Time 0.753 (0.828) Data 0.001 (0.002) Loss 2.9347 (2.8147) Prec@1 28.125 (33.172) Prec@5 65.000 (63.318) Epoch: [2][7880/11272] Time 0.736 (0.828) Data 0.001 (0.002) Loss 2.8751 (2.8148) Prec@1 34.375 (33.173) Prec@5 60.000 (63.316) Epoch: [2][7890/11272] Time 0.878 (0.828) Data 0.001 (0.002) Loss 2.4467 (2.8146) Prec@1 41.875 (33.175) Prec@5 71.250 (63.320) Epoch: [2][7900/11272] Time 0.857 (0.828) Data 0.001 (0.002) Loss 2.8180 (2.8146) Prec@1 33.125 (33.175) Prec@5 65.625 (63.320) Epoch: [2][7910/11272] Time 0.739 (0.828) Data 0.002 (0.002) Loss 2.9244 (2.8147) Prec@1 36.875 (33.173) Prec@5 60.000 (63.318) Epoch: [2][7920/11272] Time 0.754 (0.828) Data 0.001 (0.002) Loss 2.6908 (2.8146) Prec@1 35.625 (33.175) Prec@5 63.750 (63.318) Epoch: [2][7930/11272] Time 0.862 (0.828) Data 0.002 (0.002) Loss 2.7128 (2.8146) Prec@1 30.625 (33.173) Prec@5 66.875 (63.317) Epoch: [2][7940/11272] Time 0.843 (0.828) Data 0.001 (0.002) Loss 2.8410 (2.8147) Prec@1 31.875 (33.173) Prec@5 60.000 (63.315) Epoch: [2][7950/11272] Time 0.793 (0.828) Data 0.002 (0.002) Loss 2.8083 (2.8146) Prec@1 33.125 (33.174) Prec@5 61.250 (63.316) Epoch: [2][7960/11272] Time 0.754 (0.828) Data 0.001 (0.002) Loss 2.9150 (2.8147) Prec@1 30.625 (33.174) Prec@5 59.375 (63.315) Epoch: [2][7970/11272] Time 0.837 (0.828) Data 0.002 (0.002) Loss 3.0236 (2.8148) Prec@1 30.625 (33.173) Prec@5 59.375 (63.313) Epoch: [2][7980/11272] Time 0.749 (0.828) Data 0.003 (0.002) Loss 2.9391 (2.8148) Prec@1 26.250 (33.172) Prec@5 56.875 (63.313) Epoch: [2][7990/11272] Time 0.766 (0.828) Data 0.002 (0.002) Loss 3.0112 (2.8148) Prec@1 30.625 (33.173) Prec@5 59.375 (63.313) Epoch: [2][8000/11272] Time 0.847 (0.828) Data 0.001 (0.002) Loss 2.8306 (2.8148) Prec@1 28.750 (33.172) Prec@5 61.875 (63.311) Epoch: [2][8010/11272] Time 0.817 (0.828) Data 0.002 (0.002) Loss 2.9052 (2.8148) Prec@1 30.625 (33.171) Prec@5 64.375 (63.312) Epoch: [2][8020/11272] Time 0.789 (0.828) Data 0.001 (0.002) Loss 2.8754 (2.8148) Prec@1 25.000 (33.171) Prec@5 60.625 (63.313) Epoch: [2][8030/11272] Time 0.759 (0.828) Data 0.002 (0.002) Loss 2.7889 (2.8147) Prec@1 30.000 (33.172) Prec@5 66.875 (63.315) Epoch: [2][8040/11272] Time 0.858 (0.828) Data 0.001 (0.002) Loss 2.6830 (2.8146) Prec@1 34.375 (33.174) Prec@5 65.000 (63.317) Epoch: [2][8050/11272] Time 0.835 (0.828) Data 0.002 (0.002) Loss 2.5779 (2.8146) Prec@1 38.125 (33.175) Prec@5 70.000 (63.319) Epoch: [2][8060/11272] Time 0.759 (0.828) Data 0.002 (0.002) Loss 2.7766 (2.8145) Prec@1 29.375 (33.176) Prec@5 66.875 (63.321) Epoch: [2][8070/11272] Time 0.749 (0.828) Data 0.002 (0.002) Loss 2.5702 (2.8144) Prec@1 40.000 (33.180) Prec@5 63.125 (63.322) Epoch: [2][8080/11272] Time 0.875 (0.828) Data 0.002 (0.002) Loss 2.5739 (2.8144) Prec@1 41.250 (33.181) Prec@5 70.000 (63.323) Epoch: [2][8090/11272] Time 0.853 (0.828) Data 0.002 (0.002) Loss 2.8020 (2.8143) Prec@1 31.875 (33.182) Prec@5 63.125 (63.323) Epoch: [2][8100/11272] Time 0.747 (0.828) Data 0.001 (0.002) Loss 2.6361 (2.8143) Prec@1 38.750 (33.182) Prec@5 65.625 (63.323) Epoch: [2][8110/11272] Time 0.914 (0.828) Data 0.002 (0.002) Loss 2.6053 (2.8143) Prec@1 35.625 (33.183) Prec@5 66.875 (63.324) Epoch: [2][8120/11272] Time 0.864 (0.828) Data 0.001 (0.002) Loss 3.0793 (2.8142) Prec@1 30.000 (33.185) Prec@5 55.625 (63.325) Epoch: [2][8130/11272] Time 0.761 (0.827) Data 0.001 (0.002) Loss 2.7517 (2.8141) Prec@1 32.500 (33.187) Prec@5 67.500 (63.328) Epoch: [2][8140/11272] Time 0.742 (0.827) Data 0.001 (0.002) Loss 2.6824 (2.8141) Prec@1 32.500 (33.187) Prec@5 64.375 (63.328) Epoch: [2][8150/11272] Time 0.866 (0.827) Data 0.002 (0.002) Loss 2.8455 (2.8140) Prec@1 31.875 (33.188) Prec@5 61.250 (63.330) Epoch: [2][8160/11272] Time 0.855 (0.827) Data 0.001 (0.002) Loss 2.9710 (2.8140) Prec@1 29.375 (33.187) Prec@5 58.750 (63.328) Epoch: [2][8170/11272] Time 0.771 (0.827) Data 0.002 (0.002) Loss 2.9399 (2.8140) Prec@1 33.750 (33.185) Prec@5 56.250 (63.329) Epoch: [2][8180/11272] Time 0.735 (0.827) Data 0.002 (0.002) Loss 2.9274 (2.8140) Prec@1 27.500 (33.185) Prec@5 61.875 (63.330) Epoch: [2][8190/11272] Time 0.821 (0.827) Data 0.002 (0.002) Loss 2.8595 (2.8139) Prec@1 37.500 (33.188) Prec@5 61.250 (63.331) Epoch: [2][8200/11272] Time 0.848 (0.827) Data 0.001 (0.002) Loss 2.6030 (2.8139) Prec@1 36.250 (33.187) Prec@5 66.250 (63.332) Epoch: [2][8210/11272] Time 0.734 (0.827) Data 0.002 (0.002) Loss 2.7783 (2.8138) Prec@1 38.125 (33.190) Prec@5 66.250 (63.335) Epoch: [2][8220/11272] Time 0.751 (0.827) Data 0.001 (0.002) Loss 2.7030 (2.8138) Prec@1 37.500 (33.191) Prec@5 69.375 (63.339) Epoch: [2][8230/11272] Time 0.854 (0.827) Data 0.002 (0.002) Loss 2.7695 (2.8136) Prec@1 31.875 (33.192) Prec@5 66.250 (63.342) Epoch: [2][8240/11272] Time 0.838 (0.827) Data 0.001 (0.002) Loss 2.6587 (2.8135) Prec@1 35.000 (33.194) Prec@5 65.000 (63.343) Epoch: [2][8250/11272] Time 0.740 (0.827) Data 0.002 (0.002) Loss 2.9487 (2.8135) Prec@1 33.125 (33.195) Prec@5 60.000 (63.344) Epoch: [2][8260/11272] Time 0.870 (0.827) Data 0.001 (0.002) Loss 2.6954 (2.8134) Prec@1 40.625 (33.196) Prec@5 68.750 (63.347) Epoch: [2][8270/11272] Time 0.872 (0.827) Data 0.002 (0.002) Loss 2.6237 (2.8134) Prec@1 38.125 (33.197) Prec@5 66.875 (63.346) Epoch: [2][8280/11272] Time 0.752 (0.827) Data 0.001 (0.002) Loss 2.9131 (2.8133) Prec@1 31.250 (33.196) Prec@5 58.750 (63.347) Epoch: [2][8290/11272] Time 0.748 (0.827) Data 0.002 (0.002) Loss 2.7326 (2.8133) Prec@1 28.750 (33.195) Prec@5 63.750 (63.346) Epoch: [2][8300/11272] Time 0.884 (0.827) Data 0.002 (0.002) Loss 3.0023 (2.8133) Prec@1 31.250 (33.195) Prec@5 57.500 (63.347) Epoch: [2][8310/11272] Time 0.870 (0.827) Data 0.002 (0.002) Loss 2.8924 (2.8132) Prec@1 30.625 (33.197) Prec@5 60.000 (63.348) Epoch: [2][8320/11272] Time 0.754 (0.827) Data 0.002 (0.002) Loss 2.7264 (2.8132) Prec@1 38.750 (33.195) Prec@5 63.750 (63.349) Epoch: [2][8330/11272] Time 0.730 (0.827) Data 0.001 (0.002) Loss 2.6733 (2.8133) Prec@1 35.625 (33.193) Prec@5 66.250 (63.348) Epoch: [2][8340/11272] Time 0.858 (0.827) Data 0.002 (0.002) Loss 2.6425 (2.8133) Prec@1 33.125 (33.194) Prec@5 67.500 (63.349) Epoch: [2][8350/11272] Time 0.819 (0.827) Data 0.001 (0.002) Loss 3.0500 (2.8133) Prec@1 27.500 (33.193) Prec@5 63.125 (63.349) Epoch: [2][8360/11272] Time 0.726 (0.827) Data 0.001 (0.002) Loss 2.6721 (2.8133) Prec@1 37.500 (33.193) Prec@5 67.500 (63.348) Epoch: [2][8370/11272] Time 0.749 (0.827) Data 0.002 (0.002) Loss 2.7935 (2.8133) Prec@1 30.000 (33.191) Prec@5 61.250 (63.346) Epoch: [2][8380/11272] Time 0.818 (0.827) Data 0.001 (0.002) Loss 2.6564 (2.8133) Prec@1 30.000 (33.190) Prec@5 62.500 (63.346) Epoch: [2][8390/11272] Time 0.748 (0.827) Data 0.002 (0.002) Loss 2.8603 (2.8133) Prec@1 32.500 (33.192) Prec@5 63.750 (63.347) Epoch: [2][8400/11272] Time 0.730 (0.827) Data 0.001 (0.002) Loss 2.7040 (2.8132) Prec@1 32.500 (33.194) Prec@5 60.625 (63.349) Epoch: [2][8410/11272] Time 0.875 (0.827) Data 0.002 (0.002) Loss 2.5168 (2.8131) Prec@1 41.875 (33.196) Prec@5 68.125 (63.351) Epoch: [2][8420/11272] Time 0.849 (0.827) Data 0.001 (0.002) Loss 2.7320 (2.8131) Prec@1 33.125 (33.194) Prec@5 66.250 (63.352) Epoch: [2][8430/11272] Time 0.760 (0.827) Data 0.002 (0.002) Loss 2.6055 (2.8130) Prec@1 30.000 (33.194) Prec@5 67.500 (63.353) Epoch: [2][8440/11272] Time 0.732 (0.827) Data 0.001 (0.002) Loss 2.6109 (2.8130) Prec@1 38.125 (33.195) Prec@5 67.500 (63.354) Epoch: [2][8450/11272] Time 0.859 (0.826) Data 0.001 (0.002) Loss 2.8712 (2.8129) Prec@1 33.125 (33.195) Prec@5 62.500 (63.356) Epoch: [2][8460/11272] Time 0.803 (0.826) Data 0.001 (0.002) Loss 2.7157 (2.8128) Prec@1 35.000 (33.196) Prec@5 64.375 (63.357) Epoch: [2][8470/11272] Time 0.746 (0.826) Data 0.002 (0.002) Loss 2.9890 (2.8128) Prec@1 26.875 (33.195) Prec@5 63.125 (63.356) Epoch: [2][8480/11272] Time 0.730 (0.826) Data 0.001 (0.002) Loss 2.7304 (2.8129) Prec@1 32.500 (33.193) Prec@5 65.625 (63.354) Epoch: [2][8490/11272] Time 0.827 (0.826) Data 0.001 (0.002) Loss 2.9780 (2.8129) Prec@1 28.750 (33.194) Prec@5 61.250 (63.355) Epoch: [2][8500/11272] Time 0.823 (0.826) Data 0.001 (0.002) Loss 2.7884 (2.8129) Prec@1 34.375 (33.194) Prec@5 65.000 (63.356) Epoch: [2][8510/11272] Time 0.760 (0.826) Data 0.001 (0.002) Loss 3.0420 (2.8129) Prec@1 30.625 (33.192) Prec@5 58.750 (63.353) Epoch: [2][8520/11272] Time 0.864 (0.826) Data 0.001 (0.002) Loss 2.9514 (2.8129) Prec@1 30.000 (33.193) Prec@5 58.750 (63.352) Epoch: [2][8530/11272] Time 0.853 (0.826) Data 0.001 (0.002) Loss 2.9963 (2.8129) Prec@1 31.875 (33.193) Prec@5 55.625 (63.352) Epoch: [2][8540/11272] Time 0.755 (0.826) Data 0.001 (0.002) Loss 2.9316 (2.8128) Prec@1 35.625 (33.197) Prec@5 63.125 (63.354) Epoch: [2][8550/11272] Time 0.742 (0.826) Data 0.001 (0.002) Loss 2.6315 (2.8127) Prec@1 39.375 (33.199) Prec@5 64.375 (63.356) Epoch: [2][8560/11272] Time 0.873 (0.826) Data 0.001 (0.002) Loss 2.6688 (2.8126) Prec@1 42.500 (33.202) Prec@5 68.750 (63.359) Epoch: [2][8570/11272] Time 0.828 (0.826) Data 0.001 (0.002) Loss 2.6275 (2.8125) Prec@1 36.250 (33.205) Prec@5 65.000 (63.361) Epoch: [2][8580/11272] Time 0.740 (0.826) Data 0.001 (0.002) Loss 2.7898 (2.8125) Prec@1 37.500 (33.206) Prec@5 63.750 (63.363) Epoch: [2][8590/11272] Time 0.753 (0.826) Data 0.001 (0.002) Loss 2.8945 (2.8125) Prec@1 32.500 (33.207) Prec@5 59.375 (63.363) Epoch: [2][8600/11272] Time 0.902 (0.826) Data 0.001 (0.002) Loss 2.5811 (2.8125) Prec@1 38.750 (33.204) Prec@5 65.625 (63.362) Epoch: [2][8610/11272] Time 0.899 (0.826) Data 0.001 (0.002) Loss 2.8261 (2.8125) Prec@1 38.125 (33.207) Prec@5 63.750 (63.362) Epoch: [2][8620/11272] Time 0.740 (0.826) Data 0.001 (0.002) Loss 2.7352 (2.8125) Prec@1 33.750 (33.208) Prec@5 61.250 (63.362) Epoch: [2][8630/11272] Time 0.725 (0.826) Data 0.001 (0.002) Loss 2.9052 (2.8125) Prec@1 30.625 (33.208) Prec@5 58.125 (63.361) Epoch: [2][8640/11272] Time 0.883 (0.826) Data 0.001 (0.002) Loss 2.9157 (2.8125) Prec@1 32.500 (33.209) Prec@5 61.250 (63.361) Epoch: [2][8650/11272] Time 0.750 (0.826) Data 0.003 (0.002) Loss 2.9358 (2.8125) Prec@1 33.125 (33.212) Prec@5 63.125 (63.361) Epoch: [2][8660/11272] Time 0.747 (0.826) Data 0.001 (0.002) Loss 2.8822 (2.8125) Prec@1 37.500 (33.215) Prec@5 61.875 (63.361) Epoch: [2][8670/11272] Time 0.857 (0.826) Data 0.002 (0.002) Loss 2.5638 (2.8124) Prec@1 36.875 (33.216) Prec@5 67.500 (63.362) Epoch: [2][8680/11272] Time 0.817 (0.826) Data 0.001 (0.002) Loss 2.7652 (2.8125) Prec@1 31.875 (33.216) Prec@5 61.875 (63.360) Epoch: [2][8690/11272] Time 0.754 (0.826) Data 0.002 (0.002) Loss 2.6392 (2.8124) Prec@1 35.625 (33.216) Prec@5 66.250 (63.361) Epoch: [2][8700/11272] Time 0.735 (0.826) Data 0.003 (0.002) Loss 2.6006 (2.8123) Prec@1 36.250 (33.217) Prec@5 71.250 (63.364) Epoch: [2][8710/11272] Time 0.860 (0.826) Data 0.002 (0.002) Loss 2.9629 (2.8122) Prec@1 32.500 (33.218) Prec@5 58.125 (63.365) Epoch: [2][8720/11272] Time 0.840 (0.826) Data 0.001 (0.002) Loss 2.5896 (2.8122) Prec@1 38.750 (33.220) Prec@5 68.750 (63.366) Epoch: [2][8730/11272] Time 0.748 (0.826) Data 0.002 (0.002) Loss 2.6802 (2.8122) Prec@1 35.000 (33.220) Prec@5 66.250 (63.367) Epoch: [2][8740/11272] Time 0.734 (0.826) Data 0.001 (0.002) Loss 2.7050 (2.8122) Prec@1 37.500 (33.223) Prec@5 65.625 (63.366) Epoch: [2][8750/11272] Time 0.857 (0.826) Data 0.001 (0.002) Loss 2.9676 (2.8122) Prec@1 33.125 (33.223) Prec@5 61.250 (63.366) Epoch: [2][8760/11272] Time 0.845 (0.826) Data 0.001 (0.002) Loss 2.9856 (2.8121) Prec@1 28.125 (33.224) Prec@5 59.375 (63.365) Epoch: [2][8770/11272] Time 0.734 (0.826) Data 0.001 (0.002) Loss 2.6901 (2.8121) Prec@1 36.875 (33.225) Prec@5 65.000 (63.366) Epoch: [2][8780/11272] Time 0.887 (0.826) Data 0.001 (0.002) Loss 2.9254 (2.8120) Prec@1 31.250 (33.227) Prec@5 60.000 (63.367) Epoch: [2][8790/11272] Time 0.871 (0.826) Data 0.002 (0.002) Loss 2.5992 (2.8119) Prec@1 41.250 (33.229) Prec@5 65.625 (63.367) Epoch: [2][8800/11272] Time 0.725 (0.826) Data 0.001 (0.002) Loss 2.8280 (2.8120) Prec@1 33.125 (33.229) Prec@5 61.875 (63.366) Epoch: [2][8810/11272] Time 0.763 (0.826) Data 0.001 (0.002) Loss 2.6728 (2.8119) Prec@1 39.375 (33.231) Prec@5 65.000 (63.369) Epoch: [2][8820/11272] Time 0.906 (0.826) Data 0.001 (0.002) Loss 2.4651 (2.8118) Prec@1 40.625 (33.235) Prec@5 71.875 (63.372) Epoch: [2][8830/11272] Time 0.894 (0.826) Data 0.002 (0.002) Loss 2.8112 (2.8118) Prec@1 28.125 (33.234) Prec@5 63.125 (63.371) Epoch: [2][8840/11272] Time 0.739 (0.825) Data 0.001 (0.002) Loss 2.8607 (2.8119) Prec@1 31.250 (33.232) Prec@5 61.250 (63.370) Epoch: [2][8850/11272] Time 0.794 (0.825) Data 0.001 (0.002) Loss 2.6151 (2.8118) Prec@1 38.125 (33.233) Prec@5 73.125 (63.372) Epoch: [2][8860/11272] Time 0.857 (0.825) Data 0.001 (0.002) Loss 2.7895 (2.8118) Prec@1 33.750 (33.234) Prec@5 62.500 (63.374) Epoch: [2][8870/11272] Time 0.863 (0.825) Data 0.001 (0.002) Loss 2.7647 (2.8118) Prec@1 40.625 (33.234) Prec@5 66.250 (63.375) Epoch: [2][8880/11272] Time 0.720 (0.825) Data 0.001 (0.002) Loss 2.9782 (2.8118) Prec@1 30.000 (33.234) Prec@5 56.875 (63.374) Epoch: [2][8890/11272] Time 0.756 (0.825) Data 0.001 (0.002) Loss 2.5721 (2.8117) Prec@1 38.125 (33.234) Prec@5 65.000 (63.376) Epoch: [2][8900/11272] Time 0.961 (0.825) Data 0.002 (0.002) Loss 2.5073 (2.8116) Prec@1 39.375 (33.235) Prec@5 68.125 (63.376) Epoch: [2][8910/11272] Time 0.743 (0.825) Data 0.003 (0.002) Loss 2.9321 (2.8116) Prec@1 28.750 (33.236) Prec@5 61.875 (63.376) Epoch: [2][8920/11272] Time 0.739 (0.825) Data 0.001 (0.002) Loss 3.0185 (2.8117) Prec@1 32.500 (33.236) Prec@5 58.750 (63.376) Epoch: [2][8930/11272] Time 0.850 (0.825) Data 0.001 (0.002) Loss 2.7366 (2.8116) Prec@1 31.250 (33.236) Prec@5 68.125 (63.377) Epoch: [2][8940/11272] Time 0.852 (0.825) Data 0.002 (0.002) Loss 2.8493 (2.8116) Prec@1 36.250 (33.238) Prec@5 63.750 (63.378) Epoch: [2][8950/11272] Time 0.762 (0.825) Data 0.001 (0.002) Loss 2.7631 (2.8116) Prec@1 36.250 (33.238) Prec@5 63.750 (63.378) Epoch: [2][8960/11272] Time 0.769 (0.825) Data 0.002 (0.002) Loss 2.7357 (2.8116) Prec@1 35.625 (33.238) Prec@5 66.875 (63.379) Epoch: [2][8970/11272] Time 0.888 (0.825) Data 0.002 (0.002) Loss 2.6474 (2.8116) Prec@1 33.125 (33.236) Prec@5 65.000 (63.377) Epoch: [2][8980/11272] Time 0.825 (0.825) Data 0.001 (0.002) Loss 2.5674 (2.8117) Prec@1 36.875 (33.236) Prec@5 71.875 (63.378) Epoch: [2][8990/11272] Time 0.745 (0.825) Data 0.002 (0.002) Loss 2.8851 (2.8117) Prec@1 30.625 (33.236) Prec@5 64.375 (63.378) Epoch: [2][9000/11272] Time 0.771 (0.825) Data 0.002 (0.002) Loss 2.9129 (2.8117) Prec@1 31.875 (33.236) Prec@5 58.750 (63.378) Epoch: [2][9010/11272] Time 0.842 (0.825) Data 0.001 (0.002) Loss 2.9674 (2.8118) Prec@1 31.250 (33.233) Prec@5 56.875 (63.376) Epoch: [2][9020/11272] Time 0.838 (0.825) Data 0.002 (0.002) Loss 2.8225 (2.8117) Prec@1 38.750 (33.236) Prec@5 61.875 (63.378) Epoch: [2][9030/11272] Time 0.765 (0.825) Data 0.002 (0.002) Loss 2.9284 (2.8117) Prec@1 31.875 (33.235) Prec@5 60.000 (63.377) Epoch: [2][9040/11272] Time 0.925 (0.825) Data 0.001 (0.002) Loss 2.8459 (2.8117) Prec@1 25.000 (33.235) Prec@5 65.000 (63.378) Epoch: [2][9050/11272] Time 0.867 (0.825) Data 0.002 (0.002) Loss 2.7966 (2.8116) Prec@1 31.250 (33.236) Prec@5 65.625 (63.380) Epoch: [2][9060/11272] Time 0.756 (0.825) Data 0.001 (0.002) Loss 2.6998 (2.8115) Prec@1 35.000 (33.238) Prec@5 63.125 (63.382) Epoch: [2][9070/11272] Time 0.756 (0.825) Data 0.002 (0.002) Loss 2.7611 (2.8115) Prec@1 34.375 (33.239) Prec@5 66.875 (63.383) Epoch: [2][9080/11272] Time 0.893 (0.825) Data 0.002 (0.002) Loss 2.6263 (2.8115) Prec@1 36.875 (33.240) Prec@5 66.875 (63.383) Epoch: [2][9090/11272] Time 0.884 (0.825) Data 0.002 (0.002) Loss 3.0131 (2.8116) Prec@1 30.625 (33.238) Prec@5 60.625 (63.383) Epoch: [2][9100/11272] Time 0.742 (0.825) Data 0.001 (0.002) Loss 2.8407 (2.8115) Prec@1 32.500 (33.241) Prec@5 60.625 (63.383) Epoch: [2][9110/11272] Time 0.735 (0.825) Data 0.001 (0.002) Loss 2.9058 (2.8116) Prec@1 33.125 (33.240) Prec@5 60.000 (63.383) Epoch: [2][9120/11272] Time 0.866 (0.825) Data 0.001 (0.002) Loss 2.7424 (2.8115) Prec@1 29.375 (33.240) Prec@5 68.125 (63.385) Epoch: [2][9130/11272] Time 0.868 (0.825) Data 0.002 (0.002) Loss 2.9396 (2.8114) Prec@1 28.125 (33.240) Prec@5 60.625 (63.386) Epoch: [2][9140/11272] Time 0.752 (0.825) Data 0.002 (0.002) Loss 2.9418 (2.8114) Prec@1 26.875 (33.241) Prec@5 62.500 (63.387) Epoch: [2][9150/11272] Time 0.746 (0.825) Data 0.002 (0.002) Loss 2.5915 (2.8113) Prec@1 35.625 (33.242) Prec@5 66.250 (63.389) Epoch: [2][9160/11272] Time 0.848 (0.825) Data 0.001 (0.002) Loss 2.9921 (2.8113) Prec@1 29.375 (33.242) Prec@5 57.500 (63.388) Epoch: [2][9170/11272] Time 0.839 (0.825) Data 0.002 (0.002) Loss 2.8331 (2.8114) Prec@1 33.750 (33.242) Prec@5 64.375 (63.386) Epoch: [2][9180/11272] Time 0.780 (0.825) Data 0.002 (0.002) Loss 2.8611 (2.8114) Prec@1 32.500 (33.243) Prec@5 63.125 (63.386) Epoch: [2][9190/11272] Time 0.940 (0.825) Data 0.002 (0.002) Loss 2.5831 (2.8115) Prec@1 38.750 (33.243) Prec@5 71.250 (63.386) Epoch: [2][9200/11272] Time 0.857 (0.825) Data 0.001 (0.002) Loss 2.9814 (2.8115) Prec@1 28.750 (33.242) Prec@5 60.625 (63.385) Epoch: [2][9210/11272] Time 0.770 (0.825) Data 0.001 (0.002) Loss 2.8439 (2.8114) Prec@1 33.750 (33.243) Prec@5 62.500 (63.387) Epoch: [2][9220/11272] Time 0.726 (0.825) Data 0.001 (0.002) Loss 2.5749 (2.8113) Prec@1 42.500 (33.245) Prec@5 68.125 (63.389) Epoch: [2][9230/11272] Time 0.827 (0.825) Data 0.001 (0.002) Loss 2.8329 (2.8112) Prec@1 36.250 (33.247) Prec@5 65.000 (63.391) Epoch: [2][9240/11272] Time 0.877 (0.825) Data 0.002 (0.002) Loss 3.0556 (2.8112) Prec@1 30.625 (33.249) Prec@5 56.875 (63.392) Epoch: [2][9250/11272] Time 0.762 (0.825) Data 0.001 (0.002) Loss 2.8645 (2.8111) Prec@1 35.625 (33.251) Prec@5 59.375 (63.394) Epoch: [2][9260/11272] Time 0.775 (0.825) Data 0.002 (0.002) Loss 2.4266 (2.8110) Prec@1 40.000 (33.253) Prec@5 76.250 (63.397) Epoch: [2][9270/11272] Time 0.874 (0.825) Data 0.002 (0.002) Loss 2.8238 (2.8109) Prec@1 37.500 (33.255) Prec@5 61.875 (63.400) Epoch: [2][9280/11272] Time 0.830 (0.825) Data 0.002 (0.002) Loss 2.7245 (2.8108) Prec@1 34.375 (33.255) Prec@5 66.875 (63.400) Epoch: [2][9290/11272] Time 0.740 (0.825) Data 0.002 (0.002) Loss 2.9230 (2.8108) Prec@1 35.000 (33.254) Prec@5 56.250 (63.399) Epoch: [2][9300/11272] Time 0.771 (0.825) Data 0.002 (0.002) Loss 2.7770 (2.8108) Prec@1 35.625 (33.255) Prec@5 65.000 (63.399) Epoch: [2][9310/11272] Time 0.847 (0.824) Data 0.002 (0.002) Loss 2.5680 (2.8108) Prec@1 29.375 (33.257) Prec@5 69.375 (63.401) Epoch: [2][9320/11272] Time 0.749 (0.824) Data 0.001 (0.002) Loss 2.9057 (2.8107) Prec@1 31.875 (33.255) Prec@5 63.125 (63.404) Epoch: [2][9330/11272] Time 0.745 (0.824) Data 0.001 (0.002) Loss 2.7519 (2.8108) Prec@1 38.750 (33.256) Prec@5 61.250 (63.403) Epoch: [2][9340/11272] Time 0.824 (0.824) Data 0.001 (0.002) Loss 2.7535 (2.8108) Prec@1 32.500 (33.257) Prec@5 63.125 (63.404) Epoch: [2][9350/11272] Time 0.843 (0.824) Data 0.002 (0.002) Loss 2.5837 (2.8107) Prec@1 35.000 (33.258) Prec@5 66.250 (63.405) Epoch: [2][9360/11272] Time 0.739 (0.824) Data 0.002 (0.002) Loss 2.6742 (2.8106) Prec@1 33.125 (33.260) Prec@5 66.875 (63.407) Epoch: [2][9370/11272] Time 0.803 (0.824) Data 0.002 (0.002) Loss 2.7125 (2.8105) Prec@1 33.125 (33.263) Prec@5 66.875 (63.408) Epoch: [2][9380/11272] Time 0.884 (0.824) Data 0.001 (0.002) Loss 2.6808 (2.8105) Prec@1 32.500 (33.262) Prec@5 65.000 (63.407) Epoch: [2][9390/11272] Time 0.866 (0.824) Data 0.001 (0.002) Loss 2.8842 (2.8106) Prec@1 32.500 (33.263) Prec@5 61.875 (63.408) Epoch: [2][9400/11272] Time 0.744 (0.824) Data 0.001 (0.002) Loss 2.7865 (2.8105) Prec@1 32.500 (33.264) Prec@5 58.750 (63.409) Epoch: [2][9410/11272] Time 0.740 (0.824) Data 0.002 (0.002) Loss 3.1409 (2.8105) Prec@1 29.375 (33.263) Prec@5 55.625 (63.409) Epoch: [2][9420/11272] Time 0.884 (0.824) Data 0.002 (0.002) Loss 2.9285 (2.8105) Prec@1 31.250 (33.264) Prec@5 62.500 (63.409) Epoch: [2][9430/11272] Time 0.832 (0.824) Data 0.002 (0.002) Loss 2.7022 (2.8104) Prec@1 35.000 (33.265) Prec@5 65.625 (63.410) Epoch: [2][9440/11272] Time 0.750 (0.824) Data 0.001 (0.002) Loss 3.0230 (2.8105) Prec@1 27.500 (33.264) Prec@5 60.625 (63.408) Epoch: [2][9450/11272] Time 0.899 (0.824) Data 0.001 (0.002) Loss 2.6010 (2.8104) Prec@1 38.750 (33.265) Prec@5 68.125 (63.408) Epoch: [2][9460/11272] Time 0.849 (0.824) Data 0.001 (0.002) Loss 2.7980 (2.8104) Prec@1 35.000 (33.265) Prec@5 65.000 (63.409) Epoch: [2][9470/11272] Time 0.743 (0.824) Data 0.001 (0.002) Loss 3.0509 (2.8103) Prec@1 29.375 (33.265) Prec@5 56.875 (63.409) Epoch: [2][9480/11272] Time 0.742 (0.824) Data 0.001 (0.002) Loss 2.7861 (2.8103) Prec@1 40.000 (33.266) Prec@5 58.750 (63.408) Epoch: [2][9490/11272] Time 0.875 (0.824) Data 0.002 (0.002) Loss 3.0744 (2.8103) Prec@1 27.500 (33.267) Prec@5 55.000 (63.407) Epoch: [2][9500/11272] Time 0.842 (0.824) Data 0.001 (0.002) Loss 2.8290 (2.8103) Prec@1 35.000 (33.268) Prec@5 63.750 (63.408) Epoch: [2][9510/11272] Time 0.762 (0.824) Data 0.002 (0.002) Loss 2.6317 (2.8102) Prec@1 35.625 (33.269) Prec@5 66.250 (63.409) Epoch: [2][9520/11272] Time 0.733 (0.824) Data 0.001 (0.002) Loss 2.6492 (2.8100) Prec@1 41.250 (33.273) Prec@5 63.750 (63.412) Epoch: [2][9530/11272] Time 0.823 (0.824) Data 0.001 (0.002) Loss 2.8347 (2.8101) Prec@1 36.250 (33.272) Prec@5 59.375 (63.410) Epoch: [2][9540/11272] Time 0.818 (0.824) Data 0.001 (0.002) Loss 2.7901 (2.8100) Prec@1 33.125 (33.273) Prec@5 64.375 (63.413) Epoch: [2][9550/11272] Time 0.781 (0.824) Data 0.003 (0.002) Loss 2.6959 (2.8099) Prec@1 36.250 (33.273) Prec@5 65.625 (63.413) Epoch: [2][9560/11272] Time 0.745 (0.824) Data 0.001 (0.002) Loss 2.5433 (2.8099) Prec@1 40.000 (33.273) Prec@5 70.625 (63.414) Epoch: [2][9570/11272] Time 0.836 (0.824) Data 0.001 (0.002) Loss 2.7716 (2.8099) Prec@1 31.875 (33.273) Prec@5 66.875 (63.413) Epoch: [2][9580/11272] Time 0.732 (0.824) Data 0.003 (0.002) Loss 2.7058 (2.8098) Prec@1 31.250 (33.274) Prec@5 64.375 (63.414) Epoch: [2][9590/11272] Time 0.713 (0.824) Data 0.001 (0.002) Loss 2.7487 (2.8098) Prec@1 35.625 (33.275) Prec@5 64.375 (63.413) Epoch: [2][9600/11272] Time 0.865 (0.824) Data 0.001 (0.002) Loss 2.7958 (2.8098) Prec@1 30.000 (33.274) Prec@5 64.375 (63.414) Epoch: [2][9610/11272] Time 0.834 (0.824) Data 0.001 (0.002) Loss 2.8704 (2.8098) Prec@1 35.625 (33.275) Prec@5 61.875 (63.414) Epoch: [2][9620/11272] Time 0.761 (0.824) Data 0.001 (0.002) Loss 2.8252 (2.8097) Prec@1 35.000 (33.278) Prec@5 60.000 (63.413) Epoch: [2][9630/11272] Time 0.732 (0.824) Data 0.001 (0.002) Loss 2.7075 (2.8097) Prec@1 36.250 (33.278) Prec@5 63.125 (63.413) Epoch: [2][9640/11272] Time 0.869 (0.824) Data 0.001 (0.002) Loss 2.6675 (2.8096) Prec@1 35.000 (33.279) Prec@5 66.250 (63.414) Epoch: [2][9650/11272] Time 0.846 (0.824) Data 0.001 (0.002) Loss 2.7148 (2.8097) Prec@1 32.500 (33.279) Prec@5 61.250 (63.412) Epoch: [2][9660/11272] Time 0.747 (0.824) Data 0.001 (0.002) Loss 2.7465 (2.8096) Prec@1 30.000 (33.279) Prec@5 66.250 (63.413) Epoch: [2][9670/11272] Time 0.760 (0.824) Data 0.002 (0.002) Loss 2.6014 (2.8096) Prec@1 38.750 (33.281) Prec@5 63.750 (63.413) Epoch: [2][9680/11272] Time 0.866 (0.824) Data 0.001 (0.002) Loss 2.5803 (2.8096) Prec@1 30.625 (33.279) Prec@5 65.625 (63.412) Epoch: [2][9690/11272] Time 0.828 (0.824) Data 0.001 (0.002) Loss 2.6654 (2.8097) Prec@1 37.500 (33.278) Prec@5 70.000 (63.412) Epoch: [2][9700/11272] Time 0.747 (0.824) Data 0.002 (0.002) Loss 2.8109 (2.8096) Prec@1 32.500 (33.278) Prec@5 59.375 (63.412) Epoch: [2][9710/11272] Time 0.852 (0.824) Data 0.001 (0.002) Loss 2.6069 (2.8096) Prec@1 38.750 (33.278) Prec@5 65.000 (63.413) Epoch: [2][9720/11272] Time 0.944 (0.824) Data 0.002 (0.002) Loss 2.7920 (2.8095) Prec@1 33.750 (33.281) Prec@5 61.875 (63.414) Epoch: [2][9730/11272] Time 0.806 (0.824) Data 0.002 (0.002) Loss 2.6625 (2.8094) Prec@1 32.500 (33.281) Prec@5 64.375 (63.415) Epoch: [2][9740/11272] Time 0.747 (0.824) Data 0.001 (0.002) Loss 3.0482 (2.8094) Prec@1 25.625 (33.282) Prec@5 58.750 (63.416) Epoch: [2][9750/11272] Time 0.858 (0.824) Data 0.002 (0.002) Loss 2.7796 (2.8093) Prec@1 30.625 (33.280) Prec@5 67.500 (63.416) Epoch: [2][9760/11272] Time 0.898 (0.824) Data 0.001 (0.002) Loss 3.0577 (2.8093) Prec@1 34.375 (33.282) Prec@5 58.750 (63.417) Epoch: [2][9770/11272] Time 0.744 (0.824) Data 0.001 (0.002) Loss 2.6802 (2.8093) Prec@1 33.750 (33.281) Prec@5 66.875 (63.418) Epoch: [2][9780/11272] Time 0.753 (0.824) Data 0.001 (0.002) Loss 2.6627 (2.8092) Prec@1 35.000 (33.281) Prec@5 70.000 (63.420) Epoch: [2][9790/11272] Time 0.883 (0.824) Data 0.001 (0.002) Loss 2.5893 (2.8092) Prec@1 36.875 (33.281) Prec@5 67.500 (63.419) Epoch: [2][9800/11272] Time 0.851 (0.824) Data 0.001 (0.002) Loss 2.7426 (2.8091) Prec@1 30.625 (33.283) Prec@5 65.000 (63.421) Epoch: [2][9810/11272] Time 0.780 (0.824) Data 0.003 (0.002) Loss 3.0048 (2.8090) Prec@1 30.000 (33.284) Prec@5 58.750 (63.422) Epoch: [2][9820/11272] Time 0.731 (0.824) Data 0.001 (0.002) Loss 2.7285 (2.8091) Prec@1 31.875 (33.283) Prec@5 63.750 (63.420) Epoch: [2][9830/11272] Time 0.847 (0.824) Data 0.001 (0.002) Loss 2.8114 (2.8092) Prec@1 35.625 (33.282) Prec@5 66.875 (63.421) Epoch: [2][9840/11272] Time 0.734 (0.824) Data 0.003 (0.002) Loss 2.4997 (2.8092) Prec@1 38.750 (33.285) Prec@5 73.125 (63.420) Epoch: [2][9850/11272] Time 0.740 (0.824) Data 0.001 (0.002) Loss 2.7945 (2.8092) Prec@1 35.000 (33.283) Prec@5 60.625 (63.420) Epoch: [2][9860/11272] Time 0.837 (0.824) Data 0.002 (0.002) Loss 2.5457 (2.8091) Prec@1 35.000 (33.285) Prec@5 68.750 (63.421) Epoch: [2][9870/11272] Time 0.864 (0.823) Data 0.002 (0.002) Loss 2.8711 (2.8091) Prec@1 27.500 (33.283) Prec@5 60.000 (63.422) Epoch: [2][9880/11272] Time 0.747 (0.823) Data 0.002 (0.002) Loss 2.5549 (2.8091) Prec@1 38.750 (33.283) Prec@5 69.375 (63.423) Epoch: [2][9890/11272] Time 0.762 (0.823) Data 0.003 (0.002) Loss 2.5141 (2.8090) Prec@1 36.250 (33.283) Prec@5 70.625 (63.424) Epoch: [2][9900/11272] Time 0.835 (0.823) Data 0.001 (0.002) Loss 2.7952 (2.8090) Prec@1 36.875 (33.282) Prec@5 65.000 (63.423) Epoch: [2][9910/11272] Time 0.860 (0.823) Data 0.001 (0.002) Loss 2.5850 (2.8090) Prec@1 41.875 (33.283) Prec@5 65.625 (63.423) Epoch: [2][9920/11272] Time 0.727 (0.823) Data 0.001 (0.002) Loss 2.8395 (2.8089) Prec@1 32.500 (33.283) Prec@5 66.250 (63.424) Epoch: [2][9930/11272] Time 0.732 (0.823) Data 0.001 (0.002) Loss 2.9719 (2.8089) Prec@1 30.625 (33.284) Prec@5 57.500 (63.425) Epoch: [2][9940/11272] Time 0.859 (0.823) Data 0.001 (0.002) Loss 2.8534 (2.8088) Prec@1 31.250 (33.287) Prec@5 58.125 (63.426) Epoch: [2][9950/11272] Time 0.847 (0.823) Data 0.001 (0.002) Loss 2.6919 (2.8088) Prec@1 36.875 (33.288) Prec@5 66.875 (63.428) Epoch: [2][9960/11272] Time 0.766 (0.823) Data 0.001 (0.002) Loss 2.9487 (2.8087) Prec@1 28.125 (33.288) Prec@5 64.375 (63.430) Epoch: [2][9970/11272] Time 0.904 (0.823) Data 0.001 (0.002) Loss 2.4068 (2.8086) Prec@1 41.250 (33.291) Prec@5 73.125 (63.432) Epoch: [2][9980/11272] Time 0.879 (0.823) Data 0.001 (0.002) Loss 2.8185 (2.8085) Prec@1 32.500 (33.292) Prec@5 64.375 (63.434) Epoch: [2][9990/11272] Time 0.717 (0.823) Data 0.001 (0.002) Loss 3.0345 (2.8086) Prec@1 26.250 (33.292) Prec@5 58.125 (63.435) Epoch: [2][10000/11272] Time 0.746 (0.823) Data 0.002 (0.002) Loss 2.6233 (2.8085) Prec@1 32.500 (33.292) Prec@5 70.000 (63.435) Epoch: [2][10010/11272] Time 0.859 (0.823) Data 0.002 (0.002) Loss 2.6899 (2.8085) Prec@1 35.000 (33.292) Prec@5 66.250 (63.436) Epoch: [2][10020/11272] Time 0.862 (0.823) Data 0.001 (0.002) Loss 3.0402 (2.8086) Prec@1 31.250 (33.291) Prec@5 60.000 (63.436) Epoch: [2][10030/11272] Time 0.740 (0.823) Data 0.001 (0.002) Loss 2.3711 (2.8085) Prec@1 42.500 (33.293) Prec@5 73.125 (63.436) Epoch: [2][10040/11272] Time 0.760 (0.823) Data 0.002 (0.002) Loss 2.7460 (2.8084) Prec@1 31.250 (33.296) Prec@5 61.875 (63.439) Epoch: [2][10050/11272] Time 0.877 (0.823) Data 0.002 (0.002) Loss 2.8270 (2.8084) Prec@1 29.375 (33.296) Prec@5 63.125 (63.438) Epoch: [2][10060/11272] Time 0.828 (0.823) Data 0.001 (0.002) Loss 2.8414 (2.8085) Prec@1 35.000 (33.294) Prec@5 63.125 (63.440) Epoch: [2][10070/11272] Time 0.727 (0.823) Data 0.001 (0.002) Loss 2.8967 (2.8084) Prec@1 25.625 (33.293) Prec@5 58.125 (63.440) Epoch: [2][10080/11272] Time 0.731 (0.823) Data 0.001 (0.002) Loss 2.7479 (2.8085) Prec@1 36.250 (33.292) Prec@5 66.250 (63.439) Epoch: [2][10090/11272] Time 0.842 (0.823) Data 0.001 (0.002) Loss 2.8126 (2.8084) Prec@1 36.875 (33.293) Prec@5 60.000 (63.438) Epoch: [2][10100/11272] Time 0.835 (0.823) Data 0.002 (0.002) Loss 2.5113 (2.8084) Prec@1 39.375 (33.294) Prec@5 69.375 (63.438) Epoch: [2][10110/11272] Time 0.737 (0.823) Data 0.001 (0.002) Loss 2.7822 (2.8083) Prec@1 33.125 (33.293) Prec@5 66.875 (63.439) Epoch: [2][10120/11272] Time 0.864 (0.823) Data 0.001 (0.002) Loss 2.8693 (2.8083) Prec@1 30.000 (33.295) Prec@5 60.625 (63.440) Epoch: [2][10130/11272] Time 0.871 (0.823) Data 0.002 (0.002) Loss 2.7973 (2.8082) Prec@1 35.000 (33.296) Prec@5 66.875 (63.441) Epoch: [2][10140/11272] Time 0.735 (0.823) Data 0.001 (0.002) Loss 2.6576 (2.8082) Prec@1 34.375 (33.298) Prec@5 65.000 (63.444) Epoch: [2][10150/11272] Time 0.729 (0.823) Data 0.001 (0.002) Loss 2.7949 (2.8081) Prec@1 31.875 (33.299) Prec@5 61.875 (63.446) Epoch: [2][10160/11272] Time 0.841 (0.823) Data 0.001 (0.002) Loss 2.8765 (2.8081) Prec@1 33.750 (33.300) Prec@5 64.375 (63.445) Epoch: [2][10170/11272] Time 0.838 (0.823) Data 0.002 (0.002) Loss 2.5253 (2.8080) Prec@1 38.125 (33.299) Prec@5 70.625 (63.447) Epoch: [2][10180/11272] Time 0.744 (0.823) Data 0.001 (0.002) Loss 2.7585 (2.8079) Prec@1 35.625 (33.300) Prec@5 65.000 (63.449) Epoch: [2][10190/11272] Time 0.731 (0.823) Data 0.001 (0.002) Loss 3.1068 (2.8079) Prec@1 26.250 (33.299) Prec@5 55.625 (63.448) Epoch: [2][10200/11272] Time 0.842 (0.823) Data 0.001 (0.002) Loss 2.7466 (2.8080) Prec@1 31.250 (33.299) Prec@5 65.000 (63.447) Epoch: [2][10210/11272] Time 0.838 (0.823) Data 0.001 (0.002) Loss 2.8303 (2.8080) Prec@1 28.750 (33.297) Prec@5 65.625 (63.447) Epoch: [2][10220/11272] Time 0.750 (0.823) Data 0.001 (0.002) Loss 2.6488 (2.8079) Prec@1 41.250 (33.299) Prec@5 68.750 (63.450) Epoch: [2][10230/11272] Time 0.740 (0.823) Data 0.001 (0.002) Loss 3.0941 (2.8079) Prec@1 28.125 (33.299) Prec@5 59.375 (63.451) Epoch: [2][10240/11272] Time 0.901 (0.823) Data 0.001 (0.002) Loss 2.7615 (2.8079) Prec@1 35.000 (33.299) Prec@5 63.125 (63.451) Epoch: [2][10250/11272] Time 0.791 (0.823) Data 0.002 (0.002) Loss 2.6935 (2.8079) Prec@1 36.875 (33.300) Prec@5 64.375 (63.451) Epoch: [2][10260/11272] Time 0.770 (0.823) Data 0.002 (0.002) Loss 2.8742 (2.8079) Prec@1 36.875 (33.302) Prec@5 60.625 (63.452) Epoch: [2][10270/11272] Time 0.923 (0.823) Data 0.001 (0.002) Loss 2.7926 (2.8079) Prec@1 36.875 (33.302) Prec@5 68.125 (63.452) Epoch: [2][10280/11272] Time 0.893 (0.823) Data 0.002 (0.002) Loss 2.6616 (2.8078) Prec@1 33.125 (33.304) Prec@5 60.625 (63.453) Epoch: [2][10290/11272] Time 0.746 (0.823) Data 0.001 (0.002) Loss 3.0216 (2.8078) Prec@1 30.000 (33.303) Prec@5 58.125 (63.453) Epoch: [2][10300/11272] Time 0.776 (0.823) Data 0.001 (0.002) Loss 2.8496 (2.8079) Prec@1 32.500 (33.301) Prec@5 61.875 (63.453) Epoch: [2][10310/11272] Time 0.890 (0.823) Data 0.001 (0.002) Loss 2.8012 (2.8080) Prec@1 32.500 (33.300) Prec@5 58.125 (63.451) Epoch: [2][10320/11272] Time 0.889 (0.823) Data 0.001 (0.002) Loss 2.6183 (2.8079) Prec@1 37.500 (33.300) Prec@5 70.000 (63.451) Epoch: [2][10330/11272] Time 0.731 (0.823) Data 0.002 (0.002) Loss 2.9864 (2.8079) Prec@1 35.000 (33.302) Prec@5 58.750 (63.452) Epoch: [2][10340/11272] Time 0.768 (0.823) Data 0.001 (0.002) Loss 2.8036 (2.8079) Prec@1 31.875 (33.302) Prec@5 63.750 (63.452) Epoch: [2][10350/11272] Time 0.890 (0.823) Data 0.001 (0.002) Loss 2.7593 (2.8078) Prec@1 34.375 (33.303) Prec@5 63.125 (63.453) Epoch: [2][10360/11272] Time 0.886 (0.823) Data 0.002 (0.002) Loss 2.7380 (2.8078) Prec@1 34.375 (33.303) Prec@5 62.500 (63.453) Epoch: [2][10370/11272] Time 0.783 (0.823) Data 0.002 (0.002) Loss 2.9137 (2.8077) Prec@1 32.500 (33.305) Prec@5 63.125 (63.454) Epoch: [2][10380/11272] Time 0.859 (0.823) Data 0.001 (0.002) Loss 2.9071 (2.8078) Prec@1 29.375 (33.304) Prec@5 65.625 (63.454) Epoch: [2][10390/11272] Time 0.910 (0.823) Data 0.002 (0.002) Loss 2.6275 (2.8077) Prec@1 36.250 (33.306) Prec@5 66.875 (63.456) Epoch: [2][10400/11272] Time 0.736 (0.823) Data 0.001 (0.002) Loss 2.8985 (2.8077) Prec@1 35.000 (33.306) Prec@5 62.500 (63.456) Epoch: [2][10410/11272] Time 0.780 (0.823) Data 0.002 (0.002) Loss 2.7577 (2.8077) Prec@1 28.750 (33.305) Prec@5 63.125 (63.456) Epoch: [2][10420/11272] Time 0.882 (0.823) Data 0.002 (0.002) Loss 2.6618 (2.8077) Prec@1 35.000 (33.305) Prec@5 65.625 (63.456) Epoch: [2][10430/11272] Time 0.847 (0.823) Data 0.001 (0.002) Loss 2.7229 (2.8076) Prec@1 33.125 (33.306) Prec@5 63.750 (63.457) Epoch: [2][10440/11272] Time 0.777 (0.823) Data 0.001 (0.002) Loss 2.7388 (2.8076) Prec@1 35.000 (33.306) Prec@5 65.000 (63.458) Epoch: [2][10450/11272] Time 0.781 (0.823) Data 0.002 (0.002) Loss 2.6636 (2.8076) Prec@1 36.250 (33.307) Prec@5 65.000 (63.458) Epoch: [2][10460/11272] Time 0.869 (0.823) Data 0.002 (0.002) Loss 2.7483 (2.8077) Prec@1 37.500 (33.306) Prec@5 61.875 (63.457) Epoch: [2][10470/11272] Time 0.868 (0.823) Data 0.001 (0.002) Loss 2.6951 (2.8076) Prec@1 35.625 (33.307) Prec@5 64.375 (63.458) Epoch: [2][10480/11272] Time 0.800 (0.823) Data 0.002 (0.002) Loss 2.7538 (2.8077) Prec@1 34.375 (33.307) Prec@5 65.000 (63.458) Epoch: [2][10490/11272] Time 0.750 (0.823) Data 0.002 (0.002) Loss 2.6193 (2.8077) Prec@1 36.250 (33.308) Prec@5 66.250 (63.459) Epoch: [2][10500/11272] Time 0.899 (0.823) Data 0.001 (0.002) Loss 2.8356 (2.8077) Prec@1 33.125 (33.306) Prec@5 66.875 (63.457) Epoch: [2][10510/11272] Time 0.733 (0.823) Data 0.003 (0.002) Loss 2.8458 (2.8077) Prec@1 32.500 (33.307) Prec@5 62.500 (63.458) Epoch: [2][10520/11272] Time 0.767 (0.823) Data 0.001 (0.002) Loss 3.0766 (2.8077) Prec@1 27.500 (33.305) Prec@5 56.250 (63.457) Epoch: [2][10530/11272] Time 0.881 (0.823) Data 0.001 (0.002) Loss 2.8059 (2.8078) Prec@1 30.625 (33.305) Prec@5 68.125 (63.457) Epoch: [2][10540/11272] Time 0.906 (0.823) Data 0.002 (0.002) Loss 2.7066 (2.8077) Prec@1 36.250 (33.307) Prec@5 65.625 (63.459) Epoch: [2][10550/11272] Time 0.755 (0.823) Data 0.001 (0.002) Loss 2.5907 (2.8076) Prec@1 34.375 (33.307) Prec@5 68.750 (63.461) Epoch: [2][10560/11272] Time 0.778 (0.823) Data 0.001 (0.002) Loss 2.8440 (2.8075) Prec@1 36.250 (33.311) Prec@5 58.125 (63.461) Epoch: [2][10570/11272] Time 0.888 (0.823) Data 0.001 (0.002) Loss 2.7613 (2.8075) Prec@1 38.750 (33.313) Prec@5 61.250 (63.462) Epoch: [2][10580/11272] Time 0.915 (0.823) Data 0.002 (0.002) Loss 2.7949 (2.8073) Prec@1 33.125 (33.315) Prec@5 65.625 (63.465) Epoch: [2][10590/11272] Time 0.774 (0.823) Data 0.002 (0.002) Loss 2.5743 (2.8072) Prec@1 40.000 (33.317) Prec@5 68.750 (63.468) Epoch: [2][10600/11272] Time 0.756 (0.823) Data 0.001 (0.002) Loss 2.8284 (2.8072) Prec@1 30.625 (33.316) Prec@5 65.625 (63.468) Epoch: [2][10610/11272] Time 0.903 (0.823) Data 0.002 (0.002) Loss 2.6702 (2.8072) Prec@1 32.500 (33.315) Prec@5 68.750 (63.468) Epoch: [2][10620/11272] Time 0.923 (0.823) Data 0.002 (0.002) Loss 2.6357 (2.8073) Prec@1 35.000 (33.315) Prec@5 65.625 (63.467) Epoch: [2][10630/11272] Time 0.738 (0.823) Data 0.001 (0.002) Loss 3.0734 (2.8073) Prec@1 31.875 (33.316) Prec@5 58.750 (63.466) Epoch: [2][10640/11272] Time 0.931 (0.823) Data 0.001 (0.002) Loss 2.5774 (2.8071) Prec@1 36.875 (33.320) Prec@5 66.875 (63.469) Epoch: [2][10650/11272] Time 0.885 (0.823) Data 0.001 (0.002) Loss 2.6474 (2.8071) Prec@1 38.125 (33.320) Prec@5 70.625 (63.470) Epoch: [2][10660/11272] Time 0.749 (0.823) Data 0.001 (0.002) Loss 2.6738 (2.8070) Prec@1 34.375 (33.322) Prec@5 70.625 (63.472) Epoch: [2][10670/11272] Time 0.793 (0.823) Data 0.001 (0.002) Loss 2.8469 (2.8071) Prec@1 32.500 (33.322) Prec@5 64.375 (63.472) Epoch: [2][10680/11272] Time 0.869 (0.823) Data 0.001 (0.002) Loss 2.6532 (2.8070) Prec@1 39.375 (33.323) Prec@5 64.375 (63.473) Epoch: [2][10690/11272] Time 0.868 (0.823) Data 0.001 (0.002) Loss 2.8118 (2.8070) Prec@1 37.500 (33.323) Prec@5 60.625 (63.473) Epoch: [2][10700/11272] Time 0.768 (0.823) Data 0.001 (0.002) Loss 2.7283 (2.8070) Prec@1 33.750 (33.321) Prec@5 65.625 (63.470) Epoch: [2][10710/11272] Time 0.745 (0.823) Data 0.001 (0.002) Loss 2.7057 (2.8070) Prec@1 38.125 (33.322) Prec@5 68.750 (63.472) Epoch: [2][10720/11272] Time 0.917 (0.823) Data 0.002 (0.002) Loss 2.9431 (2.8070) Prec@1 30.625 (33.321) Prec@5 63.125 (63.472) Epoch: [2][10730/11272] Time 0.837 (0.823) Data 0.001 (0.002) Loss 2.5098 (2.8070) Prec@1 37.500 (33.323) Prec@5 70.625 (63.472) Epoch: [2][10740/11272] Time 0.785 (0.823) Data 0.001 (0.002) Loss 2.7652 (2.8070) Prec@1 31.875 (33.320) Prec@5 62.500 (63.471) Epoch: [2][10750/11272] Time 0.773 (0.823) Data 0.002 (0.002) Loss 2.8009 (2.8071) Prec@1 31.250 (33.321) Prec@5 61.875 (63.471) Epoch: [2][10760/11272] Time 0.856 (0.823) Data 0.002 (0.002) Loss 2.7634 (2.8071) Prec@1 35.625 (33.321) Prec@5 63.125 (63.469) Epoch: [2][10770/11272] Time 0.762 (0.823) Data 0.004 (0.002) Loss 2.9999 (2.8070) Prec@1 28.750 (33.322) Prec@5 60.625 (63.470) Epoch: [2][10780/11272] Time 0.762 (0.823) Data 0.001 (0.002) Loss 2.5840 (2.8070) Prec@1 40.625 (33.323) Prec@5 64.375 (63.471) Epoch: [2][10790/11272] Time 0.876 (0.823) Data 0.001 (0.002) Loss 2.7183 (2.8070) Prec@1 34.375 (33.323) Prec@5 65.000 (63.470) Epoch: [2][10800/11272] Time 0.863 (0.823) Data 0.001 (0.002) Loss 2.6959 (2.8069) Prec@1 33.750 (33.324) Prec@5 66.875 (63.473) Epoch: [2][10810/11272] Time 0.778 (0.823) Data 0.001 (0.002) Loss 2.9800 (2.8068) Prec@1 25.625 (33.327) Prec@5 63.125 (63.476) Epoch: [2][10820/11272] Time 0.767 (0.823) Data 0.001 (0.002) Loss 2.8362 (2.8069) Prec@1 30.625 (33.325) Prec@5 60.625 (63.474) Epoch: [2][10830/11272] Time 0.856 (0.823) Data 0.002 (0.002) Loss 2.6373 (2.8068) Prec@1 31.250 (33.327) Prec@5 71.250 (63.476) Epoch: [2][10840/11272] Time 0.888 (0.823) Data 0.001 (0.002) Loss 2.8084 (2.8068) Prec@1 31.875 (33.328) Prec@5 61.250 (63.477) Epoch: [2][10850/11272] Time 0.740 (0.823) Data 0.002 (0.002) Loss 2.8424 (2.8067) Prec@1 35.000 (33.328) Prec@5 61.875 (63.479) Epoch: [2][10860/11272] Time 0.742 (0.823) Data 0.001 (0.002) Loss 2.8125 (2.8068) Prec@1 33.750 (33.326) Prec@5 67.500 (63.478) Epoch: [2][10870/11272] Time 0.893 (0.823) Data 0.002 (0.002) Loss 2.5797 (2.8067) Prec@1 38.750 (33.326) Prec@5 62.500 (63.478) Epoch: [2][10880/11272] Time 0.907 (0.823) Data 0.001 (0.002) Loss 2.9085 (2.8068) Prec@1 35.625 (33.325) Prec@5 60.625 (63.477) Epoch: [2][10890/11272] Time 0.779 (0.823) Data 0.002 (0.002) Loss 2.5673 (2.8067) Prec@1 39.375 (33.327) Prec@5 65.000 (63.477) Epoch: [2][10900/11272] Time 0.895 (0.823) Data 0.001 (0.002) Loss 2.7973 (2.8066) Prec@1 35.000 (33.325) Prec@5 66.250 (63.479) Epoch: [2][10910/11272] Time 0.857 (0.823) Data 0.001 (0.002) Loss 2.7350 (2.8066) Prec@1 33.750 (33.325) Prec@5 65.625 (63.480) Epoch: [2][10920/11272] Time 0.748 (0.823) Data 0.001 (0.002) Loss 2.7588 (2.8065) Prec@1 26.875 (33.324) Prec@5 63.125 (63.480) Epoch: [2][10930/11272] Time 0.753 (0.823) Data 0.002 (0.002) Loss 2.6954 (2.8066) Prec@1 36.250 (33.323) Prec@5 64.375 (63.481) Epoch: [2][10940/11272] Time 0.882 (0.823) Data 0.001 (0.002) Loss 2.6651 (2.8065) Prec@1 35.625 (33.323) Prec@5 66.875 (63.481) Epoch: [2][10950/11272] Time 0.867 (0.823) Data 0.002 (0.002) Loss 2.8951 (2.8066) Prec@1 32.500 (33.323) Prec@5 63.750 (63.481) Epoch: [2][10960/11272] Time 0.791 (0.823) Data 0.002 (0.002) Loss 2.8077 (2.8066) Prec@1 35.000 (33.323) Prec@5 59.375 (63.480) Epoch: [2][10970/11272] Time 0.763 (0.823) Data 0.001 (0.002) Loss 2.5249 (2.8066) Prec@1 39.375 (33.323) Prec@5 71.875 (63.480) Epoch: [2][10980/11272] Time 0.942 (0.823) Data 0.001 (0.002) Loss 2.5443 (2.8065) Prec@1 37.500 (33.323) Prec@5 71.250 (63.482) Epoch: [2][10990/11272] Time 0.878 (0.823) Data 0.001 (0.002) Loss 2.7696 (2.8065) Prec@1 35.625 (33.323) Prec@5 63.750 (63.483) Epoch: [2][11000/11272] Time 0.745 (0.823) Data 0.002 (0.002) Loss 2.5685 (2.8064) Prec@1 38.750 (33.325) Prec@5 67.500 (63.486) Epoch: [2][11010/11272] Time 0.774 (0.823) Data 0.001 (0.002) Loss 2.8944 (2.8064) Prec@1 31.250 (33.325) Prec@5 66.250 (63.487) Epoch: [2][11020/11272] Time 0.906 (0.823) Data 0.001 (0.002) Loss 3.1589 (2.8064) Prec@1 26.875 (33.323) Prec@5 57.500 (63.486) Epoch: [2][11030/11272] Time 0.863 (0.823) Data 0.002 (0.002) Loss 2.6225 (2.8063) Prec@1 35.000 (33.324) Prec@5 69.375 (63.487) Epoch: [2][11040/11272] Time 0.749 (0.823) Data 0.001 (0.002) Loss 2.6736 (2.8062) Prec@1 40.000 (33.326) Prec@5 66.250 (63.490) Epoch: [2][11050/11272] Time 0.919 (0.823) Data 0.001 (0.002) Loss 2.9182 (2.8061) Prec@1 29.375 (33.327) Prec@5 62.500 (63.491) Epoch: [2][11060/11272] Time 0.942 (0.823) Data 0.001 (0.002) Loss 2.9221 (2.8061) Prec@1 31.875 (33.328) Prec@5 58.750 (63.491) Epoch: [2][11070/11272] Time 0.780 (0.823) Data 0.002 (0.002) Loss 2.8683 (2.8061) Prec@1 31.250 (33.328) Prec@5 67.500 (63.491) Epoch: [2][11080/11272] Time 0.763 (0.823) Data 0.001 (0.002) Loss 3.1063 (2.8062) Prec@1 33.750 (33.328) Prec@5 56.250 (63.490) Epoch: [2][11090/11272] Time 0.878 (0.823) Data 0.001 (0.002) Loss 2.6759 (2.8062) Prec@1 32.500 (33.328) Prec@5 67.500 (63.490) Epoch: [2][11100/11272] Time 0.883 (0.823) Data 0.002 (0.002) Loss 2.5408 (2.8061) Prec@1 38.750 (33.328) Prec@5 66.250 (63.491) Epoch: [2][11110/11272] Time 0.747 (0.823) Data 0.001 (0.002) Loss 2.6940 (2.8061) Prec@1 36.875 (33.330) Prec@5 66.875 (63.492) Epoch: [2][11120/11272] Time 0.787 (0.823) Data 0.001 (0.002) Loss 2.7445 (2.8060) Prec@1 37.500 (33.333) Prec@5 66.875 (63.496) Epoch: [2][11130/11272] Time 0.918 (0.823) Data 0.001 (0.002) Loss 2.5879 (2.8060) Prec@1 37.500 (33.333) Prec@5 65.625 (63.494) Epoch: [2][11140/11272] Time 0.847 (0.823) Data 0.001 (0.002) Loss 2.8804 (2.8060) Prec@1 34.375 (33.333) Prec@5 58.750 (63.495) Epoch: [2][11150/11272] Time 0.782 (0.823) Data 0.001 (0.002) Loss 2.6667 (2.8059) Prec@1 34.375 (33.333) Prec@5 68.750 (63.496) Epoch: [2][11160/11272] Time 0.738 (0.823) Data 0.001 (0.002) Loss 2.6817 (2.8059) Prec@1 33.750 (33.333) Prec@5 66.875 (63.497) Epoch: [2][11170/11272] Time 0.869 (0.823) Data 0.001 (0.002) Loss 2.6022 (2.8059) Prec@1 30.625 (33.331) Prec@5 68.750 (63.496) Epoch: [2][11180/11272] Time 0.762 (0.823) Data 0.001 (0.002) Loss 2.6219 (2.8059) Prec@1 38.750 (33.333) Prec@5 65.625 (63.498) Epoch: [2][11190/11272] Time 0.766 (0.823) Data 0.002 (0.002) Loss 2.7875 (2.8058) Prec@1 31.250 (33.334) Prec@5 66.875 (63.501) Epoch: [2][11200/11272] Time 0.900 (0.823) Data 0.001 (0.002) Loss 2.9352 (2.8058) Prec@1 28.125 (33.335) Prec@5 58.125 (63.500) Epoch: [2][11210/11272] Time 0.898 (0.823) Data 0.002 (0.002) Loss 2.8434 (2.8058) Prec@1 30.625 (33.335) Prec@5 63.750 (63.500) Epoch: [2][11220/11272] Time 0.780 (0.823) Data 0.001 (0.002) Loss 2.9546 (2.8057) Prec@1 28.750 (33.336) Prec@5 65.000 (63.501) Epoch: [2][11230/11272] Time 0.747 (0.823) Data 0.002 (0.002) Loss 3.0081 (2.8057) Prec@1 28.750 (33.337) Prec@5 58.125 (63.501) Epoch: [2][11240/11272] Time 0.909 (0.823) Data 0.001 (0.002) Loss 2.7235 (2.8057) Prec@1 38.125 (33.338) Prec@5 63.125 (63.502) Epoch: [2][11250/11272] Time 0.869 (0.823) Data 0.001 (0.002) Loss 2.7634 (2.8057) Prec@1 35.000 (33.339) Prec@5 68.125 (63.502) Epoch: [2][11260/11272] Time 0.761 (0.823) Data 0.001 (0.002) Loss 2.8092 (2.8056) Prec@1 34.375 (33.340) Prec@5 64.375 (63.503) Epoch: [2][11270/11272] Time 0.749 (0.823) Data 0.000 (0.002) Loss 2.9345 (2.8056) Prec@1 33.125 (33.340) Prec@5 61.250 (63.504) Test: [0/229] Time 2.418 (2.418) Loss 1.5064 (1.5064) Prec@1 50.625 (50.625) Prec@5 91.875 (91.875) Test: [10/229] Time 0.381 (0.572) Loss 1.6151 (2.2640) Prec@1 58.750 (43.636) Prec@5 93.125 (76.477) Test: [20/229] Time 0.449 (0.498) Loss 3.5814 (2.4411) Prec@1 16.875 (39.970) Prec@5 47.500 (72.024) Test: [30/229] Time 0.377 (0.463) Loss 2.3282 (2.3518) Prec@1 35.625 (41.875) Prec@5 76.250 (73.629) Test: [40/229] Time 0.421 (0.446) Loss 1.0844 (2.4066) Prec@1 76.250 (40.869) Prec@5 83.750 (72.378) Test: [50/229] Time 0.342 (0.436) Loss 3.4412 (2.4884) Prec@1 14.375 (39.240) Prec@5 51.250 (70.699) Test: [60/229] Time 0.414 (0.428) Loss 3.3904 (2.4903) Prec@1 16.250 (39.334) Prec@5 51.875 (70.113) Test: [70/229] Time 0.422 (0.422) Loss 2.2976 (2.5203) Prec@1 46.875 (38.600) Prec@5 70.000 (69.437) Test: [80/229] Time 0.367 (0.419) Loss 2.6785 (2.5348) Prec@1 34.375 (37.917) Prec@5 65.000 (69.444) Test: [90/229] Time 0.429 (0.418) Loss 2.1713 (2.5040) Prec@1 53.750 (38.290) Prec@5 73.750 (70.282) Test: [100/229] Time 0.421 (0.416) Loss 2.2599 (2.4611) Prec@1 49.375 (39.524) Prec@5 75.625 (71.052) Test: [110/229] Time 0.513 (0.415) Loss 2.3839 (2.4301) Prec@1 40.000 (40.158) Prec@5 73.125 (71.650) Test: [120/229] Time 0.425 (0.415) Loss 3.2633 (2.4418) Prec@1 20.000 (39.499) Prec@5 61.250 (71.529) Test: [130/229] Time 0.387 (0.416) Loss 1.9306 (2.4298) Prec@1 55.000 (39.909) Prec@5 83.125 (71.636) Test: [140/229] Time 0.432 (0.415) Loss 2.5319 (2.4436) Prec@1 30.000 (39.481) Prec@5 70.625 (71.401) Test: [150/229] Time 0.353 (0.414) Loss 1.7689 (2.4545) Prec@1 55.000 (39.226) Prec@5 79.375 (71.358) Test: [160/229] Time 0.481 (0.414) Loss 2.3646 (2.4575) Prec@1 46.250 (39.243) Prec@5 75.625 (71.277) Test: [170/229] Time 0.387 (0.414) Loss 2.2175 (2.4706) Prec@1 45.625 (38.849) Prec@5 80.625 (71.126) Test: [180/229] Time 0.484 (0.415) Loss 3.1069 (2.4871) Prec@1 25.000 (38.729) Prec@5 51.875 (70.701) Test: [190/229] Time 0.316 (0.413) Loss 1.9949 (2.4846) Prec@1 39.375 (38.822) Prec@5 89.375 (70.730) Test: [200/229] Time 0.359 (0.413) Loss 2.2124 (2.4777) Prec@1 41.875 (38.759) Prec@5 71.250 (70.920) Test: [210/229] Time 0.470 (0.414) Loss 1.4291 (2.4547) Prec@1 58.750 (39.218) Prec@5 85.625 (71.333) Test: [220/229] Time 0.366 (0.413) Loss 1.7295 (2.4425) Prec@1 58.750 (39.567) Prec@5 83.750 (71.465) * Prec@1 39.908 Prec@5 71.682 Epoch: [3][0/11272] Time 3.414 (3.414) Data 2.565 (2.565) Loss 2.6044 (2.6044) Prec@1 38.750 (38.750) Prec@5 68.750 (68.750) Epoch: [3][10/11272] Time 0.875 (1.075) Data 0.002 (0.235) Loss 2.6249 (2.7576) Prec@1 36.250 (34.830) Prec@5 68.750 (64.943) Epoch: [3][20/11272] Time 0.908 (0.960) Data 0.001 (0.124) Loss 2.7122 (2.7301) Prec@1 33.750 (35.774) Prec@5 62.500 (64.881) Epoch: [3][30/11272] Time 0.735 (0.918) Data 0.002 (0.085) Loss 2.6607 (2.7714) Prec@1 35.000 (35.101) Prec@5 71.250 (64.476) Epoch: [3][40/11272] Time 0.777 (0.894) Data 0.001 (0.064) Loss 2.7070 (2.7721) Prec@1 30.625 (35.000) Prec@5 65.000 (64.466) Epoch: [3][50/11272] Time 0.918 (0.880) Data 0.002 (0.052) Loss 2.6967 (2.7670) Prec@1 41.250 (34.914) Prec@5 63.750 (64.583) Epoch: [3][60/11272] Time 0.891 (0.870) Data 0.002 (0.044) Loss 2.5252 (2.7642) Prec@1 31.250 (34.549) Prec@5 67.500 (64.580) Epoch: [3][70/11272] Time 0.800 (0.864) Data 0.002 (0.038) Loss 2.6998 (2.7495) Prec@1 35.000 (34.595) Prec@5 62.500 (64.815) Epoch: [3][80/11272] Time 0.750 (0.861) Data 0.001 (0.033) Loss 2.9544 (2.7561) Prec@1 27.500 (34.390) Prec@5 58.750 (64.776) Epoch: [3][90/11272] Time 0.877 (0.861) Data 0.002 (0.030) Loss 2.8867 (2.7581) Prec@1 30.000 (34.224) Prec@5 65.000 (64.753) Epoch: [3][100/11272] Time 0.947 (0.861) Data 0.001 (0.027) Loss 2.6310 (2.7567) Prec@1 40.000 (34.264) Prec@5 65.625 (64.833) Epoch: [3][110/11272] Time 0.749 (0.858) Data 0.002 (0.025) Loss 2.7003 (2.7540) Prec@1 35.625 (34.364) Prec@5 63.125 (64.842) Epoch: [3][120/11272] Time 0.911 (0.856) Data 0.002 (0.023) Loss 2.6915 (2.7567) Prec@1 36.250 (34.292) Prec@5 64.375 (64.757) Epoch: [3][130/11272] Time 0.931 (0.855) Data 0.002 (0.021) Loss 2.8656 (2.7527) Prec@1 32.500 (34.342) Prec@5 66.250 (64.871) Epoch: [3][140/11272] Time 0.747 (0.852) Data 0.001 (0.020) Loss 2.8199 (2.7542) Prec@1 33.750 (34.362) Prec@5 61.250 (64.738) Epoch: [3][150/11272] Time 0.741 (0.849) Data 0.001 (0.019) Loss 2.4861 (2.7600) Prec@1 36.250 (34.230) Prec@5 71.875 (64.594) Epoch: [3][160/11272] Time 0.834 (0.849) Data 0.001 (0.018) Loss 2.7948 (2.7588) Prec@1 31.875 (34.309) Prec@5 66.875 (64.635) Epoch: [3][170/11272] Time 0.867 (0.846) Data 0.002 (0.017) Loss 2.7869 (2.7571) Prec@1 31.250 (34.218) Prec@5 65.625 (64.627) Epoch: [3][180/11272] Time 0.755 (0.846) Data 0.002 (0.016) Loss 2.6589 (2.7566) Prec@1 32.500 (34.185) Prec@5 63.125 (64.582) Epoch: [3][190/11272] Time 0.755 (0.846) Data 0.002 (0.015) Loss 2.7253 (2.7591) Prec@1 31.250 (34.028) Prec@5 65.000 (64.611) Epoch: [3][200/11272] Time 0.899 (0.846) Data 0.001 (0.015) Loss 2.6169 (2.7581) Prec@1 43.125 (34.092) Prec@5 66.875 (64.565) Epoch: [3][210/11272] Time 0.871 (0.845) Data 0.002 (0.014) Loss 2.6170 (2.7587) Prec@1 34.375 (34.034) Prec@5 66.875 (64.529) Epoch: [3][220/11272] Time 0.811 (0.845) Data 0.002 (0.013) Loss 2.8009 (2.7579) Prec@1 36.875 (34.075) Prec@5 60.625 (64.528) Epoch: [3][230/11272] Time 0.810 (0.844) Data 0.002 (0.013) Loss 3.0560 (2.7571) Prec@1 30.000 (34.137) Prec@5 59.375 (64.527) Epoch: [3][240/11272] Time 0.867 (0.843) Data 0.002 (0.012) Loss 2.8592 (2.7567) Prec@1 33.125 (34.108) Prec@5 57.500 (64.479) Epoch: [3][250/11272] Time 0.763 (0.843) Data 0.001 (0.012) Loss 2.4715 (2.7557) Prec@1 37.500 (34.039) Prec@5 70.000 (64.497) Epoch: [3][260/11272] Time 0.799 (0.843) Data 0.002 (0.012) Loss 2.6175 (2.7543) Prec@1 37.500 (34.068) Prec@5 65.625 (64.526) Epoch: [3][270/11272] Time 0.884 (0.843) Data 0.002 (0.011) Loss 2.8323 (2.7545) Prec@1 32.500 (34.094) Prec@5 64.375 (64.513) Epoch: [3][280/11272] Time 0.907 (0.843) Data 0.001 (0.011) Loss 2.7688 (2.7522) Prec@1 33.125 (34.104) Prec@5 65.000 (64.553) Epoch: [3][290/11272] Time 0.763 (0.842) Data 0.001 (0.011) Loss 2.8388 (2.7530) Prec@1 35.000 (34.158) Prec@5 61.875 (64.560) Epoch: [3][300/11272] Time 0.755 (0.841) Data 0.002 (0.010) Loss 2.7788 (2.7542) Prec@1 31.875 (34.161) Prec@5 65.000 (64.533) Epoch: [3][310/11272] Time 0.849 (0.841) Data 0.002 (0.010) Loss 2.7991 (2.7538) Prec@1 35.000 (34.162) Prec@5 63.750 (64.572) Epoch: [3][320/11272] Time 0.912 (0.840) Data 0.002 (0.010) Loss 2.8644 (2.7551) Prec@1 29.375 (34.153) Prec@5 61.250 (64.562) Epoch: [3][330/11272] Time 0.749 (0.840) Data 0.001 (0.010) Loss 2.9775 (2.7546) Prec@1 30.625 (34.162) Prec@5 58.125 (64.520) Epoch: [3][340/11272] Time 0.768 (0.839) Data 0.001 (0.009) Loss 2.4354 (2.7565) Prec@1 33.750 (34.139) Prec@5 74.375 (64.494) Epoch: [3][350/11272] Time 0.893 (0.839) Data 0.002 (0.009) Loss 2.7945 (2.7547) Prec@1 35.625 (34.174) Prec@5 68.125 (64.507) Epoch: [3][360/11272] Time 0.881 (0.839) Data 0.001 (0.009) Loss 2.8622 (2.7568) Prec@1 31.875 (34.131) Prec@5 64.375 (64.468) Epoch: [3][370/11272] Time 0.781 (0.839) Data 0.002 (0.009) Loss 2.8492 (2.7556) Prec@1 28.750 (34.137) Prec@5 65.000 (64.496) Epoch: [3][380/11272] Time 0.904 (0.838) Data 0.002 (0.008) Loss 2.6767 (2.7550) Prec@1 37.500 (34.135) Prec@5 65.000 (64.495) Epoch: [3][390/11272] Time 0.857 (0.838) Data 0.002 (0.008) Loss 2.7101 (2.7548) Prec@1 30.000 (34.140) Prec@5 70.000 (64.501) Epoch: [3][400/11272] Time 0.812 (0.837) Data 0.001 (0.008) Loss 3.0421 (2.7560) Prec@1 32.500 (34.123) Prec@5 56.875 (64.453) Epoch: [3][410/11272] Time 0.764 (0.837) Data 0.002 (0.008) Loss 2.5625 (2.7569) Prec@1 35.625 (34.068) Prec@5 67.500 (64.450) Epoch: [3][420/11272] Time 0.925 (0.837) Data 0.002 (0.008) Loss 2.4181 (2.7550) Prec@1 46.250 (34.100) Prec@5 72.500 (64.547) Epoch: [3][430/11272] Time 0.932 (0.837) Data 0.002 (0.008) Loss 2.9660 (2.7560) Prec@1 35.625 (34.085) Prec@5 58.750 (64.503) Epoch: [3][440/11272] Time 0.757 (0.837) Data 0.002 (0.008) Loss 2.9068 (2.7560) Prec@1 31.875 (34.080) Prec@5 63.125 (64.487) Epoch: [3][450/11272] Time 0.754 (0.837) Data 0.002 (0.007) Loss 2.7527 (2.7562) Prec@1 32.500 (34.058) Prec@5 68.750 (64.514) Epoch: [3][460/11272] Time 0.874 (0.837) Data 0.001 (0.007) Loss 2.8310 (2.7562) Prec@1 33.750 (34.075) Prec@5 63.750 (64.524) Epoch: [3][470/11272] Time 0.886 (0.837) Data 0.002 (0.007) Loss 2.6153 (2.7555) Prec@1 32.500 (34.071) Prec@5 65.625 (64.541) Epoch: [3][480/11272] Time 0.755 (0.837) Data 0.001 (0.007) Loss 2.6030 (2.7571) Prec@1 36.875 (34.049) Prec@5 63.125 (64.506) Epoch: [3][490/11272] Time 0.790 (0.837) Data 0.002 (0.007) Loss 2.8124 (2.7576) Prec@1 36.250 (34.033) Prec@5 63.125 (64.511) Epoch: [3][500/11272] Time 0.960 (0.837) Data 0.003 (0.007) Loss 2.7144 (2.7583) Prec@1 33.750 (34.019) Prec@5 63.750 (64.503) Epoch: [3][510/11272] Time 0.753 (0.837) Data 0.004 (0.007) Loss 3.0468 (2.7600) Prec@1 31.875 (34.037) Prec@5 61.875 (64.477) Epoch: [3][520/11272] Time 0.815 (0.837) Data 0.001 (0.007) Loss 2.9083 (2.7603) Prec@1 30.625 (34.044) Prec@5 63.125 (64.470) Epoch: [3][530/11272] Time 0.943 (0.838) Data 0.004 (0.007) Loss 2.7418 (2.7584) Prec@1 30.625 (34.067) Prec@5 66.875 (64.539) Epoch: [3][540/11272] Time 0.895 (0.838) Data 0.001 (0.007) Loss 3.0886 (2.7588) Prec@1 29.375 (34.065) Prec@5 56.250 (64.530) Epoch: [3][550/11272] Time 0.813 (0.838) Data 0.002 (0.006) Loss 2.6787 (2.7594) Prec@1 31.250 (34.062) Prec@5 70.625 (64.513) Epoch: [3][560/11272] Time 0.784 (0.838) Data 0.001 (0.006) Loss 2.9044 (2.7602) Prec@1 29.375 (34.033) Prec@5 61.875 (64.479) Epoch: [3][570/11272] Time 0.906 (0.838) Data 0.002 (0.006) Loss 2.5208 (2.7585) Prec@1 38.125 (34.058) Prec@5 64.375 (64.480) Epoch: [3][580/11272] Time 0.902 (0.838) Data 0.002 (0.006) Loss 2.5440 (2.7574) Prec@1 39.375 (34.076) Prec@5 70.000 (64.494) Epoch: [3][590/11272] Time 0.735 (0.838) Data 0.002 (0.006) Loss 3.0200 (2.7590) Prec@1 30.625 (34.034) Prec@5 61.875 (64.481) Epoch: [3][600/11272] Time 0.780 (0.838) Data 0.002 (0.006) Loss 2.6656 (2.7598) Prec@1 36.250 (34.001) Prec@5 69.375 (64.487) Epoch: [3][610/11272] Time 0.887 (0.837) Data 0.002 (0.006) Loss 2.5736 (2.7603) Prec@1 34.375 (33.964) Prec@5 66.250 (64.467) Epoch: [3][620/11272] Time 0.937 (0.838) Data 0.002 (0.006) Loss 2.7936 (2.7606) Prec@1 36.250 (33.986) Prec@5 67.500 (64.468) Epoch: [3][630/11272] Time 0.786 (0.837) Data 0.002 (0.006) Loss 2.8841 (2.7595) Prec@1 35.625 (34.024) Prec@5 60.625 (64.480) Epoch: [3][640/11272] Time 0.880 (0.838) Data 0.001 (0.006) Loss 2.9297 (2.7601) Prec@1 28.750 (34.025) Prec@5 58.750 (64.462) Epoch: [3][650/11272] Time 0.848 (0.838) Data 0.001 (0.006) Loss 3.0284 (2.7611) Prec@1 31.875 (34.019) Prec@5 60.000 (64.450) Epoch: [3][660/11272] Time 0.795 (0.838) Data 0.002 (0.006) Loss 2.5301 (2.7617) Prec@1 37.500 (34.002) Prec@5 66.250 (64.416) Epoch: [3][670/11272] Time 0.833 (0.839) Data 0.001 (0.006) Loss 2.5594 (2.7601) Prec@1 40.625 (34.029) Prec@5 67.500 (64.449) Epoch: [3][680/11272] Time 0.962 (0.839) Data 0.001 (0.006) Loss 2.8048 (2.7611) Prec@1 30.000 (34.000) Prec@5 68.125 (64.435) Epoch: [3][690/11272] Time 0.936 (0.839) Data 0.002 (0.006) Loss 3.1088 (2.7625) Prec@1 27.500 (33.967) Prec@5 56.875 (64.412) Epoch: [3][700/11272] Time 0.762 (0.839) Data 0.001 (0.005) Loss 2.7155 (2.7619) Prec@1 38.125 (33.983) Prec@5 67.500 (64.415) Epoch: [3][710/11272] Time 0.801 (0.839) Data 0.001 (0.005) Loss 2.8055 (2.7621) Prec@1 38.125 (33.986) Prec@5 66.250 (64.415) Epoch: [3][720/11272] Time 0.917 (0.839) Data 0.001 (0.005) Loss 2.6761 (2.7615) Prec@1 38.750 (34.015) Prec@5 65.000 (64.400) Epoch: [3][730/11272] Time 0.859 (0.839) Data 0.001 (0.005) Loss 2.9573 (2.7615) Prec@1 31.250 (34.032) Prec@5 59.375 (64.404) Epoch: [3][740/11272] Time 0.783 (0.839) Data 0.001 (0.005) Loss 2.9715 (2.7627) Prec@1 30.625 (34.000) Prec@5 61.875 (64.381) Epoch: [3][750/11272] Time 0.788 (0.839) Data 0.002 (0.005) Loss 2.7568 (2.7625) Prec@1 35.000 (34.012) Prec@5 63.750 (64.377) Epoch: [3][760/11272] Time 0.875 (0.839) Data 0.001 (0.005) Loss 3.0012 (2.7622) Prec@1 32.500 (34.025) Prec@5 63.125 (64.386) Epoch: [3][770/11272] Time 0.813 (0.840) Data 0.003 (0.005) Loss 2.7568 (2.7620) Prec@1 31.250 (34.058) Prec@5 67.500 (64.386) Epoch: [3][780/11272] Time 0.866 (0.840) Data 0.001 (0.005) Loss 2.7219 (2.7619) Prec@1 28.125 (34.033) Prec@5 63.750 (64.404) Epoch: [3][790/11272] Time 0.971 (0.840) Data 0.001 (0.005) Loss 2.6716 (2.7624) Prec@1 34.375 (34.017) Prec@5 66.250 (64.394) Epoch: [3][800/11272] Time 0.919 (0.840) Data 0.001 (0.005) Loss 2.8824 (2.7627) Prec@1 34.375 (34.013) Prec@5 57.500 (64.382) Epoch: [3][810/11272] Time 0.801 (0.840) Data 0.002 (0.005) Loss 2.6747 (2.7622) Prec@1 35.000 (34.041) Prec@5 66.250 (64.394) Epoch: [3][820/11272] Time 0.765 (0.840) Data 0.001 (0.005) Loss 2.8792 (2.7622) Prec@1 31.875 (34.049) Prec@5 66.250 (64.404) Epoch: [3][830/11272] Time 0.906 (0.841) Data 0.002 (0.005) Loss 2.7529 (2.7617) Prec@1 30.000 (34.029) Prec@5 64.375 (64.405) Epoch: [3][840/11272] Time 0.915 (0.841) Data 0.001 (0.005) Loss 2.6662 (2.7611) Prec@1 33.125 (34.040) Prec@5 67.500 (64.421) Epoch: [3][850/11272] Time 0.782 (0.841) Data 0.002 (0.005) Loss 2.8674 (2.7615) Prec@1 30.625 (34.034) Prec@5 63.125 (64.402) Epoch: [3][860/11272] Time 0.769 (0.841) Data 0.001 (0.005) Loss 2.7784 (2.7617) Prec@1 35.000 (34.051) Prec@5 62.500 (64.394) Epoch: [3][870/11272] Time 0.873 (0.841) Data 0.002 (0.005) Loss 2.7885 (2.7615) Prec@1 36.250 (34.074) Prec@5 64.375 (64.383) Epoch: [3][880/11272] Time 0.957 (0.841) Data 0.003 (0.005) Loss 2.7266 (2.7615) Prec@1 33.750 (34.073) Prec@5 68.125 (64.378) Epoch: [3][890/11272] Time 0.778 (0.841) Data 0.001 (0.005) Loss 2.8789 (2.7614) Prec@1 31.250 (34.060) Prec@5 62.500 (64.373) Epoch: [3][900/11272] Time 0.911 (0.841) Data 0.001 (0.005) Loss 2.6263 (2.7623) Prec@1 38.125 (34.058) Prec@5 63.125 (64.363) Epoch: [3][910/11272] Time 0.853 (0.841) Data 0.002 (0.005) Loss 2.9979 (2.7618) Prec@1 33.750 (34.062) Prec@5 60.625 (64.376) Epoch: [3][920/11272] Time 0.749 (0.841) Data 0.002 (0.005) Loss 2.7057 (2.7618) Prec@1 36.250 (34.053) Prec@5 64.375 (64.389) Epoch: [3][930/11272] Time 0.774 (0.841) Data 0.002 (0.005) Loss 2.3892 (2.7609) Prec@1 42.500 (34.053) Prec@5 69.375 (64.410) Epoch: [3][940/11272] Time 0.921 (0.841) Data 0.002 (0.005) Loss 2.7783 (2.7612) Prec@1 32.500 (34.042) Prec@5 64.375 (64.416) Epoch: [3][950/11272] Time 0.915 (0.841) Data 0.003 (0.004) Loss 2.5590 (2.7623) Prec@1 35.000 (34.017) Prec@5 66.250 (64.393) Epoch: [3][960/11272] Time 0.764 (0.841) Data 0.002 (0.004) Loss 2.4103 (2.7628) Prec@1 36.875 (34.012) Prec@5 71.250 (64.398) Epoch: [3][970/11272] Time 0.744 (0.841) Data 0.001 (0.004) Loss 2.4044 (2.7624) Prec@1 43.125 (34.031) Prec@5 73.125 (64.410) Epoch: [3][980/11272] Time 0.968 (0.841) Data 0.003 (0.004) Loss 2.8385 (2.7623) Prec@1 33.125 (34.037) Prec@5 64.375 (64.400) Epoch: [3][990/11272] Time 0.898 (0.841) Data 0.002 (0.004) Loss 2.8688 (2.7621) Prec@1 31.250 (34.050) Prec@5 60.625 (64.412) Epoch: [3][1000/11272] Time 0.762 (0.841) Data 0.002 (0.004) Loss 2.9920 (2.7620) Prec@1 30.625 (34.055) Prec@5 61.250 (64.413) Epoch: [3][1010/11272] Time 0.773 (0.841) Data 0.002 (0.004) Loss 2.6001 (2.7610) Prec@1 36.250 (34.057) Prec@5 65.625 (64.426) Epoch: [3][1020/11272] Time 0.862 (0.841) Data 0.001 (0.004) Loss 2.6083 (2.7606) Prec@1 35.625 (34.058) Prec@5 65.625 (64.437) Epoch: [3][1030/11272] Time 0.866 (0.841) Data 0.002 (0.004) Loss 2.9170 (2.7603) Prec@1 35.000 (34.068) Prec@5 65.000 (64.440) Epoch: [3][1040/11272] Time 0.812 (0.841) Data 0.001 (0.004) Loss 2.7320 (2.7608) Prec@1 40.625 (34.083) Prec@5 66.875 (64.440) Epoch: [3][1050/11272] Time 0.862 (0.841) Data 0.002 (0.004) Loss 2.7891 (2.7608) Prec@1 36.250 (34.090) Prec@5 66.875 (64.439) Epoch: [3][1060/11272] Time 0.825 (0.840) Data 0.001 (0.004) Loss 2.9294 (2.7615) Prec@1 31.250 (34.078) Prec@5 61.250 (64.422) Epoch: [3][1070/11272] Time 0.758 (0.840) Data 0.002 (0.004) Loss 2.8236 (2.7617) Prec@1 35.625 (34.084) Prec@5 65.000 (64.419) Epoch: [3][1080/11272] Time 0.756 (0.840) Data 0.001 (0.004) Loss 2.5277 (2.7610) Prec@1 39.375 (34.095) Prec@5 73.125 (64.428) Epoch: [3][1090/11272] Time 0.845 (0.840) Data 0.001 (0.004) Loss 3.0515 (2.7620) Prec@1 23.125 (34.072) Prec@5 61.250 (64.411) Epoch: [3][1100/11272] Time 0.861 (0.840) Data 0.001 (0.004) Loss 2.9316 (2.7623) Prec@1 27.500 (34.068) Prec@5 60.000 (64.412) Epoch: [3][1110/11272] Time 0.777 (0.839) Data 0.002 (0.004) Loss 2.6558 (2.7620) Prec@1 39.375 (34.080) Prec@5 61.875 (64.410) Epoch: [3][1120/11272] Time 0.821 (0.839) Data 0.001 (0.004) Loss 2.9373 (2.7629) Prec@1 31.250 (34.068) Prec@5 61.875 (64.385) Epoch: [3][1130/11272] Time 0.902 (0.839) Data 0.002 (0.004) Loss 2.7331 (2.7628) Prec@1 31.250 (34.054) Prec@5 66.875 (64.389) Epoch: [3][1140/11272] Time 0.912 (0.839) Data 0.002 (0.004) Loss 2.7379 (2.7625) Prec@1 29.375 (34.063) Prec@5 65.625 (64.384) Epoch: [3][1150/11272] Time 0.789 (0.839) Data 0.002 (0.004) Loss 2.5077 (2.7625) Prec@1 39.375 (34.064) Prec@5 70.000 (64.394) Epoch: [3][1160/11272] Time 0.942 (0.839) Data 0.002 (0.004) Loss 2.8105 (2.7630) Prec@1 33.125 (34.046) Prec@5 66.250 (64.399) Epoch: [3][1170/11272] Time 0.902 (0.839) Data 0.002 (0.004) Loss 2.5571 (2.7627) Prec@1 35.000 (34.052) Prec@5 69.375 (64.404) Epoch: [3][1180/11272] Time 0.774 (0.839) Data 0.001 (0.004) Loss 2.4215 (2.7625) Prec@1 41.875 (34.055) Prec@5 70.000 (64.404) Epoch: [3][1190/11272] Time 0.770 (0.839) Data 0.002 (0.004) Loss 2.8934 (2.7626) Prec@1 30.000 (34.058) Prec@5 63.750 (64.405) Epoch: [3][1200/11272] Time 0.900 (0.839) Data 0.002 (0.004) Loss 2.8200 (2.7629) Prec@1 35.000 (34.055) Prec@5 60.625 (64.395) Epoch: [3][1210/11272] Time 0.902 (0.839) Data 0.002 (0.004) Loss 2.7628 (2.7627) Prec@1 35.000 (34.051) Prec@5 67.500 (64.398) Epoch: [3][1220/11272] Time 0.790 (0.839) Data 0.002 (0.004) Loss 2.6060 (2.7623) Prec@1 39.375 (34.065) Prec@5 68.125 (64.413) Epoch: [3][1230/11272] Time 0.815 (0.839) Data 0.002 (0.004) Loss 2.7894 (2.7623) Prec@1 39.375 (34.068) Prec@5 68.125 (64.419) Epoch: [3][1240/11272] Time 0.893 (0.839) Data 0.002 (0.004) Loss 2.6688 (2.7622) Prec@1 34.375 (34.069) Prec@5 66.250 (64.422) Epoch: [3][1250/11272] Time 0.857 (0.839) Data 0.002 (0.004) Loss 3.1272 (2.7624) Prec@1 26.250 (34.063) Prec@5 56.875 (64.419) Epoch: [3][1260/11272] Time 0.754 (0.839) Data 0.001 (0.004) Loss 2.6184 (2.7624) Prec@1 37.500 (34.057) Prec@5 69.375 (64.425) Epoch: [3][1270/11272] Time 0.724 (0.839) Data 0.002 (0.004) Loss 2.6099 (2.7620) Prec@1 36.875 (34.068) Prec@5 67.500 (64.424) Epoch: [3][1280/11272] Time 0.855 (0.839) Data 0.001 (0.004) Loss 2.7630 (2.7620) Prec@1 33.125 (34.056) Prec@5 70.000 (64.434) Epoch: [3][1290/11272] Time 0.885 (0.839) Data 0.002 (0.004) Loss 2.6337 (2.7621) Prec@1 35.625 (34.051) Prec@5 68.750 (64.434) Epoch: [3][1300/11272] Time 0.814 (0.839) Data 0.002 (0.004) Loss 2.9941 (2.7615) Prec@1 25.625 (34.044) Prec@5 51.250 (64.443) Epoch: [3][1310/11272] Time 0.869 (0.839) Data 0.002 (0.004) Loss 2.8692 (2.7612) Prec@1 36.250 (34.052) Prec@5 64.375 (64.453) Epoch: [3][1320/11272] Time 0.864 (0.839) Data 0.001 (0.004) Loss 2.6728 (2.7610) Prec@1 36.250 (34.054) Prec@5 65.000 (64.468) Epoch: [3][1330/11272] Time 0.735 (0.839) Data 0.001 (0.004) Loss 2.8590 (2.7613) Prec@1 30.625 (34.056) Prec@5 64.375 (64.462) Epoch: [3][1340/11272] Time 0.792 (0.839) Data 0.002 (0.004) Loss 2.5814 (2.7612) Prec@1 35.000 (34.054) Prec@5 68.750 (64.461) Epoch: [3][1350/11272] Time 0.920 (0.839) Data 0.002 (0.004) Loss 2.9119 (2.7613) Prec@1 31.875 (34.044) Prec@5 59.375 (64.456) Epoch: [3][1360/11272] Time 0.885 (0.839) Data 0.001 (0.004) Loss 2.9433 (2.7619) Prec@1 35.625 (34.041) Prec@5 60.000 (64.441) Epoch: [3][1370/11272] Time 0.793 (0.839) Data 0.002 (0.004) Loss 2.6032 (2.7607) Prec@1 36.250 (34.063) Prec@5 66.875 (64.463) Epoch: [3][1380/11272] Time 0.742 (0.839) Data 0.001 (0.004) Loss 3.1017 (2.7607) Prec@1 28.125 (34.063) Prec@5 57.500 (64.464) Epoch: [3][1390/11272] Time 0.871 (0.839) Data 0.002 (0.004) Loss 2.7783 (2.7607) Prec@1 31.250 (34.065) Prec@5 61.250 (64.464) Epoch: [3][1400/11272] Time 0.852 (0.839) Data 0.001 (0.004) Loss 2.8591 (2.7601) Prec@1 35.000 (34.081) Prec@5 63.750 (64.474) Epoch: [3][1410/11272] Time 0.751 (0.839) Data 0.002 (0.004) Loss 2.8884 (2.7602) Prec@1 32.500 (34.078) Prec@5 62.500 (64.478) Epoch: [3][1420/11272] Time 0.774 (0.839) Data 0.002 (0.004) Loss 2.6428 (2.7594) Prec@1 28.125 (34.081) Prec@5 68.125 (64.494) Epoch: [3][1430/11272] Time 0.923 (0.839) Data 0.003 (0.004) Loss 2.8018 (2.7594) Prec@1 31.250 (34.091) Prec@5 63.750 (64.493) Epoch: [3][1440/11272] Time 0.745 (0.838) Data 0.004 (0.004) Loss 2.8004 (2.7596) Prec@1 34.375 (34.091) Prec@5 68.750 (64.505) Epoch: [3][1450/11272] Time 0.739 (0.838) Data 0.001 (0.004) Loss 2.7948 (2.7593) Prec@1 28.125 (34.092) Prec@5 65.000 (64.508) Epoch: [3][1460/11272] Time 0.890 (0.839) Data 0.001 (0.004) Loss 2.6721 (2.7585) Prec@1 32.500 (34.101) Prec@5 67.500 (64.522) Epoch: [3][1470/11272] Time 0.869 (0.839) Data 0.002 (0.004) Loss 2.3782 (2.7580) Prec@1 40.000 (34.112) Prec@5 71.875 (64.525) Epoch: [3][1480/11272] Time 0.800 (0.839) Data 0.002 (0.004) Loss 2.8618 (2.7590) Prec@1 26.250 (34.094) Prec@5 63.125 (64.502) Epoch: [3][1490/11272] Time 0.819 (0.839) Data 0.002 (0.004) Loss 2.6872 (2.7589) Prec@1 33.750 (34.098) Prec@5 66.875 (64.498) Epoch: [3][1500/11272] Time 0.865 (0.838) Data 0.001 (0.004) Loss 2.7093 (2.7588) Prec@1 35.625 (34.091) Prec@5 61.875 (64.501) Epoch: [3][1510/11272] Time 0.879 (0.838) Data 0.001 (0.003) Loss 2.7985 (2.7589) Prec@1 35.625 (34.096) Prec@5 62.500 (64.505) Epoch: [3][1520/11272] Time 0.751 (0.838) Data 0.001 (0.003) Loss 2.9888 (2.7592) Prec@1 35.000 (34.090) Prec@5 57.500 (64.498) Epoch: [3][1530/11272] Time 0.783 (0.838) Data 0.002 (0.003) Loss 2.9159 (2.7595) Prec@1 29.375 (34.099) Prec@5 58.125 (64.490) Epoch: [3][1540/11272] Time 0.913 (0.838) Data 0.002 (0.003) Loss 2.9540 (2.7603) Prec@1 27.500 (34.081) Prec@5 60.625 (64.473) Epoch: [3][1550/11272] Time 0.892 (0.838) Data 0.002 (0.003) Loss 2.7621 (2.7608) Prec@1 38.125 (34.069) Prec@5 62.500 (64.462) Epoch: [3][1560/11272] Time 0.773 (0.838) Data 0.002 (0.003) Loss 2.7597 (2.7602) Prec@1 36.875 (34.082) Prec@5 66.250 (64.475) Epoch: [3][1570/11272] Time 0.866 (0.838) Data 0.002 (0.003) Loss 2.7346 (2.7601) Prec@1 33.750 (34.080) Prec@5 66.875 (64.474) Epoch: [3][1580/11272] Time 0.918 (0.838) Data 0.002 (0.003) Loss 2.6149 (2.7600) Prec@1 31.875 (34.071) Prec@5 64.375 (64.469) Epoch: [3][1590/11272] Time 0.723 (0.838) Data 0.002 (0.003) Loss 2.7008 (2.7598) Prec@1 36.250 (34.072) Prec@5 65.625 (64.473) Epoch: [3][1600/11272] Time 0.725 (0.838) Data 0.001 (0.003) Loss 2.6618 (2.7596) Prec@1 43.750 (34.079) Prec@5 69.375 (64.473) Epoch: [3][1610/11272] Time 0.932 (0.838) Data 0.002 (0.003) Loss 3.0313 (2.7604) Prec@1 33.750 (34.074) Prec@5 65.000 (64.467) Epoch: [3][1620/11272] Time 0.912 (0.838) Data 0.001 (0.003) Loss 2.8953 (2.7598) Prec@1 28.750 (34.083) Prec@5 62.500 (64.478) Epoch: [3][1630/11272] Time 0.786 (0.838) Data 0.002 (0.003) Loss 2.7146 (2.7595) Prec@1 35.625 (34.085) Prec@5 68.750 (64.488) Epoch: [3][1640/11272] Time 0.772 (0.838) Data 0.002 (0.003) Loss 2.7594 (2.7590) Prec@1 32.500 (34.085) Prec@5 61.875 (64.496) Epoch: [3][1650/11272] Time 0.936 (0.838) Data 0.002 (0.003) Loss 2.8162 (2.7585) Prec@1 34.375 (34.090) Prec@5 64.375 (64.505) Epoch: [3][1660/11272] Time 0.907 (0.838) Data 0.002 (0.003) Loss 2.8750 (2.7582) Prec@1 29.375 (34.099) Prec@5 60.000 (64.508) Epoch: [3][1670/11272] Time 0.776 (0.838) Data 0.002 (0.003) Loss 2.4555 (2.7581) Prec@1 41.250 (34.104) Prec@5 71.250 (64.506) Epoch: [3][1680/11272] Time 0.772 (0.838) Data 0.001 (0.003) Loss 2.7112 (2.7583) Prec@1 36.875 (34.106) Prec@5 65.625 (64.498) Epoch: [3][1690/11272] Time 0.906 (0.838) Data 0.002 (0.003) Loss 2.5714 (2.7581) Prec@1 34.375 (34.113) Prec@5 66.250 (64.503) Epoch: [3][1700/11272] Time 0.741 (0.838) Data 0.003 (0.003) Loss 2.9188 (2.7579) Prec@1 30.000 (34.110) Prec@5 62.500 (64.506) Epoch: [3][1710/11272] Time 0.769 (0.838) Data 0.001 (0.003) Loss 3.0980 (2.7582) Prec@1 23.750 (34.105) Prec@5 59.375 (64.504) Epoch: [3][1720/11272] Time 0.955 (0.838) Data 0.006 (0.003) Loss 2.4659 (2.7581) Prec@1 36.875 (34.102) Prec@5 71.875 (64.498) Epoch: [3][1730/11272] Time 0.905 (0.838) Data 0.002 (0.003) Loss 2.6743 (2.7583) Prec@1 35.625 (34.098) Prec@5 66.250 (64.493) Epoch: [3][1740/11272] Time 0.791 (0.838) Data 0.002 (0.003) Loss 2.7411 (2.7579) Prec@1 35.000 (34.115) Prec@5 64.375 (64.505) Epoch: [3][1750/11272] Time 0.790 (0.838) Data 0.004 (0.003) Loss 2.6268 (2.7578) Prec@1 36.250 (34.122) Prec@5 70.000 (64.512) Epoch: [3][1760/11272] Time 0.917 (0.838) Data 0.002 (0.003) Loss 2.8523 (2.7580) Prec@1 30.000 (34.121) Prec@5 63.125 (64.510) Epoch: [3][1770/11272] Time 0.858 (0.838) Data 0.002 (0.003) Loss 2.6444 (2.7584) Prec@1 40.000 (34.125) Prec@5 67.500 (64.501) Epoch: [3][1780/11272] Time 0.816 (0.838) Data 0.001 (0.003) Loss 2.9000 (2.7586) Prec@1 30.000 (34.113) Prec@5 62.500 (64.499) Epoch: [3][1790/11272] Time 0.779 (0.838) Data 0.002 (0.003) Loss 2.7100 (2.7585) Prec@1 36.875 (34.115) Prec@5 61.250 (64.498) Epoch: [3][1800/11272] Time 0.884 (0.838) Data 0.001 (0.003) Loss 2.4863 (2.7578) Prec@1 33.750 (34.122) Prec@5 75.000 (64.515) Epoch: [3][1810/11272] Time 0.922 (0.838) Data 0.002 (0.003) Loss 2.7335 (2.7575) Prec@1 35.000 (34.128) Prec@5 64.375 (64.517) Epoch: [3][1820/11272] Time 0.792 (0.838) Data 0.002 (0.003) Loss 2.9650 (2.7572) Prec@1 29.375 (34.133) Prec@5 63.125 (64.526) Epoch: [3][1830/11272] Time 0.892 (0.838) Data 0.002 (0.003) Loss 2.7388 (2.7571) Prec@1 33.125 (34.134) Prec@5 67.500 (64.530) Epoch: [3][1840/11272] Time 0.891 (0.838) Data 0.002 (0.003) Loss 2.7406 (2.7576) Prec@1 33.750 (34.129) Prec@5 61.875 (64.518) Epoch: [3][1850/11272] Time 0.743 (0.838) Data 0.002 (0.003) Loss 2.6401 (2.7575) Prec@1 36.250 (34.127) Prec@5 70.625 (64.523) Epoch: [3][1860/11272] Time 0.767 (0.838) Data 0.002 (0.003) Loss 2.7414 (2.7577) Prec@1 38.125 (34.126) Prec@5 65.625 (64.519) Epoch: [3][1870/11272] Time 0.864 (0.838) Data 0.001 (0.003) Loss 2.5552 (2.7578) Prec@1 36.250 (34.123) Prec@5 71.250 (64.517) Epoch: [3][1880/11272] Time 0.945 (0.838) Data 0.001 (0.003) Loss 2.7913 (2.7580) Prec@1 30.625 (34.122) Prec@5 66.875 (64.514) Epoch: [3][1890/11272] Time 0.728 (0.838) Data 0.001 (0.003) Loss 2.7445 (2.7581) Prec@1 33.750 (34.122) Prec@5 65.000 (64.510) Epoch: [3][1900/11272] Time 0.749 (0.838) Data 0.002 (0.003) Loss 2.9030 (2.7584) Prec@1 30.625 (34.120) Prec@5 63.750 (64.502) Epoch: [3][1910/11272] Time 0.911 (0.838) Data 0.002 (0.003) Loss 2.8722 (2.7585) Prec@1 33.125 (34.118) Prec@5 61.875 (64.499) Epoch: [3][1920/11272] Time 0.935 (0.838) Data 0.002 (0.003) Loss 2.6753 (2.7586) Prec@1 39.375 (34.120) Prec@5 68.125 (64.497) Epoch: [3][1930/11272] Time 0.777 (0.838) Data 0.002 (0.003) Loss 2.5135 (2.7581) Prec@1 38.750 (34.124) Prec@5 66.875 (64.499) Epoch: [3][1940/11272] Time 0.788 (0.838) Data 0.001 (0.003) Loss 2.8009 (2.7583) Prec@1 32.500 (34.114) Prec@5 63.750 (64.496) Epoch: [3][1950/11272] Time 0.942 (0.838) Data 0.002 (0.003) Loss 2.6550 (2.7579) Prec@1 36.250 (34.123) Prec@5 65.625 (64.508) Epoch: [3][1960/11272] Time 0.909 (0.838) Data 0.002 (0.003) Loss 2.5846 (2.7575) Prec@1 35.625 (34.121) Prec@5 70.625 (64.511) Epoch: [3][1970/11272] Time 0.797 (0.838) Data 0.002 (0.003) Loss 2.8323 (2.7577) Prec@1 28.125 (34.109) Prec@5 64.375 (64.508) Epoch: [3][1980/11272] Time 0.943 (0.839) Data 0.002 (0.003) Loss 2.8783 (2.7578) Prec@1 35.000 (34.108) Prec@5 65.625 (64.505) Epoch: [3][1990/11272] Time 0.908 (0.839) Data 0.002 (0.003) Loss 2.6710 (2.7578) Prec@1 38.125 (34.113) Prec@5 68.750 (64.507) Epoch: [3][2000/11272] Time 0.738 (0.838) Data 0.001 (0.003) Loss 2.7147 (2.7576) Prec@1 30.625 (34.110) Prec@5 61.875 (64.508) Epoch: [3][2010/11272] Time 0.770 (0.838) Data 0.002 (0.003) Loss 2.5302 (2.7577) Prec@1 35.000 (34.107) Prec@5 71.250 (64.508) Epoch: [3][2020/11272] Time 0.908 (0.838) Data 0.002 (0.003) Loss 2.7767 (2.7575) Prec@1 35.625 (34.112) Prec@5 65.000 (64.516) Epoch: [3][2030/11272] Time 0.921 (0.838) Data 0.002 (0.003) Loss 2.8277 (2.7574) Prec@1 33.125 (34.112) Prec@5 61.875 (64.515) Epoch: [3][2040/11272] Time 0.812 (0.838) Data 0.002 (0.003) Loss 2.4299 (2.7573) Prec@1 33.750 (34.108) Prec@5 71.250 (64.516) Epoch: [3][2050/11272] Time 0.768 (0.839) Data 0.002 (0.003) Loss 2.5270 (2.7570) Prec@1 31.250 (34.110) Prec@5 71.875 (64.525) Epoch: [3][2060/11272] Time 0.899 (0.839) Data 0.001 (0.003) Loss 2.7889 (2.7566) Prec@1 31.875 (34.115) Prec@5 65.625 (64.535) Epoch: [3][2070/11272] Time 0.900 (0.839) Data 0.002 (0.003) Loss 2.8222 (2.7565) Prec@1 33.750 (34.127) Prec@5 66.875 (64.536) Epoch: [3][2080/11272] Time 0.779 (0.839) Data 0.002 (0.003) Loss 2.5568 (2.7564) Prec@1 38.125 (34.123) Prec@5 66.250 (64.534) Epoch: [3][2090/11272] Time 0.806 (0.839) Data 0.003 (0.003) Loss 2.6586 (2.7567) Prec@1 35.000 (34.121) Prec@5 64.375 (64.521) Epoch: [3][2100/11272] Time 0.927 (0.839) Data 0.002 (0.003) Loss 2.6604 (2.7565) Prec@1 33.750 (34.124) Prec@5 68.750 (64.528) Epoch: [3][2110/11272] Time 0.766 (0.839) Data 0.002 (0.003) Loss 2.7394 (2.7566) Prec@1 35.625 (34.121) Prec@5 64.375 (64.522) Epoch: [3][2120/11272] Time 0.748 (0.839) Data 0.001 (0.003) Loss 2.5918 (2.7559) Prec@1 38.750 (34.127) Prec@5 70.625 (64.534) Epoch: [3][2130/11272] Time 0.854 (0.839) Data 0.001 (0.003) Loss 2.6341 (2.7559) Prec@1 32.500 (34.137) Prec@5 63.750 (64.531) Epoch: [3][2140/11272] Time 0.851 (0.839) Data 0.001 (0.003) Loss 2.7908 (2.7559) Prec@1 35.625 (34.130) Prec@5 61.875 (64.536) Epoch: [3][2150/11272] Time 0.821 (0.839) Data 0.002 (0.003) Loss 2.7170 (2.7559) Prec@1 38.750 (34.136) Prec@5 62.500 (64.536) Epoch: [3][2160/11272] Time 0.763 (0.839) Data 0.002 (0.003) Loss 2.3786 (2.7553) Prec@1 40.625 (34.144) Prec@5 73.750 (64.544) Epoch: [3][2170/11272] Time 0.925 (0.839) Data 0.002 (0.003) Loss 2.8796 (2.7553) Prec@1 35.000 (34.143) Prec@5 63.750 (64.542) Epoch: [3][2180/11272] Time 0.938 (0.839) Data 0.001 (0.003) Loss 2.9502 (2.7554) Prec@1 30.000 (34.140) Prec@5 62.500 (64.543) Epoch: [3][2190/11272] Time 0.759 (0.839) Data 0.002 (0.003) Loss 2.5716 (2.7550) Prec@1 35.000 (34.140) Prec@5 68.125 (64.558) Epoch: [3][2200/11272] Time 0.758 (0.839) Data 0.001 (0.003) Loss 2.8343 (2.7552) Prec@1 29.375 (34.137) Prec@5 61.875 (64.548) Epoch: [3][2210/11272] Time 0.902 (0.839) Data 0.002 (0.003) Loss 2.7631 (2.7550) Prec@1 31.875 (34.138) Prec@5 66.875 (64.561) Epoch: [3][2220/11272] Time 0.826 (0.839) Data 0.001 (0.003) Loss 2.5821 (2.7553) Prec@1 38.125 (34.135) Prec@5 68.750 (64.549) Epoch: [3][2230/11272] Time 0.778 (0.839) Data 0.002 (0.003) Loss 2.7752 (2.7551) Prec@1 34.375 (34.142) Prec@5 65.625 (64.556) Epoch: [3][2240/11272] Time 0.945 (0.839) Data 0.001 (0.003) Loss 2.5381 (2.7551) Prec@1 40.000 (34.140) Prec@5 70.625 (64.565) Epoch: [3][2250/11272] Time 0.897 (0.839) Data 0.001 (0.003) Loss 2.7895 (2.7550) Prec@1 35.625 (34.146) Prec@5 63.750 (64.563) Epoch: [3][2260/11272] Time 0.808 (0.839) Data 0.002 (0.003) Loss 2.8439 (2.7552) Prec@1 27.500 (34.137) Prec@5 64.375 (64.559) Epoch: [3][2270/11272] Time 0.778 (0.839) Data 0.002 (0.003) Loss 2.6743 (2.7552) Prec@1 33.750 (34.134) Prec@5 68.125 (64.554) Epoch: [3][2280/11272] Time 0.879 (0.839) Data 0.001 (0.003) Loss 2.6896 (2.7552) Prec@1 35.000 (34.140) Prec@5 65.000 (64.556) Epoch: [3][2290/11272] Time 0.890 (0.839) Data 0.002 (0.003) Loss 2.7924 (2.7553) Prec@1 31.250 (34.139) Prec@5 61.875 (64.559) Epoch: [3][2300/11272] Time 0.750 (0.839) Data 0.001 (0.003) Loss 3.0219 (2.7557) Prec@1 35.000 (34.138) Prec@5 58.750 (64.549) Epoch: [3][2310/11272] Time 0.771 (0.839) Data 0.002 (0.003) Loss 2.9265 (2.7558) Prec@1 33.750 (34.134) Prec@5 59.375 (64.549) Epoch: [3][2320/11272] Time 0.927 (0.839) Data 0.001 (0.003) Loss 2.7171 (2.7562) Prec@1 37.500 (34.129) Prec@5 66.875 (64.542) Epoch: [3][2330/11272] Time 0.897 (0.839) Data 0.002 (0.003) Loss 2.7328 (2.7564) Prec@1 35.000 (34.131) Prec@5 62.500 (64.538) Epoch: [3][2340/11272] Time 0.747 (0.839) Data 0.002 (0.003) Loss 2.3902 (2.7563) Prec@1 41.875 (34.132) Prec@5 74.375 (64.541) Epoch: [3][2350/11272] Time 0.747 (0.839) Data 0.002 (0.003) Loss 2.5637 (2.7563) Prec@1 35.625 (34.131) Prec@5 66.875 (64.542) Epoch: [3][2360/11272] Time 0.900 (0.839) Data 0.002 (0.003) Loss 2.5591 (2.7567) Prec@1 35.000 (34.128) Prec@5 70.625 (64.538) Epoch: [3][2370/11272] Time 0.762 (0.839) Data 0.005 (0.003) Loss 2.6962 (2.7568) Prec@1 34.375 (34.127) Prec@5 66.875 (64.533) Epoch: [3][2380/11272] Time 0.777 (0.839) Data 0.001 (0.003) Loss 3.0181 (2.7567) Prec@1 28.750 (34.127) Prec@5 59.375 (64.533) Epoch: [3][2390/11272] Time 0.963 (0.839) Data 0.001 (0.003) Loss 2.8352 (2.7570) Prec@1 35.625 (34.131) Prec@5 60.000 (64.527) Epoch: [3][2400/11272] Time 0.937 (0.839) Data 0.002 (0.003) Loss 2.4589 (2.7568) Prec@1 40.625 (34.139) Prec@5 71.875 (64.529) Epoch: [3][2410/11272] Time 0.748 (0.839) Data 0.002 (0.003) Loss 2.6466 (2.7565) Prec@1 38.125 (34.146) Prec@5 63.125 (64.534) Epoch: [3][2420/11272] Time 0.764 (0.839) Data 0.001 (0.003) Loss 2.5979 (2.7565) Prec@1 40.000 (34.143) Prec@5 68.125 (64.537) Epoch: [3][2430/11272] Time 0.871 (0.839) Data 0.002 (0.003) Loss 2.5710 (2.7563) Prec@1 35.000 (34.145) Prec@5 70.000 (64.541) Epoch: [3][2440/11272] Time 0.837 (0.838) Data 0.001 (0.003) Loss 2.7950 (2.7563) Prec@1 34.375 (34.147) Prec@5 63.750 (64.538) Epoch: [3][2450/11272] Time 0.801 (0.838) Data 0.002 (0.003) Loss 2.7426 (2.7562) Prec@1 33.750 (34.144) Prec@5 66.875 (64.538) Epoch: [3][2460/11272] Time 0.794 (0.838) Data 0.002 (0.003) Loss 2.7071 (2.7562) Prec@1 35.000 (34.149) Prec@5 65.000 (64.541) Epoch: [3][2470/11272] Time 0.909 (0.838) Data 0.002 (0.003) Loss 2.7093 (2.7563) Prec@1 38.750 (34.155) Prec@5 66.250 (64.536) Epoch: [3][2480/11272] Time 0.883 (0.838) Data 0.002 (0.003) Loss 2.5751 (2.7565) Prec@1 38.125 (34.153) Prec@5 67.500 (64.529) Epoch: [3][2490/11272] Time 0.838 (0.839) Data 0.003 (0.003) Loss 2.6544 (2.7567) Prec@1 34.375 (34.150) Prec@5 63.750 (64.522) Epoch: [3][2500/11272] Time 0.944 (0.839) Data 0.001 (0.003) Loss 2.6239 (2.7569) Prec@1 35.625 (34.151) Prec@5 64.375 (64.520) Epoch: [3][2510/11272] Time 0.911 (0.839) Data 0.002 (0.003) Loss 2.5493 (2.7569) Prec@1 38.125 (34.148) Prec@5 66.250 (64.516) Epoch: [3][2520/11272] Time 0.733 (0.839) Data 0.001 (0.003) Loss 2.8482 (2.7571) Prec@1 30.000 (34.142) Prec@5 63.125 (64.511) Epoch: [3][2530/11272] Time 0.763 (0.839) Data 0.002 (0.003) Loss 2.6917 (2.7571) Prec@1 35.000 (34.144) Prec@5 65.000 (64.511) Epoch: [3][2540/11272] Time 0.938 (0.839) Data 0.001 (0.003) Loss 2.8706 (2.7570) Prec@1 32.500 (34.141) Prec@5 62.500 (64.512) Epoch: [3][2550/11272] Time 0.857 (0.839) Data 0.002 (0.003) Loss 2.9124 (2.7568) Prec@1 35.625 (34.147) Prec@5 64.375 (64.517) Epoch: [3][2560/11272] Time 0.768 (0.838) Data 0.002 (0.003) Loss 2.7956 (2.7568) Prec@1 34.375 (34.138) Prec@5 63.750 (64.522) Epoch: [3][2570/11272] Time 0.795 (0.839) Data 0.002 (0.003) Loss 2.7219 (2.7568) Prec@1 35.000 (34.139) Prec@5 63.750 (64.531) Epoch: [3][2580/11272] Time 0.912 (0.839) Data 0.001 (0.003) Loss 2.7519 (2.7568) Prec@1 29.375 (34.138) Prec@5 66.250 (64.531) Epoch: [3][2590/11272] Time 0.858 (0.838) Data 0.001 (0.003) Loss 2.5902 (2.7566) Prec@1 40.000 (34.143) Prec@5 67.500 (64.533) Epoch: [3][2600/11272] Time 0.776 (0.838) Data 0.002 (0.003) Loss 2.4973 (2.7564) Prec@1 39.375 (34.142) Prec@5 68.750 (64.535) Epoch: [3][2610/11272] Time 0.751 (0.838) Data 0.002 (0.003) Loss 2.7242 (2.7565) Prec@1 31.250 (34.142) Prec@5 63.750 (64.534) Epoch: [3][2620/11272] Time 0.933 (0.838) Data 0.001 (0.003) Loss 2.6973 (2.7566) Prec@1 33.125 (34.138) Prec@5 69.375 (64.528) Epoch: [3][2630/11272] Time 0.753 (0.838) Data 0.003 (0.003) Loss 3.1694 (2.7566) Prec@1 28.125 (34.137) Prec@5 58.750 (64.528) Epoch: [3][2640/11272] Time 0.775 (0.838) Data 0.001 (0.003) Loss 2.6792 (2.7568) Prec@1 34.375 (34.139) Prec@5 65.625 (64.523) Epoch: [3][2650/11272] Time 0.845 (0.838) Data 0.002 (0.003) Loss 2.9839 (2.7568) Prec@1 33.125 (34.142) Prec@5 58.750 (64.526) Epoch: [3][2660/11272] Time 0.877 (0.838) Data 0.001 (0.003) Loss 2.7940 (2.7571) Prec@1 33.125 (34.139) Prec@5 64.375 (64.518) Epoch: [3][2670/11272] Time 0.743 (0.838) Data 0.001 (0.003) Loss 2.6167 (2.7568) Prec@1 36.875 (34.140) Prec@5 66.875 (64.527) Epoch: [3][2680/11272] Time 0.750 (0.838) Data 0.001 (0.003) Loss 2.6442 (2.7566) Prec@1 33.125 (34.141) Prec@5 68.750 (64.528) Epoch: [3][2690/11272] Time 0.927 (0.838) Data 0.001 (0.003) Loss 3.1703 (2.7565) Prec@1 27.500 (34.147) Prec@5 59.375 (64.532) Epoch: [3][2700/11272] Time 0.867 (0.838) Data 0.001 (0.003) Loss 2.7058 (2.7564) Prec@1 36.250 (34.148) Prec@5 60.625 (64.533) Epoch: [3][2710/11272] Time 0.743 (0.838) Data 0.002 (0.003) Loss 2.8636 (2.7565) Prec@1 25.625 (34.141) Prec@5 57.500 (64.525) Epoch: [3][2720/11272] Time 0.757 (0.838) Data 0.002 (0.003) Loss 2.8537 (2.7566) Prec@1 33.125 (34.141) Prec@5 61.875 (64.524) Epoch: [3][2730/11272] Time 0.893 (0.838) Data 0.002 (0.003) Loss 2.6121 (2.7566) Prec@1 37.500 (34.146) Prec@5 69.375 (64.524) Epoch: [3][2740/11272] Time 0.845 (0.838) Data 0.003 (0.003) Loss 2.8837 (2.7567) Prec@1 26.875 (34.147) Prec@5 61.875 (64.522) Epoch: [3][2750/11272] Time 0.749 (0.838) Data 0.001 (0.003) Loss 2.7536 (2.7567) Prec@1 34.375 (34.148) Prec@5 60.000 (64.520) Epoch: [3][2760/11272] Time 0.919 (0.838) Data 0.002 (0.003) Loss 2.7972 (2.7564) Prec@1 33.750 (34.152) Prec@5 61.875 (64.528) Epoch: [3][2770/11272] Time 0.820 (0.838) Data 0.001 (0.003) Loss 2.8109 (2.7563) Prec@1 34.375 (34.154) Prec@5 59.375 (64.527) Epoch: [3][2780/11272] Time 0.765 (0.837) Data 0.002 (0.003) Loss 2.6904 (2.7564) Prec@1 38.125 (34.154) Prec@5 70.000 (64.524) Epoch: [3][2790/11272] Time 0.735 (0.837) Data 0.002 (0.003) Loss 2.6975 (2.7563) Prec@1 36.250 (34.156) Prec@5 66.875 (64.531) Epoch: [3][2800/11272] Time 0.834 (0.837) Data 0.001 (0.003) Loss 2.8311 (2.7560) Prec@1 30.625 (34.159) Prec@5 65.000 (64.535) Epoch: [3][2810/11272] Time 0.851 (0.837) Data 0.001 (0.003) Loss 2.6470 (2.7559) Prec@1 37.500 (34.167) Prec@5 65.000 (64.542) Epoch: [3][2820/11272] Time 0.741 (0.837) Data 0.002 (0.003) Loss 2.8193 (2.7558) Prec@1 33.125 (34.166) Prec@5 61.250 (64.541) Epoch: [3][2830/11272] Time 0.760 (0.837) Data 0.002 (0.003) Loss 2.8188 (2.7556) Prec@1 30.000 (34.171) Prec@5 61.250 (64.543) Epoch: [3][2840/11272] Time 0.918 (0.837) Data 0.002 (0.003) Loss 2.9216 (2.7557) Prec@1 30.000 (34.170) Prec@5 60.625 (64.534) Epoch: [3][2850/11272] Time 0.845 (0.837) Data 0.001 (0.003) Loss 2.9777 (2.7556) Prec@1 31.250 (34.172) Prec@5 60.000 (64.535) Epoch: [3][2860/11272] Time 0.753 (0.837) Data 0.001 (0.003) Loss 2.8100 (2.7557) Prec@1 33.750 (34.174) Prec@5 68.750 (64.534) Epoch: [3][2870/11272] Time 0.720 (0.837) Data 0.001 (0.003) Loss 2.5857 (2.7555) Prec@1 35.625 (34.181) Prec@5 66.250 (64.534) Epoch: [3][2880/11272] Time 0.868 (0.837) Data 0.001 (0.003) Loss 3.1499 (2.7556) Prec@1 25.000 (34.181) Prec@5 59.375 (64.531) Epoch: [3][2890/11272] Time 0.832 (0.837) Data 0.001 (0.003) Loss 2.7369 (2.7557) Prec@1 32.500 (34.178) Prec@5 68.125 (64.529) Epoch: [3][2900/11272] Time 0.763 (0.837) Data 0.002 (0.003) Loss 2.6441 (2.7557) Prec@1 38.125 (34.183) Prec@5 68.125 (64.531) Epoch: [3][2910/11272] Time 0.911 (0.837) Data 0.002 (0.003) Loss 3.0063 (2.7558) Prec@1 28.750 (34.177) Prec@5 64.375 (64.530) Epoch: [3][2920/11272] Time 0.927 (0.836) Data 0.001 (0.003) Loss 2.8090 (2.7556) Prec@1 37.500 (34.186) Prec@5 63.750 (64.535) Epoch: [3][2930/11272] Time 0.802 (0.836) Data 0.001 (0.003) Loss 2.6829 (2.7555) Prec@1 39.375 (34.186) Prec@5 63.750 (64.537) Epoch: [3][2940/11272] Time 0.772 (0.836) Data 0.002 (0.003) Loss 2.4931 (2.7551) Prec@1 35.000 (34.198) Prec@5 68.125 (64.541) Epoch: [3][2950/11272] Time 0.862 (0.836) Data 0.001 (0.003) Loss 2.8188 (2.7546) Prec@1 32.500 (34.205) Prec@5 61.875 (64.553) Epoch: [3][2960/11272] Time 0.842 (0.836) Data 0.002 (0.003) Loss 2.5847 (2.7547) Prec@1 33.125 (34.198) Prec@5 66.875 (64.551) Epoch: [3][2970/11272] Time 0.745 (0.836) Data 0.002 (0.003) Loss 2.8466 (2.7547) Prec@1 33.750 (34.197) Prec@5 62.500 (64.548) Epoch: [3][2980/11272] Time 0.770 (0.836) Data 0.002 (0.003) Loss 2.6762 (2.7547) Prec@1 33.125 (34.199) Prec@5 65.000 (64.548) Epoch: [3][2990/11272] Time 0.862 (0.836) Data 0.001 (0.003) Loss 3.2422 (2.7548) Prec@1 24.375 (34.195) Prec@5 53.750 (64.546) Epoch: [3][3000/11272] Time 0.852 (0.836) Data 0.002 (0.003) Loss 2.8047 (2.7544) Prec@1 34.375 (34.207) Prec@5 60.000 (64.556) Epoch: [3][3010/11272] Time 0.754 (0.836) Data 0.002 (0.003) Loss 2.7838 (2.7544) Prec@1 30.000 (34.205) Prec@5 65.000 (64.556) Epoch: [3][3020/11272] Time 0.740 (0.836) Data 0.002 (0.003) Loss 2.6662 (2.7543) Prec@1 35.000 (34.205) Prec@5 66.875 (64.559) Epoch: [3][3030/11272] Time 0.863 (0.836) Data 0.001 (0.003) Loss 2.5106 (2.7544) Prec@1 44.375 (34.204) Prec@5 70.000 (64.556) Epoch: [3][3040/11272] Time 0.741 (0.835) Data 0.001 (0.003) Loss 2.7127 (2.7544) Prec@1 35.000 (34.203) Prec@5 64.375 (64.557) Epoch: [3][3050/11272] Time 0.750 (0.835) Data 0.002 (0.003) Loss 2.9063 (2.7542) Prec@1 31.875 (34.206) Prec@5 59.375 (64.561) Epoch: [3][3060/11272] Time 0.885 (0.835) Data 0.002 (0.003) Loss 2.6661 (2.7543) Prec@1 34.375 (34.204) Prec@5 68.750 (64.561) Epoch: [3][3070/11272] Time 0.895 (0.835) Data 0.002 (0.003) Loss 2.7560 (2.7543) Prec@1 35.000 (34.202) Prec@5 63.750 (64.561) Epoch: [3][3080/11272] Time 0.738 (0.835) Data 0.001 (0.003) Loss 2.7374 (2.7542) Prec@1 32.500 (34.205) Prec@5 66.875 (64.564) Epoch: [3][3090/11272] Time 0.751 (0.835) Data 0.002 (0.003) Loss 2.6253 (2.7541) Prec@1 31.250 (34.204) Prec@5 70.625 (64.568) Epoch: [3][3100/11272] Time 0.882 (0.835) Data 0.002 (0.003) Loss 2.7108 (2.7546) Prec@1 36.250 (34.194) Prec@5 64.375 (64.557) Epoch: [3][3110/11272] Time 0.898 (0.835) Data 0.002 (0.003) Loss 2.5040 (2.7545) Prec@1 36.875 (34.196) Prec@5 69.375 (64.563) Epoch: [3][3120/11272] Time 0.757 (0.835) Data 0.002 (0.003) Loss 2.9291 (2.7545) Prec@1 30.625 (34.197) Prec@5 62.500 (64.566) Epoch: [3][3130/11272] Time 0.812 (0.835) Data 0.002 (0.003) Loss 2.6980 (2.7544) Prec@1 38.750 (34.200) Prec@5 65.000 (64.567) Epoch: [3][3140/11272] Time 0.923 (0.835) Data 0.002 (0.003) Loss 2.6276 (2.7545) Prec@1 36.875 (34.196) Prec@5 68.125 (64.566) Epoch: [3][3150/11272] Time 0.972 (0.835) Data 0.002 (0.003) Loss 2.7455 (2.7544) Prec@1 36.250 (34.201) Prec@5 65.625 (64.568) Epoch: [3][3160/11272] Time 0.760 (0.835) Data 0.002 (0.003) Loss 2.5764 (2.7545) Prec@1 37.500 (34.202) Prec@5 66.875 (64.569) Epoch: [3][3170/11272] Time 0.902 (0.835) Data 0.002 (0.003) Loss 2.7747 (2.7547) Prec@1 34.375 (34.199) Prec@5 65.625 (64.569) Epoch: [3][3180/11272] Time 0.859 (0.835) Data 0.002 (0.003) Loss 2.7402 (2.7548) Prec@1 34.375 (34.199) Prec@5 66.250 (64.566) Epoch: [3][3190/11272] Time 0.809 (0.835) Data 0.002 (0.003) Loss 2.7389 (2.7549) Prec@1 35.000 (34.196) Prec@5 66.250 (64.562) Epoch: [3][3200/11272] Time 0.817 (0.835) Data 0.002 (0.003) Loss 2.6416 (2.7550) Prec@1 37.500 (34.196) Prec@5 66.250 (64.566) Epoch: [3][3210/11272] Time 0.872 (0.835) Data 0.002 (0.003) Loss 2.7301 (2.7549) Prec@1 36.875 (34.198) Prec@5 65.000 (64.568) Epoch: [3][3220/11272] Time 0.963 (0.835) Data 0.002 (0.003) Loss 2.5494 (2.7548) Prec@1 36.250 (34.201) Prec@5 66.875 (64.569) Epoch: [3][3230/11272] Time 0.733 (0.835) Data 0.002 (0.003) Loss 2.9435 (2.7548) Prec@1 33.125 (34.203) Prec@5 60.000 (64.566) Epoch: [3][3240/11272] Time 0.747 (0.835) Data 0.002 (0.003) Loss 2.8506 (2.7546) Prec@1 35.625 (34.211) Prec@5 62.500 (64.573) Epoch: [3][3250/11272] Time 0.902 (0.835) Data 0.002 (0.003) Loss 2.6258 (2.7546) Prec@1 39.375 (34.212) Prec@5 67.500 (64.574) Epoch: [3][3260/11272] Time 0.829 (0.835) Data 0.001 (0.003) Loss 2.7681 (2.7546) Prec@1 38.125 (34.211) Prec@5 62.500 (64.574) Epoch: [3][3270/11272] Time 0.747 (0.835) Data 0.001 (0.003) Loss 2.8042 (2.7545) Prec@1 30.000 (34.216) Prec@5 61.875 (64.575) Epoch: [3][3280/11272] Time 0.748 (0.835) Data 0.002 (0.003) Loss 2.7695 (2.7545) Prec@1 35.625 (34.219) Prec@5 66.875 (64.573) Epoch: [3][3290/11272] Time 0.903 (0.835) Data 0.002 (0.003) Loss 2.6800 (2.7544) Prec@1 35.000 (34.217) Prec@5 62.500 (64.579) Epoch: [3][3300/11272] Time 0.742 (0.835) Data 0.003 (0.003) Loss 3.2215 (2.7545) Prec@1 25.625 (34.211) Prec@5 55.625 (64.579) Epoch: [3][3310/11272] Time 0.830 (0.835) Data 0.002 (0.003) Loss 2.6988 (2.7544) Prec@1 35.000 (34.213) Prec@5 65.625 (64.583) Epoch: [3][3320/11272] Time 0.873 (0.835) Data 0.002 (0.003) Loss 2.4410 (2.7543) Prec@1 38.125 (34.214) Prec@5 70.000 (64.578) Epoch: [3][3330/11272] Time 0.933 (0.835) Data 0.002 (0.003) Loss 2.7474 (2.7542) Prec@1 31.875 (34.218) Prec@5 68.750 (64.578) Epoch: [3][3340/11272] Time 0.759 (0.835) Data 0.002 (0.003) Loss 2.5661 (2.7544) Prec@1 37.500 (34.214) Prec@5 72.500 (64.576) Epoch: [3][3350/11272] Time 0.763 (0.835) Data 0.002 (0.003) Loss 2.9950 (2.7545) Prec@1 33.750 (34.213) Prec@5 59.375 (64.569) Epoch: [3][3360/11272] Time 0.853 (0.835) Data 0.002 (0.003) Loss 2.9153 (2.7543) Prec@1 32.500 (34.221) Prec@5 61.250 (64.569) Epoch: [3][3370/11272] Time 0.859 (0.835) Data 0.002 (0.003) Loss 2.5749 (2.7543) Prec@1 33.750 (34.221) Prec@5 69.375 (64.571) Epoch: [3][3380/11272] Time 0.755 (0.835) Data 0.001 (0.003) Loss 2.6666 (2.7545) Prec@1 40.625 (34.219) Prec@5 66.875 (64.571) Epoch: [3][3390/11272] Time 0.782 (0.835) Data 0.002 (0.003) Loss 2.5819 (2.7546) Prec@1 37.500 (34.215) Prec@5 68.750 (64.571) Epoch: [3][3400/11272] Time 0.855 (0.835) Data 0.001 (0.003) Loss 2.6944 (2.7543) Prec@1 33.125 (34.220) Prec@5 66.875 (64.579) Epoch: [3][3410/11272] Time 0.861 (0.834) Data 0.002 (0.003) Loss 2.9962 (2.7544) Prec@1 26.875 (34.218) Prec@5 61.875 (64.578) Epoch: [3][3420/11272] Time 0.743 (0.834) Data 0.002 (0.003) Loss 2.6607 (2.7541) Prec@1 36.875 (34.220) Prec@5 69.375 (64.583) Epoch: [3][3430/11272] Time 0.893 (0.834) Data 0.002 (0.003) Loss 2.8719 (2.7544) Prec@1 33.125 (34.216) Prec@5 63.750 (64.581) Epoch: [3][3440/11272] Time 0.871 (0.834) Data 0.002 (0.003) Loss 2.6692 (2.7543) Prec@1 36.250 (34.218) Prec@5 66.875 (64.588) Epoch: [3][3450/11272] Time 0.771 (0.834) Data 0.001 (0.003) Loss 2.8004 (2.7542) Prec@1 30.625 (34.219) Prec@5 66.250 (64.589) Epoch: [3][3460/11272] Time 0.783 (0.834) Data 0.002 (0.003) Loss 2.7596 (2.7541) Prec@1 33.750 (34.216) Prec@5 65.625 (64.591) Epoch: [3][3470/11272] Time 0.841 (0.834) Data 0.001 (0.003) Loss 2.7093 (2.7541) Prec@1 36.250 (34.218) Prec@5 68.125 (64.592) Epoch: [3][3480/11272] Time 0.857 (0.834) Data 0.002 (0.003) Loss 2.7116 (2.7541) Prec@1 30.000 (34.220) Prec@5 63.125 (64.593) Epoch: [3][3490/11272] Time 0.756 (0.834) Data 0.002 (0.003) Loss 2.6569 (2.7540) Prec@1 36.875 (34.222) Prec@5 65.000 (64.596) Epoch: [3][3500/11272] Time 0.735 (0.834) Data 0.002 (0.003) Loss 2.6416 (2.7539) Prec@1 33.125 (34.224) Prec@5 65.625 (64.598) Epoch: [3][3510/11272] Time 0.862 (0.834) Data 0.001 (0.003) Loss 2.7431 (2.7538) Prec@1 34.375 (34.227) Prec@5 62.500 (64.600) Epoch: [3][3520/11272] Time 0.863 (0.834) Data 0.002 (0.003) Loss 2.8968 (2.7539) Prec@1 29.375 (34.223) Prec@5 58.125 (64.598) Epoch: [3][3530/11272] Time 0.771 (0.834) Data 0.002 (0.003) Loss 2.6085 (2.7538) Prec@1 33.125 (34.222) Prec@5 66.875 (64.600) Epoch: [3][3540/11272] Time 0.750 (0.834) Data 0.002 (0.003) Loss 3.0884 (2.7538) Prec@1 31.250 (34.218) Prec@5 55.625 (64.596) Epoch: [3][3550/11272] Time 0.907 (0.834) Data 0.002 (0.003) Loss 2.9927 (2.7539) Prec@1 26.875 (34.216) Prec@5 60.000 (64.594) Epoch: [3][3560/11272] Time 0.781 (0.834) Data 0.004 (0.003) Loss 2.8353 (2.7538) Prec@1 28.125 (34.216) Prec@5 58.750 (64.593) Epoch: [3][3570/11272] Time 0.783 (0.834) Data 0.002 (0.003) Loss 2.5639 (2.7538) Prec@1 37.500 (34.219) Prec@5 68.750 (64.590) Epoch: [3][3580/11272] Time 0.889 (0.834) Data 0.002 (0.003) Loss 2.8818 (2.7541) Prec@1 32.500 (34.214) Prec@5 60.000 (64.582) Epoch: [3][3590/11272] Time 0.871 (0.834) Data 0.002 (0.002) Loss 2.7373 (2.7539) Prec@1 33.750 (34.216) Prec@5 65.000 (64.586) Epoch: [3][3600/11272] Time 0.758 (0.834) Data 0.002 (0.002) Loss 2.8775 (2.7541) Prec@1 27.500 (34.214) Prec@5 60.000 (64.583) Epoch: [3][3610/11272] Time 0.773 (0.834) Data 0.002 (0.002) Loss 2.5572 (2.7540) Prec@1 33.750 (34.214) Prec@5 65.625 (64.584) Epoch: [3][3620/11272] Time 0.979 (0.834) Data 0.002 (0.002) Loss 2.4717 (2.7540) Prec@1 41.250 (34.218) Prec@5 72.500 (64.585) Epoch: [3][3630/11272] Time 0.905 (0.834) Data 0.002 (0.002) Loss 2.6956 (2.7539) Prec@1 31.250 (34.221) Prec@5 63.750 (64.585) Epoch: [3][3640/11272] Time 0.736 (0.834) Data 0.001 (0.002) Loss 2.8189 (2.7539) Prec@1 33.750 (34.222) Prec@5 60.625 (64.586) Epoch: [3][3650/11272] Time 0.756 (0.834) Data 0.002 (0.002) Loss 2.5566 (2.7538) Prec@1 35.000 (34.228) Prec@5 69.375 (64.585) Epoch: [3][3660/11272] Time 0.895 (0.834) Data 0.001 (0.002) Loss 2.6918 (2.7536) Prec@1 32.500 (34.232) Prec@5 64.375 (64.589) Epoch: [3][3670/11272] Time 0.905 (0.834) Data 0.002 (0.002) Loss 2.5899 (2.7536) Prec@1 41.250 (34.232) Prec@5 69.375 (64.592) Epoch: [3][3680/11272] Time 0.783 (0.834) Data 0.002 (0.002) Loss 2.8302 (2.7537) Prec@1 33.125 (34.229) Prec@5 63.125 (64.591) Epoch: [3][3690/11272] Time 0.966 (0.834) Data 0.002 (0.002) Loss 2.6008 (2.7537) Prec@1 36.875 (34.232) Prec@5 63.750 (64.590) Epoch: [3][3700/11272] Time 0.913 (0.834) Data 0.002 (0.002) Loss 2.8502 (2.7537) Prec@1 31.875 (34.230) Prec@5 64.375 (64.590) Epoch: [3][3710/11272] Time 0.728 (0.834) Data 0.002 (0.002) Loss 2.5679 (2.7537) Prec@1 34.375 (34.226) Prec@5 66.250 (64.587) Epoch: [3][3720/11272] Time 0.813 (0.834) Data 0.002 (0.002) Loss 2.6577 (2.7537) Prec@1 38.125 (34.228) Prec@5 65.000 (64.586) Epoch: [3][3730/11272] Time 0.901 (0.834) Data 0.002 (0.002) Loss 2.5296 (2.7534) Prec@1 37.500 (34.233) Prec@5 72.500 (64.594) Epoch: [3][3740/11272] Time 0.911 (0.834) Data 0.002 (0.002) Loss 2.8387 (2.7534) Prec@1 35.625 (34.235) Prec@5 61.250 (64.594) Epoch: [3][3750/11272] Time 0.794 (0.834) Data 0.002 (0.002) Loss 2.6921 (2.7534) Prec@1 34.375 (34.236) Prec@5 66.875 (64.594) Epoch: [3][3760/11272] Time 0.743 (0.834) Data 0.002 (0.002) Loss 2.7281 (2.7532) Prec@1 33.750 (34.235) Prec@5 66.250 (64.599) Epoch: [3][3770/11272] Time 0.898 (0.834) Data 0.002 (0.002) Loss 2.6562 (2.7532) Prec@1 33.750 (34.236) Prec@5 67.500 (64.601) Epoch: [3][3780/11272] Time 0.845 (0.834) Data 0.001 (0.002) Loss 2.7600 (2.7533) Prec@1 31.875 (34.236) Prec@5 63.125 (64.598) Epoch: [3][3790/11272] Time 0.783 (0.834) Data 0.002 (0.002) Loss 2.9927 (2.7533) Prec@1 32.500 (34.236) Prec@5 62.500 (64.599) Epoch: [3][3800/11272] Time 0.754 (0.834) Data 0.002 (0.002) Loss 2.5993 (2.7533) Prec@1 40.000 (34.237) Prec@5 65.000 (64.598) Epoch: [3][3810/11272] Time 0.859 (0.834) Data 0.002 (0.002) Loss 2.8275 (2.7534) Prec@1 34.375 (34.237) Prec@5 61.875 (64.595) Epoch: [3][3820/11272] Time 0.875 (0.834) Data 0.002 (0.002) Loss 2.6586 (2.7534) Prec@1 34.375 (34.238) Prec@5 70.000 (64.595) Epoch: [3][3830/11272] Time 0.749 (0.834) Data 0.002 (0.002) Loss 2.7675 (2.7534) Prec@1 28.125 (34.235) Prec@5 63.125 (64.592) Epoch: [3][3840/11272] Time 0.920 (0.834) Data 0.002 (0.002) Loss 2.6817 (2.7535) Prec@1 35.625 (34.231) Prec@5 60.000 (64.588) Epoch: [3][3850/11272] Time 0.881 (0.834) Data 0.001 (0.002) Loss 2.8107 (2.7534) Prec@1 35.000 (34.233) Prec@5 64.375 (64.588) Epoch: [3][3860/11272] Time 0.813 (0.834) Data 0.002 (0.002) Loss 2.7508 (2.7533) Prec@1 31.875 (34.235) Prec@5 61.250 (64.587) Epoch: [3][3870/11272] Time 0.735 (0.834) Data 0.001 (0.002) Loss 2.8261 (2.7533) Prec@1 30.000 (34.234) Prec@5 63.125 (64.591) Epoch: [3][3880/11272] Time 0.910 (0.834) Data 0.002 (0.002) Loss 2.8222 (2.7532) Prec@1 35.000 (34.236) Prec@5 63.750 (64.593) Epoch: [3][3890/11272] Time 0.848 (0.834) Data 0.001 (0.002) Loss 2.7238 (2.7531) Prec@1 32.500 (34.237) Prec@5 67.500 (64.594) Epoch: [3][3900/11272] Time 0.817 (0.834) Data 0.002 (0.002) Loss 2.9234 (2.7532) Prec@1 29.375 (34.235) Prec@5 62.500 (64.593) Epoch: [3][3910/11272] Time 0.757 (0.834) Data 0.001 (0.002) Loss 2.5581 (2.7529) Prec@1 38.125 (34.241) Prec@5 68.125 (64.598) Epoch: [3][3920/11272] Time 0.858 (0.834) Data 0.001 (0.002) Loss 3.0759 (2.7532) Prec@1 27.500 (34.232) Prec@5 59.375 (64.592) Epoch: [3][3930/11272] Time 0.888 (0.834) Data 0.002 (0.002) Loss 2.7495 (2.7531) Prec@1 36.250 (34.235) Prec@5 65.000 (64.594) Epoch: [3][3940/11272] Time 0.770 (0.834) Data 0.002 (0.002) Loss 2.6701 (2.7531) Prec@1 36.875 (34.238) Prec@5 68.750 (64.595) Epoch: [3][3950/11272] Time 0.757 (0.834) Data 0.002 (0.002) Loss 3.1329 (2.7530) Prec@1 28.125 (34.235) Prec@5 55.625 (64.596) Epoch: [3][3960/11272] Time 0.855 (0.834) Data 0.001 (0.002) Loss 2.9150 (2.7530) Prec@1 30.625 (34.237) Prec@5 57.500 (64.597) Epoch: [3][3970/11272] Time 0.742 (0.834) Data 0.002 (0.002) Loss 3.0843 (2.7530) Prec@1 31.250 (34.238) Prec@5 58.750 (64.599) Epoch: [3][3980/11272] Time 0.768 (0.833) Data 0.002 (0.002) Loss 2.4583 (2.7531) Prec@1 41.250 (34.238) Prec@5 66.875 (64.598) Epoch: [3][3990/11272] Time 0.852 (0.833) Data 0.001 (0.002) Loss 2.7368 (2.7530) Prec@1 28.750 (34.236) Prec@5 68.750 (64.600) Epoch: [3][4000/11272] Time 0.859 (0.833) Data 0.002 (0.002) Loss 2.7207 (2.7530) Prec@1 33.750 (34.235) Prec@5 63.125 (64.598) Epoch: [3][4010/11272] Time 0.744 (0.833) Data 0.001 (0.002) Loss 2.7559 (2.7529) Prec@1 36.250 (34.239) Prec@5 63.125 (64.599) Epoch: [3][4020/11272] Time 0.765 (0.833) Data 0.002 (0.002) Loss 2.7045 (2.7528) Prec@1 31.875 (34.237) Prec@5 64.375 (64.597) Epoch: [3][4030/11272] Time 0.927 (0.833) Data 0.001 (0.002) Loss 2.7726 (2.7529) Prec@1 33.750 (34.235) Prec@5 68.125 (64.598) Epoch: [3][4040/11272] Time 0.917 (0.833) Data 0.002 (0.002) Loss 2.6636 (2.7528) Prec@1 38.750 (34.240) Prec@5 65.000 (64.599) Epoch: [3][4050/11272] Time 0.740 (0.833) Data 0.002 (0.002) Loss 2.8271 (2.7529) Prec@1 33.750 (34.237) Prec@5 58.750 (64.594) Epoch: [3][4060/11272] Time 0.782 (0.833) Data 0.002 (0.002) Loss 2.5400 (2.7528) Prec@1 41.250 (34.237) Prec@5 71.875 (64.596) Epoch: [3][4070/11272] Time 0.834 (0.833) Data 0.001 (0.002) Loss 2.7286 (2.7528) Prec@1 33.125 (34.239) Prec@5 65.625 (64.597) Epoch: [3][4080/11272] Time 0.886 (0.833) Data 0.001 (0.002) Loss 2.6514 (2.7528) Prec@1 38.750 (34.241) Prec@5 64.375 (64.596) Epoch: [3][4090/11272] Time 0.762 (0.833) Data 0.002 (0.002) Loss 2.8569 (2.7528) Prec@1 37.500 (34.241) Prec@5 62.500 (64.595) Epoch: [3][4100/11272] Time 0.902 (0.833) Data 0.002 (0.002) Loss 2.7576 (2.7528) Prec@1 28.750 (34.241) Prec@5 70.000 (64.601) Epoch: [3][4110/11272] Time 0.976 (0.833) Data 0.002 (0.002) Loss 2.6704 (2.7528) Prec@1 37.500 (34.239) Prec@5 68.750 (64.601) Epoch: [3][4120/11272] Time 0.722 (0.833) Data 0.001 (0.002) Loss 2.8232 (2.7529) Prec@1 33.125 (34.239) Prec@5 61.875 (64.599) Epoch: [3][4130/11272] Time 0.717 (0.833) Data 0.001 (0.002) Loss 2.7981 (2.7529) Prec@1 33.750 (34.239) Prec@5 63.750 (64.598) Epoch: [3][4140/11272] Time 0.918 (0.833) Data 0.002 (0.002) Loss 2.6490 (2.7527) Prec@1 35.000 (34.241) Prec@5 65.000 (64.604) Epoch: [3][4150/11272] Time 0.841 (0.833) Data 0.001 (0.002) Loss 2.6112 (2.7524) Prec@1 38.125 (34.246) Prec@5 64.375 (64.609) Epoch: [3][4160/11272] Time 0.753 (0.833) Data 0.002 (0.002) Loss 2.9333 (2.7526) Prec@1 32.500 (34.244) Prec@5 62.500 (64.607) Epoch: [3][4170/11272] Time 0.757 (0.833) Data 0.003 (0.002) Loss 2.6058 (2.7525) Prec@1 36.250 (34.244) Prec@5 69.375 (64.610) Epoch: [3][4180/11272] Time 0.916 (0.833) Data 0.002 (0.002) Loss 2.5980 (2.7525) Prec@1 33.750 (34.244) Prec@5 66.250 (64.610) Epoch: [3][4190/11272] Time 0.875 (0.833) Data 0.002 (0.002) Loss 2.6029 (2.7525) Prec@1 33.125 (34.245) Prec@5 70.625 (64.609) Epoch: [3][4200/11272] Time 0.757 (0.833) Data 0.002 (0.002) Loss 2.7746 (2.7523) Prec@1 27.500 (34.245) Prec@5 61.875 (64.610) Epoch: [3][4210/11272] Time 0.777 (0.833) Data 0.002 (0.002) Loss 3.0666 (2.7523) Prec@1 33.750 (34.245) Prec@5 59.375 (64.611) Epoch: [3][4220/11272] Time 0.920 (0.833) Data 0.002 (0.002) Loss 2.6722 (2.7522) Prec@1 34.375 (34.246) Prec@5 65.625 (64.615) Epoch: [3][4230/11272] Time 0.815 (0.833) Data 0.006 (0.002) Loss 2.9077 (2.7522) Prec@1 33.750 (34.249) Prec@5 57.500 (64.615) Epoch: [3][4240/11272] Time 0.774 (0.833) Data 0.002 (0.002) Loss 2.8433 (2.7523) Prec@1 33.750 (34.249) Prec@5 65.000 (64.615) Epoch: [3][4250/11272] Time 0.904 (0.833) Data 0.002 (0.002) Loss 2.7067 (2.7524) Prec@1 34.375 (34.249) Prec@5 66.875 (64.612) Epoch: [3][4260/11272] Time 0.887 (0.833) Data 0.002 (0.002) Loss 2.4828 (2.7522) Prec@1 36.875 (34.250) Prec@5 71.250 (64.616) Epoch: [3][4270/11272] Time 0.755 (0.833) Data 0.002 (0.002) Loss 2.8026 (2.7521) Prec@1 32.500 (34.253) Prec@5 61.875 (64.621) Epoch: [3][4280/11272] Time 0.823 (0.833) Data 0.002 (0.002) Loss 2.5943 (2.7517) Prec@1 35.000 (34.258) Prec@5 67.500 (64.630) Epoch: [3][4290/11272] Time 0.902 (0.833) Data 0.002 (0.002) Loss 2.5023 (2.7518) Prec@1 39.375 (34.257) Prec@5 70.625 (64.630) Epoch: [3][4300/11272] Time 0.885 (0.833) Data 0.002 (0.002) Loss 2.5304 (2.7517) Prec@1 36.250 (34.257) Prec@5 69.375 (64.634) Epoch: [3][4310/11272] Time 0.777 (0.833) Data 0.002 (0.002) Loss 2.7217 (2.7518) Prec@1 33.750 (34.255) Prec@5 66.875 (64.635) Epoch: [3][4320/11272] Time 0.786 (0.833) Data 0.002 (0.002) Loss 2.8543 (2.7516) Prec@1 33.125 (34.258) Prec@5 65.000 (64.637) Epoch: [3][4330/11272] Time 0.913 (0.833) Data 0.001 (0.002) Loss 2.6008 (2.7517) Prec@1 37.500 (34.257) Prec@5 65.625 (64.634) Epoch: [3][4340/11272] Time 0.912 (0.833) Data 0.002 (0.002) Loss 2.9046 (2.7515) Prec@1 34.375 (34.264) Prec@5 63.750 (64.638) Epoch: [3][4350/11272] Time 0.780 (0.833) Data 0.001 (0.002) Loss 3.0285 (2.7516) Prec@1 31.250 (34.265) Prec@5 61.875 (64.641) Epoch: [3][4360/11272] Time 0.891 (0.833) Data 0.002 (0.002) Loss 2.7508 (2.7515) Prec@1 33.750 (34.267) Prec@5 58.750 (64.644) Epoch: [3][4370/11272] Time 0.901 (0.833) Data 0.002 (0.002) Loss 2.8696 (2.7513) Prec@1 32.500 (34.270) Prec@5 63.125 (64.649) Epoch: [3][4380/11272] Time 0.787 (0.833) Data 0.002 (0.002) Loss 2.7466 (2.7514) Prec@1 33.750 (34.269) Prec@5 65.625 (64.645) Epoch: [3][4390/11272] Time 0.783 (0.833) Data 0.004 (0.002) Loss 2.5656 (2.7514) Prec@1 36.875 (34.271) Prec@5 71.250 (64.646) Epoch: [3][4400/11272] Time 0.918 (0.833) Data 0.002 (0.002) Loss 2.6984 (2.7514) Prec@1 37.500 (34.271) Prec@5 68.125 (64.646) Epoch: [3][4410/11272] Time 0.888 (0.833) Data 0.002 (0.002) Loss 2.9952 (2.7515) Prec@1 28.750 (34.271) Prec@5 60.625 (64.645) Epoch: [3][4420/11272] Time 0.740 (0.833) Data 0.002 (0.002) Loss 2.8651 (2.7515) Prec@1 30.625 (34.271) Prec@5 58.750 (64.643) Epoch: [3][4430/11272] Time 0.750 (0.833) Data 0.002 (0.002) Loss 2.5995 (2.7515) Prec@1 41.250 (34.271) Prec@5 65.625 (64.643) Epoch: [3][4440/11272] Time 0.862 (0.833) Data 0.002 (0.002) Loss 2.7652 (2.7516) Prec@1 34.375 (34.268) Prec@5 60.000 (64.643) Epoch: [3][4450/11272] Time 0.843 (0.833) Data 0.002 (0.002) Loss 2.8186 (2.7515) Prec@1 33.125 (34.270) Prec@5 65.625 (64.645) Epoch: [3][4460/11272] Time 0.796 (0.833) Data 0.002 (0.002) Loss 2.7507 (2.7515) Prec@1 33.125 (34.269) Prec@5 68.125 (64.646) Epoch: [3][4470/11272] Time 0.804 (0.833) Data 0.002 (0.002) Loss 2.4408 (2.7514) Prec@1 40.000 (34.267) Prec@5 71.250 (64.653) Epoch: [3][4480/11272] Time 0.883 (0.833) Data 0.002 (0.002) Loss 2.6683 (2.7514) Prec@1 33.125 (34.266) Prec@5 68.750 (64.653) Epoch: [3][4490/11272] Time 0.740 (0.833) Data 0.003 (0.002) Loss 2.8195 (2.7515) Prec@1 36.250 (34.265) Prec@5 64.375 (64.654) Epoch: [3][4500/11272] Time 0.764 (0.833) Data 0.002 (0.002) Loss 2.7050 (2.7515) Prec@1 31.875 (34.262) Prec@5 64.375 (64.654) Epoch: [3][4510/11272] Time 0.944 (0.833) Data 0.002 (0.002) Loss 2.6347 (2.7512) Prec@1 36.875 (34.265) Prec@5 66.875 (64.659) Epoch: [3][4520/11272] Time 0.890 (0.833) Data 0.002 (0.002) Loss 2.7178 (2.7512) Prec@1 39.375 (34.264) Prec@5 66.250 (64.660) Epoch: [3][4530/11272] Time 0.744 (0.833) Data 0.002 (0.002) Loss 2.8123 (2.7512) Prec@1 36.250 (34.265) Prec@5 60.625 (64.658) Epoch: [3][4540/11272] Time 0.770 (0.833) Data 0.002 (0.002) Loss 2.6840 (2.7512) Prec@1 33.750 (34.262) Prec@5 61.250 (64.657) Epoch: [3][4550/11272] Time 0.893 (0.833) Data 0.002 (0.002) Loss 2.6475 (2.7512) Prec@1 38.125 (34.262) Prec@5 68.125 (64.655) Epoch: [3][4560/11272] Time 0.848 (0.833) Data 0.001 (0.002) Loss 2.6423 (2.7513) Prec@1 37.500 (34.259) Prec@5 67.500 (64.653) Epoch: [3][4570/11272] Time 0.748 (0.833) Data 0.002 (0.002) Loss 2.8359 (2.7512) Prec@1 35.000 (34.260) Prec@5 62.500 (64.654) Epoch: [3][4580/11272] Time 0.763 (0.833) Data 0.002 (0.002) Loss 2.5450 (2.7513) Prec@1 36.250 (34.258) Prec@5 68.125 (64.654) Epoch: [3][4590/11272] Time 0.962 (0.833) Data 0.002 (0.002) Loss 2.6360 (2.7514) Prec@1 37.500 (34.255) Prec@5 68.125 (64.651) Epoch: [3][4600/11272] Time 0.906 (0.833) Data 0.002 (0.002) Loss 2.8163 (2.7514) Prec@1 28.750 (34.257) Prec@5 61.250 (64.652) Epoch: [3][4610/11272] Time 0.757 (0.833) Data 0.002 (0.002) Loss 2.6957 (2.7515) Prec@1 35.625 (34.256) Prec@5 64.375 (64.648) Epoch: [3][4620/11272] Time 0.881 (0.833) Data 0.002 (0.002) Loss 2.7908 (2.7516) Prec@1 29.375 (34.256) Prec@5 61.875 (64.645) Epoch: [3][4630/11272] Time 0.871 (0.832) Data 0.002 (0.002) Loss 2.5285 (2.7518) Prec@1 38.750 (34.254) Prec@5 76.250 (64.644) Epoch: [3][4640/11272] Time 0.742 (0.832) Data 0.002 (0.002) Loss 2.8077 (2.7517) Prec@1 25.625 (34.254) Prec@5 63.125 (64.643) Epoch: [3][4650/11272] Time 0.760 (0.832) Data 0.002 (0.002) Loss 2.8536 (2.7518) Prec@1 31.250 (34.249) Prec@5 62.500 (64.642) Epoch: [3][4660/11272] Time 0.857 (0.832) Data 0.001 (0.002) Loss 2.7768 (2.7518) Prec@1 33.750 (34.248) Prec@5 63.125 (64.641) Epoch: [3][4670/11272] Time 0.859 (0.832) Data 0.002 (0.002) Loss 2.7709 (2.7517) Prec@1 35.625 (34.253) Prec@5 63.750 (64.646) Epoch: [3][4680/11272] Time 0.742 (0.832) Data 0.002 (0.002) Loss 2.9066 (2.7517) Prec@1 28.125 (34.251) Prec@5 63.750 (64.646) Epoch: [3][4690/11272] Time 0.717 (0.832) Data 0.001 (0.002) Loss 2.6775 (2.7516) Prec@1 37.500 (34.252) Prec@5 63.750 (64.647) Epoch: [3][4700/11272] Time 0.872 (0.832) Data 0.003 (0.002) Loss 2.5950 (2.7513) Prec@1 38.750 (34.258) Prec@5 68.750 (64.654) Epoch: [3][4710/11272] Time 0.835 (0.832) Data 0.001 (0.002) Loss 2.7782 (2.7513) Prec@1 33.750 (34.257) Prec@5 64.375 (64.654) Epoch: [3][4720/11272] Time 0.785 (0.832) Data 0.002 (0.002) Loss 2.8064 (2.7512) Prec@1 34.375 (34.260) Prec@5 61.875 (64.652) Epoch: [3][4730/11272] Time 0.758 (0.832) Data 0.002 (0.002) Loss 2.7715 (2.7511) Prec@1 35.000 (34.263) Prec@5 67.500 (64.653) Epoch: [3][4740/11272] Time 0.889 (0.832) Data 0.002 (0.002) Loss 2.7688 (2.7509) Prec@1 38.750 (34.268) Prec@5 67.500 (64.660) Epoch: [3][4750/11272] Time 0.867 (0.832) Data 0.002 (0.002) Loss 2.8240 (2.7510) Prec@1 34.375 (34.268) Prec@5 58.125 (64.657) Epoch: [3][4760/11272] Time 0.765 (0.832) Data 0.002 (0.002) Loss 2.7468 (2.7511) Prec@1 34.375 (34.265) Prec@5 63.125 (64.657) Epoch: [3][4770/11272] Time 0.910 (0.832) Data 0.002 (0.002) Loss 2.7630 (2.7512) Prec@1 36.250 (34.265) Prec@5 66.250 (64.657) Epoch: [3][4780/11272] Time 0.857 (0.832) Data 0.001 (0.002) Loss 3.0236 (2.7513) Prec@1 28.125 (34.263) Prec@5 61.250 (64.657) Epoch: [3][4790/11272] Time 0.782 (0.832) Data 0.002 (0.002) Loss 2.6091 (2.7511) Prec@1 36.875 (34.267) Prec@5 64.375 (64.659) Epoch: [3][4800/11272] Time 0.745 (0.832) Data 0.002 (0.002) Loss 2.7511 (2.7511) Prec@1 33.125 (34.265) Prec@5 62.500 (64.658) Epoch: [3][4810/11272] Time 0.857 (0.832) Data 0.002 (0.002) Loss 2.8681 (2.7512) Prec@1 30.000 (34.263) Prec@5 58.750 (64.659) Epoch: [3][4820/11272] Time 0.833 (0.832) Data 0.001 (0.002) Loss 2.8939 (2.7512) Prec@1 31.250 (34.263) Prec@5 59.375 (64.657) Epoch: [3][4830/11272] Time 0.720 (0.832) Data 0.001 (0.002) Loss 3.0904 (2.7514) Prec@1 27.500 (34.259) Prec@5 59.375 (64.651) Epoch: [3][4840/11272] Time 0.769 (0.832) Data 0.001 (0.002) Loss 2.8509 (2.7514) Prec@1 33.125 (34.262) Prec@5 63.750 (64.651) Epoch: [3][4850/11272] Time 0.858 (0.832) Data 0.002 (0.002) Loss 2.8979 (2.7517) Prec@1 30.000 (34.258) Prec@5 60.000 (64.643) Epoch: [3][4860/11272] Time 0.869 (0.832) Data 0.002 (0.002) Loss 2.6522 (2.7516) Prec@1 34.375 (34.256) Prec@5 66.875 (64.641) Epoch: [3][4870/11272] Time 0.743 (0.832) Data 0.001 (0.002) Loss 2.8911 (2.7516) Prec@1 30.000 (34.256) Prec@5 63.125 (64.641) Epoch: [3][4880/11272] Time 0.831 (0.832) Data 0.002 (0.002) Loss 2.7200 (2.7516) Prec@1 36.875 (34.255) Prec@5 62.500 (64.639) Epoch: [3][4890/11272] Time 0.832 (0.832) Data 0.002 (0.002) Loss 2.7311 (2.7517) Prec@1 33.750 (34.253) Prec@5 66.875 (64.637) Epoch: [3][4900/11272] Time 0.737 (0.832) Data 0.002 (0.002) Loss 2.6284 (2.7516) Prec@1 38.125 (34.253) Prec@5 64.375 (64.639) Epoch: [3][4910/11272] Time 0.766 (0.832) Data 0.002 (0.002) Loss 2.5792 (2.7517) Prec@1 35.625 (34.250) Prec@5 68.750 (64.637) Epoch: [3][4920/11272] Time 0.932 (0.832) Data 0.002 (0.002) Loss 2.6803 (2.7517) Prec@1 36.250 (34.251) Prec@5 63.750 (64.639) Epoch: [3][4930/11272] Time 0.870 (0.832) Data 0.002 (0.002) Loss 2.7160 (2.7517) Prec@1 37.500 (34.251) Prec@5 66.250 (64.640) Epoch: [3][4940/11272] Time 0.747 (0.832) Data 0.002 (0.002) Loss 2.6244 (2.7515) Prec@1 40.000 (34.255) Prec@5 66.875 (64.641) Epoch: [3][4950/11272] Time 0.754 (0.832) Data 0.001 (0.002) Loss 3.0477 (2.7517) Prec@1 28.750 (34.253) Prec@5 59.375 (64.637) Epoch: [3][4960/11272] Time 0.875 (0.832) Data 0.002 (0.002) Loss 2.8545 (2.7518) Prec@1 33.750 (34.252) Prec@5 59.375 (64.635) Epoch: [3][4970/11272] Time 0.879 (0.832) Data 0.002 (0.002) Loss 2.9963 (2.7518) Prec@1 33.125 (34.254) Prec@5 58.750 (64.634) Epoch: [3][4980/11272] Time 0.780 (0.832) Data 0.002 (0.002) Loss 2.6765 (2.7517) Prec@1 35.000 (34.256) Prec@5 67.500 (64.637) Epoch: [3][4990/11272] Time 0.781 (0.832) Data 0.003 (0.002) Loss 2.9217 (2.7519) Prec@1 27.500 (34.251) Prec@5 57.500 (64.633) Epoch: [3][5000/11272] Time 0.898 (0.832) Data 0.002 (0.002) Loss 2.6373 (2.7519) Prec@1 36.875 (34.250) Prec@5 66.875 (64.633) Epoch: [3][5010/11272] Time 0.930 (0.832) Data 0.002 (0.002) Loss 2.4473 (2.7517) Prec@1 43.750 (34.253) Prec@5 74.375 (64.639) Epoch: [3][5020/11272] Time 0.753 (0.832) Data 0.001 (0.002) Loss 2.6161 (2.7518) Prec@1 34.375 (34.251) Prec@5 68.750 (64.639) Epoch: [3][5030/11272] Time 0.949 (0.832) Data 0.002 (0.002) Loss 2.8507 (2.7517) Prec@1 33.750 (34.252) Prec@5 61.250 (64.642) Epoch: [3][5040/11272] Time 0.820 (0.832) Data 0.001 (0.002) Loss 2.6358 (2.7517) Prec@1 36.250 (34.253) Prec@5 63.750 (64.642) Epoch: [3][5050/11272] Time 0.846 (0.832) Data 0.002 (0.002) Loss 2.5132 (2.7516) Prec@1 40.000 (34.256) Prec@5 66.875 (64.645) Epoch: [3][5060/11272] Time 0.768 (0.832) Data 0.002 (0.002) Loss 2.9705 (2.7517) Prec@1 31.250 (34.256) Prec@5 63.125 (64.643) Epoch: [3][5070/11272] Time 0.924 (0.832) Data 0.002 (0.002) Loss 3.1216 (2.7518) Prec@1 17.500 (34.250) Prec@5 63.125 (64.640) Epoch: [3][5080/11272] Time 0.919 (0.832) Data 0.002 (0.002) Loss 3.0495 (2.7520) Prec@1 27.500 (34.247) Prec@5 60.000 (64.638) Epoch: [3][5090/11272] Time 0.791 (0.832) Data 0.001 (0.002) Loss 2.6539 (2.7519) Prec@1 33.750 (34.250) Prec@5 64.375 (64.640) Epoch: [3][5100/11272] Time 0.881 (0.832) Data 0.002 (0.002) Loss 2.9309 (2.7520) Prec@1 35.000 (34.248) Prec@5 57.500 (64.638) Epoch: [3][5110/11272] Time 0.910 (0.832) Data 0.002 (0.002) Loss 2.7825 (2.7519) Prec@1 32.500 (34.248) Prec@5 64.375 (64.642) Epoch: [3][5120/11272] Time 0.924 (0.832) Data 0.002 (0.002) Loss 3.2059 (2.7519) Prec@1 27.500 (34.249) Prec@5 53.750 (64.641) Epoch: [3][5130/11272] Time 0.724 (0.832) Data 0.002 (0.002) Loss 2.6094 (2.7521) Prec@1 38.750 (34.250) Prec@5 65.625 (64.637) Epoch: [3][5140/11272] Time 0.795 (0.832) Data 0.002 (0.002) Loss 2.5753 (2.7520) Prec@1 36.250 (34.250) Prec@5 69.375 (64.641) Epoch: [3][5150/11272] Time 0.919 (0.832) Data 0.002 (0.002) Loss 2.5634 (2.7519) Prec@1 36.250 (34.252) Prec@5 67.500 (64.643) Epoch: [3][5160/11272] Time 0.769 (0.832) Data 0.004 (0.002) Loss 2.6799 (2.7519) Prec@1 31.875 (34.253) Prec@5 65.625 (64.642) Epoch: [3][5170/11272] Time 0.774 (0.832) Data 0.002 (0.002) Loss 3.0076 (2.7519) Prec@1 29.375 (34.253) Prec@5 60.000 (64.643) Epoch: [3][5180/11272] Time 0.856 (0.832) Data 0.001 (0.002) Loss 2.5425 (2.7518) Prec@1 41.250 (34.256) Prec@5 67.500 (64.642) Epoch: [3][5190/11272] Time 0.906 (0.832) Data 0.002 (0.002) Loss 2.7139 (2.7519) Prec@1 36.875 (34.257) Prec@5 66.250 (64.640) Epoch: [3][5200/11272] Time 0.784 (0.832) Data 0.002 (0.002) Loss 2.5567 (2.7518) Prec@1 36.250 (34.259) Prec@5 65.625 (64.640) Epoch: [3][5210/11272] Time 0.723 (0.832) Data 0.001 (0.002) Loss 2.7594 (2.7517) Prec@1 34.375 (34.263) Prec@5 63.750 (64.641) Epoch: [3][5220/11272] Time 0.882 (0.832) Data 0.001 (0.002) Loss 2.5628 (2.7517) Prec@1 38.750 (34.263) Prec@5 65.000 (64.643) Epoch: [3][5230/11272] Time 0.901 (0.832) Data 0.002 (0.002) Loss 2.7437 (2.7516) Prec@1 35.625 (34.263) Prec@5 65.625 (64.643) Epoch: [3][5240/11272] Time 0.823 (0.832) Data 0.002 (0.002) Loss 2.8210 (2.7516) Prec@1 32.500 (34.265) Prec@5 65.000 (64.644) Epoch: [3][5250/11272] Time 0.747 (0.832) Data 0.002 (0.002) Loss 2.9381 (2.7516) Prec@1 33.750 (34.262) Prec@5 61.250 (64.642) Epoch: [3][5260/11272] Time 0.887 (0.832) Data 0.002 (0.002) Loss 2.8795 (2.7516) Prec@1 33.750 (34.267) Prec@5 63.125 (64.639) Epoch: [3][5270/11272] Time 0.873 (0.832) Data 0.002 (0.002) Loss 2.6667 (2.7515) Prec@1 35.000 (34.268) Prec@5 62.500 (64.641) Epoch: [3][5280/11272] Time 0.765 (0.832) Data 0.002 (0.002) Loss 2.6988 (2.7515) Prec@1 35.000 (34.268) Prec@5 68.750 (64.640) Epoch: [3][5290/11272] Time 0.897 (0.832) Data 0.001 (0.002) Loss 2.9595 (2.7514) Prec@1 31.250 (34.270) Prec@5 62.500 (64.644) Epoch: [3][5300/11272] Time 0.899 (0.832) Data 0.002 (0.002) Loss 2.4257 (2.7512) Prec@1 44.375 (34.274) Prec@5 73.125 (64.647) Epoch: [3][5310/11272] Time 0.743 (0.832) Data 0.002 (0.002) Loss 2.7261 (2.7513) Prec@1 35.000 (34.273) Prec@5 61.875 (64.646) Epoch: [3][5320/11272] Time 0.790 (0.832) Data 0.002 (0.002) Loss 2.6494 (2.7513) Prec@1 38.750 (34.272) Prec@5 72.500 (64.648) Epoch: [3][5330/11272] Time 0.902 (0.832) Data 0.002 (0.002) Loss 2.7727 (2.7512) Prec@1 36.250 (34.273) Prec@5 65.625 (64.648) Epoch: [3][5340/11272] Time 0.933 (0.832) Data 0.002 (0.002) Loss 2.8015 (2.7511) Prec@1 35.000 (34.275) Prec@5 67.500 (64.653) Epoch: [3][5350/11272] Time 0.765 (0.831) Data 0.002 (0.002) Loss 2.8157 (2.7511) Prec@1 30.625 (34.276) Prec@5 60.000 (64.653) Epoch: [3][5360/11272] Time 0.778 (0.831) Data 0.002 (0.002) Loss 2.7828 (2.7510) Prec@1 32.500 (34.279) Prec@5 66.875 (64.655) Epoch: [3][5370/11272] Time 0.878 (0.831) Data 0.002 (0.002) Loss 2.6700 (2.7510) Prec@1 33.750 (34.277) Prec@5 68.125 (64.651) Epoch: [3][5380/11272] Time 0.856 (0.831) Data 0.001 (0.002) Loss 2.4006 (2.7509) Prec@1 41.250 (34.278) Prec@5 71.250 (64.652) Epoch: [3][5390/11272] Time 0.742 (0.831) Data 0.002 (0.002) Loss 2.6762 (2.7508) Prec@1 35.625 (34.280) Prec@5 62.500 (64.653) Epoch: [3][5400/11272] Time 0.776 (0.831) Data 0.002 (0.002) Loss 2.6810 (2.7507) Prec@1 38.750 (34.283) Prec@5 66.875 (64.657) Epoch: [3][5410/11272] Time 0.933 (0.831) Data 0.002 (0.002) Loss 2.5262 (2.7506) Prec@1 35.000 (34.284) Prec@5 68.125 (64.657) Epoch: [3][5420/11272] Time 0.808 (0.831) Data 0.004 (0.002) Loss 2.8410 (2.7504) Prec@1 31.250 (34.286) Prec@5 61.250 (64.658) Epoch: [3][5430/11272] Time 0.814 (0.831) Data 0.002 (0.002) Loss 2.6225 (2.7504) Prec@1 38.125 (34.287) Prec@5 68.125 (64.657) Epoch: [3][5440/11272] Time 0.890 (0.831) Data 0.002 (0.002) Loss 2.8334 (2.7504) Prec@1 30.625 (34.288) Prec@5 62.500 (64.659) Epoch: [3][5450/11272] Time 0.926 (0.831) Data 0.003 (0.002) Loss 2.7531 (2.7502) Prec@1 33.750 (34.291) Prec@5 66.875 (64.664) Epoch: [3][5460/11272] Time 0.809 (0.831) Data 0.002 (0.002) Loss 2.5695 (2.7503) Prec@1 39.375 (34.290) Prec@5 70.000 (64.663) Epoch: [3][5470/11272] Time 0.798 (0.832) Data 0.002 (0.002) Loss 3.0111 (2.7502) Prec@1 31.250 (34.290) Prec@5 56.250 (64.664) Epoch: [3][5480/11272] Time 0.944 (0.832) Data 0.002 (0.002) Loss 2.6910 (2.7503) Prec@1 33.750 (34.288) Prec@5 66.250 (64.664) Epoch: [3][5490/11272] Time 0.873 (0.832) Data 0.002 (0.002) Loss 2.4209 (2.7503) Prec@1 38.750 (34.286) Prec@5 71.875 (64.663) Epoch: [3][5500/11272] Time 0.799 (0.832) Data 0.002 (0.002) Loss 2.6404 (2.7502) Prec@1 31.250 (34.286) Prec@5 64.375 (64.664) Epoch: [3][5510/11272] Time 0.753 (0.832) Data 0.002 (0.002) Loss 2.8702 (2.7501) Prec@1 35.000 (34.290) Prec@5 61.875 (64.668) Epoch: [3][5520/11272] Time 0.890 (0.832) Data 0.002 (0.002) Loss 2.7931 (2.7501) Prec@1 33.125 (34.290) Prec@5 63.125 (64.666) Epoch: [3][5530/11272] Time 0.844 (0.832) Data 0.001 (0.002) Loss 2.7158 (2.7501) Prec@1 31.875 (34.288) Prec@5 62.500 (64.666) Epoch: [3][5540/11272] Time 0.789 (0.832) Data 0.002 (0.002) Loss 2.8733 (2.7502) Prec@1 33.750 (34.289) Prec@5 59.375 (64.665) Epoch: [3][5550/11272] Time 0.918 (0.832) Data 0.002 (0.002) Loss 2.6368 (2.7504) Prec@1 33.750 (34.288) Prec@5 68.125 (64.661) Epoch: [3][5560/11272] Time 0.842 (0.832) Data 0.002 (0.002) Loss 2.9469 (2.7505) Prec@1 30.000 (34.286) Prec@5 59.375 (64.658) Epoch: [3][5570/11272] Time 0.802 (0.832) Data 0.002 (0.002) Loss 2.7439 (2.7505) Prec@1 36.875 (34.285) Prec@5 67.500 (64.655) Epoch: [3][5580/11272] Time 0.759 (0.832) Data 0.002 (0.002) Loss 2.6895 (2.7505) Prec@1 34.375 (34.287) Prec@5 66.875 (64.657) Epoch: [3][5590/11272] Time 0.848 (0.832) Data 0.002 (0.002) Loss 2.5897 (2.7506) Prec@1 39.375 (34.287) Prec@5 63.125 (64.654) Epoch: [3][5600/11272] Time 0.940 (0.832) Data 0.002 (0.002) Loss 2.8493 (2.7505) Prec@1 31.875 (34.283) Prec@5 65.000 (64.655) Epoch: [3][5610/11272] Time 0.766 (0.832) Data 0.002 (0.002) Loss 2.6660 (2.7505) Prec@1 39.375 (34.282) Prec@5 68.125 (64.658) Epoch: [3][5620/11272] Time 0.783 (0.832) Data 0.002 (0.002) Loss 2.8023 (2.7505) Prec@1 33.125 (34.283) Prec@5 60.625 (64.654) Epoch: [3][5630/11272] Time 0.893 (0.832) Data 0.002 (0.002) Loss 2.8022 (2.7504) Prec@1 30.000 (34.285) Prec@5 69.375 (64.659) Epoch: [3][5640/11272] Time 0.851 (0.832) Data 0.002 (0.002) Loss 2.4302 (2.7504) Prec@1 43.125 (34.285) Prec@5 76.250 (64.660) Epoch: [3][5650/11272] Time 0.766 (0.832) Data 0.002 (0.002) Loss 2.6383 (2.7503) Prec@1 38.125 (34.287) Prec@5 65.625 (64.659) Epoch: [3][5660/11272] Time 0.831 (0.832) Data 0.002 (0.002) Loss 2.6118 (2.7502) Prec@1 38.125 (34.287) Prec@5 68.125 (64.660) Epoch: [3][5670/11272] Time 0.893 (0.832) Data 0.002 (0.002) Loss 2.7453 (2.7502) Prec@1 33.125 (34.286) Prec@5 64.375 (64.659) Epoch: [3][5680/11272] Time 0.794 (0.832) Data 0.001 (0.002) Loss 2.8683 (2.7503) Prec@1 35.000 (34.286) Prec@5 62.500 (64.660) Epoch: [3][5690/11272] Time 0.738 (0.832) Data 0.002 (0.002) Loss 2.9428 (2.7503) Prec@1 31.875 (34.287) Prec@5 63.125 (64.660) Epoch: [3][5700/11272] Time 0.878 (0.832) Data 0.002 (0.002) Loss 2.8534 (2.7503) Prec@1 31.250 (34.286) Prec@5 61.250 (64.662) Epoch: [3][5710/11272] Time 0.968 (0.832) Data 0.003 (0.002) Loss 2.5774 (2.7502) Prec@1 37.500 (34.288) Prec@5 71.250 (64.663) Epoch: [3][5720/11272] Time 0.767 (0.832) Data 0.001 (0.002) Loss 2.7414 (2.7502) Prec@1 41.250 (34.289) Prec@5 68.750 (64.665) Epoch: [3][5730/11272] Time 0.765 (0.832) Data 0.002 (0.002) Loss 2.6373 (2.7502) Prec@1 39.375 (34.290) Prec@5 67.500 (64.665) Epoch: [3][5740/11272] Time 0.873 (0.832) Data 0.002 (0.002) Loss 2.7659 (2.7502) Prec@1 35.625 (34.291) Prec@5 68.125 (64.665) Epoch: [3][5750/11272] Time 0.934 (0.832) Data 0.001 (0.002) Loss 2.6474 (2.7502) Prec@1 43.125 (34.295) Prec@5 68.750 (64.666) Epoch: [3][5760/11272] Time 0.790 (0.832) Data 0.002 (0.002) Loss 2.9341 (2.7502) Prec@1 35.625 (34.297) Prec@5 63.750 (64.665) Epoch: [3][5770/11272] Time 0.869 (0.832) Data 0.002 (0.002) Loss 2.8008 (2.7502) Prec@1 33.125 (34.296) Prec@5 63.125 (64.664) Epoch: [3][5780/11272] Time 0.849 (0.832) Data 0.002 (0.002) Loss 2.8971 (2.7503) Prec@1 31.250 (34.296) Prec@5 61.875 (64.663) Epoch: [3][5790/11272] Time 0.935 (0.832) Data 0.002 (0.002) Loss 2.6998 (2.7504) Prec@1 35.625 (34.295) Prec@5 62.500 (64.661) Epoch: [3][5800/11272] Time 0.776 (0.832) Data 0.002 (0.002) Loss 3.0086 (2.7503) Prec@1 34.375 (34.298) Prec@5 58.750 (64.663) Epoch: [3][5810/11272] Time 0.732 (0.832) Data 0.002 (0.002) Loss 2.6352 (2.7504) Prec@1 38.125 (34.295) Prec@5 64.375 (64.661) Epoch: [3][5820/11272] Time 0.862 (0.832) Data 0.002 (0.002) Loss 2.6277 (2.7504) Prec@1 30.625 (34.291) Prec@5 66.875 (64.660) Epoch: [3][5830/11272] Time 0.732 (0.832) Data 0.001 (0.002) Loss 2.7233 (2.7503) Prec@1 31.250 (34.293) Prec@5 67.500 (64.660) Epoch: [3][5840/11272] Time 0.740 (0.832) Data 0.001 (0.002) Loss 2.5435 (2.7502) Prec@1 40.000 (34.297) Prec@5 68.750 (64.661) Epoch: [3][5850/11272] Time 0.887 (0.832) Data 0.002 (0.002) Loss 2.6603 (2.7501) Prec@1 36.250 (34.298) Prec@5 63.750 (64.662) Epoch: [3][5860/11272] Time 0.799 (0.832) Data 0.001 (0.002) Loss 2.7759 (2.7502) Prec@1 36.250 (34.295) Prec@5 63.750 (64.660) Epoch: [3][5870/11272] Time 0.748 (0.832) Data 0.002 (0.002) Loss 2.5416 (2.7502) Prec@1 35.625 (34.297) Prec@5 68.750 (64.660) Epoch: [3][5880/11272] Time 0.757 (0.832) Data 0.002 (0.002) Loss 2.8409 (2.7502) Prec@1 36.250 (34.297) Prec@5 60.000 (64.658) Epoch: [3][5890/11272] Time 0.906 (0.832) Data 0.002 (0.002) Loss 2.6207 (2.7501) Prec@1 34.375 (34.299) Prec@5 65.000 (64.658) Epoch: [3][5900/11272] Time 0.869 (0.832) Data 0.002 (0.002) Loss 2.6369 (2.7501) Prec@1 35.000 (34.302) Prec@5 68.750 (64.659) Epoch: [3][5910/11272] Time 0.783 (0.832) Data 0.001 (0.002) Loss 2.9097 (2.7502) Prec@1 34.375 (34.301) Prec@5 59.375 (64.657) Epoch: [3][5920/11272] Time 0.785 (0.832) Data 0.002 (0.002) Loss 2.6185 (2.7500) Prec@1 36.875 (34.303) Prec@5 66.250 (64.662) Epoch: [3][5930/11272] Time 0.921 (0.832) Data 0.002 (0.002) Loss 2.9469 (2.7501) Prec@1 28.750 (34.301) Prec@5 60.000 (64.657) Epoch: [3][5940/11272] Time 0.871 (0.832) Data 0.002 (0.002) Loss 2.7427 (2.7501) Prec@1 33.125 (34.302) Prec@5 65.625 (64.656) Epoch: [3][5950/11272] Time 0.758 (0.832) Data 0.002 (0.002) Loss 2.8359 (2.7502) Prec@1 33.750 (34.299) Prec@5 62.500 (64.655) Epoch: [3][5960/11272] Time 0.886 (0.832) Data 0.002 (0.002) Loss 2.9473 (2.7502) Prec@1 32.500 (34.301) Prec@5 59.375 (64.655) Epoch: [3][5970/11272] Time 0.831 (0.832) Data 0.001 (0.002) Loss 2.7904 (2.7502) Prec@1 30.625 (34.296) Prec@5 63.750 (64.654) Epoch: [3][5980/11272] Time 0.760 (0.832) Data 0.002 (0.002) Loss 2.7454 (2.7501) Prec@1 31.875 (34.296) Prec@5 65.625 (64.657) Epoch: [3][5990/11272] Time 0.801 (0.832) Data 0.002 (0.002) Loss 2.7505 (2.7500) Prec@1 33.125 (34.299) Prec@5 62.500 (64.660) Epoch: [3][6000/11272] Time 0.923 (0.832) Data 0.002 (0.002) Loss 2.7086 (2.7501) Prec@1 39.375 (34.298) Prec@5 66.250 (64.660) Epoch: [3][6010/11272] Time 0.849 (0.832) Data 0.002 (0.002) Loss 3.0045 (2.7502) Prec@1 31.875 (34.300) Prec@5 60.625 (64.659) Epoch: [3][6020/11272] Time 0.790 (0.832) Data 0.002 (0.002) Loss 2.6407 (2.7502) Prec@1 36.875 (34.304) Prec@5 65.000 (64.660) Epoch: [3][6030/11272] Time 0.777 (0.832) Data 0.002 (0.002) Loss 2.7762 (2.7501) Prec@1 32.500 (34.307) Prec@5 68.125 (64.661) Epoch: [3][6040/11272] Time 0.988 (0.832) Data 0.002 (0.002) Loss 2.7485 (2.7501) Prec@1 40.000 (34.306) Prec@5 65.000 (64.659) Epoch: [3][6050/11272] Time 0.846 (0.832) Data 0.002 (0.002) Loss 2.9360 (2.7502) Prec@1 30.625 (34.306) Prec@5 58.750 (64.657) Epoch: [3][6060/11272] Time 0.758 (0.832) Data 0.002 (0.002) Loss 2.8028 (2.7502) Prec@1 32.500 (34.305) Prec@5 64.375 (64.659) Epoch: [3][6070/11272] Time 0.761 (0.832) Data 0.002 (0.002) Loss 2.8392 (2.7503) Prec@1 30.000 (34.303) Prec@5 61.250 (64.659) Epoch: [3][6080/11272] Time 0.917 (0.832) Data 0.002 (0.002) Loss 3.0803 (2.7503) Prec@1 30.000 (34.302) Prec@5 58.125 (64.658) Epoch: [3][6090/11272] Time 0.757 (0.832) Data 0.005 (0.002) Loss 2.7404 (2.7503) Prec@1 32.500 (34.301) Prec@5 68.125 (64.658) Epoch: [3][6100/11272] Time 0.793 (0.832) Data 0.002 (0.002) Loss 2.6429 (2.7503) Prec@1 36.250 (34.301) Prec@5 65.000 (64.659) Epoch: [3][6110/11272] Time 0.910 (0.832) Data 0.002 (0.002) Loss 2.9298 (2.7504) Prec@1 31.250 (34.299) Prec@5 61.250 (64.658) Epoch: [3][6120/11272] Time 1.004 (0.832) Data 0.002 (0.002) Loss 2.8242 (2.7505) Prec@1 35.625 (34.297) Prec@5 64.375 (64.656) Epoch: [3][6130/11272] Time 0.773 (0.832) Data 0.002 (0.002) Loss 2.6015 (2.7504) Prec@1 40.000 (34.297) Prec@5 66.875 (64.659) Epoch: [3][6140/11272] Time 0.771 (0.832) Data 0.002 (0.002) Loss 2.7566 (2.7504) Prec@1 32.500 (34.298) Prec@5 68.125 (64.660) Epoch: [3][6150/11272] Time 0.913 (0.832) Data 0.002 (0.002) Loss 2.5800 (2.7505) Prec@1 41.250 (34.297) Prec@5 66.875 (64.661) Epoch: [3][6160/11272] Time 0.861 (0.832) Data 0.001 (0.002) Loss 2.5732 (2.7505) Prec@1 34.375 (34.296) Prec@5 68.750 (64.663) Epoch: [3][6170/11272] Time 0.788 (0.832) Data 0.001 (0.002) Loss 2.7908 (2.7504) Prec@1 27.500 (34.297) Prec@5 69.375 (64.664) Epoch: [3][6180/11272] Time 0.715 (0.832) Data 0.001 (0.002) Loss 2.7750 (2.7504) Prec@1 33.125 (34.296) Prec@5 65.625 (64.664) Epoch: [3][6190/11272] Time 0.948 (0.832) Data 0.002 (0.002) Loss 2.5563 (2.7504) Prec@1 38.125 (34.295) Prec@5 66.875 (64.664) Epoch: [3][6200/11272] Time 0.888 (0.832) Data 0.002 (0.002) Loss 2.7621 (2.7505) Prec@1 39.375 (34.294) Prec@5 65.000 (64.662) Epoch: [3][6210/11272] Time 0.819 (0.832) Data 0.002 (0.002) Loss 2.8048 (2.7506) Prec@1 29.375 (34.291) Prec@5 66.250 (64.661) Epoch: [3][6220/11272] Time 0.926 (0.832) Data 0.002 (0.002) Loss 2.6953 (2.7505) Prec@1 31.875 (34.290) Prec@5 63.750 (64.662) Epoch: [3][6230/11272] Time 0.910 (0.832) Data 0.002 (0.002) Loss 2.7138 (2.7506) Prec@1 34.375 (34.288) Prec@5 64.375 (64.660) Epoch: [3][6240/11272] Time 0.775 (0.832) Data 0.002 (0.002) Loss 2.8950 (2.7506) Prec@1 26.875 (34.285) Prec@5 63.125 (64.662) Epoch: [3][6250/11272] Time 0.740 (0.832) Data 0.001 (0.002) Loss 2.7070 (2.7506) Prec@1 35.000 (34.284) Prec@5 66.250 (64.660) Epoch: [3][6260/11272] Time 0.967 (0.832) Data 0.002 (0.002) Loss 2.5394 (2.7506) Prec@1 36.875 (34.283) Prec@5 69.375 (64.660) Epoch: [3][6270/11272] Time 0.890 (0.832) Data 0.002 (0.002) Loss 2.4141 (2.7505) Prec@1 36.875 (34.284) Prec@5 71.250 (64.664) Epoch: [3][6280/11272] Time 0.743 (0.832) Data 0.002 (0.002) Loss 2.7831 (2.7504) Prec@1 32.500 (34.282) Prec@5 61.875 (64.663) Epoch: [3][6290/11272] Time 0.775 (0.832) Data 0.002 (0.002) Loss 2.7473 (2.7505) Prec@1 35.000 (34.281) Prec@5 65.625 (64.663) Epoch: [3][6300/11272] Time 0.881 (0.832) Data 0.002 (0.002) Loss 2.7540 (2.7505) Prec@1 33.750 (34.282) Prec@5 64.375 (64.662) Epoch: [3][6310/11272] Time 0.842 (0.832) Data 0.002 (0.002) Loss 2.8798 (2.7506) Prec@1 34.375 (34.279) Prec@5 64.375 (64.661) Epoch: [3][6320/11272] Time 0.755 (0.832) Data 0.002 (0.002) Loss 2.5382 (2.7507) Prec@1 33.125 (34.276) Prec@5 70.000 (64.659) Epoch: [3][6330/11272] Time 0.779 (0.832) Data 0.002 (0.002) Loss 2.6613 (2.7508) Prec@1 32.500 (34.277) Prec@5 67.500 (64.659) Epoch: [3][6340/11272] Time 0.919 (0.832) Data 0.002 (0.002) Loss 2.4795 (2.7505) Prec@1 35.625 (34.280) Prec@5 71.875 (64.664) Epoch: [3][6350/11272] Time 0.781 (0.832) Data 0.004 (0.002) Loss 2.6407 (2.7504) Prec@1 34.375 (34.282) Prec@5 65.000 (64.664) Epoch: [3][6360/11272] Time 0.747 (0.832) Data 0.002 (0.002) Loss 2.7992 (2.7505) Prec@1 32.500 (34.281) Prec@5 61.250 (64.665) Epoch: [3][6370/11272] Time 0.910 (0.832) Data 0.002 (0.002) Loss 2.6725 (2.7507) Prec@1 33.750 (34.279) Prec@5 66.875 (64.661) Epoch: [3][6380/11272] Time 0.957 (0.832) Data 0.002 (0.002) Loss 2.6776 (2.7506) Prec@1 35.625 (34.280) Prec@5 65.625 (64.662) Epoch: [3][6390/11272] Time 0.753 (0.832) Data 0.002 (0.002) Loss 2.7558 (2.7506) Prec@1 35.625 (34.279) Prec@5 65.625 (64.665) Epoch: [3][6400/11272] Time 0.762 (0.832) Data 0.002 (0.002) Loss 2.8421 (2.7507) Prec@1 29.375 (34.276) Prec@5 65.000 (64.664) Epoch: [3][6410/11272] Time 0.841 (0.832) Data 0.001 (0.002) Loss 3.0230 (2.7507) Prec@1 24.375 (34.279) Prec@5 58.125 (64.664) Epoch: [3][6420/11272] Time 0.906 (0.832) Data 0.002 (0.002) Loss 2.6191 (2.7505) Prec@1 35.625 (34.283) Prec@5 66.250 (64.665) Epoch: [3][6430/11272] Time 0.782 (0.832) Data 0.002 (0.002) Loss 2.6716 (2.7505) Prec@1 32.500 (34.284) Prec@5 65.625 (64.665) Epoch: [3][6440/11272] Time 0.785 (0.832) Data 0.002 (0.002) Loss 2.9151 (2.7505) Prec@1 30.000 (34.282) Prec@5 62.500 (64.663) Epoch: [3][6450/11272] Time 0.884 (0.832) Data 0.002 (0.002) Loss 2.9652 (2.7505) Prec@1 31.250 (34.282) Prec@5 58.750 (64.663) Epoch: [3][6460/11272] Time 0.888 (0.833) Data 0.002 (0.002) Loss 2.7828 (2.7506) Prec@1 33.750 (34.282) Prec@5 67.500 (64.663) Epoch: [3][6470/11272] Time 0.792 (0.833) Data 0.002 (0.002) Loss 3.0037 (2.7507) Prec@1 28.125 (34.279) Prec@5 63.125 (64.660) Epoch: [3][6480/11272] Time 0.905 (0.833) Data 0.002 (0.002) Loss 2.9529 (2.7508) Prec@1 33.125 (34.278) Prec@5 62.500 (64.660) Epoch: [3][6490/11272] Time 0.945 (0.833) Data 0.002 (0.002) Loss 2.8869 (2.7509) Prec@1 35.000 (34.276) Prec@5 60.625 (64.658) Epoch: [3][6500/11272] Time 0.764 (0.833) Data 0.001 (0.002) Loss 2.7634 (2.7510) Prec@1 35.000 (34.274) Prec@5 63.125 (64.655) Epoch: [3][6510/11272] Time 0.760 (0.833) Data 0.002 (0.002) Loss 2.7582 (2.7509) Prec@1 34.375 (34.278) Prec@5 63.750 (64.659) Epoch: [3][6520/11272] Time 0.912 (0.833) Data 0.002 (0.002) Loss 2.8150 (2.7510) Prec@1 31.250 (34.277) Prec@5 66.875 (64.657) Epoch: [3][6530/11272] Time 0.972 (0.833) Data 0.002 (0.002) Loss 2.3881 (2.7510) Prec@1 42.500 (34.278) Prec@5 72.500 (64.658) Epoch: [3][6540/11272] Time 0.832 (0.833) Data 0.002 (0.002) Loss 2.7986 (2.7509) Prec@1 35.000 (34.281) Prec@5 61.250 (64.659) Epoch: [3][6550/11272] Time 0.794 (0.833) Data 0.002 (0.002) Loss 2.8338 (2.7509) Prec@1 32.500 (34.280) Prec@5 60.625 (64.657) Epoch: [3][6560/11272] Time 0.880 (0.833) Data 0.002 (0.002) Loss 2.6431 (2.7509) Prec@1 30.000 (34.282) Prec@5 67.500 (64.656) Epoch: [3][6570/11272] Time 0.874 (0.833) Data 0.002 (0.002) Loss 2.6855 (2.7509) Prec@1 33.125 (34.280) Prec@5 63.125 (64.653) Epoch: [3][6580/11272] Time 0.785 (0.833) Data 0.002 (0.002) Loss 2.6262 (2.7510) Prec@1 37.500 (34.282) Prec@5 62.500 (64.651) Epoch: [3][6590/11272] Time 0.747 (0.833) Data 0.002 (0.002) Loss 2.5681 (2.7509) Prec@1 35.000 (34.283) Prec@5 68.125 (64.652) Epoch: [3][6600/11272] Time 0.866 (0.833) Data 0.001 (0.002) Loss 2.7092 (2.7509) Prec@1 33.750 (34.282) Prec@5 67.500 (64.652) Epoch: [3][6610/11272] Time 0.863 (0.833) Data 0.002 (0.002) Loss 3.0205 (2.7510) Prec@1 26.875 (34.283) Prec@5 63.125 (64.654) Epoch: [3][6620/11272] Time 0.755 (0.833) Data 0.002 (0.002) Loss 3.0371 (2.7510) Prec@1 29.375 (34.283) Prec@5 55.625 (64.651) Epoch: [3][6630/11272] Time 0.887 (0.833) Data 0.002 (0.002) Loss 2.8361 (2.7511) Prec@1 31.250 (34.281) Prec@5 61.250 (64.649) Epoch: [3][6640/11272] Time 0.855 (0.833) Data 0.002 (0.002) Loss 2.6530 (2.7512) Prec@1 38.750 (34.281) Prec@5 65.625 (64.646) Epoch: [3][6650/11272] Time 0.801 (0.833) Data 0.002 (0.002) Loss 2.8523 (2.7512) Prec@1 34.375 (34.282) Prec@5 65.000 (64.646) Epoch: [3][6660/11272] Time 0.740 (0.833) Data 0.002 (0.002) Loss 2.8947 (2.7513) Prec@1 30.625 (34.281) Prec@5 66.875 (64.647) Epoch: [3][6670/11272] Time 0.862 (0.833) Data 0.002 (0.002) Loss 3.1431 (2.7514) Prec@1 29.375 (34.279) Prec@5 55.000 (64.643) Epoch: [3][6680/11272] Time 0.930 (0.833) Data 0.002 (0.002) Loss 2.5454 (2.7513) Prec@1 35.000 (34.282) Prec@5 68.125 (64.647) Epoch: [3][6690/11272] Time 0.766 (0.833) Data 0.002 (0.002) Loss 2.8566 (2.7513) Prec@1 42.500 (34.282) Prec@5 66.250 (64.647) Epoch: [3][6700/11272] Time 0.748 (0.833) Data 0.002 (0.002) Loss 2.7417 (2.7514) Prec@1 37.500 (34.281) Prec@5 65.000 (64.644) Epoch: [3][6710/11272] Time 0.851 (0.833) Data 0.002 (0.002) Loss 2.9118 (2.7516) Prec@1 33.750 (34.281) Prec@5 57.500 (64.640) Epoch: [3][6720/11272] Time 0.897 (0.833) Data 0.002 (0.002) Loss 2.9701 (2.7515) Prec@1 31.250 (34.281) Prec@5 59.375 (64.643) Epoch: [3][6730/11272] Time 0.746 (0.833) Data 0.001 (0.002) Loss 2.8172 (2.7516) Prec@1 33.750 (34.279) Prec@5 66.875 (64.641) Epoch: [3][6740/11272] Time 0.797 (0.833) Data 0.002 (0.002) Loss 2.8519 (2.7516) Prec@1 30.000 (34.278) Prec@5 60.000 (64.641) Epoch: [3][6750/11272] Time 0.880 (0.833) Data 0.002 (0.002) Loss 2.6098 (2.7516) Prec@1 39.375 (34.278) Prec@5 63.750 (64.639) Epoch: [3][6760/11272] Time 0.775 (0.833) Data 0.002 (0.002) Loss 2.8986 (2.7516) Prec@1 31.250 (34.278) Prec@5 61.250 (64.639) Epoch: [3][6770/11272] Time 0.752 (0.833) Data 0.002 (0.002) Loss 2.5809 (2.7516) Prec@1 40.000 (34.276) Prec@5 65.625 (64.636) Epoch: [3][6780/11272] Time 0.865 (0.833) Data 0.001 (0.002) Loss 2.7692 (2.7515) Prec@1 34.375 (34.275) Prec@5 66.875 (64.638) Epoch: [3][6790/11272] Time 0.872 (0.833) Data 0.001 (0.002) Loss 2.8850 (2.7515) Prec@1 29.375 (34.275) Prec@5 60.625 (64.638) Epoch: [3][6800/11272] Time 0.800 (0.833) Data 0.002 (0.002) Loss 2.5798 (2.7514) Prec@1 40.625 (34.278) Prec@5 70.625 (64.641) Epoch: [3][6810/11272] Time 0.748 (0.833) Data 0.002 (0.002) Loss 2.6896 (2.7514) Prec@1 35.000 (34.278) Prec@5 64.375 (64.640) Epoch: [3][6820/11272] Time 0.889 (0.833) Data 0.002 (0.002) Loss 2.7090 (2.7513) Prec@1 39.375 (34.279) Prec@5 67.500 (64.644) Epoch: [3][6830/11272] Time 0.878 (0.833) Data 0.002 (0.002) Loss 2.8329 (2.7512) Prec@1 33.750 (34.281) Prec@5 63.125 (64.647) Epoch: [3][6840/11272] Time 0.754 (0.833) Data 0.002 (0.002) Loss 2.4207 (2.7512) Prec@1 35.000 (34.284) Prec@5 71.250 (64.648) Epoch: [3][6850/11272] Time 0.732 (0.833) Data 0.002 (0.002) Loss 2.8718 (2.7513) Prec@1 37.500 (34.283) Prec@5 56.875 (64.645) Epoch: [3][6860/11272] Time 0.892 (0.833) Data 0.002 (0.002) Loss 2.8344 (2.7514) Prec@1 31.875 (34.282) Prec@5 64.375 (64.644) Epoch: [3][6870/11272] Time 0.847 (0.832) Data 0.001 (0.002) Loss 2.7835 (2.7515) Prec@1 28.750 (34.282) Prec@5 62.500 (64.642) Epoch: [3][6880/11272] Time 0.771 (0.832) Data 0.002 (0.002) Loss 2.5620 (2.7514) Prec@1 40.000 (34.283) Prec@5 68.750 (64.642) Epoch: [3][6890/11272] Time 0.851 (0.832) Data 0.002 (0.002) Loss 2.8957 (2.7514) Prec@1 29.375 (34.283) Prec@5 60.625 (64.643) Epoch: [3][6900/11272] Time 0.929 (0.832) Data 0.003 (0.002) Loss 2.6051 (2.7512) Prec@1 38.125 (34.288) Prec@5 66.250 (64.648) Epoch: [3][6910/11272] Time 0.737 (0.832) Data 0.002 (0.002) Loss 2.6276 (2.7512) Prec@1 40.625 (34.288) Prec@5 68.750 (64.646) Epoch: [3][6920/11272] Time 0.813 (0.832) Data 0.002 (0.002) Loss 2.7264 (2.7512) Prec@1 35.000 (34.285) Prec@5 56.875 (64.645) Epoch: [3][6930/11272] Time 0.886 (0.832) Data 0.002 (0.002) Loss 2.6532 (2.7511) Prec@1 38.750 (34.286) Prec@5 66.875 (64.646) Epoch: [3][6940/11272] Time 0.927 (0.832) Data 0.002 (0.002) Loss 3.0580 (2.7511) Prec@1 25.000 (34.289) Prec@5 56.875 (64.646) Epoch: [3][6950/11272] Time 0.784 (0.832) Data 0.002 (0.002) Loss 2.9309 (2.7511) Prec@1 34.375 (34.288) Prec@5 60.625 (64.646) Epoch: [3][6960/11272] Time 0.765 (0.832) Data 0.001 (0.002) Loss 2.8206 (2.7512) Prec@1 29.375 (34.286) Prec@5 62.500 (64.645) Epoch: [3][6970/11272] Time 0.943 (0.832) Data 0.002 (0.002) Loss 2.5257 (2.7512) Prec@1 39.375 (34.287) Prec@5 68.750 (64.645) Epoch: [3][6980/11272] Time 0.851 (0.832) Data 0.002 (0.002) Loss 2.9050 (2.7512) Prec@1 33.750 (34.287) Prec@5 59.375 (64.644) Epoch: [3][6990/11272] Time 0.737 (0.832) Data 0.001 (0.002) Loss 2.7179 (2.7512) Prec@1 35.625 (34.291) Prec@5 63.125 (64.647) Epoch: [3][7000/11272] Time 0.739 (0.832) Data 0.002 (0.002) Loss 2.8631 (2.7512) Prec@1 33.125 (34.292) Prec@5 63.750 (64.647) Epoch: [3][7010/11272] Time 0.822 (0.832) Data 0.001 (0.002) Loss 2.6300 (2.7512) Prec@1 35.625 (34.292) Prec@5 66.250 (64.648) Epoch: [3][7020/11272] Time 0.768 (0.832) Data 0.003 (0.002) Loss 2.6994 (2.7511) Prec@1 33.750 (34.294) Prec@5 65.625 (64.651) Epoch: [3][7030/11272] Time 0.794 (0.832) Data 0.002 (0.002) Loss 2.5152 (2.7512) Prec@1 39.375 (34.295) Prec@5 64.375 (64.650) Epoch: [3][7040/11272] Time 0.864 (0.832) Data 0.001 (0.002) Loss 2.5266 (2.7511) Prec@1 39.375 (34.295) Prec@5 68.750 (64.649) Epoch: [3][7050/11272] Time 0.889 (0.832) Data 0.002 (0.002) Loss 2.6660 (2.7511) Prec@1 34.375 (34.295) Prec@5 65.625 (64.650) Epoch: [3][7060/11272] Time 0.732 (0.832) Data 0.001 (0.002) Loss 2.6097 (2.7509) Prec@1 37.500 (34.299) Prec@5 68.750 (64.652) Epoch: [3][7070/11272] Time 0.800 (0.832) Data 0.002 (0.002) Loss 2.7728 (2.7508) Prec@1 32.500 (34.301) Prec@5 64.375 (64.654) Epoch: [3][7080/11272] Time 0.932 (0.832) Data 0.001 (0.002) Loss 2.6990 (2.7507) Prec@1 30.625 (34.300) Prec@5 64.375 (64.655) Epoch: [3][7090/11272] Time 0.900 (0.832) Data 0.002 (0.002) Loss 2.7012 (2.7506) Prec@1 39.375 (34.302) Prec@5 63.750 (64.656) Epoch: [3][7100/11272] Time 0.769 (0.832) Data 0.002 (0.002) Loss 2.6592 (2.7507) Prec@1 40.625 (34.303) Prec@5 64.375 (64.653) Epoch: [3][7110/11272] Time 0.762 (0.832) Data 0.002 (0.002) Loss 2.7957 (2.7507) Prec@1 33.750 (34.303) Prec@5 63.750 (64.652) Epoch: [3][7120/11272] Time 0.871 (0.832) Data 0.002 (0.002) Loss 2.6820 (2.7508) Prec@1 35.000 (34.304) Prec@5 62.500 (64.650) Epoch: [3][7130/11272] Time 0.862 (0.832) Data 0.002 (0.002) Loss 2.6165 (2.7508) Prec@1 38.125 (34.306) Prec@5 67.500 (64.651) Epoch: [3][7140/11272] Time 0.749 (0.832) Data 0.002 (0.002) Loss 2.7866 (2.7507) Prec@1 32.500 (34.306) Prec@5 62.500 (64.654) Epoch: [3][7150/11272] Time 0.876 (0.832) Data 0.002 (0.002) Loss 2.6029 (2.7506) Prec@1 35.625 (34.307) Prec@5 71.875 (64.656) Epoch: [3][7160/11272] Time 0.873 (0.832) Data 0.002 (0.002) Loss 2.8210 (2.7506) Prec@1 28.750 (34.307) Prec@5 63.750 (64.656) Epoch: [3][7170/11272] Time 0.746 (0.832) Data 0.002 (0.002) Loss 2.6354 (2.7506) Prec@1 39.375 (34.308) Prec@5 68.125 (64.657) Epoch: [3][7180/11272] Time 0.768 (0.832) Data 0.002 (0.002) Loss 2.5318 (2.7505) Prec@1 45.000 (34.312) Prec@5 70.000 (64.660) Epoch: [3][7190/11272] Time 0.886 (0.832) Data 0.002 (0.002) Loss 2.6354 (2.7504) Prec@1 38.750 (34.313) Prec@5 64.375 (64.661) Epoch: [3][7200/11272] Time 0.905 (0.832) Data 0.003 (0.002) Loss 2.7316 (2.7503) Prec@1 33.125 (34.316) Prec@5 65.625 (64.662) Epoch: [3][7210/11272] Time 0.769 (0.832) Data 0.002 (0.002) Loss 2.6416 (2.7503) Prec@1 36.875 (34.315) Prec@5 67.500 (64.663) Epoch: [3][7220/11272] Time 0.737 (0.832) Data 0.002 (0.002) Loss 2.5727 (2.7503) Prec@1 38.125 (34.315) Prec@5 65.625 (64.664) Epoch: [3][7230/11272] Time 1.004 (0.832) Data 0.002 (0.002) Loss 2.3851 (2.7502) Prec@1 42.500 (34.318) Prec@5 73.125 (64.665) Epoch: [3][7240/11272] Time 0.908 (0.832) Data 0.002 (0.002) Loss 2.7764 (2.7502) Prec@1 36.250 (34.317) Prec@5 64.375 (64.665) Epoch: [3][7250/11272] Time 0.739 (0.832) Data 0.001 (0.002) Loss 2.7612 (2.7501) Prec@1 36.875 (34.318) Prec@5 61.250 (64.666) Epoch: [3][7260/11272] Time 0.755 (0.832) Data 0.002 (0.002) Loss 2.7576 (2.7502) Prec@1 33.750 (34.319) Prec@5 60.625 (64.665) Epoch: [3][7270/11272] Time 0.876 (0.832) Data 0.002 (0.002) Loss 2.9055 (2.7502) Prec@1 33.125 (34.317) Prec@5 60.625 (64.664) Epoch: [3][7280/11272] Time 0.765 (0.832) Data 0.005 (0.002) Loss 2.6316 (2.7502) Prec@1 34.375 (34.318) Prec@5 65.625 (64.666) Epoch: [3][7290/11272] Time 0.715 (0.832) Data 0.002 (0.002) Loss 2.6809 (2.7501) Prec@1 35.000 (34.318) Prec@5 68.750 (64.668) Epoch: [3][7300/11272] Time 0.911 (0.832) Data 0.002 (0.002) Loss 2.7855 (2.7501) Prec@1 35.000 (34.319) Prec@5 63.125 (64.667) Epoch: [3][7310/11272] Time 0.842 (0.832) Data 0.002 (0.002) Loss 2.8085 (2.7500) Prec@1 35.625 (34.320) Prec@5 63.125 (64.667) Epoch: [3][7320/11272] Time 0.786 (0.832) Data 0.002 (0.002) Loss 2.8932 (2.7500) Prec@1 28.125 (34.322) Prec@5 61.875 (64.668) Epoch: [3][7330/11272] Time 0.769 (0.832) Data 0.002 (0.002) Loss 2.9461 (2.7500) Prec@1 35.625 (34.321) Prec@5 66.875 (64.671) Epoch: [3][7340/11272] Time 0.885 (0.832) Data 0.001 (0.002) Loss 2.8131 (2.7500) Prec@1 34.375 (34.321) Prec@5 62.500 (64.671) Epoch: [3][7350/11272] Time 0.878 (0.832) Data 0.002 (0.002) Loss 2.8032 (2.7501) Prec@1 30.625 (34.319) Prec@5 66.875 (64.668) Epoch: [3][7360/11272] Time 0.775 (0.832) Data 0.001 (0.002) Loss 2.7408 (2.7501) Prec@1 38.125 (34.320) Prec@5 66.875 (64.669) Epoch: [3][7370/11272] Time 0.769 (0.832) Data 0.002 (0.002) Loss 3.2199 (2.7502) Prec@1 23.750 (34.319) Prec@5 55.625 (64.669) Epoch: [3][7380/11272] Time 0.922 (0.832) Data 0.002 (0.002) Loss 2.6784 (2.7501) Prec@1 33.750 (34.318) Prec@5 65.000 (64.670) Epoch: [3][7390/11272] Time 0.881 (0.832) Data 0.002 (0.002) Loss 3.0743 (2.7502) Prec@1 30.000 (34.316) Prec@5 56.875 (64.669) Epoch: [3][7400/11272] Time 0.745 (0.832) Data 0.002 (0.002) Loss 2.7540 (2.7502) Prec@1 32.500 (34.316) Prec@5 66.250 (64.669) Epoch: [3][7410/11272] Time 0.916 (0.832) Data 0.002 (0.002) Loss 2.6207 (2.7502) Prec@1 38.125 (34.318) Prec@5 65.000 (64.669) Epoch: [3][7420/11272] Time 0.888 (0.832) Data 0.001 (0.002) Loss 2.6190 (2.7502) Prec@1 38.750 (34.317) Prec@5 66.875 (64.669) Epoch: [3][7430/11272] Time 0.794 (0.832) Data 0.002 (0.002) Loss 2.7781 (2.7502) Prec@1 35.625 (34.317) Prec@5 63.750 (64.668) Epoch: [3][7440/11272] Time 0.765 (0.832) Data 0.002 (0.002) Loss 2.6944 (2.7501) Prec@1 36.250 (34.318) Prec@5 66.875 (64.669) Epoch: [3][7450/11272] Time 0.936 (0.832) Data 0.002 (0.002) Loss 2.7230 (2.7500) Prec@1 40.000 (34.320) Prec@5 61.875 (64.672) Epoch: [3][7460/11272] Time 0.928 (0.832) Data 0.002 (0.002) Loss 2.8564 (2.7500) Prec@1 31.875 (34.320) Prec@5 60.625 (64.671) Epoch: [3][7470/11272] Time 0.824 (0.832) Data 0.001 (0.002) Loss 2.7117 (2.7501) Prec@1 36.875 (34.319) Prec@5 70.000 (64.671) Epoch: [3][7480/11272] Time 0.751 (0.832) Data 0.002 (0.002) Loss 2.6746 (2.7501) Prec@1 40.000 (34.317) Prec@5 67.500 (64.670) Epoch: [3][7490/11272] Time 0.911 (0.832) Data 0.001 (0.002) Loss 2.7521 (2.7501) Prec@1 33.750 (34.318) Prec@5 65.000 (64.672) Epoch: [3][7500/11272] Time 0.953 (0.832) Data 0.003 (0.002) Loss 2.5892 (2.7500) Prec@1 39.375 (34.317) Prec@5 69.375 (64.672) Epoch: [3][7510/11272] Time 0.768 (0.832) Data 0.002 (0.002) Loss 2.8437 (2.7500) Prec@1 36.250 (34.318) Prec@5 61.875 (64.673) Epoch: [3][7520/11272] Time 0.775 (0.832) Data 0.002 (0.002) Loss 2.8266 (2.7499) Prec@1 35.000 (34.318) Prec@5 60.000 (64.673) Epoch: [3][7530/11272] Time 0.913 (0.832) Data 0.002 (0.002) Loss 2.6462 (2.7499) Prec@1 35.000 (34.318) Prec@5 66.250 (64.673) Epoch: [3][7540/11272] Time 0.903 (0.832) Data 0.002 (0.002) Loss 2.6935 (2.7499) Prec@1 32.500 (34.318) Prec@5 65.000 (64.672) Epoch: [3][7550/11272] Time 0.746 (0.832) Data 0.001 (0.002) Loss 2.5784 (2.7498) Prec@1 45.000 (34.321) Prec@5 66.250 (64.675) Epoch: [3][7560/11272] Time 0.859 (0.832) Data 0.001 (0.002) Loss 2.7739 (2.7498) Prec@1 28.750 (34.320) Prec@5 61.875 (64.674) Epoch: [3][7570/11272] Time 0.893 (0.832) Data 0.002 (0.002) Loss 2.6855 (2.7497) Prec@1 35.000 (34.322) Prec@5 65.625 (64.677) Epoch: [3][7580/11272] Time 0.749 (0.832) Data 0.001 (0.002) Loss 2.4180 (2.7497) Prec@1 41.250 (34.321) Prec@5 68.750 (64.678) Epoch: [3][7590/11272] Time 0.760 (0.832) Data 0.001 (0.002) Loss 2.5965 (2.7497) Prec@1 34.375 (34.320) Prec@5 65.625 (64.677) Epoch: [3][7600/11272] Time 0.891 (0.832) Data 0.002 (0.002) Loss 2.5871 (2.7497) Prec@1 33.125 (34.318) Prec@5 67.500 (64.676) Epoch: [3][7610/11272] Time 0.927 (0.832) Data 0.001 (0.002) Loss 2.5130 (2.7497) Prec@1 41.875 (34.319) Prec@5 66.875 (64.674) Epoch: [3][7620/11272] Time 0.735 (0.832) Data 0.001 (0.002) Loss 2.5545 (2.7497) Prec@1 31.875 (34.319) Prec@5 69.375 (64.676) Epoch: [3][7630/11272] Time 0.783 (0.832) Data 0.002 (0.002) Loss 2.4954 (2.7495) Prec@1 42.500 (34.325) Prec@5 70.000 (64.678) Epoch: [3][7640/11272] Time 0.934 (0.832) Data 0.002 (0.002) Loss 2.6140 (2.7494) Prec@1 33.750 (34.326) Prec@5 66.875 (64.679) Epoch: [3][7650/11272] Time 0.916 (0.832) Data 0.002 (0.002) Loss 2.7192 (2.7495) Prec@1 31.875 (34.324) Prec@5 65.000 (64.679) Epoch: [3][7660/11272] Time 0.756 (0.832) Data 0.002 (0.002) Loss 2.6265 (2.7493) Prec@1 39.375 (34.328) Prec@5 68.125 (64.682) Epoch: [3][7670/11272] Time 0.752 (0.832) Data 0.002 (0.002) Loss 2.7616 (2.7493) Prec@1 38.750 (34.328) Prec@5 63.125 (64.681) Epoch: [3][7680/11272] Time 0.838 (0.832) Data 0.001 (0.002) Loss 2.8909 (2.7493) Prec@1 35.000 (34.330) Prec@5 59.375 (64.680) Epoch: [3][7690/11272] Time 0.759 (0.832) Data 0.002 (0.002) Loss 2.3102 (2.7492) Prec@1 41.875 (34.331) Prec@5 71.875 (64.684) Epoch: [3][7700/11272] Time 0.759 (0.832) Data 0.001 (0.002) Loss 2.8042 (2.7492) Prec@1 29.375 (34.329) Prec@5 59.375 (64.683) Epoch: [3][7710/11272] Time 0.896 (0.832) Data 0.001 (0.002) Loss 2.8186 (2.7492) Prec@1 33.125 (34.329) Prec@5 67.500 (64.684) Epoch: [3][7720/11272] Time 0.866 (0.832) Data 0.002 (0.002) Loss 2.7577 (2.7492) Prec@1 30.000 (34.328) Prec@5 64.375 (64.685) Epoch: [3][7730/11272] Time 0.803 (0.832) Data 0.002 (0.002) Loss 2.7633 (2.7492) Prec@1 34.375 (34.327) Prec@5 64.375 (64.684) Epoch: [3][7740/11272] Time 0.744 (0.832) Data 0.002 (0.002) Loss 2.8166 (2.7493) Prec@1 33.125 (34.323) Prec@5 58.750 (64.682) Epoch: [3][7750/11272] Time 0.893 (0.832) Data 0.001 (0.002) Loss 2.6634 (2.7492) Prec@1 40.625 (34.325) Prec@5 65.625 (64.683) Epoch: [3][7760/11272] Time 0.869 (0.832) Data 0.002 (0.002) Loss 2.7014 (2.7492) Prec@1 35.000 (34.326) Prec@5 67.500 (64.683) Epoch: [3][7770/11272] Time 0.758 (0.832) Data 0.002 (0.002) Loss 2.7721 (2.7492) Prec@1 35.625 (34.326) Prec@5 61.875 (64.683) Epoch: [3][7780/11272] Time 0.787 (0.832) Data 0.002 (0.002) Loss 2.6483 (2.7492) Prec@1 38.125 (34.328) Prec@5 66.875 (64.682) Epoch: [3][7790/11272] Time 0.950 (0.832) Data 0.002 (0.002) Loss 2.8316 (2.7492) Prec@1 31.250 (34.327) Prec@5 61.875 (64.684) Epoch: [3][7800/11272] Time 0.885 (0.832) Data 0.001 (0.002) Loss 2.6372 (2.7492) Prec@1 36.875 (34.328) Prec@5 64.375 (64.682) Epoch: [3][7810/11272] Time 0.809 (0.832) Data 0.001 (0.002) Loss 2.7359 (2.7491) Prec@1 36.250 (34.329) Prec@5 63.750 (64.683) Epoch: [3][7820/11272] Time 0.890 (0.832) Data 0.002 (0.002) Loss 2.7194 (2.7490) Prec@1 33.125 (34.329) Prec@5 63.125 (64.685) Epoch: [3][7830/11272] Time 0.912 (0.832) Data 0.002 (0.002) Loss 2.9267 (2.7491) Prec@1 30.000 (34.329) Prec@5 63.125 (64.685) Epoch: [3][7840/11272] Time 0.757 (0.832) Data 0.002 (0.002) Loss 2.8145 (2.7490) Prec@1 33.750 (34.331) Prec@5 63.750 (64.686) Epoch: [3][7850/11272] Time 0.721 (0.832) Data 0.002 (0.002) Loss 2.6266 (2.7490) Prec@1 34.375 (34.330) Prec@5 68.125 (64.686) Epoch: [3][7860/11272] Time 0.874 (0.832) Data 0.002 (0.002) Loss 2.8844 (2.7490) Prec@1 35.000 (34.332) Prec@5 63.125 (64.686) Epoch: [3][7870/11272] Time 0.889 (0.832) Data 0.002 (0.002) Loss 2.6794 (2.7490) Prec@1 36.250 (34.334) Prec@5 64.375 (64.687) Epoch: [3][7880/11272] Time 0.744 (0.832) Data 0.001 (0.002) Loss 2.6590 (2.7489) Prec@1 38.125 (34.333) Prec@5 63.750 (64.687) Epoch: [3][7890/11272] Time 0.748 (0.832) Data 0.002 (0.002) Loss 2.9706 (2.7489) Prec@1 31.875 (34.334) Prec@5 58.125 (64.687) Epoch: [3][7900/11272] Time 0.862 (0.832) Data 0.001 (0.002) Loss 2.4194 (2.7488) Prec@1 38.125 (34.336) Prec@5 70.000 (64.688) Epoch: [3][7910/11272] Time 0.911 (0.832) Data 0.002 (0.002) Loss 2.8927 (2.7488) Prec@1 32.500 (34.338) Prec@5 65.625 (64.687) Epoch: [3][7920/11272] Time 0.755 (0.832) Data 0.002 (0.002) Loss 2.8219 (2.7488) Prec@1 33.125 (34.339) Prec@5 59.375 (64.684) Epoch: [3][7930/11272] Time 0.778 (0.832) Data 0.002 (0.002) Loss 2.6489 (2.7487) Prec@1 31.250 (34.341) Prec@5 69.375 (64.686) Epoch: [3][7940/11272] Time 0.873 (0.832) Data 0.002 (0.002) Loss 2.4542 (2.7488) Prec@1 40.625 (34.341) Prec@5 70.625 (64.684) Epoch: [3][7950/11272] Time 0.820 (0.832) Data 0.004 (0.002) Loss 2.6389 (2.7487) Prec@1 37.500 (34.343) Prec@5 63.750 (64.684) Epoch: [3][7960/11272] Time 0.756 (0.832) Data 0.002 (0.002) Loss 2.7721 (2.7486) Prec@1 32.500 (34.344) Prec@5 62.500 (64.686) Epoch: [3][7970/11272] Time 0.933 (0.832) Data 0.002 (0.002) Loss 2.3472 (2.7485) Prec@1 39.375 (34.344) Prec@5 71.250 (64.688) Epoch: [3][7980/11272] Time 0.905 (0.832) Data 0.002 (0.002) Loss 2.6787 (2.7484) Prec@1 36.250 (34.346) Prec@5 71.250 (64.690) Epoch: [3][7990/11272] Time 0.798 (0.832) Data 0.002 (0.002) Loss 2.5302 (2.7483) Prec@1 35.625 (34.348) Prec@5 70.625 (64.692) Epoch: [3][8000/11272] Time 0.736 (0.832) Data 0.001 (0.002) Loss 2.8047 (2.7483) Prec@1 33.750 (34.349) Prec@5 66.875 (64.693) Epoch: [3][8010/11272] Time 0.942 (0.832) Data 0.002 (0.002) Loss 2.5872 (2.7483) Prec@1 38.125 (34.349) Prec@5 66.875 (64.693) Epoch: [3][8020/11272] Time 0.869 (0.832) Data 0.002 (0.002) Loss 2.4733 (2.7483) Prec@1 40.625 (34.349) Prec@5 68.125 (64.693) Epoch: [3][8030/11272] Time 0.761 (0.832) Data 0.002 (0.002) Loss 2.8055 (2.7482) Prec@1 32.500 (34.351) Prec@5 62.500 (64.693) Epoch: [3][8040/11272] Time 0.735 (0.832) Data 0.001 (0.002) Loss 2.8079 (2.7482) Prec@1 28.750 (34.352) Prec@5 65.625 (64.693) Epoch: [3][8050/11272] Time 0.897 (0.832) Data 0.002 (0.002) Loss 2.5622 (2.7481) Prec@1 38.125 (34.351) Prec@5 68.125 (64.694) Epoch: [3][8060/11272] Time 0.926 (0.832) Data 0.002 (0.002) Loss 2.8722 (2.7480) Prec@1 28.125 (34.351) Prec@5 65.000 (64.694) Epoch: [3][8070/11272] Time 0.756 (0.832) Data 0.002 (0.002) Loss 2.5854 (2.7480) Prec@1 36.250 (34.350) Prec@5 68.750 (64.693) Epoch: [3][8080/11272] Time 0.916 (0.832) Data 0.002 (0.002) Loss 2.7392 (2.7480) Prec@1 32.500 (34.352) Prec@5 61.250 (64.692) Epoch: [3][8090/11272] Time 0.830 (0.832) Data 0.001 (0.002) Loss 2.6717 (2.7480) Prec@1 36.875 (34.354) Prec@5 66.250 (64.693) Epoch: [3][8100/11272] Time 0.788 (0.832) Data 0.003 (0.002) Loss 2.6956 (2.7480) Prec@1 33.750 (34.352) Prec@5 68.125 (64.693) Epoch: [3][8110/11272] Time 0.738 (0.832) Data 0.002 (0.002) Loss 2.8694 (2.7480) Prec@1 33.750 (34.352) Prec@5 64.375 (64.695) Epoch: [3][8120/11272] Time 0.914 (0.832) Data 0.002 (0.002) Loss 2.8463 (2.7480) Prec@1 31.250 (34.353) Prec@5 61.875 (64.695) Epoch: [3][8130/11272] Time 0.860 (0.832) Data 0.001 (0.002) Loss 2.9205 (2.7480) Prec@1 33.750 (34.351) Prec@5 63.125 (64.696) Epoch: [3][8140/11272] Time 0.775 (0.832) Data 0.002 (0.002) Loss 2.8066 (2.7480) Prec@1 31.250 (34.350) Prec@5 62.500 (64.694) Epoch: [3][8150/11272] Time 0.781 (0.832) Data 0.002 (0.002) Loss 2.8986 (2.7481) Prec@1 31.875 (34.349) Prec@5 61.875 (64.692) Epoch: [3][8160/11272] Time 0.860 (0.832) Data 0.002 (0.002) Loss 2.8383 (2.7481) Prec@1 30.625 (34.350) Prec@5 62.500 (64.693) Epoch: [3][8170/11272] Time 0.905 (0.832) Data 0.002 (0.002) Loss 2.7157 (2.7480) Prec@1 31.250 (34.350) Prec@5 62.500 (64.693) Epoch: [3][8180/11272] Time 0.748 (0.832) Data 0.002 (0.002) Loss 2.7383 (2.7480) Prec@1 36.875 (34.349) Prec@5 61.875 (64.692) Epoch: [3][8190/11272] Time 0.743 (0.832) Data 0.002 (0.002) Loss 2.8655 (2.7480) Prec@1 36.250 (34.350) Prec@5 63.750 (64.690) Epoch: [3][8200/11272] Time 0.948 (0.832) Data 0.002 (0.002) Loss 2.4559 (2.7479) Prec@1 36.250 (34.352) Prec@5 70.000 (64.690) Epoch: [3][8210/11272] Time 0.739 (0.832) Data 0.004 (0.002) Loss 2.8724 (2.7479) Prec@1 33.750 (34.352) Prec@5 63.125 (64.690) Epoch: [3][8220/11272] Time 0.760 (0.832) Data 0.002 (0.002) Loss 2.6380 (2.7478) Prec@1 41.250 (34.353) Prec@5 66.250 (64.691) Epoch: [3][8230/11272] Time 0.885 (0.832) Data 0.002 (0.002) Loss 2.8803 (2.7478) Prec@1 32.500 (34.354) Prec@5 60.625 (64.691) Epoch: [3][8240/11272] Time 0.864 (0.832) Data 0.002 (0.002) Loss 2.7971 (2.7478) Prec@1 31.250 (34.352) Prec@5 63.125 (64.691) Epoch: [3][8250/11272] Time 0.770 (0.832) Data 0.002 (0.002) Loss 2.9170 (2.7478) Prec@1 32.500 (34.351) Prec@5 60.625 (64.691) Epoch: [3][8260/11272] Time 0.750 (0.832) Data 0.002 (0.002) Loss 2.7310 (2.7477) Prec@1 33.125 (34.352) Prec@5 67.500 (64.693) Epoch: [3][8270/11272] Time 0.939 (0.832) Data 0.002 (0.002) Loss 2.7963 (2.7477) Prec@1 37.500 (34.352) Prec@5 61.250 (64.694) Epoch: [3][8280/11272] Time 0.821 (0.832) Data 0.001 (0.002) Loss 2.8445 (2.7478) Prec@1 27.500 (34.350) Prec@5 64.375 (64.692) Epoch: [3][8290/11272] Time 0.746 (0.832) Data 0.002 (0.002) Loss 2.7871 (2.7478) Prec@1 31.875 (34.351) Prec@5 65.000 (64.693) Epoch: [3][8300/11272] Time 0.743 (0.832) Data 0.002 (0.002) Loss 2.5748 (2.7477) Prec@1 36.250 (34.352) Prec@5 66.250 (64.694) Epoch: [3][8310/11272] Time 0.896 (0.832) Data 0.002 (0.002) Loss 2.6327 (2.7477) Prec@1 36.875 (34.353) Prec@5 66.875 (64.694) Epoch: [3][8320/11272] Time 0.857 (0.832) Data 0.002 (0.002) Loss 2.7103 (2.7478) Prec@1 40.625 (34.351) Prec@5 65.000 (64.691) Epoch: [3][8330/11272] Time 0.781 (0.832) Data 0.002 (0.002) Loss 2.6781 (2.7477) Prec@1 33.125 (34.351) Prec@5 64.375 (64.691) Epoch: [3][8340/11272] Time 0.860 (0.832) Data 0.001 (0.002) Loss 2.7801 (2.7477) Prec@1 36.875 (34.350) Prec@5 65.625 (64.690) Epoch: [3][8350/11272] Time 1.009 (0.832) Data 0.002 (0.002) Loss 2.6573 (2.7477) Prec@1 35.000 (34.352) Prec@5 63.750 (64.691) Epoch: [3][8360/11272] Time 0.737 (0.832) Data 0.001 (0.002) Loss 2.5567 (2.7477) Prec@1 33.750 (34.350) Prec@5 73.125 (64.692) Epoch: [3][8370/11272] Time 0.744 (0.832) Data 0.002 (0.002) Loss 2.7330 (2.7477) Prec@1 35.000 (34.350) Prec@5 66.250 (64.693) Epoch: [3][8380/11272] Time 0.867 (0.832) Data 0.001 (0.002) Loss 2.6765 (2.7477) Prec@1 31.250 (34.351) Prec@5 69.375 (64.695) Epoch: [3][8390/11272] Time 0.880 (0.832) Data 0.002 (0.002) Loss 2.3677 (2.7477) Prec@1 44.375 (34.352) Prec@5 69.375 (64.694) Epoch: [3][8400/11272] Time 0.755 (0.832) Data 0.002 (0.002) Loss 2.6634 (2.7476) Prec@1 36.250 (34.351) Prec@5 65.625 (64.696) Epoch: [3][8410/11272] Time 0.741 (0.832) Data 0.002 (0.002) Loss 2.7358 (2.7477) Prec@1 36.875 (34.351) Prec@5 62.500 (64.694) Epoch: [3][8420/11272] Time 0.908 (0.832) Data 0.002 (0.002) Loss 2.4996 (2.7477) Prec@1 38.750 (34.352) Prec@5 70.000 (64.697) Epoch: [3][8430/11272] Time 0.906 (0.832) Data 0.002 (0.002) Loss 2.8603 (2.7477) Prec@1 32.500 (34.352) Prec@5 64.375 (64.697) Epoch: [3][8440/11272] Time 0.753 (0.832) Data 0.002 (0.002) Loss 2.7223 (2.7478) Prec@1 35.625 (34.353) Prec@5 66.875 (64.695) Epoch: [3][8450/11272] Time 0.776 (0.832) Data 0.002 (0.002) Loss 2.7759 (2.7478) Prec@1 30.000 (34.352) Prec@5 65.625 (64.694) Epoch: [3][8460/11272] Time 0.914 (0.832) Data 0.002 (0.002) Loss 2.7614 (2.7477) Prec@1 33.750 (34.351) Prec@5 65.000 (64.695) Epoch: [3][8470/11272] Time 0.906 (0.832) Data 0.002 (0.002) Loss 2.5552 (2.7476) Prec@1 35.625 (34.354) Prec@5 70.625 (64.696) Epoch: [3][8480/11272] Time 0.750 (0.832) Data 0.002 (0.002) Loss 2.8184 (2.7477) Prec@1 35.000 (34.353) Prec@5 63.125 (64.695) Epoch: [3][8490/11272] Time 0.907 (0.832) Data 0.002 (0.002) Loss 2.7358 (2.7476) Prec@1 35.000 (34.354) Prec@5 62.500 (64.696) Epoch: [3][8500/11272] Time 0.938 (0.832) Data 0.002 (0.002) Loss 2.8017 (2.7476) Prec@1 31.875 (34.353) Prec@5 60.625 (64.698) Epoch: [3][8510/11272] Time 0.742 (0.832) Data 0.002 (0.002) Loss 2.7472 (2.7477) Prec@1 36.250 (34.351) Prec@5 62.500 (64.695) Epoch: [3][8520/11272] Time 0.723 (0.832) Data 0.002 (0.002) Loss 2.6276 (2.7477) Prec@1 40.625 (34.352) Prec@5 68.125 (64.696) Epoch: [3][8530/11272] Time 0.891 (0.832) Data 0.002 (0.002) Loss 2.5820 (2.7476) Prec@1 40.625 (34.355) Prec@5 66.875 (64.697) Epoch: [3][8540/11272] Time 0.867 (0.832) Data 0.002 (0.002) Loss 2.6747 (2.7476) Prec@1 36.250 (34.356) Prec@5 66.250 (64.697) Epoch: [3][8550/11272] Time 0.786 (0.832) Data 0.002 (0.002) Loss 2.9096 (2.7475) Prec@1 30.000 (34.357) Prec@5 59.375 (64.696) Epoch: [3][8560/11272] Time 0.748 (0.832) Data 0.002 (0.002) Loss 2.8536 (2.7476) Prec@1 35.000 (34.356) Prec@5 64.375 (64.694) Epoch: [3][8570/11272] Time 0.872 (0.832) Data 0.002 (0.002) Loss 3.0229 (2.7475) Prec@1 31.250 (34.357) Prec@5 58.750 (64.697) Epoch: [3][8580/11272] Time 0.879 (0.832) Data 0.002 (0.002) Loss 2.5086 (2.7474) Prec@1 38.750 (34.357) Prec@5 67.500 (64.699) Epoch: [3][8590/11272] Time 0.747 (0.832) Data 0.002 (0.002) Loss 2.9314 (2.7475) Prec@1 27.500 (34.354) Prec@5 59.375 (64.698) Epoch: [3][8600/11272] Time 0.743 (0.832) Data 0.001 (0.002) Loss 2.6883 (2.7475) Prec@1 32.500 (34.354) Prec@5 68.125 (64.697) Epoch: [3][8610/11272] Time 0.858 (0.832) Data 0.002 (0.002) Loss 2.8200 (2.7476) Prec@1 29.375 (34.353) Prec@5 65.625 (64.696) Epoch: [3][8620/11272] Time 0.806 (0.832) Data 0.001 (0.002) Loss 2.7626 (2.7476) Prec@1 32.500 (34.354) Prec@5 65.625 (64.697) Epoch: [3][8630/11272] Time 0.821 (0.832) Data 0.002 (0.002) Loss 2.6705 (2.7475) Prec@1 35.000 (34.355) Prec@5 66.875 (64.699) Epoch: [3][8640/11272] Time 0.924 (0.832) Data 0.002 (0.002) Loss 2.5475 (2.7474) Prec@1 39.375 (34.357) Prec@5 66.250 (64.700) Epoch: [3][8650/11272] Time 0.908 (0.832) Data 0.002 (0.002) Loss 2.6499 (2.7474) Prec@1 38.125 (34.357) Prec@5 65.625 (64.700) Epoch: [3][8660/11272] Time 0.756 (0.832) Data 0.001 (0.002) Loss 2.7837 (2.7475) Prec@1 36.250 (34.358) Prec@5 61.875 (64.699) Epoch: [3][8670/11272] Time 0.756 (0.832) Data 0.002 (0.002) Loss 2.6686 (2.7475) Prec@1 35.000 (34.358) Prec@5 67.500 (64.701) Epoch: [3][8680/11272] Time 0.853 (0.832) Data 0.002 (0.002) Loss 3.0995 (2.7474) Prec@1 30.625 (34.359) Prec@5 60.000 (64.703) Epoch: [3][8690/11272] Time 0.900 (0.832) Data 0.002 (0.002) Loss 3.0418 (2.7474) Prec@1 32.500 (34.357) Prec@5 60.000 (64.703) Epoch: [3][8700/11272] Time 0.763 (0.832) Data 0.002 (0.002) Loss 2.8289 (2.7474) Prec@1 30.000 (34.357) Prec@5 64.375 (64.704) Epoch: [3][8710/11272] Time 0.798 (0.832) Data 0.002 (0.002) Loss 2.6998 (2.7474) Prec@1 39.375 (34.359) Prec@5 66.875 (64.704) Epoch: [3][8720/11272] Time 0.896 (0.832) Data 0.002 (0.002) Loss 2.9820 (2.7474) Prec@1 31.250 (34.358) Prec@5 63.750 (64.702) Epoch: [3][8730/11272] Time 0.899 (0.832) Data 0.002 (0.002) Loss 2.6875 (2.7475) Prec@1 33.750 (34.356) Prec@5 68.750 (64.702) Epoch: [3][8740/11272] Time 0.782 (0.832) Data 0.003 (0.002) Loss 2.5711 (2.7475) Prec@1 38.750 (34.358) Prec@5 66.875 (64.702) Epoch: [3][8750/11272] Time 0.885 (0.832) Data 0.002 (0.002) Loss 2.9379 (2.7475) Prec@1 30.000 (34.357) Prec@5 60.000 (64.702) Epoch: [3][8760/11272] Time 0.923 (0.832) Data 0.002 (0.002) Loss 2.7544 (2.7474) Prec@1 37.500 (34.358) Prec@5 65.625 (64.702) Epoch: [3][8770/11272] Time 0.728 (0.832) Data 0.001 (0.002) Loss 2.8469 (2.7474) Prec@1 32.500 (34.359) Prec@5 60.625 (64.704) Epoch: [3][8780/11272] Time 0.792 (0.832) Data 0.002 (0.002) Loss 2.8785 (2.7473) Prec@1 33.750 (34.357) Prec@5 63.125 (64.706) Epoch: [3][8790/11272] Time 0.934 (0.832) Data 0.002 (0.002) Loss 3.0122 (2.7474) Prec@1 26.875 (34.357) Prec@5 61.250 (64.706) Epoch: [3][8800/11272] Time 0.872 (0.832) Data 0.004 (0.002) Loss 2.9314 (2.7474) Prec@1 35.625 (34.358) Prec@5 58.125 (64.704) Epoch: [3][8810/11272] Time 0.739 (0.832) Data 0.002 (0.002) Loss 2.6442 (2.7473) Prec@1 33.750 (34.358) Prec@5 70.000 (64.705) Epoch: [3][8820/11272] Time 0.792 (0.832) Data 0.002 (0.002) Loss 2.6389 (2.7473) Prec@1 37.500 (34.359) Prec@5 63.125 (64.706) Epoch: [3][8830/11272] Time 0.939 (0.832) Data 0.002 (0.002) Loss 2.5067 (2.7472) Prec@1 40.000 (34.362) Prec@5 69.375 (64.708) Epoch: [3][8840/11272] Time 0.828 (0.832) Data 0.002 (0.002) Loss 2.7845 (2.7472) Prec@1 32.500 (34.363) Prec@5 64.375 (64.711) Epoch: [3][8850/11272] Time 0.750 (0.832) Data 0.001 (0.002) Loss 2.5815 (2.7471) Prec@1 41.250 (34.365) Prec@5 70.000 (64.713) Epoch: [3][8860/11272] Time 0.758 (0.832) Data 0.002 (0.002) Loss 2.9330 (2.7472) Prec@1 28.125 (34.363) Prec@5 60.625 (64.714) Epoch: [3][8870/11272] Time 0.898 (0.832) Data 0.002 (0.002) Loss 2.6990 (2.7471) Prec@1 38.125 (34.364) Prec@5 63.125 (64.715) Epoch: [3][8880/11272] Time 0.823 (0.832) Data 0.003 (0.002) Loss 2.6226 (2.7471) Prec@1 36.250 (34.367) Prec@5 70.625 (64.717) Epoch: [3][8890/11272] Time 0.745 (0.832) Data 0.001 (0.002) Loss 2.9661 (2.7470) Prec@1 29.375 (34.367) Prec@5 58.125 (64.716) Epoch: [3][8900/11272] Time 0.897 (0.832) Data 0.002 (0.002) Loss 2.7872 (2.7470) Prec@1 36.875 (34.368) Prec@5 67.500 (64.717) Epoch: [3][8910/11272] Time 0.880 (0.832) Data 0.002 (0.002) Loss 2.7298 (2.7470) Prec@1 36.250 (34.368) Prec@5 64.375 (64.716) Epoch: [3][8920/11272] Time 0.739 (0.832) Data 0.002 (0.002) Loss 2.9560 (2.7470) Prec@1 26.250 (34.368) Prec@5 58.125 (64.717) Epoch: [3][8930/11272] Time 0.735 (0.832) Data 0.002 (0.002) Loss 2.7357 (2.7470) Prec@1 30.625 (34.369) Prec@5 65.000 (64.719) Epoch: [3][8940/11272] Time 0.977 (0.832) Data 0.002 (0.002) Loss 2.7800 (2.7470) Prec@1 35.625 (34.369) Prec@5 66.875 (64.719) Epoch: [3][8950/11272] Time 0.859 (0.832) Data 0.001 (0.002) Loss 2.7426 (2.7469) Prec@1 36.875 (34.370) Prec@5 58.750 (64.720) Epoch: [3][8960/11272] Time 0.746 (0.832) Data 0.002 (0.002) Loss 2.8311 (2.7469) Prec@1 31.250 (34.369) Prec@5 63.125 (64.719) Epoch: [3][8970/11272] Time 0.743 (0.832) Data 0.002 (0.002) Loss 2.9844 (2.7470) Prec@1 32.500 (34.368) Prec@5 58.750 (64.719) Epoch: [3][8980/11272] Time 0.911 (0.832) Data 0.002 (0.002) Loss 2.5120 (2.7470) Prec@1 38.125 (34.369) Prec@5 68.125 (64.720) Epoch: [3][8990/11272] Time 0.857 (0.832) Data 0.002 (0.002) Loss 2.7680 (2.7469) Prec@1 32.500 (34.369) Prec@5 64.375 (64.721) Epoch: [3][9000/11272] Time 0.747 (0.832) Data 0.001 (0.002) Loss 2.6828 (2.7469) Prec@1 33.750 (34.367) Prec@5 62.500 (64.721) Epoch: [3][9010/11272] Time 0.954 (0.832) Data 0.002 (0.002) Loss 2.5563 (2.7469) Prec@1 41.875 (34.366) Prec@5 68.750 (64.722) Epoch: [3][9020/11272] Time 0.896 (0.832) Data 0.002 (0.002) Loss 2.8493 (2.7469) Prec@1 35.625 (34.367) Prec@5 61.875 (64.722) Epoch: [3][9030/11272] Time 0.752 (0.832) Data 0.001 (0.002) Loss 2.6876 (2.7468) Prec@1 37.500 (34.367) Prec@5 65.625 (64.722) Epoch: [3][9040/11272] Time 0.795 (0.832) Data 0.004 (0.002) Loss 2.9058 (2.7469) Prec@1 28.750 (34.368) Prec@5 58.125 (64.721) Epoch: [3][9050/11272] Time 0.876 (0.832) Data 0.002 (0.002) Loss 2.6415 (2.7468) Prec@1 36.250 (34.366) Prec@5 66.250 (64.722) Epoch: [3][9060/11272] Time 0.890 (0.832) Data 0.002 (0.002) Loss 2.6466 (2.7468) Prec@1 39.375 (34.367) Prec@5 63.125 (64.722) Epoch: [3][9070/11272] Time 0.800 (0.832) Data 0.002 (0.002) Loss 2.6991 (2.7468) Prec@1 34.375 (34.368) Prec@5 65.625 (64.725) Epoch: [3][9080/11272] Time 0.746 (0.832) Data 0.002 (0.002) Loss 2.5827 (2.7467) Prec@1 35.000 (34.369) Prec@5 69.375 (64.727) Epoch: [3][9090/11272] Time 0.893 (0.832) Data 0.002 (0.002) Loss 2.7923 (2.7467) Prec@1 38.125 (34.370) Prec@5 64.375 (64.727) Epoch: [3][9100/11272] Time 0.878 (0.832) Data 0.002 (0.002) Loss 2.9608 (2.7468) Prec@1 33.125 (34.369) Prec@5 60.625 (64.726) Epoch: [3][9110/11272] Time 0.747 (0.832) Data 0.002 (0.002) Loss 2.9871 (2.7468) Prec@1 35.625 (34.370) Prec@5 61.875 (64.726) Epoch: [3][9120/11272] Time 0.763 (0.832) Data 0.002 (0.002) Loss 2.5054 (2.7467) Prec@1 33.750 (34.370) Prec@5 73.750 (64.727) Epoch: [3][9130/11272] Time 0.916 (0.832) Data 0.001 (0.002) Loss 2.7365 (2.7467) Prec@1 36.250 (34.370) Prec@5 63.125 (64.725) Epoch: [3][9140/11272] Time 0.750 (0.832) Data 0.005 (0.002) Loss 2.8495 (2.7467) Prec@1 35.000 (34.370) Prec@5 63.750 (64.725) Epoch: [3][9150/11272] Time 0.765 (0.832) Data 0.002 (0.002) Loss 2.6886 (2.7467) Prec@1 37.500 (34.370) Prec@5 66.875 (64.726) Epoch: [3][9160/11272] Time 0.897 (0.832) Data 0.002 (0.002) Loss 2.6418 (2.7467) Prec@1 36.875 (34.369) Prec@5 65.625 (64.726) Epoch: [3][9170/11272] Time 0.879 (0.832) Data 0.002 (0.002) Loss 2.3975 (2.7467) Prec@1 38.125 (34.371) Prec@5 71.875 (64.726) Epoch: [3][9180/11272] Time 0.743 (0.832) Data 0.002 (0.002) Loss 2.6125 (2.7467) Prec@1 35.000 (34.372) Prec@5 66.250 (64.727) Epoch: [3][9190/11272] Time 0.731 (0.832) Data 0.002 (0.002) Loss 2.6531 (2.7467) Prec@1 36.250 (34.372) Prec@5 65.000 (64.725) Epoch: [3][9200/11272] Time 0.881 (0.832) Data 0.001 (0.002) Loss 2.4323 (2.7467) Prec@1 38.125 (34.372) Prec@5 68.750 (64.725) Epoch: [3][9210/11272] Time 0.888 (0.832) Data 0.002 (0.002) Loss 2.5556 (2.7467) Prec@1 35.625 (34.370) Prec@5 66.875 (64.724) Epoch: [3][9220/11272] Time 0.777 (0.832) Data 0.002 (0.002) Loss 2.6232 (2.7467) Prec@1 33.750 (34.371) Prec@5 68.750 (64.724) Epoch: [3][9230/11272] Time 0.737 (0.832) Data 0.002 (0.002) Loss 2.7354 (2.7468) Prec@1 35.000 (34.369) Prec@5 62.500 (64.722) Epoch: [3][9240/11272] Time 0.955 (0.832) Data 0.050 (0.002) Loss 2.8934 (2.7468) Prec@1 26.875 (34.368) Prec@5 63.750 (64.721) Epoch: [3][9250/11272] Time 0.841 (0.832) Data 0.002 (0.002) Loss 2.7923 (2.7468) Prec@1 30.625 (34.368) Prec@5 64.375 (64.719) Epoch: [3][9260/11272] Time 0.835 (0.832) Data 0.002 (0.002) Loss 2.4480 (2.7467) Prec@1 40.000 (34.369) Prec@5 71.875 (64.722) Epoch: [3][9270/11272] Time 0.908 (0.832) Data 0.002 (0.002) Loss 2.8649 (2.7467) Prec@1 28.750 (34.369) Prec@5 61.875 (64.721) Epoch: [3][9280/11272] Time 0.950 (0.832) Data 0.002 (0.002) Loss 2.6282 (2.7468) Prec@1 36.875 (34.369) Prec@5 65.000 (64.719) Epoch: [3][9290/11272] Time 0.745 (0.832) Data 0.002 (0.002) Loss 2.8252 (2.7468) Prec@1 33.750 (34.368) Prec@5 62.500 (64.719) Epoch: [3][9300/11272] Time 0.742 (0.832) Data 0.001 (0.002) Loss 2.9091 (2.7468) Prec@1 30.625 (34.369) Prec@5 63.125 (64.716) Epoch: [3][9310/11272] Time 0.850 (0.832) Data 0.001 (0.002) Loss 2.5727 (2.7469) Prec@1 36.875 (34.368) Prec@5 70.625 (64.716) Epoch: [3][9320/11272] Time 0.956 (0.832) Data 0.002 (0.002) Loss 2.7535 (2.7469) Prec@1 35.625 (34.368) Prec@5 63.750 (64.715) Epoch: [3][9330/11272] Time 0.794 (0.832) Data 0.002 (0.002) Loss 2.8344 (2.7469) Prec@1 33.125 (34.368) Prec@5 61.250 (64.715) Epoch: [3][9340/11272] Time 0.786 (0.832) Data 0.002 (0.002) Loss 2.3140 (2.7469) Prec@1 40.000 (34.368) Prec@5 75.000 (64.713) Epoch: [3][9350/11272] Time 0.957 (0.832) Data 0.002 (0.002) Loss 2.6992 (2.7469) Prec@1 36.250 (34.370) Prec@5 68.750 (64.716) Epoch: [3][9360/11272] Time 0.961 (0.832) Data 0.002 (0.002) Loss 2.6271 (2.7469) Prec@1 40.000 (34.369) Prec@5 66.875 (64.716) Epoch: [3][9370/11272] Time 0.745 (0.832) Data 0.002 (0.002) Loss 2.7181 (2.7469) Prec@1 35.000 (34.370) Prec@5 66.250 (64.716) Epoch: [3][9380/11272] Time 0.770 (0.832) Data 0.001 (0.002) Loss 2.6772 (2.7468) Prec@1 32.500 (34.371) Prec@5 66.875 (64.718) Epoch: [3][9390/11272] Time 0.894 (0.832) Data 0.002 (0.002) Loss 2.6858 (2.7468) Prec@1 38.125 (34.369) Prec@5 65.000 (64.717) Epoch: [3][9400/11272] Time 0.901 (0.832) Data 0.002 (0.002) Loss 3.0881 (2.7469) Prec@1 27.500 (34.367) Prec@5 60.625 (64.715) Epoch: [3][9410/11272] Time 0.761 (0.832) Data 0.001 (0.002) Loss 2.7736 (2.7471) Prec@1 31.875 (34.364) Prec@5 60.000 (64.710) Epoch: [3][9420/11272] Time 0.973 (0.832) Data 0.002 (0.002) Loss 2.6054 (2.7470) Prec@1 39.375 (34.365) Prec@5 66.875 (64.713) Epoch: [3][9430/11272] Time 0.865 (0.832) Data 0.001 (0.002) Loss 2.9243 (2.7470) Prec@1 33.125 (34.365) Prec@5 58.750 (64.712) Epoch: [3][9440/11272] Time 0.741 (0.832) Data 0.002 (0.002) Loss 2.4641 (2.7469) Prec@1 36.250 (34.365) Prec@5 69.375 (64.713) Epoch: [3][9450/11272] Time 0.787 (0.832) Data 0.002 (0.002) Loss 2.7815 (2.7469) Prec@1 34.375 (34.364) Prec@5 65.000 (64.714) Epoch: [3][9460/11272] Time 0.949 (0.832) Data 0.001 (0.002) Loss 2.6843 (2.7469) Prec@1 35.625 (34.365) Prec@5 61.875 (64.713) Epoch: [3][9470/11272] Time 0.905 (0.832) Data 0.002 (0.002) Loss 2.7397 (2.7469) Prec@1 33.125 (34.363) Prec@5 60.625 (64.713) Epoch: [3][9480/11272] Time 0.811 (0.832) Data 0.002 (0.002) Loss 2.6057 (2.7470) Prec@1 32.500 (34.362) Prec@5 70.625 (64.711) Epoch: [3][9490/11272] Time 0.746 (0.832) Data 0.001 (0.002) Loss 2.7961 (2.7470) Prec@1 35.000 (34.362) Prec@5 63.125 (64.712) Epoch: [3][9500/11272] Time 0.885 (0.832) Data 0.002 (0.002) Loss 2.8268 (2.7469) Prec@1 31.875 (34.362) Prec@5 62.500 (64.713) Epoch: [3][9510/11272] Time 0.888 (0.832) Data 0.001 (0.002) Loss 2.5837 (2.7469) Prec@1 40.625 (34.363) Prec@5 66.250 (64.714) Epoch: [3][9520/11272] Time 0.760 (0.832) Data 0.002 (0.002) Loss 2.7847 (2.7467) Prec@1 30.625 (34.366) Prec@5 69.375 (64.718) Epoch: [3][9530/11272] Time 0.740 (0.832) Data 0.001 (0.002) Loss 2.7817 (2.7468) Prec@1 31.875 (34.365) Prec@5 63.125 (64.717) Epoch: [3][9540/11272] Time 0.937 (0.832) Data 0.002 (0.002) Loss 2.7597 (2.7467) Prec@1 35.000 (34.366) Prec@5 63.750 (64.718) Epoch: [3][9550/11272] Time 0.822 (0.832) Data 0.002 (0.002) Loss 2.6750 (2.7467) Prec@1 31.250 (34.365) Prec@5 71.250 (64.720) Epoch: [3][9560/11272] Time 0.753 (0.832) Data 0.002 (0.002) Loss 2.7403 (2.7466) Prec@1 31.875 (34.365) Prec@5 61.250 (64.720) Epoch: [3][9570/11272] Time 0.865 (0.832) Data 0.001 (0.002) Loss 2.9941 (2.7466) Prec@1 26.250 (34.363) Prec@5 61.875 (64.720) Epoch: [3][9580/11272] Time 0.870 (0.832) Data 0.002 (0.002) Loss 2.7261 (2.7466) Prec@1 32.500 (34.364) Prec@5 61.250 (64.721) Epoch: [3][9590/11272] Time 0.775 (0.832) Data 0.001 (0.002) Loss 2.7230 (2.7466) Prec@1 30.625 (34.362) Prec@5 61.250 (64.720) Epoch: [3][9600/11272] Time 0.753 (0.832) Data 0.002 (0.002) Loss 2.7944 (2.7466) Prec@1 30.000 (34.362) Prec@5 62.500 (64.720) Epoch: [3][9610/11272] Time 0.943 (0.832) Data 0.002 (0.002) Loss 2.6548 (2.7466) Prec@1 38.125 (34.362) Prec@5 65.625 (64.719) Epoch: [3][9620/11272] Time 0.930 (0.832) Data 0.002 (0.002) Loss 2.5955 (2.7465) Prec@1 35.625 (34.364) Prec@5 67.500 (64.721) Epoch: [3][9630/11272] Time 0.795 (0.832) Data 0.002 (0.002) Loss 2.9325 (2.7466) Prec@1 32.500 (34.363) Prec@5 59.375 (64.720) Epoch: [3][9640/11272] Time 0.742 (0.832) Data 0.002 (0.002) Loss 2.7066 (2.7465) Prec@1 33.125 (34.364) Prec@5 69.375 (64.723) Epoch: [3][9650/11272] Time 0.896 (0.832) Data 0.002 (0.002) Loss 2.8146 (2.7465) Prec@1 30.000 (34.363) Prec@5 66.250 (64.723) Epoch: [3][9660/11272] Time 0.898 (0.832) Data 0.002 (0.002) Loss 2.7312 (2.7464) Prec@1 32.500 (34.365) Prec@5 67.500 (64.726) Epoch: [3][9670/11272] Time 0.767 (0.832) Data 0.002 (0.002) Loss 2.6765 (2.7464) Prec@1 34.375 (34.363) Prec@5 67.500 (64.727) Epoch: [3][9680/11272] Time 0.907 (0.832) Data 0.002 (0.002) Loss 2.6251 (2.7464) Prec@1 36.875 (34.363) Prec@5 68.125 (64.727) Epoch: [3][9690/11272] Time 0.929 (0.832) Data 0.002 (0.002) Loss 2.4332 (2.7465) Prec@1 38.750 (34.362) Prec@5 70.625 (64.726) Epoch: [3][9700/11272] Time 0.741 (0.832) Data 0.002 (0.002) Loss 2.7715 (2.7464) Prec@1 27.500 (34.362) Prec@5 66.875 (64.727) Epoch: [3][9710/11272] Time 0.758 (0.832) Data 0.002 (0.002) Loss 2.7491 (2.7465) Prec@1 35.000 (34.361) Prec@5 62.500 (64.726) Epoch: [3][9720/11272] Time 0.937 (0.832) Data 0.002 (0.002) Loss 2.8004 (2.7464) Prec@1 35.000 (34.361) Prec@5 63.125 (64.727) Epoch: [3][9730/11272] Time 0.972 (0.832) Data 0.002 (0.002) Loss 2.4631 (2.7463) Prec@1 37.500 (34.364) Prec@5 70.625 (64.729) Epoch: [3][9740/11272] Time 0.761 (0.832) Data 0.001 (0.002) Loss 2.9060 (2.7463) Prec@1 34.375 (34.365) Prec@5 62.500 (64.731) Epoch: [3][9750/11272] Time 0.758 (0.832) Data 0.002 (0.002) Loss 2.5869 (2.7462) Prec@1 37.500 (34.367) Prec@5 66.250 (64.731) Epoch: [3][9760/11272] Time 0.861 (0.832) Data 0.001 (0.002) Loss 2.7473 (2.7461) Prec@1 36.875 (34.367) Prec@5 65.625 (64.732) Epoch: [3][9770/11272] Time 0.846 (0.832) Data 0.001 (0.002) Loss 2.5688 (2.7461) Prec@1 35.625 (34.367) Prec@5 69.375 (64.734) Epoch: [3][9780/11272] Time 0.783 (0.832) Data 0.001 (0.002) Loss 2.8500 (2.7460) Prec@1 28.125 (34.367) Prec@5 62.500 (64.737) Epoch: [3][9790/11272] Time 0.785 (0.832) Data 0.002 (0.002) Loss 2.5531 (2.7460) Prec@1 40.000 (34.367) Prec@5 66.875 (64.737) Epoch: [3][9800/11272] Time 0.869 (0.832) Data 0.002 (0.002) Loss 2.6063 (2.7461) Prec@1 40.000 (34.366) Prec@5 68.750 (64.736) Epoch: [3][9810/11272] Time 0.736 (0.832) Data 0.003 (0.002) Loss 2.6777 (2.7460) Prec@1 33.125 (34.367) Prec@5 66.875 (64.737) Epoch: [3][9820/11272] Time 0.716 (0.832) Data 0.001 (0.002) Loss 2.7025 (2.7460) Prec@1 38.125 (34.365) Prec@5 68.750 (64.737) Epoch: [3][9830/11272] Time 0.897 (0.832) Data 0.002 (0.002) Loss 2.6272 (2.7461) Prec@1 39.375 (34.364) Prec@5 68.125 (64.736) Epoch: [3][9840/11272] Time 0.834 (0.832) Data 0.002 (0.002) Loss 2.8122 (2.7461) Prec@1 33.125 (34.366) Prec@5 64.375 (64.737) Epoch: [3][9850/11272] Time 0.777 (0.832) Data 0.001 (0.002) Loss 2.8722 (2.7461) Prec@1 27.500 (34.366) Prec@5 62.500 (64.736) Epoch: [3][9860/11272] Time 0.729 (0.832) Data 0.002 (0.002) Loss 2.4995 (2.7460) Prec@1 41.875 (34.370) Prec@5 70.625 (64.737) Epoch: [3][9870/11272] Time 0.853 (0.832) Data 0.002 (0.002) Loss 2.8576 (2.7461) Prec@1 35.000 (34.368) Prec@5 65.000 (64.736) Epoch: [3][9880/11272] Time 0.839 (0.832) Data 0.002 (0.002) Loss 2.8772 (2.7460) Prec@1 35.000 (34.369) Prec@5 62.500 (64.738) Epoch: [3][9890/11272] Time 0.768 (0.832) Data 0.002 (0.002) Loss 2.5660 (2.7459) Prec@1 35.625 (34.370) Prec@5 69.375 (64.739) Epoch: [3][9900/11272] Time 0.750 (0.832) Data 0.002 (0.002) Loss 2.6898 (2.7458) Prec@1 34.375 (34.371) Prec@5 65.000 (64.742) Epoch: [3][9910/11272] Time 0.866 (0.832) Data 0.001 (0.002) Loss 2.6421 (2.7458) Prec@1 42.500 (34.372) Prec@5 67.500 (64.741) Epoch: [3][9920/11272] Time 0.848 (0.832) Data 0.002 (0.002) Loss 2.7226 (2.7457) Prec@1 39.375 (34.374) Prec@5 64.375 (64.743) Epoch: [3][9930/11272] Time 0.755 (0.832) Data 0.001 (0.002) Loss 2.5743 (2.7457) Prec@1 36.875 (34.377) Prec@5 66.875 (64.744) Epoch: [3][9940/11272] Time 0.901 (0.832) Data 0.002 (0.002) Loss 2.6920 (2.7456) Prec@1 29.375 (34.378) Prec@5 63.125 (64.745) Epoch: [3][9950/11272] Time 0.887 (0.832) Data 0.002 (0.002) Loss 2.6864 (2.7455) Prec@1 33.750 (34.380) Prec@5 65.000 (64.747) Epoch: [3][9960/11272] Time 0.773 (0.832) Data 0.002 (0.002) Loss 2.9323 (2.7455) Prec@1 29.375 (34.380) Prec@5 61.875 (64.747) Epoch: [3][9970/11272] Time 0.748 (0.832) Data 0.002 (0.002) Loss 2.5583 (2.7455) Prec@1 36.250 (34.380) Prec@5 68.750 (64.747) Epoch: [3][9980/11272] Time 0.840 (0.832) Data 0.001 (0.002) Loss 2.7039 (2.7455) Prec@1 33.750 (34.379) Prec@5 65.625 (64.747) Epoch: [3][9990/11272] Time 0.940 (0.832) Data 0.002 (0.002) Loss 2.7804 (2.7455) Prec@1 36.250 (34.378) Prec@5 56.875 (64.746) Epoch: [3][10000/11272] Time 0.735 (0.832) Data 0.002 (0.002) Loss 2.7983 (2.7456) Prec@1 35.625 (34.377) Prec@5 59.375 (64.746) Epoch: [3][10010/11272] Time 0.726 (0.832) Data 0.002 (0.002) Loss 3.0328 (2.7456) Prec@1 27.500 (34.376) Prec@5 59.375 (64.744) Epoch: [3][10020/11272] Time 0.875 (0.832) Data 0.001 (0.002) Loss 2.8780 (2.7456) Prec@1 29.375 (34.377) Prec@5 58.125 (64.744) Epoch: [3][10030/11272] Time 0.835 (0.832) Data 0.002 (0.002) Loss 2.7134 (2.7455) Prec@1 36.875 (34.377) Prec@5 65.625 (64.744) Epoch: [3][10040/11272] Time 0.811 (0.832) Data 0.049 (0.002) Loss 2.7386 (2.7455) Prec@1 34.375 (34.377) Prec@5 65.625 (64.745) Epoch: [3][10050/11272] Time 0.737 (0.832) Data 0.002 (0.002) Loss 2.8532 (2.7455) Prec@1 32.500 (34.378) Prec@5 62.500 (64.746) Epoch: [3][10060/11272] Time 0.835 (0.832) Data 0.001 (0.002) Loss 2.7559 (2.7454) Prec@1 28.125 (34.379) Prec@5 64.375 (64.747) Epoch: [3][10070/11272] Time 0.730 (0.832) Data 0.003 (0.002) Loss 2.4834 (2.7454) Prec@1 41.250 (34.380) Prec@5 69.375 (64.747) Epoch: [3][10080/11272] Time 0.771 (0.832) Data 0.001 (0.002) Loss 2.8760 (2.7454) Prec@1 34.375 (34.379) Prec@5 64.375 (64.746) Epoch: [3][10090/11272] Time 0.838 (0.832) Data 0.001 (0.002) Loss 2.7814 (2.7454) Prec@1 36.250 (34.378) Prec@5 61.875 (64.746) Epoch: [3][10100/11272] Time 0.848 (0.832) Data 0.002 (0.002) Loss 2.8027 (2.7454) Prec@1 31.875 (34.379) Prec@5 61.250 (64.747) Epoch: [3][10110/11272] Time 0.751 (0.832) Data 0.002 (0.002) Loss 2.9298 (2.7454) Prec@1 35.000 (34.381) Prec@5 60.625 (64.748) Epoch: [3][10120/11272] Time 0.749 (0.832) Data 0.002 (0.002) Loss 2.7588 (2.7453) Prec@1 30.625 (34.381) Prec@5 65.000 (64.748) Epoch: [3][10130/11272] Time 0.970 (0.832) Data 0.002 (0.002) Loss 2.8438 (2.7453) Prec@1 33.750 (34.382) Prec@5 63.125 (64.749) Epoch: [3][10140/11272] Time 0.887 (0.832) Data 0.001 (0.002) Loss 2.6970 (2.7453) Prec@1 36.250 (34.382) Prec@5 66.250 (64.750) Epoch: [3][10150/11272] Time 0.777 (0.832) Data 0.002 (0.002) Loss 2.8271 (2.7452) Prec@1 33.750 (34.385) Prec@5 61.875 (64.750) Epoch: [3][10160/11272] Time 0.738 (0.832) Data 0.002 (0.002) Loss 2.5041 (2.7452) Prec@1 38.125 (34.384) Prec@5 69.375 (64.750) Epoch: [3][10170/11272] Time 0.849 (0.832) Data 0.001 (0.002) Loss 2.6813 (2.7453) Prec@1 33.750 (34.384) Prec@5 64.375 (64.749) Epoch: [3][10180/11272] Time 0.830 (0.832) Data 0.002 (0.002) Loss 2.6858 (2.7452) Prec@1 35.625 (34.383) Prec@5 69.375 (64.749) Epoch: [3][10190/11272] Time 0.764 (0.832) Data 0.001 (0.002) Loss 2.6895 (2.7451) Prec@1 36.250 (34.385) Prec@5 60.000 (64.750) Epoch: [3][10200/11272] Time 0.900 (0.832) Data 0.002 (0.002) Loss 2.7330 (2.7451) Prec@1 35.625 (34.385) Prec@5 64.375 (64.750) Epoch: [3][10210/11272] Time 0.896 (0.832) Data 0.002 (0.002) Loss 3.0364 (2.7452) Prec@1 29.375 (34.384) Prec@5 56.250 (64.748) Epoch: [3][10220/11272] Time 0.751 (0.832) Data 0.002 (0.002) Loss 3.0413 (2.7452) Prec@1 31.250 (34.383) Prec@5 58.750 (64.748) Epoch: [3][10230/11272] Time 0.772 (0.832) Data 0.002 (0.002) Loss 2.6001 (2.7451) Prec@1 33.750 (34.385) Prec@5 65.000 (64.748) Epoch: [3][10240/11272] Time 0.850 (0.832) Data 0.001 (0.002) Loss 2.6533 (2.7451) Prec@1 34.375 (34.384) Prec@5 65.625 (64.748) Epoch: [3][10250/11272] Time 0.863 (0.832) Data 0.001 (0.002) Loss 2.6522 (2.7450) Prec@1 34.375 (34.385) Prec@5 66.875 (64.749) Epoch: [3][10260/11272] Time 0.734 (0.832) Data 0.002 (0.002) Loss 2.8612 (2.7450) Prec@1 30.625 (34.385) Prec@5 59.375 (64.751) Epoch: [3][10270/11272] Time 0.756 (0.832) Data 0.002 (0.002) Loss 2.8367 (2.7450) Prec@1 37.500 (34.387) Prec@5 63.750 (64.750) Epoch: [3][10280/11272] Time 0.888 (0.831) Data 0.002 (0.002) Loss 2.6442 (2.7449) Prec@1 33.750 (34.389) Prec@5 67.500 (64.752) Epoch: [3][10290/11272] Time 0.901 (0.831) Data 0.002 (0.002) Loss 2.8667 (2.7449) Prec@1 37.500 (34.388) Prec@5 63.750 (64.752) Epoch: [3][10300/11272] Time 0.735 (0.831) Data 0.002 (0.002) Loss 2.8793 (2.7449) Prec@1 28.125 (34.390) Prec@5 60.000 (64.753) Epoch: [3][10310/11272] Time 0.777 (0.831) Data 0.002 (0.002) Loss 2.7620 (2.7448) Prec@1 36.250 (34.390) Prec@5 65.000 (64.754) Epoch: [3][10320/11272] Time 0.879 (0.831) Data 0.002 (0.002) Loss 2.6238 (2.7448) Prec@1 41.875 (34.391) Prec@5 68.125 (64.753) Epoch: [3][10330/11272] Time 0.876 (0.831) Data 0.002 (0.002) Loss 3.2011 (2.7449) Prec@1 28.125 (34.389) Prec@5 55.625 (64.751) Epoch: [3][10340/11272] Time 0.729 (0.831) Data 0.001 (0.002) Loss 3.0553 (2.7449) Prec@1 32.500 (34.390) Prec@5 59.375 (64.751) Epoch: [3][10350/11272] Time 0.869 (0.831) Data 0.002 (0.002) Loss 2.6967 (2.7449) Prec@1 41.250 (34.390) Prec@5 66.875 (64.752) Epoch: [3][10360/11272] Time 0.892 (0.831) Data 0.002 (0.002) Loss 2.7312 (2.7449) Prec@1 33.750 (34.390) Prec@5 63.125 (64.751) Epoch: [3][10370/11272] Time 0.718 (0.831) Data 0.001 (0.002) Loss 2.6745 (2.7449) Prec@1 38.125 (34.389) Prec@5 67.500 (64.751) Epoch: [3][10380/11272] Time 0.776 (0.831) Data 0.001 (0.002) Loss 2.5951 (2.7449) Prec@1 35.625 (34.389) Prec@5 71.875 (64.752) Epoch: [3][10390/11272] Time 0.839 (0.831) Data 0.001 (0.002) Loss 2.4665 (2.7449) Prec@1 40.000 (34.388) Prec@5 71.875 (64.752) Epoch: [3][10400/11272] Time 0.902 (0.831) Data 0.002 (0.002) Loss 2.6364 (2.7448) Prec@1 37.500 (34.388) Prec@5 68.750 (64.753) Epoch: [3][10410/11272] Time 0.742 (0.831) Data 0.002 (0.002) Loss 2.6195 (2.7448) Prec@1 33.750 (34.389) Prec@5 64.375 (64.754) Epoch: [3][10420/11272] Time 0.751 (0.831) Data 0.002 (0.002) Loss 2.6453 (2.7448) Prec@1 37.500 (34.390) Prec@5 66.875 (64.755) Epoch: [3][10430/11272] Time 0.860 (0.831) Data 0.003 (0.002) Loss 2.6589 (2.7447) Prec@1 35.000 (34.391) Prec@5 66.250 (64.757) Epoch: [3][10440/11272] Time 0.903 (0.831) Data 0.001 (0.002) Loss 2.9079 (2.7446) Prec@1 34.375 (34.392) Prec@5 56.250 (64.758) Epoch: [3][10450/11272] Time 0.737 (0.831) Data 0.002 (0.002) Loss 2.4756 (2.7446) Prec@1 38.750 (34.392) Prec@5 68.750 (64.757) Epoch: [3][10460/11272] Time 0.736 (0.831) Data 0.002 (0.002) Loss 2.4520 (2.7446) Prec@1 42.500 (34.394) Prec@5 70.625 (64.757) Epoch: [3][10470/11272] Time 0.830 (0.831) Data 0.002 (0.002) Loss 2.7605 (2.7447) Prec@1 37.500 (34.394) Prec@5 62.500 (64.756) Epoch: [3][10480/11272] Time 0.746 (0.831) Data 0.002 (0.002) Loss 2.7291 (2.7447) Prec@1 33.750 (34.394) Prec@5 65.000 (64.755) Epoch: [3][10490/11272] Time 0.775 (0.831) Data 0.002 (0.002) Loss 2.6158 (2.7447) Prec@1 37.500 (34.395) Prec@5 65.000 (64.757) Epoch: [3][10500/11272] Time 0.929 (0.831) Data 0.002 (0.002) Loss 2.6883 (2.7447) Prec@1 34.375 (34.395) Prec@5 65.000 (64.756) Epoch: [3][10510/11272] Time 0.852 (0.831) Data 0.002 (0.002) Loss 2.6519 (2.7447) Prec@1 34.375 (34.395) Prec@5 63.750 (64.756) Epoch: [3][10520/11272] Time 0.739 (0.831) Data 0.002 (0.002) Loss 2.6442 (2.7446) Prec@1 37.500 (34.396) Prec@5 63.125 (64.757) Epoch: [3][10530/11272] Time 0.790 (0.831) Data 0.002 (0.002) Loss 2.5947 (2.7445) Prec@1 38.750 (34.397) Prec@5 65.000 (64.759) Epoch: [3][10540/11272] Time 0.869 (0.831) Data 0.002 (0.002) Loss 2.7649 (2.7445) Prec@1 34.375 (34.398) Prec@5 61.875 (64.759) Epoch: [3][10550/11272] Time 0.859 (0.831) Data 0.002 (0.002) Loss 2.5914 (2.7445) Prec@1 38.750 (34.398) Prec@5 67.500 (64.760) Epoch: [3][10560/11272] Time 0.791 (0.831) Data 0.002 (0.002) Loss 2.7301 (2.7446) Prec@1 36.875 (34.398) Prec@5 62.500 (64.758) Epoch: [3][10570/11272] Time 0.798 (0.831) Data 0.002 (0.002) Loss 2.8857 (2.7446) Prec@1 30.000 (34.398) Prec@5 63.125 (64.757) Epoch: [3][10580/11272] Time 0.936 (0.831) Data 0.002 (0.002) Loss 2.4475 (2.7445) Prec@1 37.500 (34.398) Prec@5 71.250 (64.759) Epoch: [3][10590/11272] Time 0.841 (0.831) Data 0.001 (0.002) Loss 2.8816 (2.7445) Prec@1 29.375 (34.399) Prec@5 60.000 (64.759) Epoch: [3][10600/11272] Time 0.767 (0.831) Data 0.002 (0.002) Loss 2.6845 (2.7446) Prec@1 38.125 (34.399) Prec@5 62.500 (64.759) Epoch: [3][10610/11272] Time 0.950 (0.831) Data 0.002 (0.002) Loss 2.7107 (2.7445) Prec@1 38.125 (34.401) Prec@5 67.500 (64.761) Epoch: [3][10620/11272] Time 0.890 (0.831) Data 0.002 (0.002) Loss 2.6717 (2.7444) Prec@1 40.000 (34.402) Prec@5 62.500 (64.761) Epoch: [3][10630/11272] Time 0.727 (0.831) Data 0.001 (0.002) Loss 2.3572 (2.7444) Prec@1 42.500 (34.404) Prec@5 74.375 (64.763) Epoch: [3][10640/11272] Time 0.759 (0.831) Data 0.002 (0.002) Loss 2.8288 (2.7443) Prec@1 37.500 (34.405) Prec@5 63.125 (64.764) Epoch: [3][10650/11272] Time 0.950 (0.831) Data 0.002 (0.002) Loss 2.6780 (2.7443) Prec@1 34.375 (34.405) Prec@5 67.500 (64.763) Epoch: [3][10660/11272] Time 0.833 (0.831) Data 0.002 (0.002) Loss 2.7705 (2.7442) Prec@1 35.625 (34.405) Prec@5 63.750 (64.764) Epoch: [3][10670/11272] Time 0.756 (0.831) Data 0.002 (0.002) Loss 2.7673 (2.7443) Prec@1 35.625 (34.406) Prec@5 65.000 (64.764) Epoch: [3][10680/11272] Time 0.834 (0.831) Data 0.002 (0.002) Loss 2.9834 (2.7443) Prec@1 28.125 (34.404) Prec@5 62.500 (64.765) Epoch: [3][10690/11272] Time 0.894 (0.831) Data 0.002 (0.002) Loss 2.7777 (2.7443) Prec@1 31.875 (34.403) Prec@5 64.375 (64.764) Epoch: [3][10700/11272] Time 0.849 (0.831) Data 0.002 (0.002) Loss 2.8374 (2.7442) Prec@1 32.500 (34.404) Prec@5 61.250 (64.764) Epoch: [3][10710/11272] Time 0.757 (0.831) Data 0.001 (0.002) Loss 2.6750 (2.7443) Prec@1 31.875 (34.403) Prec@5 69.375 (64.764) Epoch: [3][10720/11272] Time 0.736 (0.831) Data 0.001 (0.002) Loss 2.8059 (2.7442) Prec@1 33.125 (34.405) Prec@5 61.875 (64.763) Epoch: [3][10730/11272] Time 0.848 (0.831) Data 0.001 (0.002) Loss 2.7650 (2.7443) Prec@1 34.375 (34.405) Prec@5 60.625 (64.761) Epoch: [3][10740/11272] Time 0.773 (0.831) Data 0.004 (0.002) Loss 2.8719 (2.7443) Prec@1 31.875 (34.403) Prec@5 66.875 (64.761) Epoch: [3][10750/11272] Time 0.754 (0.831) Data 0.002 (0.002) Loss 3.0607 (2.7443) Prec@1 28.750 (34.403) Prec@5 61.875 (64.761) Epoch: [3][10760/11272] Time 0.933 (0.831) Data 0.001 (0.002) Loss 2.6106 (2.7443) Prec@1 39.375 (34.404) Prec@5 69.375 (64.762) Epoch: [3][10770/11272] Time 0.926 (0.831) Data 0.002 (0.002) Loss 2.6306 (2.7443) Prec@1 33.750 (34.402) Prec@5 64.375 (64.761) Epoch: [3][10780/11272] Time 0.735 (0.831) Data 0.001 (0.002) Loss 2.9744 (2.7442) Prec@1 33.125 (34.402) Prec@5 58.750 (64.761) Epoch: [3][10790/11272] Time 0.759 (0.831) Data 0.002 (0.002) Loss 2.7705 (2.7442) Prec@1 32.500 (34.402) Prec@5 68.125 (64.761) Epoch: [3][10800/11272] Time 0.884 (0.831) Data 0.002 (0.002) Loss 2.7380 (2.7442) Prec@1 31.250 (34.402) Prec@5 67.500 (64.760) Epoch: [3][10810/11272] Time 0.897 (0.831) Data 0.002 (0.002) Loss 2.9514 (2.7442) Prec@1 30.000 (34.402) Prec@5 61.875 (64.760) Epoch: [3][10820/11272] Time 0.744 (0.831) Data 0.002 (0.002) Loss 2.7766 (2.7442) Prec@1 32.500 (34.404) Prec@5 63.750 (64.760) Epoch: [3][10830/11272] Time 0.774 (0.831) Data 0.002 (0.002) Loss 2.7133 (2.7442) Prec@1 37.500 (34.404) Prec@5 66.875 (64.761) Epoch: [3][10840/11272] Time 0.834 (0.831) Data 0.001 (0.002) Loss 2.8545 (2.7442) Prec@1 31.875 (34.403) Prec@5 60.625 (64.761) Epoch: [3][10850/11272] Time 0.865 (0.831) Data 0.001 (0.002) Loss 2.6644 (2.7442) Prec@1 34.375 (34.403) Prec@5 65.625 (64.760) Epoch: [3][10860/11272] Time 0.814 (0.831) Data 0.002 (0.002) Loss 2.7753 (2.7441) Prec@1 31.250 (34.403) Prec@5 63.750 (64.760) Epoch: [3][10870/11272] Time 0.907 (0.831) Data 0.002 (0.002) Loss 2.8667 (2.7442) Prec@1 35.625 (34.404) Prec@5 64.375 (64.761) Epoch: [3][10880/11272] Time 0.853 (0.831) Data 0.002 (0.002) Loss 2.5749 (2.7441) Prec@1 34.375 (34.403) Prec@5 71.250 (64.761) Epoch: [3][10890/11272] Time 0.747 (0.831) Data 0.002 (0.002) Loss 2.8104 (2.7441) Prec@1 33.750 (34.403) Prec@5 64.375 (64.762) Epoch: [3][10900/11272] Time 0.730 (0.831) Data 0.001 (0.002) Loss 2.7763 (2.7440) Prec@1 36.250 (34.406) Prec@5 62.500 (64.763) Epoch: [3][10910/11272] Time 0.969 (0.831) Data 0.002 (0.002) Loss 2.5415 (2.7439) Prec@1 41.250 (34.408) Prec@5 68.750 (64.763) Epoch: [3][10920/11272] Time 0.937 (0.831) Data 0.002 (0.002) Loss 2.8220 (2.7439) Prec@1 34.375 (34.407) Prec@5 63.750 (64.764) Epoch: [3][10930/11272] Time 0.749 (0.831) Data 0.002 (0.002) Loss 2.9672 (2.7440) Prec@1 30.000 (34.408) Prec@5 58.750 (64.764) Epoch: [3][10940/11272] Time 0.811 (0.831) Data 0.002 (0.002) Loss 2.7273 (2.7440) Prec@1 33.125 (34.408) Prec@5 63.125 (64.764) Epoch: [3][10950/11272] Time 0.915 (0.831) Data 0.002 (0.002) Loss 2.6957 (2.7440) Prec@1 41.875 (34.409) Prec@5 65.000 (64.764) Epoch: [3][10960/11272] Time 0.823 (0.831) Data 0.001 (0.002) Loss 2.7257 (2.7440) Prec@1 38.125 (34.408) Prec@5 65.000 (64.763) Epoch: [3][10970/11272] Time 0.739 (0.831) Data 0.002 (0.002) Loss 2.8014 (2.7440) Prec@1 34.375 (34.407) Prec@5 63.125 (64.763) Epoch: [3][10980/11272] Time 0.756 (0.831) Data 0.002 (0.002) Loss 2.8919 (2.7440) Prec@1 32.500 (34.407) Prec@5 60.000 (64.763) Epoch: [3][10990/11272] Time 0.899 (0.831) Data 0.002 (0.002) Loss 2.7200 (2.7439) Prec@1 36.875 (34.409) Prec@5 66.250 (64.764) Epoch: [3][11000/11272] Time 0.755 (0.831) Data 0.005 (0.002) Loss 2.7336 (2.7439) Prec@1 38.750 (34.410) Prec@5 63.125 (64.765) Epoch: [3][11010/11272] Time 0.763 (0.831) Data 0.002 (0.002) Loss 2.8461 (2.7438) Prec@1 31.250 (34.410) Prec@5 62.500 (64.766) Epoch: [3][11020/11272] Time 0.905 (0.831) Data 0.001 (0.002) Loss 2.7897 (2.7439) Prec@1 32.500 (34.409) Prec@5 61.875 (64.766) Epoch: [3][11030/11272] Time 0.854 (0.831) Data 0.002 (0.002) Loss 2.9476 (2.7438) Prec@1 26.875 (34.408) Prec@5 60.625 (64.766) Epoch: [3][11040/11272] Time 0.740 (0.831) Data 0.002 (0.002) Loss 2.7058 (2.7438) Prec@1 32.500 (34.408) Prec@5 66.875 (64.766) Epoch: [3][11050/11272] Time 0.765 (0.831) Data 0.002 (0.002) Loss 2.7574 (2.7438) Prec@1 40.000 (34.407) Prec@5 63.125 (64.767) Epoch: [3][11060/11272] Time 0.893 (0.831) Data 0.002 (0.002) Loss 2.7402 (2.7438) Prec@1 35.625 (34.408) Prec@5 67.500 (64.767) Epoch: [3][11070/11272] Time 0.826 (0.831) Data 0.001 (0.002) Loss 2.7065 (2.7437) Prec@1 35.625 (34.408) Prec@5 65.625 (64.769) Epoch: [3][11080/11272] Time 0.731 (0.831) Data 0.001 (0.002) Loss 2.7497 (2.7437) Prec@1 33.750 (34.408) Prec@5 65.000 (64.768) Epoch: [3][11090/11272] Time 0.772 (0.831) Data 0.002 (0.002) Loss 2.7430 (2.7436) Prec@1 31.250 (34.409) Prec@5 63.125 (64.770) Epoch: [3][11100/11272] Time 0.862 (0.831) Data 0.002 (0.002) Loss 2.6827 (2.7436) Prec@1 33.125 (34.409) Prec@5 66.875 (64.771) Epoch: [3][11110/11272] Time 0.868 (0.831) Data 0.002 (0.002) Loss 2.7812 (2.7436) Prec@1 33.750 (34.408) Prec@5 63.750 (64.771) Epoch: [3][11120/11272] Time 0.732 (0.831) Data 0.002 (0.002) Loss 2.7839 (2.7435) Prec@1 32.500 (34.409) Prec@5 64.375 (64.772) Epoch: [3][11130/11272] Time 0.934 (0.831) Data 0.002 (0.002) Loss 2.4355 (2.7436) Prec@1 38.750 (34.410) Prec@5 71.250 (64.771) Epoch: [3][11140/11272] Time 0.876 (0.831) Data 0.002 (0.002) Loss 2.5988 (2.7435) Prec@1 36.250 (34.410) Prec@5 65.625 (64.772) Epoch: [3][11150/11272] Time 0.746 (0.831) Data 0.001 (0.002) Loss 2.9908 (2.7436) Prec@1 36.875 (34.410) Prec@5 59.375 (64.771) Epoch: [3][11160/11272] Time 0.754 (0.831) Data 0.002 (0.002) Loss 2.6348 (2.7435) Prec@1 35.000 (34.412) Prec@5 68.125 (64.772) Epoch: [3][11170/11272] Time 0.884 (0.831) Data 0.001 (0.002) Loss 2.4906 (2.7435) Prec@1 39.375 (34.412) Prec@5 66.875 (64.773) Epoch: [3][11180/11272] Time 0.889 (0.831) Data 0.002 (0.002) Loss 2.9920 (2.7435) Prec@1 27.500 (34.411) Prec@5 59.375 (64.772) Epoch: [3][11190/11272] Time 0.742 (0.831) Data 0.002 (0.002) Loss 2.7270 (2.7435) Prec@1 35.000 (34.413) Prec@5 66.250 (64.774) Epoch: [3][11200/11272] Time 0.747 (0.831) Data 0.001 (0.002) Loss 2.7897 (2.7435) Prec@1 33.125 (34.411) Prec@5 63.750 (64.772) Epoch: [3][11210/11272] Time 0.890 (0.831) Data 0.002 (0.002) Loss 2.6378 (2.7435) Prec@1 37.500 (34.410) Prec@5 66.875 (64.772) Epoch: [3][11220/11272] Time 0.876 (0.831) Data 0.002 (0.002) Loss 2.4271 (2.7435) Prec@1 38.125 (34.410) Prec@5 70.000 (64.772) Epoch: [3][11230/11272] Time 0.806 (0.831) Data 0.002 (0.002) Loss 2.8555 (2.7436) Prec@1 31.875 (34.408) Prec@5 60.625 (64.770) Epoch: [3][11240/11272] Time 0.744 (0.831) Data 0.002 (0.002) Loss 2.5006 (2.7436) Prec@1 35.000 (34.410) Prec@5 73.750 (64.771) Epoch: [3][11250/11272] Time 0.860 (0.831) Data 0.001 (0.002) Loss 2.7738 (2.7436) Prec@1 35.000 (34.409) Prec@5 63.750 (64.770) Epoch: [3][11260/11272] Time 0.849 (0.831) Data 0.001 (0.002) Loss 2.6449 (2.7436) Prec@1 38.750 (34.409) Prec@5 65.625 (64.770) Epoch: [3][11270/11272] Time 0.727 (0.831) Data 0.000 (0.002) Loss 2.8534 (2.7436) Prec@1 36.250 (34.407) Prec@5 68.125 (64.771) Test: [0/229] Time 3.220 (3.220) Loss 1.3429 (1.3429) Prec@1 66.250 (66.250) Prec@5 90.625 (90.625) Test: [10/229] Time 0.476 (0.664) Loss 1.6054 (2.2080) Prec@1 54.375 (44.148) Prec@5 90.625 (77.614) Test: [20/229] Time 0.344 (0.534) Loss 2.3572 (2.2442) Prec@1 43.125 (43.750) Prec@5 79.375 (76.339) Test: [30/229] Time 0.382 (0.493) Loss 2.0534 (2.1665) Prec@1 41.250 (45.504) Prec@5 80.000 (77.077) Test: [40/229] Time 0.461 (0.475) Loss 1.0218 (2.1612) Prec@1 76.875 (45.564) Prec@5 88.750 (76.494) Test: [50/229] Time 0.380 (0.460) Loss 2.6917 (2.2000) Prec@1 26.875 (44.975) Prec@5 68.750 (75.000) Test: [60/229] Time 0.429 (0.450) Loss 2.7254 (2.2249) Prec@1 28.125 (44.426) Prec@5 66.250 (74.447) Test: [70/229] Time 0.379 (0.442) Loss 2.0708 (2.2536) Prec@1 50.625 (43.442) Prec@5 78.750 (74.040) Test: [80/229] Time 0.465 (0.438) Loss 2.5449 (2.2799) Prec@1 37.500 (42.523) Prec@5 67.500 (73.727) Test: [90/229] Time 0.343 (0.433) Loss 2.5330 (2.3044) Prec@1 36.875 (41.717) Prec@5 72.500 (73.523) Test: [100/229] Time 0.358 (0.429) Loss 2.3377 (2.3068) Prec@1 43.750 (41.925) Prec@5 78.750 (73.620) Test: [110/229] Time 0.417 (0.427) Loss 2.2096 (2.2950) Prec@1 36.875 (42.021) Prec@5 78.750 (73.902) Test: [120/229] Time 0.393 (0.426) Loss 2.8211 (2.3103) Prec@1 31.875 (41.581) Prec@5 65.625 (73.776) Test: [130/229] Time 0.419 (0.425) Loss 1.7275 (2.2892) Prec@1 50.000 (42.047) Prec@5 83.125 (74.051) Test: [140/229] Time 0.369 (0.422) Loss 2.5887 (2.3027) Prec@1 28.750 (41.613) Prec@5 67.500 (74.020) Test: [150/229] Time 0.421 (0.421) Loss 1.6276 (2.3259) Prec@1 62.500 (41.217) Prec@5 83.125 (73.502) Test: [160/229] Time 0.459 (0.421) Loss 2.0153 (2.3328) Prec@1 59.375 (41.165) Prec@5 78.750 (73.331) Test: [170/229] Time 0.380 (0.421) Loss 2.4848 (2.3470) Prec@1 36.250 (40.822) Prec@5 78.125 (73.121) Test: [180/229] Time 0.413 (0.421) Loss 2.5500 (2.3508) Prec@1 27.500 (40.867) Prec@5 69.375 (72.990) Test: [190/229] Time 0.398 (0.420) Loss 1.7279 (2.3535) Prec@1 51.250 (40.848) Prec@5 88.750 (73.020) Test: [200/229] Time 0.421 (0.420) Loss 2.7063 (2.3479) Prec@1 27.500 (40.821) Prec@5 65.000 (73.268) Test: [210/229] Time 0.326 (0.419) Loss 1.8775 (2.3418) Prec@1 33.750 (40.865) Prec@5 89.375 (73.353) Test: [220/229] Time 0.368 (0.419) Loss 1.9995 (2.3323) Prec@1 47.500 (41.210) Prec@5 82.500 (73.467) * Prec@1 41.558 Prec@5 73.647 Epoch: [4][0/11272] Time 4.124 (4.124) Data 3.120 (3.120) Loss 2.8117 (2.8117) Prec@1 28.750 (28.750) Prec@5 65.000 (65.000) Epoch: [4][10/11272] Time 0.750 (1.141) Data 0.002 (0.285) Loss 2.9465 (2.7028) Prec@1 31.250 (35.795) Prec@5 60.625 (66.080) Epoch: [4][20/11272] Time 0.902 (1.014) Data 0.002 (0.150) Loss 2.9547 (2.7113) Prec@1 31.250 (35.238) Prec@5 61.250 (66.101) Epoch: [4][30/11272] Time 0.902 (0.950) Data 0.002 (0.102) Loss 2.5386 (2.7125) Prec@1 38.125 (34.496) Prec@5 68.750 (65.625) Epoch: [4][40/11272] Time 0.804 (0.918) Data 0.002 (0.078) Loss 2.7336 (2.7071) Prec@1 35.000 (34.817) Prec@5 65.000 (65.640) Epoch: [4][50/11272] Time 0.749 (0.896) Data 0.002 (0.063) Loss 2.5486 (2.7012) Prec@1 40.625 (34.939) Prec@5 65.625 (65.662) Epoch: [4][60/11272] Time 0.848 (0.882) Data 0.002 (0.053) Loss 2.5165 (2.7018) Prec@1 36.250 (35.051) Prec@5 67.500 (65.615) Epoch: [4][70/11272] Time 0.888 (0.874) Data 0.002 (0.046) Loss 2.6558 (2.7035) Prec@1 33.125 (35.044) Prec@5 71.250 (65.783) Epoch: [4][80/11272] Time 0.746 (0.868) Data 0.002 (0.040) Loss 2.5250 (2.6969) Prec@1 37.500 (35.231) Prec@5 70.000 (65.856) Epoch: [4][90/11272] Time 0.915 (0.861) Data 0.001 (0.036) Loss 2.9172 (2.7062) Prec@1 33.750 (35.082) Prec@5 59.375 (65.543) Epoch: [4][100/11272] Time 0.876 (0.856) Data 0.002 (0.033) Loss 2.4925 (2.7121) Prec@1 41.250 (35.043) Prec@5 70.000 (65.408) Epoch: [4][110/11272] Time 0.737 (0.852) Data 0.002 (0.030) Loss 2.8101 (2.7141) Prec@1 30.625 (35.011) Prec@5 65.625 (65.434) Epoch: [4][120/11272] Time 0.769 (0.850) Data 0.002 (0.027) Loss 2.7776 (2.7133) Prec@1 30.000 (34.948) Prec@5 66.250 (65.449) Epoch: [4][130/11272] Time 0.855 (0.847) Data 0.002 (0.025) Loss 2.5353 (2.7109) Prec@1 38.750 (34.967) Prec@5 68.750 (65.429) Epoch: [4][140/11272] Time 0.866 (0.845) Data 0.001 (0.024) Loss 2.6523 (2.7087) Prec@1 36.875 (34.947) Prec@5 65.625 (65.519) Epoch: [4][150/11272] Time 0.741 (0.843) Data 0.001 (0.022) Loss 2.7133 (2.7111) Prec@1 37.500 (34.880) Prec@5 67.500 (65.509) Epoch: [4][160/11272] Time 0.762 (0.841) Data 0.002 (0.021) Loss 3.0051 (2.7098) Prec@1 29.375 (34.868) Prec@5 59.375 (65.512) Epoch: [4][170/11272] Time 0.866 (0.841) Data 0.002 (0.020) Loss 2.8054 (2.7082) Prec@1 33.125 (34.836) Prec@5 65.000 (65.537) Epoch: [4][180/11272] Time 0.898 (0.839) Data 0.002 (0.019) Loss 2.6537 (2.7050) Prec@1 38.125 (34.927) Prec@5 66.250 (65.663) Epoch: [4][190/11272] Time 0.773 (0.838) Data 0.002 (0.018) Loss 2.5466 (2.7037) Prec@1 39.375 (34.974) Prec@5 67.500 (65.654) Epoch: [4][200/11272] Time 0.916 (0.838) Data 0.002 (0.017) Loss 2.7288 (2.7062) Prec@1 30.000 (34.857) Prec@5 67.500 (65.647) Epoch: [4][210/11272] Time 0.838 (0.836) Data 0.001 (0.016) Loss 2.6694 (2.7065) Prec@1 38.125 (34.837) Prec@5 63.125 (65.652) Epoch: [4][220/11272] Time 0.762 (0.836) Data 0.002 (0.016) Loss 2.7134 (2.7052) Prec@1 35.625 (34.850) Prec@5 61.875 (65.636) Epoch: [4][230/11272] Time 0.783 (0.835) Data 0.002 (0.015) Loss 2.6503 (2.7056) Prec@1 34.375 (34.848) Prec@5 69.375 (65.674) Epoch: [4][240/11272] Time 0.849 (0.834) Data 0.002 (0.015) Loss 2.6916 (2.7040) Prec@1 35.625 (34.876) Prec@5 63.125 (65.669) Epoch: [4][250/11272] Time 0.867 (0.834) Data 0.002 (0.014) Loss 2.7641 (2.7052) Prec@1 36.250 (34.888) Prec@5 60.000 (65.690) Epoch: [4][260/11272] Time 0.764 (0.833) Data 0.002 (0.014) Loss 2.4472 (2.7056) Prec@1 43.125 (34.931) Prec@5 68.125 (65.651) Epoch: [4][270/11272] Time 0.794 (0.834) Data 0.002 (0.013) Loss 2.8356 (2.7085) Prec@1 30.625 (34.924) Prec@5 61.250 (65.590) Epoch: [4][280/11272] Time 0.849 (0.833) Data 0.002 (0.013) Loss 2.7683 (2.7098) Prec@1 36.250 (34.871) Prec@5 65.000 (65.585) Epoch: [4][290/11272] Time 0.865 (0.832) Data 0.002 (0.012) Loss 3.1058 (2.7130) Prec@1 29.375 (34.792) Prec@5 57.500 (65.543) Epoch: [4][300/11272] Time 0.816 (0.832) Data 0.002 (0.012) Loss 2.8331 (2.7124) Prec@1 31.875 (34.826) Prec@5 65.000 (65.592) Epoch: [4][310/11272] Time 0.771 (0.832) Data 0.002 (0.012) Loss 2.7487 (2.7140) Prec@1 33.750 (34.793) Prec@5 66.250 (65.601) Epoch: [4][320/11272] Time 0.916 (0.832) Data 0.002 (0.011) Loss 2.6709 (2.7142) Prec@1 37.500 (34.786) Prec@5 63.750 (65.541) Epoch: [4][330/11272] Time 0.875 (0.832) Data 0.002 (0.011) Loss 2.7431 (2.7139) Prec@1 35.000 (34.794) Prec@5 65.000 (65.549) Epoch: [4][340/11272] Time 0.742 (0.831) Data 0.001 (0.011) Loss 3.0259 (2.7174) Prec@1 30.000 (34.716) Prec@5 58.125 (65.500) Epoch: [4][350/11272] Time 0.916 (0.831) Data 0.002 (0.011) Loss 2.8020 (2.7185) Prec@1 32.500 (34.687) Prec@5 65.000 (65.511) Epoch: [4][360/11272] Time 0.901 (0.831) Data 0.001 (0.010) Loss 2.4484 (2.7174) Prec@1 38.125 (34.681) Prec@5 67.500 (65.502) Epoch: [4][370/11272] Time 0.725 (0.830) Data 0.001 (0.010) Loss 2.5529 (2.7149) Prec@1 42.500 (34.754) Prec@5 64.375 (65.497) Epoch: [4][380/11272] Time 0.751 (0.830) Data 0.002 (0.010) Loss 2.7796 (2.7148) Prec@1 31.250 (34.760) Prec@5 60.625 (65.461) Epoch: [4][390/11272] Time 0.874 (0.830) Data 0.002 (0.010) Loss 2.6491 (2.7150) Prec@1 35.000 (34.763) Prec@5 71.250 (65.510) Epoch: [4][400/11272] Time 0.897 (0.829) Data 0.002 (0.009) Loss 2.7127 (2.7142) Prec@1 35.000 (34.774) Prec@5 66.250 (65.488) Epoch: [4][410/11272] Time 0.751 (0.829) Data 0.002 (0.009) Loss 2.9254 (2.7137) Prec@1 37.500 (34.811) Prec@5 61.875 (65.455) Epoch: [4][420/11272] Time 0.752 (0.828) Data 0.002 (0.009) Loss 2.6706 (2.7146) Prec@1 33.125 (34.814) Prec@5 63.750 (65.442) Epoch: [4][430/11272] Time 0.837 (0.828) Data 0.001 (0.009) Loss 2.7522 (2.7151) Prec@1 38.750 (34.840) Prec@5 64.375 (65.455) Epoch: [4][440/11272] Time 0.900 (0.828) Data 0.002 (0.009) Loss 2.8370 (2.7160) Prec@1 31.250 (34.823) Prec@5 60.625 (65.441) Epoch: [4][450/11272] Time 0.756 (0.829) Data 0.002 (0.009) Loss 2.8275 (2.7154) Prec@1 34.375 (34.856) Prec@5 64.375 (65.423) Epoch: [4][460/11272] Time 0.775 (0.828) Data 0.002 (0.008) Loss 2.7845 (2.7146) Prec@1 30.000 (34.845) Prec@5 63.750 (65.431) Epoch: [4][470/11272] Time 0.860 (0.828) Data 0.002 (0.008) Loss 2.6249 (2.7153) Prec@1 39.375 (34.813) Prec@5 68.750 (65.437) Epoch: [4][480/11272] Time 0.766 (0.828) Data 0.003 (0.008) Loss 2.5376 (2.7155) Prec@1 40.000 (34.827) Prec@5 64.375 (65.425) Epoch: [4][490/11272] Time 0.760 (0.828) Data 0.002 (0.008) Loss 2.6008 (2.7171) Prec@1 35.000 (34.796) Prec@5 70.000 (65.373) Epoch: [4][500/11272] Time 0.876 (0.829) Data 0.002 (0.008) Loss 2.7377 (2.7173) Prec@1 34.375 (34.788) Prec@5 63.750 (65.361) Epoch: [4][510/11272] Time 0.847 (0.829) Data 0.002 (0.008) Loss 2.7156 (2.7177) Prec@1 39.375 (34.793) Prec@5 60.625 (65.361) Epoch: [4][520/11272] Time 0.757 (0.829) Data 0.001 (0.008) Loss 2.8271 (2.7179) Prec@1 28.125 (34.786) Prec@5 65.625 (65.367) Epoch: [4][530/11272] Time 0.824 (0.829) Data 0.002 (0.008) Loss 2.6538 (2.7178) Prec@1 36.250 (34.794) Prec@5 67.500 (65.380) Epoch: [4][540/11272] Time 0.928 (0.829) Data 0.002 (0.007) Loss 2.8092 (2.7178) Prec@1 36.250 (34.806) Prec@5 66.250 (65.370) Epoch: [4][550/11272] Time 0.853 (0.829) Data 0.002 (0.007) Loss 2.7284 (2.7175) Prec@1 38.125 (34.825) Prec@5 64.375 (65.369) Epoch: [4][560/11272] Time 0.738 (0.829) Data 0.001 (0.007) Loss 2.8162 (2.7167) Prec@1 35.000 (34.867) Prec@5 61.250 (65.406) Epoch: [4][570/11272] Time 0.763 (0.828) Data 0.001 (0.007) Loss 2.8652 (2.7164) Prec@1 35.000 (34.883) Prec@5 62.500 (65.439) Epoch: [4][580/11272] Time 0.868 (0.828) Data 0.002 (0.007) Loss 2.6296 (2.7173) Prec@1 34.375 (34.856) Prec@5 64.375 (65.398) Epoch: [4][590/11272] Time 0.852 (0.828) Data 0.002 (0.007) Loss 2.6541 (2.7188) Prec@1 36.875 (34.837) Prec@5 66.875 (65.367) Epoch: [4][600/11272] Time 0.744 (0.828) Data 0.001 (0.007) Loss 2.7727 (2.7183) Prec@1 31.250 (34.843) Prec@5 65.000 (65.364) Epoch: [4][610/11272] Time 0.845 (0.828) Data 0.001 (0.007) Loss 2.6537 (2.7174) Prec@1 33.125 (34.856) Prec@5 70.625 (65.408) Epoch: [4][620/11272] Time 0.864 (0.828) Data 0.001 (0.007) Loss 2.8698 (2.7167) Prec@1 34.375 (34.867) Prec@5 61.250 (65.394) Epoch: [4][630/11272] Time 0.723 (0.828) Data 0.001 (0.007) Loss 2.6554 (2.7167) Prec@1 34.375 (34.877) Prec@5 68.750 (65.382) Epoch: [4][640/11272] Time 0.752 (0.828) Data 0.002 (0.007) Loss 2.8772 (2.7168) Prec@1 33.125 (34.859) Prec@5 62.500 (65.388) Epoch: [4][650/11272] Time 0.892 (0.828) Data 0.002 (0.006) Loss 2.8943 (2.7153) Prec@1 30.625 (34.898) Prec@5 61.875 (65.421) Epoch: [4][660/11272] Time 0.855 (0.827) Data 0.002 (0.006) Loss 2.9431 (2.7151) Prec@1 32.500 (34.901) Prec@5 61.250 (65.414) Epoch: [4][670/11272] Time 0.741 (0.827) Data 0.002 (0.006) Loss 2.9365 (2.7156) Prec@1 32.500 (34.902) Prec@5 65.000 (65.414) Epoch: [4][680/11272] Time 0.779 (0.826) Data 0.001 (0.006) Loss 2.7188 (2.7147) Prec@1 39.375 (34.933) Prec@5 63.750 (65.414) Epoch: [4][690/11272] Time 0.873 (0.826) Data 0.001 (0.006) Loss 2.6231 (2.7145) Prec@1 34.375 (34.937) Prec@5 68.750 (65.415) Epoch: [4][700/11272] Time 0.899 (0.826) Data 0.002 (0.006) Loss 2.6549 (2.7140) Prec@1 39.375 (34.974) Prec@5 63.125 (65.432) Epoch: [4][710/11272] Time 0.722 (0.826) Data 0.001 (0.006) Loss 2.6669 (2.7142) Prec@1 37.500 (34.981) Prec@5 64.375 (65.409) Epoch: [4][720/11272] Time 0.744 (0.826) Data 0.002 (0.006) Loss 2.6991 (2.7142) Prec@1 36.250 (34.984) Prec@5 66.875 (65.396) Epoch: [4][730/11272] Time 0.892 (0.826) Data 0.002 (0.006) Loss 2.6399 (2.7143) Prec@1 35.625 (34.975) Prec@5 67.500 (65.380) Epoch: [4][740/11272] Time 0.743 (0.826) Data 0.003 (0.006) Loss 2.6859 (2.7140) Prec@1 31.250 (34.968) Prec@5 66.250 (65.399) Epoch: [4][750/11272] Time 0.758 (0.826) Data 0.002 (0.006) Loss 2.8755 (2.7141) Prec@1 33.750 (34.973) Prec@5 68.750 (65.406) Epoch: [4][760/11272] Time 0.848 (0.826) Data 0.001 (0.006) Loss 2.7162 (2.7131) Prec@1 30.000 (34.992) Prec@5 70.000 (65.433) Epoch: [4][770/11272] Time 0.855 (0.825) Data 0.002 (0.006) Loss 2.7565 (2.7137) Prec@1 32.500 (34.988) Prec@5 68.125 (65.438) Epoch: [4][780/11272] Time 0.773 (0.826) Data 0.002 (0.006) Loss 2.8270 (2.7134) Prec@1 30.000 (34.998) Prec@5 60.625 (65.429) Epoch: [4][790/11272] Time 0.724 (0.826) Data 0.002 (0.006) Loss 2.8020 (2.7133) Prec@1 35.625 (35.001) Prec@5 58.125 (65.416) Epoch: [4][800/11272] Time 0.892 (0.826) Data 0.002 (0.006) Loss 2.7133 (2.7132) Prec@1 36.250 (34.988) Prec@5 67.500 (65.403) Epoch: [4][810/11272] Time 0.855 (0.825) Data 0.002 (0.006) Loss 2.7939 (2.7134) Prec@1 36.250 (34.990) Prec@5 66.875 (65.393) Epoch: [4][820/11272] Time 0.755 (0.825) Data 0.001 (0.005) Loss 2.7111 (2.7131) Prec@1 33.750 (34.994) Prec@5 66.875 (65.397) Epoch: [4][830/11272] Time 0.780 (0.825) Data 0.003 (0.005) Loss 2.8767 (2.7136) Prec@1 33.750 (35.001) Prec@5 61.250 (65.365) Epoch: [4][840/11272] Time 0.874 (0.825) Data 0.002 (0.005) Loss 2.6421 (2.7145) Prec@1 38.125 (35.004) Prec@5 66.875 (65.349) Epoch: [4][850/11272] Time 0.913 (0.825) Data 0.002 (0.005) Loss 2.7501 (2.7145) Prec@1 35.000 (34.994) Prec@5 67.500 (65.351) Epoch: [4][860/11272] Time 0.762 (0.825) Data 0.002 (0.005) Loss 2.9366 (2.7160) Prec@1 31.875 (34.969) Prec@5 65.000 (65.330) Epoch: [4][870/11272] Time 0.932 (0.825) Data 0.002 (0.005) Loss 2.8125 (2.7167) Prec@1 33.125 (34.953) Prec@5 65.625 (65.314) Epoch: [4][880/11272] Time 0.895 (0.825) Data 0.002 (0.005) Loss 2.7413 (2.7169) Prec@1 35.000 (34.964) Prec@5 67.500 (65.315) Epoch: [4][890/11272] Time 0.740 (0.825) Data 0.001 (0.005) Loss 2.8290 (2.7169) Prec@1 36.250 (34.966) Prec@5 59.375 (65.313) Epoch: [4][900/11272] Time 0.740 (0.825) Data 0.001 (0.005) Loss 2.4739 (2.7169) Prec@1 40.625 (34.973) Prec@5 71.250 (65.321) Epoch: [4][910/11272] Time 0.872 (0.825) Data 0.001 (0.005) Loss 2.7372 (2.7170) Prec@1 33.750 (34.970) Prec@5 65.000 (65.327) Epoch: [4][920/11272] Time 0.948 (0.825) Data 0.002 (0.005) Loss 2.7111 (2.7172) Prec@1 33.750 (34.974) Prec@5 65.000 (65.320) Epoch: [4][930/11272] Time 0.736 (0.825) Data 0.001 (0.005) Loss 2.4700 (2.7163) Prec@1 40.000 (34.991) Prec@5 71.250 (65.332) Epoch: [4][940/11272] Time 0.743 (0.824) Data 0.002 (0.005) Loss 2.3962 (2.7161) Prec@1 41.250 (35.009) Prec@5 69.375 (65.330) Epoch: [4][950/11272] Time 0.913 (0.824) Data 0.002 (0.005) Loss 2.6763 (2.7152) Prec@1 36.250 (35.014) Prec@5 65.000 (65.339) Epoch: [4][960/11272] Time 0.873 (0.824) Data 0.002 (0.005) Loss 2.8771 (2.7167) Prec@1 31.250 (35.003) Prec@5 64.375 (65.304) Epoch: [4][970/11272] Time 0.741 (0.824) Data 0.001 (0.005) Loss 2.6138 (2.7164) Prec@1 36.875 (35.015) Prec@5 63.125 (65.304) Epoch: [4][980/11272] Time 0.729 (0.824) Data 0.002 (0.005) Loss 2.5440 (2.7168) Prec@1 39.375 (34.996) Prec@5 68.750 (65.299) Epoch: [4][990/11272] Time 0.887 (0.824) Data 0.002 (0.005) Loss 2.6071 (2.7167) Prec@1 41.875 (34.991) Prec@5 63.750 (65.297) Epoch: [4][1000/11272] Time 0.817 (0.824) Data 0.001 (0.005) Loss 2.7495 (2.7172) Prec@1 35.625 (34.985) Prec@5 63.125 (65.293) Epoch: [4][1010/11272] Time 0.740 (0.824) Data 0.001 (0.005) Loss 2.7882 (2.7162) Prec@1 31.875 (34.997) Prec@5 62.500 (65.300) Epoch: [4][1020/11272] Time 0.956 (0.824) Data 0.002 (0.005) Loss 2.8870 (2.7164) Prec@1 33.750 (35.005) Prec@5 65.625 (65.298) Epoch: [4][1030/11272] Time 0.872 (0.824) Data 0.002 (0.005) Loss 2.8957 (2.7171) Prec@1 34.375 (34.985) Prec@5 62.500 (65.287) Epoch: [4][1040/11272] Time 0.759 (0.824) Data 0.002 (0.005) Loss 2.5930 (2.7165) Prec@1 39.375 (34.991) Prec@5 66.875 (65.302) Epoch: [4][1050/11272] Time 0.749 (0.823) Data 0.001 (0.005) Loss 2.5282 (2.7152) Prec@1 35.625 (35.008) Prec@5 70.000 (65.331) Epoch: [4][1060/11272] Time 0.878 (0.823) Data 0.002 (0.005) Loss 2.5417 (2.7156) Prec@1 41.875 (34.990) Prec@5 67.500 (65.315) Epoch: [4][1070/11272] Time 0.844 (0.823) Data 0.001 (0.005) Loss 2.5540 (2.7156) Prec@1 41.875 (34.986) Prec@5 69.375 (65.317) Epoch: [4][1080/11272] Time 0.746 (0.823) Data 0.002 (0.005) Loss 2.4345 (2.7151) Prec@1 35.625 (34.985) Prec@5 70.000 (65.323) Epoch: [4][1090/11272] Time 0.760 (0.823) Data 0.001 (0.005) Loss 2.6121 (2.7152) Prec@1 34.375 (34.981) Prec@5 69.375 (65.323) Epoch: [4][1100/11272] Time 0.815 (0.823) Data 0.001 (0.005) Loss 2.8538 (2.7155) Prec@1 31.875 (34.981) Prec@5 63.750 (65.311) Epoch: [4][1110/11272] Time 0.854 (0.823) Data 0.002 (0.004) Loss 2.4670 (2.7156) Prec@1 35.000 (34.972) Prec@5 71.250 (65.300) Epoch: [4][1120/11272] Time 0.728 (0.823) Data 0.001 (0.004) Loss 2.8903 (2.7159) Prec@1 31.875 (34.976) Prec@5 63.125 (65.302) Epoch: [4][1130/11272] Time 0.744 (0.822) Data 0.002 (0.004) Loss 2.6199 (2.7157) Prec@1 43.750 (34.997) Prec@5 67.500 (65.319) Epoch: [4][1140/11272] Time 0.870 (0.822) Data 0.002 (0.004) Loss 2.9668 (2.7160) Prec@1 32.500 (34.994) Prec@5 60.625 (65.311) Epoch: [4][1150/11272] Time 0.728 (0.822) Data 0.002 (0.004) Loss 2.9122 (2.7161) Prec@1 34.375 (34.995) Prec@5 59.375 (65.301) Epoch: [4][1160/11272] Time 0.741 (0.822) Data 0.001 (0.004) Loss 2.8293 (2.7165) Prec@1 36.250 (34.992) Prec@5 61.250 (65.296) Epoch: [4][1170/11272] Time 0.844 (0.822) Data 0.001 (0.004) Loss 2.6728 (2.7165) Prec@1 37.500 (34.985) Prec@5 64.375 (65.299) Epoch: [4][1180/11272] Time 0.855 (0.822) Data 0.002 (0.004) Loss 2.5289 (2.7161) Prec@1 36.875 (34.993) Prec@5 73.125 (65.319) Epoch: [4][1190/11272] Time 0.749 (0.822) Data 0.002 (0.004) Loss 2.7134 (2.7167) Prec@1 38.750 (34.974) Prec@5 67.500 (65.313) Epoch: [4][1200/11272] Time 0.724 (0.822) Data 0.001 (0.004) Loss 2.8187 (2.7172) Prec@1 35.625 (34.957) Prec@5 65.625 (65.307) Epoch: [4][1210/11272] Time 0.880 (0.822) Data 0.002 (0.004) Loss 2.7287 (2.7175) Prec@1 32.500 (34.952) Prec@5 64.375 (65.298) Epoch: [4][1220/11272] Time 0.872 (0.822) Data 0.002 (0.004) Loss 2.6710 (2.7184) Prec@1 36.250 (34.936) Prec@5 70.000 (65.284) Epoch: [4][1230/11272] Time 0.770 (0.821) Data 0.001 (0.004) Loss 2.7664 (2.7186) Prec@1 33.125 (34.937) Prec@5 63.125 (65.271) Epoch: [4][1240/11272] Time 0.749 (0.821) Data 0.002 (0.004) Loss 2.4518 (2.7180) Prec@1 35.000 (34.943) Prec@5 68.125 (65.280) Epoch: [4][1250/11272] Time 0.859 (0.821) Data 0.001 (0.004) Loss 2.5883 (2.7186) Prec@1 31.875 (34.924) Prec@5 62.500 (65.256) Epoch: [4][1260/11272] Time 0.860 (0.821) Data 0.001 (0.004) Loss 2.7413 (2.7184) Prec@1 34.375 (34.923) Prec@5 63.750 (65.255) Epoch: [4][1270/11272] Time 0.731 (0.821) Data 0.001 (0.004) Loss 2.7354 (2.7181) Prec@1 31.875 (34.915) Prec@5 66.250 (65.271) Epoch: [4][1280/11272] Time 0.908 (0.821) Data 0.001 (0.004) Loss 2.7363 (2.7189) Prec@1 34.375 (34.895) Prec@5 66.250 (65.258) Epoch: [4][1290/11272] Time 0.878 (0.821) Data 0.002 (0.004) Loss 2.5060 (2.7183) Prec@1 36.875 (34.906) Prec@5 71.875 (65.270) Epoch: [4][1300/11272] Time 0.749 (0.821) Data 0.002 (0.004) Loss 2.7033 (2.7181) Prec@1 33.750 (34.900) Prec@5 66.250 (65.274) Epoch: [4][1310/11272] Time 0.744 (0.820) Data 0.002 (0.004) Loss 2.8064 (2.7181) Prec@1 29.375 (34.906) Prec@5 60.625 (65.271) Epoch: [4][1320/11272] Time 0.905 (0.820) Data 0.001 (0.004) Loss 2.8583 (2.7182) Prec@1 36.250 (34.901) Prec@5 65.625 (65.275) Epoch: [4][1330/11272] Time 0.839 (0.820) Data 0.001 (0.004) Loss 2.6757 (2.7187) Prec@1 35.000 (34.888) Prec@5 63.750 (65.260) Epoch: [4][1340/11272] Time 0.776 (0.820) Data 0.002 (0.004) Loss 2.8503 (2.7186) Prec@1 33.125 (34.891) Prec@5 61.250 (65.260) Epoch: [4][1350/11272] Time 0.714 (0.820) Data 0.002 (0.004) Loss 2.4253 (2.7183) Prec@1 41.875 (34.894) Prec@5 70.625 (65.263) Epoch: [4][1360/11272] Time 0.849 (0.820) Data 0.001 (0.004) Loss 2.9869 (2.7186) Prec@1 31.875 (34.900) Prec@5 61.250 (65.261) Epoch: [4][1370/11272] Time 0.826 (0.820) Data 0.002 (0.004) Loss 2.6425 (2.7182) Prec@1 37.500 (34.908) Prec@5 68.125 (65.274) Epoch: [4][1380/11272] Time 0.740 (0.820) Data 0.001 (0.004) Loss 2.5862 (2.7180) Prec@1 30.625 (34.906) Prec@5 68.750 (65.276) Epoch: [4][1390/11272] Time 0.751 (0.820) Data 0.002 (0.004) Loss 2.7450 (2.7180) Prec@1 31.250 (34.900) Prec@5 63.750 (65.275) Epoch: [4][1400/11272] Time 0.896 (0.820) Data 0.002 (0.004) Loss 2.7278 (2.7178) Prec@1 35.625 (34.907) Prec@5 65.000 (65.274) Epoch: [4][1410/11272] Time 0.754 (0.820) Data 0.004 (0.004) Loss 2.9375 (2.7179) Prec@1 28.750 (34.907) Prec@5 58.750 (65.271) Epoch: [4][1420/11272] Time 0.752 (0.820) Data 0.001 (0.004) Loss 2.8438 (2.7177) Prec@1 33.750 (34.901) Prec@5 64.375 (65.271) Epoch: [4][1430/11272] Time 0.871 (0.820) Data 0.001 (0.004) Loss 2.5698 (2.7171) Prec@1 33.125 (34.903) Prec@5 70.000 (65.286) Epoch: [4][1440/11272] Time 0.898 (0.820) Data 0.002 (0.004) Loss 2.6668 (2.7167) Prec@1 34.375 (34.907) Prec@5 67.500 (65.294) Epoch: [4][1450/11272] Time 0.769 (0.820) Data 0.002 (0.004) Loss 2.8093 (2.7168) Prec@1 33.750 (34.907) Prec@5 60.625 (65.290) Epoch: [4][1460/11272] Time 0.732 (0.820) Data 0.002 (0.004) Loss 2.7632 (2.7167) Prec@1 35.000 (34.910) Prec@5 59.375 (65.285) Epoch: [4][1470/11272] Time 0.815 (0.820) Data 0.001 (0.004) Loss 2.6728 (2.7168) Prec@1 33.125 (34.906) Prec@5 68.125 (65.285) Epoch: [4][1480/11272] Time 0.889 (0.820) Data 0.001 (0.004) Loss 2.5991 (2.7169) Prec@1 37.500 (34.901) Prec@5 72.500 (65.284) Epoch: [4][1490/11272] Time 0.786 (0.820) Data 0.003 (0.004) Loss 2.4991 (2.7166) Prec@1 36.875 (34.904) Prec@5 68.125 (65.293) Epoch: [4][1500/11272] Time 0.757 (0.820) Data 0.002 (0.004) Loss 2.7230 (2.7165) Prec@1 32.500 (34.906) Prec@5 63.750 (65.296) Epoch: [4][1510/11272] Time 0.864 (0.820) Data 0.002 (0.004) Loss 2.5984 (2.7161) Prec@1 39.375 (34.908) Prec@5 68.125 (65.302) Epoch: [4][1520/11272] Time 0.912 (0.820) Data 0.003 (0.004) Loss 2.8128 (2.7161) Prec@1 30.625 (34.904) Prec@5 64.375 (65.307) Epoch: [4][1530/11272] Time 0.811 (0.820) Data 0.002 (0.004) Loss 2.6038 (2.7157) Prec@1 36.250 (34.905) Prec@5 61.875 (65.314) Epoch: [4][1540/11272] Time 0.911 (0.821) Data 0.001 (0.004) Loss 2.7171 (2.7163) Prec@1 33.125 (34.890) Prec@5 70.625 (65.309) Epoch: [4][1550/11272] Time 0.906 (0.821) Data 0.002 (0.004) Loss 2.7228 (2.7163) Prec@1 36.875 (34.892) Prec@5 61.250 (65.310) Epoch: [4][1560/11272] Time 0.744 (0.821) Data 0.001 (0.004) Loss 2.5313 (2.7160) Prec@1 40.000 (34.892) Prec@5 70.000 (65.316) Epoch: [4][1570/11272] Time 0.771 (0.821) Data 0.002 (0.004) Loss 2.5928 (2.7156) Prec@1 41.250 (34.907) Prec@5 64.375 (65.329) Epoch: [4][1580/11272] Time 0.980 (0.821) Data 0.002 (0.004) Loss 2.6051 (2.7153) Prec@1 34.375 (34.912) Prec@5 66.250 (65.332) Epoch: [4][1590/11272] Time 0.892 (0.821) Data 0.002 (0.004) Loss 2.8456 (2.7155) Prec@1 33.750 (34.910) Prec@5 66.875 (65.341) Epoch: [4][1600/11272] Time 0.795 (0.821) Data 0.002 (0.004) Loss 2.6862 (2.7155) Prec@1 36.875 (34.913) Prec@5 66.875 (65.345) Epoch: [4][1610/11272] Time 0.742 (0.821) Data 0.002 (0.004) Loss 2.6274 (2.7154) Prec@1 35.000 (34.914) Prec@5 65.625 (65.345) Epoch: [4][1620/11272] Time 0.867 (0.821) Data 0.002 (0.004) Loss 2.8546 (2.7147) Prec@1 34.375 (34.926) Prec@5 63.750 (65.354) Epoch: [4][1630/11272] Time 0.881 (0.821) Data 0.002 (0.004) Loss 2.6943 (2.7144) Prec@1 30.625 (34.927) Prec@5 69.375 (65.363) Epoch: [4][1640/11272] Time 0.745 (0.821) Data 0.002 (0.004) Loss 2.7491 (2.7141) Prec@1 33.125 (34.915) Prec@5 61.875 (65.371) Epoch: [4][1650/11272] Time 0.742 (0.821) Data 0.002 (0.004) Loss 2.6796 (2.7145) Prec@1 36.875 (34.903) Prec@5 66.250 (65.358) Epoch: [4][1660/11272] Time 0.884 (0.821) Data 0.002 (0.004) Loss 2.5321 (2.7148) Prec@1 40.000 (34.898) Prec@5 67.500 (65.354) Epoch: [4][1670/11272] Time 0.756 (0.821) Data 0.003 (0.004) Loss 2.7400 (2.7149) Prec@1 33.750 (34.898) Prec@5 61.250 (65.349) Epoch: [4][1680/11272] Time 0.749 (0.821) Data 0.002 (0.004) Loss 2.5823 (2.7145) Prec@1 34.375 (34.897) Prec@5 72.500 (65.358) Epoch: [4][1690/11272] Time 0.946 (0.821) Data 0.002 (0.004) Loss 2.8260 (2.7150) Prec@1 33.125 (34.888) Prec@5 65.625 (65.353) Epoch: [4][1700/11272] Time 0.848 (0.821) Data 0.001 (0.004) Loss 2.2443 (2.7149) Prec@1 42.500 (34.896) Prec@5 75.625 (65.354) Epoch: [4][1710/11272] Time 0.753 (0.821) Data 0.002 (0.004) Loss 2.8699 (2.7150) Prec@1 32.500 (34.896) Prec@5 63.750 (65.349) Epoch: [4][1720/11272] Time 0.785 (0.821) Data 0.002 (0.003) Loss 2.7190 (2.7150) Prec@1 36.875 (34.895) Prec@5 66.250 (65.347) Epoch: [4][1730/11272] Time 0.880 (0.821) Data 0.002 (0.003) Loss 2.5079 (2.7151) Prec@1 33.125 (34.889) Prec@5 72.500 (65.356) Epoch: [4][1740/11272] Time 0.843 (0.821) Data 0.001 (0.003) Loss 2.4184 (2.7146) Prec@1 41.250 (34.903) Prec@5 68.125 (65.366) Epoch: [4][1750/11272] Time 0.747 (0.821) Data 0.002 (0.003) Loss 2.9729 (2.7150) Prec@1 31.250 (34.891) Prec@5 61.250 (65.358) Epoch: [4][1760/11272] Time 0.742 (0.821) Data 0.002 (0.003) Loss 2.5407 (2.7148) Prec@1 39.375 (34.896) Prec@5 68.125 (65.356) Epoch: [4][1770/11272] Time 0.867 (0.821) Data 0.001 (0.003) Loss 2.8747 (2.7147) Prec@1 32.500 (34.903) Prec@5 60.000 (65.357) Epoch: [4][1780/11272] Time 0.830 (0.821) Data 0.001 (0.003) Loss 3.0700 (2.7149) Prec@1 32.500 (34.908) Prec@5 57.500 (65.354) Epoch: [4][1790/11272] Time 0.729 (0.820) Data 0.002 (0.003) Loss 2.7880 (2.7146) Prec@1 36.875 (34.916) Prec@5 60.000 (65.354) Epoch: [4][1800/11272] Time 0.913 (0.820) Data 0.002 (0.003) Loss 2.6803 (2.7144) Prec@1 31.250 (34.918) Prec@5 67.500 (65.352) Epoch: [4][1810/11272] Time 0.892 (0.820) Data 0.001 (0.003) Loss 2.6734 (2.7148) Prec@1 35.625 (34.910) Prec@5 65.000 (65.342) Epoch: [4][1820/11272] Time 0.758 (0.821) Data 0.002 (0.003) Loss 2.7702 (2.7148) Prec@1 32.500 (34.916) Prec@5 63.750 (65.339) Epoch: [4][1830/11272] Time 0.834 (0.821) Data 0.002 (0.003) Loss 2.7971 (2.7148) Prec@1 31.875 (34.913) Prec@5 63.125 (65.337) Epoch: [4][1840/11272] Time 0.900 (0.821) Data 0.002 (0.003) Loss 2.5716 (2.7146) Prec@1 34.375 (34.915) Prec@5 66.250 (65.334) Epoch: [4][1850/11272] Time 0.846 (0.821) Data 0.001 (0.003) Loss 2.6190 (2.7149) Prec@1 35.000 (34.906) Prec@5 69.375 (65.329) Epoch: [4][1860/11272] Time 0.749 (0.821) Data 0.002 (0.003) Loss 2.7557 (2.7152) Prec@1 31.250 (34.909) Prec@5 62.500 (65.318) Epoch: [4][1870/11272] Time 0.734 (0.821) Data 0.001 (0.003) Loss 3.1044 (2.7154) Prec@1 33.750 (34.908) Prec@5 58.750 (65.312) Epoch: [4][1880/11272] Time 0.934 (0.821) Data 0.002 (0.003) Loss 2.7193 (2.7151) Prec@1 30.625 (34.915) Prec@5 65.625 (65.322) Epoch: [4][1890/11272] Time 0.901 (0.821) Data 0.003 (0.003) Loss 2.7277 (2.7150) Prec@1 30.000 (34.908) Prec@5 66.250 (65.326) Epoch: [4][1900/11272] Time 0.748 (0.821) Data 0.002 (0.003) Loss 2.6077 (2.7152) Prec@1 33.750 (34.912) Prec@5 70.625 (65.324) Epoch: [4][1910/11272] Time 0.748 (0.821) Data 0.002 (0.003) Loss 2.8667 (2.7153) Prec@1 32.500 (34.915) Prec@5 62.500 (65.322) Epoch: [4][1920/11272] Time 0.839 (0.821) Data 0.001 (0.003) Loss 2.6207 (2.7154) Prec@1 30.000 (34.910) Prec@5 66.250 (65.321) Epoch: [4][1930/11272] Time 0.910 (0.821) Data 0.002 (0.003) Loss 2.4337 (2.7153) Prec@1 37.500 (34.916) Prec@5 71.250 (65.319) Epoch: [4][1940/11272] Time 0.738 (0.821) Data 0.002 (0.003) Loss 2.8249 (2.7154) Prec@1 31.250 (34.916) Prec@5 65.000 (65.311) Epoch: [4][1950/11272] Time 0.883 (0.821) Data 0.001 (0.003) Loss 2.7087 (2.7155) Prec@1 38.125 (34.917) Prec@5 65.625 (65.317) Epoch: [4][1960/11272] Time 0.874 (0.821) Data 0.002 (0.003) Loss 2.6047 (2.7154) Prec@1 38.125 (34.924) Prec@5 71.250 (65.323) Epoch: [4][1970/11272] Time 0.733 (0.820) Data 0.001 (0.003) Loss 2.9401 (2.7155) Prec@1 29.375 (34.922) Prec@5 57.500 (65.324) Epoch: [4][1980/11272] Time 0.741 (0.820) Data 0.002 (0.003) Loss 2.9580 (2.7160) Prec@1 34.375 (34.915) Prec@5 60.000 (65.318) Epoch: [4][1990/11272] Time 0.924 (0.820) Data 0.001 (0.003) Loss 2.8640 (2.7160) Prec@1 33.750 (34.911) Prec@5 64.375 (65.314) Epoch: [4][2000/11272] Time 0.890 (0.820) Data 0.004 (0.003) Loss 2.9387 (2.7159) Prec@1 31.250 (34.909) Prec@5 61.250 (65.320) Epoch: [4][2010/11272] Time 0.736 (0.820) Data 0.001 (0.003) Loss 2.5879 (2.7157) Prec@1 38.125 (34.915) Prec@5 68.750 (65.325) Epoch: [4][2020/11272] Time 0.778 (0.820) Data 0.002 (0.003) Loss 2.5925 (2.7153) Prec@1 35.625 (34.928) Prec@5 70.000 (65.333) Epoch: [4][2030/11272] Time 0.854 (0.820) Data 0.001 (0.003) Loss 2.5638 (2.7155) Prec@1 37.500 (34.926) Prec@5 66.875 (65.325) Epoch: [4][2040/11272] Time 0.863 (0.820) Data 0.002 (0.003) Loss 2.6318 (2.7153) Prec@1 36.250 (34.919) Prec@5 65.625 (65.331) Epoch: [4][2050/11272] Time 0.729 (0.820) Data 0.001 (0.003) Loss 2.6748 (2.7152) Prec@1 40.000 (34.918) Prec@5 66.875 (65.335) Epoch: [4][2060/11272] Time 0.744 (0.820) Data 0.002 (0.003) Loss 2.9354 (2.7149) Prec@1 29.375 (34.921) Prec@5 62.500 (65.345) Epoch: [4][2070/11272] Time 0.825 (0.820) Data 0.001 (0.003) Loss 2.6948 (2.7148) Prec@1 35.625 (34.922) Prec@5 66.875 (65.346) Epoch: [4][2080/11272] Time 0.734 (0.820) Data 0.002 (0.003) Loss 2.5102 (2.7151) Prec@1 36.250 (34.920) Prec@5 71.250 (65.340) Epoch: [4][2090/11272] Time 0.743 (0.820) Data 0.002 (0.003) Loss 2.6286 (2.7148) Prec@1 40.000 (34.928) Prec@5 66.250 (65.352) Epoch: [4][2100/11272] Time 0.899 (0.820) Data 0.002 (0.003) Loss 2.9304 (2.7144) Prec@1 36.250 (34.934) Prec@5 60.625 (65.361) Epoch: [4][2110/11272] Time 0.862 (0.820) Data 0.008 (0.003) Loss 2.5592 (2.7139) Prec@1 41.250 (34.942) Prec@5 66.250 (65.370) Epoch: [4][2120/11272] Time 0.735 (0.820) Data 0.001 (0.003) Loss 2.8013 (2.7138) Prec@1 36.875 (34.941) Prec@5 65.000 (65.376) Epoch: [4][2130/11272] Time 0.754 (0.820) Data 0.002 (0.003) Loss 2.4709 (2.7134) Prec@1 38.750 (34.950) Prec@5 72.500 (65.383) Epoch: [4][2140/11272] Time 0.861 (0.820) Data 0.002 (0.003) Loss 2.6297 (2.7133) Prec@1 39.375 (34.952) Prec@5 67.500 (65.381) Epoch: [4][2150/11272] Time 0.869 (0.820) Data 0.002 (0.003) Loss 2.7268 (2.7129) Prec@1 31.875 (34.954) Prec@5 63.750 (65.388) Epoch: [4][2160/11272] Time 0.756 (0.820) Data 0.001 (0.003) Loss 2.8409 (2.7127) Prec@1 33.125 (34.955) Prec@5 64.375 (65.390) Epoch: [4][2170/11272] Time 0.781 (0.820) Data 0.002 (0.003) Loss 2.6849 (2.7130) Prec@1 35.000 (34.950) Prec@5 61.875 (65.383) Epoch: [4][2180/11272] Time 0.885 (0.820) Data 0.002 (0.003) Loss 2.5436 (2.7130) Prec@1 39.375 (34.950) Prec@5 63.750 (65.384) Epoch: [4][2190/11272] Time 0.853 (0.820) Data 0.001 (0.003) Loss 2.5489 (2.7124) Prec@1 37.500 (34.962) Prec@5 64.375 (65.393) Epoch: [4][2200/11272] Time 0.742 (0.820) Data 0.001 (0.003) Loss 2.4164 (2.7124) Prec@1 38.750 (34.957) Prec@5 73.125 (65.396) Epoch: [4][2210/11272] Time 0.871 (0.820) Data 0.002 (0.003) Loss 2.7036 (2.7129) Prec@1 36.875 (34.952) Prec@5 68.125 (65.393) Epoch: [4][2220/11272] Time 0.911 (0.820) Data 0.002 (0.003) Loss 2.9809 (2.7131) Prec@1 31.250 (34.951) Prec@5 62.500 (65.390) Epoch: [4][2230/11272] Time 0.783 (0.820) Data 0.001 (0.003) Loss 2.8427 (2.7133) Prec@1 30.000 (34.951) Prec@5 62.500 (65.382) Epoch: [4][2240/11272] Time 0.749 (0.820) Data 0.002 (0.003) Loss 2.6171 (2.7132) Prec@1 35.625 (34.950) Prec@5 63.125 (65.380) Epoch: [4][2250/11272] Time 0.866 (0.820) Data 0.002 (0.003) Loss 2.7515 (2.7137) Prec@1 38.125 (34.941) Prec@5 64.375 (65.365) Epoch: [4][2260/11272] Time 0.931 (0.820) Data 0.001 (0.003) Loss 2.5567 (2.7142) Prec@1 38.750 (34.926) Prec@5 69.375 (65.357) Epoch: [4][2270/11272] Time 0.743 (0.820) Data 0.002 (0.003) Loss 2.6097 (2.7141) Prec@1 36.250 (34.927) Prec@5 66.250 (65.357) Epoch: [4][2280/11272] Time 0.738 (0.820) Data 0.002 (0.003) Loss 2.5660 (2.7141) Prec@1 36.875 (34.920) Prec@5 68.750 (65.358) Epoch: [4][2290/11272] Time 0.889 (0.820) Data 0.002 (0.003) Loss 2.8920 (2.7143) Prec@1 33.750 (34.916) Prec@5 62.500 (65.352) Epoch: [4][2300/11272] Time 0.896 (0.820) Data 0.002 (0.003) Loss 2.7633 (2.7142) Prec@1 38.125 (34.920) Prec@5 61.250 (65.355) Epoch: [4][2310/11272] Time 0.814 (0.820) Data 0.002 (0.003) Loss 2.6959 (2.7144) Prec@1 33.750 (34.919) Prec@5 68.750 (65.350) Epoch: [4][2320/11272] Time 0.821 (0.820) Data 0.002 (0.003) Loss 2.6837 (2.7141) Prec@1 37.500 (34.920) Prec@5 68.125 (65.353) Epoch: [4][2330/11272] Time 0.903 (0.820) Data 0.002 (0.003) Loss 2.4654 (2.7141) Prec@1 33.125 (34.918) Prec@5 71.875 (65.351) Epoch: [4][2340/11272] Time 0.754 (0.820) Data 0.004 (0.003) Loss 2.7593 (2.7142) Prec@1 40.000 (34.918) Prec@5 60.625 (65.353) Epoch: [4][2350/11272] Time 0.740 (0.820) Data 0.001 (0.003) Loss 2.6055 (2.7140) Prec@1 37.500 (34.922) Prec@5 66.875 (65.352) Epoch: [4][2360/11272] Time 0.903 (0.820) Data 0.003 (0.003) Loss 2.7407 (2.7143) Prec@1 36.250 (34.915) Prec@5 63.125 (65.345) Epoch: [4][2370/11272] Time 0.849 (0.820) Data 0.002 (0.003) Loss 2.6588 (2.7142) Prec@1 39.375 (34.916) Prec@5 66.875 (65.350) Epoch: [4][2380/11272] Time 0.771 (0.820) Data 0.002 (0.003) Loss 2.7836 (2.7144) Prec@1 30.000 (34.906) Prec@5 65.000 (65.343) Epoch: [4][2390/11272] Time 0.740 (0.820) Data 0.002 (0.003) Loss 2.9053 (2.7142) Prec@1 28.750 (34.910) Prec@5 65.625 (65.349) Epoch: [4][2400/11272] Time 0.960 (0.820) Data 0.002 (0.003) Loss 2.8483 (2.7144) Prec@1 33.750 (34.914) Prec@5 66.250 (65.344) Epoch: [4][2410/11272] Time 0.864 (0.820) Data 0.001 (0.003) Loss 2.4709 (2.7144) Prec@1 39.375 (34.911) Prec@5 68.750 (65.342) Epoch: [4][2420/11272] Time 0.745 (0.820) Data 0.002 (0.003) Loss 2.5597 (2.7141) Prec@1 33.750 (34.912) Prec@5 67.500 (65.347) Epoch: [4][2430/11272] Time 0.776 (0.820) Data 0.002 (0.003) Loss 2.7438 (2.7143) Prec@1 37.500 (34.911) Prec@5 65.625 (65.338) Epoch: [4][2440/11272] Time 0.886 (0.820) Data 0.002 (0.003) Loss 2.5132 (2.7143) Prec@1 35.625 (34.914) Prec@5 69.375 (65.337) Epoch: [4][2450/11272] Time 0.859 (0.820) Data 0.002 (0.003) Loss 2.5179 (2.7141) Prec@1 40.000 (34.922) Prec@5 69.375 (65.346) Epoch: [4][2460/11272] Time 0.749 (0.820) Data 0.002 (0.003) Loss 2.7069 (2.7142) Prec@1 35.000 (34.922) Prec@5 61.250 (65.345) Epoch: [4][2470/11272] Time 0.867 (0.820) Data 0.002 (0.003) Loss 2.8501 (2.7144) Prec@1 36.250 (34.919) Prec@5 62.500 (65.341) Epoch: [4][2480/11272] Time 0.861 (0.820) Data 0.002 (0.003) Loss 2.8383 (2.7140) Prec@1 31.875 (34.927) Prec@5 65.625 (65.349) Epoch: [4][2490/11272] Time 0.779 (0.820) Data 0.002 (0.003) Loss 2.9289 (2.7142) Prec@1 26.250 (34.919) Prec@5 63.125 (65.344) Epoch: [4][2500/11272] Time 0.761 (0.820) Data 0.002 (0.003) Loss 2.6863 (2.7140) Prec@1 36.250 (34.931) Prec@5 68.125 (65.353) Epoch: [4][2510/11272] Time 0.893 (0.820) Data 0.002 (0.003) Loss 2.6393 (2.7136) Prec@1 31.250 (34.933) Prec@5 64.375 (65.359) Epoch: [4][2520/11272] Time 0.852 (0.820) Data 0.001 (0.003) Loss 2.9258 (2.7137) Prec@1 28.125 (34.926) Prec@5 59.375 (65.353) Epoch: [4][2530/11272] Time 0.770 (0.820) Data 0.002 (0.003) Loss 2.3888 (2.7136) Prec@1 35.000 (34.922) Prec@5 74.375 (65.352) Epoch: [4][2540/11272] Time 0.780 (0.820) Data 0.002 (0.003) Loss 2.7013 (2.7137) Prec@1 36.875 (34.919) Prec@5 67.500 (65.355) Epoch: [4][2550/11272] Time 0.881 (0.820) Data 0.002 (0.003) Loss 2.5926 (2.7139) Prec@1 31.250 (34.912) Prec@5 67.500 (65.353) Epoch: [4][2560/11272] Time 0.848 (0.820) Data 0.002 (0.003) Loss 2.5343 (2.7140) Prec@1 32.500 (34.907) Prec@5 68.750 (65.349) Epoch: [4][2570/11272] Time 0.770 (0.820) Data 0.002 (0.003) Loss 2.5375 (2.7139) Prec@1 36.875 (34.908) Prec@5 69.375 (65.354) Epoch: [4][2580/11272] Time 0.746 (0.820) Data 0.002 (0.003) Loss 2.5427 (2.7136) Prec@1 38.125 (34.912) Prec@5 71.250 (65.360) Epoch: [4][2590/11272] Time 0.888 (0.820) Data 0.002 (0.003) Loss 2.4881 (2.7138) Prec@1 36.250 (34.901) Prec@5 67.500 (65.354) Epoch: [4][2600/11272] Time 0.772 (0.820) Data 0.004 (0.003) Loss 2.6225 (2.7136) Prec@1 40.625 (34.900) Prec@5 65.000 (65.360) Epoch: [4][2610/11272] Time 0.767 (0.820) Data 0.002 (0.003) Loss 2.6989 (2.7135) Prec@1 34.375 (34.904) Prec@5 65.000 (65.359) Epoch: [4][2620/11272] Time 0.907 (0.821) Data 0.002 (0.003) Loss 2.8795 (2.7137) Prec@1 36.875 (34.902) Prec@5 63.125 (65.355) Epoch: [4][2630/11272] Time 0.863 (0.821) Data 0.002 (0.003) Loss 2.5929 (2.7134) Prec@1 41.250 (34.909) Prec@5 67.500 (65.360) Epoch: [4][2640/11272] Time 0.816 (0.821) Data 0.001 (0.003) Loss 2.5273 (2.7137) Prec@1 40.000 (34.908) Prec@5 67.500 (65.356) Epoch: [4][2650/11272] Time 0.850 (0.821) Data 0.002 (0.003) Loss 2.6712 (2.7136) Prec@1 33.750 (34.910) Prec@5 68.125 (65.355) Epoch: [4][2660/11272] Time 0.865 (0.821) Data 0.001 (0.003) Loss 2.4930 (2.7135) Prec@1 35.625 (34.913) Prec@5 71.250 (65.362) Epoch: [4][2670/11272] Time 0.993 (0.821) Data 0.002 (0.003) Loss 2.7540 (2.7138) Prec@1 36.875 (34.901) Prec@5 64.375 (65.356) Epoch: [4][2680/11272] Time 0.776 (0.821) Data 0.002 (0.003) Loss 2.6974 (2.7138) Prec@1 35.000 (34.902) Prec@5 67.500 (65.353) Epoch: [4][2690/11272] Time 0.831 (0.821) Data 0.002 (0.003) Loss 2.7300 (2.7138) Prec@1 31.875 (34.904) Prec@5 63.125 (65.347) Epoch: [4][2700/11272] Time 0.873 (0.821) Data 0.002 (0.003) Loss 2.9616 (2.7137) Prec@1 32.500 (34.909) Prec@5 61.250 (65.347) Epoch: [4][2710/11272] Time 0.920 (0.821) Data 0.002 (0.003) Loss 3.0321 (2.7139) Prec@1 30.000 (34.911) Prec@5 58.750 (65.343) Epoch: [4][2720/11272] Time 0.751 (0.821) Data 0.001 (0.003) Loss 2.6969 (2.7136) Prec@1 36.250 (34.916) Prec@5 65.625 (65.348) Epoch: [4][2730/11272] Time 0.862 (0.821) Data 0.002 (0.003) Loss 2.7415 (2.7138) Prec@1 40.000 (34.916) Prec@5 66.250 (65.345) Epoch: [4][2740/11272] Time 0.942 (0.822) Data 0.002 (0.003) Loss 2.7149 (2.7140) Prec@1 32.500 (34.916) Prec@5 66.250 (65.337) Epoch: [4][2750/11272] Time 0.766 (0.822) Data 0.002 (0.003) Loss 2.7901 (2.7141) Prec@1 36.875 (34.916) Prec@5 61.875 (65.336) Epoch: [4][2760/11272] Time 0.787 (0.822) Data 0.002 (0.003) Loss 2.3954 (2.7142) Prec@1 46.875 (34.918) Prec@5 68.750 (65.335) Epoch: [4][2770/11272] Time 0.973 (0.822) Data 0.002 (0.003) Loss 2.8076 (2.7142) Prec@1 31.250 (34.919) Prec@5 63.750 (65.330) Epoch: [4][2780/11272] Time 0.878 (0.822) Data 0.001 (0.003) Loss 2.5585 (2.7144) Prec@1 36.875 (34.914) Prec@5 67.500 (65.327) Epoch: [4][2790/11272] Time 0.747 (0.822) Data 0.002 (0.003) Loss 2.7855 (2.7144) Prec@1 35.625 (34.911) Prec@5 63.125 (65.323) Epoch: [4][2800/11272] Time 0.768 (0.822) Data 0.002 (0.003) Loss 2.8219 (2.7145) Prec@1 35.000 (34.911) Prec@5 65.625 (65.324) Epoch: [4][2810/11272] Time 0.899 (0.822) Data 0.002 (0.003) Loss 3.1063 (2.7146) Prec@1 27.500 (34.913) Prec@5 56.875 (65.322) Epoch: [4][2820/11272] Time 0.871 (0.822) Data 0.002 (0.003) Loss 2.5095 (2.7146) Prec@1 37.500 (34.913) Prec@5 69.375 (65.317) Epoch: [4][2830/11272] Time 0.774 (0.822) Data 0.001 (0.003) Loss 2.7366 (2.7146) Prec@1 33.750 (34.914) Prec@5 65.000 (65.319) Epoch: [4][2840/11272] Time 0.752 (0.822) Data 0.002 (0.003) Loss 3.0404 (2.7148) Prec@1 26.875 (34.910) Prec@5 60.000 (65.313) Epoch: [4][2850/11272] Time 0.888 (0.822) Data 0.001 (0.003) Loss 2.7572 (2.7147) Prec@1 37.500 (34.907) Prec@5 66.875 (65.317) Epoch: [4][2860/11272] Time 0.848 (0.822) Data 0.002 (0.003) Loss 2.7248 (2.7147) Prec@1 33.125 (34.902) Prec@5 65.000 (65.314) Epoch: [4][2870/11272] Time 0.754 (0.822) Data 0.002 (0.003) Loss 2.8065 (2.7146) Prec@1 35.000 (34.904) Prec@5 69.375 (65.320) Epoch: [4][2880/11272] Time 0.901 (0.822) Data 0.002 (0.003) Loss 2.8652 (2.7145) Prec@1 31.250 (34.900) Prec@5 67.500 (65.323) Epoch: [4][2890/11272] Time 0.873 (0.822) Data 0.001 (0.003) Loss 2.7520 (2.7146) Prec@1 33.750 (34.897) Prec@5 63.750 (65.324) Epoch: [4][2900/11272] Time 0.764 (0.822) Data 0.001 (0.003) Loss 2.7598 (2.7144) Prec@1 30.625 (34.895) Prec@5 63.750 (65.329) Epoch: [4][2910/11272] Time 0.756 (0.822) Data 0.001 (0.003) Loss 2.5858 (2.7142) Prec@1 39.375 (34.898) Prec@5 71.250 (65.333) Epoch: [4][2920/11272] Time 0.847 (0.822) Data 0.001 (0.003) Loss 2.6176 (2.7140) Prec@1 36.875 (34.898) Prec@5 63.125 (65.334) Epoch: [4][2930/11272] Time 0.877 (0.822) Data 0.002 (0.003) Loss 2.6236 (2.7140) Prec@1 37.500 (34.900) Prec@5 66.250 (65.334) Epoch: [4][2940/11272] Time 0.766 (0.822) Data 0.001 (0.003) Loss 2.6418 (2.7141) Prec@1 41.875 (34.905) Prec@5 69.375 (65.334) Epoch: [4][2950/11272] Time 0.773 (0.822) Data 0.002 (0.003) Loss 2.5215 (2.7139) Prec@1 40.625 (34.907) Prec@5 73.125 (65.339) Epoch: [4][2960/11272] Time 0.927 (0.822) Data 0.002 (0.003) Loss 2.7877 (2.7138) Prec@1 30.625 (34.909) Prec@5 65.625 (65.338) Epoch: [4][2970/11272] Time 0.858 (0.822) Data 0.001 (0.003) Loss 2.8271 (2.7139) Prec@1 33.125 (34.911) Prec@5 65.625 (65.335) Epoch: [4][2980/11272] Time 0.746 (0.822) Data 0.001 (0.003) Loss 2.5712 (2.7141) Prec@1 32.500 (34.906) Prec@5 70.625 (65.338) Epoch: [4][2990/11272] Time 0.867 (0.822) Data 0.002 (0.003) Loss 2.5947 (2.7139) Prec@1 40.000 (34.915) Prec@5 66.875 (65.336) Epoch: [4][3000/11272] Time 0.906 (0.822) Data 0.002 (0.003) Loss 2.6918 (2.7138) Prec@1 38.125 (34.915) Prec@5 65.625 (65.341) Epoch: [4][3010/11272] Time 0.747 (0.822) Data 0.001 (0.003) Loss 2.5369 (2.7136) Prec@1 35.625 (34.918) Prec@5 65.625 (65.341) Epoch: [4][3020/11272] Time 0.761 (0.822) Data 0.001 (0.003) Loss 2.6892 (2.7137) Prec@1 32.500 (34.915) Prec@5 63.750 (65.339) Epoch: [4][3030/11272] Time 0.917 (0.822) Data 0.001 (0.003) Loss 2.8155 (2.7137) Prec@1 29.375 (34.913) Prec@5 61.875 (65.338) Epoch: [4][3040/11272] Time 0.868 (0.822) Data 0.002 (0.003) Loss 2.9271 (2.7139) Prec@1 28.125 (34.910) Prec@5 57.500 (65.335) Epoch: [4][3050/11272] Time 0.757 (0.822) Data 0.002 (0.003) Loss 2.7435 (2.7140) Prec@1 32.500 (34.907) Prec@5 63.125 (65.332) Epoch: [4][3060/11272] Time 0.746 (0.822) Data 0.002 (0.003) Loss 2.5730 (2.7139) Prec@1 40.625 (34.910) Prec@5 64.375 (65.325) Epoch: [4][3070/11272] Time 0.865 (0.822) Data 0.002 (0.003) Loss 2.8119 (2.7141) Prec@1 33.750 (34.909) Prec@5 65.000 (65.323) Epoch: [4][3080/11272] Time 0.893 (0.822) Data 0.003 (0.003) Loss 2.6978 (2.7142) Prec@1 41.250 (34.913) Prec@5 66.875 (65.319) Epoch: [4][3090/11272] Time 0.749 (0.822) Data 0.001 (0.003) Loss 2.6554 (2.7142) Prec@1 31.875 (34.913) Prec@5 70.625 (65.321) Epoch: [4][3100/11272] Time 0.741 (0.822) Data 0.002 (0.003) Loss 2.7602 (2.7145) Prec@1 38.125 (34.909) Prec@5 65.625 (65.315) Epoch: [4][3110/11272] Time 0.857 (0.822) Data 0.001 (0.003) Loss 2.8629 (2.7145) Prec@1 31.875 (34.914) Prec@5 66.875 (65.318) Epoch: [4][3120/11272] Time 0.943 (0.822) Data 0.002 (0.003) Loss 2.6277 (2.7145) Prec@1 37.500 (34.912) Prec@5 66.250 (65.319) Epoch: [4][3130/11272] Time 0.805 (0.822) Data 0.001 (0.003) Loss 2.7677 (2.7144) Prec@1 33.125 (34.913) Prec@5 66.250 (65.320) Epoch: [4][3140/11272] Time 0.916 (0.822) Data 0.002 (0.003) Loss 2.4975 (2.7144) Prec@1 35.625 (34.913) Prec@5 73.125 (65.322) Epoch: [4][3150/11272] Time 0.860 (0.822) Data 0.002 (0.003) Loss 2.7522 (2.7143) Prec@1 35.000 (34.914) Prec@5 61.250 (65.324) Epoch: [4][3160/11272] Time 0.778 (0.822) Data 0.002 (0.003) Loss 2.7767 (2.7141) Prec@1 32.500 (34.916) Prec@5 66.875 (65.325) Epoch: [4][3170/11272] Time 0.782 (0.822) Data 0.001 (0.003) Loss 2.6051 (2.7138) Prec@1 31.875 (34.919) Prec@5 68.750 (65.332) Epoch: [4][3180/11272] Time 0.845 (0.822) Data 0.002 (0.003) Loss 2.6830 (2.7140) Prec@1 36.250 (34.915) Prec@5 65.625 (65.330) Epoch: [4][3190/11272] Time 0.842 (0.822) Data 0.001 (0.003) Loss 2.7232 (2.7143) Prec@1 33.750 (34.906) Prec@5 68.750 (65.326) Epoch: [4][3200/11272] Time 0.758 (0.822) Data 0.001 (0.003) Loss 2.6233 (2.7143) Prec@1 33.125 (34.905) Prec@5 72.500 (65.326) Epoch: [4][3210/11272] Time 0.818 (0.822) Data 0.002 (0.003) Loss 2.9023 (2.7146) Prec@1 30.625 (34.901) Prec@5 56.875 (65.315) Epoch: [4][3220/11272] Time 0.872 (0.822) Data 0.002 (0.003) Loss 2.7165 (2.7146) Prec@1 33.750 (34.901) Prec@5 64.375 (65.314) Epoch: [4][3230/11272] Time 0.877 (0.822) Data 0.002 (0.003) Loss 3.0930 (2.7149) Prec@1 30.625 (34.898) Prec@5 57.500 (65.307) Epoch: [4][3240/11272] Time 0.766 (0.822) Data 0.002 (0.003) Loss 2.6022 (2.7149) Prec@1 38.750 (34.895) Prec@5 68.125 (65.309) Epoch: [4][3250/11272] Time 0.766 (0.822) Data 0.002 (0.003) Loss 2.8618 (2.7149) Prec@1 33.750 (34.899) Prec@5 63.125 (65.307) Epoch: [4][3260/11272] Time 0.932 (0.822) Data 0.001 (0.003) Loss 2.6792 (2.7148) Prec@1 35.625 (34.901) Prec@5 63.125 (65.307) Epoch: [4][3270/11272] Time 0.738 (0.822) Data 0.003 (0.003) Loss 2.5089 (2.7149) Prec@1 41.875 (34.903) Prec@5 66.250 (65.307) Epoch: [4][3280/11272] Time 0.788 (0.822) Data 0.002 (0.003) Loss 2.6905 (2.7150) Prec@1 35.625 (34.902) Prec@5 68.125 (65.306) Epoch: [4][3290/11272] Time 0.904 (0.822) Data 0.001 (0.003) Loss 2.9405 (2.7153) Prec@1 31.250 (34.902) Prec@5 61.250 (65.305) Epoch: [4][3300/11272] Time 0.879 (0.822) Data 0.001 (0.003) Loss 2.7070 (2.7155) Prec@1 31.875 (34.897) Prec@5 64.375 (65.301) Epoch: [4][3310/11272] Time 0.805 (0.822) Data 0.001 (0.003) Loss 2.9157 (2.7157) Prec@1 31.250 (34.893) Prec@5 59.375 (65.298) Epoch: [4][3320/11272] Time 0.761 (0.822) Data 0.001 (0.003) Loss 2.8685 (2.7158) Prec@1 32.500 (34.891) Prec@5 61.250 (65.296) Epoch: [4][3330/11272] Time 0.905 (0.822) Data 0.002 (0.003) Loss 2.1462 (2.7156) Prec@1 42.500 (34.897) Prec@5 74.375 (65.303) Epoch: [4][3340/11272] Time 0.920 (0.822) Data 0.002 (0.003) Loss 2.6805 (2.7157) Prec@1 40.000 (34.899) Prec@5 67.500 (65.304) Epoch: [4][3350/11272] Time 0.733 (0.822) Data 0.002 (0.003) Loss 2.5145 (2.7157) Prec@1 36.875 (34.901) Prec@5 70.000 (65.305) Epoch: [4][3360/11272] Time 0.746 (0.822) Data 0.001 (0.003) Loss 2.5687 (2.7156) Prec@1 33.125 (34.899) Prec@5 67.500 (65.305) Epoch: [4][3370/11272] Time 0.846 (0.822) Data 0.002 (0.003) Loss 2.6452 (2.7157) Prec@1 37.500 (34.899) Prec@5 65.000 (65.303) Epoch: [4][3380/11272] Time 0.863 (0.822) Data 0.001 (0.003) Loss 2.7069 (2.7157) Prec@1 37.500 (34.899) Prec@5 66.875 (65.301) Epoch: [4][3390/11272] Time 0.786 (0.822) Data 0.002 (0.003) Loss 2.6232 (2.7155) Prec@1 40.000 (34.902) Prec@5 65.000 (65.302) Epoch: [4][3400/11272] Time 0.902 (0.822) Data 0.001 (0.003) Loss 2.6829 (2.7155) Prec@1 35.000 (34.906) Prec@5 66.250 (65.302) Epoch: [4][3410/11272] Time 0.918 (0.822) Data 0.002 (0.003) Loss 2.7109 (2.7153) Prec@1 36.875 (34.907) Prec@5 64.375 (65.303) Epoch: [4][3420/11272] Time 0.805 (0.822) Data 0.002 (0.003) Loss 2.5882 (2.7152) Prec@1 38.125 (34.914) Prec@5 69.375 (65.309) Epoch: [4][3430/11272] Time 0.754 (0.822) Data 0.002 (0.003) Loss 2.4778 (2.7149) Prec@1 38.750 (34.917) Prec@5 68.750 (65.314) Epoch: [4][3440/11272] Time 0.873 (0.822) Data 0.002 (0.003) Loss 2.5178 (2.7148) Prec@1 38.750 (34.920) Prec@5 70.625 (65.318) Epoch: [4][3450/11272] Time 0.879 (0.822) Data 0.001 (0.003) Loss 2.5027 (2.7146) Prec@1 38.125 (34.929) Prec@5 68.125 (65.321) Epoch: [4][3460/11272] Time 0.753 (0.822) Data 0.002 (0.003) Loss 2.8157 (2.7145) Prec@1 35.625 (34.931) Prec@5 63.750 (65.324) Epoch: [4][3470/11272] Time 0.760 (0.822) Data 0.002 (0.003) Loss 2.8683 (2.7145) Prec@1 33.750 (34.928) Prec@5 61.250 (65.321) Epoch: [4][3480/11272] Time 0.900 (0.822) Data 0.002 (0.003) Loss 2.8358 (2.7149) Prec@1 31.875 (34.918) Prec@5 62.500 (65.315) Epoch: [4][3490/11272] Time 0.873 (0.822) Data 0.001 (0.003) Loss 2.7181 (2.7146) Prec@1 31.875 (34.920) Prec@5 62.500 (65.320) Epoch: [4][3500/11272] Time 0.747 (0.822) Data 0.001 (0.003) Loss 2.8339 (2.7145) Prec@1 31.875 (34.922) Prec@5 65.625 (65.320) Epoch: [4][3510/11272] Time 0.745 (0.822) Data 0.001 (0.003) Loss 2.5342 (2.7145) Prec@1 41.875 (34.922) Prec@5 68.125 (65.322) Epoch: [4][3520/11272] Time 0.837 (0.822) Data 0.002 (0.003) Loss 2.4981 (2.7144) Prec@1 41.250 (34.921) Prec@5 68.750 (65.323) Epoch: [4][3530/11272] Time 0.737 (0.822) Data 0.003 (0.003) Loss 2.8048 (2.7145) Prec@1 35.625 (34.919) Prec@5 62.500 (65.325) Epoch: [4][3540/11272] Time 0.782 (0.822) Data 0.002 (0.003) Loss 2.5859 (2.7145) Prec@1 36.250 (34.919) Prec@5 66.875 (65.325) Epoch: [4][3550/11272] Time 0.891 (0.822) Data 0.002 (0.003) Loss 2.8674 (2.7146) Prec@1 33.750 (34.918) Prec@5 65.000 (65.323) Epoch: [4][3560/11272] Time 0.952 (0.822) Data 0.002 (0.003) Loss 2.8345 (2.7146) Prec@1 27.500 (34.919) Prec@5 62.500 (65.324) Epoch: [4][3570/11272] Time 0.840 (0.822) Data 0.002 (0.003) Loss 2.8491 (2.7146) Prec@1 39.375 (34.919) Prec@5 61.250 (65.322) Epoch: [4][3580/11272] Time 0.744 (0.822) Data 0.002 (0.003) Loss 2.6086 (2.7144) Prec@1 41.875 (34.925) Prec@5 67.500 (65.326) Epoch: [4][3590/11272] Time 0.878 (0.822) Data 0.001 (0.003) Loss 2.7931 (2.7143) Prec@1 32.500 (34.927) Prec@5 65.625 (65.328) Epoch: [4][3600/11272] Time 0.902 (0.822) Data 0.002 (0.003) Loss 2.8165 (2.7143) Prec@1 33.125 (34.932) Prec@5 61.875 (65.329) Epoch: [4][3610/11272] Time 0.800 (0.822) Data 0.002 (0.003) Loss 2.6879 (2.7144) Prec@1 37.500 (34.934) Prec@5 67.500 (65.326) Epoch: [4][3620/11272] Time 0.744 (0.822) Data 0.001 (0.003) Loss 2.7203 (2.7145) Prec@1 35.000 (34.931) Prec@5 61.875 (65.324) Epoch: [4][3630/11272] Time 0.850 (0.822) Data 0.001 (0.003) Loss 2.5851 (2.7145) Prec@1 41.875 (34.935) Prec@5 71.250 (65.326) Epoch: [4][3640/11272] Time 0.851 (0.822) Data 0.001 (0.003) Loss 2.7253 (2.7144) Prec@1 35.625 (34.940) Prec@5 65.625 (65.328) Epoch: [4][3650/11272] Time 0.748 (0.822) Data 0.002 (0.003) Loss 3.0797 (2.7145) Prec@1 26.875 (34.936) Prec@5 55.000 (65.324) Epoch: [4][3660/11272] Time 0.862 (0.822) Data 0.002 (0.003) Loss 2.7293 (2.7145) Prec@1 33.125 (34.935) Prec@5 63.750 (65.325) Epoch: [4][3670/11272] Time 0.893 (0.822) Data 0.002 (0.003) Loss 2.7765 (2.7145) Prec@1 40.000 (34.934) Prec@5 66.875 (65.325) Epoch: [4][3680/11272] Time 0.777 (0.822) Data 0.002 (0.003) Loss 2.5539 (2.7145) Prec@1 39.375 (34.938) Prec@5 67.500 (65.320) Epoch: [4][3690/11272] Time 0.749 (0.822) Data 0.001 (0.003) Loss 2.8519 (2.7146) Prec@1 33.750 (34.937) Prec@5 60.625 (65.319) Epoch: [4][3700/11272] Time 0.858 (0.822) Data 0.001 (0.003) Loss 2.9637 (2.7148) Prec@1 29.375 (34.935) Prec@5 58.750 (65.315) Epoch: [4][3710/11272] Time 0.882 (0.822) Data 0.002 (0.003) Loss 2.7950 (2.7148) Prec@1 29.375 (34.936) Prec@5 63.125 (65.314) Epoch: [4][3720/11272] Time 0.750 (0.822) Data 0.001 (0.003) Loss 2.5674 (2.7146) Prec@1 39.375 (34.936) Prec@5 69.375 (65.318) Epoch: [4][3730/11272] Time 0.753 (0.822) Data 0.001 (0.003) Loss 2.9143 (2.7147) Prec@1 34.375 (34.938) Prec@5 60.000 (65.315) Epoch: [4][3740/11272] Time 0.875 (0.822) Data 0.001 (0.003) Loss 2.6900 (2.7146) Prec@1 31.250 (34.940) Prec@5 66.250 (65.317) Epoch: [4][3750/11272] Time 0.876 (0.822) Data 0.002 (0.003) Loss 2.7304 (2.7147) Prec@1 30.000 (34.937) Prec@5 68.750 (65.317) Epoch: [4][3760/11272] Time 0.758 (0.822) Data 0.002 (0.003) Loss 2.6865 (2.7147) Prec@1 32.500 (34.935) Prec@5 67.500 (65.316) Epoch: [4][3770/11272] Time 0.759 (0.822) Data 0.002 (0.003) Loss 2.6735 (2.7148) Prec@1 40.000 (34.935) Prec@5 63.125 (65.311) Epoch: [4][3780/11272] Time 0.873 (0.822) Data 0.002 (0.003) Loss 2.8163 (2.7147) Prec@1 33.750 (34.935) Prec@5 60.625 (65.309) Epoch: [4][3790/11272] Time 0.854 (0.822) Data 0.002 (0.003) Loss 2.5815 (2.7146) Prec@1 40.625 (34.938) Prec@5 68.125 (65.309) Epoch: [4][3800/11272] Time 0.759 (0.822) Data 0.002 (0.003) Loss 2.7814 (2.7146) Prec@1 32.500 (34.939) Prec@5 65.000 (65.308) Epoch: [4][3810/11272] Time 0.902 (0.822) Data 0.002 (0.003) Loss 2.6493 (2.7146) Prec@1 33.750 (34.941) Prec@5 67.500 (65.310) Epoch: [4][3820/11272] Time 0.882 (0.822) Data 0.001 (0.003) Loss 2.5948 (2.7147) Prec@1 35.625 (34.937) Prec@5 66.875 (65.306) Epoch: [4][3830/11272] Time 0.738 (0.822) Data 0.002 (0.003) Loss 2.6519 (2.7146) Prec@1 40.000 (34.942) Prec@5 70.000 (65.313) Epoch: [4][3840/11272] Time 0.757 (0.822) Data 0.002 (0.003) Loss 2.6594 (2.7145) Prec@1 33.750 (34.947) Prec@5 68.750 (65.316) Epoch: [4][3850/11272] Time 0.891 (0.822) Data 0.001 (0.003) Loss 2.7304 (2.7145) Prec@1 35.625 (34.949) Prec@5 61.875 (65.315) Epoch: [4][3860/11272] Time 0.868 (0.822) Data 0.001 (0.003) Loss 2.6606 (2.7145) Prec@1 33.125 (34.947) Prec@5 66.250 (65.313) Epoch: [4][3870/11272] Time 0.823 (0.822) Data 0.001 (0.003) Loss 2.5657 (2.7145) Prec@1 36.250 (34.948) Prec@5 69.375 (65.314) Epoch: [4][3880/11272] Time 0.751 (0.822) Data 0.001 (0.002) Loss 2.4896 (2.7143) Prec@1 40.000 (34.953) Prec@5 69.375 (65.318) Epoch: [4][3890/11272] Time 0.848 (0.822) Data 0.001 (0.002) Loss 2.7844 (2.7144) Prec@1 35.625 (34.952) Prec@5 63.125 (65.318) Epoch: [4][3900/11272] Time 0.855 (0.822) Data 0.001 (0.002) Loss 2.9575 (2.7145) Prec@1 35.625 (34.950) Prec@5 60.625 (65.315) Epoch: [4][3910/11272] Time 0.723 (0.822) Data 0.001 (0.002) Loss 2.7930 (2.7145) Prec@1 36.250 (34.953) Prec@5 63.125 (65.317) Epoch: [4][3920/11272] Time 0.784 (0.822) Data 0.002 (0.002) Loss 2.7760 (2.7146) Prec@1 33.750 (34.951) Prec@5 61.250 (65.310) Epoch: [4][3930/11272] Time 0.838 (0.821) Data 0.002 (0.002) Loss 2.8736 (2.7145) Prec@1 31.250 (34.953) Prec@5 62.500 (65.312) Epoch: [4][3940/11272] Time 0.816 (0.822) Data 0.002 (0.002) Loss 2.7874 (2.7146) Prec@1 31.875 (34.955) Prec@5 62.500 (65.309) Epoch: [4][3950/11272] Time 0.752 (0.821) Data 0.001 (0.002) Loss 2.5926 (2.7144) Prec@1 39.375 (34.957) Prec@5 65.625 (65.312) Epoch: [4][3960/11272] Time 0.903 (0.822) Data 0.001 (0.002) Loss 2.6657 (2.7147) Prec@1 32.500 (34.953) Prec@5 68.125 (65.306) Epoch: [4][3970/11272] Time 0.840 (0.822) Data 0.002 (0.002) Loss 2.7199 (2.7148) Prec@1 34.375 (34.956) Prec@5 67.500 (65.307) Epoch: [4][3980/11272] Time 0.735 (0.821) Data 0.001 (0.002) Loss 2.6727 (2.7149) Prec@1 34.375 (34.951) Prec@5 64.375 (65.307) Epoch: [4][3990/11272] Time 0.759 (0.822) Data 0.002 (0.002) Loss 2.4137 (2.7148) Prec@1 39.375 (34.952) Prec@5 73.750 (65.311) Epoch: [4][4000/11272] Time 0.851 (0.821) Data 0.001 (0.002) Loss 2.9428 (2.7147) Prec@1 26.875 (34.953) Prec@5 62.500 (65.311) Epoch: [4][4010/11272] Time 0.844 (0.821) Data 0.001 (0.002) Loss 2.7204 (2.7150) Prec@1 34.375 (34.950) Prec@5 64.375 (65.307) Epoch: [4][4020/11272] Time 0.746 (0.821) Data 0.002 (0.002) Loss 2.5408 (2.7151) Prec@1 40.625 (34.952) Prec@5 70.000 (65.305) Epoch: [4][4030/11272] Time 0.733 (0.821) Data 0.002 (0.002) Loss 2.7670 (2.7150) Prec@1 38.125 (34.950) Prec@5 61.250 (65.303) Epoch: [4][4040/11272] Time 0.806 (0.821) Data 0.001 (0.002) Loss 2.8545 (2.7150) Prec@1 26.875 (34.948) Prec@5 63.125 (65.303) Epoch: [4][4050/11272] Time 0.829 (0.821) Data 0.001 (0.002) Loss 2.7748 (2.7151) Prec@1 35.000 (34.945) Prec@5 60.625 (65.302) Epoch: [4][4060/11272] Time 0.753 (0.821) Data 0.001 (0.002) Loss 2.7582 (2.7151) Prec@1 33.750 (34.939) Prec@5 65.000 (65.304) Epoch: [4][4070/11272] Time 0.894 (0.821) Data 0.001 (0.002) Loss 2.5956 (2.7152) Prec@1 35.000 (34.938) Prec@5 68.125 (65.303) Epoch: [4][4080/11272] Time 0.859 (0.821) Data 0.005 (0.002) Loss 2.9270 (2.7152) Prec@1 30.625 (34.939) Prec@5 61.250 (65.301) Epoch: [4][4090/11272] Time 0.783 (0.821) Data 0.002 (0.002) Loss 2.6987 (2.7154) Prec@1 32.500 (34.935) Prec@5 66.875 (65.296) Epoch: [4][4100/11272] Time 0.757 (0.821) Data 0.001 (0.002) Loss 2.6810 (2.7153) Prec@1 33.750 (34.935) Prec@5 70.000 (65.299) Epoch: [4][4110/11272] Time 0.860 (0.821) Data 0.001 (0.002) Loss 2.7386 (2.7154) Prec@1 34.375 (34.931) Prec@5 62.500 (65.296) Epoch: [4][4120/11272] Time 0.861 (0.821) Data 0.001 (0.002) Loss 2.9014 (2.7154) Prec@1 33.125 (34.929) Prec@5 63.125 (65.297) Epoch: [4][4130/11272] Time 0.741 (0.821) Data 0.001 (0.002) Loss 2.8881 (2.7155) Prec@1 33.750 (34.927) Prec@5 61.250 (65.298) Epoch: [4][4140/11272] Time 0.748 (0.821) Data 0.001 (0.002) Loss 2.6956 (2.7156) Prec@1 35.625 (34.924) Prec@5 61.875 (65.295) Epoch: [4][4150/11272] Time 0.845 (0.821) Data 0.001 (0.002) Loss 2.4071 (2.7152) Prec@1 41.250 (34.934) Prec@5 70.625 (65.301) Epoch: [4][4160/11272] Time 0.840 (0.821) Data 0.001 (0.002) Loss 2.4943 (2.7151) Prec@1 35.625 (34.935) Prec@5 70.625 (65.301) Epoch: [4][4170/11272] Time 0.767 (0.821) Data 0.002 (0.002) Loss 2.5895 (2.7151) Prec@1 41.250 (34.939) Prec@5 66.875 (65.302) Epoch: [4][4180/11272] Time 0.746 (0.821) Data 0.001 (0.002) Loss 2.6581 (2.7152) Prec@1 35.625 (34.940) Prec@5 65.000 (65.299) Epoch: [4][4190/11272] Time 0.856 (0.821) Data 0.001 (0.002) Loss 2.7960 (2.7152) Prec@1 29.375 (34.941) Prec@5 65.625 (65.301) Epoch: [4][4200/11272] Time 0.770 (0.821) Data 0.004 (0.002) Loss 2.6446 (2.7153) Prec@1 34.375 (34.938) Prec@5 66.875 (65.303) Epoch: [4][4210/11272] Time 0.764 (0.821) Data 0.001 (0.002) Loss 2.4740 (2.7152) Prec@1 36.250 (34.936) Prec@5 73.125 (65.306) Epoch: [4][4220/11272] Time 0.849 (0.821) Data 0.002 (0.002) Loss 2.7102 (2.7150) Prec@1 28.125 (34.937) Prec@5 64.375 (65.309) Epoch: [4][4230/11272] Time 0.870 (0.821) Data 0.001 (0.002) Loss 2.6075 (2.7148) Prec@1 36.875 (34.942) Prec@5 68.125 (65.312) Epoch: [4][4240/11272] Time 0.733 (0.821) Data 0.001 (0.002) Loss 2.4682 (2.7148) Prec@1 39.375 (34.944) Prec@5 68.750 (65.312) Epoch: [4][4250/11272] Time 0.758 (0.821) Data 0.001 (0.002) Loss 2.8631 (2.7148) Prec@1 32.500 (34.944) Prec@5 60.000 (65.311) Epoch: [4][4260/11272] Time 0.802 (0.820) Data 0.001 (0.002) Loss 2.9255 (2.7149) Prec@1 34.375 (34.946) Prec@5 60.000 (65.308) Epoch: [4][4270/11272] Time 0.831 (0.820) Data 0.001 (0.002) Loss 2.5812 (2.7147) Prec@1 37.500 (34.951) Prec@5 67.500 (65.309) Epoch: [4][4280/11272] Time 0.744 (0.820) Data 0.002 (0.002) Loss 2.3319 (2.7146) Prec@1 41.875 (34.951) Prec@5 70.625 (65.309) Epoch: [4][4290/11272] Time 0.755 (0.820) Data 0.002 (0.002) Loss 2.5984 (2.7145) Prec@1 35.000 (34.955) Prec@5 67.500 (65.312) Epoch: [4][4300/11272] Time 0.836 (0.820) Data 0.002 (0.002) Loss 2.8209 (2.7146) Prec@1 28.750 (34.951) Prec@5 67.500 (65.309) Epoch: [4][4310/11272] Time 0.841 (0.820) Data 0.002 (0.002) Loss 2.6358 (2.7146) Prec@1 36.875 (34.952) Prec@5 69.375 (65.308) Epoch: [4][4320/11272] Time 0.738 (0.820) Data 0.001 (0.002) Loss 2.5490 (2.7145) Prec@1 39.375 (34.957) Prec@5 68.750 (65.309) Epoch: [4][4330/11272] Time 0.866 (0.820) Data 0.001 (0.002) Loss 2.9766 (2.7143) Prec@1 25.000 (34.956) Prec@5 60.000 (65.314) Epoch: [4][4340/11272] Time 0.846 (0.820) Data 0.001 (0.002) Loss 2.8259 (2.7143) Prec@1 36.875 (34.958) Prec@5 66.875 (65.315) Epoch: [4][4350/11272] Time 0.768 (0.820) Data 0.002 (0.002) Loss 2.8535 (2.7143) Prec@1 33.125 (34.961) Prec@5 58.750 (65.315) Epoch: [4][4360/11272] Time 0.737 (0.820) Data 0.002 (0.002) Loss 2.6822 (2.7143) Prec@1 38.125 (34.962) Prec@5 66.875 (65.313) Epoch: [4][4370/11272] Time 0.878 (0.820) Data 0.002 (0.002) Loss 2.8334 (2.7142) Prec@1 33.125 (34.963) Prec@5 61.250 (65.314) Epoch: [4][4380/11272] Time 0.846 (0.820) Data 0.002 (0.002) Loss 2.5458 (2.7142) Prec@1 39.375 (34.963) Prec@5 66.875 (65.313) Epoch: [4][4390/11272] Time 0.743 (0.820) Data 0.001 (0.002) Loss 2.7278 (2.7141) Prec@1 31.250 (34.964) Prec@5 68.750 (65.320) Epoch: [4][4400/11272] Time 0.736 (0.820) Data 0.001 (0.002) Loss 2.7049 (2.7139) Prec@1 38.125 (34.971) Prec@5 66.250 (65.324) Epoch: [4][4410/11272] Time 0.881 (0.820) Data 0.002 (0.002) Loss 2.5597 (2.7138) Prec@1 40.625 (34.973) Prec@5 67.500 (65.326) Epoch: [4][4420/11272] Time 0.847 (0.820) Data 0.002 (0.002) Loss 2.5357 (2.7137) Prec@1 40.625 (34.974) Prec@5 68.125 (65.328) Epoch: [4][4430/11272] Time 0.751 (0.820) Data 0.002 (0.002) Loss 2.7514 (2.7138) Prec@1 30.625 (34.971) Prec@5 68.125 (65.332) Epoch: [4][4440/11272] Time 0.744 (0.820) Data 0.002 (0.002) Loss 2.5474 (2.7137) Prec@1 36.250 (34.971) Prec@5 65.000 (65.331) Epoch: [4][4450/11272] Time 0.862 (0.820) Data 0.002 (0.002) Loss 2.6190 (2.7135) Prec@1 38.125 (34.975) Prec@5 64.375 (65.334) Epoch: [4][4460/11272] Time 0.734 (0.820) Data 0.003 (0.002) Loss 2.8853 (2.7134) Prec@1 33.750 (34.980) Prec@5 59.375 (65.339) Epoch: [4][4470/11272] Time 0.774 (0.820) Data 0.002 (0.002) Loss 2.4237 (2.7134) Prec@1 41.875 (34.982) Prec@5 75.000 (65.340) Epoch: [4][4480/11272] Time 0.863 (0.820) Data 0.002 (0.002) Loss 2.7501 (2.7135) Prec@1 36.875 (34.982) Prec@5 63.125 (65.338) Epoch: [4][4490/11272] Time 0.833 (0.820) Data 0.001 (0.002) Loss 2.6960 (2.7136) Prec@1 33.750 (34.981) Prec@5 66.250 (65.334) Epoch: [4][4500/11272] Time 0.735 (0.820) Data 0.001 (0.002) Loss 2.6420 (2.7137) Prec@1 34.375 (34.976) Prec@5 66.250 (65.330) Epoch: [4][4510/11272] Time 0.757 (0.820) Data 0.002 (0.002) Loss 2.8383 (2.7139) Prec@1 33.125 (34.972) Prec@5 57.500 (65.327) Epoch: [4][4520/11272] Time 0.922 (0.820) Data 0.002 (0.002) Loss 2.5223 (2.7138) Prec@1 41.875 (34.972) Prec@5 69.375 (65.329) Epoch: [4][4530/11272] Time 0.874 (0.820) Data 0.002 (0.002) Loss 2.6972 (2.7137) Prec@1 33.750 (34.973) Prec@5 70.000 (65.332) Epoch: [4][4540/11272] Time 0.757 (0.820) Data 0.001 (0.002) Loss 2.5811 (2.7138) Prec@1 37.500 (34.969) Prec@5 68.750 (65.331) Epoch: [4][4550/11272] Time 0.790 (0.820) Data 0.002 (0.002) Loss 2.6812 (2.7138) Prec@1 37.500 (34.970) Prec@5 66.250 (65.330) Epoch: [4][4560/11272] Time 0.925 (0.820) Data 0.001 (0.002) Loss 3.1092 (2.7140) Prec@1 31.250 (34.969) Prec@5 55.000 (65.326) Epoch: [4][4570/11272] Time 0.823 (0.820) Data 0.001 (0.002) Loss 2.7107 (2.7139) Prec@1 37.500 (34.970) Prec@5 68.750 (65.329) Epoch: [4][4580/11272] Time 0.741 (0.820) Data 0.001 (0.002) Loss 2.9966 (2.7140) Prec@1 30.000 (34.970) Prec@5 55.000 (65.326) Epoch: [4][4590/11272] Time 0.883 (0.820) Data 0.002 (0.002) Loss 2.5913 (2.7140) Prec@1 37.500 (34.968) Prec@5 68.125 (65.326) Epoch: [4][4600/11272] Time 0.851 (0.820) Data 0.001 (0.002) Loss 2.6438 (2.7140) Prec@1 41.875 (34.971) Prec@5 67.500 (65.325) Epoch: [4][4610/11272] Time 0.768 (0.820) Data 0.002 (0.002) Loss 2.6171 (2.7139) Prec@1 37.500 (34.972) Prec@5 67.500 (65.328) Epoch: [4][4620/11272] Time 0.750 (0.820) Data 0.001 (0.002) Loss 2.9734 (2.7138) Prec@1 27.500 (34.972) Prec@5 58.750 (65.326) Epoch: [4][4630/11272] Time 0.949 (0.820) Data 0.002 (0.002) Loss 2.5552 (2.7137) Prec@1 36.875 (34.975) Prec@5 65.000 (65.326) Epoch: [4][4640/11272] Time 0.955 (0.820) Data 0.003 (0.002) Loss 2.8858 (2.7139) Prec@1 29.375 (34.973) Prec@5 59.375 (65.322) Epoch: [4][4650/11272] Time 0.771 (0.820) Data 0.001 (0.002) Loss 2.8688 (2.7138) Prec@1 27.500 (34.976) Prec@5 62.500 (65.324) Epoch: [4][4660/11272] Time 0.771 (0.820) Data 0.001 (0.002) Loss 2.4159 (2.7137) Prec@1 38.125 (34.977) Prec@5 69.375 (65.327) Epoch: [4][4670/11272] Time 0.914 (0.820) Data 0.001 (0.002) Loss 3.0007 (2.7137) Prec@1 33.125 (34.978) Prec@5 57.500 (65.328) Epoch: [4][4680/11272] Time 0.955 (0.820) Data 0.002 (0.002) Loss 2.7039 (2.7136) Prec@1 35.625 (34.976) Prec@5 65.625 (65.330) Epoch: [4][4690/11272] Time 0.785 (0.820) Data 0.002 (0.002) Loss 2.7401 (2.7136) Prec@1 35.000 (34.975) Prec@5 64.375 (65.331) Epoch: [4][4700/11272] Time 0.819 (0.820) Data 0.002 (0.002) Loss 2.7009 (2.7138) Prec@1 37.500 (34.972) Prec@5 67.500 (65.329) Epoch: [4][4710/11272] Time 0.864 (0.820) Data 0.002 (0.002) Loss 2.6183 (2.7137) Prec@1 38.750 (34.971) Prec@5 68.125 (65.328) Epoch: [4][4720/11272] Time 0.854 (0.820) Data 0.001 (0.002) Loss 2.7600 (2.7137) Prec@1 30.625 (34.969) Prec@5 63.750 (65.329) Epoch: [4][4730/11272] Time 0.752 (0.820) Data 0.002 (0.002) Loss 2.5523 (2.7135) Prec@1 36.250 (34.969) Prec@5 69.375 (65.334) Epoch: [4][4740/11272] Time 0.886 (0.820) Data 0.001 (0.002) Loss 2.6348 (2.7135) Prec@1 33.125 (34.969) Prec@5 63.750 (65.334) Epoch: [4][4750/11272] Time 0.887 (0.820) Data 0.002 (0.002) Loss 2.8652 (2.7137) Prec@1 37.500 (34.965) Prec@5 63.125 (65.330) Epoch: [4][4760/11272] Time 0.755 (0.820) Data 0.002 (0.002) Loss 2.8783 (2.7136) Prec@1 37.500 (34.967) Prec@5 63.125 (65.333) Epoch: [4][4770/11272] Time 0.746 (0.820) Data 0.001 (0.002) Loss 2.6237 (2.7135) Prec@1 33.125 (34.964) Prec@5 65.625 (65.339) Epoch: [4][4780/11272] Time 0.916 (0.820) Data 0.002 (0.002) Loss 2.5998 (2.7134) Prec@1 39.375 (34.966) Prec@5 65.625 (65.338) Epoch: [4][4790/11272] Time 0.900 (0.820) Data 0.002 (0.002) Loss 2.7089 (2.7134) Prec@1 34.375 (34.964) Prec@5 64.375 (65.338) Epoch: [4][4800/11272] Time 0.789 (0.820) Data 0.002 (0.002) Loss 2.6505 (2.7135) Prec@1 33.125 (34.963) Prec@5 60.625 (65.335) Epoch: [4][4810/11272] Time 0.736 (0.820) Data 0.001 (0.002) Loss 2.8556 (2.7135) Prec@1 33.750 (34.963) Prec@5 65.000 (65.333) Epoch: [4][4820/11272] Time 0.870 (0.820) Data 0.001 (0.002) Loss 2.5565 (2.7133) Prec@1 38.750 (34.969) Prec@5 66.875 (65.335) Epoch: [4][4830/11272] Time 0.936 (0.820) Data 0.002 (0.002) Loss 2.6901 (2.7133) Prec@1 35.000 (34.967) Prec@5 63.750 (65.335) Epoch: [4][4840/11272] Time 0.787 (0.820) Data 0.002 (0.002) Loss 2.8007 (2.7133) Prec@1 31.875 (34.970) Prec@5 66.250 (65.337) Epoch: [4][4850/11272] Time 0.802 (0.820) Data 0.002 (0.002) Loss 2.6581 (2.7132) Prec@1 38.125 (34.973) Prec@5 71.250 (65.341) Epoch: [4][4860/11272] Time 0.801 (0.820) Data 0.001 (0.002) Loss 2.6380 (2.7131) Prec@1 36.250 (34.973) Prec@5 69.375 (65.343) Epoch: [4][4870/11272] Time 0.777 (0.820) Data 0.001 (0.002) Loss 2.6138 (2.7129) Prec@1 38.750 (34.978) Prec@5 65.000 (65.349) Epoch: [4][4880/11272] Time 0.851 (0.820) Data 0.002 (0.002) Loss 2.6398 (2.7129) Prec@1 36.875 (34.977) Prec@5 66.250 (65.349) Epoch: [4][4890/11272] Time 0.872 (0.820) Data 0.002 (0.002) Loss 2.8472 (2.7130) Prec@1 30.000 (34.972) Prec@5 63.125 (65.349) Epoch: [4][4900/11272] Time 0.830 (0.820) Data 0.001 (0.002) Loss 2.8539 (2.7130) Prec@1 30.000 (34.971) Prec@5 65.625 (65.352) Epoch: [4][4910/11272] Time 0.740 (0.820) Data 0.002 (0.002) Loss 2.7315 (2.7129) Prec@1 33.125 (34.973) Prec@5 68.750 (65.353) Epoch: [4][4920/11272] Time 0.783 (0.820) Data 0.002 (0.002) Loss 2.7794 (2.7130) Prec@1 28.125 (34.971) Prec@5 63.125 (65.355) Epoch: [4][4930/11272] Time 0.907 (0.820) Data 0.002 (0.002) Loss 2.7836 (2.7128) Prec@1 33.750 (34.973) Prec@5 65.000 (65.359) Epoch: [4][4940/11272] Time 0.904 (0.820) Data 0.001 (0.002) Loss 2.7394 (2.7129) Prec@1 38.750 (34.975) Prec@5 61.875 (65.356) Epoch: [4][4950/11272] Time 0.751 (0.820) Data 0.002 (0.002) Loss 2.5316 (2.7129) Prec@1 38.750 (34.978) Prec@5 68.750 (65.359) Epoch: [4][4960/11272] Time 0.757 (0.820) Data 0.001 (0.002) Loss 2.5970 (2.7126) Prec@1 35.625 (34.982) Prec@5 65.625 (65.365) Epoch: [4][4970/11272] Time 0.915 (0.820) Data 0.002 (0.002) Loss 2.5099 (2.7124) Prec@1 40.000 (34.989) Prec@5 71.250 (65.370) Epoch: [4][4980/11272] Time 0.863 (0.820) Data 0.002 (0.002) Loss 2.9435 (2.7124) Prec@1 32.500 (34.989) Prec@5 58.750 (65.368) Epoch: [4][4990/11272] Time 0.748 (0.820) Data 0.001 (0.002) Loss 2.8510 (2.7124) Prec@1 33.750 (34.988) Prec@5 61.250 (65.369) Epoch: [4][5000/11272] Time 0.887 (0.820) Data 0.002 (0.002) Loss 2.7140 (2.7124) Prec@1 33.125 (34.986) Prec@5 63.125 (65.368) Epoch: [4][5010/11272] Time 0.873 (0.820) Data 0.002 (0.002) Loss 2.7059 (2.7124) Prec@1 36.875 (34.986) Prec@5 63.125 (65.368) Epoch: [4][5020/11272] Time 0.746 (0.820) Data 0.002 (0.002) Loss 2.9603 (2.7123) Prec@1 36.250 (34.989) Prec@5 58.750 (65.368) Epoch: [4][5030/11272] Time 0.758 (0.820) Data 0.002 (0.002) Loss 2.7294 (2.7123) Prec@1 33.750 (34.989) Prec@5 66.875 (65.370) Epoch: [4][5040/11272] Time 0.880 (0.820) Data 0.001 (0.002) Loss 2.6328 (2.7124) Prec@1 35.000 (34.985) Prec@5 67.500 (65.369) Epoch: [4][5050/11272] Time 0.880 (0.820) Data 0.001 (0.002) Loss 2.6519 (2.7124) Prec@1 39.375 (34.985) Prec@5 69.375 (65.368) Epoch: [4][5060/11272] Time 0.775 (0.820) Data 0.002 (0.002) Loss 2.9219 (2.7124) Prec@1 29.375 (34.985) Prec@5 63.125 (65.371) Epoch: [4][5070/11272] Time 0.745 (0.820) Data 0.001 (0.002) Loss 2.6731 (2.7124) Prec@1 36.875 (34.986) Prec@5 64.375 (65.371) Epoch: [4][5080/11272] Time 0.872 (0.820) Data 0.002 (0.002) Loss 2.6191 (2.7122) Prec@1 36.875 (34.989) Prec@5 64.375 (65.372) Epoch: [4][5090/11272] Time 0.858 (0.820) Data 0.002 (0.002) Loss 2.8998 (2.7123) Prec@1 34.375 (34.988) Prec@5 60.625 (65.369) Epoch: [4][5100/11272] Time 0.745 (0.820) Data 0.002 (0.002) Loss 2.8295 (2.7124) Prec@1 33.125 (34.986) Prec@5 61.250 (65.367) Epoch: [4][5110/11272] Time 0.721 (0.820) Data 0.002 (0.002) Loss 2.7950 (2.7123) Prec@1 30.625 (34.986) Prec@5 64.375 (65.369) Epoch: [4][5120/11272] Time 0.859 (0.820) Data 0.002 (0.002) Loss 2.6505 (2.7123) Prec@1 34.375 (34.987) Prec@5 66.250 (65.370) Epoch: [4][5130/11272] Time 0.804 (0.820) Data 0.004 (0.002) Loss 2.7701 (2.7123) Prec@1 28.750 (34.987) Prec@5 65.000 (65.371) Epoch: [4][5140/11272] Time 0.757 (0.820) Data 0.001 (0.002) Loss 2.6771 (2.7122) Prec@1 33.125 (34.986) Prec@5 60.625 (65.370) Epoch: [4][5150/11272] Time 0.848 (0.820) Data 0.001 (0.002) Loss 2.6052 (2.7123) Prec@1 40.000 (34.985) Prec@5 64.375 (65.367) Epoch: [4][5160/11272] Time 0.859 (0.820) Data 0.002 (0.002) Loss 2.6300 (2.7122) Prec@1 36.875 (34.986) Prec@5 63.750 (65.367) Epoch: [4][5170/11272] Time 0.726 (0.820) Data 0.001 (0.002) Loss 2.7018 (2.7121) Prec@1 38.125 (34.989) Prec@5 66.250 (65.369) Epoch: [4][5180/11272] Time 0.737 (0.820) Data 0.001 (0.002) Loss 2.9319 (2.7121) Prec@1 30.000 (34.990) Prec@5 56.250 (65.368) Epoch: [4][5190/11272] Time 0.854 (0.820) Data 0.001 (0.002) Loss 3.0861 (2.7121) Prec@1 25.625 (34.989) Prec@5 56.875 (65.366) Epoch: [4][5200/11272] Time 0.911 (0.820) Data 0.002 (0.002) Loss 2.8006 (2.7122) Prec@1 35.000 (34.986) Prec@5 63.750 (65.362) Epoch: [4][5210/11272] Time 0.763 (0.820) Data 0.002 (0.002) Loss 2.9148 (2.7122) Prec@1 33.125 (34.987) Prec@5 63.125 (65.363) Epoch: [4][5220/11272] Time 0.744 (0.820) Data 0.001 (0.002) Loss 2.8643 (2.7122) Prec@1 35.000 (34.983) Prec@5 60.625 (65.361) Epoch: [4][5230/11272] Time 0.940 (0.820) Data 0.002 (0.002) Loss 2.7827 (2.7122) Prec@1 33.125 (34.984) Prec@5 60.625 (65.360) Epoch: [4][5240/11272] Time 0.843 (0.820) Data 0.001 (0.002) Loss 2.5985 (2.7121) Prec@1 41.875 (34.989) Prec@5 68.125 (65.362) Epoch: [4][5250/11272] Time 0.757 (0.820) Data 0.001 (0.002) Loss 2.6497 (2.7120) Prec@1 37.500 (34.989) Prec@5 68.750 (65.360) Epoch: [4][5260/11272] Time 0.826 (0.820) Data 0.001 (0.002) Loss 2.9383 (2.7121) Prec@1 32.500 (34.989) Prec@5 57.500 (65.360) Epoch: [4][5270/11272] Time 0.851 (0.820) Data 0.002 (0.002) Loss 2.9179 (2.7122) Prec@1 28.125 (34.986) Prec@5 58.125 (65.358) Epoch: [4][5280/11272] Time 0.799 (0.820) Data 0.002 (0.002) Loss 2.6809 (2.7122) Prec@1 30.000 (34.985) Prec@5 67.500 (65.360) Epoch: [4][5290/11272] Time 0.800 (0.820) Data 0.001 (0.002) Loss 2.5608 (2.7117) Prec@1 35.625 (34.993) Prec@5 69.375 (65.370) Epoch: [4][5300/11272] Time 0.845 (0.820) Data 0.001 (0.002) Loss 2.7362 (2.7118) Prec@1 33.125 (34.990) Prec@5 67.500 (65.369) Epoch: [4][5310/11272] Time 0.878 (0.820) Data 0.001 (0.002) Loss 2.9604 (2.7118) Prec@1 28.125 (34.993) Prec@5 65.000 (65.373) Epoch: [4][5320/11272] Time 0.827 (0.820) Data 0.002 (0.002) Loss 2.7733 (2.7117) Prec@1 31.250 (34.992) Prec@5 61.250 (65.374) Epoch: [4][5330/11272] Time 0.746 (0.820) Data 0.001 (0.002) Loss 2.6807 (2.7117) Prec@1 33.750 (34.992) Prec@5 65.000 (65.374) Epoch: [4][5340/11272] Time 0.869 (0.820) Data 0.001 (0.002) Loss 3.0882 (2.7117) Prec@1 26.250 (34.993) Prec@5 55.000 (65.373) Epoch: [4][5350/11272] Time 0.839 (0.820) Data 0.002 (0.002) Loss 2.7598 (2.7119) Prec@1 31.875 (34.992) Prec@5 64.375 (65.373) Epoch: [4][5360/11272] Time 0.765 (0.820) Data 0.001 (0.002) Loss 2.7420 (2.7119) Prec@1 36.875 (34.994) Prec@5 62.500 (65.371) Epoch: [4][5370/11272] Time 0.792 (0.820) Data 0.002 (0.002) Loss 2.4378 (2.7120) Prec@1 36.250 (34.992) Prec@5 68.125 (65.367) Epoch: [4][5380/11272] Time 0.915 (0.820) Data 0.001 (0.002) Loss 2.7553 (2.7120) Prec@1 37.500 (34.992) Prec@5 65.000 (65.362) Epoch: [4][5390/11272] Time 0.735 (0.820) Data 0.003 (0.002) Loss 2.4543 (2.7119) Prec@1 38.750 (34.997) Prec@5 71.250 (65.364) Epoch: [4][5400/11272] Time 0.807 (0.820) Data 0.002 (0.002) Loss 2.9017 (2.7119) Prec@1 30.000 (34.995) Prec@5 60.625 (65.362) Epoch: [4][5410/11272] Time 0.864 (0.820) Data 0.001 (0.002) Loss 2.8615 (2.7118) Prec@1 28.750 (34.996) Prec@5 61.875 (65.364) Epoch: [4][5420/11272] Time 0.929 (0.820) Data 0.001 (0.002) Loss 2.8866 (2.7118) Prec@1 28.750 (34.997) Prec@5 61.875 (65.365) Epoch: [4][5430/11272] Time 0.763 (0.820) Data 0.002 (0.002) Loss 2.7140 (2.7116) Prec@1 31.250 (34.999) Prec@5 66.250 (65.365) Epoch: [4][5440/11272] Time 0.742 (0.820) Data 0.002 (0.002) Loss 2.8533 (2.7116) Prec@1 32.500 (34.999) Prec@5 62.500 (65.367) Epoch: [4][5450/11272] Time 0.891 (0.820) Data 0.001 (0.002) Loss 2.8546 (2.7117) Prec@1 33.125 (34.999) Prec@5 65.625 (65.365) Epoch: [4][5460/11272] Time 0.869 (0.820) Data 0.001 (0.002) Loss 2.5205 (2.7115) Prec@1 35.000 (35.003) Prec@5 72.500 (65.373) Epoch: [4][5470/11272] Time 0.776 (0.820) Data 0.001 (0.002) Loss 2.6848 (2.7115) Prec@1 33.125 (35.002) Prec@5 65.000 (65.371) Epoch: [4][5480/11272] Time 0.767 (0.820) Data 0.002 (0.002) Loss 2.6229 (2.7117) Prec@1 33.750 (34.997) Prec@5 66.875 (65.365) Epoch: [4][5490/11272] Time 0.857 (0.820) Data 0.001 (0.002) Loss 2.7098 (2.7118) Prec@1 35.625 (34.995) Prec@5 62.500 (65.366) Epoch: [4][5500/11272] Time 0.856 (0.820) Data 0.001 (0.002) Loss 2.8622 (2.7117) Prec@1 30.625 (34.996) Prec@5 65.625 (65.366) Epoch: [4][5510/11272] Time 0.738 (0.820) Data 0.002 (0.002) Loss 2.6724 (2.7117) Prec@1 33.125 (34.996) Prec@5 63.750 (65.368) Epoch: [4][5520/11272] Time 0.878 (0.820) Data 0.001 (0.002) Loss 2.5016 (2.7116) Prec@1 33.125 (34.997) Prec@5 68.125 (65.369) Epoch: [4][5530/11272] Time 0.848 (0.820) Data 0.001 (0.002) Loss 2.9798 (2.7116) Prec@1 31.875 (34.996) Prec@5 58.750 (65.368) Epoch: [4][5540/11272] Time 0.754 (0.820) Data 0.002 (0.002) Loss 2.6213 (2.7116) Prec@1 36.875 (34.994) Prec@5 71.250 (65.367) Epoch: [4][5550/11272] Time 0.760 (0.820) Data 0.002 (0.002) Loss 2.6004 (2.7118) Prec@1 35.000 (34.991) Prec@5 71.875 (65.365) Epoch: [4][5560/11272] Time 0.890 (0.820) Data 0.001 (0.002) Loss 2.5457 (2.7116) Prec@1 35.000 (34.994) Prec@5 68.750 (65.370) Epoch: [4][5570/11272] Time 0.875 (0.820) Data 0.002 (0.002) Loss 2.7743 (2.7115) Prec@1 35.625 (34.996) Prec@5 67.500 (65.371) Epoch: [4][5580/11272] Time 0.767 (0.820) Data 0.001 (0.002) Loss 2.8672 (2.7117) Prec@1 33.125 (34.994) Prec@5 62.500 (65.370) Epoch: [4][5590/11272] Time 0.743 (0.820) Data 0.001 (0.002) Loss 2.8101 (2.7117) Prec@1 32.500 (34.994) Prec@5 66.250 (65.372) Epoch: [4][5600/11272] Time 0.916 (0.820) Data 0.001 (0.002) Loss 2.5420 (2.7117) Prec@1 38.750 (34.993) Prec@5 68.750 (65.370) Epoch: [4][5610/11272] Time 0.866 (0.820) Data 0.001 (0.002) Loss 2.7863 (2.7116) Prec@1 32.500 (34.995) Prec@5 64.375 (65.372) Epoch: [4][5620/11272] Time 0.749 (0.820) Data 0.002 (0.002) Loss 2.6731 (2.7117) Prec@1 36.875 (34.994) Prec@5 68.125 (65.372) Epoch: [4][5630/11272] Time 0.755 (0.820) Data 0.002 (0.002) Loss 2.6829 (2.7118) Prec@1 42.500 (34.990) Prec@5 68.750 (65.370) Epoch: [4][5640/11272] Time 0.839 (0.820) Data 0.002 (0.002) Loss 2.5707 (2.7118) Prec@1 40.000 (34.992) Prec@5 68.750 (65.372) Epoch: [4][5650/11272] Time 0.871 (0.820) Data 0.002 (0.002) Loss 2.8703 (2.7120) Prec@1 33.125 (34.989) Prec@5 65.000 (65.370) Epoch: [4][5660/11272] Time 0.743 (0.820) Data 0.002 (0.002) Loss 2.4924 (2.7120) Prec@1 33.125 (34.989) Prec@5 68.750 (65.370) Epoch: [4][5670/11272] Time 0.912 (0.820) Data 0.001 (0.002) Loss 2.6246 (2.7120) Prec@1 33.750 (34.988) Prec@5 68.750 (65.372) Epoch: [4][5680/11272] Time 0.848 (0.820) Data 0.001 (0.002) Loss 2.9360 (2.7120) Prec@1 30.000 (34.986) Prec@5 60.625 (65.369) Epoch: [4][5690/11272] Time 0.800 (0.820) Data 0.002 (0.002) Loss 2.8253 (2.7121) Prec@1 24.375 (34.982) Prec@5 64.375 (65.367) Epoch: [4][5700/11272] Time 0.763 (0.820) Data 0.002 (0.002) Loss 2.9121 (2.7122) Prec@1 35.000 (34.982) Prec@5 61.875 (65.366) Epoch: [4][5710/11272] Time 0.961 (0.820) Data 0.002 (0.002) Loss 2.4985 (2.7121) Prec@1 38.750 (34.986) Prec@5 71.875 (65.369) Epoch: [4][5720/11272] Time 0.906 (0.820) Data 0.002 (0.002) Loss 2.5109 (2.7121) Prec@1 41.250 (34.988) Prec@5 66.250 (65.368) Epoch: [4][5730/11272] Time 0.790 (0.820) Data 0.001 (0.002) Loss 2.9007 (2.7120) Prec@1 30.625 (34.988) Prec@5 63.125 (65.370) Epoch: [4][5740/11272] Time 0.759 (0.820) Data 0.001 (0.002) Loss 2.6905 (2.7120) Prec@1 33.750 (34.988) Prec@5 66.250 (65.371) Epoch: [4][5750/11272] Time 0.883 (0.820) Data 0.001 (0.002) Loss 2.7490 (2.7121) Prec@1 33.750 (34.985) Prec@5 66.875 (65.368) Epoch: [4][5760/11272] Time 0.868 (0.820) Data 0.002 (0.002) Loss 2.6495 (2.7121) Prec@1 35.625 (34.985) Prec@5 64.375 (65.369) Epoch: [4][5770/11272] Time 0.778 (0.820) Data 0.002 (0.002) Loss 2.8954 (2.7121) Prec@1 29.375 (34.985) Prec@5 62.500 (65.367) Epoch: [4][5780/11272] Time 0.800 (0.820) Data 0.002 (0.002) Loss 2.9148 (2.7121) Prec@1 31.875 (34.986) Prec@5 63.125 (65.370) Epoch: [4][5790/11272] Time 0.854 (0.820) Data 0.002 (0.002) Loss 2.8318 (2.7121) Prec@1 33.750 (34.983) Prec@5 63.125 (65.369) Epoch: [4][5800/11272] Time 0.800 (0.820) Data 0.002 (0.002) Loss 2.7858 (2.7122) Prec@1 33.125 (34.979) Prec@5 66.250 (65.368) Epoch: [4][5810/11272] Time 0.757 (0.820) Data 0.001 (0.002) Loss 3.0259 (2.7123) Prec@1 33.125 (34.979) Prec@5 62.500 (65.366) Epoch: [4][5820/11272] Time 0.890 (0.820) Data 0.002 (0.002) Loss 2.6867 (2.7122) Prec@1 35.625 (34.981) Prec@5 61.250 (65.369) Epoch: [4][5830/11272] Time 0.856 (0.820) Data 0.001 (0.002) Loss 2.7665 (2.7122) Prec@1 28.750 (34.980) Prec@5 61.875 (65.369) Epoch: [4][5840/11272] Time 0.750 (0.820) Data 0.002 (0.002) Loss 2.8007 (2.7121) Prec@1 34.375 (34.981) Prec@5 65.625 (65.370) Epoch: [4][5850/11272] Time 0.768 (0.820) Data 0.002 (0.002) Loss 2.7257 (2.7121) Prec@1 34.375 (34.979) Prec@5 65.625 (65.370) Epoch: [4][5860/11272] Time 0.858 (0.820) Data 0.002 (0.002) Loss 2.6468 (2.7120) Prec@1 36.250 (34.981) Prec@5 68.125 (65.371) Epoch: [4][5870/11272] Time 0.900 (0.820) Data 0.002 (0.002) Loss 2.7518 (2.7119) Prec@1 36.875 (34.982) Prec@5 67.500 (65.374) Epoch: [4][5880/11272] Time 0.786 (0.820) Data 0.002 (0.002) Loss 2.9146 (2.7119) Prec@1 31.250 (34.983) Prec@5 60.000 (65.374) Epoch: [4][5890/11272] Time 0.771 (0.820) Data 0.002 (0.002) Loss 2.8733 (2.7120) Prec@1 33.750 (34.981) Prec@5 61.875 (65.372) Epoch: [4][5900/11272] Time 0.906 (0.820) Data 0.002 (0.002) Loss 2.6389 (2.7121) Prec@1 39.375 (34.980) Prec@5 68.125 (65.370) Epoch: [4][5910/11272] Time 0.879 (0.820) Data 0.002 (0.002) Loss 2.7732 (2.7121) Prec@1 35.000 (34.980) Prec@5 65.625 (65.372) Epoch: [4][5920/11272] Time 0.821 (0.820) Data 0.001 (0.002) Loss 2.8375 (2.7120) Prec@1 32.500 (34.980) Prec@5 65.000 (65.371) Epoch: [4][5930/11272] Time 0.832 (0.820) Data 0.001 (0.002) Loss 2.5064 (2.7121) Prec@1 36.250 (34.980) Prec@5 69.375 (65.370) Epoch: [4][5940/11272] Time 0.873 (0.820) Data 0.001 (0.002) Loss 2.8534 (2.7122) Prec@1 29.375 (34.978) Prec@5 61.250 (65.370) Epoch: [4][5950/11272] Time 0.736 (0.820) Data 0.001 (0.002) Loss 2.9876 (2.7123) Prec@1 30.000 (34.978) Prec@5 56.250 (65.366) Epoch: [4][5960/11272] Time 0.814 (0.820) Data 0.002 (0.002) Loss 2.9648 (2.7125) Prec@1 28.125 (34.975) Prec@5 58.125 (65.362) Epoch: [4][5970/11272] Time 0.853 (0.820) Data 0.001 (0.002) Loss 2.5924 (2.7124) Prec@1 39.375 (34.977) Prec@5 66.875 (65.362) Epoch: [4][5980/11272] Time 0.952 (0.820) Data 0.003 (0.002) Loss 2.7532 (2.7124) Prec@1 38.750 (34.980) Prec@5 63.750 (65.362) Epoch: [4][5990/11272] Time 0.750 (0.820) Data 0.001 (0.002) Loss 2.5308 (2.7122) Prec@1 38.125 (34.984) Prec@5 67.500 (65.366) Epoch: [4][6000/11272] Time 0.748 (0.820) Data 0.001 (0.002) Loss 2.6227 (2.7122) Prec@1 35.625 (34.984) Prec@5 66.250 (65.365) Epoch: [4][6010/11272] Time 0.872 (0.820) Data 0.002 (0.002) Loss 2.6491 (2.7121) Prec@1 33.125 (34.984) Prec@5 69.375 (65.368) Epoch: [4][6020/11272] Time 0.848 (0.820) Data 0.002 (0.002) Loss 2.9408 (2.7122) Prec@1 27.500 (34.982) Prec@5 58.750 (65.365) Epoch: [4][6030/11272] Time 0.773 (0.820) Data 0.002 (0.002) Loss 2.8338 (2.7122) Prec@1 31.875 (34.982) Prec@5 63.125 (65.364) Epoch: [4][6040/11272] Time 0.762 (0.820) Data 0.002 (0.002) Loss 2.9666 (2.7122) Prec@1 30.625 (34.980) Prec@5 61.875 (65.364) Epoch: [4][6050/11272] Time 0.842 (0.820) Data 0.002 (0.002) Loss 2.5368 (2.7122) Prec@1 38.125 (34.982) Prec@5 68.125 (65.364) Epoch: [4][6060/11272] Time 0.750 (0.820) Data 0.003 (0.002) Loss 2.8252 (2.7122) Prec@1 34.375 (34.984) Prec@5 61.875 (65.365) Epoch: [4][6070/11272] Time 0.831 (0.820) Data 0.002 (0.002) Loss 2.7020 (2.7120) Prec@1 30.000 (34.985) Prec@5 68.750 (65.369) Epoch: [4][6080/11272] Time 0.893 (0.820) Data 0.001 (0.002) Loss 2.5842 (2.7120) Prec@1 41.250 (34.985) Prec@5 67.500 (65.369) Epoch: [4][6090/11272] Time 0.868 (0.820) Data 0.002 (0.002) Loss 2.8575 (2.7120) Prec@1 33.125 (34.986) Prec@5 65.000 (65.370) Epoch: [4][6100/11272] Time 0.754 (0.820) Data 0.001 (0.002) Loss 2.4398 (2.7119) Prec@1 38.125 (34.985) Prec@5 71.875 (65.374) Epoch: [4][6110/11272] Time 0.736 (0.820) Data 0.002 (0.002) Loss 2.7424 (2.7118) Prec@1 33.125 (34.986) Prec@5 63.750 (65.374) Epoch: [4][6120/11272] Time 0.859 (0.820) Data 0.001 (0.002) Loss 2.6114 (2.7118) Prec@1 31.875 (34.985) Prec@5 65.000 (65.375) Epoch: [4][6130/11272] Time 0.855 (0.820) Data 0.002 (0.002) Loss 2.8202 (2.7119) Prec@1 29.375 (34.982) Prec@5 60.625 (65.374) Epoch: [4][6140/11272] Time 0.765 (0.820) Data 0.002 (0.002) Loss 2.6327 (2.7118) Prec@1 29.375 (34.984) Prec@5 68.750 (65.376) Epoch: [4][6150/11272] Time 0.737 (0.820) Data 0.003 (0.002) Loss 3.0540 (2.7118) Prec@1 29.375 (34.983) Prec@5 56.875 (65.378) Epoch: [4][6160/11272] Time 0.875 (0.820) Data 0.001 (0.002) Loss 2.5988 (2.7117) Prec@1 39.375 (34.987) Prec@5 68.125 (65.380) Epoch: [4][6170/11272] Time 0.869 (0.820) Data 0.002 (0.002) Loss 2.8152 (2.7116) Prec@1 40.000 (34.991) Prec@5 66.875 (65.383) Epoch: [4][6180/11272] Time 0.763 (0.820) Data 0.002 (0.002) Loss 3.0870 (2.7116) Prec@1 31.875 (34.989) Prec@5 61.250 (65.383) Epoch: [4][6190/11272] Time 0.873 (0.820) Data 0.002 (0.002) Loss 3.0813 (2.7116) Prec@1 28.750 (34.990) Prec@5 58.125 (65.382) Epoch: [4][6200/11272] Time 0.882 (0.820) Data 0.001 (0.002) Loss 3.1081 (2.7117) Prec@1 25.000 (34.988) Prec@5 56.250 (65.380) Epoch: [4][6210/11272] Time 0.745 (0.820) Data 0.002 (0.002) Loss 2.8198 (2.7117) Prec@1 36.875 (34.985) Prec@5 61.875 (65.378) Epoch: [4][6220/11272] Time 0.802 (0.820) Data 0.001 (0.002) Loss 2.6752 (2.7117) Prec@1 35.000 (34.986) Prec@5 65.625 (65.379) Epoch: [4][6230/11272] Time 0.917 (0.820) Data 0.002 (0.002) Loss 2.7266 (2.7117) Prec@1 32.500 (34.984) Prec@5 66.875 (65.379) Epoch: [4][6240/11272] Time 0.827 (0.820) Data 0.001 (0.002) Loss 2.7948 (2.7118) Prec@1 32.500 (34.981) Prec@5 63.750 (65.377) Epoch: [4][6250/11272] Time 0.791 (0.820) Data 0.002 (0.002) Loss 2.6620 (2.7118) Prec@1 36.250 (34.977) Prec@5 64.375 (65.375) Epoch: [4][6260/11272] Time 0.762 (0.820) Data 0.002 (0.002) Loss 2.7576 (2.7119) Prec@1 37.500 (34.974) Prec@5 64.375 (65.374) Epoch: [4][6270/11272] Time 0.852 (0.820) Data 0.002 (0.002) Loss 2.7689 (2.7120) Prec@1 33.125 (34.972) Prec@5 63.125 (65.372) Epoch: [4][6280/11272] Time 0.838 (0.820) Data 0.002 (0.002) Loss 2.5017 (2.7120) Prec@1 36.250 (34.971) Prec@5 71.250 (65.375) Epoch: [4][6290/11272] Time 0.798 (0.820) Data 0.002 (0.002) Loss 2.6938 (2.7119) Prec@1 35.000 (34.973) Prec@5 61.875 (65.376) Epoch: [4][6300/11272] Time 0.739 (0.820) Data 0.001 (0.002) Loss 2.6641 (2.7120) Prec@1 38.750 (34.970) Prec@5 66.250 (65.373) Epoch: [4][6310/11272] Time 0.879 (0.820) Data 0.001 (0.002) Loss 2.9961 (2.7122) Prec@1 26.875 (34.968) Prec@5 60.625 (65.369) Epoch: [4][6320/11272] Time 0.791 (0.820) Data 0.003 (0.002) Loss 2.9586 (2.7121) Prec@1 31.875 (34.968) Prec@5 60.000 (65.369) Epoch: [4][6330/11272] Time 0.759 (0.820) Data 0.002 (0.002) Loss 2.6689 (2.7120) Prec@1 37.500 (34.971) Prec@5 67.500 (65.372) Epoch: [4][6340/11272] Time 0.902 (0.820) Data 0.002 (0.002) Loss 2.8648 (2.7121) Prec@1 33.750 (34.970) Prec@5 63.750 (65.371) Epoch: [4][6350/11272] Time 0.881 (0.820) Data 0.002 (0.002) Loss 2.9717 (2.7123) Prec@1 30.000 (34.969) Prec@5 60.625 (65.369) Epoch: [4][6360/11272] Time 0.742 (0.820) Data 0.002 (0.002) Loss 2.7672 (2.7123) Prec@1 37.500 (34.970) Prec@5 62.500 (65.368) Epoch: [4][6370/11272] Time 0.773 (0.820) Data 0.002 (0.002) Loss 2.7133 (2.7122) Prec@1 30.625 (34.969) Prec@5 66.875 (65.372) Epoch: [4][6380/11272] Time 0.846 (0.820) Data 0.001 (0.002) Loss 2.6127 (2.7121) Prec@1 40.625 (34.972) Prec@5 63.125 (65.372) Epoch: [4][6390/11272] Time 0.807 (0.820) Data 0.001 (0.002) Loss 2.6992 (2.7121) Prec@1 34.375 (34.972) Prec@5 70.000 (65.374) Epoch: [4][6400/11272] Time 0.735 (0.820) Data 0.001 (0.002) Loss 2.5256 (2.7123) Prec@1 40.625 (34.970) Prec@5 69.375 (65.371) Epoch: [4][6410/11272] Time 0.750 (0.820) Data 0.002 (0.002) Loss 2.5804 (2.7122) Prec@1 36.250 (34.973) Prec@5 68.125 (65.372) Epoch: [4][6420/11272] Time 0.837 (0.820) Data 0.001 (0.002) Loss 2.7091 (2.7121) Prec@1 34.375 (34.974) Prec@5 68.750 (65.373) Epoch: [4][6430/11272] Time 0.854 (0.820) Data 0.001 (0.002) Loss 2.8952 (2.7122) Prec@1 33.125 (34.972) Prec@5 62.500 (65.372) Epoch: [4][6440/11272] Time 0.741 (0.820) Data 0.001 (0.002) Loss 2.8029 (2.7121) Prec@1 29.375 (34.973) Prec@5 61.250 (65.370) Epoch: [4][6450/11272] Time 0.926 (0.820) Data 0.002 (0.002) Loss 3.0112 (2.7123) Prec@1 24.375 (34.971) Prec@5 56.875 (65.365) Epoch: [4][6460/11272] Time 0.908 (0.820) Data 0.002 (0.002) Loss 2.7544 (2.7123) Prec@1 33.125 (34.973) Prec@5 65.625 (65.365) Epoch: [4][6470/11272] Time 0.775 (0.820) Data 0.002 (0.002) Loss 2.7023 (2.7123) Prec@1 36.250 (34.977) Prec@5 67.500 (65.367) Epoch: [4][6480/11272] Time 0.797 (0.820) Data 0.001 (0.002) Loss 2.7253 (2.7122) Prec@1 36.875 (34.977) Prec@5 61.875 (65.367) Epoch: [4][6490/11272] Time 0.910 (0.820) Data 0.001 (0.002) Loss 2.6024 (2.7121) Prec@1 36.875 (34.976) Prec@5 68.125 (65.369) Epoch: [4][6500/11272] Time 0.862 (0.820) Data 0.001 (0.002) Loss 2.8819 (2.7122) Prec@1 29.375 (34.977) Prec@5 61.250 (65.367) Epoch: [4][6510/11272] Time 0.757 (0.820) Data 0.002 (0.002) Loss 2.5942 (2.7121) Prec@1 36.250 (34.978) Prec@5 71.250 (65.368) Epoch: [4][6520/11272] Time 0.843 (0.820) Data 0.003 (0.002) Loss 2.7588 (2.7122) Prec@1 33.125 (34.979) Prec@5 61.875 (65.368) Epoch: [4][6530/11272] Time 0.858 (0.820) Data 0.002 (0.002) Loss 2.8285 (2.7122) Prec@1 31.875 (34.981) Prec@5 61.250 (65.367) Epoch: [4][6540/11272] Time 0.865 (0.820) Data 0.001 (0.002) Loss 2.5911 (2.7120) Prec@1 34.375 (34.983) Prec@5 64.375 (65.371) Epoch: [4][6550/11272] Time 0.764 (0.820) Data 0.001 (0.002) Loss 2.9013 (2.7120) Prec@1 35.000 (34.986) Prec@5 65.625 (65.370) Epoch: [4][6560/11272] Time 0.762 (0.820) Data 0.002 (0.002) Loss 2.9160 (2.7120) Prec@1 28.125 (34.985) Prec@5 61.875 (65.369) Epoch: [4][6570/11272] Time 0.840 (0.820) Data 0.001 (0.002) Loss 2.7259 (2.7119) Prec@1 31.875 (34.987) Prec@5 65.000 (65.372) Epoch: [4][6580/11272] Time 0.922 (0.820) Data 0.002 (0.002) Loss 2.5072 (2.7118) Prec@1 38.125 (34.987) Prec@5 66.875 (65.373) Epoch: [4][6590/11272] Time 0.787 (0.820) Data 0.001 (0.002) Loss 2.6509 (2.7118) Prec@1 39.375 (34.987) Prec@5 65.000 (65.373) Epoch: [4][6600/11272] Time 0.856 (0.820) Data 0.001 (0.002) Loss 2.5711 (2.7118) Prec@1 41.250 (34.988) Prec@5 70.000 (65.375) Epoch: [4][6610/11272] Time 0.854 (0.820) Data 0.002 (0.002) Loss 2.8362 (2.7118) Prec@1 33.125 (34.989) Prec@5 60.625 (65.376) Epoch: [4][6620/11272] Time 0.760 (0.820) Data 0.001 (0.002) Loss 2.6905 (2.7119) Prec@1 32.500 (34.987) Prec@5 70.625 (65.375) Epoch: [4][6630/11272] Time 0.753 (0.820) Data 0.002 (0.002) Loss 2.5154 (2.7118) Prec@1 36.250 (34.986) Prec@5 71.250 (65.378) Epoch: [4][6640/11272] Time 0.872 (0.820) Data 0.001 (0.002) Loss 2.6292 (2.7116) Prec@1 40.625 (34.989) Prec@5 68.125 (65.379) Epoch: [4][6650/11272] Time 0.872 (0.820) Data 0.002 (0.002) Loss 2.8788 (2.7117) Prec@1 31.875 (34.986) Prec@5 63.750 (65.378) Epoch: [4][6660/11272] Time 0.795 (0.820) Data 0.002 (0.002) Loss 2.8259 (2.7116) Prec@1 34.375 (34.988) Prec@5 59.375 (65.378) Epoch: [4][6670/11272] Time 0.770 (0.820) Data 0.002 (0.002) Loss 2.8039 (2.7117) Prec@1 33.750 (34.988) Prec@5 68.125 (65.377) Epoch: [4][6680/11272] Time 0.846 (0.820) Data 0.001 (0.002) Loss 2.6165 (2.7116) Prec@1 34.375 (34.987) Prec@5 69.375 (65.379) Epoch: [4][6690/11272] Time 0.847 (0.820) Data 0.001 (0.002) Loss 2.9162 (2.7115) Prec@1 31.250 (34.990) Prec@5 60.625 (65.381) Epoch: [4][6700/11272] Time 0.745 (0.820) Data 0.001 (0.002) Loss 2.4535 (2.7114) Prec@1 38.750 (34.994) Prec@5 69.375 (65.380) Epoch: [4][6710/11272] Time 0.758 (0.820) Data 0.002 (0.002) Loss 2.5472 (2.7116) Prec@1 35.000 (34.992) Prec@5 69.375 (65.377) Epoch: [4][6720/11272] Time 0.851 (0.820) Data 0.001 (0.002) Loss 2.5655 (2.7116) Prec@1 31.875 (34.991) Prec@5 73.125 (65.380) Epoch: [4][6730/11272] Time 0.741 (0.820) Data 0.001 (0.002) Loss 2.6537 (2.7116) Prec@1 36.250 (34.989) Prec@5 65.000 (65.377) Epoch: [4][6740/11272] Time 0.742 (0.820) Data 0.002 (0.002) Loss 2.7934 (2.7117) Prec@1 31.875 (34.989) Prec@5 64.375 (65.375) Epoch: [4][6750/11272] Time 0.861 (0.820) Data 0.001 (0.002) Loss 2.5467 (2.7116) Prec@1 42.500 (34.990) Prec@5 68.750 (65.376) Epoch: [4][6760/11272] Time 0.899 (0.820) Data 0.003 (0.002) Loss 2.6808 (2.7116) Prec@1 31.875 (34.987) Prec@5 68.125 (65.375) Epoch: [4][6770/11272] Time 0.738 (0.819) Data 0.001 (0.002) Loss 2.5015 (2.7116) Prec@1 35.625 (34.987) Prec@5 68.750 (65.375) Epoch: [4][6780/11272] Time 0.756 (0.819) Data 0.002 (0.002) Loss 2.7620 (2.7115) Prec@1 31.875 (34.988) Prec@5 68.750 (65.377) Epoch: [4][6790/11272] Time 0.849 (0.819) Data 0.001 (0.002) Loss 2.7433 (2.7114) Prec@1 32.500 (34.989) Prec@5 62.500 (65.378) Epoch: [4][6800/11272] Time 0.872 (0.819) Data 0.002 (0.002) Loss 2.5179 (2.7114) Prec@1 41.875 (34.990) Prec@5 70.625 (65.381) Epoch: [4][6810/11272] Time 0.744 (0.819) Data 0.001 (0.002) Loss 2.4799 (2.7114) Prec@1 43.750 (34.989) Prec@5 70.000 (65.380) Epoch: [4][6820/11272] Time 0.741 (0.819) Data 0.002 (0.002) Loss 2.8861 (2.7113) Prec@1 33.750 (34.989) Prec@5 65.000 (65.382) Epoch: [4][6830/11272] Time 0.882 (0.819) Data 0.002 (0.002) Loss 2.8074 (2.7113) Prec@1 31.875 (34.989) Prec@5 62.500 (65.384) Epoch: [4][6840/11272] Time 0.822 (0.819) Data 0.001 (0.002) Loss 2.6200 (2.7112) Prec@1 33.750 (34.988) Prec@5 63.750 (65.385) Epoch: [4][6850/11272] Time 0.752 (0.819) Data 0.002 (0.002) Loss 2.8627 (2.7113) Prec@1 33.750 (34.987) Prec@5 66.875 (65.384) Epoch: [4][6860/11272] Time 0.851 (0.819) Data 0.001 (0.002) Loss 2.5328 (2.7113) Prec@1 42.500 (34.989) Prec@5 70.625 (65.387) Epoch: [4][6870/11272] Time 0.882 (0.819) Data 0.002 (0.002) Loss 2.6005 (2.7113) Prec@1 35.625 (34.989) Prec@5 68.125 (65.387) Epoch: [4][6880/11272] Time 0.770 (0.819) Data 0.002 (0.002) Loss 2.6163 (2.7113) Prec@1 35.625 (34.990) Prec@5 68.125 (65.385) Epoch: [4][6890/11272] Time 0.740 (0.819) Data 0.001 (0.002) Loss 2.7249 (2.7112) Prec@1 31.875 (34.992) Prec@5 67.500 (65.387) Epoch: [4][6900/11272] Time 0.862 (0.819) Data 0.002 (0.002) Loss 2.9683 (2.7113) Prec@1 29.375 (34.990) Prec@5 58.125 (65.385) Epoch: [4][6910/11272] Time 0.908 (0.819) Data 0.002 (0.002) Loss 2.7069 (2.7113) Prec@1 33.125 (34.990) Prec@5 63.750 (65.385) Epoch: [4][6920/11272] Time 0.743 (0.819) Data 0.001 (0.002) Loss 2.5381 (2.7113) Prec@1 36.250 (34.990) Prec@5 73.125 (65.386) Epoch: [4][6930/11272] Time 0.755 (0.819) Data 0.002 (0.002) Loss 2.8315 (2.7113) Prec@1 33.750 (34.989) Prec@5 64.375 (65.386) Epoch: [4][6940/11272] Time 0.847 (0.819) Data 0.001 (0.002) Loss 2.7055 (2.7112) Prec@1 37.500 (34.990) Prec@5 66.250 (65.388) Epoch: [4][6950/11272] Time 0.903 (0.819) Data 0.001 (0.002) Loss 2.6394 (2.7113) Prec@1 36.875 (34.989) Prec@5 66.250 (65.388) Epoch: [4][6960/11272] Time 0.778 (0.819) Data 0.001 (0.002) Loss 2.3524 (2.7113) Prec@1 39.375 (34.990) Prec@5 74.375 (65.388) Epoch: [4][6970/11272] Time 0.771 (0.819) Data 0.002 (0.002) Loss 2.6460 (2.7112) Prec@1 41.250 (34.990) Prec@5 66.250 (65.388) Epoch: [4][6980/11272] Time 0.834 (0.819) Data 0.001 (0.002) Loss 2.6190 (2.7112) Prec@1 37.500 (34.991) Prec@5 63.750 (65.388) Epoch: [4][6990/11272] Time 0.737 (0.819) Data 0.003 (0.002) Loss 2.6575 (2.7111) Prec@1 38.125 (34.993) Prec@5 68.125 (65.391) Epoch: [4][7000/11272] Time 0.753 (0.819) Data 0.001 (0.002) Loss 2.8905 (2.7111) Prec@1 32.500 (34.990) Prec@5 61.875 (65.390) Epoch: [4][7010/11272] Time 0.885 (0.819) Data 0.002 (0.002) Loss 2.8223 (2.7113) Prec@1 29.375 (34.986) Prec@5 65.625 (65.388) Epoch: [4][7020/11272] Time 0.851 (0.819) Data 0.001 (0.002) Loss 2.5553 (2.7112) Prec@1 36.250 (34.985) Prec@5 66.875 (65.391) Epoch: [4][7030/11272] Time 0.764 (0.819) Data 0.004 (0.002) Loss 2.7503 (2.7111) Prec@1 33.125 (34.985) Prec@5 62.500 (65.392) Epoch: [4][7040/11272] Time 0.745 (0.819) Data 0.001 (0.002) Loss 2.7601 (2.7110) Prec@1 37.500 (34.986) Prec@5 63.750 (65.391) Epoch: [4][7050/11272] Time 0.916 (0.819) Data 0.002 (0.002) Loss 2.7081 (2.7110) Prec@1 36.250 (34.988) Prec@5 64.375 (65.392) Epoch: [4][7060/11272] Time 0.831 (0.819) Data 0.001 (0.002) Loss 2.7361 (2.7111) Prec@1 30.000 (34.985) Prec@5 68.125 (65.393) Epoch: [4][7070/11272] Time 0.745 (0.819) Data 0.002 (0.002) Loss 2.7058 (2.7112) Prec@1 38.750 (34.983) Prec@5 71.250 (65.393) Epoch: [4][7080/11272] Time 0.747 (0.819) Data 0.001 (0.002) Loss 2.7825 (2.7112) Prec@1 31.250 (34.982) Prec@5 61.875 (65.393) Epoch: [4][7090/11272] Time 0.894 (0.819) Data 0.001 (0.002) Loss 2.4272 (2.7111) Prec@1 33.750 (34.983) Prec@5 68.750 (65.393) Epoch: [4][7100/11272] Time 0.890 (0.819) Data 0.015 (0.002) Loss 2.7715 (2.7111) Prec@1 35.625 (34.981) Prec@5 63.750 (65.392) Epoch: [4][7110/11272] Time 0.740 (0.819) Data 0.001 (0.002) Loss 2.6649 (2.7111) Prec@1 38.750 (34.979) Prec@5 67.500 (65.393) Epoch: [4][7120/11272] Time 0.931 (0.819) Data 0.017 (0.002) Loss 2.6395 (2.7111) Prec@1 38.750 (34.979) Prec@5 66.250 (65.394) Epoch: [4][7130/11272] Time 0.886 (0.819) Data 0.002 (0.002) Loss 2.7969 (2.7111) Prec@1 33.125 (34.980) Prec@5 65.625 (65.394) Epoch: [4][7140/11272] Time 0.788 (0.819) Data 0.002 (0.002) Loss 2.9481 (2.7111) Prec@1 28.750 (34.979) Prec@5 61.250 (65.394) Epoch: [4][7150/11272] Time 0.752 (0.819) Data 0.002 (0.002) Loss 2.4997 (2.7111) Prec@1 39.375 (34.981) Prec@5 71.250 (65.395) Epoch: [4][7160/11272] Time 0.867 (0.819) Data 0.002 (0.002) Loss 2.6648 (2.7110) Prec@1 35.000 (34.983) Prec@5 65.625 (65.397) Epoch: [4][7170/11272] Time 0.871 (0.819) Data 0.001 (0.002) Loss 2.6972 (2.7110) Prec@1 33.750 (34.985) Prec@5 67.500 (65.396) Epoch: [4][7180/11272] Time 0.781 (0.819) Data 0.002 (0.002) Loss 2.6593 (2.7110) Prec@1 32.500 (34.986) Prec@5 65.000 (65.396) Epoch: [4][7190/11272] Time 0.741 (0.819) Data 0.001 (0.002) Loss 2.9257 (2.7110) Prec@1 33.125 (34.984) Prec@5 64.375 (65.397) Epoch: [4][7200/11272] Time 1.117 (0.819) Data 0.325 (0.002) Loss 2.8197 (2.7109) Prec@1 32.500 (34.983) Prec@5 61.875 (65.398) Epoch: [4][7210/11272] Time 0.896 (0.819) Data 0.001 (0.002) Loss 2.5172 (2.7109) Prec@1 38.750 (34.982) Prec@5 68.125 (65.401) Epoch: [4][7220/11272] Time 0.753 (0.819) Data 0.002 (0.002) Loss 2.8796 (2.7108) Prec@1 31.875 (34.982) Prec@5 56.250 (65.402) Epoch: [4][7230/11272] Time 0.774 (0.819) Data 0.002 (0.002) Loss 2.6685 (2.7108) Prec@1 37.500 (34.984) Prec@5 67.500 (65.404) Epoch: [4][7240/11272] Time 0.854 (0.819) Data 0.001 (0.002) Loss 2.7698 (2.7107) Prec@1 36.250 (34.985) Prec@5 63.125 (65.404) Epoch: [4][7250/11272] Time 0.772 (0.819) Data 0.003 (0.002) Loss 2.7224 (2.7107) Prec@1 35.625 (34.983) Prec@5 64.375 (65.404) Epoch: [4][7260/11272] Time 0.746 (0.819) Data 0.002 (0.002) Loss 2.7189 (2.7107) Prec@1 31.250 (34.983) Prec@5 68.750 (65.404) Epoch: [4][7270/11272] Time 0.910 (0.819) Data 0.003 (0.002) Loss 2.5200 (2.7108) Prec@1 39.375 (34.983) Prec@5 70.000 (65.404) Epoch: [4][7280/11272] Time 0.852 (0.819) Data 0.002 (0.002) Loss 2.9700 (2.7108) Prec@1 28.750 (34.981) Prec@5 59.375 (65.403) Epoch: [4][7290/11272] Time 0.748 (0.819) Data 0.002 (0.002) Loss 2.6715 (2.7107) Prec@1 37.500 (34.981) Prec@5 66.250 (65.403) Epoch: [4][7300/11272] Time 0.733 (0.819) Data 0.001 (0.002) Loss 2.7862 (2.7107) Prec@1 35.000 (34.982) Prec@5 66.875 (65.405) Epoch: [4][7310/11272] Time 0.844 (0.820) Data 0.001 (0.002) Loss 2.9270 (2.7108) Prec@1 36.250 (34.981) Prec@5 63.125 (65.402) Epoch: [4][7320/11272] Time 0.956 (0.820) Data 0.099 (0.002) Loss 2.5868 (2.7107) Prec@1 33.750 (34.980) Prec@5 67.500 (65.403) Epoch: [4][7330/11272] Time 0.738 (0.820) Data 0.002 (0.003) Loss 2.7610 (2.7108) Prec@1 35.625 (34.980) Prec@5 66.875 (65.402) Epoch: [4][7340/11272] Time 1.241 (0.820) Data 0.457 (0.003) Loss 2.4739 (2.7107) Prec@1 36.875 (34.981) Prec@5 66.250 (65.405) Epoch: [4][7350/11272] Time 0.843 (0.820) Data 0.002 (0.003) Loss 2.6244 (2.7106) Prec@1 42.500 (34.984) Prec@5 68.750 (65.408) Epoch: [4][7360/11272] Time 0.922 (0.820) Data 0.001 (0.003) Loss 2.7267 (2.7106) Prec@1 33.125 (34.983) Prec@5 64.375 (65.408) Epoch: [4][7370/11272] Time 0.770 (0.820) Data 0.001 (0.003) Loss 3.0985 (2.7107) Prec@1 28.125 (34.983) Prec@5 56.875 (65.407) Epoch: [4][7380/11272] Time 0.890 (0.820) Data 0.002 (0.003) Loss 2.7225 (2.7106) Prec@1 38.125 (34.985) Prec@5 65.000 (65.408) Epoch: [4][7390/11272] Time 0.891 (0.820) Data 0.002 (0.003) Loss 2.8475 (2.7105) Prec@1 38.125 (34.985) Prec@5 63.750 (65.411) Epoch: [4][7400/11272] Time 0.803 (0.820) Data 0.002 (0.003) Loss 2.6793 (2.7106) Prec@1 31.875 (34.982) Prec@5 67.500 (65.410) Epoch: [4][7410/11272] Time 0.750 (0.820) Data 0.002 (0.003) Loss 2.7514 (2.7105) Prec@1 29.375 (34.982) Prec@5 61.875 (65.411) Epoch: [4][7420/11272] Time 0.881 (0.820) Data 0.001 (0.003) Loss 2.6064 (2.7105) Prec@1 36.875 (34.982) Prec@5 65.625 (65.413) Epoch: [4][7430/11272] Time 0.858 (0.820) Data 0.001 (0.003) Loss 2.4735 (2.7105) Prec@1 36.875 (34.981) Prec@5 71.250 (65.412) Epoch: [4][7440/11272] Time 0.932 (0.820) Data 0.191 (0.003) Loss 2.5267 (2.7105) Prec@1 37.500 (34.978) Prec@5 70.000 (65.412) Epoch: [4][7450/11272] Time 0.738 (0.820) Data 0.002 (0.003) Loss 2.6238 (2.7106) Prec@1 35.625 (34.977) Prec@5 68.750 (65.410) Epoch: [4][7460/11272] Time 0.865 (0.820) Data 0.001 (0.003) Loss 2.5593 (2.7106) Prec@1 38.750 (34.977) Prec@5 69.375 (65.409) Epoch: [4][7470/11272] Time 0.866 (0.820) Data 0.001 (0.003) Loss 2.6686 (2.7106) Prec@1 35.000 (34.978) Prec@5 65.000 (65.408) Epoch: [4][7480/11272] Time 0.843 (0.820) Data 0.002 (0.003) Loss 2.6879 (2.7108) Prec@1 35.625 (34.978) Prec@5 66.875 (65.406) Epoch: [4][7490/11272] Time 0.758 (0.820) Data 0.001 (0.003) Loss 2.8387 (2.7108) Prec@1 34.375 (34.977) Prec@5 61.250 (65.405) Epoch: [4][7500/11272] Time 0.861 (0.820) Data 0.002 (0.003) Loss 2.9184 (2.7107) Prec@1 36.250 (34.979) Prec@5 61.250 (65.406) Epoch: [4][7510/11272] Time 0.844 (0.820) Data 0.002 (0.003) Loss 2.7419 (2.7107) Prec@1 33.125 (34.979) Prec@5 68.125 (65.406) Epoch: [4][7520/11272] Time 0.784 (0.820) Data 0.002 (0.003) Loss 2.6293 (2.7107) Prec@1 35.000 (34.978) Prec@5 66.250 (65.406) Epoch: [4][7530/11272] Time 0.928 (0.820) Data 0.001 (0.003) Loss 2.6385 (2.7108) Prec@1 36.250 (34.977) Prec@5 66.250 (65.404) Epoch: [4][7540/11272] Time 0.882 (0.820) Data 0.002 (0.003) Loss 2.8599 (2.7108) Prec@1 33.750 (34.981) Prec@5 65.000 (65.403) Epoch: [4][7550/11272] Time 0.784 (0.820) Data 0.002 (0.003) Loss 2.7645 (2.7109) Prec@1 37.500 (34.978) Prec@5 65.000 (65.403) Epoch: [4][7560/11272] Time 0.734 (0.820) Data 0.002 (0.003) Loss 2.4351 (2.7109) Prec@1 44.375 (34.978) Prec@5 69.375 (65.405) Epoch: [4][7570/11272] Time 0.906 (0.820) Data 0.002 (0.003) Loss 2.5136 (2.7108) Prec@1 41.875 (34.980) Prec@5 65.625 (65.405) Epoch: [4][7580/11272] Time 0.918 (0.820) Data 0.002 (0.003) Loss 2.6504 (2.7107) Prec@1 36.875 (34.982) Prec@5 61.875 (65.406) Epoch: [4][7590/11272] Time 0.799 (0.820) Data 0.002 (0.003) Loss 2.6768 (2.7107) Prec@1 36.250 (34.982) Prec@5 61.250 (65.404) Epoch: [4][7600/11272] Time 0.764 (0.820) Data 0.001 (0.003) Loss 2.6927 (2.7107) Prec@1 41.875 (34.984) Prec@5 66.875 (65.403) Epoch: [4][7610/11272] Time 0.860 (0.820) Data 0.001 (0.003) Loss 2.6629 (2.7107) Prec@1 37.500 (34.987) Prec@5 66.875 (65.403) Epoch: [4][7620/11272] Time 0.863 (0.820) Data 0.001 (0.003) Loss 2.8407 (2.7107) Prec@1 26.250 (34.985) Prec@5 66.875 (65.403) Epoch: [4][7630/11272] Time 0.790 (0.820) Data 0.002 (0.003) Loss 2.8582 (2.7107) Prec@1 36.250 (34.985) Prec@5 65.000 (65.403) Epoch: [4][7640/11272] Time 0.851 (0.820) Data 0.086 (0.003) Loss 2.6592 (2.7106) Prec@1 31.875 (34.984) Prec@5 65.000 (65.405) Epoch: [4][7650/11272] Time 0.839 (0.820) Data 0.003 (0.003) Loss 2.8271 (2.7106) Prec@1 33.750 (34.985) Prec@5 58.750 (65.404) Epoch: [4][7660/11272] Time 0.752 (0.820) Data 0.001 (0.003) Loss 2.9087 (2.7107) Prec@1 34.375 (34.983) Prec@5 62.500 (65.402) Epoch: [4][7670/11272] Time 0.802 (0.820) Data 0.002 (0.003) Loss 2.5094 (2.7108) Prec@1 38.125 (34.983) Prec@5 70.000 (65.401) Epoch: [4][7680/11272] Time 0.902 (0.820) Data 0.002 (0.003) Loss 2.6924 (2.7108) Prec@1 36.875 (34.984) Prec@5 67.500 (65.400) Epoch: [4][7690/11272] Time 0.840 (0.821) Data 0.002 (0.003) Loss 2.8303 (2.7108) Prec@1 30.000 (34.982) Prec@5 62.500 (65.402) Epoch: [4][7700/11272] Time 0.887 (0.821) Data 0.111 (0.003) Loss 2.7500 (2.7108) Prec@1 35.625 (34.982) Prec@5 68.125 (65.403) Epoch: [4][7710/11272] Time 0.737 (0.821) Data 0.002 (0.003) Loss 2.6350 (2.7107) Prec@1 36.875 (34.983) Prec@5 68.125 (65.405) Epoch: [4][7720/11272] Time 0.867 (0.821) Data 0.002 (0.003) Loss 2.9792 (2.7106) Prec@1 28.750 (34.982) Prec@5 57.500 (65.405) Epoch: [4][7730/11272] Time 0.947 (0.821) Data 0.001 (0.003) Loss 2.4678 (2.7104) Prec@1 38.125 (34.985) Prec@5 70.625 (65.409) Epoch: [4][7740/11272] Time 0.745 (0.821) Data 0.002 (0.003) Loss 2.4308 (2.7103) Prec@1 44.375 (34.987) Prec@5 72.500 (65.412) Epoch: [4][7750/11272] Time 0.727 (0.821) Data 0.001 (0.003) Loss 2.7443 (2.7104) Prec@1 34.375 (34.985) Prec@5 66.875 (65.411) Epoch: [4][7760/11272] Time 0.844 (0.821) Data 0.002 (0.003) Loss 2.4816 (2.7103) Prec@1 40.625 (34.984) Prec@5 71.250 (65.411) Epoch: [4][7770/11272] Time 0.841 (0.821) Data 0.001 (0.004) Loss 2.7181 (2.7103) Prec@1 34.375 (34.983) Prec@5 61.250 (65.411) Epoch: [4][7780/11272] Time 0.725 (0.821) Data 0.002 (0.004) Loss 2.5950 (2.7102) Prec@1 31.875 (34.983) Prec@5 70.000 (65.414) Epoch: [4][7790/11272] Time 0.911 (0.821) Data 0.002 (0.004) Loss 2.9221 (2.7104) Prec@1 30.000 (34.979) Prec@5 60.000 (65.412) Epoch: [4][7800/11272] Time 1.533 (0.821) Data 0.688 (0.004) Loss 2.5687 (2.7103) Prec@1 41.875 (34.981) Prec@5 68.750 (65.414) Epoch: [4][7810/11272] Time 0.736 (0.821) Data 0.002 (0.004) Loss 2.7631 (2.7103) Prec@1 31.875 (34.980) Prec@5 63.125 (65.412) Epoch: [4][7820/11272] Time 0.906 (0.821) Data 0.131 (0.004) Loss 2.5452 (2.7103) Prec@1 40.000 (34.981) Prec@5 66.875 (65.413) Epoch: [4][7830/11272] Time 0.874 (0.821) Data 0.002 (0.004) Loss 2.7751 (2.7103) Prec@1 33.750 (34.982) Prec@5 64.375 (65.413) Epoch: [4][7840/11272] Time 0.885 (0.821) Data 0.002 (0.004) Loss 2.9032 (2.7102) Prec@1 31.875 (34.983) Prec@5 61.250 (65.413) Epoch: [4][7850/11272] Time 0.784 (0.821) Data 0.002 (0.004) Loss 2.9115 (2.7102) Prec@1 31.250 (34.982) Prec@5 62.500 (65.412) Epoch: [4][7860/11272] Time 0.744 (0.821) Data 0.001 (0.004) Loss 2.7314 (2.7102) Prec@1 34.375 (34.982) Prec@5 69.375 (65.413) Epoch: [4][7870/11272] Time 0.906 (0.821) Data 0.001 (0.004) Loss 2.5843 (2.7101) Prec@1 35.625 (34.984) Prec@5 67.500 (65.414) Epoch: [4][7880/11272] Time 1.033 (0.821) Data 0.172 (0.004) Loss 2.7346 (2.7101) Prec@1 36.250 (34.987) Prec@5 68.750 (65.416) Epoch: [4][7890/11272] Time 0.766 (0.821) Data 0.002 (0.004) Loss 2.9054 (2.7102) Prec@1 30.625 (34.986) Prec@5 66.250 (65.416) Epoch: [4][7900/11272] Time 0.804 (0.821) Data 0.002 (0.004) Loss 2.6121 (2.7101) Prec@1 32.500 (34.986) Prec@5 70.625 (65.418) Epoch: [4][7910/11272] Time 0.942 (0.821) Data 0.026 (0.004) Loss 2.8690 (2.7102) Prec@1 27.500 (34.984) Prec@5 60.625 (65.417) Epoch: [4][7920/11272] Time 0.766 (0.821) Data 0.003 (0.004) Loss 2.7388 (2.7101) Prec@1 36.250 (34.985) Prec@5 61.875 (65.421) Epoch: [4][7930/11272] Time 0.769 (0.821) Data 0.002 (0.004) Loss 2.5138 (2.7101) Prec@1 41.875 (34.984) Prec@5 72.500 (65.423) Epoch: [4][7940/11272] Time 0.887 (0.821) Data 0.002 (0.004) Loss 2.5298 (2.7100) Prec@1 37.500 (34.984) Prec@5 68.125 (65.424) Epoch: [4][7950/11272] Time 0.885 (0.821) Data 0.001 (0.004) Loss 2.7520 (2.7100) Prec@1 30.000 (34.984) Prec@5 64.375 (65.425) Epoch: [4][7960/11272] Time 0.743 (0.821) Data 0.001 (0.004) Loss 2.4536 (2.7099) Prec@1 43.125 (34.986) Prec@5 70.625 (65.426) Epoch: [4][7970/11272] Time 0.719 (0.821) Data 0.001 (0.004) Loss 2.7499 (2.7098) Prec@1 31.250 (34.986) Prec@5 66.250 (65.428) Epoch: [4][7980/11272] Time 0.837 (0.821) Data 0.001 (0.004) Loss 2.3723 (2.7097) Prec@1 43.125 (34.988) Prec@5 70.625 (65.431) Epoch: [4][7990/11272] Time 0.820 (0.821) Data 0.001 (0.004) Loss 2.8815 (2.7097) Prec@1 35.000 (34.989) Prec@5 61.250 (65.431) Epoch: [4][8000/11272] Time 0.737 (0.821) Data 0.001 (0.004) Loss 3.1460 (2.7098) Prec@1 24.375 (34.988) Prec@5 57.500 (65.429) Epoch: [4][8010/11272] Time 0.770 (0.821) Data 0.002 (0.004) Loss 2.7157 (2.7097) Prec@1 37.500 (34.988) Prec@5 66.250 (65.430) Epoch: [4][8020/11272] Time 0.874 (0.821) Data 0.002 (0.004) Loss 2.3196 (2.7098) Prec@1 38.750 (34.986) Prec@5 74.375 (65.430) Epoch: [4][8030/11272] Time 0.838 (0.821) Data 0.002 (0.004) Loss 2.7666 (2.7098) Prec@1 37.500 (34.986) Prec@5 67.500 (65.430) Epoch: [4][8040/11272] Time 0.745 (0.821) Data 0.001 (0.004) Loss 2.8432 (2.7098) Prec@1 36.875 (34.985) Prec@5 61.875 (65.429) Epoch: [4][8050/11272] Time 0.865 (0.821) Data 0.002 (0.004) Loss 2.4514 (2.7098) Prec@1 40.625 (34.987) Prec@5 71.875 (65.429) Epoch: [4][8060/11272] Time 1.086 (0.821) Data 0.189 (0.004) Loss 2.5320 (2.7097) Prec@1 36.875 (34.987) Prec@5 68.125 (65.429) Epoch: [4][8070/11272] Time 0.797 (0.821) Data 0.002 (0.004) Loss 2.8014 (2.7097) Prec@1 31.875 (34.986) Prec@5 65.000 (65.430) Epoch: [4][8080/11272] Time 0.803 (0.821) Data 0.002 (0.004) Loss 2.6588 (2.7098) Prec@1 36.250 (34.984) Prec@5 66.250 (65.428) Epoch: [4][8090/11272] Time 0.962 (0.821) Data 0.002 (0.004) Loss 2.8606 (2.7098) Prec@1 31.875 (34.985) Prec@5 58.750 (65.428) Epoch: [4][8100/11272] Time 1.028 (0.821) Data 0.096 (0.004) Loss 2.7301 (2.7097) Prec@1 30.000 (34.986) Prec@5 65.625 (65.430) Epoch: [4][8110/11272] Time 0.732 (0.821) Data 0.001 (0.004) Loss 2.5739 (2.7096) Prec@1 33.750 (34.985) Prec@5 66.875 (65.431) Epoch: [4][8120/11272] Time 0.940 (0.821) Data 0.166 (0.004) Loss 2.7045 (2.7095) Prec@1 26.875 (34.985) Prec@5 68.125 (65.432) Epoch: [4][8130/11272] Time 0.923 (0.821) Data 0.002 (0.004) Loss 2.5943 (2.7095) Prec@1 36.250 (34.985) Prec@5 63.750 (65.432) Epoch: [4][8140/11272] Time 1.101 (0.822) Data 0.233 (0.005) Loss 2.8318 (2.7095) Prec@1 29.375 (34.984) Prec@5 66.875 (65.433) Epoch: [4][8150/11272] Time 0.746 (0.822) Data 0.002 (0.005) Loss 2.8687 (2.7096) Prec@1 33.125 (34.982) Prec@5 68.750 (65.432) Epoch: [4][8160/11272] Time 1.060 (0.822) Data 0.295 (0.005) Loss 2.6631 (2.7096) Prec@1 32.500 (34.983) Prec@5 62.500 (65.430) Epoch: [4][8170/11272] Time 0.852 (0.822) Data 0.002 (0.005) Loss 2.5545 (2.7097) Prec@1 38.750 (34.984) Prec@5 66.875 (65.429) Epoch: [4][8180/11272] Time 0.987 (0.822) Data 0.216 (0.005) Loss 2.5715 (2.7097) Prec@1 39.375 (34.985) Prec@5 65.000 (65.428) Epoch: [4][8190/11272] Time 0.769 (0.822) Data 0.003 (0.005) Loss 2.8758 (2.7097) Prec@1 30.000 (34.986) Prec@5 61.875 (65.430) Epoch: [4][8200/11272] Time 0.858 (0.822) Data 0.002 (0.005) Loss 2.6387 (2.7096) Prec@1 32.500 (34.985) Prec@5 72.500 (65.432) Epoch: [4][8210/11272] Time 0.896 (0.822) Data 0.002 (0.005) Loss 2.7105 (2.7096) Prec@1 33.125 (34.987) Prec@5 66.875 (65.434) Epoch: [4][8220/11272] Time 0.753 (0.822) Data 0.001 (0.005) Loss 2.7795 (2.7095) Prec@1 29.375 (34.988) Prec@5 70.000 (65.437) Epoch: [4][8230/11272] Time 0.765 (0.822) Data 0.002 (0.005) Loss 2.5334 (2.7095) Prec@1 31.875 (34.986) Prec@5 70.625 (65.437) Epoch: [4][8240/11272] Time 0.902 (0.822) Data 0.002 (0.005) Loss 2.6075 (2.7095) Prec@1 42.500 (34.987) Prec@5 67.500 (65.437) Epoch: [4][8250/11272] Time 0.860 (0.822) Data 0.002 (0.005) Loss 2.6736 (2.7095) Prec@1 40.000 (34.988) Prec@5 67.500 (65.438) Epoch: [4][8260/11272] Time 0.805 (0.822) Data 0.035 (0.005) Loss 2.9558 (2.7095) Prec@1 28.750 (34.986) Prec@5 64.375 (65.440) Epoch: [4][8270/11272] Time 0.755 (0.822) Data 0.002 (0.005) Loss 2.9957 (2.7095) Prec@1 30.625 (34.988) Prec@5 65.000 (65.440) Epoch: [4][8280/11272] Time 0.966 (0.822) Data 0.002 (0.005) Loss 2.7806 (2.7096) Prec@1 33.750 (34.987) Prec@5 63.125 (65.439) Epoch: [4][8290/11272] Time 0.916 (0.822) Data 0.002 (0.005) Loss 2.6405 (2.7096) Prec@1 30.000 (34.986) Prec@5 67.500 (65.438) Epoch: [4][8300/11272] Time 0.784 (0.822) Data 0.001 (0.005) Loss 2.6178 (2.7096) Prec@1 35.625 (34.989) Prec@5 68.750 (65.440) Epoch: [4][8310/11272] Time 0.880 (0.822) Data 0.002 (0.005) Loss 2.6741 (2.7096) Prec@1 38.750 (34.988) Prec@5 63.750 (65.437) Epoch: [4][8320/11272] Time 0.916 (0.822) Data 0.002 (0.005) Loss 2.7875 (2.7096) Prec@1 36.250 (34.989) Prec@5 61.875 (65.439) Epoch: [4][8330/11272] Time 0.764 (0.822) Data 0.001 (0.005) Loss 2.6365 (2.7096) Prec@1 38.125 (34.991) Prec@5 66.875 (65.439) Epoch: [4][8340/11272] Time 0.770 (0.822) Data 0.001 (0.005) Loss 2.7898 (2.7097) Prec@1 33.125 (34.989) Prec@5 61.875 (65.436) Epoch: [4][8350/11272] Time 0.866 (0.822) Data 0.002 (0.005) Loss 2.8925 (2.7096) Prec@1 33.750 (34.990) Prec@5 65.625 (65.437) Epoch: [4][8360/11272] Time 1.119 (0.822) Data 0.228 (0.005) Loss 2.5102 (2.7096) Prec@1 37.500 (34.989) Prec@5 71.250 (65.438) Epoch: [4][8370/11272] Time 0.772 (0.822) Data 0.002 (0.005) Loss 2.6582 (2.7096) Prec@1 36.875 (34.989) Prec@5 70.000 (65.438) Epoch: [4][8380/11272] Time 0.805 (0.822) Data 0.024 (0.005) Loss 2.5315 (2.7095) Prec@1 41.250 (34.989) Prec@5 74.375 (65.439) Epoch: [4][8390/11272] Time 0.871 (0.822) Data 0.001 (0.005) Loss 2.9420 (2.7095) Prec@1 31.250 (34.992) Prec@5 61.875 (65.439) Epoch: [4][8400/11272] Time 1.208 (0.823) Data 0.305 (0.005) Loss 2.9878 (2.7095) Prec@1 26.875 (34.990) Prec@5 61.250 (65.438) Epoch: [4][8410/11272] Time 0.783 (0.823) Data 0.002 (0.005) Loss 2.7041 (2.7095) Prec@1 34.375 (34.992) Prec@5 66.250 (65.439) Epoch: [4][8420/11272] Time 0.773 (0.823) Data 0.002 (0.005) Loss 2.5522 (2.7095) Prec@1 39.375 (34.991) Prec@5 73.125 (65.440) Epoch: [4][8430/11272] Time 0.838 (0.823) Data 0.001 (0.006) Loss 2.7835 (2.7095) Prec@1 33.125 (34.989) Prec@5 62.500 (65.438) Epoch: [4][8440/11272] Time 0.909 (0.823) Data 0.038 (0.006) Loss 2.7491 (2.7094) Prec@1 32.500 (34.992) Prec@5 63.750 (65.441) Epoch: [4][8450/11272] Time 0.782 (0.823) Data 0.002 (0.006) Loss 2.7143 (2.7094) Prec@1 31.250 (34.993) Prec@5 66.250 (65.441) Epoch: [4][8460/11272] Time 0.911 (0.823) Data 0.001 (0.006) Loss 2.9624 (2.7094) Prec@1 30.625 (34.995) Prec@5 61.250 (65.441) Epoch: [4][8470/11272] Time 0.906 (0.823) Data 0.002 (0.006) Loss 2.9557 (2.7093) Prec@1 25.000 (34.994) Prec@5 58.125 (65.442) Epoch: [4][8480/11272] Time 0.729 (0.823) Data 0.002 (0.006) Loss 2.5957 (2.7094) Prec@1 39.375 (34.994) Prec@5 68.125 (65.442) Epoch: [4][8490/11272] Time 0.842 (0.823) Data 0.002 (0.006) Loss 2.6744 (2.7093) Prec@1 35.625 (34.996) Prec@5 67.500 (65.444) Epoch: [4][8500/11272] Time 1.622 (0.823) Data 0.696 (0.006) Loss 2.8961 (2.7092) Prec@1 35.000 (34.997) Prec@5 59.375 (65.443) Epoch: [4][8510/11272] Time 0.890 (0.824) Data 0.002 (0.006) Loss 2.8839 (2.7093) Prec@1 33.750 (34.995) Prec@5 60.000 (65.441) Epoch: [4][8520/11272] Time 0.884 (0.824) Data 0.110 (0.006) Loss 2.5515 (2.7094) Prec@1 41.250 (34.997) Prec@5 70.000 (65.440) Epoch: [4][8530/11272] Time 0.738 (0.824) Data 0.001 (0.006) Loss 2.7993 (2.7095) Prec@1 33.125 (34.998) Prec@5 63.125 (65.439) Epoch: [4][8540/11272] Time 0.858 (0.824) Data 0.001 (0.007) Loss 2.8920 (2.7096) Prec@1 35.625 (34.995) Prec@5 60.625 (65.437) Epoch: [4][8550/11272] Time 0.909 (0.824) Data 0.001 (0.007) Loss 2.4943 (2.7096) Prec@1 38.750 (34.994) Prec@5 65.625 (65.435) Epoch: [4][8560/11272] Time 0.758 (0.824) Data 0.001 (0.007) Loss 2.7760 (2.7097) Prec@1 36.875 (34.993) Prec@5 63.750 (65.435) Epoch: [4][8570/11272] Time 0.827 (0.824) Data 0.071 (0.007) Loss 2.9718 (2.7096) Prec@1 37.500 (34.994) Prec@5 58.750 (65.436) Epoch: [4][8580/11272] Time 0.845 (0.824) Data 0.001 (0.007) Loss 2.7842 (2.7096) Prec@1 34.375 (34.995) Prec@5 66.250 (65.439) Epoch: [4][8590/11272] Time 0.948 (0.824) Data 0.215 (0.007) Loss 2.4994 (2.7095) Prec@1 36.875 (34.994) Prec@5 71.875 (65.440) Epoch: [4][8600/11272] Time 0.734 (0.824) Data 0.002 (0.007) Loss 2.6840 (2.7095) Prec@1 36.250 (34.993) Prec@5 64.375 (65.440) Epoch: [4][8610/11272] Time 1.147 (0.824) Data 0.265 (0.007) Loss 2.7893 (2.7095) Prec@1 32.500 (34.992) Prec@5 65.000 (65.441) Epoch: [4][8620/11272] Time 0.933 (0.824) Data 0.002 (0.007) Loss 3.0472 (2.7096) Prec@1 30.000 (34.992) Prec@5 60.625 (65.439) Epoch: [4][8630/11272] Time 0.719 (0.824) Data 0.002 (0.007) Loss 2.6837 (2.7097) Prec@1 33.125 (34.991) Prec@5 65.000 (65.437) Epoch: [4][8640/11272] Time 0.746 (0.824) Data 0.002 (0.007) Loss 2.8755 (2.7097) Prec@1 38.125 (34.990) Prec@5 62.500 (65.436) Epoch: [4][8650/11272] Time 0.867 (0.825) Data 0.001 (0.007) Loss 2.8420 (2.7097) Prec@1 29.375 (34.992) Prec@5 59.375 (65.437) Epoch: [4][8660/11272] Time 0.899 (0.825) Data 0.002 (0.007) Loss 2.7181 (2.7097) Prec@1 35.625 (34.991) Prec@5 65.000 (65.437) Epoch: [4][8670/11272] Time 0.739 (0.825) Data 0.002 (0.007) Loss 2.7191 (2.7098) Prec@1 29.375 (34.990) Prec@5 64.375 (65.437) Epoch: [4][8680/11272] Time 0.760 (0.825) Data 0.002 (0.007) Loss 2.5082 (2.7097) Prec@1 35.625 (34.991) Prec@5 71.250 (65.437) Epoch: [4][8690/11272] Time 0.902 (0.825) Data 0.001 (0.007) Loss 2.7599 (2.7097) Prec@1 33.125 (34.991) Prec@5 70.000 (65.440) Epoch: [4][8700/11272] Time 0.838 (0.825) Data 0.001 (0.007) Loss 2.5237 (2.7098) Prec@1 36.875 (34.992) Prec@5 66.875 (65.440) Epoch: [4][8710/11272] Time 0.745 (0.825) Data 0.002 (0.007) Loss 2.5124 (2.7098) Prec@1 39.375 (34.989) Prec@5 70.000 (65.440) Epoch: [4][8720/11272] Time 0.854 (0.825) Data 0.001 (0.007) Loss 2.5953 (2.7098) Prec@1 37.500 (34.990) Prec@5 68.125 (65.440) Epoch: [4][8730/11272] Time 0.869 (0.825) Data 0.002 (0.007) Loss 2.6692 (2.7098) Prec@1 38.125 (34.991) Prec@5 66.875 (65.441) Epoch: [4][8740/11272] Time 0.741 (0.825) Data 0.001 (0.007) Loss 2.9553 (2.7098) Prec@1 31.875 (34.992) Prec@5 61.875 (65.441) Epoch: [4][8750/11272] Time 0.770 (0.825) Data 0.002 (0.007) Loss 2.7915 (2.7100) Prec@1 35.625 (34.990) Prec@5 63.125 (65.438) Epoch: [4][8760/11272] Time 0.878 (0.825) Data 0.001 (0.008) Loss 2.8426 (2.7099) Prec@1 35.625 (34.989) Prec@5 62.500 (65.440) Epoch: [4][8770/11272] Time 0.886 (0.825) Data 0.001 (0.008) Loss 2.6316 (2.7098) Prec@1 35.625 (34.992) Prec@5 71.250 (65.442) Epoch: [4][8780/11272] Time 0.767 (0.825) Data 0.002 (0.008) Loss 2.8583 (2.7098) Prec@1 31.875 (34.992) Prec@5 61.250 (65.443) Epoch: [4][8790/11272] Time 0.923 (0.825) Data 0.139 (0.008) Loss 2.4627 (2.7097) Prec@1 41.875 (34.995) Prec@5 70.000 (65.446) Epoch: [4][8800/11272] Time 0.859 (0.825) Data 0.002 (0.008) Loss 2.5702 (2.7096) Prec@1 35.625 (34.998) Prec@5 70.000 (65.447) Epoch: [4][8810/11272] Time 0.990 (0.825) Data 0.076 (0.008) Loss 2.4674 (2.7095) Prec@1 41.250 (35.000) Prec@5 70.000 (65.449) Epoch: [4][8820/11272] Time 0.746 (0.825) Data 0.001 (0.008) Loss 3.0005 (2.7094) Prec@1 31.875 (35.000) Prec@5 55.625 (65.449) Epoch: [4][8830/11272] Time 0.785 (0.825) Data 0.002 (0.008) Loss 2.4028 (2.7094) Prec@1 41.250 (35.003) Prec@5 71.250 (65.449) Epoch: [4][8840/11272] Time 0.906 (0.825) Data 0.002 (0.008) Loss 2.7897 (2.7094) Prec@1 30.625 (35.002) Prec@5 60.625 (65.449) Epoch: [4][8850/11272] Time 0.750 (0.825) Data 0.003 (0.008) Loss 2.7994 (2.7095) Prec@1 30.625 (35.002) Prec@5 58.750 (65.447) Epoch: [4][8860/11272] Time 0.802 (0.825) Data 0.002 (0.008) Loss 2.7636 (2.7096) Prec@1 35.000 (35.000) Prec@5 63.125 (65.446) Epoch: [4][8870/11272] Time 0.980 (0.825) Data 0.120 (0.008) Loss 2.7166 (2.7095) Prec@1 39.375 (34.999) Prec@5 67.500 (65.447) Epoch: [4][8880/11272] Time 0.859 (0.825) Data 0.001 (0.008) Loss 2.8097 (2.7096) Prec@1 30.625 (34.997) Prec@5 63.125 (65.446) Epoch: [4][8890/11272] Time 0.773 (0.825) Data 0.002 (0.008) Loss 2.5434 (2.7096) Prec@1 38.125 (34.997) Prec@5 66.250 (65.447) Epoch: [4][8900/11272] Time 0.739 (0.825) Data 0.001 (0.008) Loss 2.5725 (2.7096) Prec@1 37.500 (34.997) Prec@5 67.500 (65.446) Epoch: [4][8910/11272] Time 0.898 (0.825) Data 0.002 (0.008) Loss 3.0928 (2.7096) Prec@1 26.875 (34.996) Prec@5 58.125 (65.446) Epoch: [4][8920/11272] Time 0.843 (0.825) Data 0.001 (0.008) Loss 2.6685 (2.7096) Prec@1 38.125 (34.995) Prec@5 65.625 (65.444) Epoch: [4][8930/11272] Time 0.800 (0.825) Data 0.002 (0.008) Loss 2.5648 (2.7096) Prec@1 35.625 (34.995) Prec@5 65.000 (65.445) Epoch: [4][8940/11272] Time 0.756 (0.825) Data 0.002 (0.008) Loss 2.5106 (2.7095) Prec@1 36.875 (34.997) Prec@5 70.000 (65.446) Epoch: [4][8950/11272] Time 0.954 (0.825) Data 0.043 (0.008) Loss 2.5313 (2.7094) Prec@1 37.500 (35.000) Prec@5 70.000 (65.447) Epoch: [4][8960/11272] Time 0.884 (0.825) Data 0.002 (0.008) Loss 2.7948 (2.7094) Prec@1 31.875 (35.001) Prec@5 64.375 (65.447) Epoch: [4][8970/11272] Time 0.879 (0.825) Data 0.105 (0.008) Loss 2.8039 (2.7094) Prec@1 33.750 (35.002) Prec@5 60.000 (65.448) Epoch: [4][8980/11272] Time 0.929 (0.825) Data 0.001 (0.008) Loss 2.9995 (2.7093) Prec@1 29.375 (35.001) Prec@5 57.500 (65.448) Epoch: [4][8990/11272] Time 0.872 (0.825) Data 0.001 (0.008) Loss 2.5659 (2.7093) Prec@1 35.625 (35.002) Prec@5 63.750 (65.449) Epoch: [4][9000/11272] Time 0.783 (0.825) Data 0.002 (0.008) Loss 2.8906 (2.7093) Prec@1 34.375 (35.002) Prec@5 58.750 (65.448) Epoch: [4][9010/11272] Time 0.748 (0.825) Data 0.002 (0.008) Loss 2.4049 (2.7092) Prec@1 42.500 (35.005) Prec@5 75.000 (65.452) Epoch: [4][9020/11272] Time 0.895 (0.825) Data 0.002 (0.008) Loss 2.6164 (2.7092) Prec@1 35.625 (35.004) Prec@5 66.250 (65.450) Epoch: [4][9030/11272] Time 0.846 (0.825) Data 0.001 (0.008) Loss 2.3962 (2.7091) Prec@1 38.750 (35.006) Prec@5 70.000 (65.452) Epoch: [4][9040/11272] Time 0.738 (0.825) Data 0.002 (0.008) Loss 2.7409 (2.7091) Prec@1 31.875 (35.008) Prec@5 67.500 (65.453) Epoch: [4][9050/11272] Time 0.776 (0.825) Data 0.002 (0.008) Loss 2.6871 (2.7090) Prec@1 35.625 (35.009) Prec@5 62.500 (65.453) Epoch: [4][9060/11272] Time 0.888 (0.825) Data 0.002 (0.008) Loss 2.7532 (2.7091) Prec@1 32.500 (35.007) Prec@5 64.375 (65.453) Epoch: [4][9070/11272] Time 0.870 (0.825) Data 0.001 (0.008) Loss 2.8911 (2.7090) Prec@1 26.250 (35.007) Prec@5 63.750 (65.454) Epoch: [4][9080/11272] Time 0.787 (0.825) Data 0.002 (0.008) Loss 2.6293 (2.7091) Prec@1 33.125 (35.008) Prec@5 65.625 (65.453) Epoch: [4][9090/11272] Time 1.021 (0.825) Data 0.224 (0.008) Loss 2.5615 (2.7090) Prec@1 37.500 (35.007) Prec@5 66.250 (65.454) Epoch: [4][9100/11272] Time 0.916 (0.825) Data 0.002 (0.008) Loss 2.5650 (2.7090) Prec@1 36.875 (35.007) Prec@5 64.375 (65.455) Epoch: [4][9110/11272] Time 0.774 (0.826) Data 0.003 (0.008) Loss 2.9391 (2.7090) Prec@1 30.625 (35.008) Prec@5 60.000 (65.456) Epoch: [4][9120/11272] Time 0.747 (0.826) Data 0.002 (0.008) Loss 2.9081 (2.7089) Prec@1 35.000 (35.011) Prec@5 59.375 (65.457) Epoch: [4][9130/11272] Time 0.896 (0.826) Data 0.001 (0.008) Loss 2.4972 (2.7089) Prec@1 40.625 (35.011) Prec@5 63.750 (65.456) Epoch: [4][9140/11272] Time 0.843 (0.826) Data 0.002 (0.008) Loss 2.8599 (2.7089) Prec@1 31.875 (35.011) Prec@5 65.000 (65.455) Epoch: [4][9150/11272] Time 0.742 (0.826) Data 0.002 (0.008) Loss 3.2025 (2.7090) Prec@1 28.125 (35.010) Prec@5 55.625 (65.453) Epoch: [4][9160/11272] Time 0.985 (0.826) Data 0.242 (0.008) Loss 2.9112 (2.7091) Prec@1 37.500 (35.008) Prec@5 56.875 (65.451) Epoch: [4][9170/11272] Time 0.930 (0.826) Data 0.002 (0.008) Loss 2.8152 (2.7091) Prec@1 28.750 (35.007) Prec@5 63.125 (65.452) Epoch: [4][9180/11272] Time 0.872 (0.826) Data 0.002 (0.008) Loss 2.8430 (2.7091) Prec@1 30.000 (35.006) Prec@5 61.875 (65.452) Epoch: [4][9190/11272] Time 0.743 (0.826) Data 0.002 (0.008) Loss 2.7448 (2.7090) Prec@1 29.375 (35.006) Prec@5 68.750 (65.453) Epoch: [4][9200/11272] Time 0.742 (0.826) Data 0.001 (0.008) Loss 2.6401 (2.7090) Prec@1 31.875 (35.006) Prec@5 63.750 (65.454) Epoch: [4][9210/11272] Time 0.876 (0.826) Data 0.001 (0.008) Loss 2.5332 (2.7089) Prec@1 36.250 (35.007) Prec@5 68.750 (65.455) Epoch: [4][9220/11272] Time 0.879 (0.826) Data 0.001 (0.008) Loss 2.7477 (2.7090) Prec@1 28.750 (35.006) Prec@5 62.500 (65.455) Epoch: [4][9230/11272] Time 0.766 (0.826) Data 0.002 (0.008) Loss 2.7173 (2.7090) Prec@1 31.250 (35.006) Prec@5 63.750 (65.456) Epoch: [4][9240/11272] Time 0.994 (0.826) Data 0.001 (0.008) Loss 2.9286 (2.7090) Prec@1 29.375 (35.004) Prec@5 60.625 (65.455) Epoch: [4][9250/11272] Time 0.880 (0.826) Data 0.002 (0.008) Loss 2.5971 (2.7089) Prec@1 33.750 (35.005) Prec@5 73.125 (65.458) Epoch: [4][9260/11272] Time 0.819 (0.826) Data 0.002 (0.008) Loss 2.4315 (2.7089) Prec@1 41.250 (35.005) Prec@5 68.750 (65.457) Epoch: [4][9270/11272] Time 0.741 (0.826) Data 0.002 (0.008) Loss 2.6628 (2.7089) Prec@1 38.125 (35.008) Prec@5 65.000 (65.459) Epoch: [4][9280/11272] Time 0.873 (0.826) Data 0.001 (0.008) Loss 2.6644 (2.7088) Prec@1 35.000 (35.009) Prec@5 64.375 (65.460) Epoch: [4][9290/11272] Time 0.876 (0.826) Data 0.001 (0.008) Loss 2.7059 (2.7088) Prec@1 32.500 (35.009) Prec@5 66.875 (65.460) Epoch: [4][9300/11272] Time 0.772 (0.826) Data 0.002 (0.009) Loss 2.6658 (2.7089) Prec@1 37.500 (35.010) Prec@5 66.875 (65.459) Epoch: [4][9310/11272] Time 1.082 (0.826) Data 0.291 (0.009) Loss 2.6887 (2.7089) Prec@1 31.250 (35.009) Prec@5 63.750 (65.457) Epoch: [4][9320/11272] Time 0.877 (0.826) Data 0.001 (0.009) Loss 2.8568 (2.7089) Prec@1 30.000 (35.009) Prec@5 60.625 (65.456) Epoch: [4][9330/11272] Time 0.890 (0.826) Data 0.001 (0.009) Loss 2.7145 (2.7089) Prec@1 28.750 (35.010) Prec@5 61.875 (65.457) Epoch: [4][9340/11272] Time 0.737 (0.826) Data 0.001 (0.009) Loss 2.7640 (2.7088) Prec@1 28.125 (35.011) Prec@5 65.000 (65.460) Epoch: [4][9350/11272] Time 0.750 (0.826) Data 0.002 (0.009) Loss 2.6031 (2.7087) Prec@1 38.750 (35.011) Prec@5 70.625 (65.461) Epoch: [4][9360/11272] Time 0.847 (0.826) Data 0.001 (0.009) Loss 2.6687 (2.7088) Prec@1 35.000 (35.009) Prec@5 61.875 (65.459) Epoch: [4][9370/11272] Time 0.871 (0.826) Data 0.001 (0.009) Loss 2.6170 (2.7088) Prec@1 33.750 (35.009) Prec@5 68.125 (65.459) Epoch: [4][9380/11272] Time 0.791 (0.826) Data 0.031 (0.009) Loss 2.8873 (2.7089) Prec@1 28.125 (35.008) Prec@5 58.750 (65.457) Epoch: [4][9390/11272] Time 0.884 (0.826) Data 0.001 (0.009) Loss 2.7262 (2.7089) Prec@1 35.000 (35.008) Prec@5 67.500 (65.458) Epoch: [4][9400/11272] Time 0.827 (0.826) Data 0.001 (0.009) Loss 2.6568 (2.7089) Prec@1 30.000 (35.008) Prec@5 70.000 (65.459) Epoch: [4][9410/11272] Time 1.005 (0.826) Data 0.266 (0.009) Loss 2.8409 (2.7089) Prec@1 32.500 (35.009) Prec@5 61.250 (65.457) Epoch: [4][9420/11272] Time 0.753 (0.826) Data 0.002 (0.009) Loss 2.5415 (2.7089) Prec@1 35.000 (35.007) Prec@5 70.000 (65.457) Epoch: [4][9430/11272] Time 0.911 (0.826) Data 0.029 (0.009) Loss 2.8879 (2.7089) Prec@1 31.875 (35.009) Prec@5 58.750 (65.458) Epoch: [4][9440/11272] Time 0.846 (0.826) Data 0.002 (0.009) Loss 2.9809 (2.7089) Prec@1 34.375 (35.010) Prec@5 60.000 (65.457) Epoch: [4][9450/11272] Time 0.742 (0.826) Data 0.002 (0.009) Loss 2.9593 (2.7088) Prec@1 30.000 (35.013) Prec@5 61.250 (65.458) Epoch: [4][9460/11272] Time 0.741 (0.826) Data 0.002 (0.009) Loss 2.3699 (2.7088) Prec@1 40.625 (35.014) Prec@5 71.875 (65.459) Epoch: [4][9470/11272] Time 0.940 (0.826) Data 0.054 (0.009) Loss 2.6715 (2.7087) Prec@1 35.625 (35.015) Prec@5 72.500 (65.463) Epoch: [4][9480/11272] Time 0.867 (0.826) Data 0.002 (0.009) Loss 2.6753 (2.7087) Prec@1 32.500 (35.014) Prec@5 65.000 (65.462) Epoch: [4][9490/11272] Time 0.779 (0.826) Data 0.002 (0.009) Loss 2.7624 (2.7087) Prec@1 29.375 (35.012) Prec@5 61.250 (65.461) Epoch: [4][9500/11272] Time 0.759 (0.826) Data 0.002 (0.009) Loss 2.7047 (2.7088) Prec@1 36.250 (35.012) Prec@5 66.875 (65.460) Epoch: [4][9510/11272] Time 0.859 (0.826) Data 0.002 (0.009) Loss 2.6230 (2.7087) Prec@1 33.750 (35.013) Prec@5 66.875 (65.461) Epoch: [4][9520/11272] Time 0.743 (0.826) Data 0.002 (0.009) Loss 2.8388 (2.7087) Prec@1 35.625 (35.013) Prec@5 61.250 (65.462) Epoch: [4][9530/11272] Time 0.745 (0.827) Data 0.001 (0.009) Loss 2.5455 (2.7086) Prec@1 38.750 (35.014) Prec@5 68.125 (65.464) Epoch: [4][9540/11272] Time 0.878 (0.827) Data 0.002 (0.009) Loss 2.7440 (2.7086) Prec@1 33.750 (35.014) Prec@5 60.625 (65.464) Epoch: [4][9550/11272] Time 0.874 (0.827) Data 0.002 (0.009) Loss 2.6509 (2.7086) Prec@1 40.000 (35.015) Prec@5 65.000 (65.463) Epoch: [4][9560/11272] Time 0.772 (0.827) Data 0.002 (0.009) Loss 2.9141 (2.7086) Prec@1 30.625 (35.015) Prec@5 61.875 (65.462) Epoch: [4][9570/11272] Time 0.923 (0.827) Data 0.147 (0.009) Loss 2.8913 (2.7086) Prec@1 29.375 (35.014) Prec@5 61.250 (65.461) Epoch: [4][9580/11272] Time 0.885 (0.827) Data 0.001 (0.009) Loss 2.6420 (2.7086) Prec@1 35.625 (35.014) Prec@5 65.625 (65.461) Epoch: [4][9590/11272] Time 1.034 (0.827) Data 0.142 (0.009) Loss 2.7024 (2.7086) Prec@1 37.500 (35.013) Prec@5 64.375 (65.461) Epoch: [4][9600/11272] Time 0.741 (0.827) Data 0.002 (0.009) Loss 2.6802 (2.7087) Prec@1 36.250 (35.012) Prec@5 66.875 (65.460) Epoch: [4][9610/11272] Time 0.748 (0.827) Data 0.002 (0.009) Loss 2.7597 (2.7086) Prec@1 37.500 (35.011) Prec@5 66.875 (65.461) Epoch: [4][9620/11272] Time 0.882 (0.827) Data 0.002 (0.009) Loss 2.7558 (2.7086) Prec@1 31.250 (35.012) Prec@5 66.875 (65.461) Epoch: [4][9630/11272] Time 0.859 (0.827) Data 0.002 (0.009) Loss 2.5875 (2.7087) Prec@1 40.000 (35.014) Prec@5 66.875 (65.462) Epoch: [4][9640/11272] Time 0.798 (0.827) Data 0.002 (0.009) Loss 2.8287 (2.7087) Prec@1 32.500 (35.014) Prec@5 65.000 (65.460) Epoch: [4][9650/11272] Time 1.123 (0.827) Data 0.184 (0.009) Loss 2.7013 (2.7087) Prec@1 33.125 (35.014) Prec@5 66.250 (65.460) Epoch: [4][9660/11272] Time 0.837 (0.827) Data 0.002 (0.009) Loss 2.6998 (2.7087) Prec@1 35.625 (35.014) Prec@5 64.375 (65.461) Epoch: [4][9670/11272] Time 1.073 (0.827) Data 0.268 (0.009) Loss 2.9178 (2.7087) Prec@1 31.875 (35.014) Prec@5 57.500 (65.461) Epoch: [4][9680/11272] Time 0.746 (0.827) Data 0.002 (0.009) Loss 2.5074 (2.7087) Prec@1 32.500 (35.011) Prec@5 71.250 (65.462) Epoch: [4][9690/11272] Time 1.282 (0.827) Data 0.324 (0.009) Loss 2.8260 (2.7088) Prec@1 28.125 (35.011) Prec@5 69.375 (65.463) Epoch: [4][9700/11272] Time 0.840 (0.827) Data 0.002 (0.009) Loss 3.0193 (2.7088) Prec@1 31.250 (35.009) Prec@5 60.625 (65.461) Epoch: [4][9710/11272] Time 0.770 (0.827) Data 0.001 (0.009) Loss 2.7472 (2.7088) Prec@1 35.000 (35.009) Prec@5 65.625 (65.461) Epoch: [4][9720/11272] Time 0.749 (0.827) Data 0.002 (0.009) Loss 2.6010 (2.7088) Prec@1 32.500 (35.008) Prec@5 69.375 (65.463) Epoch: [4][9730/11272] Time 1.084 (0.827) Data 0.168 (0.009) Loss 2.8302 (2.7087) Prec@1 33.125 (35.010) Prec@5 62.500 (65.464) Epoch: [4][9740/11272] Time 0.880 (0.827) Data 0.002 (0.009) Loss 2.6065 (2.7087) Prec@1 44.375 (35.011) Prec@5 66.875 (65.465) Epoch: [4][9750/11272] Time 0.741 (0.827) Data 0.001 (0.009) Loss 2.6712 (2.7086) Prec@1 36.250 (35.012) Prec@5 65.000 (65.466) Epoch: [4][9760/11272] Time 0.753 (0.827) Data 0.002 (0.009) Loss 2.8931 (2.7087) Prec@1 33.125 (35.012) Prec@5 65.000 (65.465) Epoch: [4][9770/11272] Time 1.023 (0.827) Data 0.113 (0.009) Loss 2.8217 (2.7087) Prec@1 33.125 (35.012) Prec@5 63.750 (65.464) Epoch: [4][9780/11272] Time 0.741 (0.827) Data 0.004 (0.009) Loss 2.8009 (2.7086) Prec@1 33.125 (35.014) Prec@5 60.000 (65.465) Epoch: [4][9790/11272] Time 0.848 (0.827) Data 0.084 (0.009) Loss 2.7002 (2.7086) Prec@1 31.250 (35.014) Prec@5 68.125 (65.468) Epoch: [4][9800/11272] Time 0.886 (0.827) Data 0.002 (0.009) Loss 2.8544 (2.7087) Prec@1 33.125 (35.012) Prec@5 61.875 (65.466) Epoch: [4][9810/11272] Time 0.907 (0.827) Data 0.067 (0.009) Loss 2.7183 (2.7086) Prec@1 33.750 (35.015) Prec@5 66.875 (65.468) Epoch: [4][9820/11272] Time 0.767 (0.827) Data 0.002 (0.010) Loss 2.5818 (2.7086) Prec@1 42.500 (35.016) Prec@5 66.875 (65.469) Epoch: [4][9830/11272] Time 0.741 (0.827) Data 0.002 (0.010) Loss 3.0024 (2.7085) Prec@1 32.500 (35.017) Prec@5 63.125 (65.470) Epoch: [4][9840/11272] Time 0.876 (0.827) Data 0.001 (0.010) Loss 2.8043 (2.7086) Prec@1 41.250 (35.017) Prec@5 61.250 (65.470) Epoch: [4][9850/11272] Time 1.079 (0.827) Data 0.198 (0.010) Loss 2.7232 (2.7086) Prec@1 39.375 (35.015) Prec@5 63.750 (65.469) Epoch: [4][9860/11272] Time 0.744 (0.827) Data 0.002 (0.010) Loss 2.5613 (2.7086) Prec@1 38.125 (35.014) Prec@5 69.375 (65.469) Epoch: [4][9870/11272] Time 0.785 (0.827) Data 0.001 (0.010) Loss 2.4832 (2.7085) Prec@1 36.875 (35.015) Prec@5 70.625 (65.468) Epoch: [4][9880/11272] Time 0.904 (0.827) Data 0.001 (0.010) Loss 2.3581 (2.7085) Prec@1 35.625 (35.013) Prec@5 71.250 (65.469) Epoch: [4][9890/11272] Time 0.822 (0.827) Data 0.001 (0.010) Loss 2.8429 (2.7085) Prec@1 34.375 (35.015) Prec@5 65.000 (65.470) Epoch: [4][9900/11272] Time 1.014 (0.827) Data 0.211 (0.010) Loss 2.6190 (2.7085) Prec@1 36.250 (35.018) Prec@5 66.250 (65.471) Epoch: [4][9910/11272] Time 0.898 (0.827) Data 0.001 (0.010) Loss 2.8086 (2.7085) Prec@1 35.625 (35.018) Prec@5 61.875 (65.471) Epoch: [4][9920/11272] Time 0.863 (0.827) Data 0.001 (0.010) Loss 2.6333 (2.7085) Prec@1 40.625 (35.018) Prec@5 68.125 (65.470) Epoch: [4][9930/11272] Time 0.782 (0.827) Data 0.011 (0.010) Loss 2.8575 (2.7085) Prec@1 33.750 (35.020) Prec@5 63.125 (65.472) Epoch: [4][9940/11272] Time 0.742 (0.827) Data 0.001 (0.010) Loss 2.6216 (2.7084) Prec@1 39.375 (35.021) Prec@5 70.000 (65.473) Epoch: [4][9950/11272] Time 1.045 (0.827) Data 0.161 (0.010) Loss 2.6269 (2.7085) Prec@1 35.000 (35.020) Prec@5 64.375 (65.473) Epoch: [4][9960/11272] Time 0.858 (0.827) Data 0.001 (0.010) Loss 2.8578 (2.7085) Prec@1 30.625 (35.019) Prec@5 63.750 (65.471) Epoch: [4][9970/11272] Time 1.266 (0.827) Data 0.482 (0.010) Loss 2.6858 (2.7085) Prec@1 36.250 (35.019) Prec@5 66.875 (65.471) Epoch: [4][9980/11272] Time 0.774 (0.827) Data 0.002 (0.010) Loss 2.8283 (2.7085) Prec@1 30.625 (35.019) Prec@5 62.500 (65.470) Epoch: [4][9990/11272] Time 0.857 (0.827) Data 0.001 (0.010) Loss 2.2738 (2.7085) Prec@1 39.375 (35.019) Prec@5 76.875 (65.470) Epoch: [4][10000/11272] Time 0.880 (0.827) Data 0.002 (0.010) Loss 2.6146 (2.7085) Prec@1 37.500 (35.020) Prec@5 67.500 (65.470) Epoch: [4][10010/11272] Time 0.817 (0.827) Data 0.016 (0.010) Loss 2.7181 (2.7085) Prec@1 34.375 (35.018) Prec@5 61.875 (65.470) Epoch: [4][10020/11272] Time 0.748 (0.827) Data 0.002 (0.010) Loss 2.7232 (2.7085) Prec@1 34.375 (35.017) Prec@5 66.250 (65.469) Epoch: [4][10030/11272] Time 0.949 (0.827) Data 0.002 (0.010) Loss 2.4893 (2.7085) Prec@1 41.250 (35.016) Prec@5 71.875 (65.470) Epoch: [4][10040/11272] Time 0.755 (0.827) Data 0.004 (0.010) Loss 2.7295 (2.7085) Prec@1 33.750 (35.015) Prec@5 64.375 (65.469) Epoch: [4][10050/11272] Time 0.767 (0.827) Data 0.002 (0.010) Loss 2.5007 (2.7085) Prec@1 39.375 (35.016) Prec@5 71.875 (65.470) Epoch: [4][10060/11272] Time 0.860 (0.827) Data 0.002 (0.010) Loss 2.8268 (2.7085) Prec@1 35.000 (35.016) Prec@5 61.250 (65.470) Epoch: [4][10070/11272] Time 0.834 (0.827) Data 0.002 (0.010) Loss 2.7461 (2.7085) Prec@1 35.625 (35.016) Prec@5 62.500 (65.469) Epoch: [4][10080/11272] Time 0.757 (0.827) Data 0.001 (0.010) Loss 2.7080 (2.7085) Prec@1 36.875 (35.016) Prec@5 64.375 (65.468) Epoch: [4][10090/11272] Time 0.745 (0.827) Data 0.002 (0.010) Loss 2.6624 (2.7085) Prec@1 36.875 (35.016) Prec@5 66.250 (65.469) Epoch: [4][10100/11272] Time 0.850 (0.828) Data 0.002 (0.010) Loss 2.7901 (2.7085) Prec@1 31.250 (35.016) Prec@5 64.375 (65.470) Epoch: [4][10110/11272] Time 0.921 (0.828) Data 0.002 (0.010) Loss 3.0175 (2.7085) Prec@1 26.875 (35.015) Prec@5 63.125 (65.472) Epoch: [4][10120/11272] Time 0.762 (0.828) Data 0.002 (0.010) Loss 2.6001 (2.7085) Prec@1 35.625 (35.012) Prec@5 68.750 (65.471) Epoch: [4][10130/11272] Time 0.734 (0.828) Data 0.002 (0.010) Loss 2.7738 (2.7086) Prec@1 33.125 (35.012) Prec@5 64.375 (65.470) Epoch: [4][10140/11272] Time 0.992 (0.828) Data 0.068 (0.010) Loss 2.7313 (2.7085) Prec@1 35.625 (35.013) Prec@5 68.125 (65.471) Epoch: [4][10150/11272] Time 0.844 (0.828) Data 0.002 (0.010) Loss 2.7718 (2.7085) Prec@1 32.500 (35.013) Prec@5 68.125 (65.471) Epoch: [4][10160/11272] Time 0.734 (0.828) Data 0.005 (0.010) Loss 2.6307 (2.7086) Prec@1 36.250 (35.013) Prec@5 70.625 (65.470) Epoch: [4][10170/11272] Time 0.905 (0.828) Data 0.002 (0.010) Loss 2.6438 (2.7085) Prec@1 31.250 (35.014) Prec@5 70.000 (65.472) Epoch: [4][10180/11272] Time 0.849 (0.828) Data 0.001 (0.010) Loss 2.8095 (2.7085) Prec@1 40.000 (35.014) Prec@5 58.125 (65.471) Epoch: [4][10190/11272] Time 0.758 (0.828) Data 0.002 (0.010) Loss 2.5913 (2.7085) Prec@1 36.250 (35.015) Prec@5 63.750 (65.472) Epoch: [4][10200/11272] Time 0.733 (0.828) Data 0.002 (0.010) Loss 2.6916 (2.7084) Prec@1 32.500 (35.016) Prec@5 63.125 (65.473) Epoch: [4][10210/11272] Time 0.873 (0.828) Data 0.002 (0.010) Loss 2.3777 (2.7084) Prec@1 38.125 (35.014) Prec@5 73.125 (65.473) Epoch: [4][10220/11272] Time 0.820 (0.828) Data 0.001 (0.010) Loss 2.7699 (2.7084) Prec@1 31.875 (35.015) Prec@5 66.250 (65.474) Epoch: [4][10230/11272] Time 0.763 (0.828) Data 0.002 (0.010) Loss 2.6562 (2.7083) Prec@1 35.625 (35.014) Prec@5 67.500 (65.475) Epoch: [4][10240/11272] Time 0.750 (0.828) Data 0.001 (0.010) Loss 2.6091 (2.7083) Prec@1 34.375 (35.014) Prec@5 69.375 (65.476) Epoch: [4][10250/11272] Time 0.938 (0.828) Data 0.002 (0.010) Loss 2.6638 (2.7082) Prec@1 43.750 (35.016) Prec@5 65.625 (65.477) Epoch: [4][10260/11272] Time 0.868 (0.828) Data 0.002 (0.010) Loss 2.6848 (2.7082) Prec@1 33.125 (35.017) Prec@5 65.000 (65.477) Epoch: [4][10270/11272] Time 0.732 (0.828) Data 0.002 (0.010) Loss 2.7568 (2.7083) Prec@1 35.625 (35.016) Prec@5 61.250 (65.475) Epoch: [4][10280/11272] Time 0.745 (0.828) Data 0.002 (0.010) Loss 2.7991 (2.7082) Prec@1 33.750 (35.016) Prec@5 60.625 (65.475) Epoch: [4][10290/11272] Time 0.892 (0.828) Data 0.002 (0.010) Loss 2.8917 (2.7082) Prec@1 37.500 (35.018) Prec@5 61.250 (65.476) Epoch: [4][10300/11272] Time 0.864 (0.828) Data 0.003 (0.010) Loss 2.6709 (2.7082) Prec@1 40.000 (35.018) Prec@5 63.125 (65.476) Epoch: [4][10310/11272] Time 0.827 (0.828) Data 0.036 (0.010) Loss 2.7719 (2.7083) Prec@1 35.000 (35.017) Prec@5 67.500 (65.477) Epoch: [4][10320/11272] Time 0.875 (0.828) Data 0.001 (0.010) Loss 2.5714 (2.7083) Prec@1 43.750 (35.015) Prec@5 66.250 (65.475) Epoch: [4][10330/11272] Time 0.919 (0.828) Data 0.044 (0.010) Loss 2.5198 (2.7083) Prec@1 36.875 (35.016) Prec@5 70.000 (65.476) Epoch: [4][10340/11272] Time 0.748 (0.828) Data 0.002 (0.010) Loss 2.4884 (2.7081) Prec@1 40.000 (35.017) Prec@5 65.625 (65.479) Epoch: [4][10350/11272] Time 0.738 (0.828) Data 0.001 (0.010) Loss 2.7444 (2.7081) Prec@1 35.000 (35.018) Prec@5 65.625 (65.479) Epoch: [4][10360/11272] Time 0.866 (0.828) Data 0.002 (0.010) Loss 2.8007 (2.7082) Prec@1 31.250 (35.016) Prec@5 65.000 (65.478) Epoch: [4][10370/11272] Time 0.841 (0.828) Data 0.002 (0.010) Loss 2.7723 (2.7083) Prec@1 36.250 (35.016) Prec@5 64.375 (65.477) Epoch: [4][10380/11272] Time 0.764 (0.828) Data 0.002 (0.010) Loss 2.9640 (2.7083) Prec@1 34.375 (35.015) Prec@5 57.500 (65.477) Epoch: [4][10390/11272] Time 0.748 (0.828) Data 0.002 (0.010) Loss 2.9638 (2.7084) Prec@1 33.125 (35.012) Prec@5 58.750 (65.474) Epoch: [4][10400/11272] Time 0.899 (0.828) Data 0.002 (0.010) Loss 2.6743 (2.7085) Prec@1 36.875 (35.011) Prec@5 63.125 (65.473) Epoch: [4][10410/11272] Time 0.870 (0.828) Data 0.002 (0.010) Loss 2.7730 (2.7084) Prec@1 27.500 (35.011) Prec@5 68.750 (65.474) Epoch: [4][10420/11272] Time 0.726 (0.828) Data 0.002 (0.010) Loss 2.5788 (2.7085) Prec@1 38.125 (35.009) Prec@5 70.625 (65.474) Epoch: [4][10430/11272] Time 0.734 (0.828) Data 0.002 (0.010) Loss 2.5509 (2.7084) Prec@1 41.250 (35.010) Prec@5 69.375 (65.476) Epoch: [4][10440/11272] Time 0.883 (0.828) Data 0.002 (0.010) Loss 2.9304 (2.7084) Prec@1 33.125 (35.009) Prec@5 63.750 (65.477) Epoch: [4][10450/11272] Time 0.771 (0.828) Data 0.002 (0.010) Loss 2.8925 (2.7084) Prec@1 29.375 (35.010) Prec@5 63.125 (65.477) Epoch: [4][10460/11272] Time 0.741 (0.828) Data 0.002 (0.010) Loss 2.9176 (2.7085) Prec@1 30.625 (35.009) Prec@5 62.500 (65.476) Epoch: [4][10470/11272] Time 0.906 (0.828) Data 0.002 (0.010) Loss 2.7667 (2.7084) Prec@1 32.500 (35.009) Prec@5 70.000 (65.477) Epoch: [4][10480/11272] Time 0.895 (0.828) Data 0.002 (0.010) Loss 2.8703 (2.7085) Prec@1 31.875 (35.007) Prec@5 60.625 (65.475) Epoch: [4][10490/11272] Time 0.771 (0.828) Data 0.002 (0.011) Loss 2.5481 (2.7084) Prec@1 40.625 (35.009) Prec@5 68.125 (65.475) Epoch: [4][10500/11272] Time 0.740 (0.828) Data 0.002 (0.011) Loss 2.7158 (2.7085) Prec@1 30.625 (35.007) Prec@5 65.000 (65.475) Epoch: [4][10510/11272] Time 0.871 (0.828) Data 0.002 (0.011) Loss 2.9164 (2.7085) Prec@1 29.375 (35.006) Prec@5 60.000 (65.475) Epoch: [4][10520/11272] Time 0.834 (0.828) Data 0.001 (0.011) Loss 2.9158 (2.7084) Prec@1 28.125 (35.005) Prec@5 63.125 (65.475) Epoch: [4][10530/11272] Time 0.763 (0.828) Data 0.002 (0.011) Loss 2.6527 (2.7085) Prec@1 36.250 (35.004) Prec@5 65.625 (65.472) Epoch: [4][10540/11272] Time 0.775 (0.828) Data 0.002 (0.011) Loss 2.6827 (2.7085) Prec@1 34.375 (35.006) Prec@5 61.875 (65.472) Epoch: [4][10550/11272] Time 1.011 (0.828) Data 0.046 (0.011) Loss 2.7530 (2.7086) Prec@1 28.125 (35.005) Prec@5 68.125 (65.470) Epoch: [4][10560/11272] Time 0.877 (0.828) Data 0.002 (0.011) Loss 2.8257 (2.7087) Prec@1 33.750 (35.005) Prec@5 64.375 (65.469) Epoch: [4][10570/11272] Time 0.749 (0.828) Data 0.001 (0.011) Loss 2.6329 (2.7087) Prec@1 35.625 (35.004) Prec@5 70.000 (65.469) Epoch: [4][10580/11272] Time 0.873 (0.828) Data 0.002 (0.011) Loss 2.5463 (2.7086) Prec@1 38.750 (35.007) Prec@5 65.000 (65.469) Epoch: [4][10590/11272] Time 1.121 (0.828) Data 0.152 (0.011) Loss 2.6147 (2.7086) Prec@1 37.500 (35.008) Prec@5 69.375 (65.471) Epoch: [4][10600/11272] Time 0.731 (0.828) Data 0.002 (0.011) Loss 2.8017 (2.7087) Prec@1 32.500 (35.006) Prec@5 61.250 (65.469) Epoch: [4][10610/11272] Time 1.049 (0.828) Data 0.271 (0.011) Loss 2.7065 (2.7086) Prec@1 40.000 (35.007) Prec@5 65.000 (65.470) Epoch: [4][10620/11272] Time 0.874 (0.828) Data 0.001 (0.011) Loss 2.5699 (2.7086) Prec@1 40.000 (35.008) Prec@5 70.000 (65.472) Epoch: [4][10630/11272] Time 0.863 (0.828) Data 0.002 (0.011) Loss 2.7969 (2.7086) Prec@1 34.375 (35.008) Prec@5 59.375 (65.473) Epoch: [4][10640/11272] Time 0.751 (0.828) Data 0.002 (0.011) Loss 2.7066 (2.7085) Prec@1 35.000 (35.010) Prec@5 66.250 (65.473) Epoch: [4][10650/11272] Time 0.765 (0.829) Data 0.002 (0.011) Loss 2.5953 (2.7084) Prec@1 32.500 (35.011) Prec@5 68.125 (65.474) Epoch: [4][10660/11272] Time 1.150 (0.829) Data 0.201 (0.011) Loss 2.9793 (2.7084) Prec@1 31.875 (35.012) Prec@5 63.750 (65.474) Epoch: [4][10670/11272] Time 0.824 (0.829) Data 0.001 (0.011) Loss 2.8819 (2.7085) Prec@1 34.375 (35.012) Prec@5 61.875 (65.474) Epoch: [4][10680/11272] Time 0.925 (0.829) Data 0.112 (0.011) Loss 2.8157 (2.7084) Prec@1 28.125 (35.012) Prec@5 64.375 (65.475) Epoch: [4][10690/11272] Time 0.743 (0.829) Data 0.001 (0.011) Loss 2.5846 (2.7084) Prec@1 39.375 (35.013) Prec@5 65.625 (65.474) Epoch: [4][10700/11272] Time 0.981 (0.829) Data 0.032 (0.011) Loss 2.5981 (2.7084) Prec@1 34.375 (35.013) Prec@5 71.250 (65.474) Epoch: [4][10710/11272] Time 0.780 (0.829) Data 0.004 (0.011) Loss 2.7154 (2.7084) Prec@1 37.500 (35.013) Prec@5 66.250 (65.475) Epoch: [4][10720/11272] Time 0.743 (0.829) Data 0.002 (0.011) Loss 2.7829 (2.7084) Prec@1 34.375 (35.014) Prec@5 64.375 (65.475) Epoch: [4][10730/11272] Time 0.896 (0.829) Data 0.002 (0.011) Loss 2.5900 (2.7082) Prec@1 41.250 (35.018) Prec@5 65.625 (65.480) Epoch: [4][10740/11272] Time 1.079 (0.829) Data 0.147 (0.011) Loss 2.4842 (2.7082) Prec@1 38.750 (35.017) Prec@5 71.250 (65.479) Epoch: [4][10750/11272] Time 0.757 (0.829) Data 0.001 (0.011) Loss 2.6849 (2.7081) Prec@1 35.000 (35.017) Prec@5 67.500 (65.481) Epoch: [4][10760/11272] Time 0.918 (0.829) Data 0.146 (0.011) Loss 2.8436 (2.7081) Prec@1 31.875 (35.017) Prec@5 61.250 (65.481) Epoch: [4][10770/11272] Time 0.916 (0.829) Data 0.002 (0.011) Loss 2.8203 (2.7081) Prec@1 40.000 (35.017) Prec@5 63.750 (65.482) Epoch: [4][10780/11272] Time 1.346 (0.829) Data 0.448 (0.011) Loss 2.6646 (2.7082) Prec@1 35.000 (35.018) Prec@5 62.500 (65.481) Epoch: [4][10790/11272] Time 0.751 (0.829) Data 0.002 (0.011) Loss 2.3660 (2.7082) Prec@1 37.500 (35.018) Prec@5 74.375 (65.480) Epoch: [4][10800/11272] Time 0.752 (0.829) Data 0.002 (0.011) Loss 2.4912 (2.7081) Prec@1 36.875 (35.019) Prec@5 70.000 (65.481) Epoch: [4][10810/11272] Time 0.906 (0.829) Data 0.002 (0.011) Loss 2.7873 (2.7082) Prec@1 38.125 (35.018) Prec@5 66.875 (65.481) Epoch: [4][10820/11272] Time 1.054 (0.829) Data 0.173 (0.011) Loss 2.9359 (2.7082) Prec@1 28.750 (35.018) Prec@5 61.250 (65.481) Epoch: [4][10830/11272] Time 0.764 (0.829) Data 0.002 (0.011) Loss 2.7985 (2.7082) Prec@1 35.000 (35.017) Prec@5 61.875 (65.479) Epoch: [4][10840/11272] Time 0.985 (0.829) Data 0.072 (0.011) Loss 2.6285 (2.7082) Prec@1 39.375 (35.019) Prec@5 70.000 (65.481) Epoch: [4][10850/11272] Time 0.938 (0.829) Data 0.002 (0.011) Loss 2.4820 (2.7082) Prec@1 36.875 (35.018) Prec@5 72.500 (65.480) Epoch: [4][10860/11272] Time 1.049 (0.829) Data 0.280 (0.011) Loss 2.7891 (2.7082) Prec@1 31.250 (35.018) Prec@5 63.125 (65.481) Epoch: [4][10870/11272] Time 0.765 (0.829) Data 0.002 (0.011) Loss 2.8203 (2.7082) Prec@1 37.500 (35.018) Prec@5 60.000 (65.480) Epoch: [4][10880/11272] Time 0.896 (0.829) Data 0.001 (0.011) Loss 2.7898 (2.7082) Prec@1 38.125 (35.019) Prec@5 65.000 (65.481) Epoch: [4][10890/11272] Time 0.904 (0.829) Data 0.002 (0.011) Loss 2.5994 (2.7082) Prec@1 35.625 (35.020) Prec@5 69.375 (65.481) Epoch: [4][10900/11272] Time 0.734 (0.829) Data 0.001 (0.011) Loss 2.8242 (2.7082) Prec@1 35.625 (35.021) Prec@5 62.500 (65.481) Epoch: [4][10910/11272] Time 0.744 (0.829) Data 0.002 (0.011) Loss 2.7528 (2.7081) Prec@1 40.000 (35.020) Prec@5 67.500 (65.482) Epoch: [4][10920/11272] Time 0.983 (0.829) Data 0.090 (0.011) Loss 2.5982 (2.7081) Prec@1 38.750 (35.022) Prec@5 66.250 (65.483) Epoch: [4][10930/11272] Time 0.874 (0.829) Data 0.002 (0.011) Loss 2.6574 (2.7081) Prec@1 36.875 (35.022) Prec@5 68.125 (65.483) Epoch: [4][10940/11272] Time 0.778 (0.829) Data 0.002 (0.011) Loss 2.7866 (2.7081) Prec@1 31.250 (35.021) Prec@5 66.875 (65.483) Epoch: [4][10950/11272] Time 0.786 (0.829) Data 0.002 (0.011) Loss 2.7598 (2.7081) Prec@1 28.750 (35.022) Prec@5 61.250 (65.484) Epoch: [4][10960/11272] Time 0.872 (0.829) Data 0.002 (0.011) Loss 2.6826 (2.7081) Prec@1 28.750 (35.021) Prec@5 70.000 (65.483) Epoch: [4][10970/11272] Time 0.762 (0.829) Data 0.003 (0.011) Loss 2.9574 (2.7081) Prec@1 30.625 (35.022) Prec@5 55.000 (65.484) Epoch: [4][10980/11272] Time 0.734 (0.829) Data 0.002 (0.011) Loss 2.7362 (2.7081) Prec@1 32.500 (35.021) Prec@5 65.000 (65.484) Epoch: [4][10990/11272] Time 0.904 (0.829) Data 0.002 (0.011) Loss 2.6618 (2.7081) Prec@1 36.250 (35.019) Prec@5 65.625 (65.483) Epoch: [4][11000/11272] Time 0.873 (0.829) Data 0.002 (0.011) Loss 2.5024 (2.7081) Prec@1 37.500 (35.021) Prec@5 66.250 (65.483) Epoch: [4][11010/11272] Time 0.746 (0.829) Data 0.004 (0.011) Loss 2.8967 (2.7081) Prec@1 36.875 (35.020) Prec@5 62.500 (65.484) Epoch: [4][11020/11272] Time 0.744 (0.829) Data 0.001 (0.011) Loss 2.8730 (2.7081) Prec@1 35.625 (35.021) Prec@5 61.875 (65.484) Epoch: [4][11030/11272] Time 0.925 (0.829) Data 0.001 (0.011) Loss 2.5791 (2.7081) Prec@1 43.125 (35.022) Prec@5 70.000 (65.485) Epoch: [4][11040/11272] Time 1.157 (0.829) Data 0.256 (0.011) Loss 2.5766 (2.7081) Prec@1 39.375 (35.023) Prec@5 68.125 (65.485) Epoch: [4][11050/11272] Time 0.751 (0.829) Data 0.001 (0.011) Loss 2.6173 (2.7081) Prec@1 35.000 (35.023) Prec@5 68.125 (65.486) Epoch: [4][11060/11272] Time 0.742 (0.829) Data 0.001 (0.011) Loss 2.9345 (2.7081) Prec@1 28.750 (35.023) Prec@5 61.250 (65.486) Epoch: [4][11070/11272] Time 0.882 (0.829) Data 0.002 (0.011) Loss 2.6516 (2.7081) Prec@1 36.875 (35.021) Prec@5 68.750 (65.485) Epoch: [4][11080/11272] Time 0.976 (0.829) Data 0.066 (0.011) Loss 2.6627 (2.7081) Prec@1 35.625 (35.023) Prec@5 66.875 (65.486) Epoch: [4][11090/11272] Time 0.736 (0.829) Data 0.002 (0.011) Loss 3.0785 (2.7081) Prec@1 30.000 (35.022) Prec@5 60.000 (65.486) Epoch: [4][11100/11272] Time 0.996 (0.829) Data 0.125 (0.011) Loss 2.5499 (2.7081) Prec@1 38.750 (35.023) Prec@5 68.750 (65.488) Epoch: [4][11110/11272] Time 1.000 (0.829) Data 0.131 (0.011) Loss 2.5669 (2.7080) Prec@1 40.000 (35.023) Prec@5 70.000 (65.488) Epoch: [4][11120/11272] Time 0.736 (0.829) Data 0.001 (0.011) Loss 2.8624 (2.7081) Prec@1 32.500 (35.022) Prec@5 61.875 (65.487) Epoch: [4][11130/11272] Time 0.771 (0.829) Data 0.002 (0.012) Loss 2.8825 (2.7081) Prec@1 31.250 (35.021) Prec@5 59.375 (65.485) Epoch: [4][11140/11272] Time 0.880 (0.829) Data 0.001 (0.012) Loss 2.6326 (2.7081) Prec@1 38.125 (35.021) Prec@5 68.125 (65.486) Epoch: [4][11150/11272] Time 1.102 (0.829) Data 0.233 (0.012) Loss 2.3616 (2.7081) Prec@1 39.375 (35.020) Prec@5 74.375 (65.487) Epoch: [4][11160/11272] Time 0.776 (0.829) Data 0.001 (0.012) Loss 2.9993 (2.7082) Prec@1 26.250 (35.019) Prec@5 54.375 (65.486) Epoch: [4][11170/11272] Time 0.745 (0.829) Data 0.001 (0.012) Loss 2.8439 (2.7082) Prec@1 31.875 (35.018) Prec@5 63.125 (65.487) Epoch: [4][11180/11272] Time 0.838 (0.829) Data 0.001 (0.012) Loss 2.4754 (2.7082) Prec@1 40.625 (35.018) Prec@5 68.125 (65.488) Epoch: [4][11190/11272] Time 0.876 (0.829) Data 0.002 (0.012) Loss 2.3871 (2.7081) Prec@1 41.250 (35.020) Prec@5 70.625 (65.489) Epoch: [4][11200/11272] Time 0.739 (0.829) Data 0.001 (0.012) Loss 2.7312 (2.7081) Prec@1 30.625 (35.020) Prec@5 63.125 (65.490) Epoch: [4][11210/11272] Time 0.739 (0.829) Data 0.002 (0.012) Loss 2.8404 (2.7081) Prec@1 31.875 (35.020) Prec@5 65.000 (65.490) Epoch: [4][11220/11272] Time 0.844 (0.829) Data 0.001 (0.012) Loss 2.8869 (2.7081) Prec@1 33.750 (35.020) Prec@5 61.875 (65.489) Epoch: [4][11230/11272] Time 0.861 (0.829) Data 0.001 (0.012) Loss 2.8510 (2.7082) Prec@1 33.750 (35.020) Prec@5 58.750 (65.488) Epoch: [4][11240/11272] Time 0.750 (0.829) Data 0.001 (0.012) Loss 2.8908 (2.7081) Prec@1 30.625 (35.019) Prec@5 61.250 (65.489) Epoch: [4][11250/11272] Time 0.898 (0.829) Data 0.020 (0.012) Loss 2.8030 (2.7080) Prec@1 35.625 (35.020) Prec@5 68.125 (65.491) Epoch: [4][11260/11272] Time 0.889 (0.829) Data 0.001 (0.012) Loss 2.6378 (2.7080) Prec@1 36.250 (35.020) Prec@5 65.000 (65.491) Epoch: [4][11270/11272] Time 0.806 (0.829) Data 0.000 (0.012) Loss 2.4610 (2.7079) Prec@1 39.375 (35.022) Prec@5 71.250 (65.493) Test: [0/229] Time 3.609 (3.609) Loss 1.2480 (1.2480) Prec@1 67.500 (67.500) Prec@5 93.125 (93.125) Test: [10/229] Time 0.402 (0.846) Loss 1.2013 (2.1657) Prec@1 62.500 (44.205) Prec@5 93.750 (77.614) Test: [20/229] Time 0.640 (0.724) Loss 2.4409 (2.2288) Prec@1 41.875 (42.530) Prec@5 73.125 (77.024) Test: [30/229] Time 0.500 (0.694) Loss 3.3910 (2.1708) Prec@1 16.250 (44.617) Prec@5 48.750 (76.996) Test: [40/229] Time 0.345 (0.664) Loss 0.7040 (2.2038) Prec@1 86.875 (43.918) Prec@5 92.500 (76.418) Test: [50/229] Time 0.404 (0.658) Loss 2.5912 (2.2514) Prec@1 33.750 (43.260) Prec@5 76.875 (75.196) Test: [60/229] Time 0.423 (0.640) Loss 3.2230 (2.2749) Prec@1 21.875 (42.633) Prec@5 57.500 (74.641) Test: [70/229] Time 0.795 (0.639) Loss 1.9275 (2.2682) Prec@1 53.125 (42.641) Prec@5 78.125 (74.762) Test: [80/229] Time 0.618 (0.630) Loss 2.7334 (2.2789) Prec@1 27.500 (42.253) Prec@5 63.750 (74.591) Test: [90/229] Time 0.414 (0.627) Loss 1.8844 (2.2768) Prec@1 56.875 (42.177) Prec@5 76.875 (74.602) Test: [100/229] Time 0.862 (0.629) Loss 2.5007 (2.2727) Prec@1 46.250 (42.450) Prec@5 73.750 (74.672) Test: [110/229] Time 0.350 (0.622) Loss 2.1496 (2.2511) Prec@1 40.000 (42.945) Prec@5 78.125 (75.045) Test: [120/229] Time 0.940 (0.623) Loss 3.0805 (2.2733) Prec@1 25.000 (42.304) Prec@5 65.625 (74.706) Test: [130/229] Time 0.425 (0.620) Loss 1.8125 (2.2536) Prec@1 51.250 (42.686) Prec@5 84.375 (75.081) Test: [140/229] Time 0.486 (0.619) Loss 2.1312 (2.2669) Prec@1 41.875 (42.389) Prec@5 76.250 (74.840) Test: [150/229] Time 0.961 (0.618) Loss 1.5003 (2.2863) Prec@1 65.000 (41.966) Prec@5 85.000 (74.541) Test: [160/229] Time 0.339 (0.615) Loss 3.1260 (2.2914) Prec@1 26.875 (41.937) Prec@5 66.875 (74.464) Test: [170/229] Time 0.792 (0.615) Loss 2.8022 (2.3191) Prec@1 26.875 (41.301) Prec@5 65.625 (73.893) Test: [180/229] Time 0.399 (0.614) Loss 2.8411 (2.3312) Prec@1 23.750 (41.122) Prec@5 67.500 (73.660) Test: [190/229] Time 0.351 (0.614) Loss 2.1425 (2.3318) Prec@1 40.625 (41.044) Prec@5 84.375 (73.717) Test: [200/229] Time 0.831 (0.614) Loss 2.5644 (2.3292) Prec@1 31.875 (41.067) Prec@5 70.000 (73.896) Test: [210/229] Time 0.347 (0.612) Loss 1.5089 (2.3099) Prec@1 57.500 (41.478) Prec@5 86.875 (74.206) Test: [220/229] Time 0.852 (0.613) Loss 1.9050 (2.3015) Prec@1 52.500 (41.838) Prec@5 85.000 (74.310) * Prec@1 42.087 Prec@5 74.409 Epoch: [5][0/11272] Time 4.582 (4.582) Data 3.560 (3.560) Loss 2.7171 (2.7171) Prec@1 33.750 (33.750) Prec@5 66.250 (66.250) Epoch: [5][10/11272] Time 0.770 (1.161) Data 0.002 (0.325) Loss 2.6414 (2.6840) Prec@1 34.375 (34.716) Prec@5 70.625 (66.818) Epoch: [5][20/11272] Time 0.729 (0.995) Data 0.001 (0.171) Loss 2.6688 (2.6676) Prec@1 40.625 (35.625) Prec@5 61.875 (66.369) Epoch: [5][30/11272] Time 0.849 (0.943) Data 0.001 (0.116) Loss 2.4978 (2.6660) Prec@1 39.375 (35.585) Prec@5 71.875 (66.492) Epoch: [5][40/11272] Time 0.746 (0.913) Data 0.002 (0.088) Loss 2.7215 (2.6762) Prec@1 35.000 (35.655) Prec@5 66.250 (66.402) Epoch: [5][50/11272] Time 0.761 (0.895) Data 0.002 (0.071) Loss 2.7707 (2.6738) Prec@1 33.125 (35.711) Prec@5 63.125 (66.225) Epoch: [5][60/11272] Time 0.850 (0.882) Data 0.001 (0.060) Loss 2.6056 (2.6693) Prec@1 39.375 (35.758) Prec@5 68.125 (66.301) Epoch: [5][70/11272] Time 0.897 (0.873) Data 0.002 (0.052) Loss 2.8627 (2.6714) Prec@1 31.875 (35.581) Prec@5 63.750 (66.171) Epoch: [5][80/11272] Time 0.807 (0.864) Data 0.002 (0.046) Loss 2.8893 (2.6751) Prec@1 35.625 (35.401) Prec@5 62.500 (66.204) Epoch: [5][90/11272] Time 0.760 (0.859) Data 0.002 (0.041) Loss 2.4330 (2.6624) Prec@1 40.625 (35.666) Prec@5 67.500 (66.408) Epoch: [5][100/11272] Time 0.897 (0.856) Data 0.002 (0.037) Loss 2.4228 (2.6616) Prec@1 38.125 (35.606) Prec@5 72.500 (66.498) Epoch: [5][110/11272] Time 0.834 (0.852) Data 0.001 (0.034) Loss 2.5009 (2.6569) Prec@1 43.750 (35.676) Prec@5 66.250 (66.588) Epoch: [5][120/11272] Time 0.759 (0.848) Data 0.002 (0.031) Loss 2.6902 (2.6538) Prec@1 34.375 (35.687) Prec@5 66.875 (66.663) Epoch: [5][130/11272] Time 0.744 (0.844) Data 0.002 (0.029) Loss 2.6388 (2.6580) Prec@1 35.625 (35.573) Prec@5 66.250 (66.660) Epoch: [5][140/11272] Time 0.847 (0.842) Data 0.002 (0.027) Loss 2.6366 (2.6615) Prec@1 38.125 (35.536) Prec@5 66.250 (66.503) Epoch: [5][150/11272] Time 0.795 (0.839) Data 0.001 (0.025) Loss 2.8198 (2.6629) Prec@1 33.750 (35.530) Prec@5 63.125 (66.494) Epoch: [5][160/11272] Time 0.756 (0.837) Data 0.002 (0.024) Loss 2.7406 (2.6649) Prec@1 32.500 (35.501) Prec@5 69.375 (66.479) Epoch: [5][170/11272] Time 0.931 (0.836) Data 0.001 (0.023) Loss 2.3857 (2.6620) Prec@1 40.000 (35.581) Prec@5 73.125 (66.502) Epoch: [5][180/11272] Time 0.849 (0.835) Data 0.002 (0.021) Loss 2.7070 (2.6601) Prec@1 33.125 (35.597) Prec@5 61.875 (66.516) Epoch: [5][190/11272] Time 0.750 (0.833) Data 0.002 (0.020) Loss 2.5189 (2.6606) Prec@1 35.625 (35.609) Prec@5 68.125 (66.551) Epoch: [5][200/11272] Time 0.789 (0.833) Data 0.002 (0.019) Loss 2.7230 (2.6576) Prec@1 36.250 (35.706) Prec@5 66.875 (66.592) Epoch: [5][210/11272] Time 0.858 (0.832) Data 0.001 (0.019) Loss 2.6934 (2.6583) Prec@1 31.875 (35.666) Prec@5 68.125 (66.579) Epoch: [5][220/11272] Time 0.876 (0.832) Data 0.002 (0.018) Loss 2.5492 (2.6605) Prec@1 38.125 (35.667) Prec@5 70.000 (66.519) Epoch: [5][230/11272] Time 0.762 (0.832) Data 0.003 (0.017) Loss 2.8668 (2.6655) Prec@1 29.375 (35.571) Prec@5 60.625 (66.453) Epoch: [5][240/11272] Time 0.764 (0.831) Data 0.002 (0.017) Loss 2.7138 (2.6703) Prec@1 41.250 (35.560) Prec@5 64.375 (66.374) Epoch: [5][250/11272] Time 0.876 (0.832) Data 0.001 (0.016) Loss 2.5421 (2.6705) Prec@1 31.875 (35.528) Prec@5 68.125 (66.379) Epoch: [5][260/11272] Time 0.873 (0.832) Data 0.002 (0.015) Loss 2.6991 (2.6713) Prec@1 33.125 (35.534) Prec@5 70.000 (66.398) Epoch: [5][270/11272] Time 0.839 (0.833) Data 0.002 (0.015) Loss 2.6941 (2.6705) Prec@1 30.000 (35.540) Prec@5 65.625 (66.432) Epoch: [5][280/11272] Time 0.756 (0.833) Data 0.001 (0.014) Loss 2.6270 (2.6759) Prec@1 36.875 (35.487) Prec@5 68.750 (66.292) Epoch: [5][290/11272] Time 0.909 (0.833) Data 0.002 (0.014) Loss 2.8187 (2.6771) Prec@1 39.375 (35.509) Prec@5 60.625 (66.231) Epoch: [5][300/11272] Time 0.737 (0.832) Data 0.003 (0.014) Loss 2.4729 (2.6778) Prec@1 43.125 (35.519) Prec@5 69.375 (66.208) Epoch: [5][310/11272] Time 0.742 (0.832) Data 0.001 (0.013) Loss 2.5253 (2.6776) Prec@1 41.250 (35.537) Prec@5 70.000 (66.180) Epoch: [5][320/11272] Time 0.871 (0.831) Data 0.002 (0.013) Loss 2.3575 (2.6748) Prec@1 46.875 (35.596) Prec@5 75.000 (66.238) Epoch: [5][330/11272] Time 0.943 (0.831) Data 0.002 (0.013) Loss 2.8631 (2.6771) Prec@1 33.125 (35.521) Prec@5 65.000 (66.203) Epoch: [5][340/11272] Time 0.749 (0.831) Data 0.002 (0.012) Loss 2.6902 (2.6775) Prec@1 28.750 (35.493) Prec@5 66.250 (66.195) Epoch: [5][350/11272] Time 0.788 (0.831) Data 0.002 (0.012) Loss 2.6274 (2.6785) Prec@1 35.000 (35.443) Prec@5 68.125 (66.182) Epoch: [5][360/11272] Time 0.870 (0.831) Data 0.002 (0.012) Loss 2.8233 (2.6784) Prec@1 37.500 (35.471) Prec@5 60.625 (66.172) Epoch: [5][370/11272] Time 0.925 (0.832) Data 0.002 (0.011) Loss 2.7339 (2.6789) Prec@1 36.875 (35.446) Prec@5 64.375 (66.147) Epoch: [5][380/11272] Time 0.796 (0.831) Data 0.002 (0.011) Loss 2.5629 (2.6786) Prec@1 42.500 (35.489) Prec@5 65.625 (66.135) Epoch: [5][390/11272] Time 0.747 (0.831) Data 0.001 (0.011) Loss 2.6527 (2.6784) Prec@1 36.875 (35.470) Prec@5 67.500 (66.149) Epoch: [5][400/11272] Time 0.886 (0.832) Data 0.002 (0.011) Loss 2.6082 (2.6785) Prec@1 36.250 (35.466) Prec@5 62.500 (66.089) Epoch: [5][410/11272] Time 0.839 (0.832) Data 0.002 (0.010) Loss 2.8271 (2.6780) Prec@1 36.875 (35.474) Prec@5 63.125 (66.099) Epoch: [5][420/11272] Time 0.748 (0.831) Data 0.002 (0.010) Loss 2.9616 (2.6794) Prec@1 30.000 (35.460) Prec@5 59.375 (66.045) Epoch: [5][430/11272] Time 0.925 (0.832) Data 0.002 (0.010) Loss 2.8016 (2.6774) Prec@1 35.625 (35.519) Prec@5 61.250 (66.077) Epoch: [5][440/11272] Time 0.930 (0.832) Data 0.002 (0.010) Loss 2.3446 (2.6785) Prec@1 40.625 (35.509) Prec@5 73.750 (66.025) Epoch: [5][450/11272] Time 0.776 (0.832) Data 0.002 (0.010) Loss 2.5310 (2.6787) Prec@1 39.375 (35.527) Prec@5 71.875 (66.028) Epoch: [5][460/11272] Time 0.746 (0.832) Data 0.001 (0.009) Loss 2.7944 (2.6797) Prec@1 36.875 (35.518) Prec@5 62.500 (65.999) Epoch: [5][470/11272] Time 0.980 (0.831) Data 0.002 (0.009) Loss 2.4305 (2.6779) Prec@1 37.500 (35.516) Prec@5 74.375 (66.036) Epoch: [5][480/11272] Time 0.833 (0.831) Data 0.001 (0.009) Loss 2.8923 (2.6776) Prec@1 38.750 (35.516) Prec@5 60.625 (66.064) Epoch: [5][490/11272] Time 0.757 (0.831) Data 0.002 (0.009) Loss 2.5931 (2.6785) Prec@1 35.625 (35.516) Prec@5 68.750 (66.057) Epoch: [5][500/11272] Time 0.848 (0.830) Data 0.002 (0.009) Loss 2.7860 (2.6784) Prec@1 33.750 (35.515) Prec@5 65.000 (66.059) Epoch: [5][510/11272] Time 0.836 (0.830) Data 0.002 (0.009) Loss 2.9899 (2.6789) Prec@1 35.000 (35.533) Prec@5 58.125 (66.067) Epoch: [5][520/11272] Time 0.842 (0.831) Data 0.001 (0.009) Loss 2.5658 (2.6781) Prec@1 31.875 (35.536) Prec@5 69.375 (66.084) Epoch: [5][530/11272] Time 0.784 (0.831) Data 0.002 (0.008) Loss 2.6920 (2.6775) Prec@1 40.000 (35.526) Prec@5 68.125 (66.121) Epoch: [5][540/11272] Time 0.829 (0.831) Data 0.002 (0.008) Loss 2.6727 (2.6785) Prec@1 33.125 (35.530) Prec@5 64.375 (66.114) Epoch: [5][550/11272] Time 0.905 (0.831) Data 0.002 (0.008) Loss 3.0922 (2.6790) Prec@1 28.750 (35.508) Prec@5 61.250 (66.118) Epoch: [5][560/11272] Time 0.754 (0.831) Data 0.003 (0.008) Loss 2.4960 (2.6794) Prec@1 36.250 (35.507) Prec@5 69.375 (66.114) Epoch: [5][570/11272] Time 0.755 (0.831) Data 0.002 (0.008) Loss 2.5867 (2.6786) Prec@1 37.500 (35.539) Prec@5 68.125 (66.135) Epoch: [5][580/11272] Time 0.854 (0.831) Data 0.001 (0.008) Loss 2.3649 (2.6785) Prec@1 38.125 (35.553) Prec@5 73.750 (66.132) Epoch: [5][590/11272] Time 0.922 (0.831) Data 0.001 (0.008) Loss 2.6063 (2.6773) Prec@1 35.625 (35.569) Prec@5 71.875 (66.160) Epoch: [5][600/11272] Time 0.812 (0.831) Data 0.002 (0.008) Loss 2.5899 (2.6763) Prec@1 36.250 (35.563) Prec@5 63.750 (66.177) Epoch: [5][610/11272] Time 0.767 (0.831) Data 0.003 (0.008) Loss 2.5324 (2.6763) Prec@1 35.625 (35.567) Prec@5 71.250 (66.173) Epoch: [5][620/11272] Time 0.929 (0.831) Data 0.002 (0.007) Loss 2.7484 (2.6757) Prec@1 35.000 (35.567) Prec@5 67.500 (66.178) Epoch: [5][630/11272] Time 0.877 (0.831) Data 0.002 (0.007) Loss 2.4912 (2.6747) Prec@1 43.125 (35.572) Prec@5 70.000 (66.193) Epoch: [5][640/11272] Time 0.761 (0.831) Data 0.001 (0.007) Loss 2.3794 (2.6740) Prec@1 48.750 (35.614) Prec@5 76.875 (66.192) Epoch: [5][650/11272] Time 0.742 (0.831) Data 0.002 (0.007) Loss 2.6847 (2.6763) Prec@1 35.625 (35.575) Prec@5 66.250 (66.136) Epoch: [5][660/11272] Time 0.903 (0.831) Data 0.002 (0.007) Loss 2.4285 (2.6750) Prec@1 39.375 (35.596) Prec@5 69.375 (66.157) Epoch: [5][670/11272] Time 0.872 (0.831) Data 0.002 (0.007) Loss 2.9350 (2.6754) Prec@1 23.750 (35.579) Prec@5 60.000 (66.156) Epoch: [5][680/11272] Time 0.773 (0.831) Data 0.002 (0.007) Loss 2.6015 (2.6758) Prec@1 37.500 (35.581) Prec@5 65.625 (66.127) Epoch: [5][690/11272] Time 0.934 (0.831) Data 0.002 (0.007) Loss 2.8089 (2.6766) Prec@1 33.125 (35.559) Prec@5 65.000 (66.103) Epoch: [5][700/11272] Time 0.991 (0.831) Data 0.002 (0.007) Loss 2.6499 (2.6769) Prec@1 38.125 (35.551) Prec@5 63.750 (66.101) Epoch: [5][710/11272] Time 0.750 (0.831) Data 0.001 (0.007) Loss 2.8477 (2.6787) Prec@1 35.625 (35.531) Prec@5 63.750 (66.078) Epoch: [5][720/11272] Time 0.753 (0.831) Data 0.002 (0.007) Loss 2.7418 (2.6795) Prec@1 38.125 (35.512) Prec@5 67.500 (66.077) Epoch: [5][730/11272] Time 0.902 (0.831) Data 0.001 (0.007) Loss 2.9579 (2.6801) Prec@1 30.625 (35.506) Prec@5 63.750 (66.059) Epoch: [5][740/11272] Time 0.859 (0.831) Data 0.001 (0.007) Loss 2.4713 (2.6794) Prec@1 40.625 (35.518) Prec@5 71.250 (66.075) Epoch: [5][750/11272] Time 0.754 (0.831) Data 0.002 (0.007) Loss 2.7062 (2.6790) Prec@1 30.000 (35.492) Prec@5 65.625 (66.095) Epoch: [5][760/11272] Time 0.798 (0.831) Data 0.002 (0.006) Loss 2.9172 (2.6793) Prec@1 28.125 (35.477) Prec@5 64.375 (66.082) Epoch: [5][770/11272] Time 0.942 (0.831) Data 0.002 (0.006) Loss 2.7493 (2.6799) Prec@1 36.250 (35.471) Prec@5 64.375 (66.077) Epoch: [5][780/11272] Time 0.895 (0.831) Data 0.002 (0.006) Loss 2.5056 (2.6793) Prec@1 38.750 (35.475) Prec@5 66.875 (66.072) Epoch: [5][790/11272] Time 0.779 (0.831) Data 0.002 (0.006) Loss 2.8114 (2.6788) Prec@1 35.625 (35.480) Prec@5 60.000 (66.084) Epoch: [5][800/11272] Time 0.757 (0.831) Data 0.002 (0.006) Loss 2.8799 (2.6793) Prec@1 34.375 (35.487) Prec@5 61.250 (66.071) Epoch: [5][810/11272] Time 0.917 (0.831) Data 0.001 (0.006) Loss 2.6623 (2.6793) Prec@1 31.250 (35.462) Prec@5 68.125 (66.069) Epoch: [5][820/11272] Time 0.905 (0.831) Data 0.002 (0.006) Loss 2.5761 (2.6782) Prec@1 36.250 (35.481) Prec@5 67.500 (66.095) Epoch: [5][830/11272] Time 0.764 (0.831) Data 0.002 (0.006) Loss 2.5447 (2.6774) Prec@1 35.000 (35.499) Prec@5 68.750 (66.109) Epoch: [5][840/11272] Time 0.815 (0.831) Data 0.001 (0.006) Loss 2.5314 (2.6776) Prec@1 37.500 (35.505) Prec@5 65.000 (66.103) Epoch: [5][850/11272] Time 0.874 (0.831) Data 0.001 (0.006) Loss 2.4102 (2.6765) Prec@1 43.125 (35.526) Prec@5 71.250 (66.121) Epoch: [5][860/11272] Time 0.761 (0.831) Data 0.002 (0.006) Loss 2.7018 (2.6764) Prec@1 33.750 (35.536) Prec@5 65.625 (66.113) Epoch: [5][870/11272] Time 0.793 (0.831) Data 0.002 (0.006) Loss 2.7955 (2.6757) Prec@1 33.125 (35.549) Prec@5 61.875 (66.122) Epoch: [5][880/11272] Time 0.911 (0.831) Data 0.001 (0.006) Loss 2.8416 (2.6758) Prec@1 35.000 (35.549) Prec@5 61.875 (66.118) Epoch: [5][890/11272] Time 0.881 (0.831) Data 0.002 (0.006) Loss 2.7662 (2.6756) Prec@1 36.875 (35.553) Prec@5 66.250 (66.129) Epoch: [5][900/11272] Time 0.762 (0.831) Data 0.002 (0.006) Loss 2.5222 (2.6765) Prec@1 42.500 (35.547) Prec@5 70.000 (66.102) Epoch: [5][910/11272] Time 0.751 (0.831) Data 0.002 (0.006) Loss 2.7004 (2.6772) Prec@1 32.500 (35.530) Prec@5 63.750 (66.097) Epoch: [5][920/11272] Time 0.834 (0.831) Data 0.001 (0.006) Loss 2.6031 (2.6773) Prec@1 36.250 (35.541) Prec@5 66.875 (66.111) Epoch: [5][930/11272] Time 0.855 (0.831) Data 0.002 (0.006) Loss 2.5703 (2.6769) Prec@1 38.125 (35.571) Prec@5 68.750 (66.116) Epoch: [5][940/11272] Time 0.771 (0.831) Data 0.001 (0.006) Loss 2.5037 (2.6760) Prec@1 40.000 (35.590) Prec@5 68.750 (66.143) Epoch: [5][950/11272] Time 0.741 (0.831) Data 0.002 (0.006) Loss 2.5015 (2.6754) Prec@1 36.250 (35.602) Prec@5 65.625 (66.144) Epoch: [5][960/11272] Time 0.898 (0.831) Data 0.001 (0.005) Loss 2.8324 (2.6756) Prec@1 32.500 (35.594) Prec@5 64.375 (66.136) Epoch: [5][970/11272] Time 0.774 (0.831) Data 0.002 (0.005) Loss 2.5709 (2.6768) Prec@1 37.500 (35.576) Prec@5 66.875 (66.113) Epoch: [5][980/11272] Time 0.748 (0.831) Data 0.001 (0.005) Loss 2.6967 (2.6761) Prec@1 31.250 (35.576) Prec@5 67.500 (66.131) Epoch: [5][990/11272] Time 0.875 (0.831) Data 0.001 (0.005) Loss 2.8468 (2.6765) Prec@1 35.625 (35.585) Prec@5 63.125 (66.126) Epoch: [5][1000/11272] Time 0.836 (0.831) Data 0.002 (0.005) Loss 2.8669 (2.6773) Prec@1 32.500 (35.554) Prec@5 61.250 (66.106) Epoch: [5][1010/11272] Time 0.770 (0.831) Data 0.002 (0.005) Loss 2.6896 (2.6766) Prec@1 34.375 (35.560) Prec@5 67.500 (66.126) Epoch: [5][1020/11272] Time 0.795 (0.831) Data 0.002 (0.005) Loss 2.8421 (2.6766) Prec@1 35.000 (35.574) Prec@5 61.250 (66.127) Epoch: [5][1030/11272] Time 0.876 (0.831) Data 0.001 (0.005) Loss 2.6711 (2.6769) Prec@1 33.125 (35.562) Prec@5 63.125 (66.116) Epoch: [5][1040/11272] Time 0.881 (0.831) Data 0.002 (0.005) Loss 2.6222 (2.6775) Prec@1 36.250 (35.541) Prec@5 63.125 (66.095) Epoch: [5][1050/11272] Time 0.752 (0.831) Data 0.002 (0.005) Loss 2.7548 (2.6776) Prec@1 34.375 (35.541) Prec@5 62.500 (66.095) Epoch: [5][1060/11272] Time 0.810 (0.831) Data 0.002 (0.005) Loss 2.5843 (2.6784) Prec@1 36.250 (35.530) Prec@5 70.000 (66.096) Epoch: [5][1070/11272] Time 0.891 (0.831) Data 0.002 (0.005) Loss 2.6406 (2.6785) Prec@1 35.000 (35.530) Prec@5 63.750 (66.084) Epoch: [5][1080/11272] Time 0.897 (0.831) Data 0.002 (0.005) Loss 2.9022 (2.6787) Prec@1 36.250 (35.528) Prec@5 63.125 (66.088) Epoch: [5][1090/11272] Time 0.816 (0.832) Data 0.002 (0.005) Loss 2.6063 (2.6794) Prec@1 37.500 (35.508) Prec@5 70.000 (66.078) Epoch: [5][1100/11272] Time 0.922 (0.832) Data 0.002 (0.005) Loss 2.7986 (2.6795) Prec@1 31.875 (35.507) Prec@5 65.625 (66.090) Epoch: [5][1110/11272] Time 0.939 (0.832) Data 0.002 (0.005) Loss 2.8176 (2.6805) Prec@1 34.375 (35.476) Prec@5 68.125 (66.064) Epoch: [5][1120/11272] Time 0.753 (0.832) Data 0.002 (0.005) Loss 2.9766 (2.6808) Prec@1 26.875 (35.460) Prec@5 63.125 (66.062) Epoch: [5][1130/11272] Time 0.737 (0.832) Data 0.001 (0.005) Loss 2.7882 (2.6804) Prec@1 34.375 (35.463) Prec@5 63.750 (66.059) Epoch: [5][1140/11272] Time 0.965 (0.832) Data 0.002 (0.005) Loss 2.3651 (2.6798) Prec@1 40.000 (35.475) Prec@5 71.875 (66.070) Epoch: [5][1150/11272] Time 0.903 (0.832) Data 0.001 (0.005) Loss 2.7651 (2.6801) Prec@1 33.750 (35.475) Prec@5 66.875 (66.062) Epoch: [5][1160/11272] Time 0.778 (0.832) Data 0.002 (0.005) Loss 2.6702 (2.6807) Prec@1 31.875 (35.454) Prec@5 68.125 (66.051) Epoch: [5][1170/11272] Time 0.808 (0.832) Data 0.002 (0.005) Loss 2.7024 (2.6808) Prec@1 36.875 (35.450) Prec@5 63.125 (66.042) Epoch: [5][1180/11272] Time 0.875 (0.832) Data 0.002 (0.005) Loss 2.7584 (2.6813) Prec@1 31.875 (35.420) Prec@5 64.375 (66.045) Epoch: [5][1190/11272] Time 0.882 (0.832) Data 0.001 (0.005) Loss 2.8900 (2.6809) Prec@1 28.750 (35.418) Prec@5 61.250 (66.062) Epoch: [5][1200/11272] Time 0.713 (0.832) Data 0.002 (0.005) Loss 2.7127 (2.6815) Prec@1 36.250 (35.415) Prec@5 58.750 (66.049) Epoch: [5][1210/11272] Time 0.794 (0.832) Data 0.002 (0.005) Loss 2.7675 (2.6812) Prec@1 37.500 (35.423) Prec@5 64.375 (66.048) Epoch: [5][1220/11272] Time 0.850 (0.832) Data 0.002 (0.005) Loss 2.5424 (2.6808) Prec@1 37.500 (35.417) Prec@5 65.625 (66.053) Epoch: [5][1230/11272] Time 0.764 (0.832) Data 0.002 (0.005) Loss 2.7231 (2.6805) Prec@1 38.750 (35.420) Prec@5 63.750 (66.049) Epoch: [5][1240/11272] Time 0.833 (0.832) Data 0.002 (0.005) Loss 2.4466 (2.6806) Prec@1 38.750 (35.417) Prec@5 71.250 (66.053) Epoch: [5][1250/11272] Time 0.845 (0.832) Data 0.001 (0.005) Loss 2.6367 (2.6804) Prec@1 35.000 (35.420) Prec@5 66.250 (66.064) Epoch: [5][1260/11272] Time 0.916 (0.832) Data 0.002 (0.005) Loss 2.8270 (2.6808) Prec@1 36.250 (35.414) Prec@5 63.125 (66.057) Epoch: [5][1270/11272] Time 0.762 (0.832) Data 0.001 (0.005) Loss 2.8120 (2.6815) Prec@1 32.500 (35.400) Prec@5 63.750 (66.044) Epoch: [5][1280/11272] Time 0.786 (0.832) Data 0.002 (0.005) Loss 2.9449 (2.6815) Prec@1 24.375 (35.391) Prec@5 58.750 (66.042) Epoch: [5][1290/11272] Time 0.933 (0.832) Data 0.002 (0.005) Loss 2.6722 (2.6819) Prec@1 35.625 (35.377) Prec@5 68.750 (66.042) Epoch: [5][1300/11272] Time 0.877 (0.832) Data 0.002 (0.005) Loss 3.1720 (2.6824) Prec@1 27.500 (35.377) Prec@5 58.125 (66.037) Epoch: [5][1310/11272] Time 0.755 (0.833) Data 0.002 (0.004) Loss 2.9410 (2.6826) Prec@1 31.875 (35.370) Prec@5 60.000 (66.026) Epoch: [5][1320/11272] Time 0.750 (0.833) Data 0.002 (0.004) Loss 2.6383 (2.6822) Prec@1 31.875 (35.361) Prec@5 70.625 (66.036) Epoch: [5][1330/11272] Time 0.841 (0.833) Data 0.001 (0.004) Loss 2.6318 (2.6824) Prec@1 33.125 (35.369) Prec@5 65.625 (66.034) Epoch: [5][1340/11272] Time 0.880 (0.833) Data 0.002 (0.004) Loss 2.4201 (2.6829) Prec@1 40.000 (35.356) Prec@5 69.375 (66.020) Epoch: [5][1350/11272] Time 0.759 (0.833) Data 0.002 (0.004) Loss 2.9310 (2.6834) Prec@1 32.500 (35.350) Prec@5 59.375 (66.014) Epoch: [5][1360/11272] Time 0.939 (0.833) Data 0.002 (0.004) Loss 2.9258 (2.6844) Prec@1 28.750 (35.332) Prec@5 63.125 (66.000) Epoch: [5][1370/11272] Time 0.848 (0.833) Data 0.002 (0.004) Loss 2.7484 (2.6844) Prec@1 44.375 (35.341) Prec@5 61.875 (65.993) Epoch: [5][1380/11272] Time 0.762 (0.833) Data 0.001 (0.004) Loss 2.9304 (2.6844) Prec@1 28.125 (35.342) Prec@5 59.375 (65.986) Epoch: [5][1390/11272] Time 0.762 (0.833) Data 0.001 (0.004) Loss 3.0925 (2.6841) Prec@1 30.625 (35.349) Prec@5 58.750 (65.993) Epoch: [5][1400/11272] Time 0.907 (0.833) Data 0.001 (0.004) Loss 3.0105 (2.6846) Prec@1 26.875 (35.333) Prec@5 60.000 (65.988) Epoch: [5][1410/11272] Time 0.865 (0.833) Data 0.002 (0.004) Loss 2.4931 (2.6845) Prec@1 44.375 (35.330) Prec@5 70.000 (65.985) Epoch: [5][1420/11272] Time 0.845 (0.832) Data 0.003 (0.004) Loss 2.5002 (2.6844) Prec@1 38.750 (35.333) Prec@5 70.625 (65.983) Epoch: [5][1430/11272] Time 0.777 (0.832) Data 0.002 (0.004) Loss 2.7168 (2.6838) Prec@1 30.625 (35.348) Prec@5 68.750 (66.009) Epoch: [5][1440/11272] Time 0.874 (0.832) Data 0.002 (0.004) Loss 2.8419 (2.6834) Prec@1 28.750 (35.348) Prec@5 61.875 (66.018) Epoch: [5][1450/11272] Time 0.938 (0.832) Data 0.001 (0.004) Loss 2.5862 (2.6837) Prec@1 36.875 (35.332) Prec@5 67.500 (66.011) Epoch: [5][1460/11272] Time 0.750 (0.832) Data 0.002 (0.004) Loss 2.6460 (2.6839) Prec@1 33.750 (35.331) Prec@5 69.375 (66.016) Epoch: [5][1470/11272] Time 0.772 (0.832) Data 0.002 (0.004) Loss 2.7497 (2.6842) Prec@1 40.625 (35.330) Prec@5 62.500 (66.014) Epoch: [5][1480/11272] Time 0.903 (0.832) Data 0.002 (0.004) Loss 2.7603 (2.6845) Prec@1 35.625 (35.309) Prec@5 66.875 (66.005) Epoch: [5][1490/11272] Time 0.755 (0.832) Data 0.003 (0.004) Loss 2.6304 (2.6844) Prec@1 34.375 (35.307) Prec@5 69.375 (66.008) Epoch: [5][1500/11272] Time 0.791 (0.832) Data 0.002 (0.004) Loss 3.0077 (2.6842) Prec@1 31.875 (35.307) Prec@5 58.750 (66.010) Epoch: [5][1510/11272] Time 0.886 (0.832) Data 0.002 (0.004) Loss 2.6513 (2.6842) Prec@1 33.125 (35.314) Prec@5 70.625 (66.011) Epoch: [5][1520/11272] Time 0.869 (0.832) Data 0.002 (0.004) Loss 2.7909 (2.6840) Prec@1 33.125 (35.312) Prec@5 61.250 (66.017) Epoch: [5][1530/11272] Time 0.797 (0.832) Data 0.002 (0.004) Loss 2.8766 (2.6839) Prec@1 31.875 (35.325) Prec@5 59.375 (66.018) Epoch: [5][1540/11272] Time 0.807 (0.832) Data 0.002 (0.004) Loss 2.4588 (2.6838) Prec@1 33.125 (35.331) Prec@5 73.750 (66.025) Epoch: [5][1550/11272] Time 0.906 (0.832) Data 0.002 (0.004) Loss 2.6704 (2.6838) Prec@1 38.750 (35.327) Prec@5 66.250 (66.028) Epoch: [5][1560/11272] Time 0.885 (0.832) Data 0.002 (0.004) Loss 2.7133 (2.6837) Prec@1 33.750 (35.323) Prec@5 68.750 (66.033) Epoch: [5][1570/11272] Time 0.813 (0.832) Data 0.002 (0.004) Loss 2.8729 (2.6837) Prec@1 34.375 (35.329) Prec@5 62.500 (66.028) Epoch: [5][1580/11272] Time 0.746 (0.832) Data 0.002 (0.004) Loss 2.6158 (2.6837) Prec@1 36.875 (35.337) Prec@5 67.500 (66.021) Epoch: [5][1590/11272] Time 0.904 (0.832) Data 0.002 (0.004) Loss 2.5997 (2.6836) Prec@1 36.250 (35.332) Prec@5 69.375 (66.021) Epoch: [5][1600/11272] Time 0.877 (0.833) Data 0.002 (0.004) Loss 2.6466 (2.6835) Prec@1 38.750 (35.332) Prec@5 63.125 (66.019) Epoch: [5][1610/11272] Time 0.797 (0.832) Data 0.002 (0.004) Loss 2.6105 (2.6834) Prec@1 38.750 (35.332) Prec@5 68.125 (66.018) Epoch: [5][1620/11272] Time 0.914 (0.832) Data 0.002 (0.004) Loss 2.5450 (2.6827) Prec@1 38.125 (35.345) Prec@5 66.875 (66.028) Epoch: [5][1630/11272] Time 0.960 (0.832) Data 0.002 (0.004) Loss 2.7487 (2.6824) Prec@1 33.750 (35.349) Prec@5 63.750 (66.036) Epoch: [5][1640/11272] Time 0.794 (0.833) Data 0.001 (0.004) Loss 2.7196 (2.6823) Prec@1 31.875 (35.347) Prec@5 63.750 (66.036) Epoch: [5][1650/11272] Time 0.791 (0.833) Data 0.002 (0.004) Loss 2.5756 (2.6824) Prec@1 39.375 (35.344) Prec@5 67.500 (66.037) Epoch: [5][1660/11272] Time 0.942 (0.833) Data 0.002 (0.004) Loss 2.6634 (2.6820) Prec@1 31.250 (35.346) Prec@5 67.500 (66.054) Epoch: [5][1670/11272] Time 0.910 (0.833) Data 0.002 (0.004) Loss 2.3803 (2.6819) Prec@1 40.625 (35.344) Prec@5 71.875 (66.054) Epoch: [5][1680/11272] Time 0.778 (0.833) Data 0.002 (0.004) Loss 2.8334 (2.6818) Prec@1 31.250 (35.346) Prec@5 60.000 (66.057) Epoch: [5][1690/11272] Time 0.836 (0.833) Data 0.002 (0.004) Loss 2.4169 (2.6820) Prec@1 40.625 (35.344) Prec@5 69.375 (66.043) Epoch: [5][1700/11272] Time 0.836 (0.833) Data 0.001 (0.004) Loss 2.5679 (2.6821) Prec@1 31.875 (35.340) Prec@5 66.875 (66.037) Epoch: [5][1710/11272] Time 0.874 (0.833) Data 0.002 (0.004) Loss 2.8648 (2.6821) Prec@1 32.500 (35.339) Prec@5 63.125 (66.032) Epoch: [5][1720/11272] Time 0.811 (0.833) Data 0.002 (0.004) Loss 2.4194 (2.6820) Prec@1 37.500 (35.344) Prec@5 71.250 (66.033) Epoch: [5][1730/11272] Time 0.763 (0.833) Data 0.002 (0.004) Loss 2.8680 (2.6821) Prec@1 30.000 (35.346) Prec@5 62.500 (66.032) Epoch: [5][1740/11272] Time 0.860 (0.833) Data 0.002 (0.004) Loss 2.6292 (2.6823) Prec@1 36.250 (35.342) Prec@5 68.125 (66.031) Epoch: [5][1750/11272] Time 0.849 (0.833) Data 0.001 (0.004) Loss 2.5577 (2.6825) Prec@1 34.375 (35.331) Prec@5 69.375 (66.030) Epoch: [5][1760/11272] Time 0.780 (0.833) Data 0.002 (0.004) Loss 2.5521 (2.6828) Prec@1 34.375 (35.325) Prec@5 65.000 (66.021) Epoch: [5][1770/11272] Time 0.878 (0.833) Data 0.001 (0.004) Loss 2.6895 (2.6826) Prec@1 31.875 (35.320) Prec@5 67.500 (66.025) Epoch: [5][1780/11272] Time 0.904 (0.833) Data 0.002 (0.004) Loss 2.9376 (2.6826) Prec@1 33.750 (35.325) Prec@5 65.000 (66.026) Epoch: [5][1790/11272] Time 0.772 (0.833) Data 0.002 (0.004) Loss 2.5360 (2.6826) Prec@1 39.375 (35.325) Prec@5 67.500 (66.030) Epoch: [5][1800/11272] Time 0.779 (0.833) Data 0.002 (0.004) Loss 2.6144 (2.6824) Prec@1 39.375 (35.337) Prec@5 66.250 (66.028) Epoch: [5][1810/11272] Time 0.928 (0.833) Data 0.001 (0.004) Loss 2.4871 (2.6821) Prec@1 42.500 (35.348) Prec@5 70.625 (66.037) Epoch: [5][1820/11272] Time 0.841 (0.833) Data 0.001 (0.004) Loss 2.7851 (2.6822) Prec@1 31.250 (35.336) Prec@5 67.500 (66.033) Epoch: [5][1830/11272] Time 0.763 (0.833) Data 0.001 (0.004) Loss 2.8323 (2.6828) Prec@1 34.375 (35.324) Prec@5 64.375 (66.025) Epoch: [5][1840/11272] Time 0.757 (0.833) Data 0.001 (0.004) Loss 2.5781 (2.6830) Prec@1 41.250 (35.323) Prec@5 68.750 (66.027) Epoch: [5][1850/11272] Time 0.870 (0.833) Data 0.001 (0.004) Loss 2.6952 (2.6829) Prec@1 34.375 (35.329) Prec@5 66.875 (66.026) Epoch: [5][1860/11272] Time 0.862 (0.833) Data 0.001 (0.004) Loss 2.7391 (2.6830) Prec@1 35.000 (35.328) Prec@5 62.500 (66.025) Epoch: [5][1870/11272] Time 0.805 (0.833) Data 0.002 (0.004) Loss 2.5412 (2.6830) Prec@1 36.250 (35.329) Prec@5 65.000 (66.023) Epoch: [5][1880/11272] Time 0.788 (0.833) Data 0.001 (0.004) Loss 2.7802 (2.6831) Prec@1 36.250 (35.319) Prec@5 62.500 (66.017) Epoch: [5][1890/11272] Time 0.896 (0.833) Data 0.001 (0.004) Loss 2.6839 (2.6829) Prec@1 33.125 (35.318) Prec@5 68.125 (66.026) Epoch: [5][1900/11272] Time 0.764 (0.833) Data 0.002 (0.004) Loss 2.5056 (2.6827) Prec@1 37.500 (35.328) Prec@5 69.375 (66.027) Epoch: [5][1910/11272] Time 0.761 (0.833) Data 0.002 (0.004) Loss 2.8157 (2.6827) Prec@1 28.125 (35.325) Prec@5 63.750 (66.031) Epoch: [5][1920/11272] Time 0.944 (0.833) Data 0.001 (0.004) Loss 2.7465 (2.6829) Prec@1 38.750 (35.317) Prec@5 66.250 (66.029) Epoch: [5][1930/11272] Time 0.933 (0.833) Data 0.001 (0.004) Loss 2.7304 (2.6830) Prec@1 35.000 (35.310) Prec@5 65.000 (66.021) Epoch: [5][1940/11272] Time 0.808 (0.833) Data 0.008 (0.004) Loss 2.5441 (2.6829) Prec@1 43.125 (35.317) Prec@5 72.500 (66.024) Epoch: [5][1950/11272] Time 0.786 (0.833) Data 0.001 (0.004) Loss 2.4360 (2.6826) Prec@1 39.375 (35.324) Prec@5 70.625 (66.031) Epoch: [5][1960/11272] Time 0.906 (0.833) Data 0.002 (0.004) Loss 2.6173 (2.6829) Prec@1 41.250 (35.318) Prec@5 63.750 (66.027) Epoch: [5][1970/11272] Time 0.933 (0.833) Data 0.002 (0.004) Loss 2.5378 (2.6831) Prec@1 40.625 (35.313) Prec@5 68.125 (66.019) Epoch: [5][1980/11272] Time 0.763 (0.833) Data 0.001 (0.004) Loss 2.6550 (2.6832) Prec@1 36.250 (35.313) Prec@5 67.500 (66.024) Epoch: [5][1990/11272] Time 0.814 (0.833) Data 0.002 (0.004) Loss 2.7246 (2.6834) Prec@1 31.875 (35.305) Prec@5 62.500 (66.020) Epoch: [5][2000/11272] Time 1.022 (0.833) Data 0.002 (0.004) Loss 2.5274 (2.6835) Prec@1 37.500 (35.305) Prec@5 68.125 (66.015) Epoch: [5][2010/11272] Time 0.875 (0.834) Data 0.001 (0.004) Loss 2.8634 (2.6838) Prec@1 31.250 (35.303) Prec@5 63.750 (66.014) Epoch: [5][2020/11272] Time 0.744 (0.834) Data 0.001 (0.004) Loss 2.7196 (2.6837) Prec@1 33.750 (35.299) Prec@5 68.125 (66.012) Epoch: [5][2030/11272] Time 0.890 (0.834) Data 0.002 (0.004) Loss 2.6509 (2.6841) Prec@1 39.375 (35.290) Prec@5 67.500 (66.003) Epoch: [5][2040/11272] Time 0.863 (0.834) Data 0.002 (0.004) Loss 2.9064 (2.6838) Prec@1 33.750 (35.301) Prec@5 61.875 (66.007) Epoch: [5][2050/11272] Time 0.812 (0.834) Data 0.002 (0.004) Loss 2.4456 (2.6839) Prec@1 39.375 (35.304) Prec@5 70.000 (66.004) Epoch: [5][2060/11272] Time 0.790 (0.834) Data 0.002 (0.004) Loss 2.6646 (2.6839) Prec@1 34.375 (35.300) Prec@5 66.250 (66.000) Epoch: [5][2070/11272] Time 0.928 (0.834) Data 0.002 (0.003) Loss 2.6896 (2.6842) Prec@1 35.000 (35.296) Prec@5 67.500 (65.997) Epoch: [5][2080/11272] Time 0.931 (0.834) Data 0.002 (0.003) Loss 2.8641 (2.6845) Prec@1 31.875 (35.296) Prec@5 61.875 (65.993) Epoch: [5][2090/11272] Time 0.766 (0.834) Data 0.002 (0.003) Loss 2.6410 (2.6847) Prec@1 32.500 (35.286) Prec@5 68.125 (65.984) Epoch: [5][2100/11272] Time 0.743 (0.834) Data 0.001 (0.003) Loss 2.5624 (2.6848) Prec@1 37.500 (35.291) Prec@5 68.125 (65.977) Epoch: [5][2110/11272] Time 0.992 (0.834) Data 0.002 (0.003) Loss 2.4967 (2.6847) Prec@1 36.250 (35.288) Prec@5 70.000 (65.978) Epoch: [5][2120/11272] Time 0.910 (0.834) Data 0.004 (0.003) Loss 2.6594 (2.6846) Prec@1 36.875 (35.283) Prec@5 71.250 (65.974) Epoch: [5][2130/11272] Time 0.769 (0.834) Data 0.002 (0.003) Loss 2.8938 (2.6847) Prec@1 30.625 (35.286) Prec@5 68.750 (65.977) Epoch: [5][2140/11272] Time 0.793 (0.835) Data 0.002 (0.003) Loss 2.9714 (2.6847) Prec@1 31.875 (35.288) Prec@5 61.875 (65.978) Epoch: [5][2150/11272] Time 0.879 (0.835) Data 0.002 (0.003) Loss 2.8994 (2.6851) Prec@1 33.125 (35.279) Prec@5 62.500 (65.972) Epoch: [5][2160/11272] Time 0.810 (0.835) Data 0.004 (0.003) Loss 2.7029 (2.6849) Prec@1 37.500 (35.283) Prec@5 68.125 (65.980) Epoch: [5][2170/11272] Time 0.748 (0.835) Data 0.002 (0.003) Loss 2.6646 (2.6848) Prec@1 36.875 (35.283) Prec@5 65.625 (65.977) Epoch: [5][2180/11272] Time 0.951 (0.835) Data 0.002 (0.003) Loss 2.6878 (2.6849) Prec@1 30.625 (35.273) Prec@5 67.500 (65.976) Epoch: [5][2190/11272] Time 0.896 (0.835) Data 0.002 (0.003) Loss 2.5400 (2.6852) Prec@1 39.375 (35.270) Prec@5 70.000 (65.969) Epoch: [5][2200/11272] Time 0.784 (0.835) Data 0.002 (0.003) Loss 2.8319 (2.6852) Prec@1 40.000 (35.273) Prec@5 62.500 (65.966) Epoch: [5][2210/11272] Time 0.768 (0.835) Data 0.002 (0.003) Loss 2.7512 (2.6855) Prec@1 35.000 (35.270) Prec@5 65.000 (65.961) Epoch: [5][2220/11272] Time 0.875 (0.835) Data 0.001 (0.003) Loss 2.4384 (2.6854) Prec@1 40.000 (35.277) Prec@5 69.375 (65.956) Epoch: [5][2230/11272] Time 0.934 (0.835) Data 0.001 (0.003) Loss 2.4984 (2.6851) Prec@1 38.750 (35.281) Prec@5 71.250 (65.964) Epoch: [5][2240/11272] Time 0.732 (0.835) Data 0.001 (0.003) Loss 2.7506 (2.6850) Prec@1 31.250 (35.287) Prec@5 65.000 (65.968) Epoch: [5][2250/11272] Time 0.774 (0.835) Data 0.002 (0.003) Loss 2.5985 (2.6852) Prec@1 40.625 (35.287) Prec@5 68.125 (65.968) Epoch: [5][2260/11272] Time 0.883 (0.835) Data 0.001 (0.003) Loss 3.0164 (2.6852) Prec@1 31.250 (35.288) Prec@5 60.625 (65.969) Epoch: [5][2270/11272] Time 0.886 (0.835) Data 0.001 (0.003) Loss 2.8091 (2.6857) Prec@1 35.000 (35.275) Prec@5 67.500 (65.957) Epoch: [5][2280/11272] Time 0.761 (0.835) Data 0.001 (0.003) Loss 2.4395 (2.6854) Prec@1 38.125 (35.278) Prec@5 70.625 (65.963) Epoch: [5][2290/11272] Time 0.912 (0.835) Data 0.001 (0.003) Loss 2.7234 (2.6853) Prec@1 35.000 (35.283) Prec@5 61.250 (65.962) Epoch: [5][2300/11272] Time 0.919 (0.835) Data 0.002 (0.003) Loss 2.7780 (2.6851) Prec@1 36.250 (35.288) Prec@5 61.875 (65.959) Epoch: [5][2310/11272] Time 0.756 (0.835) Data 0.002 (0.003) Loss 2.7225 (2.6852) Prec@1 36.875 (35.291) Prec@5 63.750 (65.955) Epoch: [5][2320/11272] Time 0.771 (0.835) Data 0.002 (0.003) Loss 2.6204 (2.6850) Prec@1 36.250 (35.295) Prec@5 67.500 (65.960) Epoch: [5][2330/11272] Time 0.904 (0.835) Data 0.002 (0.003) Loss 2.4890 (2.6847) Prec@1 40.000 (35.298) Prec@5 67.500 (65.966) Epoch: [5][2340/11272] Time 0.917 (0.835) Data 0.002 (0.003) Loss 2.7820 (2.6849) Prec@1 30.000 (35.297) Prec@5 65.000 (65.961) Epoch: [5][2350/11272] Time 0.770 (0.835) Data 0.002 (0.003) Loss 2.6869 (2.6850) Prec@1 34.375 (35.297) Prec@5 65.625 (65.957) Epoch: [5][2360/11272] Time 0.767 (0.836) Data 0.002 (0.003) Loss 2.6473 (2.6846) Prec@1 34.375 (35.307) Prec@5 66.875 (65.966) Epoch: [5][2370/11272] Time 0.910 (0.836) Data 0.001 (0.003) Loss 2.5538 (2.6848) Prec@1 40.000 (35.305) Prec@5 65.625 (65.960) Epoch: [5][2380/11272] Time 0.904 (0.836) Data 0.001 (0.003) Loss 2.8761 (2.6849) Prec@1 28.750 (35.298) Prec@5 68.750 (65.961) Epoch: [5][2390/11272] Time 0.781 (0.836) Data 0.002 (0.003) Loss 2.9091 (2.6848) Prec@1 32.500 (35.301) Prec@5 61.250 (65.958) Epoch: [5][2400/11272] Time 0.783 (0.836) Data 0.002 (0.003) Loss 2.4033 (2.6845) Prec@1 38.125 (35.306) Prec@5 71.250 (65.961) Epoch: [5][2410/11272] Time 0.913 (0.836) Data 0.001 (0.003) Loss 2.6233 (2.6846) Prec@1 37.500 (35.310) Prec@5 68.125 (65.963) Epoch: [5][2420/11272] Time 0.732 (0.836) Data 0.003 (0.003) Loss 2.6177 (2.6843) Prec@1 35.000 (35.313) Prec@5 66.250 (65.972) Epoch: [5][2430/11272] Time 0.796 (0.836) Data 0.002 (0.003) Loss 2.5455 (2.6842) Prec@1 40.625 (35.315) Prec@5 67.500 (65.977) Epoch: [5][2440/11272] Time 0.931 (0.836) Data 0.001 (0.003) Loss 3.1597 (2.6841) Prec@1 26.875 (35.318) Prec@5 56.875 (65.977) Epoch: [5][2450/11272] Time 0.992 (0.836) Data 0.003 (0.003) Loss 2.4996 (2.6838) Prec@1 38.125 (35.324) Prec@5 65.625 (65.980) Epoch: [5][2460/11272] Time 0.837 (0.836) Data 0.002 (0.003) Loss 2.6681 (2.6838) Prec@1 37.500 (35.328) Prec@5 70.625 (65.984) Epoch: [5][2470/11272] Time 0.777 (0.836) Data 0.002 (0.003) Loss 2.6956 (2.6839) Prec@1 35.000 (35.327) Prec@5 61.875 (65.976) Epoch: [5][2480/11272] Time 0.939 (0.837) Data 0.002 (0.003) Loss 2.5843 (2.6839) Prec@1 34.375 (35.325) Prec@5 65.625 (65.971) Epoch: [5][2490/11272] Time 0.932 (0.837) Data 0.002 (0.003) Loss 2.6734 (2.6844) Prec@1 38.125 (35.316) Prec@5 66.250 (65.966) Epoch: [5][2500/11272] Time 0.794 (0.837) Data 0.001 (0.003) Loss 2.7443 (2.6845) Prec@1 28.125 (35.311) Prec@5 64.375 (65.962) Epoch: [5][2510/11272] Time 0.794 (0.837) Data 0.001 (0.003) Loss 2.7967 (2.6842) Prec@1 31.250 (35.313) Prec@5 66.250 (65.967) Epoch: [5][2520/11272] Time 0.928 (0.837) Data 0.003 (0.003) Loss 2.7917 (2.6842) Prec@1 40.000 (35.323) Prec@5 60.000 (65.970) Epoch: [5][2530/11272] Time 0.921 (0.837) Data 0.002 (0.003) Loss 2.4669 (2.6842) Prec@1 41.875 (35.324) Prec@5 65.625 (65.968) Epoch: [5][2540/11272] Time 0.761 (0.837) Data 0.002 (0.003) Loss 3.0342 (2.6841) Prec@1 30.000 (35.320) Prec@5 55.625 (65.966) Epoch: [5][2550/11272] Time 0.971 (0.837) Data 0.001 (0.003) Loss 2.9922 (2.6846) Prec@1 33.125 (35.314) Prec@5 61.250 (65.958) Epoch: [5][2560/11272] Time 0.906 (0.837) Data 0.002 (0.003) Loss 2.5970 (2.6848) Prec@1 36.875 (35.312) Prec@5 69.375 (65.955) Epoch: [5][2570/11272] Time 0.767 (0.837) Data 0.002 (0.003) Loss 2.7712 (2.6848) Prec@1 32.500 (35.311) Prec@5 59.375 (65.951) Epoch: [5][2580/11272] Time 0.779 (0.837) Data 0.002 (0.003) Loss 2.7378 (2.6850) Prec@1 34.375 (35.306) Prec@5 61.250 (65.948) Epoch: [5][2590/11272] Time 0.919 (0.837) Data 0.001 (0.003) Loss 2.3961 (2.6849) Prec@1 40.625 (35.309) Prec@5 72.500 (65.950) Epoch: [5][2600/11272] Time 0.950 (0.837) Data 0.002 (0.003) Loss 2.8462 (2.6850) Prec@1 30.625 (35.309) Prec@5 58.750 (65.947) Epoch: [5][2610/11272] Time 0.769 (0.837) Data 0.002 (0.003) Loss 2.6407 (2.6847) Prec@1 36.875 (35.312) Prec@5 65.625 (65.951) Epoch: [5][2620/11272] Time 0.765 (0.837) Data 0.001 (0.003) Loss 2.9010 (2.6855) Prec@1 29.375 (35.302) Prec@5 62.500 (65.937) Epoch: [5][2630/11272] Time 0.933 (0.837) Data 0.003 (0.003) Loss 2.6906 (2.6856) Prec@1 33.750 (35.301) Prec@5 60.625 (65.932) Epoch: [5][2640/11272] Time 0.960 (0.838) Data 0.002 (0.003) Loss 2.6539 (2.6856) Prec@1 39.375 (35.305) Prec@5 68.125 (65.931) Epoch: [5][2650/11272] Time 0.743 (0.838) Data 0.002 (0.003) Loss 2.5005 (2.6857) Prec@1 38.750 (35.303) Prec@5 66.875 (65.927) Epoch: [5][2660/11272] Time 0.814 (0.838) Data 0.002 (0.003) Loss 2.4990 (2.6856) Prec@1 37.500 (35.303) Prec@5 70.000 (65.932) Epoch: [5][2670/11272] Time 0.968 (0.838) Data 0.002 (0.003) Loss 2.8323 (2.6859) Prec@1 35.000 (35.300) Prec@5 62.500 (65.923) Epoch: [5][2680/11272] Time 0.886 (0.838) Data 0.001 (0.003) Loss 2.6163 (2.6860) Prec@1 39.375 (35.299) Prec@5 68.125 (65.925) Epoch: [5][2690/11272] Time 0.815 (0.838) Data 0.002 (0.003) Loss 2.5352 (2.6861) Prec@1 34.375 (35.302) Prec@5 74.375 (65.929) Epoch: [5][2700/11272] Time 0.928 (0.838) Data 0.002 (0.003) Loss 2.7072 (2.6858) Prec@1 33.750 (35.305) Prec@5 67.500 (65.931) Epoch: [5][2710/11272] Time 0.908 (0.838) Data 0.002 (0.003) Loss 2.5929 (2.6860) Prec@1 36.875 (35.307) Prec@5 68.750 (65.930) Epoch: [5][2720/11272] Time 0.761 (0.838) Data 0.001 (0.003) Loss 2.6878 (2.6860) Prec@1 30.000 (35.305) Prec@5 65.625 (65.932) Epoch: [5][2730/11272] Time 0.773 (0.838) Data 0.002 (0.003) Loss 2.5943 (2.6861) Prec@1 35.000 (35.306) Prec@5 63.125 (65.930) Epoch: [5][2740/11272] Time 0.937 (0.838) Data 0.001 (0.003) Loss 2.8322 (2.6860) Prec@1 30.625 (35.306) Prec@5 63.125 (65.931) Epoch: [5][2750/11272] Time 0.879 (0.838) Data 0.002 (0.003) Loss 2.8619 (2.6860) Prec@1 35.625 (35.303) Prec@5 65.000 (65.934) Epoch: [5][2760/11272] Time 0.768 (0.838) Data 0.001 (0.003) Loss 2.7951 (2.6862) Prec@1 33.750 (35.301) Prec@5 63.750 (65.930) Epoch: [5][2770/11272] Time 0.759 (0.838) Data 0.002 (0.003) Loss 2.7201 (2.6863) Prec@1 31.250 (35.302) Prec@5 67.500 (65.928) Epoch: [5][2780/11272] Time 0.958 (0.838) Data 0.002 (0.003) Loss 2.8386 (2.6862) Prec@1 32.500 (35.304) Prec@5 63.125 (65.929) Epoch: [5][2790/11272] Time 0.892 (0.838) Data 0.002 (0.003) Loss 2.6170 (2.6858) Prec@1 41.875 (35.307) Prec@5 65.625 (65.935) Epoch: [5][2800/11272] Time 0.785 (0.838) Data 0.002 (0.003) Loss 2.2501 (2.6856) Prec@1 43.125 (35.314) Prec@5 75.625 (65.940) Epoch: [5][2810/11272] Time 0.788 (0.838) Data 0.001 (0.003) Loss 2.7507 (2.6856) Prec@1 32.500 (35.313) Prec@5 61.250 (65.942) Epoch: [5][2820/11272] Time 0.984 (0.838) Data 0.002 (0.003) Loss 2.6516 (2.6855) Prec@1 35.625 (35.311) Prec@5 71.250 (65.944) Epoch: [5][2830/11272] Time 0.811 (0.838) Data 0.001 (0.003) Loss 2.7727 (2.6854) Prec@1 31.875 (35.307) Prec@5 68.125 (65.950) Epoch: [5][2840/11272] Time 0.750 (0.838) Data 0.002 (0.003) Loss 2.8221 (2.6857) Prec@1 32.500 (35.301) Prec@5 59.375 (65.944) Epoch: [5][2850/11272] Time 0.869 (0.838) Data 0.001 (0.003) Loss 2.5734 (2.6857) Prec@1 36.875 (35.300) Prec@5 67.500 (65.951) Epoch: [5][2860/11272] Time 0.923 (0.838) Data 0.001 (0.003) Loss 2.4809 (2.6855) Prec@1 40.625 (35.303) Prec@5 68.125 (65.954) Epoch: [5][2870/11272] Time 0.815 (0.839) Data 0.002 (0.003) Loss 3.0016 (2.6856) Prec@1 35.000 (35.306) Prec@5 60.625 (65.953) Epoch: [5][2880/11272] Time 0.735 (0.839) Data 0.001 (0.003) Loss 2.6327 (2.6857) Prec@1 37.500 (35.302) Prec@5 65.000 (65.949) Epoch: [5][2890/11272] Time 0.907 (0.839) Data 0.001 (0.003) Loss 2.4685 (2.6854) Prec@1 39.375 (35.306) Prec@5 70.625 (65.955) Epoch: [5][2900/11272] Time 0.912 (0.839) Data 0.001 (0.003) Loss 2.6517 (2.6856) Prec@1 30.625 (35.299) Prec@5 60.625 (65.947) Epoch: [5][2910/11272] Time 0.765 (0.839) Data 0.002 (0.003) Loss 2.8952 (2.6857) Prec@1 29.375 (35.298) Prec@5 60.625 (65.950) Epoch: [5][2920/11272] Time 0.769 (0.839) Data 0.002 (0.003) Loss 3.2196 (2.6859) Prec@1 25.625 (35.294) Prec@5 52.500 (65.947) Epoch: [5][2930/11272] Time 0.957 (0.839) Data 0.002 (0.003) Loss 2.8155 (2.6860) Prec@1 33.125 (35.290) Prec@5 64.375 (65.946) Epoch: [5][2940/11272] Time 0.873 (0.839) Data 0.002 (0.003) Loss 2.8162 (2.6857) Prec@1 31.250 (35.293) Prec@5 65.000 (65.953) Epoch: [5][2950/11272] Time 0.814 (0.839) Data 0.003 (0.003) Loss 2.6142 (2.6858) Prec@1 36.875 (35.289) Prec@5 67.500 (65.951) Epoch: [5][2960/11272] Time 0.948 (0.839) Data 0.002 (0.003) Loss 2.8501 (2.6858) Prec@1 30.000 (35.290) Prec@5 66.250 (65.954) Epoch: [5][2970/11272] Time 0.940 (0.839) Data 0.002 (0.003) Loss 2.8332 (2.6858) Prec@1 30.000 (35.289) Prec@5 64.375 (65.952) Epoch: [5][2980/11272] Time 0.788 (0.839) Data 0.001 (0.003) Loss 2.7104 (2.6859) Prec@1 30.625 (35.291) Prec@5 64.375 (65.947) Epoch: [5][2990/11272] Time 0.768 (0.839) Data 0.002 (0.003) Loss 2.7157 (2.6858) Prec@1 33.750 (35.294) Prec@5 66.875 (65.951) Epoch: [5][3000/11272] Time 0.843 (0.839) Data 0.001 (0.003) Loss 2.6811 (2.6857) Prec@1 34.375 (35.296) Prec@5 64.375 (65.954) Epoch: [5][3010/11272] Time 0.900 (0.839) Data 0.002 (0.003) Loss 2.5280 (2.6855) Prec@1 36.250 (35.298) Prec@5 70.625 (65.961) Epoch: [5][3020/11272] Time 0.745 (0.839) Data 0.001 (0.003) Loss 2.8834 (2.6858) Prec@1 25.000 (35.295) Prec@5 62.500 (65.949) Epoch: [5][3030/11272] Time 0.814 (0.839) Data 0.002 (0.003) Loss 2.7267 (2.6857) Prec@1 35.625 (35.297) Prec@5 65.000 (65.951) Epoch: [5][3040/11272] Time 0.925 (0.839) Data 0.002 (0.003) Loss 2.8740 (2.6859) Prec@1 33.125 (35.292) Prec@5 61.875 (65.946) Epoch: [5][3050/11272] Time 0.878 (0.839) Data 0.001 (0.003) Loss 2.5470 (2.6859) Prec@1 37.500 (35.288) Prec@5 66.875 (65.945) Epoch: [5][3060/11272] Time 0.782 (0.839) Data 0.001 (0.003) Loss 2.5627 (2.6858) Prec@1 39.375 (35.288) Prec@5 67.500 (65.955) Epoch: [5][3070/11272] Time 0.750 (0.839) Data 0.002 (0.003) Loss 2.7965 (2.6859) Prec@1 33.750 (35.287) Prec@5 65.625 (65.956) Epoch: [5][3080/11272] Time 0.927 (0.839) Data 0.002 (0.003) Loss 2.8376 (2.6863) Prec@1 37.500 (35.283) Prec@5 61.250 (65.948) Epoch: [5][3090/11272] Time 0.770 (0.839) Data 0.004 (0.003) Loss 2.8476 (2.6865) Prec@1 35.000 (35.285) Prec@5 63.125 (65.942) Epoch: [5][3100/11272] Time 0.761 (0.839) Data 0.002 (0.003) Loss 2.4221 (2.6865) Prec@1 38.125 (35.284) Prec@5 71.250 (65.943) Epoch: [5][3110/11272] Time 0.978 (0.839) Data 0.002 (0.003) Loss 2.8198 (2.6865) Prec@1 34.375 (35.284) Prec@5 62.500 (65.946) Epoch: [5][3120/11272] Time 0.902 (0.839) Data 0.001 (0.003) Loss 2.8285 (2.6865) Prec@1 35.000 (35.287) Prec@5 63.125 (65.948) Epoch: [5][3130/11272] Time 0.781 (0.839) Data 0.003 (0.003) Loss 2.4940 (2.6866) Prec@1 36.250 (35.285) Prec@5 73.750 (65.946) Epoch: [5][3140/11272] Time 0.747 (0.839) Data 0.002 (0.003) Loss 2.9528 (2.6863) Prec@1 31.875 (35.290) Prec@5 60.000 (65.957) Epoch: [5][3150/11272] Time 0.867 (0.839) Data 0.002 (0.003) Loss 2.5322 (2.6863) Prec@1 38.750 (35.293) Prec@5 73.125 (65.959) Epoch: [5][3160/11272] Time 0.946 (0.839) Data 0.002 (0.003) Loss 2.5149 (2.6860) Prec@1 39.375 (35.298) Prec@5 70.625 (65.965) Epoch: [5][3170/11272] Time 0.772 (0.839) Data 0.001 (0.003) Loss 2.9064 (2.6857) Prec@1 35.625 (35.307) Prec@5 60.000 (65.971) Epoch: [5][3180/11272] Time 0.758 (0.839) Data 0.002 (0.003) Loss 2.6808 (2.6859) Prec@1 41.875 (35.307) Prec@5 65.000 (65.965) Epoch: [5][3190/11272] Time 0.850 (0.839) Data 0.001 (0.003) Loss 2.3972 (2.6860) Prec@1 42.500 (35.303) Prec@5 71.875 (65.962) Epoch: [5][3200/11272] Time 0.883 (0.839) Data 0.001 (0.003) Loss 2.6514 (2.6861) Prec@1 33.125 (35.300) Prec@5 66.250 (65.964) Epoch: [5][3210/11272] Time 0.765 (0.840) Data 0.002 (0.003) Loss 2.3512 (2.6858) Prec@1 40.000 (35.301) Prec@5 71.875 (65.970) Epoch: [5][3220/11272] Time 0.928 (0.840) Data 0.002 (0.003) Loss 2.5240 (2.6856) Prec@1 40.000 (35.302) Prec@5 69.375 (65.976) Epoch: [5][3230/11272] Time 0.943 (0.840) Data 0.001 (0.003) Loss 2.8066 (2.6855) Prec@1 24.375 (35.300) Prec@5 63.125 (65.978) Epoch: [5][3240/11272] Time 0.740 (0.840) Data 0.001 (0.003) Loss 2.9298 (2.6855) Prec@1 31.250 (35.305) Prec@5 63.750 (65.981) Epoch: [5][3250/11272] Time 0.739 (0.840) Data 0.001 (0.003) Loss 2.7378 (2.6855) Prec@1 35.625 (35.303) Prec@5 65.000 (65.980) Epoch: [5][3260/11272] Time 0.839 (0.840) Data 0.002 (0.003) Loss 2.6119 (2.6856) Prec@1 37.500 (35.302) Prec@5 66.875 (65.980) Epoch: [5][3270/11272] Time 0.996 (0.840) Data 0.001 (0.003) Loss 2.7677 (2.6857) Prec@1 36.250 (35.296) Prec@5 60.625 (65.973) Epoch: [5][3280/11272] Time 0.752 (0.840) Data 0.002 (0.003) Loss 2.5086 (2.6853) Prec@1 42.500 (35.302) Prec@5 68.125 (65.984) Epoch: [5][3290/11272] Time 0.816 (0.840) Data 0.002 (0.003) Loss 3.0106 (2.6855) Prec@1 26.875 (35.299) Prec@5 57.500 (65.977) Epoch: [5][3300/11272] Time 0.853 (0.840) Data 0.001 (0.003) Loss 2.7604 (2.6853) Prec@1 35.625 (35.301) Prec@5 63.125 (65.982) Epoch: [5][3310/11272] Time 0.933 (0.840) Data 0.002 (0.003) Loss 2.2435 (2.6852) Prec@1 46.250 (35.298) Prec@5 72.500 (65.984) Epoch: [5][3320/11272] Time 0.783 (0.840) Data 0.001 (0.003) Loss 2.5016 (2.6851) Prec@1 36.875 (35.300) Prec@5 68.125 (65.984) Epoch: [5][3330/11272] Time 0.760 (0.840) Data 0.001 (0.003) Loss 2.4896 (2.6852) Prec@1 42.500 (35.303) Prec@5 66.875 (65.983) Epoch: [5][3340/11272] Time 0.905 (0.840) Data 0.001 (0.003) Loss 2.6487 (2.6852) Prec@1 40.000 (35.303) Prec@5 66.250 (65.986) Epoch: [5][3350/11272] Time 0.737 (0.840) Data 0.003 (0.003) Loss 2.3581 (2.6853) Prec@1 40.625 (35.296) Prec@5 71.250 (65.984) Epoch: [5][3360/11272] Time 0.744 (0.840) Data 0.002 (0.003) Loss 2.8709 (2.6856) Prec@1 31.875 (35.296) Prec@5 63.750 (65.980) Epoch: [5][3370/11272] Time 0.887 (0.840) Data 0.001 (0.003) Loss 2.7403 (2.6855) Prec@1 35.000 (35.297) Prec@5 65.625 (65.980) Epoch: [5][3380/11272] Time 0.891 (0.840) Data 0.001 (0.003) Loss 2.6177 (2.6855) Prec@1 33.125 (35.298) Prec@5 66.875 (65.984) Epoch: [5][3390/11272] Time 0.745 (0.840) Data 0.001 (0.003) Loss 2.5628 (2.6856) Prec@1 40.000 (35.298) Prec@5 70.625 (65.986) Epoch: [5][3400/11272] Time 0.784 (0.840) Data 0.002 (0.003) Loss 2.7778 (2.6857) Prec@1 31.875 (35.297) Prec@5 61.250 (65.981) Epoch: [5][3410/11272] Time 0.876 (0.839) Data 0.001 (0.003) Loss 2.7409 (2.6855) Prec@1 33.750 (35.296) Prec@5 65.000 (65.983) Epoch: [5][3420/11272] Time 0.849 (0.839) Data 0.002 (0.003) Loss 2.8572 (2.6856) Prec@1 29.375 (35.299) Prec@5 63.125 (65.982) Epoch: [5][3430/11272] Time 0.741 (0.839) Data 0.002 (0.003) Loss 2.8860 (2.6856) Prec@1 32.500 (35.299) Prec@5 63.125 (65.980) Epoch: [5][3440/11272] Time 0.752 (0.839) Data 0.002 (0.003) Loss 2.7634 (2.6856) Prec@1 36.875 (35.297) Prec@5 65.000 (65.979) Epoch: [5][3450/11272] Time 0.886 (0.839) Data 0.002 (0.003) Loss 2.4106 (2.6854) Prec@1 41.875 (35.304) Prec@5 71.250 (65.981) Epoch: [5][3460/11272] Time 0.906 (0.839) Data 0.002 (0.003) Loss 2.4646 (2.6851) Prec@1 36.875 (35.307) Prec@5 65.000 (65.987) Epoch: [5][3470/11272] Time 0.764 (0.839) Data 0.002 (0.003) Loss 2.6773 (2.6851) Prec@1 33.750 (35.303) Prec@5 65.000 (65.985) Epoch: [5][3480/11272] Time 0.969 (0.839) Data 0.002 (0.003) Loss 2.8834 (2.6853) Prec@1 36.250 (35.302) Prec@5 63.750 (65.984) Epoch: [5][3490/11272] Time 0.872 (0.839) Data 0.002 (0.003) Loss 2.9420 (2.6855) Prec@1 35.000 (35.299) Prec@5 60.625 (65.983) Epoch: [5][3500/11272] Time 0.806 (0.839) Data 0.002 (0.003) Loss 2.9245 (2.6856) Prec@1 34.375 (35.302) Prec@5 59.375 (65.982) Epoch: [5][3510/11272] Time 0.812 (0.839) Data 0.002 (0.003) Loss 2.6575 (2.6858) Prec@1 34.375 (35.298) Prec@5 63.750 (65.974) Epoch: [5][3520/11272] Time 0.943 (0.839) Data 0.002 (0.003) Loss 2.6364 (2.6857) Prec@1 36.875 (35.299) Prec@5 65.625 (65.972) Epoch: [5][3530/11272] Time 0.919 (0.839) Data 0.003 (0.003) Loss 2.6821 (2.6857) Prec@1 36.250 (35.299) Prec@5 66.250 (65.971) Epoch: [5][3540/11272] Time 0.741 (0.839) Data 0.002 (0.003) Loss 2.6085 (2.6857) Prec@1 35.625 (35.299) Prec@5 63.125 (65.972) Epoch: [5][3550/11272] Time 0.733 (0.839) Data 0.002 (0.003) Loss 2.8921 (2.6858) Prec@1 36.875 (35.298) Prec@5 61.875 (65.972) Epoch: [5][3560/11272] Time 0.936 (0.839) Data 0.002 (0.003) Loss 2.8016 (2.6856) Prec@1 35.625 (35.298) Prec@5 62.500 (65.976) Epoch: [5][3570/11272] Time 0.872 (0.839) Data 0.002 (0.003) Loss 2.4960 (2.6855) Prec@1 44.375 (35.304) Prec@5 70.625 (65.976) Epoch: [5][3580/11272] Time 0.797 (0.839) Data 0.002 (0.003) Loss 2.7515 (2.6856) Prec@1 36.250 (35.305) Prec@5 65.000 (65.973) Epoch: [5][3590/11272] Time 0.773 (0.839) Data 0.002 (0.003) Loss 2.4309 (2.6855) Prec@1 41.875 (35.312) Prec@5 69.375 (65.976) Epoch: [5][3600/11272] Time 0.871 (0.840) Data 0.002 (0.003) Loss 2.6775 (2.6855) Prec@1 36.875 (35.313) Prec@5 60.625 (65.974) Epoch: [5][3610/11272] Time 0.890 (0.840) Data 0.002 (0.003) Loss 2.5874 (2.6855) Prec@1 38.125 (35.312) Prec@5 68.750 (65.974) Epoch: [5][3620/11272] Time 0.754 (0.840) Data 0.002 (0.003) Loss 2.5693 (2.6853) Prec@1 35.625 (35.315) Prec@5 70.000 (65.975) Epoch: [5][3630/11272] Time 0.928 (0.840) Data 0.002 (0.003) Loss 2.6376 (2.6856) Prec@1 38.125 (35.312) Prec@5 65.000 (65.968) Epoch: [5][3640/11272] Time 0.935 (0.840) Data 0.002 (0.003) Loss 2.7930 (2.6857) Prec@1 38.125 (35.312) Prec@5 62.500 (65.964) Epoch: [5][3650/11272] Time 0.782 (0.839) Data 0.002 (0.003) Loss 2.7050 (2.6858) Prec@1 38.750 (35.310) Prec@5 63.750 (65.962) Epoch: [5][3660/11272] Time 0.751 (0.839) Data 0.002 (0.003) Loss 2.9580 (2.6860) Prec@1 36.250 (35.310) Prec@5 61.875 (65.960) Epoch: [5][3670/11272] Time 0.888 (0.839) Data 0.002 (0.003) Loss 2.7129 (2.6859) Prec@1 37.500 (35.316) Prec@5 70.000 (65.964) Epoch: [5][3680/11272] Time 0.852 (0.839) Data 0.001 (0.003) Loss 2.6037 (2.6859) Prec@1 31.875 (35.316) Prec@5 64.375 (65.964) Epoch: [5][3690/11272] Time 0.734 (0.839) Data 0.001 (0.003) Loss 2.8377 (2.6859) Prec@1 35.000 (35.314) Prec@5 61.250 (65.966) Epoch: [5][3700/11272] Time 0.757 (0.839) Data 0.002 (0.003) Loss 2.6570 (2.6859) Prec@1 35.000 (35.319) Prec@5 62.500 (65.965) Epoch: [5][3710/11272] Time 0.895 (0.839) Data 0.002 (0.003) Loss 2.5047 (2.6857) Prec@1 39.375 (35.324) Prec@5 70.000 (65.971) Epoch: [5][3720/11272] Time 0.956 (0.839) Data 0.002 (0.003) Loss 2.7928 (2.6858) Prec@1 32.500 (35.320) Prec@5 64.375 (65.972) Epoch: [5][3730/11272] Time 0.742 (0.839) Data 0.001 (0.003) Loss 2.8812 (2.6859) Prec@1 31.250 (35.323) Prec@5 60.000 (65.970) Epoch: [5][3740/11272] Time 0.780 (0.839) Data 0.003 (0.003) Loss 2.7071 (2.6859) Prec@1 31.875 (35.319) Prec@5 65.000 (65.972) Epoch: [5][3750/11272] Time 0.900 (0.839) Data 0.002 (0.003) Loss 2.4704 (2.6860) Prec@1 45.625 (35.322) Prec@5 68.750 (65.968) Epoch: [5][3760/11272] Time 0.786 (0.839) Data 0.002 (0.003) Loss 2.7926 (2.6860) Prec@1 37.500 (35.322) Prec@5 64.375 (65.965) Epoch: [5][3770/11272] Time 0.762 (0.839) Data 0.001 (0.003) Loss 2.6875 (2.6862) Prec@1 35.000 (35.321) Prec@5 67.500 (65.961) Epoch: [5][3780/11272] Time 0.872 (0.839) Data 0.002 (0.003) Loss 2.9107 (2.6862) Prec@1 32.500 (35.323) Prec@5 62.500 (65.957) Epoch: [5][3790/11272] Time 0.854 (0.839) Data 0.001 (0.003) Loss 2.7054 (2.6860) Prec@1 33.125 (35.321) Prec@5 64.375 (65.962) Epoch: [5][3800/11272] Time 0.763 (0.839) Data 0.002 (0.003) Loss 3.0440 (2.6862) Prec@1 31.875 (35.319) Prec@5 60.000 (65.958) Epoch: [5][3810/11272] Time 0.754 (0.839) Data 0.002 (0.003) Loss 2.4636 (2.6862) Prec@1 41.875 (35.320) Prec@5 71.250 (65.960) Epoch: [5][3820/11272] Time 0.911 (0.839) Data 0.002 (0.003) Loss 2.6945 (2.6861) Prec@1 38.125 (35.322) Prec@5 68.125 (65.962) Epoch: [5][3830/11272] Time 0.871 (0.839) Data 0.002 (0.003) Loss 2.6407 (2.6862) Prec@1 30.625 (35.324) Prec@5 67.500 (65.959) Epoch: [5][3840/11272] Time 0.759 (0.839) Data 0.001 (0.003) Loss 2.6425 (2.6861) Prec@1 40.000 (35.324) Prec@5 68.125 (65.962) Epoch: [5][3850/11272] Time 0.786 (0.839) Data 0.002 (0.003) Loss 2.7666 (2.6862) Prec@1 35.625 (35.321) Prec@5 63.125 (65.957) Epoch: [5][3860/11272] Time 0.936 (0.839) Data 0.002 (0.003) Loss 2.7956 (2.6863) Prec@1 30.000 (35.319) Prec@5 64.375 (65.955) Epoch: [5][3870/11272] Time 0.856 (0.839) Data 0.001 (0.003) Loss 2.8098 (2.6863) Prec@1 35.000 (35.318) Prec@5 61.250 (65.954) Epoch: [5][3880/11272] Time 0.773 (0.839) Data 0.001 (0.003) Loss 2.9930 (2.6867) Prec@1 33.750 (35.317) Prec@5 61.250 (65.945) Epoch: [5][3890/11272] Time 0.894 (0.839) Data 0.002 (0.003) Loss 3.0032 (2.6869) Prec@1 28.125 (35.314) Prec@5 61.875 (65.941) Epoch: [5][3900/11272] Time 0.924 (0.839) Data 0.002 (0.003) Loss 2.5684 (2.6870) Prec@1 37.500 (35.318) Prec@5 65.000 (65.940) Epoch: [5][3910/11272] Time 0.745 (0.839) Data 0.001 (0.003) Loss 2.5032 (2.6867) Prec@1 38.125 (35.323) Prec@5 70.000 (65.947) Epoch: [5][3920/11272] Time 0.713 (0.839) Data 0.001 (0.003) Loss 2.8613 (2.6867) Prec@1 28.750 (35.320) Prec@5 61.875 (65.944) Epoch: [5][3930/11272] Time 0.885 (0.839) Data 0.001 (0.003) Loss 2.7923 (2.6870) Prec@1 30.625 (35.316) Prec@5 61.875 (65.940) Epoch: [5][3940/11272] Time 0.848 (0.839) Data 0.001 (0.003) Loss 2.7439 (2.6870) Prec@1 37.500 (35.316) Prec@5 62.500 (65.942) Epoch: [5][3950/11272] Time 0.743 (0.839) Data 0.001 (0.003) Loss 2.6600 (2.6870) Prec@1 35.000 (35.316) Prec@5 69.375 (65.944) Epoch: [5][3960/11272] Time 0.797 (0.839) Data 0.003 (0.003) Loss 2.6214 (2.6870) Prec@1 36.875 (35.315) Prec@5 63.125 (65.942) Epoch: [5][3970/11272] Time 0.920 (0.839) Data 0.002 (0.003) Loss 2.7112 (2.6868) Prec@1 39.375 (35.319) Prec@5 69.375 (65.947) Epoch: [5][3980/11272] Time 0.893 (0.839) Data 0.001 (0.003) Loss 2.9219 (2.6867) Prec@1 29.375 (35.322) Prec@5 60.000 (65.948) Epoch: [5][3990/11272] Time 0.749 (0.839) Data 0.002 (0.003) Loss 2.5107 (2.6867) Prec@1 36.875 (35.319) Prec@5 68.750 (65.951) Epoch: [5][4000/11272] Time 0.735 (0.839) Data 0.001 (0.003) Loss 2.7563 (2.6866) Prec@1 39.375 (35.326) Prec@5 63.125 (65.951) Epoch: [5][4010/11272] Time 0.893 (0.839) Data 0.002 (0.003) Loss 2.4197 (2.6865) Prec@1 40.000 (35.326) Prec@5 68.125 (65.954) Epoch: [5][4020/11272] Time 0.753 (0.839) Data 0.003 (0.003) Loss 2.6427 (2.6866) Prec@1 32.500 (35.323) Prec@5 66.250 (65.952) Epoch: [5][4030/11272] Time 0.794 (0.839) Data 0.002 (0.003) Loss 2.7560 (2.6867) Prec@1 38.125 (35.324) Prec@5 65.000 (65.953) Epoch: [5][4040/11272] Time 0.908 (0.839) Data 0.002 (0.003) Loss 2.3918 (2.6865) Prec@1 37.500 (35.323) Prec@5 71.250 (65.954) Epoch: [5][4050/11272] Time 0.894 (0.839) Data 0.003 (0.003) Loss 2.5584 (2.6865) Prec@1 38.750 (35.323) Prec@5 66.875 (65.953) Epoch: [5][4060/11272] Time 0.741 (0.839) Data 0.002 (0.003) Loss 2.8693 (2.6864) Prec@1 35.625 (35.323) Prec@5 63.125 (65.955) Epoch: [5][4070/11272] Time 0.750 (0.839) Data 0.002 (0.003) Loss 2.7375 (2.6863) Prec@1 33.125 (35.327) Prec@5 62.500 (65.953) Epoch: [5][4080/11272] Time 0.849 (0.839) Data 0.002 (0.003) Loss 2.5738 (2.6862) Prec@1 36.250 (35.328) Prec@5 67.500 (65.953) Epoch: [5][4090/11272] Time 0.872 (0.839) Data 0.002 (0.003) Loss 2.7915 (2.6862) Prec@1 34.375 (35.329) Prec@5 65.000 (65.953) Epoch: [5][4100/11272] Time 0.750 (0.839) Data 0.002 (0.003) Loss 2.6956 (2.6863) Prec@1 33.750 (35.328) Prec@5 63.750 (65.951) Epoch: [5][4110/11272] Time 0.763 (0.838) Data 0.001 (0.003) Loss 2.9662 (2.6864) Prec@1 31.875 (35.324) Prec@5 58.750 (65.952) Epoch: [5][4120/11272] Time 0.908 (0.838) Data 0.002 (0.003) Loss 2.6814 (2.6865) Prec@1 36.250 (35.320) Prec@5 70.000 (65.952) Epoch: [5][4130/11272] Time 0.965 (0.838) Data 0.002 (0.003) Loss 2.3473 (2.6863) Prec@1 38.125 (35.323) Prec@5 75.000 (65.958) Epoch: [5][4140/11272] Time 0.745 (0.838) Data 0.001 (0.003) Loss 2.7048 (2.6864) Prec@1 33.125 (35.323) Prec@5 65.625 (65.957) Epoch: [5][4150/11272] Time 0.908 (0.838) Data 0.002 (0.003) Loss 2.4410 (2.6863) Prec@1 39.375 (35.323) Prec@5 69.375 (65.958) Epoch: [5][4160/11272] Time 0.912 (0.838) Data 0.001 (0.003) Loss 2.6517 (2.6862) Prec@1 36.250 (35.326) Prec@5 64.375 (65.960) Epoch: [5][4170/11272] Time 0.779 (0.838) Data 0.001 (0.003) Loss 2.4160 (2.6861) Prec@1 41.250 (35.328) Prec@5 73.125 (65.962) Epoch: [5][4180/11272] Time 0.747 (0.838) Data 0.001 (0.003) Loss 2.6354 (2.6860) Prec@1 37.500 (35.335) Prec@5 70.625 (65.965) Epoch: [5][4190/11272] Time 0.879 (0.839) Data 0.001 (0.003) Loss 2.6264 (2.6860) Prec@1 35.000 (35.337) Prec@5 66.250 (65.967) Epoch: [5][4200/11272] Time 0.947 (0.838) Data 0.002 (0.003) Loss 2.5466 (2.6860) Prec@1 39.375 (35.334) Prec@5 68.750 (65.965) Epoch: [5][4210/11272] Time 0.781 (0.838) Data 0.001 (0.003) Loss 2.4136 (2.6859) Prec@1 40.000 (35.335) Prec@5 68.125 (65.967) Epoch: [5][4220/11272] Time 0.794 (0.838) Data 0.002 (0.003) Loss 2.4312 (2.6858) Prec@1 40.625 (35.336) Prec@5 70.000 (65.967) Epoch: [5][4230/11272] Time 0.865 (0.838) Data 0.001 (0.003) Loss 2.5266 (2.6857) Prec@1 41.875 (35.336) Prec@5 70.000 (65.969) Epoch: [5][4240/11272] Time 0.858 (0.838) Data 0.001 (0.003) Loss 2.4734 (2.6857) Prec@1 36.250 (35.334) Prec@5 71.875 (65.973) Epoch: [5][4250/11272] Time 0.771 (0.838) Data 0.002 (0.003) Loss 2.6517 (2.6856) Prec@1 35.625 (35.336) Prec@5 66.875 (65.978) Epoch: [5][4260/11272] Time 0.746 (0.838) Data 0.001 (0.003) Loss 2.6752 (2.6857) Prec@1 37.500 (35.337) Prec@5 65.000 (65.976) Epoch: [5][4270/11272] Time 0.890 (0.838) Data 0.002 (0.003) Loss 2.9457 (2.6857) Prec@1 38.125 (35.340) Prec@5 61.250 (65.976) Epoch: [5][4280/11272] Time 0.754 (0.838) Data 0.004 (0.003) Loss 2.4624 (2.6854) Prec@1 41.875 (35.347) Prec@5 73.750 (65.982) Epoch: [5][4290/11272] Time 0.744 (0.838) Data 0.001 (0.003) Loss 2.5818 (2.6855) Prec@1 34.375 (35.348) Prec@5 68.125 (65.979) Epoch: [5][4300/11272] Time 0.898 (0.838) Data 0.002 (0.003) Loss 2.6788 (2.6856) Prec@1 33.750 (35.345) Prec@5 66.250 (65.976) Epoch: [5][4310/11272] Time 0.932 (0.838) Data 0.001 (0.003) Loss 2.6358 (2.6857) Prec@1 40.000 (35.343) Prec@5 65.625 (65.973) Epoch: [5][4320/11272] Time 0.762 (0.838) Data 0.002 (0.003) Loss 2.6230 (2.6858) Prec@1 36.875 (35.342) Prec@5 71.250 (65.970) Epoch: [5][4330/11272] Time 0.770 (0.838) Data 0.001 (0.003) Loss 2.4141 (2.6856) Prec@1 46.250 (35.346) Prec@5 67.500 (65.976) Epoch: [5][4340/11272] Time 0.840 (0.838) Data 0.001 (0.003) Loss 2.8456 (2.6855) Prec@1 32.500 (35.344) Prec@5 63.125 (65.981) Epoch: [5][4350/11272] Time 0.927 (0.838) Data 0.001 (0.003) Loss 2.5689 (2.6853) Prec@1 40.625 (35.347) Prec@5 69.375 (65.985) Epoch: [5][4360/11272] Time 0.741 (0.838) Data 0.001 (0.003) Loss 2.3399 (2.6851) Prec@1 40.000 (35.350) Prec@5 75.625 (65.993) Epoch: [5][4370/11272] Time 0.798 (0.838) Data 0.001 (0.003) Loss 2.5988 (2.6851) Prec@1 40.625 (35.352) Prec@5 68.750 (65.994) Epoch: [5][4380/11272] Time 0.865 (0.838) Data 0.001 (0.003) Loss 2.7689 (2.6850) Prec@1 34.375 (35.357) Prec@5 62.500 (65.992) Epoch: [5][4390/11272] Time 0.928 (0.838) Data 0.001 (0.003) Loss 2.6329 (2.6849) Prec@1 33.125 (35.364) Prec@5 68.750 (65.993) Epoch: [5][4400/11272] Time 0.781 (0.838) Data 0.001 (0.003) Loss 2.6920 (2.6849) Prec@1 33.750 (35.366) Prec@5 65.625 (65.992) Epoch: [5][4410/11272] Time 0.948 (0.838) Data 0.002 (0.003) Loss 2.6376 (2.6850) Prec@1 40.625 (35.369) Prec@5 66.250 (65.990) Epoch: [5][4420/11272] Time 0.914 (0.838) Data 0.001 (0.003) Loss 2.9071 (2.6852) Prec@1 31.250 (35.364) Prec@5 65.625 (65.985) Epoch: [5][4430/11272] Time 0.769 (0.838) Data 0.001 (0.003) Loss 2.6611 (2.6852) Prec@1 42.500 (35.366) Prec@5 60.625 (65.982) Epoch: [5][4440/11272] Time 0.727 (0.838) Data 0.001 (0.003) Loss 2.5781 (2.6853) Prec@1 34.375 (35.364) Prec@5 67.500 (65.979) Epoch: [5][4450/11272] Time 0.887 (0.838) Data 0.001 (0.003) Loss 2.5329 (2.6853) Prec@1 43.750 (35.360) Prec@5 69.375 (65.979) Epoch: [5][4460/11272] Time 0.889 (0.838) Data 0.002 (0.003) Loss 2.6465 (2.6854) Prec@1 31.875 (35.355) Prec@5 67.500 (65.974) Epoch: [5][4470/11272] Time 0.770 (0.838) Data 0.002 (0.003) Loss 2.7253 (2.6855) Prec@1 37.500 (35.355) Prec@5 63.750 (65.971) Epoch: [5][4480/11272] Time 0.746 (0.838) Data 0.002 (0.003) Loss 2.6977 (2.6856) Prec@1 40.625 (35.355) Prec@5 63.750 (65.966) Epoch: [5][4490/11272] Time 0.862 (0.838) Data 0.001 (0.003) Loss 2.6565 (2.6856) Prec@1 36.250 (35.356) Prec@5 68.125 (65.965) Epoch: [5][4500/11272] Time 0.888 (0.838) Data 0.002 (0.003) Loss 2.9098 (2.6858) Prec@1 26.875 (35.352) Prec@5 61.250 (65.961) Epoch: [5][4510/11272] Time 0.763 (0.838) Data 0.002 (0.003) Loss 2.8109 (2.6857) Prec@1 34.375 (35.356) Prec@5 60.000 (65.963) Epoch: [5][4520/11272] Time 0.754 (0.838) Data 0.001 (0.003) Loss 2.5733 (2.6857) Prec@1 38.125 (35.356) Prec@5 66.875 (65.961) Epoch: [5][4530/11272] Time 0.892 (0.838) Data 0.001 (0.003) Loss 2.6815 (2.6858) Prec@1 38.750 (35.355) Prec@5 63.125 (65.959) Epoch: [5][4540/11272] Time 0.867 (0.838) Data 0.002 (0.003) Loss 2.7240 (2.6858) Prec@1 34.375 (35.357) Prec@5 64.375 (65.956) Epoch: [5][4550/11272] Time 0.748 (0.838) Data 0.001 (0.003) Loss 2.5550 (2.6859) Prec@1 38.125 (35.356) Prec@5 70.625 (65.956) Epoch: [5][4560/11272] Time 0.865 (0.838) Data 0.002 (0.003) Loss 2.6672 (2.6858) Prec@1 33.125 (35.354) Prec@5 69.375 (65.958) Epoch: [5][4570/11272] Time 0.945 (0.838) Data 0.002 (0.003) Loss 2.6883 (2.6857) Prec@1 35.625 (35.357) Prec@5 66.250 (65.963) Epoch: [5][4580/11272] Time 0.749 (0.838) Data 0.001 (0.003) Loss 2.4751 (2.6853) Prec@1 37.500 (35.365) Prec@5 70.000 (65.970) Epoch: [5][4590/11272] Time 0.751 (0.838) Data 0.001 (0.003) Loss 2.6203 (2.6853) Prec@1 36.875 (35.363) Prec@5 67.500 (65.969) Epoch: [5][4600/11272] Time 0.869 (0.838) Data 0.001 (0.003) Loss 2.7641 (2.6854) Prec@1 35.625 (35.362) Prec@5 64.375 (65.968) Epoch: [5][4610/11272] Time 0.854 (0.838) Data 0.001 (0.003) Loss 2.6589 (2.6855) Prec@1 38.750 (35.363) Prec@5 64.375 (65.967) Epoch: [5][4620/11272] Time 0.772 (0.838) Data 0.001 (0.003) Loss 2.3395 (2.6853) Prec@1 41.875 (35.367) Prec@5 72.500 (65.970) Epoch: [5][4630/11272] Time 0.805 (0.838) Data 0.001 (0.003) Loss 2.5678 (2.6853) Prec@1 38.750 (35.366) Prec@5 68.750 (65.973) Epoch: [5][4640/11272] Time 0.911 (0.838) Data 0.001 (0.003) Loss 2.7238 (2.6854) Prec@1 37.500 (35.362) Prec@5 65.625 (65.968) Epoch: [5][4650/11272] Time 0.948 (0.838) Data 0.002 (0.003) Loss 2.8777 (2.6853) Prec@1 29.375 (35.360) Prec@5 59.375 (65.971) Epoch: [5][4660/11272] Time 0.785 (0.838) Data 0.002 (0.002) Loss 2.7778 (2.6854) Prec@1 31.875 (35.360) Prec@5 64.375 (65.970) Epoch: [5][4670/11272] Time 0.771 (0.838) Data 0.002 (0.002) Loss 2.7807 (2.6854) Prec@1 38.750 (35.360) Prec@5 63.125 (65.972) Epoch: [5][4680/11272] Time 0.933 (0.838) Data 0.002 (0.002) Loss 3.0177 (2.6855) Prec@1 32.500 (35.358) Prec@5 58.750 (65.968) Epoch: [5][4690/11272] Time 0.794 (0.838) Data 0.001 (0.002) Loss 2.5347 (2.6853) Prec@1 42.500 (35.364) Prec@5 74.375 (65.972) Epoch: [5][4700/11272] Time 0.762 (0.838) Data 0.002 (0.002) Loss 2.6770 (2.6853) Prec@1 33.125 (35.363) Prec@5 63.125 (65.970) Epoch: [5][4710/11272] Time 0.949 (0.838) Data 0.002 (0.002) Loss 2.4050 (2.6854) Prec@1 40.625 (35.366) Prec@5 68.125 (65.966) Epoch: [5][4720/11272] Time 0.924 (0.838) Data 0.002 (0.002) Loss 2.7672 (2.6854) Prec@1 35.000 (35.367) Prec@5 66.875 (65.966) Epoch: [5][4730/11272] Time 0.772 (0.838) Data 0.003 (0.002) Loss 2.8704 (2.6854) Prec@1 31.875 (35.366) Prec@5 63.750 (65.966) Epoch: [5][4740/11272] Time 0.760 (0.838) Data 0.001 (0.002) Loss 2.4833 (2.6854) Prec@1 38.750 (35.368) Prec@5 72.500 (65.967) Epoch: [5][4750/11272] Time 0.886 (0.838) Data 0.002 (0.002) Loss 2.6922 (2.6854) Prec@1 35.625 (35.368) Prec@5 63.750 (65.966) Epoch: [5][4760/11272] Time 0.903 (0.838) Data 0.001 (0.002) Loss 2.9687 (2.6855) Prec@1 28.125 (35.365) Prec@5 61.250 (65.964) Epoch: [5][4770/11272] Time 0.758 (0.838) Data 0.001 (0.002) Loss 2.6045 (2.6856) Prec@1 38.125 (35.364) Prec@5 66.250 (65.963) Epoch: [5][4780/11272] Time 0.787 (0.838) Data 0.002 (0.002) Loss 2.6457 (2.6857) Prec@1 36.250 (35.364) Prec@5 68.750 (65.962) Epoch: [5][4790/11272] Time 0.930 (0.838) Data 0.001 (0.002) Loss 2.7939 (2.6858) Prec@1 31.250 (35.363) Prec@5 62.500 (65.959) Epoch: [5][4800/11272] Time 0.846 (0.838) Data 0.001 (0.002) Loss 2.6073 (2.6857) Prec@1 33.750 (35.362) Prec@5 68.750 (65.964) Epoch: [5][4810/11272] Time 0.787 (0.838) Data 0.002 (0.002) Loss 2.7457 (2.6856) Prec@1 36.250 (35.367) Prec@5 66.875 (65.966) Epoch: [5][4820/11272] Time 0.906 (0.838) Data 0.003 (0.002) Loss 2.9108 (2.6857) Prec@1 31.250 (35.366) Prec@5 59.375 (65.966) Epoch: [5][4830/11272] Time 0.917 (0.838) Data 0.002 (0.002) Loss 2.8036 (2.6857) Prec@1 33.125 (35.366) Prec@5 63.125 (65.966) Epoch: [5][4840/11272] Time 0.774 (0.838) Data 0.001 (0.002) Loss 2.9026 (2.6858) Prec@1 30.000 (35.362) Prec@5 64.375 (65.961) Epoch: [5][4850/11272] Time 0.743 (0.838) Data 0.001 (0.002) Loss 2.5873 (2.6858) Prec@1 34.375 (35.362) Prec@5 65.625 (65.960) Epoch: [5][4860/11272] Time 0.943 (0.838) Data 0.002 (0.002) Loss 2.9367 (2.6860) Prec@1 34.375 (35.359) Prec@5 63.125 (65.954) Epoch: [5][4870/11272] Time 0.872 (0.838) Data 0.001 (0.002) Loss 2.7071 (2.6860) Prec@1 32.500 (35.362) Prec@5 66.875 (65.952) Epoch: [5][4880/11272] Time 0.745 (0.838) Data 0.001 (0.002) Loss 2.8570 (2.6860) Prec@1 30.625 (35.364) Prec@5 58.750 (65.951) Epoch: [5][4890/11272] Time 0.747 (0.838) Data 0.002 (0.002) Loss 2.6897 (2.6860) Prec@1 36.250 (35.360) Prec@5 65.000 (65.952) Epoch: [5][4900/11272] Time 0.852 (0.837) Data 0.001 (0.002) Loss 2.5730 (2.6859) Prec@1 34.375 (35.362) Prec@5 69.375 (65.951) Epoch: [5][4910/11272] Time 0.863 (0.837) Data 0.002 (0.002) Loss 2.7448 (2.6859) Prec@1 35.625 (35.361) Prec@5 65.000 (65.951) Epoch: [5][4920/11272] Time 0.743 (0.837) Data 0.001 (0.002) Loss 2.5496 (2.6859) Prec@1 35.625 (35.361) Prec@5 68.125 (65.952) Epoch: [5][4930/11272] Time 0.786 (0.837) Data 0.002 (0.002) Loss 2.7546 (2.6859) Prec@1 32.500 (35.365) Prec@5 63.750 (65.952) Epoch: [5][4940/11272] Time 0.853 (0.837) Data 0.001 (0.002) Loss 2.7306 (2.6859) Prec@1 30.625 (35.367) Prec@5 63.125 (65.952) Epoch: [5][4950/11272] Time 0.789 (0.837) Data 0.004 (0.002) Loss 2.6677 (2.6860) Prec@1 33.125 (35.366) Prec@5 68.125 (65.950) Epoch: [5][4960/11272] Time 0.736 (0.837) Data 0.001 (0.002) Loss 2.4096 (2.6858) Prec@1 46.250 (35.367) Prec@5 70.625 (65.952) Epoch: [5][4970/11272] Time 0.989 (0.837) Data 0.002 (0.002) Loss 2.5321 (2.6857) Prec@1 43.750 (35.367) Prec@5 73.125 (65.956) Epoch: [5][4980/11272] Time 0.870 (0.837) Data 0.002 (0.002) Loss 2.7722 (2.6859) Prec@1 36.875 (35.363) Prec@5 65.625 (65.951) Epoch: [5][4990/11272] Time 0.757 (0.837) Data 0.002 (0.002) Loss 2.5611 (2.6858) Prec@1 35.000 (35.365) Prec@5 70.000 (65.954) Epoch: [5][5000/11272] Time 0.788 (0.837) Data 0.002 (0.002) Loss 2.7317 (2.6858) Prec@1 36.250 (35.368) Prec@5 64.375 (65.956) Epoch: [5][5010/11272] Time 0.947 (0.837) Data 0.001 (0.002) Loss 2.4042 (2.6858) Prec@1 36.875 (35.364) Prec@5 75.000 (65.956) Epoch: [5][5020/11272] Time 0.895 (0.837) Data 0.002 (0.002) Loss 2.7083 (2.6859) Prec@1 33.750 (35.364) Prec@5 61.250 (65.954) Epoch: [5][5030/11272] Time 0.758 (0.837) Data 0.001 (0.002) Loss 2.8831 (2.6861) Prec@1 32.500 (35.361) Prec@5 60.625 (65.949) Epoch: [5][5040/11272] Time 0.739 (0.837) Data 0.002 (0.002) Loss 2.6521 (2.6861) Prec@1 33.125 (35.363) Prec@5 63.750 (65.948) Epoch: [5][5050/11272] Time 0.878 (0.837) Data 0.002 (0.002) Loss 2.7226 (2.6862) Prec@1 33.750 (35.364) Prec@5 61.875 (65.947) Epoch: [5][5060/11272] Time 0.876 (0.837) Data 0.001 (0.002) Loss 2.9709 (2.6862) Prec@1 27.500 (35.363) Prec@5 63.125 (65.945) Epoch: [5][5070/11272] Time 0.754 (0.837) Data 0.001 (0.002) Loss 2.9711 (2.6863) Prec@1 31.250 (35.363) Prec@5 59.375 (65.944) Epoch: [5][5080/11272] Time 0.984 (0.837) Data 0.002 (0.002) Loss 2.5730 (2.6863) Prec@1 35.000 (35.362) Prec@5 68.125 (65.944) Epoch: [5][5090/11272] Time 0.896 (0.837) Data 0.002 (0.002) Loss 2.7621 (2.6863) Prec@1 35.000 (35.366) Prec@5 63.750 (65.944) Epoch: [5][5100/11272] Time 0.763 (0.837) Data 0.002 (0.002) Loss 2.9303 (2.6863) Prec@1 27.500 (35.365) Prec@5 65.625 (65.944) Epoch: [5][5110/11272] Time 0.795 (0.837) Data 0.002 (0.002) Loss 2.5493 (2.6862) Prec@1 38.125 (35.367) Prec@5 71.875 (65.944) Epoch: [5][5120/11272] Time 0.892 (0.837) Data 0.002 (0.002) Loss 2.4980 (2.6861) Prec@1 41.250 (35.371) Prec@5 66.875 (65.945) Epoch: [5][5130/11272] Time 0.965 (0.837) Data 0.002 (0.002) Loss 2.6605 (2.6861) Prec@1 33.125 (35.368) Prec@5 67.500 (65.943) Epoch: [5][5140/11272] Time 0.778 (0.837) Data 0.001 (0.002) Loss 2.4304 (2.6860) Prec@1 38.750 (35.370) Prec@5 71.250 (65.944) Epoch: [5][5150/11272] Time 0.738 (0.837) Data 0.001 (0.002) Loss 2.5928 (2.6859) Prec@1 35.625 (35.369) Prec@5 64.375 (65.943) Epoch: [5][5160/11272] Time 0.902 (0.837) Data 0.001 (0.002) Loss 2.6720 (2.6859) Prec@1 36.875 (35.369) Prec@5 66.250 (65.945) Epoch: [5][5170/11272] Time 0.889 (0.837) Data 0.002 (0.002) Loss 2.6105 (2.6860) Prec@1 32.500 (35.368) Prec@5 69.375 (65.946) Epoch: [5][5180/11272] Time 0.762 (0.837) Data 0.002 (0.002) Loss 2.8108 (2.6861) Prec@1 30.625 (35.365) Prec@5 65.625 (65.944) Epoch: [5][5190/11272] Time 0.775 (0.837) Data 0.002 (0.002) Loss 2.6634 (2.6859) Prec@1 35.625 (35.367) Prec@5 68.125 (65.950) Epoch: [5][5200/11272] Time 0.964 (0.837) Data 0.002 (0.002) Loss 2.8279 (2.6859) Prec@1 32.500 (35.365) Prec@5 62.500 (65.947) Epoch: [5][5210/11272] Time 0.740 (0.837) Data 0.004 (0.002) Loss 2.9344 (2.6859) Prec@1 31.875 (35.365) Prec@5 56.875 (65.947) Epoch: [5][5220/11272] Time 0.741 (0.837) Data 0.001 (0.002) Loss 2.6479 (2.6859) Prec@1 36.250 (35.365) Prec@5 63.125 (65.947) Epoch: [5][5230/11272] Time 0.916 (0.837) Data 0.002 (0.002) Loss 2.6422 (2.6859) Prec@1 33.750 (35.363) Prec@5 68.125 (65.948) Epoch: [5][5240/11272] Time 0.885 (0.837) Data 0.002 (0.002) Loss 3.0263 (2.6860) Prec@1 34.375 (35.364) Prec@5 62.500 (65.946) Epoch: [5][5250/11272] Time 0.770 (0.837) Data 0.002 (0.002) Loss 2.7945 (2.6861) Prec@1 33.750 (35.361) Prec@5 62.500 (65.944) Epoch: [5][5260/11272] Time 0.751 (0.837) Data 0.002 (0.002) Loss 3.0570 (2.6862) Prec@1 30.000 (35.360) Prec@5 60.000 (65.944) Epoch: [5][5270/11272] Time 0.901 (0.837) Data 0.002 (0.002) Loss 2.6400 (2.6862) Prec@1 38.125 (35.359) Prec@5 66.250 (65.944) Epoch: [5][5280/11272] Time 0.862 (0.837) Data 0.001 (0.002) Loss 2.4329 (2.6862) Prec@1 37.500 (35.359) Prec@5 72.500 (65.945) Epoch: [5][5290/11272] Time 0.763 (0.837) Data 0.001 (0.002) Loss 2.5414 (2.6860) Prec@1 39.375 (35.362) Prec@5 68.750 (65.947) Epoch: [5][5300/11272] Time 0.764 (0.837) Data 0.001 (0.002) Loss 2.5650 (2.6856) Prec@1 40.625 (35.368) Prec@5 65.625 (65.954) Epoch: [5][5310/11272] Time 0.882 (0.837) Data 0.001 (0.002) Loss 2.6660 (2.6855) Prec@1 36.250 (35.370) Prec@5 63.125 (65.956) Epoch: [5][5320/11272] Time 0.884 (0.837) Data 0.001 (0.002) Loss 2.6710 (2.6856) Prec@1 41.875 (35.373) Prec@5 63.125 (65.953) Epoch: [5][5330/11272] Time 0.738 (0.837) Data 0.002 (0.002) Loss 2.7420 (2.6856) Prec@1 33.125 (35.372) Prec@5 66.875 (65.954) Epoch: [5][5340/11272] Time 0.903 (0.837) Data 0.001 (0.002) Loss 2.7403 (2.6856) Prec@1 31.875 (35.372) Prec@5 65.000 (65.953) Epoch: [5][5350/11272] Time 0.891 (0.837) Data 0.001 (0.002) Loss 2.8368 (2.6856) Prec@1 34.375 (35.374) Prec@5 63.125 (65.953) Epoch: [5][5360/11272] Time 0.747 (0.837) Data 0.001 (0.002) Loss 2.5752 (2.6855) Prec@1 38.125 (35.372) Prec@5 66.875 (65.955) Epoch: [5][5370/11272] Time 0.749 (0.837) Data 0.001 (0.002) Loss 2.5944 (2.6856) Prec@1 35.625 (35.371) Prec@5 71.250 (65.954) Epoch: [5][5380/11272] Time 0.901 (0.837) Data 0.002 (0.002) Loss 2.5987 (2.6857) Prec@1 38.750 (35.368) Prec@5 65.000 (65.952) Epoch: [5][5390/11272] Time 0.846 (0.837) Data 0.002 (0.002) Loss 2.7008 (2.6855) Prec@1 41.250 (35.370) Prec@5 68.125 (65.957) Epoch: [5][5400/11272] Time 0.765 (0.837) Data 0.002 (0.002) Loss 2.7667 (2.6855) Prec@1 36.250 (35.372) Prec@5 63.750 (65.958) Epoch: [5][5410/11272] Time 0.803 (0.837) Data 0.002 (0.002) Loss 2.7393 (2.6855) Prec@1 31.250 (35.375) Prec@5 63.125 (65.956) Epoch: [5][5420/11272] Time 0.919 (0.837) Data 0.002 (0.002) Loss 2.5867 (2.6856) Prec@1 38.125 (35.374) Prec@5 70.000 (65.955) Epoch: [5][5430/11272] Time 0.876 (0.836) Data 0.002 (0.002) Loss 2.6959 (2.6856) Prec@1 33.125 (35.374) Prec@5 65.625 (65.956) Epoch: [5][5440/11272] Time 0.744 (0.836) Data 0.002 (0.002) Loss 2.6695 (2.6855) Prec@1 35.625 (35.374) Prec@5 67.500 (65.961) Epoch: [5][5450/11272] Time 0.738 (0.836) Data 0.002 (0.002) Loss 2.6244 (2.6855) Prec@1 31.250 (35.374) Prec@5 68.750 (65.961) Epoch: [5][5460/11272] Time 0.936 (0.836) Data 0.001 (0.002) Loss 2.8738 (2.6856) Prec@1 30.625 (35.371) Prec@5 60.000 (65.960) Epoch: [5][5470/11272] Time 0.845 (0.836) Data 0.002 (0.002) Loss 2.5524 (2.6855) Prec@1 39.375 (35.372) Prec@5 73.125 (65.960) Epoch: [5][5480/11272] Time 0.805 (0.836) Data 0.001 (0.002) Loss 2.9992 (2.6856) Prec@1 30.625 (35.370) Prec@5 58.750 (65.958) Epoch: [5][5490/11272] Time 0.925 (0.836) Data 0.002 (0.002) Loss 2.8790 (2.6855) Prec@1 32.500 (35.372) Prec@5 62.500 (65.957) Epoch: [5][5500/11272] Time 0.935 (0.836) Data 0.002 (0.002) Loss 2.7887 (2.6854) Prec@1 31.250 (35.374) Prec@5 61.875 (65.959) Epoch: [5][5510/11272] Time 0.754 (0.836) Data 0.001 (0.002) Loss 2.7304 (2.6855) Prec@1 30.000 (35.372) Prec@5 63.750 (65.956) Epoch: [5][5520/11272] Time 0.734 (0.836) Data 0.002 (0.002) Loss 2.7258 (2.6855) Prec@1 37.500 (35.369) Prec@5 66.250 (65.955) Epoch: [5][5530/11272] Time 0.925 (0.836) Data 0.001 (0.002) Loss 2.5500 (2.6855) Prec@1 35.000 (35.368) Prec@5 69.375 (65.955) Epoch: [5][5540/11272] Time 0.910 (0.836) Data 0.002 (0.002) Loss 2.8827 (2.6855) Prec@1 32.500 (35.366) Prec@5 63.125 (65.954) Epoch: [5][5550/11272] Time 0.790 (0.836) Data 0.002 (0.002) Loss 2.6596 (2.6855) Prec@1 38.750 (35.367) Prec@5 65.000 (65.955) Epoch: [5][5560/11272] Time 0.767 (0.836) Data 0.002 (0.002) Loss 2.4962 (2.6856) Prec@1 39.375 (35.366) Prec@5 70.625 (65.953) Epoch: [5][5570/11272] Time 0.918 (0.836) Data 0.002 (0.002) Loss 2.6308 (2.6856) Prec@1 37.500 (35.367) Prec@5 67.500 (65.954) Epoch: [5][5580/11272] Time 0.867 (0.836) Data 0.001 (0.002) Loss 2.6567 (2.6857) Prec@1 33.125 (35.367) Prec@5 65.000 (65.952) Epoch: [5][5590/11272] Time 0.757 (0.836) Data 0.001 (0.002) Loss 2.3928 (2.6856) Prec@1 39.375 (35.369) Prec@5 72.500 (65.956) Epoch: [5][5600/11272] Time 0.779 (0.836) Data 0.001 (0.002) Loss 2.8169 (2.6857) Prec@1 30.625 (35.364) Prec@5 61.250 (65.953) Epoch: [5][5610/11272] Time 0.966 (0.836) Data 0.002 (0.002) Loss 2.3349 (2.6856) Prec@1 37.500 (35.364) Prec@5 76.250 (65.956) Epoch: [5][5620/11272] Time 0.770 (0.836) Data 0.002 (0.002) Loss 2.5517 (2.6854) Prec@1 31.875 (35.368) Prec@5 70.625 (65.961) Epoch: [5][5630/11272] Time 0.809 (0.836) Data 0.001 (0.002) Loss 3.0418 (2.6854) Prec@1 27.500 (35.365) Prec@5 58.125 (65.961) Epoch: [5][5640/11272] Time 0.912 (0.836) Data 0.001 (0.002) Loss 2.6616 (2.6854) Prec@1 33.125 (35.367) Prec@5 68.750 (65.959) Epoch: [5][5650/11272] Time 0.873 (0.836) Data 0.002 (0.002) Loss 2.6455 (2.6853) Prec@1 32.500 (35.369) Prec@5 64.375 (65.961) Epoch: [5][5660/11272] Time 0.796 (0.836) Data 0.002 (0.002) Loss 2.5180 (2.6853) Prec@1 42.500 (35.371) Prec@5 68.750 (65.958) Epoch: [5][5670/11272] Time 0.756 (0.836) Data 0.001 (0.002) Loss 2.9084 (2.6855) Prec@1 36.875 (35.363) Prec@5 62.500 (65.954) Epoch: [5][5680/11272] Time 0.871 (0.836) Data 0.001 (0.002) Loss 2.8154 (2.6855) Prec@1 31.875 (35.362) Prec@5 60.625 (65.953) Epoch: [5][5690/11272] Time 0.880 (0.836) Data 0.001 (0.002) Loss 2.5834 (2.6856) Prec@1 33.125 (35.359) Prec@5 66.875 (65.950) Epoch: [5][5700/11272] Time 0.740 (0.836) Data 0.002 (0.002) Loss 2.6134 (2.6856) Prec@1 38.750 (35.358) Prec@5 65.000 (65.948) Epoch: [5][5710/11272] Time 0.762 (0.836) Data 0.002 (0.002) Loss 2.9397 (2.6857) Prec@1 31.875 (35.355) Prec@5 61.875 (65.948) Epoch: [5][5720/11272] Time 0.941 (0.836) Data 0.002 (0.002) Loss 2.6567 (2.6857) Prec@1 31.250 (35.352) Prec@5 68.125 (65.946) Epoch: [5][5730/11272] Time 0.863 (0.836) Data 0.002 (0.002) Loss 2.7875 (2.6857) Prec@1 30.000 (35.350) Prec@5 68.750 (65.947) Epoch: [5][5740/11272] Time 0.710 (0.836) Data 0.001 (0.002) Loss 2.7513 (2.6858) Prec@1 35.625 (35.348) Prec@5 63.750 (65.945) Epoch: [5][5750/11272] Time 1.019 (0.836) Data 0.003 (0.002) Loss 2.4167 (2.6857) Prec@1 41.875 (35.351) Prec@5 68.750 (65.944) Epoch: [5][5760/11272] Time 0.973 (0.836) Data 0.002 (0.002) Loss 2.9665 (2.6858) Prec@1 28.125 (35.352) Prec@5 61.250 (65.943) Epoch: [5][5770/11272] Time 0.743 (0.836) Data 0.001 (0.002) Loss 2.9939 (2.6858) Prec@1 34.375 (35.354) Prec@5 63.750 (65.943) Epoch: [5][5780/11272] Time 0.776 (0.836) Data 0.001 (0.002) Loss 2.6971 (2.6857) Prec@1 33.750 (35.356) Prec@5 63.750 (65.947) Epoch: [5][5790/11272] Time 0.910 (0.836) Data 0.001 (0.002) Loss 2.7287 (2.6857) Prec@1 39.375 (35.355) Prec@5 66.875 (65.948) Epoch: [5][5800/11272] Time 0.927 (0.836) Data 0.001 (0.002) Loss 2.6511 (2.6856) Prec@1 35.625 (35.357) Prec@5 65.000 (65.949) Epoch: [5][5810/11272] Time 0.741 (0.836) Data 0.001 (0.002) Loss 2.7249 (2.6855) Prec@1 36.250 (35.359) Prec@5 59.375 (65.951) Epoch: [5][5820/11272] Time 0.777 (0.836) Data 0.001 (0.002) Loss 2.6474 (2.6856) Prec@1 37.500 (35.357) Prec@5 65.625 (65.947) Epoch: [5][5830/11272] Time 0.886 (0.836) Data 0.001 (0.002) Loss 2.8431 (2.6857) Prec@1 31.875 (35.354) Prec@5 65.625 (65.946) Epoch: [5][5840/11272] Time 0.877 (0.836) Data 0.001 (0.002) Loss 2.9692 (2.6856) Prec@1 30.625 (35.354) Prec@5 58.750 (65.946) Epoch: [5][5850/11272] Time 0.743 (0.836) Data 0.001 (0.002) Loss 2.7515 (2.6856) Prec@1 37.500 (35.355) Prec@5 60.625 (65.947) Epoch: [5][5860/11272] Time 0.738 (0.836) Data 0.001 (0.002) Loss 2.6920 (2.6855) Prec@1 35.000 (35.355) Prec@5 63.750 (65.947) Epoch: [5][5870/11272] Time 0.929 (0.836) Data 0.001 (0.002) Loss 2.7292 (2.6857) Prec@1 40.625 (35.352) Prec@5 68.750 (65.943) Epoch: [5][5880/11272] Time 0.756 (0.836) Data 0.003 (0.002) Loss 2.6325 (2.6856) Prec@1 35.625 (35.355) Prec@5 66.875 (65.943) Epoch: [5][5890/11272] Time 0.775 (0.836) Data 0.001 (0.002) Loss 2.5675 (2.6856) Prec@1 38.125 (35.355) Prec@5 65.625 (65.943) Epoch: [5][5900/11272] Time 0.856 (0.836) Data 0.001 (0.002) Loss 2.5669 (2.6855) Prec@1 38.125 (35.357) Prec@5 67.500 (65.945) Epoch: [5][5910/11272] Time 0.907 (0.836) Data 0.002 (0.002) Loss 2.7126 (2.6855) Prec@1 36.250 (35.359) Prec@5 61.250 (65.946) Epoch: [5][5920/11272] Time 0.783 (0.836) Data 0.002 (0.002) Loss 2.5582 (2.6853) Prec@1 37.500 (35.363) Prec@5 71.875 (65.947) Epoch: [5][5930/11272] Time 0.745 (0.836) Data 0.001 (0.002) Loss 2.6976 (2.6853) Prec@1 31.250 (35.363) Prec@5 61.250 (65.949) Epoch: [5][5940/11272] Time 0.879 (0.836) Data 0.002 (0.002) Loss 2.4810 (2.6852) Prec@1 40.625 (35.364) Prec@5 73.125 (65.953) Epoch: [5][5950/11272] Time 0.866 (0.836) Data 0.001 (0.002) Loss 2.6854 (2.6851) Prec@1 36.250 (35.368) Prec@5 68.750 (65.952) Epoch: [5][5960/11272] Time 0.763 (0.836) Data 0.001 (0.002) Loss 2.8035 (2.6852) Prec@1 34.375 (35.368) Prec@5 63.125 (65.950) Epoch: [5][5970/11272] Time 0.768 (0.836) Data 0.001 (0.002) Loss 2.8007 (2.6853) Prec@1 28.750 (35.365) Prec@5 62.500 (65.947) Epoch: [5][5980/11272] Time 0.888 (0.836) Data 0.002 (0.002) Loss 2.7819 (2.6854) Prec@1 31.875 (35.361) Prec@5 65.000 (65.945) Epoch: [5][5990/11272] Time 0.878 (0.836) Data 0.001 (0.002) Loss 2.6740 (2.6854) Prec@1 35.625 (35.362) Prec@5 67.500 (65.945) Epoch: [5][6000/11272] Time 0.746 (0.836) Data 0.001 (0.002) Loss 2.7805 (2.6854) Prec@1 35.000 (35.363) Prec@5 65.000 (65.946) Epoch: [5][6010/11272] Time 0.914 (0.836) Data 0.002 (0.002) Loss 2.7564 (2.6853) Prec@1 36.250 (35.364) Prec@5 67.500 (65.947) Epoch: [5][6020/11272] Time 0.916 (0.836) Data 0.002 (0.002) Loss 2.4774 (2.6854) Prec@1 33.125 (35.363) Prec@5 71.250 (65.946) Epoch: [5][6030/11272] Time 0.767 (0.836) Data 0.002 (0.002) Loss 2.8090 (2.6853) Prec@1 33.750 (35.363) Prec@5 61.250 (65.947) Epoch: [5][6040/11272] Time 0.768 (0.836) Data 0.001 (0.002) Loss 2.7217 (2.6852) Prec@1 31.875 (35.363) Prec@5 64.375 (65.949) Epoch: [5][6050/11272] Time 0.872 (0.836) Data 0.001 (0.002) Loss 2.7186 (2.6852) Prec@1 31.875 (35.363) Prec@5 66.875 (65.948) Epoch: [5][6060/11272] Time 0.901 (0.836) Data 0.002 (0.002) Loss 2.7419 (2.6851) Prec@1 35.000 (35.364) Prec@5 63.750 (65.948) Epoch: [5][6070/11272] Time 0.776 (0.836) Data 0.001 (0.002) Loss 2.5744 (2.6850) Prec@1 36.250 (35.368) Prec@5 71.875 (65.951) Epoch: [5][6080/11272] Time 0.761 (0.836) Data 0.004 (0.002) Loss 2.4850 (2.6850) Prec@1 36.250 (35.370) Prec@5 70.625 (65.948) Epoch: [5][6090/11272] Time 0.858 (0.836) Data 0.002 (0.002) Loss 2.6502 (2.6850) Prec@1 40.625 (35.370) Prec@5 69.375 (65.949) Epoch: [5][6100/11272] Time 0.899 (0.836) Data 0.001 (0.002) Loss 2.8317 (2.6852) Prec@1 32.500 (35.368) Prec@5 60.625 (65.945) Epoch: [5][6110/11272] Time 0.763 (0.836) Data 0.002 (0.002) Loss 2.7956 (2.6851) Prec@1 31.875 (35.369) Prec@5 64.375 (65.947) Epoch: [5][6120/11272] Time 0.770 (0.836) Data 0.001 (0.002) Loss 2.7840 (2.6851) Prec@1 34.375 (35.370) Prec@5 67.500 (65.947) Epoch: [5][6130/11272] Time 0.870 (0.836) Data 0.001 (0.002) Loss 2.7104 (2.6853) Prec@1 32.500 (35.369) Prec@5 61.875 (65.944) Epoch: [5][6140/11272] Time 0.745 (0.836) Data 0.003 (0.002) Loss 2.6810 (2.6852) Prec@1 37.500 (35.369) Prec@5 61.875 (65.944) Epoch: [5][6150/11272] Time 0.781 (0.836) Data 0.001 (0.002) Loss 2.5969 (2.6853) Prec@1 38.750 (35.368) Prec@5 71.250 (65.942) Epoch: [5][6160/11272] Time 0.856 (0.836) Data 0.001 (0.002) Loss 2.3619 (2.6852) Prec@1 38.750 (35.366) Prec@5 73.750 (65.943) Epoch: [5][6170/11272] Time 0.877 (0.836) Data 0.002 (0.002) Loss 2.7797 (2.6852) Prec@1 33.125 (35.364) Prec@5 63.125 (65.942) Epoch: [5][6180/11272] Time 0.748 (0.836) Data 0.002 (0.002) Loss 2.8142 (2.6853) Prec@1 34.375 (35.363) Prec@5 67.500 (65.942) Epoch: [5][6190/11272] Time 0.790 (0.836) Data 0.001 (0.002) Loss 2.8702 (2.6854) Prec@1 27.500 (35.359) Prec@5 63.750 (65.940) Epoch: [5][6200/11272] Time 0.879 (0.836) Data 0.001 (0.002) Loss 2.5708 (2.6854) Prec@1 37.500 (35.361) Prec@5 68.125 (65.939) Epoch: [5][6210/11272] Time 0.949 (0.836) Data 0.002 (0.002) Loss 2.4972 (2.6854) Prec@1 35.625 (35.360) Prec@5 70.000 (65.939) Epoch: [5][6220/11272] Time 0.732 (0.836) Data 0.002 (0.002) Loss 2.8805 (2.6855) Prec@1 30.625 (35.360) Prec@5 60.000 (65.937) Epoch: [5][6230/11272] Time 0.767 (0.836) Data 0.001 (0.002) Loss 2.3854 (2.6855) Prec@1 40.000 (35.359) Prec@5 70.000 (65.937) Epoch: [5][6240/11272] Time 0.926 (0.836) Data 0.002 (0.002) Loss 2.6940 (2.6857) Prec@1 39.375 (35.355) Prec@5 65.625 (65.934) Epoch: [5][6250/11272] Time 0.912 (0.836) Data 0.001 (0.002) Loss 3.0216 (2.6858) Prec@1 31.250 (35.356) Prec@5 57.500 (65.933) Epoch: [5][6260/11272] Time 0.799 (0.836) Data 0.002 (0.002) Loss 2.5989 (2.6859) Prec@1 40.000 (35.356) Prec@5 67.500 (65.932) Epoch: [5][6270/11272] Time 0.951 (0.836) Data 0.002 (0.002) Loss 2.5362 (2.6857) Prec@1 37.500 (35.360) Prec@5 73.125 (65.936) Epoch: [5][6280/11272] Time 0.890 (0.836) Data 0.002 (0.002) Loss 2.6919 (2.6857) Prec@1 36.250 (35.361) Prec@5 63.125 (65.936) Epoch: [5][6290/11272] Time 0.796 (0.836) Data 0.002 (0.002) Loss 2.6743 (2.6857) Prec@1 31.875 (35.360) Prec@5 62.500 (65.935) Epoch: [5][6300/11272] Time 0.763 (0.836) Data 0.001 (0.002) Loss 2.7836 (2.6858) Prec@1 30.625 (35.359) Prec@5 64.375 (65.934) Epoch: [5][6310/11272] Time 0.881 (0.836) Data 0.001 (0.002) Loss 2.6394 (2.6857) Prec@1 43.750 (35.361) Prec@5 65.000 (65.934) Epoch: [5][6320/11272] Time 0.871 (0.836) Data 0.001 (0.002) Loss 2.9667 (2.6858) Prec@1 32.500 (35.360) Prec@5 62.500 (65.934) Epoch: [5][6330/11272] Time 0.753 (0.836) Data 0.002 (0.002) Loss 2.5511 (2.6857) Prec@1 36.250 (35.362) Prec@5 70.000 (65.936) Epoch: [5][6340/11272] Time 0.752 (0.836) Data 0.001 (0.002) Loss 2.8496 (2.6856) Prec@1 27.500 (35.363) Prec@5 67.500 (65.938) Epoch: [5][6350/11272] Time 0.883 (0.836) Data 0.002 (0.002) Loss 2.7509 (2.6857) Prec@1 33.750 (35.362) Prec@5 67.500 (65.938) Epoch: [5][6360/11272] Time 0.863 (0.836) Data 0.002 (0.002) Loss 2.4973 (2.6859) Prec@1 39.375 (35.358) Prec@5 69.375 (65.933) Epoch: [5][6370/11272] Time 0.774 (0.836) Data 0.002 (0.002) Loss 2.5796 (2.6859) Prec@1 35.625 (35.359) Prec@5 69.375 (65.935) Epoch: [5][6380/11272] Time 0.749 (0.836) Data 0.002 (0.002) Loss 3.0941 (2.6861) Prec@1 23.750 (35.357) Prec@5 55.625 (65.932) Epoch: [5][6390/11272] Time 0.901 (0.836) Data 0.001 (0.002) Loss 2.7897 (2.6861) Prec@1 28.750 (35.357) Prec@5 63.125 (65.931) Epoch: [5][6400/11272] Time 0.857 (0.836) Data 0.002 (0.002) Loss 2.4568 (2.6861) Prec@1 36.875 (35.358) Prec@5 72.500 (65.932) Epoch: [5][6410/11272] Time 0.765 (0.836) Data 0.002 (0.002) Loss 2.7544 (2.6861) Prec@1 40.000 (35.359) Prec@5 65.625 (65.932) Epoch: [5][6420/11272] Time 0.907 (0.836) Data 0.002 (0.002) Loss 2.6875 (2.6862) Prec@1 32.500 (35.359) Prec@5 68.125 (65.932) Epoch: [5][6430/11272] Time 0.945 (0.836) Data 0.002 (0.002) Loss 2.7206 (2.6861) Prec@1 40.000 (35.360) Prec@5 63.750 (65.932) Epoch: [5][6440/11272] Time 0.801 (0.836) Data 0.002 (0.002) Loss 2.5510 (2.6860) Prec@1 36.875 (35.363) Prec@5 66.250 (65.934) Epoch: [5][6450/11272] Time 0.721 (0.836) Data 0.001 (0.002) Loss 2.7952 (2.6861) Prec@1 30.625 (35.361) Prec@5 58.750 (65.931) Epoch: [5][6460/11272] Time 0.938 (0.836) Data 0.002 (0.002) Loss 2.8039 (2.6860) Prec@1 36.250 (35.364) Prec@5 63.125 (65.934) Epoch: [5][6470/11272] Time 0.871 (0.836) Data 0.001 (0.002) Loss 2.7010 (2.6861) Prec@1 35.625 (35.361) Prec@5 67.500 (65.931) Epoch: [5][6480/11272] Time 0.785 (0.836) Data 0.003 (0.002) Loss 2.6953 (2.6860) Prec@1 33.125 (35.362) Prec@5 68.125 (65.934) Epoch: [5][6490/11272] Time 0.786 (0.836) Data 0.002 (0.002) Loss 2.6125 (2.6860) Prec@1 35.625 (35.362) Prec@5 70.000 (65.935) Epoch: [5][6500/11272] Time 0.876 (0.836) Data 0.001 (0.002) Loss 2.7871 (2.6860) Prec@1 33.750 (35.363) Prec@5 64.375 (65.935) Epoch: [5][6510/11272] Time 0.920 (0.836) Data 0.001 (0.002) Loss 2.9405 (2.6860) Prec@1 30.000 (35.360) Prec@5 62.500 (65.935) Epoch: [5][6520/11272] Time 0.832 (0.836) Data 0.003 (0.002) Loss 2.3890 (2.6858) Prec@1 42.500 (35.361) Prec@5 67.500 (65.940) Epoch: [5][6530/11272] Time 0.937 (0.836) Data 0.001 (0.002) Loss 2.5322 (2.6857) Prec@1 35.625 (35.363) Prec@5 70.000 (65.943) Epoch: [5][6540/11272] Time 0.912 (0.836) Data 0.002 (0.002) Loss 2.7510 (2.6855) Prec@1 36.250 (35.368) Prec@5 63.125 (65.945) Epoch: [5][6550/11272] Time 0.783 (0.836) Data 0.002 (0.002) Loss 2.8306 (2.6855) Prec@1 33.125 (35.368) Prec@5 61.875 (65.945) Epoch: [5][6560/11272] Time 0.777 (0.836) Data 0.001 (0.002) Loss 2.8946 (2.6855) Prec@1 31.875 (35.366) Prec@5 61.250 (65.943) Epoch: [5][6570/11272] Time 0.936 (0.836) Data 0.002 (0.002) Loss 2.6168 (2.6854) Prec@1 36.875 (35.369) Prec@5 68.750 (65.946) Epoch: [5][6580/11272] Time 0.941 (0.836) Data 0.002 (0.002) Loss 2.7394 (2.6854) Prec@1 33.125 (35.371) Prec@5 66.250 (65.946) Epoch: [5][6590/11272] Time 0.817 (0.836) Data 0.002 (0.002) Loss 2.7712 (2.6853) Prec@1 32.500 (35.371) Prec@5 63.125 (65.947) Epoch: [5][6600/11272] Time 0.772 (0.836) Data 0.002 (0.002) Loss 2.6233 (2.6853) Prec@1 36.250 (35.371) Prec@5 65.000 (65.946) Epoch: [5][6610/11272] Time 0.912 (0.836) Data 0.002 (0.002) Loss 2.8096 (2.6854) Prec@1 35.000 (35.372) Prec@5 64.375 (65.946) Epoch: [5][6620/11272] Time 0.907 (0.836) Data 0.002 (0.002) Loss 2.8461 (2.6853) Prec@1 32.500 (35.372) Prec@5 60.000 (65.945) Epoch: [5][6630/11272] Time 0.751 (0.836) Data 0.002 (0.002) Loss 2.6361 (2.6854) Prec@1 38.125 (35.370) Prec@5 69.375 (65.944) Epoch: [5][6640/11272] Time 0.792 (0.836) Data 0.006 (0.002) Loss 2.5714 (2.6855) Prec@1 41.875 (35.370) Prec@5 66.875 (65.941) Epoch: [5][6650/11272] Time 0.895 (0.836) Data 0.002 (0.002) Loss 2.5355 (2.6855) Prec@1 36.250 (35.369) Prec@5 66.250 (65.942) Epoch: [5][6660/11272] Time 0.918 (0.836) Data 0.003 (0.002) Loss 2.6343 (2.6856) Prec@1 36.250 (35.368) Prec@5 63.125 (65.941) Epoch: [5][6670/11272] Time 0.745 (0.836) Data 0.002 (0.002) Loss 2.7723 (2.6857) Prec@1 35.000 (35.368) Prec@5 64.375 (65.941) Epoch: [5][6680/11272] Time 0.932 (0.836) Data 0.001 (0.002) Loss 2.8349 (2.6857) Prec@1 33.125 (35.365) Prec@5 61.250 (65.941) Epoch: [5][6690/11272] Time 0.857 (0.836) Data 0.001 (0.002) Loss 2.5030 (2.6857) Prec@1 40.000 (35.369) Prec@5 70.000 (65.942) Epoch: [5][6700/11272] Time 0.791 (0.836) Data 0.001 (0.002) Loss 2.6513 (2.6856) Prec@1 33.750 (35.372) Prec@5 65.000 (65.942) Epoch: [5][6710/11272] Time 0.776 (0.836) Data 0.002 (0.002) Loss 2.5403 (2.6856) Prec@1 36.875 (35.370) Prec@5 68.750 (65.944) Epoch: [5][6720/11272] Time 0.889 (0.836) Data 0.001 (0.002) Loss 2.5975 (2.6856) Prec@1 38.125 (35.371) Prec@5 70.625 (65.943) Epoch: [5][6730/11272] Time 0.926 (0.836) Data 0.002 (0.002) Loss 2.7438 (2.6855) Prec@1 34.375 (35.373) Prec@5 63.125 (65.945) Epoch: [5][6740/11272] Time 0.749 (0.836) Data 0.001 (0.002) Loss 2.9369 (2.6855) Prec@1 33.125 (35.371) Prec@5 61.250 (65.943) Epoch: [5][6750/11272] Time 0.753 (0.836) Data 0.002 (0.002) Loss 2.8040 (2.6856) Prec@1 33.125 (35.367) Prec@5 63.125 (65.942) Epoch: [5][6760/11272] Time 0.848 (0.836) Data 0.001 (0.002) Loss 2.9532 (2.6857) Prec@1 31.250 (35.366) Prec@5 59.375 (65.940) Epoch: [5][6770/11272] Time 0.888 (0.836) Data 0.002 (0.002) Loss 2.9987 (2.6857) Prec@1 29.375 (35.365) Prec@5 60.000 (65.938) Epoch: [5][6780/11272] Time 0.742 (0.836) Data 0.001 (0.002) Loss 2.6797 (2.6857) Prec@1 36.250 (35.364) Prec@5 66.875 (65.938) Epoch: [5][6790/11272] Time 0.775 (0.836) Data 0.002 (0.002) Loss 2.6732 (2.6857) Prec@1 35.000 (35.364) Prec@5 66.250 (65.937) Epoch: [5][6800/11272] Time 0.879 (0.836) Data 0.001 (0.002) Loss 2.8093 (2.6857) Prec@1 32.500 (35.364) Prec@5 63.125 (65.937) Epoch: [5][6810/11272] Time 0.786 (0.836) Data 0.005 (0.002) Loss 2.4509 (2.6857) Prec@1 42.500 (35.365) Prec@5 70.000 (65.937) Epoch: [5][6820/11272] Time 0.788 (0.836) Data 0.002 (0.002) Loss 2.6513 (2.6857) Prec@1 35.625 (35.364) Prec@5 66.250 (65.938) Epoch: [5][6830/11272] Time 0.967 (0.836) Data 0.002 (0.002) Loss 3.0669 (2.6857) Prec@1 32.500 (35.364) Prec@5 61.250 (65.940) Epoch: [5][6840/11272] Time 0.939 (0.836) Data 0.002 (0.002) Loss 2.7034 (2.6857) Prec@1 25.000 (35.363) Prec@5 69.375 (65.939) Epoch: [5][6850/11272] Time 0.799 (0.836) Data 0.002 (0.002) Loss 2.5064 (2.6858) Prec@1 36.875 (35.361) Prec@5 70.625 (65.938) Epoch: [5][6860/11272] Time 0.776 (0.836) Data 0.002 (0.002) Loss 2.8593 (2.6860) Prec@1 33.750 (35.357) Prec@5 61.875 (65.933) Epoch: [5][6870/11272] Time 0.886 (0.836) Data 0.002 (0.002) Loss 2.5940 (2.6860) Prec@1 37.500 (35.355) Prec@5 67.500 (65.933) Epoch: [5][6880/11272] Time 0.925 (0.836) Data 0.002 (0.002) Loss 2.7107 (2.6860) Prec@1 36.250 (35.356) Prec@5 68.750 (65.934) Epoch: [5][6890/11272] Time 0.742 (0.836) Data 0.001 (0.002) Loss 2.8743 (2.6860) Prec@1 36.875 (35.357) Prec@5 63.750 (65.935) Epoch: [5][6900/11272] Time 0.773 (0.836) Data 0.002 (0.002) Loss 2.8187 (2.6859) Prec@1 38.125 (35.358) Prec@5 64.375 (65.935) Epoch: [5][6910/11272] Time 0.926 (0.836) Data 0.002 (0.002) Loss 2.6427 (2.6859) Prec@1 36.250 (35.359) Prec@5 67.500 (65.935) Epoch: [5][6920/11272] Time 0.933 (0.836) Data 0.006 (0.002) Loss 2.9849 (2.6859) Prec@1 33.125 (35.357) Prec@5 56.250 (65.935) Epoch: [5][6930/11272] Time 0.768 (0.836) Data 0.002 (0.002) Loss 2.4788 (2.6859) Prec@1 36.250 (35.358) Prec@5 70.000 (65.937) Epoch: [5][6940/11272] Time 0.948 (0.836) Data 0.001 (0.002) Loss 2.6095 (2.6859) Prec@1 35.625 (35.359) Prec@5 65.000 (65.936) Epoch: [5][6950/11272] Time 0.946 (0.836) Data 0.002 (0.002) Loss 2.8610 (2.6860) Prec@1 31.875 (35.358) Prec@5 58.750 (65.933) Epoch: [5][6960/11272] Time 0.779 (0.836) Data 0.001 (0.002) Loss 2.4938 (2.6859) Prec@1 41.875 (35.359) Prec@5 66.875 (65.936) Epoch: [5][6970/11272] Time 0.757 (0.836) Data 0.001 (0.002) Loss 2.7256 (2.6858) Prec@1 37.500 (35.360) Prec@5 67.500 (65.937) Epoch: [5][6980/11272] Time 0.919 (0.836) Data 0.001 (0.002) Loss 2.5759 (2.6859) Prec@1 35.000 (35.358) Prec@5 67.500 (65.938) Epoch: [5][6990/11272] Time 0.862 (0.836) Data 0.002 (0.002) Loss 2.7472 (2.6859) Prec@1 33.750 (35.357) Prec@5 63.750 (65.936) Epoch: [5][7000/11272] Time 0.781 (0.836) Data 0.002 (0.002) Loss 2.8178 (2.6859) Prec@1 35.000 (35.358) Prec@5 61.875 (65.934) Epoch: [5][7010/11272] Time 0.786 (0.836) Data 0.002 (0.002) Loss 2.6143 (2.6859) Prec@1 35.625 (35.356) Prec@5 65.000 (65.934) Epoch: [5][7020/11272] Time 0.893 (0.836) Data 0.001 (0.002) Loss 2.6921 (2.6861) Prec@1 32.500 (35.354) Prec@5 66.250 (65.931) Epoch: [5][7030/11272] Time 0.928 (0.836) Data 0.001 (0.002) Loss 2.7004 (2.6860) Prec@1 39.375 (35.357) Prec@5 66.875 (65.931) Epoch: [5][7040/11272] Time 0.742 (0.836) Data 0.001 (0.002) Loss 2.8233 (2.6861) Prec@1 33.125 (35.356) Prec@5 62.500 (65.928) Epoch: [5][7050/11272] Time 0.781 (0.836) Data 0.002 (0.002) Loss 2.6844 (2.6862) Prec@1 35.625 (35.357) Prec@5 68.750 (65.927) Epoch: [5][7060/11272] Time 0.886 (0.837) Data 0.002 (0.002) Loss 2.8004 (2.6861) Prec@1 37.500 (35.359) Prec@5 63.125 (65.928) Epoch: [5][7070/11272] Time 0.764 (0.837) Data 0.003 (0.002) Loss 2.9081 (2.6861) Prec@1 32.500 (35.359) Prec@5 61.875 (65.926) Epoch: [5][7080/11272] Time 0.775 (0.837) Data 0.002 (0.002) Loss 2.6288 (2.6860) Prec@1 33.750 (35.360) Prec@5 68.750 (65.926) Epoch: [5][7090/11272] Time 0.869 (0.837) Data 0.001 (0.002) Loss 2.7738 (2.6860) Prec@1 37.500 (35.363) Prec@5 60.625 (65.926) Epoch: [5][7100/11272] Time 0.871 (0.837) Data 0.002 (0.002) Loss 2.6550 (2.6861) Prec@1 35.625 (35.360) Prec@5 66.250 (65.924) Epoch: [5][7110/11272] Time 0.794 (0.837) Data 0.003 (0.002) Loss 2.8909 (2.6861) Prec@1 30.625 (35.361) Prec@5 65.000 (65.925) Epoch: [5][7120/11272] Time 0.750 (0.837) Data 0.001 (0.002) Loss 2.7081 (2.6861) Prec@1 39.375 (35.361) Prec@5 66.250 (65.923) Epoch: [5][7130/11272] Time 0.929 (0.837) Data 0.001 (0.002) Loss 2.4530 (2.6861) Prec@1 40.000 (35.362) Prec@5 71.875 (65.923) Epoch: [5][7140/11272] Time 0.975 (0.837) Data 0.002 (0.002) Loss 2.6293 (2.6860) Prec@1 40.625 (35.364) Prec@5 68.750 (65.925) Epoch: [5][7150/11272] Time 0.753 (0.837) Data 0.002 (0.002) Loss 2.6861 (2.6859) Prec@1 32.500 (35.363) Prec@5 65.625 (65.926) Epoch: [5][7160/11272] Time 0.781 (0.837) Data 0.002 (0.002) Loss 2.7182 (2.6859) Prec@1 38.750 (35.367) Prec@5 66.250 (65.928) Epoch: [5][7170/11272] Time 0.924 (0.837) Data 0.001 (0.002) Loss 2.7087 (2.6859) Prec@1 38.750 (35.366) Prec@5 63.750 (65.928) Epoch: [5][7180/11272] Time 0.956 (0.837) Data 0.002 (0.002) Loss 2.5534 (2.6859) Prec@1 38.125 (35.365) Prec@5 66.250 (65.926) Epoch: [5][7190/11272] Time 0.762 (0.837) Data 0.002 (0.002) Loss 2.4373 (2.6858) Prec@1 39.375 (35.364) Prec@5 73.750 (65.929) Epoch: [5][7200/11272] Time 0.915 (0.837) Data 0.002 (0.002) Loss 2.6332 (2.6858) Prec@1 37.500 (35.367) Prec@5 66.250 (65.929) Epoch: [5][7210/11272] Time 0.856 (0.837) Data 0.001 (0.002) Loss 2.6865 (2.6856) Prec@1 29.375 (35.369) Prec@5 70.000 (65.932) Epoch: [5][7220/11272] Time 0.789 (0.837) Data 0.002 (0.002) Loss 2.8628 (2.6858) Prec@1 30.625 (35.367) Prec@5 61.875 (65.930) Epoch: [5][7230/11272] Time 0.811 (0.837) Data 0.002 (0.002) Loss 2.5898 (2.6857) Prec@1 32.500 (35.367) Prec@5 66.875 (65.931) Epoch: [5][7240/11272] Time 0.886 (0.837) Data 0.001 (0.002) Loss 2.9355 (2.6858) Prec@1 31.875 (35.366) Prec@5 60.000 (65.928) Epoch: [5][7250/11272] Time 0.864 (0.837) Data 0.001 (0.002) Loss 2.5963 (2.6857) Prec@1 41.250 (35.368) Prec@5 67.500 (65.929) Epoch: [5][7260/11272] Time 0.833 (0.837) Data 0.002 (0.002) Loss 2.6918 (2.6857) Prec@1 39.375 (35.368) Prec@5 65.625 (65.930) Epoch: [5][7270/11272] Time 0.777 (0.837) Data 0.002 (0.002) Loss 2.6790 (2.6857) Prec@1 32.500 (35.368) Prec@5 65.625 (65.931) Epoch: [5][7280/11272] Time 0.928 (0.837) Data 0.002 (0.002) Loss 2.5209 (2.6858) Prec@1 35.000 (35.366) Prec@5 68.750 (65.930) Epoch: [5][7290/11272] Time 0.903 (0.837) Data 0.002 (0.002) Loss 2.6388 (2.6858) Prec@1 36.875 (35.367) Prec@5 65.000 (65.929) Epoch: [5][7300/11272] Time 0.798 (0.837) Data 0.003 (0.002) Loss 2.4986 (2.6858) Prec@1 36.250 (35.368) Prec@5 70.625 (65.929) Epoch: [5][7310/11272] Time 0.755 (0.837) Data 0.002 (0.002) Loss 2.7057 (2.6858) Prec@1 36.875 (35.367) Prec@5 63.750 (65.928) Epoch: [5][7320/11272] Time 0.890 (0.837) Data 0.002 (0.002) Loss 2.4748 (2.6857) Prec@1 41.250 (35.369) Prec@5 67.500 (65.930) Epoch: [5][7330/11272] Time 0.879 (0.837) Data 0.001 (0.002) Loss 2.5738 (2.6857) Prec@1 38.125 (35.368) Prec@5 66.875 (65.929) Epoch: [5][7340/11272] Time 0.743 (0.837) Data 0.002 (0.002) Loss 2.6094 (2.6857) Prec@1 40.625 (35.371) Prec@5 66.250 (65.930) Epoch: [5][7350/11272] Time 0.935 (0.837) Data 0.002 (0.002) Loss 2.6514 (2.6855) Prec@1 31.875 (35.373) Prec@5 68.125 (65.933) Epoch: [5][7360/11272] Time 0.919 (0.837) Data 0.002 (0.002) Loss 2.6130 (2.6856) Prec@1 36.250 (35.373) Prec@5 63.750 (65.931) Epoch: [5][7370/11272] Time 0.787 (0.837) Data 0.002 (0.002) Loss 2.8342 (2.6856) Prec@1 32.500 (35.372) Prec@5 65.625 (65.932) Epoch: [5][7380/11272] Time 0.779 (0.837) Data 0.001 (0.002) Loss 2.5503 (2.6856) Prec@1 35.625 (35.373) Prec@5 71.875 (65.934) Epoch: [5][7390/11272] Time 0.870 (0.837) Data 0.002 (0.002) Loss 2.6939 (2.6856) Prec@1 38.750 (35.373) Prec@5 68.125 (65.933) Epoch: [5][7400/11272] Time 0.893 (0.837) Data 0.002 (0.002) Loss 2.5988 (2.6855) Prec@1 39.375 (35.374) Prec@5 65.625 (65.934) Epoch: [5][7410/11272] Time 0.816 (0.837) Data 0.001 (0.002) Loss 2.4883 (2.6856) Prec@1 40.000 (35.373) Prec@5 71.875 (65.932) Epoch: [5][7420/11272] Time 0.742 (0.837) Data 0.002 (0.002) Loss 2.6649 (2.6856) Prec@1 40.625 (35.374) Prec@5 61.875 (65.933) Epoch: [5][7430/11272] Time 0.972 (0.837) Data 0.002 (0.002) Loss 2.6481 (2.6856) Prec@1 35.000 (35.378) Prec@5 68.750 (65.934) Epoch: [5][7440/11272] Time 0.848 (0.837) Data 0.001 (0.002) Loss 2.6881 (2.6857) Prec@1 33.750 (35.376) Prec@5 65.625 (65.931) Epoch: [5][7450/11272] Time 0.749 (0.837) Data 0.002 (0.002) Loss 2.9322 (2.6857) Prec@1 29.375 (35.374) Prec@5 61.250 (65.931) Epoch: [5][7460/11272] Time 0.756 (0.837) Data 0.001 (0.002) Loss 2.6854 (2.6857) Prec@1 36.875 (35.376) Prec@5 66.250 (65.930) Epoch: [5][7470/11272] Time 0.923 (0.837) Data 0.002 (0.002) Loss 2.5998 (2.6857) Prec@1 34.375 (35.374) Prec@5 71.250 (65.931) Epoch: [5][7480/11272] Time 0.751 (0.837) Data 0.001 (0.002) Loss 2.7119 (2.6857) Prec@1 32.500 (35.375) Prec@5 64.375 (65.929) Epoch: [5][7490/11272] Time 0.759 (0.837) Data 0.001 (0.002) Loss 2.7542 (2.6857) Prec@1 34.375 (35.374) Prec@5 65.000 (65.929) Epoch: [5][7500/11272] Time 0.885 (0.837) Data 0.001 (0.002) Loss 2.4926 (2.6857) Prec@1 39.375 (35.375) Prec@5 66.250 (65.927) Epoch: [5][7510/11272] Time 0.842 (0.837) Data 0.001 (0.002) Loss 2.8541 (2.6857) Prec@1 27.500 (35.373) Prec@5 63.125 (65.927) Epoch: [5][7520/11272] Time 0.835 (0.837) Data 0.003 (0.002) Loss 2.9023 (2.6858) Prec@1 35.000 (35.372) Prec@5 60.000 (65.925) Epoch: [5][7530/11272] Time 0.814 (0.837) Data 0.002 (0.002) Loss 2.7051 (2.6858) Prec@1 40.000 (35.373) Prec@5 63.125 (65.926) Epoch: [5][7540/11272] Time 0.894 (0.837) Data 0.002 (0.002) Loss 2.7290 (2.6857) Prec@1 37.500 (35.375) Prec@5 66.250 (65.928) Epoch: [5][7550/11272] Time 0.852 (0.837) Data 0.001 (0.002) Loss 2.7375 (2.6856) Prec@1 35.000 (35.377) Prec@5 68.125 (65.929) Epoch: [5][7560/11272] Time 0.824 (0.837) Data 0.001 (0.002) Loss 2.4543 (2.6857) Prec@1 43.750 (35.379) Prec@5 68.125 (65.929) Epoch: [5][7570/11272] Time 0.758 (0.837) Data 0.001 (0.002) Loss 2.4416 (2.6857) Prec@1 35.625 (35.380) Prec@5 67.500 (65.929) Epoch: [5][7580/11272] Time 0.923 (0.837) Data 0.002 (0.002) Loss 2.9664 (2.6858) Prec@1 31.250 (35.379) Prec@5 63.750 (65.927) Epoch: [5][7590/11272] Time 0.845 (0.837) Data 0.001 (0.002) Loss 2.5744 (2.6856) Prec@1 35.625 (35.383) Prec@5 69.375 (65.931) Epoch: [5][7600/11272] Time 0.759 (0.837) Data 0.001 (0.002) Loss 2.7227 (2.6856) Prec@1 34.375 (35.383) Prec@5 65.000 (65.931) Epoch: [5][7610/11272] Time 0.911 (0.837) Data 0.002 (0.002) Loss 2.6367 (2.6855) Prec@1 35.000 (35.384) Prec@5 68.750 (65.931) Epoch: [5][7620/11272] Time 0.898 (0.837) Data 0.002 (0.002) Loss 2.9325 (2.6855) Prec@1 30.000 (35.386) Prec@5 60.000 (65.932) Epoch: [5][7630/11272] Time 0.772 (0.837) Data 0.001 (0.002) Loss 2.4763 (2.6854) Prec@1 36.250 (35.388) Prec@5 67.500 (65.934) Epoch: [5][7640/11272] Time 0.771 (0.837) Data 0.002 (0.002) Loss 2.6998 (2.6854) Prec@1 41.250 (35.388) Prec@5 63.750 (65.935) Epoch: [5][7650/11272] Time 0.901 (0.837) Data 0.001 (0.002) Loss 2.7210 (2.6854) Prec@1 33.125 (35.388) Prec@5 63.125 (65.936) Epoch: [5][7660/11272] Time 0.978 (0.837) Data 0.002 (0.002) Loss 2.3413 (2.6854) Prec@1 43.125 (35.390) Prec@5 71.250 (65.936) Epoch: [5][7670/11272] Time 0.783 (0.837) Data 0.002 (0.002) Loss 2.6609 (2.6853) Prec@1 34.375 (35.390) Prec@5 65.000 (65.936) Epoch: [5][7680/11272] Time 0.775 (0.837) Data 0.001 (0.002) Loss 2.4389 (2.6852) Prec@1 44.375 (35.392) Prec@5 69.375 (65.939) Epoch: [5][7690/11272] Time 0.962 (0.837) Data 0.002 (0.002) Loss 2.5792 (2.6852) Prec@1 35.625 (35.391) Prec@5 68.750 (65.941) Epoch: [5][7700/11272] Time 0.890 (0.837) Data 0.003 (0.002) Loss 2.7281 (2.6853) Prec@1 32.500 (35.389) Prec@5 64.375 (65.938) Epoch: [5][7710/11272] Time 0.770 (0.837) Data 0.002 (0.002) Loss 2.7403 (2.6854) Prec@1 35.000 (35.386) Prec@5 58.125 (65.936) Epoch: [5][7720/11272] Time 0.792 (0.837) Data 0.001 (0.002) Loss 2.8905 (2.6854) Prec@1 32.500 (35.385) Prec@5 59.375 (65.936) Epoch: [5][7730/11272] Time 0.903 (0.837) Data 0.002 (0.002) Loss 2.7829 (2.6853) Prec@1 33.125 (35.386) Prec@5 62.500 (65.937) Epoch: [5][7740/11272] Time 0.799 (0.837) Data 0.003 (0.002) Loss 2.6498 (2.6854) Prec@1 37.500 (35.386) Prec@5 64.375 (65.936) Epoch: [5][7750/11272] Time 0.813 (0.837) Data 0.002 (0.002) Loss 2.6469 (2.6853) Prec@1 35.625 (35.388) Prec@5 61.875 (65.935) Epoch: [5][7760/11272] Time 0.907 (0.837) Data 0.001 (0.002) Loss 2.8986 (2.6852) Prec@1 31.875 (35.388) Prec@5 65.625 (65.937) Epoch: [5][7770/11272] Time 0.943 (0.837) Data 0.003 (0.002) Loss 2.3794 (2.6853) Prec@1 39.375 (35.389) Prec@5 71.875 (65.935) Epoch: [5][7780/11272] Time 0.785 (0.837) Data 0.001 (0.002) Loss 2.6687 (2.6854) Prec@1 35.625 (35.389) Prec@5 66.250 (65.934) Epoch: [5][7790/11272] Time 0.835 (0.837) Data 0.002 (0.002) Loss 2.6018 (2.6853) Prec@1 41.875 (35.390) Prec@5 66.875 (65.935) Epoch: [5][7800/11272] Time 0.886 (0.837) Data 0.001 (0.002) Loss 2.4212 (2.6853) Prec@1 39.375 (35.390) Prec@5 76.875 (65.936) Epoch: [5][7810/11272] Time 0.921 (0.837) Data 0.001 (0.002) Loss 2.6824 (2.6853) Prec@1 34.375 (35.390) Prec@5 63.750 (65.935) Epoch: [5][7820/11272] Time 0.791 (0.837) Data 0.002 (0.002) Loss 2.6568 (2.6854) Prec@1 37.500 (35.388) Prec@5 66.250 (65.933) Epoch: [5][7830/11272] Time 0.744 (0.837) Data 0.001 (0.002) Loss 2.7241 (2.6854) Prec@1 37.500 (35.386) Prec@5 66.875 (65.934) Epoch: [5][7840/11272] Time 0.937 (0.837) Data 0.001 (0.002) Loss 2.7690 (2.6854) Prec@1 40.000 (35.387) Prec@5 68.125 (65.934) Epoch: [5][7850/11272] Time 0.937 (0.837) Data 0.001 (0.002) Loss 2.5976 (2.6854) Prec@1 39.375 (35.387) Prec@5 66.250 (65.934) Epoch: [5][7860/11272] Time 0.794 (0.837) Data 0.002 (0.002) Loss 2.4770 (2.6854) Prec@1 38.125 (35.384) Prec@5 71.875 (65.933) Epoch: [5][7870/11272] Time 0.983 (0.837) Data 0.001 (0.002) Loss 2.9161 (2.6855) Prec@1 34.375 (35.384) Prec@5 61.875 (65.932) Epoch: [5][7880/11272] Time 0.994 (0.837) Data 0.002 (0.002) Loss 2.7371 (2.6855) Prec@1 33.125 (35.384) Prec@5 66.250 (65.932) Epoch: [5][7890/11272] Time 0.737 (0.837) Data 0.001 (0.002) Loss 2.6544 (2.6855) Prec@1 33.125 (35.383) Prec@5 70.000 (65.933) Epoch: [5][7900/11272] Time 0.777 (0.837) Data 0.002 (0.002) Loss 2.6098 (2.6855) Prec@1 37.500 (35.381) Prec@5 62.500 (65.931) Epoch: [5][7910/11272] Time 0.889 (0.837) Data 0.002 (0.002) Loss 2.5083 (2.6855) Prec@1 41.250 (35.383) Prec@5 66.875 (65.932) Epoch: [5][7920/11272] Time 0.929 (0.837) Data 0.002 (0.002) Loss 2.4444 (2.6855) Prec@1 33.125 (35.383) Prec@5 72.500 (65.932) Epoch: [5][7930/11272] Time 0.763 (0.837) Data 0.001 (0.002) Loss 2.6389 (2.6854) Prec@1 41.250 (35.383) Prec@5 67.500 (65.933) Epoch: [5][7940/11272] Time 0.797 (0.837) Data 0.001 (0.002) Loss 2.5110 (2.6853) Prec@1 38.125 (35.384) Prec@5 71.250 (65.936) Epoch: [5][7950/11272] Time 0.872 (0.837) Data 0.001 (0.002) Loss 2.6593 (2.6853) Prec@1 35.000 (35.384) Prec@5 68.125 (65.936) Epoch: [5][7960/11272] Time 0.866 (0.837) Data 0.001 (0.002) Loss 2.7454 (2.6853) Prec@1 30.000 (35.384) Prec@5 62.500 (65.936) Epoch: [5][7970/11272] Time 0.834 (0.837) Data 0.002 (0.002) Loss 2.6078 (2.6853) Prec@1 35.625 (35.383) Prec@5 66.875 (65.937) Epoch: [5][7980/11272] Time 0.773 (0.837) Data 0.001 (0.002) Loss 2.6153 (2.6852) Prec@1 38.750 (35.387) Prec@5 65.625 (65.937) Epoch: [5][7990/11272] Time 0.882 (0.837) Data 0.001 (0.002) Loss 2.7145 (2.6852) Prec@1 33.125 (35.385) Prec@5 63.750 (65.937) Epoch: [5][8000/11272] Time 0.761 (0.837) Data 0.004 (0.002) Loss 2.5440 (2.6853) Prec@1 39.375 (35.383) Prec@5 71.250 (65.937) Epoch: [5][8010/11272] Time 0.740 (0.837) Data 0.001 (0.002) Loss 2.6778 (2.6854) Prec@1 30.625 (35.379) Prec@5 66.875 (65.935) Epoch: [5][8020/11272] Time 0.860 (0.837) Data 0.002 (0.002) Loss 2.7424 (2.6855) Prec@1 31.875 (35.377) Prec@5 66.250 (65.933) Epoch: [5][8030/11272] Time 0.935 (0.837) Data 0.001 (0.002) Loss 2.7323 (2.6855) Prec@1 34.375 (35.379) Prec@5 66.250 (65.935) Epoch: [5][8040/11272] Time 0.760 (0.837) Data 0.002 (0.002) Loss 2.7691 (2.6854) Prec@1 38.750 (35.380) Prec@5 66.875 (65.936) Epoch: [5][8050/11272] Time 0.763 (0.837) Data 0.002 (0.002) Loss 2.7459 (2.6854) Prec@1 30.625 (35.380) Prec@5 61.250 (65.935) Epoch: [5][8060/11272] Time 0.897 (0.837) Data 0.002 (0.002) Loss 2.5762 (2.6853) Prec@1 37.500 (35.382) Prec@5 66.875 (65.936) Epoch: [5][8070/11272] Time 0.869 (0.837) Data 0.001 (0.002) Loss 2.8439 (2.6853) Prec@1 30.625 (35.379) Prec@5 63.125 (65.937) Epoch: [5][8080/11272] Time 0.792 (0.837) Data 0.002 (0.002) Loss 2.6091 (2.6853) Prec@1 35.625 (35.380) Prec@5 70.625 (65.939) Epoch: [5][8090/11272] Time 0.755 (0.837) Data 0.001 (0.002) Loss 2.4448 (2.6853) Prec@1 38.750 (35.381) Prec@5 68.750 (65.939) Epoch: [5][8100/11272] Time 0.925 (0.837) Data 0.001 (0.002) Loss 2.8052 (2.6852) Prec@1 28.750 (35.381) Prec@5 62.500 (65.939) Epoch: [5][8110/11272] Time 0.934 (0.837) Data 0.002 (0.002) Loss 2.7165 (2.6852) Prec@1 32.500 (35.379) Prec@5 63.125 (65.940) Epoch: [5][8120/11272] Time 0.771 (0.837) Data 0.002 (0.002) Loss 2.6884 (2.6852) Prec@1 33.750 (35.380) Prec@5 66.875 (65.941) Epoch: [5][8130/11272] Time 0.890 (0.837) Data 0.002 (0.002) Loss 2.7265 (2.6852) Prec@1 38.750 (35.381) Prec@5 66.875 (65.940) Epoch: [5][8140/11272] Time 0.881 (0.837) Data 0.001 (0.002) Loss 2.7439 (2.6852) Prec@1 34.375 (35.381) Prec@5 63.125 (65.939) Epoch: [5][8150/11272] Time 0.748 (0.837) Data 0.001 (0.002) Loss 2.7687 (2.6852) Prec@1 31.875 (35.378) Prec@5 65.000 (65.939) Epoch: [5][8160/11272] Time 0.747 (0.837) Data 0.001 (0.002) Loss 2.4843 (2.6853) Prec@1 40.625 (35.379) Prec@5 70.625 (65.938) Epoch: [5][8170/11272] Time 0.877 (0.837) Data 0.001 (0.002) Loss 2.7192 (2.6853) Prec@1 33.125 (35.379) Prec@5 65.625 (65.938) Epoch: [5][8180/11272] Time 0.929 (0.837) Data 0.002 (0.002) Loss 2.7069 (2.6852) Prec@1 33.750 (35.379) Prec@5 65.000 (65.939) Epoch: [5][8190/11272] Time 0.763 (0.837) Data 0.001 (0.002) Loss 2.5765 (2.6852) Prec@1 34.375 (35.378) Prec@5 65.625 (65.939) Epoch: [5][8200/11272] Time 0.766 (0.837) Data 0.002 (0.002) Loss 2.8378 (2.6853) Prec@1 30.625 (35.376) Prec@5 61.250 (65.937) Epoch: [5][8210/11272] Time 0.928 (0.837) Data 0.002 (0.002) Loss 2.8831 (2.6853) Prec@1 32.500 (35.376) Prec@5 61.250 (65.939) Epoch: [5][8220/11272] Time 0.883 (0.837) Data 0.001 (0.002) Loss 2.4256 (2.6853) Prec@1 43.125 (35.376) Prec@5 70.625 (65.938) Epoch: [5][8230/11272] Time 0.739 (0.837) Data 0.002 (0.002) Loss 2.7155 (2.6853) Prec@1 35.625 (35.376) Prec@5 64.375 (65.939) Epoch: [5][8240/11272] Time 0.770 (0.837) Data 0.002 (0.002) Loss 2.3549 (2.6853) Prec@1 42.500 (35.377) Prec@5 71.250 (65.938) Epoch: [5][8250/11272] Time 0.926 (0.837) Data 0.001 (0.002) Loss 2.5066 (2.6852) Prec@1 40.625 (35.377) Prec@5 68.750 (65.939) Epoch: [5][8260/11272] Time 0.900 (0.837) Data 0.001 (0.002) Loss 2.6684 (2.6853) Prec@1 35.625 (35.376) Prec@5 70.625 (65.939) Epoch: [5][8270/11272] Time 0.748 (0.837) Data 0.001 (0.002) Loss 2.5640 (2.6852) Prec@1 37.500 (35.376) Prec@5 70.625 (65.939) Epoch: [5][8280/11272] Time 0.911 (0.837) Data 0.001 (0.002) Loss 2.6575 (2.6852) Prec@1 36.875 (35.376) Prec@5 65.000 (65.939) Epoch: [5][8290/11272] Time 0.818 (0.837) Data 0.001 (0.002) Loss 2.7153 (2.6853) Prec@1 37.500 (35.377) Prec@5 64.375 (65.938) Epoch: [5][8300/11272] Time 0.792 (0.837) Data 0.001 (0.002) Loss 2.6255 (2.6853) Prec@1 36.875 (35.376) Prec@5 67.500 (65.939) Epoch: [5][8310/11272] Time 0.755 (0.837) Data 0.001 (0.002) Loss 2.7288 (2.6854) Prec@1 33.125 (35.375) Prec@5 68.125 (65.938) Epoch: [5][8320/11272] Time 0.951 (0.837) Data 0.003 (0.002) Loss 2.6138 (2.6853) Prec@1 35.000 (35.376) Prec@5 66.875 (65.939) Epoch: [5][8330/11272] Time 0.856 (0.837) Data 0.001 (0.002) Loss 2.5814 (2.6853) Prec@1 36.250 (35.376) Prec@5 65.625 (65.939) Epoch: [5][8340/11272] Time 0.747 (0.837) Data 0.001 (0.002) Loss 2.3860 (2.6852) Prec@1 39.375 (35.378) Prec@5 71.250 (65.940) Epoch: [5][8350/11272] Time 0.786 (0.837) Data 0.001 (0.002) Loss 2.4782 (2.6851) Prec@1 41.250 (35.379) Prec@5 68.750 (65.943) Epoch: [5][8360/11272] Time 0.899 (0.837) Data 0.002 (0.002) Loss 2.3925 (2.6851) Prec@1 41.875 (35.380) Prec@5 70.625 (65.943) Epoch: [5][8370/11272] Time 0.875 (0.837) Data 0.001 (0.002) Loss 2.2526 (2.6851) Prec@1 43.750 (35.380) Prec@5 71.875 (65.945) Epoch: [5][8380/11272] Time 0.737 (0.837) Data 0.001 (0.002) Loss 2.5228 (2.6850) Prec@1 38.750 (35.381) Prec@5 66.875 (65.943) Epoch: [5][8390/11272] Time 0.744 (0.837) Data 0.001 (0.002) Loss 2.4042 (2.6851) Prec@1 45.000 (35.381) Prec@5 71.250 (65.942) Epoch: [5][8400/11272] Time 1.009 (0.837) Data 0.001 (0.002) Loss 2.7056 (2.6851) Prec@1 32.500 (35.379) Prec@5 66.875 (65.941) Epoch: [5][8410/11272] Time 0.751 (0.837) Data 0.003 (0.002) Loss 2.7239 (2.6851) Prec@1 33.125 (35.379) Prec@5 64.375 (65.943) Epoch: [5][8420/11272] Time 0.740 (0.837) Data 0.002 (0.002) Loss 2.3773 (2.6851) Prec@1 41.250 (35.380) Prec@5 74.375 (65.945) Epoch: [5][8430/11272] Time 0.942 (0.837) Data 0.002 (0.002) Loss 2.9149 (2.6851) Prec@1 28.750 (35.379) Prec@5 58.750 (65.942) Epoch: [5][8440/11272] Time 0.928 (0.837) Data 0.003 (0.002) Loss 2.6068 (2.6851) Prec@1 32.500 (35.378) Prec@5 68.125 (65.944) Epoch: [5][8450/11272] Time 0.747 (0.837) Data 0.002 (0.002) Loss 2.7378 (2.6852) Prec@1 34.375 (35.376) Prec@5 58.750 (65.941) Epoch: [5][8460/11272] Time 0.774 (0.837) Data 0.003 (0.002) Loss 2.7237 (2.6853) Prec@1 40.000 (35.376) Prec@5 63.125 (65.940) Epoch: [5][8470/11272] Time 0.927 (0.837) Data 0.002 (0.002) Loss 2.4294 (2.6852) Prec@1 40.000 (35.377) Prec@5 71.250 (65.941) Epoch: [5][8480/11272] Time 0.861 (0.837) Data 0.001 (0.002) Loss 2.7693 (2.6852) Prec@1 35.625 (35.378) Prec@5 69.375 (65.942) Epoch: [5][8490/11272] Time 0.763 (0.837) Data 0.002 (0.002) Loss 2.5394 (2.6853) Prec@1 43.750 (35.379) Prec@5 68.125 (65.941) Epoch: [5][8500/11272] Time 0.751 (0.837) Data 0.002 (0.002) Loss 2.5186 (2.6851) Prec@1 36.250 (35.379) Prec@5 68.125 (65.942) Epoch: [5][8510/11272] Time 0.862 (0.837) Data 0.001 (0.002) Loss 2.6369 (2.6852) Prec@1 35.000 (35.377) Prec@5 63.125 (65.940) Epoch: [5][8520/11272] Time 0.900 (0.837) Data 0.002 (0.002) Loss 2.4777 (2.6851) Prec@1 39.375 (35.378) Prec@5 67.500 (65.940) Epoch: [5][8530/11272] Time 0.752 (0.837) Data 0.002 (0.002) Loss 2.6753 (2.6852) Prec@1 34.375 (35.377) Prec@5 66.250 (65.939) Epoch: [5][8540/11272] Time 0.942 (0.837) Data 0.002 (0.002) Loss 2.6960 (2.6852) Prec@1 33.750 (35.375) Prec@5 66.250 (65.938) Epoch: [5][8550/11272] Time 0.936 (0.837) Data 0.002 (0.002) Loss 2.5533 (2.6853) Prec@1 36.250 (35.375) Prec@5 72.500 (65.938) Epoch: [5][8560/11272] Time 0.808 (0.837) Data 0.003 (0.002) Loss 2.5509 (2.6853) Prec@1 40.000 (35.375) Prec@5 69.375 (65.937) Epoch: [5][8570/11272] Time 0.753 (0.837) Data 0.001 (0.002) Loss 2.6906 (2.6853) Prec@1 36.875 (35.376) Prec@5 65.625 (65.938) Epoch: [5][8580/11272] Time 0.926 (0.837) Data 0.002 (0.002) Loss 2.4223 (2.6852) Prec@1 45.000 (35.378) Prec@5 71.875 (65.940) Epoch: [5][8590/11272] Time 0.892 (0.837) Data 0.001 (0.002) Loss 2.8597 (2.6851) Prec@1 31.250 (35.380) Prec@5 68.750 (65.941) Epoch: [5][8600/11272] Time 0.772 (0.837) Data 0.001 (0.002) Loss 2.7305 (2.6851) Prec@1 30.000 (35.380) Prec@5 66.875 (65.942) Epoch: [5][8610/11272] Time 0.747 (0.837) Data 0.001 (0.002) Loss 2.6722 (2.6851) Prec@1 30.625 (35.381) Prec@5 63.125 (65.942) Epoch: [5][8620/11272] Time 0.985 (0.837) Data 0.002 (0.002) Loss 2.8944 (2.6852) Prec@1 28.125 (35.380) Prec@5 62.500 (65.940) Epoch: [5][8630/11272] Time 0.866 (0.837) Data 0.001 (0.002) Loss 2.8670 (2.6851) Prec@1 30.625 (35.382) Prec@5 65.000 (65.943) Epoch: [5][8640/11272] Time 0.731 (0.837) Data 0.002 (0.002) Loss 2.6281 (2.6850) Prec@1 36.250 (35.383) Prec@5 66.875 (65.944) Epoch: [5][8650/11272] Time 0.775 (0.837) Data 0.002 (0.002) Loss 2.8424 (2.6851) Prec@1 31.250 (35.383) Prec@5 60.625 (65.942) Epoch: [5][8660/11272] Time 0.853 (0.837) Data 0.001 (0.002) Loss 2.4313 (2.6851) Prec@1 37.500 (35.384) Prec@5 68.750 (65.943) Epoch: [5][8670/11272] Time 0.776 (0.837) Data 0.003 (0.002) Loss 2.5306 (2.6851) Prec@1 40.625 (35.385) Prec@5 66.875 (65.942) Epoch: [5][8680/11272] Time 0.742 (0.837) Data 0.001 (0.002) Loss 2.2687 (2.6850) Prec@1 46.875 (35.387) Prec@5 73.125 (65.941) Epoch: [5][8690/11272] Time 0.875 (0.837) Data 0.002 (0.002) Loss 2.6622 (2.6850) Prec@1 35.000 (35.387) Prec@5 69.375 (65.942) Epoch: [5][8700/11272] Time 0.856 (0.837) Data 0.001 (0.002) Loss 2.9824 (2.6851) Prec@1 32.500 (35.386) Prec@5 63.125 (65.942) Epoch: [5][8710/11272] Time 0.761 (0.837) Data 0.002 (0.002) Loss 2.7012 (2.6851) Prec@1 41.250 (35.388) Prec@5 63.750 (65.942) Epoch: [5][8720/11272] Time 0.785 (0.837) Data 0.002 (0.002) Loss 2.7844 (2.6852) Prec@1 35.000 (35.387) Prec@5 63.750 (65.941) Epoch: [5][8730/11272] Time 0.960 (0.837) Data 0.002 (0.002) Loss 2.9491 (2.6852) Prec@1 30.000 (35.387) Prec@5 62.500 (65.941) Epoch: [5][8740/11272] Time 0.856 (0.837) Data 0.001 (0.002) Loss 2.7035 (2.6851) Prec@1 30.625 (35.389) Prec@5 68.125 (65.943) Epoch: [5][8750/11272] Time 0.746 (0.837) Data 0.001 (0.002) Loss 2.7960 (2.6852) Prec@1 33.125 (35.388) Prec@5 60.625 (65.941) Epoch: [5][8760/11272] Time 0.748 (0.837) Data 0.001 (0.002) Loss 2.5237 (2.6852) Prec@1 38.125 (35.390) Prec@5 70.000 (65.942) Epoch: [5][8770/11272] Time 0.931 (0.837) Data 0.002 (0.002) Loss 2.6922 (2.6851) Prec@1 37.500 (35.392) Prec@5 63.750 (65.944) Epoch: [5][8780/11272] Time 0.911 (0.837) Data 0.001 (0.002) Loss 2.5768 (2.6851) Prec@1 33.750 (35.393) Prec@5 63.750 (65.943) Epoch: [5][8790/11272] Time 0.752 (0.837) Data 0.003 (0.002) Loss 2.4515 (2.6850) Prec@1 36.250 (35.394) Prec@5 73.750 (65.944) Epoch: [5][8800/11272] Time 0.908 (0.837) Data 0.001 (0.002) Loss 2.6382 (2.6849) Prec@1 39.375 (35.395) Prec@5 68.750 (65.944) Epoch: [5][8810/11272] Time 1.023 (0.837) Data 0.002 (0.002) Loss 2.8668 (2.6851) Prec@1 32.500 (35.392) Prec@5 63.750 (65.943) Epoch: [5][8820/11272] Time 0.762 (0.837) Data 0.001 (0.002) Loss 2.8110 (2.6851) Prec@1 34.375 (35.391) Prec@5 58.125 (65.942) Epoch: [5][8830/11272] Time 0.779 (0.837) Data 0.001 (0.002) Loss 2.7367 (2.6852) Prec@1 37.500 (35.390) Prec@5 66.250 (65.940) Epoch: [5][8840/11272] Time 0.904 (0.837) Data 0.001 (0.002) Loss 2.6604 (2.6851) Prec@1 40.000 (35.392) Prec@5 64.375 (65.942) Epoch: [5][8850/11272] Time 0.845 (0.837) Data 0.001 (0.002) Loss 2.7520 (2.6850) Prec@1 33.750 (35.391) Prec@5 62.500 (65.942) Epoch: [5][8860/11272] Time 0.794 (0.837) Data 0.001 (0.002) Loss 2.6801 (2.6850) Prec@1 36.250 (35.392) Prec@5 65.000 (65.943) Epoch: [5][8870/11272] Time 0.830 (0.837) Data 0.001 (0.002) Loss 2.6897 (2.6850) Prec@1 31.250 (35.391) Prec@5 66.250 (65.944) Epoch: [5][8880/11272] Time 0.899 (0.837) Data 0.001 (0.002) Loss 2.6663 (2.6850) Prec@1 32.500 (35.390) Prec@5 70.000 (65.944) Epoch: [5][8890/11272] Time 0.899 (0.837) Data 0.003 (0.002) Loss 2.6422 (2.6850) Prec@1 35.000 (35.391) Prec@5 67.500 (65.944) Epoch: [5][8900/11272] Time 0.771 (0.837) Data 0.002 (0.002) Loss 2.5474 (2.6851) Prec@1 36.875 (35.389) Prec@5 69.375 (65.944) Epoch: [5][8910/11272] Time 0.745 (0.837) Data 0.001 (0.002) Loss 2.8052 (2.6851) Prec@1 31.250 (35.388) Prec@5 58.125 (65.943) Epoch: [5][8920/11272] Time 0.867 (0.837) Data 0.002 (0.002) Loss 2.8668 (2.6851) Prec@1 30.000 (35.389) Prec@5 65.000 (65.944) Epoch: [5][8930/11272] Time 0.765 (0.837) Data 0.003 (0.002) Loss 2.9679 (2.6851) Prec@1 31.875 (35.387) Prec@5 60.000 (65.944) Epoch: [5][8940/11272] Time 0.788 (0.837) Data 0.002 (0.002) Loss 2.6561 (2.6851) Prec@1 29.375 (35.385) Prec@5 63.750 (65.943) Epoch: [5][8950/11272] Time 0.919 (0.837) Data 0.001 (0.002) Loss 2.7998 (2.6851) Prec@1 34.375 (35.383) Prec@5 63.125 (65.942) Epoch: [5][8960/11272] Time 0.922 (0.837) Data 0.001 (0.002) Loss 2.7389 (2.6852) Prec@1 35.000 (35.383) Prec@5 66.875 (65.942) Epoch: [5][8970/11272] Time 0.791 (0.837) Data 0.002 (0.002) Loss 2.8803 (2.6852) Prec@1 34.375 (35.384) Prec@5 61.875 (65.943) Epoch: [5][8980/11272] Time 0.778 (0.837) Data 0.001 (0.002) Loss 2.6474 (2.6853) Prec@1 34.375 (35.382) Prec@5 68.750 (65.943) Epoch: [5][8990/11272] Time 0.840 (0.837) Data 0.002 (0.002) Loss 2.4971 (2.6853) Prec@1 43.750 (35.382) Prec@5 72.500 (65.943) Epoch: [5][9000/11272] Time 0.949 (0.837) Data 0.002 (0.002) Loss 2.7862 (2.6853) Prec@1 29.375 (35.383) Prec@5 65.000 (65.943) Epoch: [5][9010/11272] Time 0.804 (0.837) Data 0.002 (0.002) Loss 2.8703 (2.6853) Prec@1 30.000 (35.382) Prec@5 60.625 (65.942) Epoch: [5][9020/11272] Time 0.773 (0.837) Data 0.001 (0.002) Loss 2.5971 (2.6853) Prec@1 40.000 (35.381) Prec@5 67.500 (65.942) Epoch: [5][9030/11272] Time 0.882 (0.837) Data 0.001 (0.002) Loss 2.5934 (2.6853) Prec@1 38.750 (35.381) Prec@5 66.250 (65.941) Epoch: [5][9040/11272] Time 0.869 (0.837) Data 0.002 (0.002) Loss 2.6894 (2.6854) Prec@1 36.875 (35.380) Prec@5 65.625 (65.940) Epoch: [5][9050/11272] Time 0.793 (0.837) Data 0.001 (0.002) Loss 2.6119 (2.6853) Prec@1 42.500 (35.381) Prec@5 65.000 (65.940) Epoch: [5][9060/11272] Time 0.926 (0.837) Data 0.002 (0.002) Loss 2.5357 (2.6853) Prec@1 42.500 (35.384) Prec@5 68.125 (65.942) Epoch: [5][9070/11272] Time 0.905 (0.837) Data 0.002 (0.002) Loss 2.7037 (2.6852) Prec@1 34.375 (35.385) Prec@5 67.500 (65.943) Epoch: [5][9080/11272] Time 0.768 (0.837) Data 0.001 (0.002) Loss 2.6684 (2.6852) Prec@1 33.750 (35.385) Prec@5 66.250 (65.942) Epoch: [5][9090/11272] Time 0.768 (0.837) Data 0.001 (0.002) Loss 2.8993 (2.6853) Prec@1 35.000 (35.385) Prec@5 59.375 (65.940) Epoch: [5][9100/11272] Time 0.821 (0.837) Data 0.004 (0.002) Loss 2.6394 (2.6853) Prec@1 35.000 (35.388) Prec@5 67.500 (65.942) Epoch: [5][9110/11272] Time 0.881 (0.837) Data 0.001 (0.002) Loss 2.7807 (2.6852) Prec@1 32.500 (35.387) Prec@5 64.375 (65.942) Epoch: [5][9120/11272] Time 0.771 (0.837) Data 0.002 (0.002) Loss 2.8962 (2.6853) Prec@1 38.125 (35.388) Prec@5 61.875 (65.942) Epoch: [5][9130/11272] Time 0.763 (0.837) Data 0.001 (0.002) Loss 2.7819 (2.6852) Prec@1 31.250 (35.388) Prec@5 62.500 (65.943) Epoch: [5][9140/11272] Time 0.868 (0.837) Data 0.001 (0.002) Loss 2.8805 (2.6852) Prec@1 27.500 (35.388) Prec@5 60.625 (65.943) Epoch: [5][9150/11272] Time 0.908 (0.837) Data 0.002 (0.002) Loss 2.8201 (2.6852) Prec@1 39.375 (35.390) Prec@5 63.125 (65.942) Epoch: [5][9160/11272] Time 0.791 (0.837) Data 0.002 (0.002) Loss 2.5958 (2.6852) Prec@1 33.750 (35.390) Prec@5 68.750 (65.944) Epoch: [5][9170/11272] Time 0.740 (0.837) Data 0.001 (0.002) Loss 2.8720 (2.6852) Prec@1 35.625 (35.392) Prec@5 62.500 (65.944) Epoch: [5][9180/11272] Time 0.897 (0.837) Data 0.002 (0.002) Loss 2.8470 (2.6852) Prec@1 31.875 (35.391) Prec@5 58.750 (65.942) Epoch: [5][9190/11272] Time 0.894 (0.837) Data 0.002 (0.002) Loss 2.8358 (2.6851) Prec@1 33.750 (35.393) Prec@5 59.375 (65.944) Epoch: [5][9200/11272] Time 0.792 (0.837) Data 0.003 (0.002) Loss 2.4212 (2.6850) Prec@1 41.250 (35.394) Prec@5 69.375 (65.944) Epoch: [5][9210/11272] Time 0.868 (0.837) Data 0.002 (0.002) Loss 2.8556 (2.6850) Prec@1 33.125 (35.395) Prec@5 63.125 (65.945) Epoch: [5][9220/11272] Time 0.911 (0.837) Data 0.001 (0.002) Loss 2.5859 (2.6850) Prec@1 36.250 (35.394) Prec@5 67.500 (65.945) Epoch: [5][9230/11272] Time 0.771 (0.837) Data 0.002 (0.002) Loss 2.5167 (2.6849) Prec@1 32.500 (35.395) Prec@5 67.500 (65.945) Epoch: [5][9240/11272] Time 0.773 (0.837) Data 0.002 (0.002) Loss 2.3627 (2.6849) Prec@1 40.000 (35.395) Prec@5 66.250 (65.945) Epoch: [5][9250/11272] Time 0.902 (0.837) Data 0.003 (0.002) Loss 2.7098 (2.6849) Prec@1 36.875 (35.395) Prec@5 61.875 (65.944) Epoch: [5][9260/11272] Time 0.863 (0.837) Data 0.002 (0.002) Loss 2.8742 (2.6850) Prec@1 30.625 (35.394) Prec@5 62.500 (65.943) Epoch: [5][9270/11272] Time 0.824 (0.837) Data 0.002 (0.002) Loss 2.5923 (2.6849) Prec@1 37.500 (35.396) Prec@5 71.250 (65.945) Epoch: [5][9280/11272] Time 0.762 (0.837) Data 0.001 (0.002) Loss 2.6084 (2.6849) Prec@1 36.250 (35.396) Prec@5 65.625 (65.945) Epoch: [5][9290/11272] Time 0.985 (0.837) Data 0.002 (0.002) Loss 2.8199 (2.6849) Prec@1 31.875 (35.395) Prec@5 68.125 (65.945) Epoch: [5][9300/11272] Time 0.912 (0.837) Data 0.001 (0.002) Loss 2.4140 (2.6849) Prec@1 39.375 (35.396) Prec@5 71.250 (65.946) Epoch: [5][9310/11272] Time 0.738 (0.837) Data 0.001 (0.002) Loss 2.6208 (2.6848) Prec@1 35.625 (35.397) Prec@5 71.250 (65.948) Epoch: [5][9320/11272] Time 0.741 (0.837) Data 0.002 (0.002) Loss 2.5612 (2.6848) Prec@1 40.625 (35.399) Prec@5 68.750 (65.949) Epoch: [5][9330/11272] Time 0.915 (0.837) Data 0.001 (0.002) Loss 2.7517 (2.6847) Prec@1 36.250 (35.401) Prec@5 63.750 (65.950) Epoch: [5][9340/11272] Time 0.817 (0.837) Data 0.002 (0.002) Loss 2.3018 (2.6847) Prec@1 38.125 (35.401) Prec@5 73.125 (65.950) Epoch: [5][9350/11272] Time 0.758 (0.837) Data 0.002 (0.002) Loss 2.7456 (2.6847) Prec@1 29.375 (35.400) Prec@5 63.750 (65.950) Epoch: [5][9360/11272] Time 0.913 (0.837) Data 0.001 (0.002) Loss 2.8050 (2.6847) Prec@1 29.375 (35.400) Prec@5 65.000 (65.951) Epoch: [5][9370/11272] Time 0.901 (0.837) Data 0.001 (0.002) Loss 2.8086 (2.6847) Prec@1 31.250 (35.401) Prec@5 63.750 (65.950) Epoch: [5][9380/11272] Time 0.774 (0.837) Data 0.002 (0.002) Loss 2.6330 (2.6847) Prec@1 36.250 (35.401) Prec@5 63.125 (65.948) Epoch: [5][9390/11272] Time 0.742 (0.837) Data 0.001 (0.002) Loss 2.8097 (2.6847) Prec@1 36.875 (35.402) Prec@5 65.625 (65.947) Epoch: [5][9400/11272] Time 0.917 (0.837) Data 0.001 (0.002) Loss 2.6405 (2.6847) Prec@1 43.125 (35.402) Prec@5 65.625 (65.948) Epoch: [5][9410/11272] Time 0.858 (0.837) Data 0.001 (0.002) Loss 2.3913 (2.6847) Prec@1 38.125 (35.402) Prec@5 71.250 (65.947) Epoch: [5][9420/11272] Time 0.770 (0.837) Data 0.000 (0.002) Loss 2.5227 (2.6847) Prec@1 40.000 (35.402) Prec@5 70.625 (65.947) Epoch: [5][9430/11272] Time 0.735 (0.837) Data 0.001 (0.002) Loss 2.8773 (2.6848) Prec@1 32.500 (35.402) Prec@5 64.375 (65.946) Epoch: [5][9440/11272] Time 0.887 (0.837) Data 0.001 (0.002) Loss 2.6911 (2.6848) Prec@1 35.625 (35.402) Prec@5 66.250 (65.945) Epoch: [5][9450/11272] Time 0.848 (0.837) Data 0.001 (0.002) Loss 2.5723 (2.6848) Prec@1 36.250 (35.403) Prec@5 70.000 (65.946) Epoch: [5][9460/11272] Time 0.769 (0.837) Data 0.002 (0.002) Loss 2.4401 (2.6849) Prec@1 42.500 (35.401) Prec@5 73.750 (65.946) Epoch: [5][9470/11272] Time 0.871 (0.837) Data 0.001 (0.002) Loss 2.7693 (2.6849) Prec@1 36.875 (35.400) Prec@5 67.500 (65.945) Epoch: [5][9480/11272] Time 0.924 (0.837) Data 0.002 (0.002) Loss 2.6707 (2.6849) Prec@1 35.000 (35.401) Prec@5 66.875 (65.947) Epoch: [5][9490/11272] Time 0.714 (0.837) Data 0.002 (0.002) Loss 2.8638 (2.6849) Prec@1 31.875 (35.400) Prec@5 65.000 (65.948) Epoch: [5][9500/11272] Time 0.750 (0.837) Data 0.001 (0.002) Loss 2.6467 (2.6850) Prec@1 33.750 (35.397) Prec@5 66.250 (65.946) Epoch: [5][9510/11272] Time 0.867 (0.837) Data 0.001 (0.002) Loss 2.5954 (2.6849) Prec@1 38.750 (35.399) Prec@5 66.250 (65.948) Epoch: [5][9520/11272] Time 0.885 (0.837) Data 0.002 (0.002) Loss 2.6627 (2.6849) Prec@1 33.750 (35.399) Prec@5 64.375 (65.948) Epoch: [5][9530/11272] Time 0.737 (0.837) Data 0.001 (0.002) Loss 2.7838 (2.6849) Prec@1 33.125 (35.398) Prec@5 66.250 (65.948) Epoch: [5][9540/11272] Time 0.780 (0.837) Data 0.002 (0.002) Loss 2.5473 (2.6848) Prec@1 41.250 (35.400) Prec@5 66.875 (65.949) Epoch: [5][9550/11272] Time 0.902 (0.837) Data 0.001 (0.002) Loss 2.6095 (2.6848) Prec@1 34.375 (35.400) Prec@5 66.875 (65.949) Epoch: [5][9560/11272] Time 0.908 (0.837) Data 0.001 (0.002) Loss 2.7095 (2.6847) Prec@1 33.125 (35.399) Prec@5 65.000 (65.951) Epoch: [5][9570/11272] Time 0.774 (0.837) Data 0.001 (0.002) Loss 2.5803 (2.6847) Prec@1 38.750 (35.401) Prec@5 67.500 (65.951) Epoch: [5][9580/11272] Time 0.814 (0.837) Data 0.002 (0.002) Loss 2.7492 (2.6847) Prec@1 34.375 (35.399) Prec@5 65.000 (65.952) Epoch: [5][9590/11272] Time 0.910 (0.837) Data 0.002 (0.002) Loss 2.8560 (2.6847) Prec@1 34.375 (35.401) Prec@5 63.125 (65.953) Epoch: [5][9600/11272] Time 0.767 (0.837) Data 0.003 (0.002) Loss 2.7912 (2.6847) Prec@1 35.625 (35.401) Prec@5 63.750 (65.954) Epoch: [5][9610/11272] Time 0.771 (0.837) Data 0.001 (0.002) Loss 2.7880 (2.6847) Prec@1 28.125 (35.399) Prec@5 61.250 (65.953) Epoch: [5][9620/11272] Time 0.896 (0.837) Data 0.001 (0.002) Loss 2.5952 (2.6847) Prec@1 36.875 (35.398) Prec@5 68.125 (65.954) Epoch: [5][9630/11272] Time 0.878 (0.837) Data 0.001 (0.002) Loss 2.8427 (2.6847) Prec@1 35.000 (35.398) Prec@5 65.000 (65.954) Epoch: [5][9640/11272] Time 0.767 (0.837) Data 0.001 (0.002) Loss 2.4045 (2.6847) Prec@1 37.500 (35.399) Prec@5 68.750 (65.953) Epoch: [5][9650/11272] Time 0.800 (0.837) Data 0.001 (0.002) Loss 2.7813 (2.6847) Prec@1 37.500 (35.399) Prec@5 63.750 (65.953) Epoch: [5][9660/11272] Time 0.913 (0.837) Data 0.001 (0.002) Loss 2.4991 (2.6847) Prec@1 40.625 (35.398) Prec@5 68.750 (65.953) Epoch: [5][9670/11272] Time 0.935 (0.837) Data 0.002 (0.002) Loss 2.5511 (2.6846) Prec@1 38.750 (35.399) Prec@5 64.375 (65.952) Epoch: [5][9680/11272] Time 0.756 (0.837) Data 0.002 (0.002) Loss 2.5710 (2.6846) Prec@1 36.250 (35.400) Prec@5 66.875 (65.952) Epoch: [5][9690/11272] Time 0.767 (0.837) Data 0.002 (0.002) Loss 2.4578 (2.6845) Prec@1 36.250 (35.400) Prec@5 70.625 (65.953) Epoch: [5][9700/11272] Time 0.886 (0.837) Data 0.002 (0.002) Loss 2.8319 (2.6846) Prec@1 33.125 (35.398) Prec@5 62.500 (65.953) Epoch: [5][9710/11272] Time 0.819 (0.837) Data 0.001 (0.002) Loss 2.5511 (2.6846) Prec@1 36.250 (35.398) Prec@5 66.875 (65.950) Epoch: [5][9720/11272] Time 0.777 (0.837) Data 0.002 (0.002) Loss 2.5562 (2.6847) Prec@1 40.625 (35.399) Prec@5 66.250 (65.950) Epoch: [5][9730/11272] Time 0.987 (0.837) Data 0.001 (0.002) Loss 2.6838 (2.6846) Prec@1 33.750 (35.401) Prec@5 61.250 (65.950) Epoch: [5][9740/11272] Time 0.823 (0.837) Data 0.001 (0.002) Loss 2.7168 (2.6845) Prec@1 35.000 (35.403) Prec@5 65.625 (65.950) Epoch: [5][9750/11272] Time 0.765 (0.837) Data 0.002 (0.002) Loss 3.0132 (2.6845) Prec@1 33.125 (35.403) Prec@5 59.375 (65.950) Epoch: [5][9760/11272] Time 0.792 (0.837) Data 0.002 (0.002) Loss 2.6587 (2.6845) Prec@1 36.250 (35.404) Prec@5 66.250 (65.952) Epoch: [5][9770/11272] Time 0.849 (0.837) Data 0.002 (0.002) Loss 2.7210 (2.6845) Prec@1 35.625 (35.403) Prec@5 63.125 (65.951) Epoch: [5][9780/11272] Time 0.884 (0.837) Data 0.002 (0.002) Loss 2.5159 (2.6844) Prec@1 41.250 (35.403) Prec@5 63.750 (65.951) Epoch: [5][9790/11272] Time 0.808 (0.837) Data 0.002 (0.002) Loss 2.6478 (2.6844) Prec@1 32.500 (35.403) Prec@5 70.625 (65.952) Epoch: [5][9800/11272] Time 0.766 (0.837) Data 0.001 (0.002) Loss 2.9063 (2.6844) Prec@1 34.375 (35.402) Prec@5 59.375 (65.950) Epoch: [5][9810/11272] Time 0.867 (0.837) Data 0.001 (0.002) Loss 2.7288 (2.6844) Prec@1 30.000 (35.404) Prec@5 67.500 (65.951) Epoch: [5][9820/11272] Time 0.879 (0.837) Data 0.001 (0.002) Loss 2.7483 (2.6844) Prec@1 37.500 (35.404) Prec@5 64.375 (65.951) Epoch: [5][9830/11272] Time 0.743 (0.836) Data 0.001 (0.002) Loss 2.8327 (2.6845) Prec@1 31.875 (35.403) Prec@5 61.875 (65.950) Epoch: [5][9840/11272] Time 0.747 (0.837) Data 0.002 (0.002) Loss 2.8203 (2.6845) Prec@1 31.875 (35.404) Prec@5 63.750 (65.949) Epoch: [5][9850/11272] Time 0.909 (0.837) Data 0.001 (0.002) Loss 2.8998 (2.6845) Prec@1 33.750 (35.403) Prec@5 59.375 (65.947) Epoch: [5][9860/11272] Time 0.760 (0.837) Data 0.004 (0.002) Loss 2.7564 (2.6846) Prec@1 33.750 (35.402) Prec@5 63.750 (65.945) Epoch: [5][9870/11272] Time 0.754 (0.837) Data 0.002 (0.002) Loss 2.8745 (2.6846) Prec@1 34.375 (35.403) Prec@5 60.625 (65.944) Epoch: [5][9880/11272] Time 0.848 (0.837) Data 0.002 (0.002) Loss 2.7222 (2.6846) Prec@1 30.625 (35.402) Prec@5 60.625 (65.943) Epoch: [5][9890/11272] Time 0.874 (0.836) Data 0.001 (0.002) Loss 2.5338 (2.6847) Prec@1 33.125 (35.400) Prec@5 66.875 (65.942) Epoch: [5][9900/11272] Time 0.769 (0.836) Data 0.001 (0.002) Loss 2.5279 (2.6846) Prec@1 38.125 (35.401) Prec@5 70.625 (65.942) Epoch: [5][9910/11272] Time 0.766 (0.836) Data 0.001 (0.002) Loss 2.3974 (2.6846) Prec@1 43.125 (35.402) Prec@5 74.375 (65.943) Epoch: [5][9920/11272] Time 0.886 (0.837) Data 0.002 (0.002) Loss 2.7196 (2.6846) Prec@1 36.250 (35.402) Prec@5 65.000 (65.942) Epoch: [5][9930/11272] Time 0.860 (0.837) Data 0.001 (0.002) Loss 2.9921 (2.6846) Prec@1 32.500 (35.402) Prec@5 58.750 (65.942) Epoch: [5][9940/11272] Time 0.751 (0.837) Data 0.001 (0.002) Loss 2.8289 (2.6846) Prec@1 35.625 (35.403) Prec@5 60.625 (65.943) Epoch: [5][9950/11272] Time 0.758 (0.837) Data 0.001 (0.002) Loss 2.8005 (2.6846) Prec@1 34.375 (35.403) Prec@5 66.250 (65.945) Epoch: [5][9960/11272] Time 0.936 (0.837) Data 0.002 (0.002) Loss 2.7452 (2.6846) Prec@1 30.000 (35.402) Prec@5 66.875 (65.945) Epoch: [5][9970/11272] Time 0.925 (0.837) Data 0.002 (0.002) Loss 2.6045 (2.6846) Prec@1 37.500 (35.400) Prec@5 71.250 (65.946) Epoch: [5][9980/11272] Time 0.795 (0.837) Data 0.001 (0.002) Loss 2.6733 (2.6846) Prec@1 36.250 (35.400) Prec@5 61.250 (65.945) Epoch: [5][9990/11272] Time 0.983 (0.837) Data 0.003 (0.002) Loss 2.7312 (2.6847) Prec@1 36.250 (35.398) Prec@5 63.125 (65.944) Epoch: [5][10000/11272] Time 0.957 (0.837) Data 0.002 (0.002) Loss 2.7441 (2.6847) Prec@1 34.375 (35.397) Prec@5 64.375 (65.944) Epoch: [5][10010/11272] Time 0.764 (0.837) Data 0.001 (0.002) Loss 2.6335 (2.6848) Prec@1 35.000 (35.397) Prec@5 63.125 (65.944) Epoch: [5][10020/11272] Time 0.777 (0.837) Data 0.001 (0.002) Loss 2.9055 (2.6848) Prec@1 39.375 (35.397) Prec@5 66.875 (65.945) Epoch: [5][10030/11272] Time 0.885 (0.837) Data 0.002 (0.002) Loss 2.5322 (2.6848) Prec@1 40.000 (35.397) Prec@5 68.125 (65.945) Epoch: [5][10040/11272] Time 0.913 (0.837) Data 0.001 (0.002) Loss 2.6091 (2.6848) Prec@1 32.500 (35.399) Prec@5 64.375 (65.946) Epoch: [5][10050/11272] Time 0.777 (0.837) Data 0.002 (0.002) Loss 2.7245 (2.6848) Prec@1 35.000 (35.399) Prec@5 65.625 (65.946) Epoch: [5][10060/11272] Time 0.792 (0.837) Data 0.002 (0.002) Loss 2.6985 (2.6848) Prec@1 35.625 (35.399) Prec@5 67.500 (65.946) Epoch: [5][10070/11272] Time 0.900 (0.837) Data 0.001 (0.002) Loss 2.8559 (2.6849) Prec@1 31.875 (35.398) Prec@5 64.375 (65.946) Epoch: [5][10080/11272] Time 0.879 (0.837) Data 0.001 (0.002) Loss 2.8709 (2.6849) Prec@1 32.500 (35.398) Prec@5 60.625 (65.946) Epoch: [5][10090/11272] Time 0.750 (0.837) Data 0.001 (0.002) Loss 2.5684 (2.6849) Prec@1 44.375 (35.399) Prec@5 69.375 (65.947) Epoch: [5][10100/11272] Time 0.745 (0.837) Data 0.001 (0.002) Loss 2.6519 (2.6848) Prec@1 40.625 (35.401) Prec@5 71.250 (65.948) Epoch: [5][10110/11272] Time 0.874 (0.837) Data 0.001 (0.002) Loss 2.8458 (2.6848) Prec@1 33.750 (35.403) Prec@5 62.500 (65.947) Epoch: [5][10120/11272] Time 0.905 (0.837) Data 0.001 (0.002) Loss 2.6557 (2.6849) Prec@1 37.500 (35.402) Prec@5 68.125 (65.946) Epoch: [5][10130/11272] Time 0.763 (0.837) Data 0.002 (0.002) Loss 2.5368 (2.6849) Prec@1 34.375 (35.402) Prec@5 70.625 (65.947) Epoch: [5][10140/11272] Time 0.962 (0.837) Data 0.002 (0.002) Loss 2.6416 (2.6849) Prec@1 31.875 (35.402) Prec@5 69.375 (65.948) Epoch: [5][10150/11272] Time 0.944 (0.837) Data 0.001 (0.002) Loss 2.7084 (2.6849) Prec@1 35.000 (35.403) Prec@5 63.125 (65.946) Epoch: [5][10160/11272] Time 0.754 (0.837) Data 0.002 (0.002) Loss 2.4010 (2.6849) Prec@1 40.000 (35.402) Prec@5 66.875 (65.946) Epoch: [5][10170/11272] Time 0.757 (0.837) Data 0.001 (0.002) Loss 2.5699 (2.6849) Prec@1 34.375 (35.402) Prec@5 69.375 (65.946) Epoch: [5][10180/11272] Time 0.870 (0.837) Data 0.001 (0.002) Loss 2.4647 (2.6849) Prec@1 36.250 (35.402) Prec@5 71.875 (65.946) Epoch: [5][10190/11272] Time 0.898 (0.837) Data 0.001 (0.002) Loss 2.7844 (2.6849) Prec@1 33.125 (35.403) Prec@5 68.125 (65.948) Epoch: [5][10200/11272] Time 0.742 (0.837) Data 0.001 (0.002) Loss 2.7585 (2.6849) Prec@1 38.750 (35.403) Prec@5 65.625 (65.948) Epoch: [5][10210/11272] Time 0.768 (0.837) Data 0.002 (0.002) Loss 2.5379 (2.6850) Prec@1 37.500 (35.401) Prec@5 68.750 (65.946) Epoch: [5][10220/11272] Time 0.871 (0.837) Data 0.001 (0.002) Loss 2.6235 (2.6849) Prec@1 31.250 (35.401) Prec@5 65.625 (65.947) Epoch: [5][10230/11272] Time 0.866 (0.837) Data 0.001 (0.002) Loss 2.6649 (2.6849) Prec@1 35.000 (35.401) Prec@5 63.750 (65.946) Epoch: [5][10240/11272] Time 0.772 (0.836) Data 0.002 (0.002) Loss 2.4905 (2.6849) Prec@1 41.250 (35.400) Prec@5 72.500 (65.946) Epoch: [5][10250/11272] Time 0.904 (0.836) Data 0.002 (0.002) Loss 2.7556 (2.6849) Prec@1 33.750 (35.399) Prec@5 61.250 (65.946) Epoch: [5][10260/11272] Time 0.843 (0.836) Data 0.001 (0.002) Loss 2.5215 (2.6848) Prec@1 42.500 (35.402) Prec@5 66.250 (65.948) Epoch: [5][10270/11272] Time 0.782 (0.836) Data 0.002 (0.002) Loss 2.5354 (2.6847) Prec@1 37.500 (35.404) Prec@5 65.625 (65.949) Epoch: [5][10280/11272] Time 0.753 (0.836) Data 0.001 (0.002) Loss 2.3034 (2.6847) Prec@1 38.750 (35.403) Prec@5 73.750 (65.949) Epoch: [5][10290/11272] Time 0.879 (0.836) Data 0.001 (0.002) Loss 2.9072 (2.6848) Prec@1 33.125 (35.403) Prec@5 63.750 (65.948) Epoch: [5][10300/11272] Time 0.918 (0.836) Data 0.002 (0.002) Loss 2.4759 (2.6847) Prec@1 43.125 (35.404) Prec@5 70.000 (65.949) Epoch: [5][10310/11272] Time 0.755 (0.836) Data 0.004 (0.002) Loss 2.6577 (2.6847) Prec@1 35.625 (35.403) Prec@5 66.250 (65.951) Epoch: [5][10320/11272] Time 0.748 (0.836) Data 0.001 (0.002) Loss 2.9232 (2.6846) Prec@1 33.125 (35.404) Prec@5 58.750 (65.952) Epoch: [5][10330/11272] Time 0.907 (0.836) Data 0.002 (0.002) Loss 2.6639 (2.6846) Prec@1 33.750 (35.405) Prec@5 69.375 (65.953) Epoch: [5][10340/11272] Time 0.894 (0.836) Data 0.001 (0.002) Loss 2.6237 (2.6846) Prec@1 31.250 (35.404) Prec@5 61.875 (65.952) Epoch: [5][10350/11272] Time 0.746 (0.836) Data 0.001 (0.002) Loss 2.4302 (2.6846) Prec@1 38.125 (35.404) Prec@5 69.375 (65.953) Epoch: [5][10360/11272] Time 0.789 (0.836) Data 0.002 (0.002) Loss 2.8426 (2.6846) Prec@1 31.875 (35.403) Prec@5 62.500 (65.953) Epoch: [5][10370/11272] Time 0.898 (0.836) Data 0.001 (0.002) Loss 2.7462 (2.6847) Prec@1 36.250 (35.403) Prec@5 66.875 (65.952) Epoch: [5][10380/11272] Time 0.889 (0.836) Data 0.002 (0.002) Loss 2.8386 (2.6847) Prec@1 34.375 (35.401) Prec@5 65.625 (65.949) Epoch: [5][10390/11272] Time 0.757 (0.836) Data 0.001 (0.002) Loss 2.7780 (2.6847) Prec@1 33.750 (35.401) Prec@5 64.375 (65.950) Epoch: [5][10400/11272] Time 1.017 (0.837) Data 0.002 (0.002) Loss 2.4507 (2.6846) Prec@1 40.625 (35.404) Prec@5 75.000 (65.953) Epoch: [5][10410/11272] Time 0.925 (0.837) Data 0.002 (0.002) Loss 2.5881 (2.6846) Prec@1 38.750 (35.404) Prec@5 68.125 (65.952) Epoch: [5][10420/11272] Time 0.743 (0.837) Data 0.001 (0.002) Loss 2.5788 (2.6846) Prec@1 34.375 (35.404) Prec@5 66.875 (65.953) Epoch: [5][10430/11272] Time 0.780 (0.837) Data 0.003 (0.002) Loss 2.8447 (2.6846) Prec@1 32.500 (35.405) Prec@5 59.375 (65.953) Epoch: [5][10440/11272] Time 0.934 (0.837) Data 0.002 (0.002) Loss 2.8175 (2.6846) Prec@1 31.875 (35.404) Prec@5 65.625 (65.953) Epoch: [5][10450/11272] Time 0.875 (0.837) Data 0.002 (0.002) Loss 2.6726 (2.6847) Prec@1 35.000 (35.403) Prec@5 62.500 (65.951) Epoch: [5][10460/11272] Time 0.749 (0.837) Data 0.002 (0.002) Loss 2.5522 (2.6846) Prec@1 37.500 (35.402) Prec@5 71.875 (65.952) Epoch: [5][10470/11272] Time 0.834 (0.837) Data 0.003 (0.002) Loss 2.7168 (2.6847) Prec@1 35.625 (35.400) Prec@5 66.875 (65.949) Epoch: [5][10480/11272] Time 0.862 (0.837) Data 0.001 (0.002) Loss 2.6201 (2.6847) Prec@1 38.750 (35.400) Prec@5 68.125 (65.950) Epoch: [5][10490/11272] Time 0.877 (0.837) Data 0.001 (0.002) Loss 2.4346 (2.6847) Prec@1 40.000 (35.400) Prec@5 72.500 (65.951) Epoch: [5][10500/11272] Time 0.747 (0.837) Data 0.001 (0.002) Loss 2.5976 (2.6847) Prec@1 37.500 (35.398) Prec@5 64.375 (65.948) Epoch: [5][10510/11272] Time 0.772 (0.837) Data 0.002 (0.002) Loss 2.4883 (2.6846) Prec@1 35.625 (35.400) Prec@5 70.625 (65.950) Epoch: [5][10520/11272] Time 0.903 (0.837) Data 0.002 (0.002) Loss 2.6547 (2.6846) Prec@1 33.125 (35.398) Prec@5 68.125 (65.950) Epoch: [5][10530/11272] Time 0.734 (0.837) Data 0.003 (0.002) Loss 2.4907 (2.6847) Prec@1 42.500 (35.400) Prec@5 74.375 (65.949) Epoch: [5][10540/11272] Time 0.791 (0.837) Data 0.001 (0.002) Loss 2.5565 (2.6846) Prec@1 38.125 (35.400) Prec@5 65.625 (65.949) Epoch: [5][10550/11272] Time 0.895 (0.837) Data 0.002 (0.002) Loss 2.5582 (2.6846) Prec@1 38.750 (35.401) Prec@5 71.875 (65.951) Epoch: [5][10560/11272] Time 0.899 (0.837) Data 0.002 (0.002) Loss 2.4672 (2.6846) Prec@1 40.000 (35.402) Prec@5 71.250 (65.951) Epoch: [5][10570/11272] Time 0.783 (0.837) Data 0.002 (0.002) Loss 2.7156 (2.6846) Prec@1 28.750 (35.401) Prec@5 67.500 (65.950) Epoch: [5][10580/11272] Time 0.857 (0.837) Data 0.002 (0.002) Loss 2.5475 (2.6847) Prec@1 40.000 (35.400) Prec@5 68.750 (65.949) Epoch: [5][10590/11272] Time 0.966 (0.837) Data 0.002 (0.002) Loss 2.4887 (2.6847) Prec@1 37.500 (35.400) Prec@5 73.125 (65.950) Epoch: [5][10600/11272] Time 0.919 (0.837) Data 0.002 (0.002) Loss 2.7556 (2.6847) Prec@1 38.125 (35.400) Prec@5 63.750 (65.950) Epoch: [5][10610/11272] Time 0.760 (0.837) Data 0.002 (0.002) Loss 2.9222 (2.6847) Prec@1 32.500 (35.401) Prec@5 57.500 (65.949) Epoch: [5][10620/11272] Time 0.778 (0.837) Data 0.002 (0.002) Loss 2.7775 (2.6846) Prec@1 29.375 (35.402) Prec@5 63.125 (65.951) Epoch: [5][10630/11272] Time 0.867 (0.837) Data 0.001 (0.002) Loss 2.7894 (2.6846) Prec@1 29.375 (35.402) Prec@5 64.375 (65.951) Epoch: [5][10640/11272] Time 0.957 (0.837) Data 0.002 (0.002) Loss 2.5187 (2.6846) Prec@1 35.000 (35.402) Prec@5 68.125 (65.951) Epoch: [5][10650/11272] Time 0.746 (0.837) Data 0.001 (0.002) Loss 2.5136 (2.6846) Prec@1 40.000 (35.403) Prec@5 66.875 (65.952) Epoch: [5][10660/11272] Time 0.924 (0.837) Data 0.002 (0.002) Loss 2.6068 (2.6846) Prec@1 33.750 (35.402) Prec@5 68.125 (65.952) Epoch: [5][10670/11272] Time 0.889 (0.837) Data 0.002 (0.002) Loss 2.9628 (2.6846) Prec@1 29.375 (35.401) Prec@5 63.125 (65.953) Epoch: [5][10680/11272] Time 0.797 (0.837) Data 0.003 (0.002) Loss 2.5065 (2.6845) Prec@1 39.375 (35.402) Prec@5 70.000 (65.954) Epoch: [5][10690/11272] Time 0.787 (0.837) Data 0.002 (0.002) Loss 2.7128 (2.6846) Prec@1 35.000 (35.402) Prec@5 64.375 (65.953) Epoch: [5][10700/11272] Time 0.929 (0.837) Data 0.001 (0.002) Loss 2.5534 (2.6846) Prec@1 41.875 (35.402) Prec@5 71.875 (65.952) Epoch: [5][10710/11272] Time 0.966 (0.837) Data 0.001 (0.002) Loss 2.3012 (2.6845) Prec@1 42.500 (35.403) Prec@5 73.125 (65.953) Epoch: [5][10720/11272] Time 0.760 (0.837) Data 0.001 (0.002) Loss 2.7888 (2.6844) Prec@1 36.250 (35.406) Prec@5 66.250 (65.955) Epoch: [5][10730/11272] Time 0.791 (0.837) Data 0.002 (0.002) Loss 2.8999 (2.6845) Prec@1 30.625 (35.405) Prec@5 58.750 (65.953) Epoch: [5][10740/11272] Time 0.967 (0.837) Data 0.002 (0.002) Loss 2.8419 (2.6845) Prec@1 32.500 (35.405) Prec@5 65.625 (65.952) Epoch: [5][10750/11272] Time 0.855 (0.837) Data 0.001 (0.002) Loss 2.4137 (2.6845) Prec@1 45.625 (35.406) Prec@5 70.625 (65.952) Epoch: [5][10760/11272] Time 0.761 (0.837) Data 0.002 (0.002) Loss 2.6684 (2.6844) Prec@1 34.375 (35.408) Prec@5 66.875 (65.953) Epoch: [5][10770/11272] Time 0.765 (0.837) Data 0.002 (0.002) Loss 2.6493 (2.6845) Prec@1 37.500 (35.406) Prec@5 66.250 (65.952) Epoch: [5][10780/11272] Time 0.877 (0.837) Data 0.001 (0.002) Loss 2.7050 (2.6844) Prec@1 37.500 (35.407) Prec@5 65.000 (65.953) Epoch: [5][10790/11272] Time 0.855 (0.837) Data 0.003 (0.002) Loss 2.8424 (2.6844) Prec@1 36.875 (35.407) Prec@5 63.125 (65.951) Epoch: [5][10800/11272] Time 0.753 (0.837) Data 0.001 (0.002) Loss 2.7008 (2.6844) Prec@1 34.375 (35.407) Prec@5 63.125 (65.950) Epoch: [5][10810/11272] Time 0.954 (0.837) Data 0.002 (0.002) Loss 2.8083 (2.6845) Prec@1 28.125 (35.405) Prec@5 64.375 (65.950) Epoch: [5][10820/11272] Time 0.905 (0.837) Data 0.002 (0.002) Loss 2.6063 (2.6845) Prec@1 36.250 (35.405) Prec@5 69.375 (65.950) Epoch: [5][10830/11272] Time 0.814 (0.837) Data 0.002 (0.002) Loss 2.4148 (2.6844) Prec@1 36.875 (35.405) Prec@5 68.750 (65.951) Epoch: [5][10840/11272] Time 0.795 (0.837) Data 0.002 (0.002) Loss 3.0026 (2.6844) Prec@1 31.875 (35.405) Prec@5 60.625 (65.951) Epoch: [5][10850/11272] Time 0.930 (0.837) Data 0.001 (0.002) Loss 2.4901 (2.6843) Prec@1 36.250 (35.407) Prec@5 70.625 (65.954) Epoch: [5][10860/11272] Time 0.907 (0.837) Data 0.002 (0.002) Loss 2.6822 (2.6844) Prec@1 35.000 (35.406) Prec@5 65.000 (65.954) Epoch: [5][10870/11272] Time 0.772 (0.837) Data 0.002 (0.002) Loss 2.6700 (2.6844) Prec@1 37.500 (35.406) Prec@5 67.500 (65.955) Epoch: [5][10880/11272] Time 0.747 (0.837) Data 0.002 (0.002) Loss 2.4295 (2.6844) Prec@1 41.875 (35.407) Prec@5 71.875 (65.955) Epoch: [5][10890/11272] Time 0.909 (0.837) Data 0.001 (0.002) Loss 2.7285 (2.6844) Prec@1 39.375 (35.406) Prec@5 66.250 (65.953) Epoch: [5][10900/11272] Time 0.962 (0.837) Data 0.002 (0.002) Loss 2.7458 (2.6845) Prec@1 28.750 (35.405) Prec@5 63.750 (65.954) Epoch: [5][10910/11272] Time 0.757 (0.837) Data 0.002 (0.002) Loss 2.5234 (2.6845) Prec@1 41.875 (35.403) Prec@5 63.750 (65.952) Epoch: [5][10920/11272] Time 0.979 (0.837) Data 0.002 (0.002) Loss 2.8735 (2.6845) Prec@1 35.000 (35.403) Prec@5 60.000 (65.950) Epoch: [5][10930/11272] Time 0.943 (0.837) Data 0.002 (0.002) Loss 2.5121 (2.6845) Prec@1 40.625 (35.404) Prec@5 71.250 (65.951) Epoch: [5][10940/11272] Time 0.791 (0.837) Data 0.002 (0.002) Loss 2.6541 (2.6845) Prec@1 32.500 (35.404) Prec@5 69.375 (65.950) Epoch: [5][10950/11272] Time 0.787 (0.837) Data 0.002 (0.002) Loss 2.7346 (2.6845) Prec@1 32.500 (35.404) Prec@5 63.125 (65.950) Epoch: [5][10960/11272] Time 0.982 (0.837) Data 0.001 (0.002) Loss 2.4455 (2.6845) Prec@1 40.625 (35.403) Prec@5 65.625 (65.950) Epoch: [5][10970/11272] Time 0.890 (0.837) Data 0.002 (0.002) Loss 2.7157 (2.6845) Prec@1 33.125 (35.402) Prec@5 70.000 (65.949) Epoch: [5][10980/11272] Time 0.819 (0.837) Data 0.002 (0.002) Loss 2.5753 (2.6846) Prec@1 38.750 (35.402) Prec@5 70.000 (65.949) Epoch: [5][10990/11272] Time 0.817 (0.837) Data 0.001 (0.002) Loss 2.8350 (2.6846) Prec@1 33.750 (35.401) Prec@5 64.375 (65.948) Epoch: [5][11000/11272] Time 0.994 (0.837) Data 0.002 (0.002) Loss 2.8220 (2.6846) Prec@1 34.375 (35.401) Prec@5 60.625 (65.948) Epoch: [5][11010/11272] Time 0.920 (0.837) Data 0.002 (0.002) Loss 2.5179 (2.6846) Prec@1 35.000 (35.401) Prec@5 72.500 (65.948) Epoch: [5][11020/11272] Time 0.766 (0.837) Data 0.002 (0.002) Loss 2.7010 (2.6845) Prec@1 34.375 (35.402) Prec@5 65.625 (65.950) Epoch: [5][11030/11272] Time 0.779 (0.837) Data 0.003 (0.002) Loss 2.5961 (2.6846) Prec@1 33.750 (35.402) Prec@5 68.125 (65.948) Epoch: [5][11040/11272] Time 0.951 (0.837) Data 0.002 (0.002) Loss 2.7954 (2.6846) Prec@1 31.875 (35.401) Prec@5 62.500 (65.948) Epoch: [5][11050/11272] Time 0.864 (0.837) Data 0.002 (0.002) Loss 2.7422 (2.6845) Prec@1 35.625 (35.402) Prec@5 65.000 (65.949) Epoch: [5][11060/11272] Time 0.769 (0.837) Data 0.001 (0.002) Loss 2.7054 (2.6846) Prec@1 36.875 (35.402) Prec@5 67.500 (65.948) Epoch: [5][11070/11272] Time 0.940 (0.837) Data 0.002 (0.002) Loss 3.0088 (2.6847) Prec@1 31.250 (35.400) Prec@5 56.250 (65.945) Epoch: [5][11080/11272] Time 0.910 (0.837) Data 0.002 (0.002) Loss 2.5800 (2.6847) Prec@1 33.125 (35.400) Prec@5 69.375 (65.944) Epoch: [5][11090/11272] Time 0.765 (0.837) Data 0.002 (0.002) Loss 2.5298 (2.6846) Prec@1 36.250 (35.403) Prec@5 69.375 (65.945) Epoch: [5][11100/11272] Time 0.748 (0.837) Data 0.002 (0.002) Loss 2.6837 (2.6847) Prec@1 33.750 (35.403) Prec@5 65.625 (65.945) Epoch: [5][11110/11272] Time 0.901 (0.837) Data 0.002 (0.002) Loss 2.3384 (2.6846) Prec@1 43.125 (35.404) Prec@5 72.500 (65.946) Epoch: [5][11120/11272] Time 0.852 (0.837) Data 0.002 (0.002) Loss 2.7165 (2.6846) Prec@1 33.750 (35.404) Prec@5 65.000 (65.946) Epoch: [5][11130/11272] Time 0.839 (0.837) Data 0.002 (0.002) Loss 2.6422 (2.6846) Prec@1 35.000 (35.404) Prec@5 66.875 (65.945) Epoch: [5][11140/11272] Time 0.752 (0.837) Data 0.001 (0.002) Loss 2.6216 (2.6846) Prec@1 36.250 (35.406) Prec@5 68.125 (65.945) Epoch: [5][11150/11272] Time 0.843 (0.837) Data 0.001 (0.002) Loss 2.6939 (2.6845) Prec@1 35.000 (35.407) Prec@5 65.625 (65.946) Epoch: [5][11160/11272] Time 0.894 (0.837) Data 0.001 (0.002) Loss 2.7500 (2.6845) Prec@1 35.625 (35.408) Prec@5 63.750 (65.945) Epoch: [5][11170/11272] Time 0.796 (0.837) Data 0.001 (0.002) Loss 2.6648 (2.6845) Prec@1 34.375 (35.408) Prec@5 62.500 (65.943) Epoch: [5][11180/11272] Time 0.784 (0.837) Data 0.002 (0.002) Loss 2.8807 (2.6845) Prec@1 28.125 (35.408) Prec@5 61.250 (65.943) Epoch: [5][11190/11272] Time 0.928 (0.837) Data 0.003 (0.002) Loss 2.5625 (2.6845) Prec@1 37.500 (35.408) Prec@5 67.500 (65.942) Epoch: [5][11200/11272] Time 0.827 (0.837) Data 0.002 (0.002) Loss 2.5977 (2.6846) Prec@1 35.625 (35.406) Prec@5 67.500 (65.941) Epoch: [5][11210/11272] Time 0.808 (0.837) Data 0.001 (0.002) Loss 2.7876 (2.6847) Prec@1 32.500 (35.405) Prec@5 69.375 (65.940) Epoch: [5][11220/11272] Time 0.932 (0.837) Data 0.002 (0.002) Loss 2.6667 (2.6846) Prec@1 34.375 (35.406) Prec@5 68.125 (65.942) Epoch: [5][11230/11272] Time 0.979 (0.837) Data 0.003 (0.002) Loss 2.6417 (2.6846) Prec@1 41.875 (35.408) Prec@5 67.500 (65.943) Epoch: [5][11240/11272] Time 0.778 (0.837) Data 0.001 (0.002) Loss 3.0967 (2.6845) Prec@1 27.500 (35.407) Prec@5 56.875 (65.943) Epoch: [5][11250/11272] Time 0.736 (0.837) Data 0.001 (0.002) Loss 2.8654 (2.6846) Prec@1 31.250 (35.406) Prec@5 65.000 (65.943) Epoch: [5][11260/11272] Time 0.956 (0.837) Data 0.002 (0.002) Loss 2.7376 (2.6845) Prec@1 36.875 (35.406) Prec@5 65.625 (65.943) Epoch: [5][11270/11272] Time 0.866 (0.837) Data 0.001 (0.002) Loss 2.8605 (2.6845) Prec@1 30.000 (35.408) Prec@5 61.250 (65.943) Test: [0/229] Time 3.314 (3.314) Loss 1.9380 (1.9380) Prec@1 37.500 (37.500) Prec@5 88.125 (88.125) Test: [10/229] Time 0.468 (0.850) Loss 1.8565 (2.3131) Prec@1 43.750 (41.080) Prec@5 87.500 (75.227) Test: [20/229] Time 0.342 (0.741) Loss 2.1807 (2.4055) Prec@1 50.625 (40.536) Prec@5 73.125 (71.935) Test: [30/229] Time 0.558 (0.693) Loss 2.1283 (2.3170) Prec@1 43.125 (42.117) Prec@5 84.375 (73.427) Test: [40/229] Time 0.342 (0.668) Loss 0.9772 (2.3201) Prec@1 76.875 (41.951) Prec@5 91.250 (73.110) Test: [50/229] Time 0.412 (0.665) Loss 3.9836 (2.3939) Prec@1 8.750 (40.882) Prec@5 36.875 (71.348) Test: [60/229] Time 0.642 (0.658) Loss 2.8578 (2.4401) Prec@1 21.250 (39.652) Prec@5 68.750 (70.215) Test: [70/229] Time 0.315 (0.645) Loss 2.5166 (2.4378) Prec@1 41.250 (39.463) Prec@5 67.500 (70.185) Test: [80/229] Time 0.612 (0.640) Loss 2.6136 (2.4560) Prec@1 29.375 (38.796) Prec@5 68.125 (70.378) Test: [90/229] Time 0.354 (0.644) Loss 1.9734 (2.4340) Prec@1 54.375 (38.970) Prec@5 74.375 (71.078) Test: [100/229] Time 0.731 (0.644) Loss 3.1813 (2.4268) Prec@1 28.125 (39.264) Prec@5 59.375 (71.176) Test: [110/229] Time 0.918 (0.643) Loss 2.4679 (2.4029) Prec@1 37.500 (39.718) Prec@5 68.125 (71.678) Test: [120/229] Time 0.538 (0.637) Loss 2.8898 (2.4185) Prec@1 26.875 (39.075) Prec@5 65.000 (71.560) Test: [130/229] Time 0.639 (0.634) Loss 1.9343 (2.4065) Prec@1 46.875 (39.432) Prec@5 80.625 (71.765) Test: [140/229] Time 0.930 (0.634) Loss 2.4765 (2.4112) Prec@1 35.625 (39.371) Prec@5 76.250 (71.866) Test: [150/229] Time 0.942 (0.633) Loss 1.9208 (2.4290) Prec@1 62.500 (38.911) Prec@5 79.375 (71.714) Test: [160/229] Time 0.431 (0.629) Loss 2.2546 (2.4348) Prec@1 48.125 (38.824) Prec@5 80.000 (71.646) Test: [170/229] Time 1.119 (0.632) Loss 2.9088 (2.4556) Prec@1 30.625 (38.385) Prec@5 65.625 (71.283) Test: [180/229] Time 0.347 (0.628) Loss 3.6058 (2.4722) Prec@1 21.875 (38.287) Prec@5 41.250 (70.829) Test: [190/229] Time 0.649 (0.626) Loss 2.4891 (2.4593) Prec@1 30.000 (38.649) Prec@5 77.500 (71.060) Test: [200/229] Time 0.366 (0.626) Loss 2.3864 (2.4426) Prec@1 38.125 (38.989) Prec@5 70.625 (71.499) Test: [210/229] Time 0.346 (0.624) Loss 1.5073 (2.4324) Prec@1 57.500 (39.304) Prec@5 88.750 (71.694) Test: [220/229] Time 0.876 (0.627) Loss 2.3673 (2.4243) Prec@1 39.375 (39.618) Prec@5 75.625 (71.796) * Prec@1 39.870 Prec@5 71.964 Epoch: [6][0/11272] Time 5.994 (5.994) Data 5.047 (5.047) Loss 2.6584 (2.6584) Prec@1 34.375 (34.375) Prec@5 66.875 (66.875) Epoch: [6][10/11272] Time 0.795 (1.293) Data 0.002 (0.460) Loss 2.5984 (2.6633) Prec@1 38.125 (36.250) Prec@5 66.875 (66.364) Epoch: [6][20/11272] Time 0.754 (1.071) Data 0.001 (0.242) Loss 2.5939 (2.6658) Prec@1 40.625 (36.726) Prec@5 66.250 (66.815) Epoch: [6][30/11272] Time 0.881 (0.995) Data 0.001 (0.164) Loss 2.6314 (2.6689) Prec@1 38.750 (36.169) Prec@5 63.125 (66.633) Epoch: [6][40/11272] Time 0.929 (0.954) Data 0.001 (0.125) Loss 2.5818 (2.6633) Prec@1 40.625 (36.433) Prec@5 68.750 (66.662) Epoch: [6][50/11272] Time 0.761 (0.927) Data 0.002 (0.101) Loss 2.6545 (2.6549) Prec@1 37.500 (36.385) Prec@5 67.500 (66.789) Epoch: [6][60/11272] Time 0.835 (0.913) Data 0.001 (0.084) Loss 2.5345 (2.6520) Prec@1 41.250 (36.537) Prec@5 69.375 (66.783) Epoch: [6][70/11272] Time 0.855 (0.901) Data 0.002 (0.073) Loss 2.4943 (2.6508) Prec@1 41.875 (36.408) Prec@5 66.875 (66.813) Epoch: [6][80/11272] Time 0.874 (0.893) Data 0.001 (0.064) Loss 2.4569 (2.6525) Prec@1 44.375 (36.474) Prec@5 69.375 (66.674) Epoch: [6][90/11272] Time 0.808 (0.884) Data 0.002 (0.057) Loss 2.4684 (2.6563) Prec@1 39.375 (36.277) Prec@5 66.875 (66.635) Epoch: [6][100/11272] Time 0.806 (0.879) Data 0.002 (0.052) Loss 2.5342 (2.6551) Prec@1 37.500 (36.200) Prec@5 67.500 (66.702) Epoch: [6][110/11272] Time 0.878 (0.877) Data 0.001 (0.047) Loss 2.6268 (2.6499) Prec@1 35.000 (36.334) Prec@5 66.250 (66.807) Epoch: [6][120/11272] Time 0.868 (0.874) Data 0.001 (0.043) Loss 2.4275 (2.6464) Prec@1 37.500 (36.348) Prec@5 70.625 (66.952) Epoch: [6][130/11272] Time 0.762 (0.872) Data 0.001 (0.040) Loss 2.6304 (2.6476) Prec@1 36.250 (36.336) Prec@5 66.250 (67.028) Epoch: [6][140/11272] Time 0.907 (0.871) Data 0.001 (0.037) Loss 2.5150 (2.6432) Prec@1 32.500 (36.423) Prec@5 70.625 (67.057) Epoch: [6][150/11272] Time 0.894 (0.868) Data 0.001 (0.035) Loss 2.4206 (2.6448) Prec@1 36.250 (36.420) Prec@5 70.000 (67.020) Epoch: [6][160/11272] Time 0.745 (0.865) Data 0.002 (0.033) Loss 2.7777 (2.6423) Prec@1 36.875 (36.475) Prec@5 66.875 (67.003) Epoch: [6][170/11272] Time 0.764 (0.862) Data 0.002 (0.031) Loss 2.5675 (2.6419) Prec@1 40.000 (36.524) Prec@5 68.750 (67.065) Epoch: [6][180/11272] Time 0.937 (0.860) Data 0.001 (0.030) Loss 2.6901 (2.6453) Prec@1 32.500 (36.347) Prec@5 64.375 (66.972) Epoch: [6][190/11272] Time 0.923 (0.858) Data 0.002 (0.028) Loss 2.5334 (2.6458) Prec@1 36.250 (36.365) Prec@5 72.500 (66.999) Epoch: [6][200/11272] Time 0.765 (0.857) Data 0.002 (0.027) Loss 2.5011 (2.6472) Prec@1 41.250 (36.396) Prec@5 68.125 (66.984) Epoch: [6][210/11272] Time 0.762 (0.856) Data 0.002 (0.026) Loss 2.7385 (2.6486) Prec@1 33.750 (36.345) Prec@5 68.750 (66.988) Epoch: [6][220/11272] Time 0.925 (0.857) Data 0.001 (0.024) Loss 2.9418 (2.6514) Prec@1 30.000 (36.309) Prec@5 60.625 (66.869) Epoch: [6][230/11272] Time 0.852 (3.441) Data 0.001 (0.023) Loss 2.4663 (2.6510) Prec@1 41.250 (36.356) Prec@5 71.250 (66.889) Epoch: [6][240/11272] Time 0.713 (3.332) Data 0.001 (0.023) Loss 2.5457 (2.6537) Prec@1 37.500 (36.310) Prec@5 70.000 (66.779) Epoch: [6][250/11272] Time 0.739 (3.232) Data 0.001 (0.022) Loss 2.8278 (2.6556) Prec@1 31.250 (36.223) Prec@5 62.500 (66.726) Epoch: [6][260/11272] Time 0.883 (3.141) Data 0.001 (0.021) Loss 2.5376 (2.6527) Prec@1 38.125 (36.262) Prec@5 65.000 (66.743) Epoch: [6][270/11272] Time 0.739 (3.055) Data 0.004 (0.020) Loss 2.3843 (2.6506) Prec@1 41.250 (36.308) Prec@5 73.125 (66.787) Epoch: [6][280/11272] Time 0.755 (2.976) Data 0.002 (0.020) Loss 2.7835 (2.6522) Prec@1 28.750 (36.257) Prec@5 63.750 (66.737) Epoch: [6][290/11272] Time 0.882 (2.902) Data 0.001 (0.019) Loss 2.7366 (2.6504) Prec@1 34.375 (36.306) Prec@5 66.875 (66.755) Epoch: [6][300/11272] Time 0.896 (2.833) Data 0.001 (0.018) Loss 2.7993 (2.6514) Prec@1 32.500 (36.265) Prec@5 60.625 (66.730) Epoch: [6][310/11272] Time 0.733 (2.768) Data 0.002 (0.018) Loss 2.7482 (2.6517) Prec@1 31.250 (36.228) Prec@5 61.250 (66.682) Epoch: [6][320/11272] Time 0.738 (2.707) Data 0.002 (0.017) Loss 2.8063 (2.6527) Prec@1 28.750 (36.194) Prec@5 62.500 (66.626) Epoch: [6][330/11272] Time 0.901 (2.651) Data 0.002 (0.017) Loss 2.5970 (2.6552) Prec@1 36.875 (36.163) Prec@5 65.625 (66.584) Epoch: [6][340/11272] Time 1.001 (2.597) Data 0.002 (0.016) Loss 2.9298 (2.6576) Prec@1 31.875 (36.109) Prec@5 63.750 (66.541) Epoch: [6][350/11272] Time 0.713 (2.546) Data 0.001 (0.016) Loss 2.9804 (2.6596) Prec@1 34.375 (36.088) Prec@5 60.625 (66.522) Epoch: [6][360/11272] Time 0.743 (2.498) Data 0.001 (0.016) Loss 2.5883 (2.6608) Prec@1 40.625 (36.068) Prec@5 70.625 (66.477) Epoch: [6][370/11272] Time 0.878 (2.453) Data 0.002 (0.015) Loss 2.6502 (2.6598) Prec@1 31.875 (36.050) Prec@5 67.500 (66.509) Epoch: [6][380/11272] Time 0.841 (2.410) Data 0.002 (0.015) Loss 2.7691 (2.6610) Prec@1 35.625 (36.035) Prec@5 61.875 (66.463) Epoch: [6][390/11272] Time 0.743 (2.369) Data 0.001 (0.015) Loss 2.4877 (2.6613) Prec@1 38.125 (36.037) Prec@5 66.250 (66.437) Epoch: [6][400/11272] Time 0.901 (2.331) Data 0.002 (0.014) Loss 2.7966 (2.6632) Prec@1 29.375 (35.982) Prec@5 66.250 (66.403) Epoch: [6][410/11272] Time 0.837 (2.295) Data 0.002 (0.014) Loss 2.5797 (2.6621) Prec@1 36.250 (36.026) Prec@5 63.750 (66.407) Epoch: [6][420/11272] Time 0.753 (2.260) Data 0.002 (0.014) Loss 2.7930 (2.6631) Prec@1 32.500 (35.975) Prec@5 63.750 (66.366) Epoch: [6][430/11272] Time 0.745 (2.227) Data 0.002 (0.013) Loss 2.9651 (2.6649) Prec@1 31.250 (35.916) Prec@5 60.625 (66.317) Epoch: [6][440/11272] Time 0.943 (2.195) Data 0.003 (0.013) Loss 2.7875 (2.6675) Prec@1 30.000 (35.879) Prec@5 63.750 (66.278) Epoch: [6][450/11272] Time 0.957 (2.165) Data 0.002 (0.013) Loss 2.6041 (2.6670) Prec@1 33.125 (35.881) Prec@5 68.125 (66.275) Epoch: [6][460/11272] Time 0.752 (2.135) Data 0.002 (0.013) Loss 2.4903 (2.6671) Prec@1 45.625 (35.877) Prec@5 71.875 (66.276) Epoch: [6][470/11272] Time 0.766 (2.107) Data 0.002 (0.012) Loss 2.5668 (2.6664) Prec@1 38.125 (35.890) Prec@5 68.750 (66.296) Epoch: [6][480/11272] Time 0.841 (2.081) Data 0.001 (0.012) Loss 2.5951 (2.6669) Prec@1 35.000 (35.854) Prec@5 70.625 (66.315) Epoch: [6][490/11272] Time 0.858 (2.055) Data 0.001 (0.012) Loss 2.7534 (2.6678) Prec@1 28.750 (35.821) Prec@5 65.625 (66.288) Epoch: [6][500/11272] Time 0.733 (2.030) Data 0.001 (0.012) Loss 2.7588 (2.6692) Prec@1 35.625 (35.817) Prec@5 65.000 (66.249) Epoch: [6][510/11272] Time 0.728 (2.007) Data 0.002 (0.011) Loss 2.9214 (2.6691) Prec@1 31.875 (35.806) Prec@5 63.750 (66.267) Epoch: [6][520/11272] Time 0.898 (1.984) Data 0.002 (0.011) Loss 2.5048 (2.6683) Prec@1 38.750 (35.830) Prec@5 65.000 (66.269) Epoch: [6][530/11272] Time 0.764 (1.962) Data 0.004 (0.011) Loss 2.7781 (2.6679) Prec@1 34.375 (35.847) Prec@5 66.250 (66.282) Epoch: [6][540/11272] Time 0.728 (1.941) Data 0.002 (0.011) Loss 2.7735 (2.6673) Prec@1 32.500 (35.856) Prec@5 66.250 (66.272) Epoch: [6][550/11272] Time 0.874 (1.921) Data 0.001 (0.011) Loss 2.6707 (2.6667) Prec@1 37.500 (35.855) Prec@5 68.125 (66.286) Epoch: [6][560/11272] Time 0.852 (1.901) Data 0.002 (0.011) Loss 2.7866 (2.6662) Prec@1 34.375 (35.892) Prec@5 63.750 (66.275) Epoch: [6][570/11272] Time 0.816 (1.883) Data 0.002 (0.010) Loss 3.0323 (2.6673) Prec@1 27.500 (35.916) Prec@5 60.625 (66.260) Epoch: [6][580/11272] Time 0.755 (1.865) Data 0.002 (0.010) Loss 2.6239 (2.6674) Prec@1 33.125 (35.909) Prec@5 70.625 (66.254) Epoch: [6][590/11272] Time 0.969 (1.847) Data 0.001 (0.010) Loss 2.5746 (2.6675) Prec@1 36.250 (35.912) Prec@5 70.000 (66.257) Epoch: [6][600/11272] Time 0.906 (1.830) Data 0.002 (0.010) Loss 2.8118 (2.6674) Prec@1 35.625 (35.926) Prec@5 63.125 (66.253) Epoch: [6][610/11272] Time 0.767 (1.814) Data 0.001 (0.010) Loss 2.4845 (2.6670) Prec@1 41.875 (35.929) Prec@5 66.250 (66.235) Epoch: [6][620/11272] Time 0.773 (1.798) Data 0.001 (0.010) Loss 2.7155 (2.6669) Prec@1 28.125 (35.915) Prec@5 66.250 (66.248) Epoch: [6][630/11272] Time 0.834 (1.782) Data 0.002 (0.010) Loss 2.5150 (2.6669) Prec@1 36.250 (35.913) Prec@5 64.375 (66.229) Epoch: [6][640/11272] Time 0.865 (1.767) Data 0.002 (0.009) Loss 2.7172 (2.6669) Prec@1 38.125 (35.929) Prec@5 63.125 (66.226) Epoch: [6][650/11272] Time 0.757 (1.752) Data 0.001 (0.009) Loss 2.7063 (2.6672) Prec@1 36.250 (35.932) Prec@5 65.000 (66.233) Epoch: [6][660/11272] Time 0.930 (1.738) Data 0.001 (0.009) Loss 2.6385 (2.6670) Prec@1 37.500 (35.927) Prec@5 67.500 (66.239) Epoch: [6][670/11272] Time 0.840 (1.725) Data 0.002 (0.009) Loss 2.7099 (2.6685) Prec@1 38.750 (35.920) Prec@5 56.250 (66.186) Epoch: [6][680/11272] Time 0.749 (1.711) Data 0.002 (0.009) Loss 2.8290 (2.6696) Prec@1 32.500 (35.903) Prec@5 61.875 (66.160) Epoch: [6][690/11272] Time 0.745 (1.699) Data 0.002 (0.009) Loss 2.9571 (2.6700) Prec@1 33.750 (35.903) Prec@5 61.250 (66.161) Epoch: [6][700/11272] Time 0.906 (1.686) Data 0.001 (0.009) Loss 2.8606 (2.6705) Prec@1 32.500 (35.884) Prec@5 65.625 (66.166) Epoch: [6][710/11272] Time 0.906 (1.674) Data 0.002 (0.009) Loss 2.9400 (2.6714) Prec@1 30.625 (35.861) Prec@5 61.875 (66.154) Epoch: [6][720/11272] Time 0.781 (1.662) Data 0.001 (0.009) Loss 2.5428 (2.6706) Prec@1 36.875 (35.881) Prec@5 71.250 (66.155) Epoch: [6][730/11272] Time 0.737 (1.650) Data 0.001 (0.009) Loss 2.7235 (2.6684) Prec@1 36.875 (35.911) Prec@5 63.125 (66.210) Epoch: [6][740/11272] Time 0.908 (1.639) Data 0.002 (0.008) Loss 2.5861 (2.6681) Prec@1 40.625 (35.925) Prec@5 66.875 (66.214) Epoch: [6][750/11272] Time 0.891 (1.629) Data 0.002 (0.008) Loss 2.8371 (2.6686) Prec@1 36.875 (35.912) Prec@5 68.125 (66.216) Epoch: [6][760/11272] Time 0.743 (1.618) Data 0.002 (0.008) Loss 2.7599 (2.6690) Prec@1 32.500 (35.898) Prec@5 62.500 (66.197) Epoch: [6][770/11272] Time 0.735 (1.608) Data 0.001 (0.008) Loss 2.8748 (2.6694) Prec@1 28.125 (35.877) Prec@5 60.625 (66.177) Epoch: [6][780/11272] Time 0.865 (1.598) Data 0.001 (0.008) Loss 2.6216 (2.6686) Prec@1 34.375 (35.891) Prec@5 68.750 (66.189) Epoch: [6][790/11272] Time 0.879 (1.588) Data 0.002 (0.008) Loss 2.5979 (2.6681) Prec@1 34.375 (35.898) Prec@5 68.125 (66.196) Epoch: [6][800/11272] Time 0.751 (1.578) Data 0.003 (0.008) Loss 2.6985 (2.6679) Prec@1 36.250 (35.889) Prec@5 67.500 (66.203) Epoch: [6][810/11272] Time 0.901 (1.569) Data 0.002 (0.008) Loss 2.7820 (2.6674) Prec@1 30.000 (35.896) Prec@5 65.000 (66.203) Epoch: [6][820/11272] Time 0.886 (1.560) Data 0.002 (0.008) Loss 2.7618 (2.6684) Prec@1 34.375 (35.869) Prec@5 60.000 (66.170) Epoch: [6][830/11272] Time 0.763 (1.551) Data 0.002 (0.008) Loss 2.6038 (2.6684) Prec@1 38.750 (35.844) Prec@5 65.625 (66.181) Epoch: [6][840/11272] Time 0.766 (1.542) Data 0.001 (0.008) Loss 2.7146 (2.6691) Prec@1 34.375 (35.836) Prec@5 67.500 (66.176) Epoch: [6][850/11272] Time 0.836 (1.534) Data 0.002 (0.008) Loss 2.2726 (2.6681) Prec@1 45.625 (35.866) Prec@5 76.875 (66.194) Epoch: [6][860/11272] Time 0.823 (1.525) Data 0.001 (0.007) Loss 2.6513 (2.6679) Prec@1 35.000 (35.874) Prec@5 70.625 (66.191) Epoch: [6][870/11272] Time 0.753 (1.517) Data 0.002 (0.007) Loss 2.8109 (2.6687) Prec@1 31.250 (35.863) Prec@5 61.875 (66.177) Epoch: [6][880/11272] Time 0.754 (1.509) Data 0.002 (0.007) Loss 2.4945 (2.6685) Prec@1 39.375 (35.853) Prec@5 65.000 (66.165) Epoch: [6][890/11272] Time 0.891 (1.501) Data 0.001 (0.007) Loss 2.3949 (2.6686) Prec@1 45.000 (35.852) Prec@5 71.250 (66.163) Epoch: [6][900/11272] Time 0.860 (1.494) Data 0.001 (0.007) Loss 2.7610 (2.6685) Prec@1 34.375 (35.843) Prec@5 68.125 (66.173) Epoch: [6][910/11272] Time 0.763 (1.487) Data 0.002 (0.007) Loss 2.4439 (2.6680) Prec@1 40.000 (35.856) Prec@5 67.500 (66.188) Epoch: [6][920/11272] Time 0.752 (1.480) Data 0.002 (0.007) Loss 2.7120 (2.6681) Prec@1 36.250 (35.857) Prec@5 65.000 (66.198) Epoch: [6][930/11272] Time 0.920 (1.473) Data 0.002 (0.007) Loss 2.4876 (2.6684) Prec@1 39.375 (35.851) Prec@5 71.250 (66.204) Epoch: [6][940/11272] Time 0.751 (1.466) Data 0.001 (0.007) Loss 2.9279 (2.6682) Prec@1 30.625 (35.859) Prec@5 65.000 (66.191) Epoch: [6][950/11272] Time 0.712 (1.459) Data 0.001 (0.007) Loss 2.9892 (2.6693) Prec@1 34.375 (35.840) Prec@5 60.000 (66.162) Epoch: [6][960/11272] Time 0.908 (1.453) Data 0.002 (0.007) Loss 2.7185 (2.6695) Prec@1 33.125 (35.840) Prec@5 63.750 (66.155) Epoch: [6][970/11272] Time 0.857 (1.446) Data 0.002 (0.007) Loss 2.6568 (2.6696) Prec@1 39.375 (35.835) Prec@5 64.375 (66.148) Epoch: [6][980/11272] Time 0.736 (1.440) Data 0.001 (0.007) Loss 2.5600 (2.6693) Prec@1 36.250 (35.840) Prec@5 68.750 (66.158) Epoch: [6][990/11272] Time 0.751 (1.433) Data 0.002 (0.007) Loss 2.8537 (2.6692) Prec@1 31.250 (35.843) Prec@5 58.125 (66.156) Epoch: [6][1000/11272] Time 0.887 (1.427) Data 0.002 (0.007) Loss 2.4718 (2.6697) Prec@1 40.000 (35.852) Prec@5 70.625 (66.148) Epoch: [6][1010/11272] Time 0.859 (1.421) Data 0.002 (0.007) Loss 2.8292 (2.6693) Prec@1 37.500 (35.867) Prec@5 63.125 (66.163) Epoch: [6][1020/11272] Time 0.786 (1.416) Data 0.003 (0.007) Loss 2.2963 (2.6686) Prec@1 40.000 (35.870) Prec@5 76.250 (66.172) Epoch: [6][1030/11272] Time 0.766 (1.410) Data 0.001 (0.007) Loss 2.6035 (2.6681) Prec@1 33.750 (35.875) Prec@5 68.125 (66.201) Epoch: [6][1040/11272] Time 0.895 (1.404) Data 0.001 (0.006) Loss 2.7899 (2.6681) Prec@1 34.375 (35.878) Prec@5 63.750 (66.200) Epoch: [6][1050/11272] Time 0.892 (1.399) Data 0.001 (0.006) Loss 2.6796 (2.6682) Prec@1 36.250 (35.875) Prec@5 67.500 (66.202) Epoch: [6][1060/11272] Time 0.782 (1.393) Data 0.002 (0.006) Loss 2.4991 (2.6681) Prec@1 40.000 (35.889) Prec@5 70.625 (66.205) Epoch: [6][1070/11272] Time 0.876 (1.388) Data 0.001 (0.006) Loss 2.5855 (2.6681) Prec@1 41.250 (35.903) Prec@5 68.125 (66.198) Epoch: [6][1080/11272] Time 0.886 (1.383) Data 0.002 (0.006) Loss 2.6945 (2.6680) Prec@1 34.375 (35.917) Prec@5 68.125 (66.199) Epoch: [6][1090/11272] Time 0.741 (1.378) Data 0.001 (0.006) Loss 2.4142 (2.6683) Prec@1 37.500 (35.918) Prec@5 70.625 (66.193) Epoch: [6][1100/11272] Time 0.773 (1.373) Data 0.002 (0.006) Loss 2.6848 (2.6685) Prec@1 35.625 (35.907) Prec@5 67.500 (66.193) Epoch: [6][1110/11272] Time 0.873 (1.368) Data 0.001 (0.006) Loss 2.4988 (2.6692) Prec@1 41.250 (35.886) Prec@5 68.125 (66.187) Epoch: [6][1120/11272] Time 0.848 (1.363) Data 0.001 (0.006) Loss 2.8908 (2.6695) Prec@1 31.250 (35.877) Prec@5 61.875 (66.180) Epoch: [6][1130/11272] Time 0.747 (1.358) Data 0.002 (0.006) Loss 2.6734 (2.6691) Prec@1 36.875 (35.880) Prec@5 66.250 (66.188) Epoch: [6][1140/11272] Time 0.744 (1.353) Data 0.001 (0.006) Loss 2.4508 (2.6688) Prec@1 41.250 (35.882) Prec@5 68.125 (66.193) Epoch: [6][1150/11272] Time 0.901 (1.348) Data 0.001 (0.006) Loss 2.8371 (2.6689) Prec@1 38.125 (35.881) Prec@5 63.125 (66.186) Epoch: [6][1160/11272] Time 0.868 (1.344) Data 0.002 (0.006) Loss 2.7300 (2.6696) Prec@1 35.625 (35.863) Prec@5 63.125 (66.174) Epoch: [6][1170/11272] Time 0.693 (1.339) Data 0.001 (0.006) Loss 2.6815 (2.6694) Prec@1 40.625 (35.858) Prec@5 64.375 (66.184) Epoch: [6][1180/11272] Time 0.729 (1.335) Data 0.001 (0.006) Loss 2.4454 (2.6692) Prec@1 39.375 (35.857) Prec@5 71.875 (66.185) Epoch: [6][1190/11272] Time 0.847 (1.330) Data 0.002 (0.006) Loss 2.5862 (2.6694) Prec@1 41.250 (35.863) Prec@5 65.625 (66.180) Epoch: [6][1200/11272] Time 0.730 (1.326) Data 0.003 (0.006) Loss 2.5410 (2.6697) Prec@1 38.125 (35.870) Prec@5 65.625 (66.159) Epoch: [6][1210/11272] Time 0.728 (1.322) Data 0.001 (0.006) Loss 2.6410 (2.6699) Prec@1 35.625 (35.870) Prec@5 69.375 (66.159) Epoch: [6][1220/11272] Time 0.936 (1.318) Data 0.002 (0.006) Loss 3.0274 (2.6702) Prec@1 29.375 (35.875) Prec@5 61.250 (66.152) Epoch: [6][1230/11272] Time 0.939 (1.314) Data 0.002 (0.006) Loss 2.5057 (2.6702) Prec@1 36.250 (35.871) Prec@5 66.875 (66.159) Epoch: [6][1240/11272] Time 0.764 (1.310) Data 0.002 (0.006) Loss 2.7093 (2.6700) Prec@1 33.750 (35.870) Prec@5 63.750 (66.163) Epoch: [6][1250/11272] Time 0.759 (1.306) Data 0.002 (0.006) Loss 2.6894 (2.6700) Prec@1 38.750 (35.875) Prec@5 64.375 (66.162) Epoch: [6][1260/11272] Time 0.849 (1.302) Data 0.001 (0.006) Loss 2.5680 (2.6697) Prec@1 38.125 (35.875) Prec@5 68.125 (66.162) Epoch: [6][1270/11272] Time 0.885 (1.299) Data 0.001 (0.006) Loss 2.4214 (2.6696) Prec@1 36.250 (35.868) Prec@5 66.250 (66.167) Epoch: [6][1280/11272] Time 0.753 (1.295) Data 0.002 (0.006) Loss 2.9709 (2.6697) Prec@1 30.625 (35.867) Prec@5 61.875 (66.170) Epoch: [6][1290/11272] Time 0.794 (1.291) Data 0.002 (0.006) Loss 2.6074 (2.6693) Prec@1 38.125 (35.876) Prec@5 66.250 (66.177) Epoch: [6][1300/11272] Time 0.871 (1.288) Data 0.002 (0.006) Loss 2.5854 (2.6692) Prec@1 33.125 (35.875) Prec@5 70.000 (66.189) Epoch: [6][1310/11272] Time 0.865 (1.284) Data 0.002 (0.005) Loss 2.6267 (2.6690) Prec@1 40.000 (35.885) Prec@5 70.625 (66.190) Epoch: [6][1320/11272] Time 0.763 (1.280) Data 0.002 (0.005) Loss 2.7011 (2.6692) Prec@1 38.750 (35.883) Prec@5 63.750 (66.182) Epoch: [6][1330/11272] Time 0.874 (1.277) Data 0.001 (0.005) Loss 2.4059 (2.6688) Prec@1 43.125 (35.888) Prec@5 70.625 (66.183) Epoch: [6][1340/11272] Time 0.852 (1.274) Data 0.001 (0.005) Loss 2.6678 (2.6697) Prec@1 38.750 (35.874) Prec@5 61.250 (66.162) Epoch: [6][1350/11272] Time 0.735 (1.270) Data 0.001 (0.005) Loss 2.6591 (2.6701) Prec@1 36.875 (35.867) Prec@5 67.500 (66.146) Epoch: [6][1360/11272] Time 0.743 (1.267) Data 0.001 (0.005) Loss 2.5507 (2.6701) Prec@1 36.250 (35.873) Prec@5 67.500 (66.143) Epoch: [6][1370/11272] Time 0.867 (1.264) Data 0.002 (0.005) Loss 2.7687 (2.6696) Prec@1 39.375 (35.885) Prec@5 65.000 (66.157) Epoch: [6][1380/11272] Time 0.861 (1.260) Data 0.001 (0.005) Loss 2.6853 (2.6694) Prec@1 36.875 (35.892) Prec@5 63.750 (66.161) Epoch: [6][1390/11272] Time 0.823 (1.257) Data 0.001 (0.005) Loss 2.5587 (2.6692) Prec@1 36.250 (35.892) Prec@5 71.250 (66.173) Epoch: [6][1400/11272] Time 0.797 (1.254) Data 0.001 (0.005) Loss 2.6528 (2.6690) Prec@1 36.250 (35.885) Prec@5 70.000 (66.182) Epoch: [6][1410/11272] Time 0.895 (1.251) Data 0.002 (0.005) Loss 2.7931 (2.6693) Prec@1 34.375 (35.888) Prec@5 61.875 (66.180) Epoch: [6][1420/11272] Time 0.902 (1.248) Data 0.001 (0.005) Loss 2.5900 (2.6690) Prec@1 33.125 (35.891) Prec@5 67.500 (66.183) Epoch: [6][1430/11272] Time 0.725 (1.245) Data 0.001 (0.005) Loss 2.6000 (2.6689) Prec@1 39.375 (35.897) Prec@5 69.375 (66.185) Epoch: [6][1440/11272] Time 0.795 (1.242) Data 0.002 (0.005) Loss 2.3810 (2.6684) Prec@1 43.125 (35.914) Prec@5 73.125 (66.190) Epoch: [6][1450/11272] Time 0.863 (1.239) Data 0.001 (0.005) Loss 2.6358 (2.6686) Prec@1 40.000 (35.916) Prec@5 61.875 (66.182) Epoch: [6][1460/11272] Time 0.766 (1.236) Data 0.003 (0.005) Loss 2.7530 (2.6686) Prec@1 30.000 (35.916) Prec@5 65.000 (66.183) Epoch: [6][1470/11272] Time 0.733 (1.233) Data 0.002 (0.005) Loss 2.8611 (2.6685) Prec@1 29.375 (35.910) Prec@5 63.750 (66.185) Epoch: [6][1480/11272] Time 0.859 (1.231) Data 0.001 (0.005) Loss 2.6538 (2.6683) Prec@1 36.250 (35.907) Prec@5 68.125 (66.185) Epoch: [6][1490/11272] Time 0.919 (1.228) Data 0.002 (0.005) Loss 2.5466 (2.6683) Prec@1 36.875 (35.906) Prec@5 70.000 (66.191) Epoch: [6][1500/11272] Time 0.751 (1.225) Data 0.001 (0.005) Loss 2.4347 (2.6678) Prec@1 41.250 (35.915) Prec@5 70.000 (66.200) Epoch: [6][1510/11272] Time 0.771 (1.223) Data 0.001 (0.005) Loss 2.5943 (2.6675) Prec@1 38.750 (35.926) Prec@5 66.250 (66.206) Epoch: [6][1520/11272] Time 0.895 (1.220) Data 0.002 (0.005) Loss 2.5068 (2.6675) Prec@1 40.625 (35.929) Prec@5 71.250 (66.207) Epoch: [6][1530/11272] Time 0.897 (1.217) Data 0.001 (0.005) Loss 2.4133 (2.6672) Prec@1 36.250 (35.925) Prec@5 72.500 (66.216) Epoch: [6][1540/11272] Time 0.719 (1.215) Data 0.001 (0.005) Loss 2.5936 (2.6679) Prec@1 39.375 (35.917) Prec@5 65.625 (66.204) Epoch: [6][1550/11272] Time 0.790 (1.212) Data 0.001 (0.005) Loss 2.6063 (2.6678) Prec@1 33.750 (35.925) Prec@5 67.500 (66.194) Epoch: [6][1560/11272] Time 0.869 (1.210) Data 0.001 (0.005) Loss 2.5949 (2.6678) Prec@1 36.875 (35.914) Prec@5 67.500 (66.196) Epoch: [6][1570/11272] Time 0.839 (1.207) Data 0.001 (0.005) Loss 2.5825 (2.6676) Prec@1 36.250 (35.914) Prec@5 65.625 (66.192) Epoch: [6][1580/11272] Time 0.761 (1.205) Data 0.001 (0.005) Loss 2.7064 (2.6682) Prec@1 36.250 (35.907) Prec@5 64.375 (66.170) Epoch: [6][1590/11272] Time 0.975 (1.202) Data 0.002 (0.005) Loss 2.8102 (2.6685) Prec@1 31.250 (35.900) Prec@5 63.750 (66.169) Epoch: [6][1600/11272] Time 0.914 (1.200) Data 0.001 (0.005) Loss 2.8159 (2.6684) Prec@1 30.000 (35.889) Prec@5 65.000 (66.178) Epoch: [6][1610/11272] Time 0.770 (1.198) Data 0.002 (0.005) Loss 2.7419 (2.6689) Prec@1 31.875 (35.876) Prec@5 63.125 (66.165) Epoch: [6][1620/11272] Time 0.750 (1.196) Data 0.001 (0.005) Loss 2.4616 (2.6686) Prec@1 41.875 (35.881) Prec@5 65.625 (66.171) Epoch: [6][1630/11272] Time 0.856 (1.193) Data 0.002 (0.005) Loss 2.4303 (2.6681) Prec@1 42.500 (35.883) Prec@5 70.625 (66.181) Epoch: [6][1640/11272] Time 0.846 (1.191) Data 0.003 (0.005) Loss 2.6792 (2.6686) Prec@1 41.250 (35.875) Prec@5 66.250 (66.173) Epoch: [6][1650/11272] Time 0.743 (1.189) Data 0.002 (0.005) Loss 2.6400 (2.6691) Prec@1 36.875 (35.865) Prec@5 68.125 (66.168) Epoch: [6][1660/11272] Time 0.718 (1.187) Data 0.002 (0.005) Loss 2.7044 (2.6695) Prec@1 33.750 (35.860) Prec@5 68.750 (66.159) Epoch: [6][1670/11272] Time 0.872 (1.184) Data 0.002 (0.005) Loss 2.5369 (2.6689) Prec@1 35.625 (35.865) Prec@5 68.750 (66.171) Epoch: [6][1680/11272] Time 0.837 (1.182) Data 0.001 (0.005) Loss 2.7026 (2.6691) Prec@1 36.875 (35.866) Prec@5 63.125 (66.168) Epoch: [6][1690/11272] Time 0.721 (1.180) Data 0.001 (0.005) Loss 2.7235 (2.6690) Prec@1 36.250 (35.874) Prec@5 68.125 (66.174) Epoch: [6][1700/11272] Time 0.723 (1.178) Data 0.001 (0.005) Loss 2.6506 (2.6690) Prec@1 31.875 (35.872) Prec@5 65.000 (66.172) Epoch: [6][1710/11272] Time 0.891 (1.176) Data 0.002 (0.005) Loss 2.5674 (2.6691) Prec@1 35.000 (35.861) Prec@5 63.750 (66.174) Epoch: [6][1720/11272] Time 0.855 (1.174) Data 0.002 (0.005) Loss 2.5928 (2.6691) Prec@1 33.750 (35.858) Prec@5 66.250 (66.167) Epoch: [6][1730/11272] Time 0.755 (1.172) Data 0.001 (0.005) Loss 2.5615 (2.6690) Prec@1 33.750 (35.857) Prec@5 68.125 (66.171) Epoch: [6][1740/11272] Time 0.993 (1.170) Data 0.002 (0.005) Loss 2.6922 (2.6688) Prec@1 33.125 (35.860) Prec@5 68.125 (66.178) Epoch: [6][1750/11272] Time 0.807 (1.168) Data 0.001 (0.005) Loss 2.8102 (2.6689) Prec@1 30.000 (35.855) Prec@5 61.875 (66.175) Epoch: [6][1760/11272] Time 0.741 (1.166) Data 0.002 (0.004) Loss 2.6522 (2.6689) Prec@1 40.000 (35.859) Prec@5 65.000 (66.170) Epoch: [6][1770/11272] Time 0.751 (1.164) Data 0.002 (0.004) Loss 2.6492 (2.6689) Prec@1 34.375 (35.853) Prec@5 68.125 (66.179) Epoch: [6][1780/11272] Time 0.880 (1.162) Data 0.001 (0.004) Loss 2.8794 (2.6693) Prec@1 33.125 (35.847) Prec@5 58.125 (66.172) Epoch: [6][1790/11272] Time 0.918 (1.160) Data 0.002 (0.004) Loss 2.4924 (2.6691) Prec@1 41.250 (35.855) Prec@5 70.625 (66.171) Epoch: [6][1800/11272] Time 0.749 (1.158) Data 0.002 (0.004) Loss 2.5839 (2.6695) Prec@1 36.875 (35.844) Prec@5 67.500 (66.164) Epoch: [6][1810/11272] Time 0.729 (1.156) Data 0.001 (0.004) Loss 2.7428 (2.6692) Prec@1 35.625 (35.848) Prec@5 66.250 (66.177) Epoch: [6][1820/11272] Time 0.894 (1.154) Data 0.002 (0.004) Loss 2.6609 (2.6696) Prec@1 34.375 (35.837) Prec@5 71.250 (66.181) Epoch: [6][1830/11272] Time 0.911 (1.153) Data 0.002 (0.004) Loss 2.5479 (2.6697) Prec@1 36.250 (35.833) Prec@5 68.750 (66.180) Epoch: [6][1840/11272] Time 0.789 (1.151) Data 0.002 (0.004) Loss 2.4855 (2.6691) Prec@1 34.375 (35.841) Prec@5 73.125 (66.198) Epoch: [6][1850/11272] Time 0.718 (1.149) Data 0.001 (0.004) Loss 2.8267 (2.6695) Prec@1 28.750 (35.831) Prec@5 62.500 (66.197) Epoch: [6][1860/11272] Time 0.995 (1.147) Data 0.002 (0.004) Loss 2.6024 (2.6700) Prec@1 35.625 (35.829) Prec@5 75.000 (66.195) Epoch: [6][1870/11272] Time 0.770 (1.146) Data 0.002 (0.004) Loss 2.6531 (2.6696) Prec@1 35.000 (35.833) Prec@5 66.250 (66.206) Epoch: [6][1880/11272] Time 0.726 (1.144) Data 0.001 (0.004) Loss 3.0763 (2.6696) Prec@1 32.500 (35.835) Prec@5 61.250 (66.205) Epoch: [6][1890/11272] Time 0.883 (1.142) Data 0.001 (0.004) Loss 3.0050 (2.6698) Prec@1 33.125 (35.835) Prec@5 61.250 (66.196) Epoch: [6][1900/11272] Time 0.888 (1.141) Data 0.002 (0.004) Loss 2.7135 (2.6702) Prec@1 38.125 (35.826) Prec@5 66.250 (66.188) Epoch: [6][1910/11272] Time 0.806 (1.139) Data 0.001 (0.004) Loss 2.4033 (2.6707) Prec@1 37.500 (35.815) Prec@5 75.625 (66.183) Epoch: [6][1920/11272] Time 0.762 (1.137) Data 0.002 (0.004) Loss 2.7509 (2.6708) Prec@1 38.125 (35.814) Prec@5 60.625 (66.175) Epoch: [6][1930/11272] Time 0.895 (1.136) Data 0.002 (0.004) Loss 2.6056 (2.6713) Prec@1 33.750 (35.797) Prec@5 65.000 (66.161) Epoch: [6][1940/11272] Time 0.838 (1.446) Data 0.001 (0.004) Loss 2.7083 (2.6715) Prec@1 31.875 (35.787) Prec@5 68.125 (66.161) Epoch: [6][1950/11272] Time 0.729 (1.442) Data 0.001 (0.004) Loss 2.5670 (2.6715) Prec@1 38.125 (35.792) Prec@5 66.250 (66.162) Epoch: [6][1960/11272] Time 0.753 (1.439) Data 0.001 (0.004) Loss 2.5931 (2.6716) Prec@1 33.750 (35.789) Prec@5 67.500 (66.158) Epoch: [6][1970/11272] Time 0.868 (1.436) Data 0.001 (0.004) Loss 2.6689 (2.6718) Prec@1 32.500 (35.787) Prec@5 70.625 (66.152) Epoch: [6][1980/11272] Time 0.889 (1.433) Data 0.002 (0.004) Loss 2.3881 (2.6717) Prec@1 37.500 (35.788) Prec@5 75.625 (66.155) Epoch: [6][1990/11272] Time 0.742 (1.430) Data 0.002 (0.004) Loss 2.8505 (2.6715) Prec@1 31.875 (35.791) Prec@5 61.875 (66.156) Epoch: [6][2000/11272] Time 0.949 (1.427) Data 0.001 (0.004) Loss 2.6180 (2.6712) Prec@1 37.500 (35.801) Prec@5 68.750 (66.160) Epoch: [6][2010/11272] Time 0.899 (1.424) Data 0.004 (0.004) Loss 2.7739 (2.6712) Prec@1 33.750 (35.803) Prec@5 64.375 (66.159) Epoch: [6][2020/11272] Time 0.759 (1.421) Data 0.001 (0.004) Loss 2.4293 (2.6713) Prec@1 35.625 (35.803) Prec@5 71.875 (66.156) Epoch: [6][2030/11272] Time 0.775 (1.418) Data 0.002 (0.004) Loss 2.6018 (2.6711) Prec@1 35.000 (35.803) Prec@5 66.250 (66.161) Epoch: [6][2040/11272] Time 0.838 (1.415) Data 0.001 (0.004) Loss 2.7275 (2.6708) Prec@1 34.375 (35.815) Prec@5 69.375 (66.172) Epoch: [6][2050/11272] Time 0.915 (1.412) Data 0.002 (0.004) Loss 2.6762 (2.6710) Prec@1 33.750 (35.808) Prec@5 63.750 (66.166) Epoch: [6][2060/11272] Time 0.759 (1.410) Data 0.002 (0.004) Loss 2.4833 (2.6711) Prec@1 38.125 (35.804) Prec@5 71.250 (66.164) Epoch: [6][2070/11272] Time 0.754 (1.407) Data 0.002 (0.004) Loss 2.7132 (2.6711) Prec@1 38.750 (35.806) Prec@5 62.500 (66.162) Epoch: [6][2080/11272] Time 0.917 (1.404) Data 0.002 (0.004) Loss 2.7239 (2.6713) Prec@1 30.625 (35.798) Prec@5 68.125 (66.158) Epoch: [6][2090/11272] Time 0.842 (1.401) Data 0.002 (0.004) Loss 2.9176 (2.6714) Prec@1 34.375 (35.798) Prec@5 57.500 (66.152) Epoch: [6][2100/11272] Time 0.748 (1.398) Data 0.002 (0.004) Loss 2.6346 (2.6715) Prec@1 34.375 (35.789) Prec@5 64.375 (66.145) Epoch: [6][2110/11272] Time 0.775 (1.396) Data 0.002 (0.004) Loss 2.8274 (2.6718) Prec@1 35.625 (35.791) Prec@5 64.375 (66.148) Epoch: [6][2120/11272] Time 0.842 (1.393) Data 0.001 (0.004) Loss 2.6264 (2.6719) Prec@1 35.000 (35.795) Prec@5 64.375 (66.145) Epoch: [6][2130/11272] Time 0.744 (1.390) Data 0.003 (0.004) Loss 2.4318 (2.6719) Prec@1 38.750 (35.790) Prec@5 70.000 (66.146) Epoch: [6][2140/11272] Time 0.783 (1.388) Data 0.002 (0.004) Loss 2.3539 (2.6715) Prec@1 41.875 (35.798) Prec@5 70.625 (66.153) Epoch: [6][2150/11272] Time 0.956 (1.385) Data 0.002 (0.004) Loss 2.6550 (2.6716) Prec@1 41.875 (35.793) Prec@5 68.125 (66.152) Epoch: [6][2160/11272] Time 0.849 (1.383) Data 0.001 (0.004) Loss 2.4944 (2.6715) Prec@1 42.500 (35.795) Prec@5 66.250 (66.149) Epoch: [6][2170/11272] Time 0.770 (1.380) Data 0.002 (0.004) Loss 2.6676 (2.6714) Prec@1 35.625 (35.799) Prec@5 68.125 (66.146) Epoch: [6][2180/11272] Time 0.755 (1.378) Data 0.003 (0.004) Loss 2.5272 (2.6719) Prec@1 40.625 (35.788) Prec@5 68.750 (66.125) Epoch: [6][2190/11272] Time 0.927 (1.375) Data 0.002 (0.004) Loss 2.8107 (2.6717) Prec@1 33.750 (35.797) Prec@5 63.750 (66.127) Epoch: [6][2200/11272] Time 0.837 (1.373) Data 0.001 (0.004) Loss 2.8082 (2.6717) Prec@1 31.875 (35.795) Prec@5 60.625 (66.128) Epoch: [6][2210/11272] Time 0.752 (1.370) Data 0.002 (0.004) Loss 2.7374 (2.6715) Prec@1 34.375 (35.801) Prec@5 66.875 (66.137) Epoch: [6][2220/11272] Time 0.822 (1.368) Data 0.002 (0.004) Loss 2.6942 (2.6714) Prec@1 35.625 (35.803) Prec@5 63.125 (66.137) Epoch: [6][2230/11272] Time 0.878 (1.365) Data 0.002 (0.004) Loss 2.8224 (2.6716) Prec@1 33.750 (35.811) Prec@5 63.125 (66.138) Epoch: [6][2240/11272] Time 0.842 (1.363) Data 0.002 (0.004) Loss 2.7691 (2.6714) Prec@1 33.125 (35.812) Prec@5 59.375 (66.138) Epoch: [6][2250/11272] Time 0.762 (1.361) Data 0.001 (0.004) Loss 2.6751 (2.6714) Prec@1 28.750 (35.805) Prec@5 66.250 (66.141) Epoch: [6][2260/11272] Time 0.897 (1.358) Data 0.002 (0.004) Loss 2.5349 (2.6716) Prec@1 40.625 (35.804) Prec@5 68.125 (66.137) Epoch: [6][2270/11272] Time 0.873 (1.356) Data 0.001 (0.004) Loss 2.5238 (2.6715) Prec@1 40.000 (35.806) Prec@5 69.375 (66.141) Epoch: [6][2280/11272] Time 0.827 (1.354) Data 0.002 (0.004) Loss 2.8459 (2.6720) Prec@1 31.250 (35.796) Prec@5 65.000 (66.130) Epoch: [6][2290/11272] Time 0.806 (1.352) Data 0.002 (0.004) Loss 2.8040 (2.6721) Prec@1 34.375 (35.795) Prec@5 63.125 (66.128) Epoch: [6][2300/11272] Time 0.890 (1.349) Data 0.002 (0.004) Loss 2.7196 (2.6723) Prec@1 35.000 (35.793) Prec@5 65.625 (66.119) Epoch: [6][2310/11272] Time 0.836 (1.347) Data 0.002 (0.004) Loss 2.8258 (2.6721) Prec@1 34.375 (35.791) Prec@5 63.125 (66.121) Epoch: [6][2320/11272] Time 0.796 (1.345) Data 0.001 (0.004) Loss 2.6351 (2.6722) Prec@1 40.000 (35.788) Prec@5 68.750 (66.119) Epoch: [6][2330/11272] Time 0.825 (1.343) Data 0.002 (0.004) Loss 2.7141 (2.6719) Prec@1 36.250 (35.794) Prec@5 64.375 (66.128) Epoch: [6][2340/11272] Time 0.935 (1.340) Data 0.002 (0.004) Loss 2.4141 (2.6719) Prec@1 43.125 (35.793) Prec@5 68.750 (66.126) Epoch: [6][2350/11272] Time 0.964 (1.338) Data 0.002 (0.004) Loss 2.5453 (2.6719) Prec@1 38.750 (35.790) Prec@5 67.500 (66.123) Epoch: [6][2360/11272] Time 0.740 (1.336) Data 0.001 (0.004) Loss 2.9338 (2.6720) Prec@1 31.250 (35.792) Prec@5 55.625 (66.116) Epoch: [6][2370/11272] Time 0.757 (1.334) Data 0.002 (0.004) Loss 2.5336 (2.6720) Prec@1 38.750 (35.794) Prec@5 70.625 (66.117) Epoch: [6][2380/11272] Time 0.981 (1.332) Data 0.002 (0.004) Loss 2.3511 (2.6719) Prec@1 41.875 (35.794) Prec@5 71.250 (66.116) Epoch: [6][2390/11272] Time 0.812 (1.330) Data 0.005 (0.004) Loss 2.7513 (2.6717) Prec@1 34.375 (35.798) Prec@5 60.625 (66.116) Epoch: [6][2400/11272] Time 0.815 (1.328) Data 0.002 (0.004) Loss 2.6199 (2.6714) Prec@1 35.625 (35.812) Prec@5 68.125 (66.125) Epoch: [6][2410/11272] Time 0.942 (1.326) Data 0.002 (0.004) Loss 2.5077 (2.6717) Prec@1 38.125 (35.804) Prec@5 68.750 (66.120) Epoch: [6][2420/11272] Time 0.950 (1.324) Data 0.002 (0.004) Loss 2.7811 (2.6718) Prec@1 32.500 (35.805) Prec@5 65.000 (66.116) Epoch: [6][2430/11272] Time 0.741 (1.322) Data 0.002 (0.004) Loss 2.5478 (2.6719) Prec@1 36.250 (35.801) Prec@5 68.750 (66.111) Epoch: [6][2440/11272] Time 0.788 (1.320) Data 0.001 (0.004) Loss 2.6952 (2.6721) Prec@1 34.375 (35.801) Prec@5 65.000 (66.109) Epoch: [6][2450/11272] Time 0.951 (1.318) Data 0.002 (0.004) Loss 2.3723 (2.6722) Prec@1 36.875 (35.792) Prec@5 75.625 (66.110) Epoch: [6][2460/11272] Time 0.820 (1.316) Data 0.001 (0.004) Loss 2.5338 (2.6720) Prec@1 36.250 (35.796) Prec@5 70.000 (66.119) Epoch: [6][2470/11272] Time 0.730 (1.314) Data 0.001 (0.004) Loss 2.5308 (2.6722) Prec@1 37.500 (35.791) Prec@5 72.500 (66.107) Epoch: [6][2480/11272] Time 0.780 (1.312) Data 0.002 (0.004) Loss 2.2914 (2.6721) Prec@1 40.625 (35.793) Prec@5 75.000 (66.109) Epoch: [6][2490/11272] Time 0.889 (1.310) Data 0.002 (0.004) Loss 2.8575 (2.6721) Prec@1 31.250 (35.792) Prec@5 63.750 (66.113) Epoch: [6][2500/11272] Time 0.861 (1.308) Data 0.001 (0.004) Loss 2.5847 (2.6720) Prec@1 34.375 (35.789) Prec@5 69.375 (66.113) Epoch: [6][2510/11272] Time 0.809 (1.306) Data 0.001 (0.004) Loss 2.8070 (2.6720) Prec@1 35.625 (35.792) Prec@5 64.375 (66.115) Epoch: [6][2520/11272] Time 0.893 (1.309) Data 0.002 (0.004) Loss 2.5976 (2.6720) Prec@1 34.375 (35.795) Prec@5 63.750 (66.115) Epoch: [6][2530/11272] Time 0.841 (1.307) Data 0.001 (0.004) Loss 2.5664 (2.6721) Prec@1 37.500 (35.794) Prec@5 64.375 (66.110) Epoch: [6][2540/11272] Time 0.750 (1.305) Data 0.001 (0.004) Loss 2.5391 (2.6720) Prec@1 38.125 (35.793) Prec@5 71.875 (66.116) Epoch: [6][2550/11272] Time 0.799 (1.303) Data 0.002 (0.004) Loss 2.8407 (2.6719) Prec@1 33.125 (35.791) Prec@5 61.875 (66.119) Epoch: [6][2560/11272] Time 0.864 (1.301) Data 0.001 (0.004) Loss 2.7614 (2.6719) Prec@1 33.125 (35.789) Prec@5 65.000 (66.118) Epoch: [6][2570/11272] Time 0.889 (1.300) Data 0.003 (0.004) Loss 2.5960 (2.6717) Prec@1 41.875 (35.794) Prec@5 68.125 (66.123) Epoch: [6][2580/11272] Time 0.761 (1.298) Data 0.002 (0.004) Loss 2.7772 (2.6715) Prec@1 31.250 (35.796) Prec@5 68.125 (66.127) Epoch: [6][2590/11272] Time 0.799 (1.296) Data 0.002 (0.004) Loss 2.7269 (2.6718) Prec@1 36.875 (35.797) Prec@5 62.500 (66.117) Epoch: [6][2600/11272] Time 0.901 (1.294) Data 0.002 (0.004) Loss 2.5052 (2.6718) Prec@1 32.500 (35.790) Prec@5 70.625 (66.122) Epoch: [6][2610/11272] Time 0.905 (1.292) Data 0.002 (0.004) Loss 2.6398 (2.6717) Prec@1 34.375 (35.790) Prec@5 67.500 (66.125) Epoch: [6][2620/11272] Time 0.793 (1.291) Data 0.004 (0.004) Loss 2.6502 (2.6717) Prec@1 34.375 (35.785) Prec@5 68.125 (66.124) Epoch: [6][2630/11272] Time 0.772 (1.289) Data 0.002 (0.004) Loss 2.7593 (2.6716) Prec@1 40.000 (35.790) Prec@5 63.750 (66.124) Epoch: [6][2640/11272] Time 0.898 (1.287) Data 0.002 (0.004) Loss 2.8210 (2.6718) Prec@1 35.000 (35.784) Prec@5 57.500 (66.120) Epoch: [6][2650/11272] Time 0.944 (1.286) Data 0.002 (0.004) Loss 2.9068 (2.6721) Prec@1 33.750 (35.780) Prec@5 63.750 (66.113) Epoch: [6][2660/11272] Time 0.771 (1.284) Data 0.002 (0.004) Loss 2.4354 (2.6716) Prec@1 37.500 (35.785) Prec@5 72.500 (66.122) Epoch: [6][2670/11272] Time 0.848 (1.282) Data 0.001 (0.004) Loss 2.8409 (2.6717) Prec@1 32.500 (35.785) Prec@5 63.125 (66.119) Epoch: [6][2680/11272] Time 0.891 (1.280) Data 0.001 (0.004) Loss 2.7729 (2.6718) Prec@1 32.500 (35.780) Prec@5 63.125 (66.116) Epoch: [6][2690/11272] Time 0.779 (1.278) Data 0.001 (0.004) Loss 2.5369 (2.6718) Prec@1 35.625 (35.780) Prec@5 68.750 (66.118) Epoch: [6][2700/11272] Time 0.768 (1.277) Data 0.002 (0.004) Loss 2.5137 (2.6714) Prec@1 46.875 (35.790) Prec@5 65.625 (66.129) Epoch: [6][2710/11272] Time 0.883 (1.275) Data 0.002 (0.003) Loss 2.7957 (2.6714) Prec@1 30.625 (35.793) Prec@5 61.250 (66.126) Epoch: [6][2720/11272] Time 0.913 (1.274) Data 0.001 (0.003) Loss 2.7546 (2.6713) Prec@1 36.250 (35.792) Prec@5 61.875 (66.126) Epoch: [6][2730/11272] Time 0.799 (1.272) Data 0.002 (0.003) Loss 2.5619 (2.6713) Prec@1 40.625 (35.793) Prec@5 67.500 (66.122) Epoch: [6][2740/11272] Time 0.739 (1.270) Data 0.001 (0.003) Loss 2.7390 (2.6712) Prec@1 34.375 (35.795) Prec@5 66.250 (66.122) Epoch: [6][2750/11272] Time 0.871 (1.269) Data 0.001 (0.003) Loss 2.5638 (2.6711) Prec@1 35.000 (35.791) Prec@5 67.500 (66.129) Epoch: [6][2760/11272] Time 0.991 (1.267) Data 0.001 (0.003) Loss 2.7063 (2.6707) Prec@1 36.875 (35.798) Prec@5 68.125 (66.137) Epoch: [6][2770/11272] Time 0.752 (1.266) Data 0.002 (0.003) Loss 2.3644 (2.6707) Prec@1 35.625 (35.795) Prec@5 71.875 (66.138) Epoch: [6][2780/11272] Time 0.775 (1.264) Data 0.002 (0.003) Loss 2.4882 (2.6706) Prec@1 40.000 (35.794) Prec@5 70.000 (66.138) Epoch: [6][2790/11272] Time 0.879 (1.263) Data 0.002 (0.003) Loss 2.6688 (2.6705) Prec@1 35.000 (35.797) Prec@5 65.000 (66.138) Epoch: [6][2800/11272] Time 0.748 (1.261) Data 0.002 (0.003) Loss 2.8839 (2.6708) Prec@1 31.250 (35.793) Prec@5 65.625 (66.137) Epoch: [6][2810/11272] Time 0.764 (1.259) Data 0.001 (0.003) Loss 2.3761 (2.6707) Prec@1 41.875 (35.797) Prec@5 75.625 (66.144) Epoch: [6][2820/11272] Time 0.878 (1.258) Data 0.002 (0.003) Loss 2.4461 (2.6704) Prec@1 41.250 (35.804) Prec@5 70.625 (66.153) Epoch: [6][2830/11272] Time 0.914 (1.256) Data 0.001 (0.003) Loss 2.8018 (2.6701) Prec@1 32.500 (35.810) Prec@5 63.750 (66.160) Epoch: [6][2840/11272] Time 0.779 (1.255) Data 0.002 (0.003) Loss 2.6042 (2.6698) Prec@1 35.000 (35.816) Prec@5 68.750 (66.168) Epoch: [6][2850/11272] Time 0.751 (1.253) Data 0.001 (0.003) Loss 2.6990 (2.6703) Prec@1 34.375 (35.809) Prec@5 63.750 (66.159) Epoch: [6][2860/11272] Time 0.934 (1.252) Data 0.001 (0.003) Loss 2.5576 (2.6704) Prec@1 37.500 (35.809) Prec@5 70.625 (66.162) Epoch: [6][2870/11272] Time 0.875 (1.250) Data 0.001 (0.003) Loss 2.5510 (2.6704) Prec@1 40.625 (35.809) Prec@5 66.250 (66.161) Epoch: [6][2880/11272] Time 0.778 (1.249) Data 0.001 (0.003) Loss 2.8281 (2.6705) Prec@1 35.625 (35.804) Prec@5 60.625 (66.152) Epoch: [6][2890/11272] Time 0.758 (1.247) Data 0.001 (0.003) Loss 2.6722 (2.6706) Prec@1 34.375 (35.804) Prec@5 68.125 (66.151) Epoch: [6][2900/11272] Time 0.888 (1.246) Data 0.001 (0.003) Loss 2.6447 (2.6706) Prec@1 34.375 (35.798) Prec@5 63.125 (66.150) Epoch: [6][2910/11272] Time 0.922 (1.245) Data 0.001 (0.003) Loss 2.9670 (2.6707) Prec@1 32.500 (35.800) Prec@5 60.000 (66.150) Epoch: [6][2920/11272] Time 0.757 (1.243) Data 0.001 (0.003) Loss 2.7853 (2.6708) Prec@1 34.375 (35.796) Prec@5 66.250 (66.151) Epoch: [6][2930/11272] Time 0.891 (1.242) Data 0.001 (0.003) Loss 2.7546 (2.6708) Prec@1 33.750 (35.797) Prec@5 61.875 (66.154) Epoch: [6][2940/11272] Time 0.853 (1.240) Data 0.002 (0.003) Loss 2.9822 (2.6706) Prec@1 32.500 (35.802) Prec@5 64.375 (66.157) Epoch: [6][2950/11272] Time 0.728 (1.239) Data 0.002 (0.003) Loss 2.5993 (2.6707) Prec@1 42.500 (35.805) Prec@5 66.250 (66.152) Epoch: [6][2960/11272] Time 0.772 (1.237) Data 0.001 (0.003) Loss 2.7808 (2.6707) Prec@1 33.750 (35.799) Prec@5 62.500 (66.152) Epoch: [6][2970/11272] Time 0.836 (1.236) Data 0.001 (0.003) Loss 2.7272 (2.6708) Prec@1 31.250 (35.800) Prec@5 65.625 (66.149) Epoch: [6][2980/11272] Time 0.906 (1.235) Data 0.001 (0.003) Loss 2.8389 (2.6711) Prec@1 30.625 (35.791) Prec@5 64.375 (66.141) Epoch: [6][2990/11272] Time 0.756 (1.233) Data 0.001 (0.003) Loss 2.4569 (2.6710) Prec@1 43.125 (35.798) Prec@5 72.500 (66.139) Epoch: [6][3000/11272] Time 0.761 (1.232) Data 0.001 (0.003) Loss 2.5732 (2.6714) Prec@1 34.375 (35.784) Prec@5 66.250 (66.131) Epoch: [6][3010/11272] Time 0.880 (1.230) Data 0.001 (0.003) Loss 2.6810 (2.6715) Prec@1 36.250 (35.777) Prec@5 63.750 (66.129) Epoch: [6][3020/11272] Time 0.931 (1.229) Data 0.001 (0.003) Loss 2.8487 (2.6718) Prec@1 33.750 (35.768) Prec@5 61.875 (66.124) Epoch: [6][3030/11272] Time 0.786 (1.228) Data 0.001 (0.003) Loss 2.6244 (2.6715) Prec@1 35.625 (35.775) Prec@5 67.500 (66.130) Epoch: [6][3040/11272] Time 0.748 (1.226) Data 0.001 (0.003) Loss 2.5510 (2.6712) Prec@1 37.500 (35.780) Prec@5 65.000 (66.134) Epoch: [6][3050/11272] Time 0.960 (1.225) Data 0.001 (0.003) Loss 2.5668 (2.6711) Prec@1 33.125 (35.782) Prec@5 69.375 (66.139) Epoch: [6][3060/11272] Time 0.741 (1.224) Data 0.003 (0.003) Loss 2.9226 (2.6709) Prec@1 35.000 (35.786) Prec@5 62.500 (66.143) Epoch: [6][3070/11272] Time 0.762 (1.223) Data 0.001 (0.003) Loss 2.6901 (2.6709) Prec@1 36.875 (35.790) Prec@5 60.625 (66.144) Epoch: [6][3080/11272] Time 0.912 (1.221) Data 0.001 (0.003) Loss 2.5288 (2.6706) Prec@1 33.125 (35.791) Prec@5 69.375 (66.152) Epoch: [6][3090/11272] Time 0.841 (1.220) Data 0.002 (0.003) Loss 2.5992 (2.6708) Prec@1 36.250 (35.793) Prec@5 71.875 (66.152) Epoch: [6][3100/11272] Time 0.755 (1.219) Data 0.002 (0.003) Loss 2.8134 (2.6709) Prec@1 32.500 (35.792) Prec@5 62.500 (66.152) Epoch: [6][3110/11272] Time 0.759 (1.217) Data 0.001 (0.003) Loss 2.7724 (2.6708) Prec@1 38.125 (35.800) Prec@5 66.250 (66.154) Epoch: [6][3120/11272] Time 0.884 (1.216) Data 0.001 (0.003) Loss 2.7467 (2.6707) Prec@1 35.000 (35.803) Prec@5 61.875 (66.153) Epoch: [6][3130/11272] Time 0.877 (1.215) Data 0.001 (0.003) Loss 2.7553 (2.6707) Prec@1 28.750 (35.804) Prec@5 62.500 (66.157) Epoch: [6][3140/11272] Time 0.751 (1.214) Data 0.001 (0.003) Loss 2.6573 (2.6706) Prec@1 36.250 (35.801) Prec@5 64.375 (66.157) Epoch: [6][3150/11272] Time 0.762 (1.212) Data 0.002 (0.003) Loss 2.7311 (2.6706) Prec@1 36.875 (35.798) Prec@5 63.750 (66.158) Epoch: [6][3160/11272] Time 0.859 (1.211) Data 0.001 (0.003) Loss 2.4971 (2.6708) Prec@1 41.250 (35.799) Prec@5 70.000 (66.151) Epoch: [6][3170/11272] Time 0.876 (1.210) Data 0.001 (0.003) Loss 2.7390 (2.6708) Prec@1 38.750 (35.797) Prec@5 68.750 (66.153) Epoch: [6][3180/11272] Time 0.749 (1.209) Data 0.001 (0.003) Loss 2.7557 (2.6708) Prec@1 32.500 (35.800) Prec@5 63.125 (66.154) Epoch: [6][3190/11272] Time 0.929 (1.208) Data 0.002 (0.003) Loss 2.7187 (2.6707) Prec@1 32.500 (35.801) Prec@5 68.750 (66.152) Epoch: [6][3200/11272] Time 0.917 (1.206) Data 0.001 (0.003) Loss 2.4979 (2.6705) Prec@1 35.625 (35.808) Prec@5 68.125 (66.153) Epoch: [6][3210/11272] Time 0.762 (1.205) Data 0.001 (0.003) Loss 2.7531 (2.6706) Prec@1 37.500 (35.813) Prec@5 63.125 (66.154) Epoch: [6][3220/11272] Time 0.775 (1.204) Data 0.001 (0.003) Loss 2.8575 (2.6708) Prec@1 34.375 (35.809) Prec@5 61.250 (66.148) Epoch: [6][3230/11272] Time 0.947 (1.203) Data 0.001 (0.003) Loss 2.5644 (2.6708) Prec@1 32.500 (35.807) Prec@5 67.500 (66.150) Epoch: [6][3240/11272] Time 0.880 (1.202) Data 0.001 (0.003) Loss 2.6678 (2.6710) Prec@1 38.125 (35.806) Prec@5 63.125 (66.146) Epoch: [6][3250/11272] Time 0.774 (1.200) Data 0.001 (0.003) Loss 2.5327 (2.6711) Prec@1 40.625 (35.803) Prec@5 67.500 (66.143) Epoch: [6][3260/11272] Time 0.759 (1.199) Data 0.001 (0.003) Loss 2.5533 (2.6712) Prec@1 38.125 (35.801) Prec@5 66.250 (66.142) Epoch: [6][3270/11272] Time 0.884 (1.198) Data 0.001 (0.003) Loss 2.9055 (2.6713) Prec@1 31.250 (35.799) Prec@5 58.125 (66.138) Epoch: [6][3280/11272] Time 0.900 (1.197) Data 0.001 (0.003) Loss 3.0005 (2.6715) Prec@1 33.750 (35.790) Prec@5 59.375 (66.136) Epoch: [6][3290/11272] Time 0.727 (1.196) Data 0.002 (0.003) Loss 2.5959 (2.6715) Prec@1 31.875 (35.788) Prec@5 66.250 (66.132) Epoch: [6][3300/11272] Time 0.739 (1.195) Data 0.001 (0.003) Loss 2.8481 (2.6714) Prec@1 36.875 (35.791) Prec@5 63.750 (66.134) Epoch: [6][3310/11272] Time 0.865 (1.194) Data 0.002 (0.003) Loss 2.6962 (2.6716) Prec@1 31.250 (35.785) Prec@5 63.750 (66.130) Epoch: [6][3320/11272] Time 0.768 (1.192) Data 0.003 (0.003) Loss 2.4406 (2.6717) Prec@1 41.875 (35.784) Prec@5 73.750 (66.131) Epoch: [6][3330/11272] Time 0.797 (1.191) Data 0.001 (0.003) Loss 2.5003 (2.6718) Prec@1 41.875 (35.782) Prec@5 69.375 (66.129) Epoch: [6][3340/11272] Time 0.858 (1.190) Data 0.001 (0.003) Loss 2.6928 (2.6716) Prec@1 33.125 (35.783) Prec@5 62.500 (66.133) Epoch: [6][3350/11272] Time 0.839 (1.189) Data 0.002 (0.003) Loss 2.6265 (2.6715) Prec@1 36.875 (35.784) Prec@5 63.125 (66.132) Epoch: [6][3360/11272] Time 0.746 (1.188) Data 0.001 (0.003) Loss 2.6941 (2.6714) Prec@1 38.125 (35.788) Prec@5 68.125 (66.133) Epoch: [6][3370/11272] Time 0.799 (1.187) Data 0.001 (0.003) Loss 2.8944 (2.6717) Prec@1 30.625 (35.787) Prec@5 63.750 (66.129) Epoch: [6][3380/11272] Time 0.903 (1.186) Data 0.002 (0.003) Loss 2.6485 (2.6717) Prec@1 44.375 (35.793) Prec@5 66.250 (66.128) Epoch: [6][3390/11272] Time 0.949 (1.185) Data 0.002 (0.003) Loss 2.5492 (2.6715) Prec@1 34.375 (35.796) Prec@5 64.375 (66.131) Epoch: [6][3400/11272] Time 0.745 (1.184) Data 0.001 (0.003) Loss 2.9816 (2.6714) Prec@1 30.000 (35.797) Prec@5 60.000 (66.130) Epoch: [6][3410/11272] Time 0.764 (1.183) Data 0.002 (0.003) Loss 2.7202 (2.6715) Prec@1 38.125 (35.797) Prec@5 66.875 (66.129) Epoch: [6][3420/11272] Time 0.892 (1.182) Data 0.001 (0.003) Loss 2.5988 (2.6713) Prec@1 33.125 (35.800) Prec@5 66.875 (66.137) Epoch: [6][3430/11272] Time 0.930 (1.181) Data 0.002 (0.003) Loss 2.4344 (2.6712) Prec@1 38.125 (35.804) Prec@5 70.000 (66.139) Epoch: [6][3440/11272] Time 0.733 (1.180) Data 0.001 (0.003) Loss 2.9067 (2.6709) Prec@1 31.250 (35.811) Prec@5 63.750 (66.147) Epoch: [6][3450/11272] Time 0.952 (1.179) Data 0.001 (0.003) Loss 2.5917 (2.6709) Prec@1 35.625 (35.808) Prec@5 65.000 (66.148) Epoch: [6][3460/11272] Time 0.848 (1.178) Data 0.002 (0.003) Loss 2.7047 (2.6708) Prec@1 35.625 (35.810) Prec@5 65.625 (66.150) Epoch: [6][3470/11272] Time 0.751 (1.177) Data 0.001 (0.003) Loss 2.6369 (2.6709) Prec@1 40.000 (35.808) Prec@5 68.125 (66.146) Epoch: [6][3480/11272] Time 0.780 (1.176) Data 0.001 (0.003) Loss 2.6012 (2.6710) Prec@1 36.875 (35.804) Prec@5 63.750 (66.144) Epoch: [6][3490/11272] Time 0.887 (1.175) Data 0.001 (0.003) Loss 2.5904 (2.6709) Prec@1 32.500 (35.803) Prec@5 64.375 (66.149) Epoch: [6][3500/11272] Time 0.895 (1.174) Data 0.001 (0.003) Loss 2.7624 (2.6710) Prec@1 35.000 (35.799) Prec@5 61.875 (66.144) Epoch: [6][3510/11272] Time 0.747 (1.173) Data 0.001 (0.003) Loss 2.5750 (2.6708) Prec@1 33.125 (35.802) Prec@5 72.500 (66.150) Epoch: [6][3520/11272] Time 0.768 (1.172) Data 0.002 (0.003) Loss 2.7508 (2.6711) Prec@1 34.375 (35.794) Prec@5 63.750 (66.141) Epoch: [6][3530/11272] Time 0.939 (1.171) Data 0.002 (0.003) Loss 2.4948 (2.6709) Prec@1 40.000 (35.798) Prec@5 70.625 (66.146) Epoch: [6][3540/11272] Time 0.917 (1.170) Data 0.001 (0.003) Loss 2.6088 (2.6707) Prec@1 36.250 (35.800) Prec@5 64.375 (66.146) Epoch: [6][3550/11272] Time 0.744 (1.169) Data 0.001 (0.003) Loss 2.4683 (2.6707) Prec@1 41.250 (35.803) Prec@5 71.250 (66.144) Epoch: [6][3560/11272] Time 0.750 (1.168) Data 0.001 (0.003) Loss 2.5298 (2.6708) Prec@1 38.750 (35.800) Prec@5 67.500 (66.143) Epoch: [6][3570/11272] Time 0.949 (1.167) Data 0.001 (0.003) Loss 2.7574 (2.6708) Prec@1 35.625 (35.804) Prec@5 63.125 (66.144) Epoch: [6][3580/11272] Time 0.846 (1.166) Data 0.002 (0.003) Loss 2.4477 (2.6707) Prec@1 44.375 (35.807) Prec@5 65.625 (66.143) Epoch: [6][3590/11272] Time 0.725 (1.165) Data 0.001 (0.003) Loss 2.7238 (2.6707) Prec@1 33.750 (35.806) Prec@5 64.375 (66.145) Epoch: [6][3600/11272] Time 0.843 (1.164) Data 0.001 (0.003) Loss 2.5973 (2.6707) Prec@1 38.125 (35.807) Prec@5 68.750 (66.144) Epoch: [6][3610/11272] Time 0.916 (1.163) Data 0.001 (0.003) Loss 2.5334 (2.6703) Prec@1 34.375 (35.814) Prec@5 69.375 (66.149) Epoch: [6][3620/11272] Time 0.749 (1.162) Data 0.001 (0.003) Loss 2.5132 (2.6705) Prec@1 42.500 (35.810) Prec@5 68.750 (66.147) Epoch: [6][3630/11272] Time 0.746 (1.161) Data 0.001 (0.003) Loss 2.6278 (2.6705) Prec@1 35.000 (35.809) Prec@5 65.625 (66.146) Epoch: [6][3640/11272] Time 0.866 (1.160) Data 0.001 (0.003) Loss 2.7477 (2.6704) Prec@1 36.250 (35.811) Prec@5 64.375 (66.149) Epoch: [6][3650/11272] Time 0.891 (1.159) Data 0.001 (0.003) Loss 2.7217 (2.6706) Prec@1 33.750 (35.805) Prec@5 69.375 (66.147) Epoch: [6][3660/11272] Time 0.765 (1.158) Data 0.002 (0.003) Loss 2.8114 (2.6706) Prec@1 35.000 (35.806) Prec@5 60.625 (66.145) Epoch: [6][3670/11272] Time 0.781 (1.157) Data 0.001 (0.003) Loss 2.5977 (2.6706) Prec@1 34.375 (35.805) Prec@5 69.375 (66.144) Epoch: [6][3680/11272] Time 0.906 (1.156) Data 0.001 (0.003) Loss 2.6827 (2.6706) Prec@1 29.375 (35.807) Prec@5 66.875 (66.144) Epoch: [6][3690/11272] Time 0.913 (1.156) Data 0.001 (0.003) Loss 2.5994 (2.6705) Prec@1 36.875 (35.807) Prec@5 67.500 (66.145) Epoch: [6][3700/11272] Time 0.752 (1.155) Data 0.001 (0.003) Loss 2.6233 (2.6703) Prec@1 40.625 (35.813) Prec@5 68.750 (66.150) Epoch: [6][3710/11272] Time 0.733 (1.154) Data 0.002 (0.003) Loss 2.3786 (2.6702) Prec@1 38.750 (35.814) Prec@5 73.750 (66.150) Epoch: [6][3720/11272] Time 0.939 (1.153) Data 0.004 (0.003) Loss 2.4817 (2.6702) Prec@1 39.375 (35.813) Prec@5 71.250 (66.154) Epoch: [6][3730/11272] Time 0.796 (1.152) Data 0.001 (0.003) Loss 2.7817 (2.6701) Prec@1 34.375 (35.814) Prec@5 61.875 (66.153) Epoch: [6][3740/11272] Time 0.758 (1.151) Data 0.001 (0.003) Loss 2.6886 (2.6698) Prec@1 32.500 (35.819) Prec@5 66.250 (66.162) Epoch: [6][3750/11272] Time 0.829 (1.150) Data 0.001 (0.003) Loss 2.9400 (2.6696) Prec@1 31.250 (35.817) Prec@5 60.000 (66.167) Epoch: [6][3760/11272] Time 0.884 (1.149) Data 0.001 (0.003) Loss 2.6677 (2.6696) Prec@1 37.500 (35.819) Prec@5 64.375 (66.168) Epoch: [6][3770/11272] Time 0.762 (1.149) Data 0.002 (0.003) Loss 2.5945 (2.6696) Prec@1 33.125 (35.815) Prec@5 68.125 (66.165) Epoch: [6][3780/11272] Time 0.753 (1.148) Data 0.001 (0.003) Loss 2.5917 (2.6698) Prec@1 40.000 (35.813) Prec@5 64.375 (66.165) Epoch: [6][3790/11272] Time 0.926 (1.147) Data 0.002 (0.003) Loss 2.6805 (2.6697) Prec@1 36.250 (35.812) Prec@5 65.000 (66.165) Epoch: [6][3800/11272] Time 0.899 (1.146) Data 0.002 (0.003) Loss 2.8505 (2.6697) Prec@1 33.125 (35.813) Prec@5 60.625 (66.166) Epoch: [6][3810/11272] Time 0.836 (1.145) Data 0.002 (0.003) Loss 2.7140 (2.6698) Prec@1 33.750 (35.813) Prec@5 65.625 (66.160) Epoch: [6][3820/11272] Time 0.811 (1.145) Data 0.002 (0.003) Loss 2.8095 (2.6697) Prec@1 33.750 (35.815) Prec@5 61.875 (66.162) Epoch: [6][3830/11272] Time 0.915 (1.144) Data 0.001 (0.003) Loss 2.7002 (2.6695) Prec@1 36.875 (35.818) Prec@5 65.000 (66.166) Epoch: [6][3840/11272] Time 0.900 (1.143) Data 0.002 (0.003) Loss 2.3805 (2.6695) Prec@1 38.750 (35.813) Prec@5 73.750 (66.167) Epoch: [6][3850/11272] Time 0.788 (1.142) Data 0.002 (0.003) Loss 2.9115 (2.6698) Prec@1 26.875 (35.811) Prec@5 65.000 (66.163) Epoch: [6][3860/11272] Time 0.936 (1.142) Data 0.002 (0.003) Loss 2.8113 (2.6698) Prec@1 26.875 (35.805) Prec@5 64.375 (66.163) Epoch: [6][3870/11272] Time 0.910 (1.141) Data 0.004 (0.003) Loss 2.7110 (2.6696) Prec@1 34.375 (35.809) Prec@5 64.375 (66.169) Epoch: [6][3880/11272] Time 0.772 (1.140) Data 0.001 (0.003) Loss 2.9635 (2.6699) Prec@1 25.625 (35.801) Prec@5 60.000 (66.161) Epoch: [6][3890/11272] Time 0.790 (1.139) Data 0.002 (0.003) Loss 2.7541 (2.6700) Prec@1 33.750 (35.800) Prec@5 66.250 (66.160) Epoch: [6][3900/11272] Time 0.949 (1.139) Data 0.001 (0.003) Loss 2.6424 (2.6700) Prec@1 37.500 (35.805) Prec@5 68.750 (66.159) Epoch: [6][3910/11272] Time 0.892 (1.138) Data 0.001 (0.003) Loss 2.6741 (2.6701) Prec@1 36.875 (35.804) Prec@5 65.625 (66.156) Epoch: [6][3920/11272] Time 0.786 (1.137) Data 0.002 (0.003) Loss 2.4828 (2.6701) Prec@1 40.000 (35.802) Prec@5 73.750 (66.158) Epoch: [6][3930/11272] Time 0.768 (1.136) Data 0.001 (0.003) Loss 2.6990 (2.6701) Prec@1 38.125 (35.801) Prec@5 61.875 (66.158) Epoch: [6][3940/11272] Time 0.976 (1.136) Data 0.002 (0.003) Loss 2.4990 (2.6701) Prec@1 40.000 (35.798) Prec@5 67.500 (66.157) Epoch: [6][3950/11272] Time 0.909 (1.135) Data 0.002 (0.003) Loss 2.5722 (2.6702) Prec@1 37.500 (35.792) Prec@5 69.375 (66.156) Epoch: [6][3960/11272] Time 0.770 (1.134) Data 0.002 (0.003) Loss 2.7046 (2.6703) Prec@1 35.625 (35.791) Prec@5 64.375 (66.154) Epoch: [6][3970/11272] Time 0.738 (1.134) Data 0.002 (0.003) Loss 2.8270 (2.6704) Prec@1 36.875 (35.789) Prec@5 60.625 (66.151) Epoch: [6][3980/11272] Time 0.890 (1.133) Data 0.002 (0.003) Loss 2.5635 (2.6704) Prec@1 33.750 (35.789) Prec@5 68.125 (66.153) Epoch: [6][3990/11272] Time 0.762 (1.132) Data 0.004 (0.003) Loss 2.5369 (2.6703) Prec@1 43.125 (35.793) Prec@5 71.250 (66.157) Epoch: [6][4000/11272] Time 0.843 (1.131) Data 0.002 (0.003) Loss 2.5686 (2.6701) Prec@1 35.000 (35.798) Prec@5 66.875 (66.160) Epoch: [6][4010/11272] Time 0.866 (1.131) Data 0.001 (0.003) Loss 2.6878 (2.6703) Prec@1 35.000 (35.793) Prec@5 63.750 (66.153) Epoch: [6][4020/11272] Time 0.921 (1.130) Data 0.008 (0.003) Loss 2.7254 (2.6704) Prec@1 39.375 (35.794) Prec@5 60.625 (66.148) Epoch: [6][4030/11272] Time 0.785 (1.129) Data 0.002 (0.003) Loss 2.7937 (2.6705) Prec@1 36.250 (35.791) Prec@5 61.250 (66.147) Epoch: [6][4040/11272] Time 0.770 (1.128) Data 0.003 (0.003) Loss 2.6382 (2.6704) Prec@1 33.750 (35.793) Prec@5 68.125 (66.148) Epoch: [6][4050/11272] Time 0.916 (1.128) Data 0.001 (0.003) Loss 2.4995 (2.6704) Prec@1 38.750 (35.792) Prec@5 66.250 (66.149) Epoch: [6][4060/11272] Time 0.893 (1.127) Data 0.002 (0.003) Loss 2.7462 (2.6701) Prec@1 38.125 (35.798) Prec@5 59.375 (66.155) Epoch: [6][4070/11272] Time 0.760 (1.126) Data 0.001 (0.003) Loss 2.5523 (2.6699) Prec@1 38.125 (35.799) Prec@5 69.375 (66.156) Epoch: [6][4080/11272] Time 0.735 (1.126) Data 0.002 (0.003) Loss 2.6821 (2.6699) Prec@1 34.375 (35.802) Prec@5 68.750 (66.159) Epoch: [6][4090/11272] Time 0.930 (1.125) Data 0.002 (0.003) Loss 2.8235 (2.6697) Prec@1 26.250 (35.808) Prec@5 63.750 (66.161) Epoch: [6][4100/11272] Time 0.892 (1.124) Data 0.001 (0.003) Loss 2.6265 (2.6696) Prec@1 33.750 (35.811) Prec@5 63.750 (66.164) Epoch: [6][4110/11272] Time 0.804 (1.124) Data 0.002 (0.003) Loss 2.8250 (2.6697) Prec@1 31.250 (35.809) Prec@5 65.625 (66.163) Epoch: [6][4120/11272] Time 0.928 (1.123) Data 0.001 (0.003) Loss 2.7633 (2.6697) Prec@1 35.000 (35.809) Prec@5 65.625 (66.162) Epoch: [6][4130/11272] Time 0.906 (1.122) Data 0.002 (0.003) Loss 2.6408 (2.6696) Prec@1 33.125 (35.810) Prec@5 66.250 (66.164) Epoch: [6][4140/11272] Time 0.763 (1.122) Data 0.002 (0.003) Loss 2.6895 (2.6696) Prec@1 37.500 (35.809) Prec@5 65.625 (66.161) Epoch: [6][4150/11272] Time 0.731 (1.121) Data 0.001 (0.003) Loss 2.5463 (2.6695) Prec@1 43.750 (35.813) Prec@5 70.000 (66.163) Epoch: [6][4160/11272] Time 0.989 (1.121) Data 0.002 (0.003) Loss 2.5790 (2.6695) Prec@1 38.125 (35.808) Prec@5 68.750 (66.165) Epoch: [6][4170/11272] Time 0.958 (1.120) Data 0.002 (0.003) Loss 2.6407 (2.6696) Prec@1 36.875 (35.811) Prec@5 67.500 (66.164) Epoch: [6][4180/11272] Time 0.761 (1.119) Data 0.001 (0.003) Loss 2.8438 (2.6696) Prec@1 29.375 (35.809) Prec@5 62.500 (66.166) Epoch: [6][4190/11272] Time 0.817 (1.119) Data 0.002 (0.003) Loss 3.0210 (2.6697) Prec@1 34.375 (35.806) Prec@5 66.875 (66.167) Epoch: [6][4200/11272] Time 0.926 (1.118) Data 0.002 (0.003) Loss 2.8056 (2.6698) Prec@1 33.750 (35.805) Prec@5 65.000 (66.165) Epoch: [6][4210/11272] Time 0.919 (1.117) Data 0.001 (0.003) Loss 2.7498 (2.6699) Prec@1 36.875 (35.806) Prec@5 63.750 (66.163) Epoch: [6][4220/11272] Time 0.749 (1.117) Data 0.002 (0.003) Loss 2.6635 (2.6699) Prec@1 34.375 (35.805) Prec@5 71.250 (66.163) Epoch: [6][4230/11272] Time 0.796 (1.116) Data 0.002 (0.003) Loss 2.7658 (2.6701) Prec@1 35.000 (35.801) Prec@5 65.000 (66.160) Epoch: [6][4240/11272] Time 0.872 (1.115) Data 0.001 (0.003) Loss 2.7656 (2.6701) Prec@1 31.875 (35.801) Prec@5 65.000 (66.160) Epoch: [6][4250/11272] Time 0.735 (1.115) Data 0.004 (0.003) Loss 2.6388 (2.6700) Prec@1 34.375 (35.802) Prec@5 71.250 (66.161) Epoch: [6][4260/11272] Time 0.755 (1.114) Data 0.002 (0.003) Loss 2.7253 (2.6701) Prec@1 30.625 (35.797) Prec@5 63.125 (66.159) Epoch: [6][4270/11272] Time 0.938 (1.114) Data 0.002 (0.003) Loss 2.4339 (2.6701) Prec@1 45.000 (35.798) Prec@5 69.375 (66.160) Epoch: [6][4280/11272] Time 0.909 (1.113) Data 0.002 (0.003) Loss 2.5351 (2.6700) Prec@1 40.000 (35.803) Prec@5 68.125 (66.161) Epoch: [6][4290/11272] Time 0.774 (1.112) Data 0.001 (0.003) Loss 2.6916 (2.6700) Prec@1 34.375 (35.801) Prec@5 67.500 (66.162) Epoch: [6][4300/11272] Time 0.755 (1.112) Data 0.001 (0.003) Loss 2.5131 (2.6701) Prec@1 39.375 (35.800) Prec@5 67.500 (66.161) Epoch: [6][4310/11272] Time 0.898 (1.111) Data 0.001 (0.003) Loss 2.7003 (2.6700) Prec@1 34.375 (35.798) Prec@5 65.000 (66.162) Epoch: [6][4320/11272] Time 0.865 (1.110) Data 0.001 (0.003) Loss 2.5831 (2.6699) Prec@1 36.250 (35.798) Prec@5 70.625 (66.164) Epoch: [6][4330/11272] Time 0.786 (1.110) Data 0.002 (0.003) Loss 2.8531 (2.6699) Prec@1 27.500 (35.793) Prec@5 64.375 (66.163) Epoch: [6][4340/11272] Time 0.772 (1.109) Data 0.001 (0.003) Loss 2.6576 (2.6698) Prec@1 36.250 (35.797) Prec@5 70.625 (66.166) Epoch: [6][4350/11272] Time 0.968 (1.109) Data 0.002 (0.003) Loss 2.5396 (2.6698) Prec@1 38.125 (35.802) Prec@5 70.000 (66.166) Epoch: [6][4360/11272] Time 0.907 (1.108) Data 0.001 (0.003) Loss 2.9883 (2.6698) Prec@1 30.000 (35.803) Prec@5 59.375 (66.167) Epoch: [6][4370/11272] Time 0.747 (1.107) Data 0.001 (0.003) Loss 2.5180 (2.6699) Prec@1 38.750 (35.804) Prec@5 70.625 (66.165) Epoch: [6][4380/11272] Time 0.863 (1.107) Data 0.001 (0.003) Loss 2.7195 (2.6698) Prec@1 31.250 (35.803) Prec@5 62.500 (66.168) Epoch: [6][4390/11272] Time 0.867 (1.162) Data 0.002 (0.003) Loss 2.4234 (2.6696) Prec@1 43.125 (35.808) Prec@5 72.500 (66.171) Epoch: [6][4400/11272] Time 0.762 (1.161) Data 0.001 (0.003) Loss 2.7766 (2.6694) Prec@1 30.625 (35.808) Prec@5 65.000 (66.174) Epoch: [6][4410/11272] Time 0.762 (1.160) Data 0.001 (0.003) Loss 2.8423 (2.6695) Prec@1 34.375 (35.809) Prec@5 63.125 (66.173) Epoch: [6][4420/11272] Time 0.919 (1.159) Data 0.003 (0.003) Loss 2.6380 (2.6694) Prec@1 35.000 (35.815) Prec@5 66.875 (66.171) Epoch: [6][4430/11272] Time 0.861 (1.159) Data 0.001 (0.003) Loss 2.6818 (2.6693) Prec@1 34.375 (35.818) Prec@5 65.625 (66.172) Epoch: [6][4440/11272] Time 0.747 (1.158) Data 0.002 (0.003) Loss 2.7614 (2.6693) Prec@1 36.250 (35.814) Prec@5 65.625 (66.173) Epoch: [6][4450/11272] Time 0.717 (1.157) Data 0.002 (0.003) Loss 2.6350 (2.6694) Prec@1 35.000 (35.813) Prec@5 70.000 (66.173) Epoch: [6][4460/11272] Time 0.876 (1.157) Data 0.001 (0.003) Loss 2.7587 (2.6693) Prec@1 34.375 (35.811) Prec@5 63.750 (66.173) Epoch: [6][4470/11272] Time 0.867 (1.156) Data 0.001 (0.003) Loss 2.5518 (2.6695) Prec@1 38.125 (35.807) Prec@5 66.875 (66.170) Epoch: [6][4480/11272] Time 0.706 (1.155) Data 0.001 (0.003) Loss 2.7851 (2.6694) Prec@1 30.625 (35.809) Prec@5 66.250 (66.174) Epoch: [6][4490/11272] Time 0.740 (1.154) Data 0.002 (0.003) Loss 2.8095 (2.6692) Prec@1 40.000 (35.812) Prec@5 66.250 (66.177) Epoch: [6][4500/11272] Time 0.868 (1.153) Data 0.001 (0.003) Loss 2.7593 (2.6692) Prec@1 30.625 (35.807) Prec@5 63.750 (66.173) Epoch: [6][4510/11272] Time 0.878 (1.153) Data 0.001 (0.003) Loss 2.8351 (2.6693) Prec@1 30.625 (35.805) Prec@5 62.500 (66.170) Epoch: [6][4520/11272] Time 0.730 (1.152) Data 0.002 (0.003) Loss 2.5345 (2.6695) Prec@1 42.500 (35.802) Prec@5 65.625 (66.167) Epoch: [6][4530/11272] Time 0.884 (1.151) Data 0.001 (0.003) Loss 2.7787 (2.6695) Prec@1 33.750 (35.803) Prec@5 64.375 (66.169) Epoch: [6][4540/11272] Time 0.862 (1.150) Data 0.002 (0.003) Loss 2.7254 (2.6694) Prec@1 34.375 (35.803) Prec@5 63.750 (66.171) Epoch: [6][4550/11272] Time 0.743 (1.150) Data 0.001 (0.003) Loss 2.8214 (2.6695) Prec@1 31.875 (35.801) Prec@5 60.000 (66.169) Epoch: [6][4560/11272] Time 0.777 (1.149) Data 0.001 (0.003) Loss 2.6721 (2.6695) Prec@1 37.500 (35.797) Prec@5 66.250 (66.167) Epoch: [6][4570/11272] Time 0.886 (1.148) Data 0.001 (0.003) Loss 2.7053 (2.6695) Prec@1 34.375 (35.796) Prec@5 66.250 (66.168) Epoch: [6][4580/11272] Time 0.863 (1.148) Data 0.001 (0.003) Loss 2.2950 (2.6694) Prec@1 44.375 (35.800) Prec@5 71.875 (66.168) Epoch: [6][4590/11272] Time 0.771 (1.147) Data 0.002 (0.003) Loss 2.6636 (2.6695) Prec@1 38.125 (35.797) Prec@5 65.625 (66.168) Epoch: [6][4600/11272] Time 0.774 (1.146) Data 0.001 (0.003) Loss 2.5617 (2.6695) Prec@1 34.375 (35.799) Prec@5 75.625 (66.169) Epoch: [6][4610/11272] Time 0.891 (1.146) Data 0.001 (0.003) Loss 2.7328 (2.6694) Prec@1 33.750 (35.799) Prec@5 63.750 (66.173) Epoch: [6][4620/11272] Time 0.858 (1.145) Data 0.001 (0.003) Loss 2.3686 (2.6694) Prec@1 40.625 (35.800) Prec@5 69.375 (66.174) Epoch: [6][4630/11272] Time 0.764 (1.144) Data 0.001 (0.003) Loss 2.7784 (2.6693) Prec@1 36.875 (35.800) Prec@5 62.500 (66.174) Epoch: [6][4640/11272] Time 0.720 (1.143) Data 0.001 (0.003) Loss 2.6752 (2.6692) Prec@1 35.000 (35.802) Prec@5 65.625 (66.176) Epoch: [6][4650/11272] Time 0.833 (1.143) Data 0.002 (0.003) Loss 2.8462 (2.6693) Prec@1 38.125 (35.804) Prec@5 58.125 (66.173) Epoch: [6][4660/11272] Time 0.721 (1.142) Data 0.001 (0.003) Loss 2.6535 (2.6692) Prec@1 35.625 (35.803) Prec@5 66.875 (66.175) Epoch: [6][4670/11272] Time 0.746 (1.141) Data 0.001 (0.003) Loss 2.6589 (2.6691) Prec@1 34.375 (35.804) Prec@5 66.875 (66.176) Epoch: [6][4680/11272] Time 0.904 (1.141) Data 0.001 (0.003) Loss 2.5411 (2.6690) Prec@1 35.000 (35.804) Prec@5 68.125 (66.180) Epoch: [6][4690/11272] Time 0.893 (1.140) Data 0.001 (0.003) Loss 2.3997 (2.6690) Prec@1 37.500 (35.805) Prec@5 73.750 (66.184) Epoch: [6][4700/11272] Time 0.784 (1.139) Data 0.001 (0.003) Loss 2.6400 (2.6691) Prec@1 34.375 (35.801) Prec@5 64.375 (66.180) Epoch: [6][4710/11272] Time 0.769 (1.139) Data 0.001 (0.003) Loss 2.8054 (2.6690) Prec@1 35.000 (35.805) Prec@5 61.875 (66.181) Epoch: [6][4720/11272] Time 0.903 (1.138) Data 0.001 (0.003) Loss 2.8031 (2.6688) Prec@1 30.000 (35.805) Prec@5 65.625 (66.184) Epoch: [6][4730/11272] Time 0.859 (1.137) Data 0.001 (0.003) Loss 2.6463 (2.6689) Prec@1 36.875 (35.802) Prec@5 68.125 (66.184) Epoch: [6][4740/11272] Time 0.744 (1.137) Data 0.001 (0.003) Loss 2.4436 (2.6688) Prec@1 42.500 (35.803) Prec@5 70.625 (66.186) Epoch: [6][4750/11272] Time 0.748 (1.136) Data 0.001 (0.003) Loss 2.6693 (2.6690) Prec@1 40.000 (35.797) Prec@5 63.750 (66.182) Epoch: [6][4760/11272] Time 0.868 (1.135) Data 0.001 (0.003) Loss 2.4851 (2.6689) Prec@1 41.875 (35.799) Prec@5 69.375 (66.185) Epoch: [6][4770/11272] Time 0.791 (1.135) Data 0.001 (0.003) Loss 2.7214 (2.6691) Prec@1 37.500 (35.795) Prec@5 66.250 (66.180) Epoch: [6][4780/11272] Time 0.767 (1.134) Data 0.001 (0.003) Loss 2.2415 (2.6691) Prec@1 45.000 (35.798) Prec@5 77.500 (66.183) Epoch: [6][4790/11272] Time 0.859 (1.133) Data 0.001 (0.003) Loss 2.7866 (2.6691) Prec@1 38.750 (35.797) Prec@5 65.000 (66.182) Epoch: [6][4800/11272] Time 0.925 (1.133) Data 0.001 (0.003) Loss 2.6684 (2.6692) Prec@1 42.500 (35.798) Prec@5 68.750 (66.182) Epoch: [6][4810/11272] Time 0.799 (1.132) Data 0.001 (0.003) Loss 2.8980 (2.6690) Prec@1 33.750 (35.800) Prec@5 63.750 (66.187) Epoch: [6][4820/11272] Time 0.827 (1.132) Data 0.002 (0.003) Loss 2.6341 (2.6690) Prec@1 34.375 (35.799) Prec@5 67.500 (66.188) Epoch: [6][4830/11272] Time 0.860 (1.131) Data 0.001 (0.003) Loss 2.9214 (2.6690) Prec@1 27.500 (35.798) Prec@5 61.250 (66.184) Epoch: [6][4840/11272] Time 0.852 (1.130) Data 0.001 (0.003) Loss 2.4963 (2.6689) Prec@1 36.875 (35.799) Prec@5 71.250 (66.186) Epoch: [6][4850/11272] Time 0.761 (1.130) Data 0.001 (0.003) Loss 2.6310 (2.6688) Prec@1 43.750 (35.800) Prec@5 65.000 (66.186) Epoch: [6][4860/11272] Time 0.784 (1.129) Data 0.002 (0.003) Loss 2.5103 (2.6686) Prec@1 31.875 (35.803) Prec@5 71.875 (66.188) Epoch: [6][4870/11272] Time 0.870 (1.128) Data 0.001 (0.003) Loss 2.6091 (2.6687) Prec@1 35.000 (35.803) Prec@5 71.875 (66.189) Epoch: [6][4880/11272] Time 0.899 (1.128) Data 0.001 (0.003) Loss 2.6874 (2.6687) Prec@1 35.000 (35.802) Prec@5 65.625 (66.189) Epoch: [6][4890/11272] Time 0.778 (1.127) Data 0.001 (0.003) Loss 2.7177 (2.6688) Prec@1 35.000 (35.801) Prec@5 63.125 (66.185) Epoch: [6][4900/11272] Time 0.761 (1.127) Data 0.001 (0.003) Loss 2.8777 (2.6689) Prec@1 33.125 (35.799) Prec@5 62.500 (66.184) Epoch: [6][4910/11272] Time 0.865 (1.126) Data 0.001 (0.003) Loss 2.6918 (2.6688) Prec@1 32.500 (35.802) Prec@5 65.000 (66.186) Epoch: [6][4920/11272] Time 0.744 (1.125) Data 0.003 (0.003) Loss 2.6243 (2.6689) Prec@1 38.125 (35.803) Prec@5 67.500 (66.184) Epoch: [6][4930/11272] Time 0.783 (1.125) Data 0.001 (0.003) Loss 2.8180 (2.6690) Prec@1 36.250 (35.806) Prec@5 64.375 (66.182) Epoch: [6][4940/11272] Time 0.927 (1.124) Data 0.001 (0.003) Loss 2.7720 (2.6691) Prec@1 35.625 (35.802) Prec@5 60.000 (66.179) Epoch: [6][4950/11272] Time 0.919 (1.124) Data 0.001 (0.003) Loss 2.8221 (2.6691) Prec@1 36.250 (35.802) Prec@5 61.250 (66.179) Epoch: [6][4960/11272] Time 0.752 (1.123) Data 0.001 (0.003) Loss 2.6372 (2.6690) Prec@1 38.750 (35.806) Prec@5 68.750 (66.181) Epoch: [6][4970/11272] Time 0.742 (1.122) Data 0.001 (0.003) Loss 2.8109 (2.6690) Prec@1 31.875 (35.804) Prec@5 63.125 (66.180) Epoch: [6][4980/11272] Time 0.901 (1.122) Data 0.001 (0.003) Loss 2.6933 (2.6689) Prec@1 31.250 (35.807) Prec@5 64.375 (66.179) Epoch: [6][4990/11272] Time 0.874 (1.121) Data 0.001 (0.003) Loss 2.7515 (2.6689) Prec@1 36.250 (35.809) Prec@5 63.750 (66.179) Epoch: [6][5000/11272] Time 0.744 (1.121) Data 0.001 (0.003) Loss 2.7753 (2.6688) Prec@1 33.125 (35.809) Prec@5 68.125 (66.179) Epoch: [6][5010/11272] Time 0.755 (1.120) Data 0.001 (0.003) Loss 2.5523 (2.6689) Prec@1 38.750 (35.809) Prec@5 70.625 (66.178) Epoch: [6][5020/11272] Time 0.867 (1.119) Data 0.001 (0.003) Loss 2.5900 (2.6687) Prec@1 31.875 (35.812) Prec@5 71.250 (66.181) Epoch: [6][5030/11272] Time 0.886 (1.119) Data 0.001 (0.003) Loss 2.6768 (2.6687) Prec@1 33.750 (35.810) Prec@5 65.625 (66.183) Epoch: [6][5040/11272] Time 0.736 (1.118) Data 0.001 (0.003) Loss 2.6099 (2.6687) Prec@1 34.375 (35.810) Prec@5 63.750 (66.183) Epoch: [6][5050/11272] Time 0.894 (1.117) Data 0.001 (0.003) Loss 2.8338 (2.6688) Prec@1 33.750 (35.808) Prec@5 61.250 (66.182) Epoch: [6][5060/11272] Time 0.848 (1.117) Data 0.001 (0.003) Loss 2.8311 (2.6687) Prec@1 33.125 (35.809) Prec@5 60.625 (66.184) Epoch: [6][5070/11272] Time 0.770 (1.116) Data 0.001 (0.003) Loss 2.5775 (2.6686) Prec@1 36.875 (35.809) Prec@5 69.375 (66.182) Epoch: [6][5080/11272] Time 0.775 (1.116) Data 0.002 (0.003) Loss 2.6303 (2.6686) Prec@1 38.750 (35.809) Prec@5 66.250 (66.182) Epoch: [6][5090/11272] Time 0.874 (1.115) Data 0.001 (0.003) Loss 2.6382 (2.6686) Prec@1 35.625 (35.807) Prec@5 69.375 (66.181) Epoch: [6][5100/11272] Time 0.866 (1.115) Data 0.001 (0.003) Loss 2.9283 (2.6685) Prec@1 32.500 (35.814) Prec@5 61.875 (66.184) Epoch: [6][5110/11272] Time 0.745 (1.114) Data 0.001 (0.003) Loss 2.3059 (2.6685) Prec@1 36.250 (35.812) Prec@5 76.250 (66.182) Epoch: [6][5120/11272] Time 0.763 (1.113) Data 0.001 (0.003) Loss 2.9496 (2.6686) Prec@1 26.875 (35.810) Prec@5 62.500 (66.182) Epoch: [6][5130/11272] Time 0.876 (1.113) Data 0.001 (0.003) Loss 2.4647 (2.6685) Prec@1 35.625 (35.810) Prec@5 71.875 (66.185) Epoch: [6][5140/11272] Time 0.866 (1.112) Data 0.001 (0.003) Loss 2.6046 (2.6684) Prec@1 38.750 (35.812) Prec@5 69.375 (66.189) Epoch: [6][5150/11272] Time 0.723 (1.112) Data 0.001 (0.003) Loss 2.5090 (2.6685) Prec@1 38.125 (35.813) Prec@5 69.375 (66.189) Epoch: [6][5160/11272] Time 0.734 (1.111) Data 0.001 (0.003) Loss 2.4671 (2.6684) Prec@1 38.750 (35.815) Prec@5 69.375 (66.188) Epoch: [6][5170/11272] Time 0.890 (1.111) Data 0.001 (0.003) Loss 2.7988 (2.6685) Prec@1 35.625 (35.814) Prec@5 61.875 (66.185) Epoch: [6][5180/11272] Time 0.756 (1.110) Data 0.003 (0.003) Loss 2.8493 (2.6685) Prec@1 30.000 (35.814) Prec@5 66.875 (66.184) Epoch: [6][5190/11272] Time 0.732 (1.109) Data 0.002 (0.003) Loss 2.4578 (2.6684) Prec@1 38.125 (35.815) Prec@5 71.250 (66.187) Epoch: [6][5200/11272] Time 0.903 (1.109) Data 0.002 (0.003) Loss 2.5584 (2.6685) Prec@1 39.375 (35.813) Prec@5 71.875 (66.187) Epoch: [6][5210/11272] Time 0.873 (1.108) Data 0.002 (0.003) Loss 2.5612 (2.6686) Prec@1 40.000 (35.808) Prec@5 67.500 (66.184) Epoch: [6][5220/11272] Time 0.753 (1.108) Data 0.001 (0.003) Loss 2.7661 (2.6687) Prec@1 36.875 (35.811) Prec@5 64.375 (66.182) Epoch: [6][5230/11272] Time 0.773 (1.107) Data 0.001 (0.003) Loss 2.8748 (2.6687) Prec@1 33.125 (35.809) Prec@5 60.000 (66.179) Epoch: [6][5240/11272] Time 0.884 (1.107) Data 0.001 (0.003) Loss 2.3551 (2.6687) Prec@1 40.625 (35.808) Prec@5 69.375 (66.176) Epoch: [6][5250/11272] Time 0.905 (1.106) Data 0.002 (0.003) Loss 2.8868 (2.6689) Prec@1 31.875 (35.806) Prec@5 60.625 (66.173) Epoch: [6][5260/11272] Time 0.755 (1.106) Data 0.002 (0.003) Loss 2.4332 (2.6687) Prec@1 41.250 (35.810) Prec@5 71.875 (66.177) Epoch: [6][5270/11272] Time 0.790 (1.105) Data 0.002 (0.003) Loss 2.5970 (2.6688) Prec@1 38.125 (35.806) Prec@5 66.250 (66.176) Epoch: [6][5280/11272] Time 0.900 (1.104) Data 0.001 (0.003) Loss 2.4448 (2.6688) Prec@1 39.375 (35.806) Prec@5 70.000 (66.179) Epoch: [6][5290/11272] Time 0.940 (1.104) Data 0.002 (0.003) Loss 2.6561 (2.6688) Prec@1 38.750 (35.804) Prec@5 67.500 (66.179) Epoch: [6][5300/11272] Time 0.740 (1.103) Data 0.001 (0.003) Loss 2.6496 (2.6689) Prec@1 33.750 (35.804) Prec@5 65.625 (66.179) Epoch: [6][5310/11272] Time 0.911 (1.103) Data 0.001 (0.003) Loss 2.6629 (2.6688) Prec@1 34.375 (35.805) Prec@5 64.375 (66.181) Epoch: [6][5320/11272] Time 0.997 (1.102) Data 0.002 (0.003) Loss 2.5736 (2.6689) Prec@1 37.500 (35.804) Prec@5 65.000 (66.182) Epoch: [6][5330/11272] Time 0.758 (1.102) Data 0.002 (0.003) Loss 2.3870 (2.6688) Prec@1 41.875 (35.804) Prec@5 70.000 (66.184) Epoch: [6][5340/11272] Time 0.802 (1.101) Data 0.001 (0.003) Loss 2.5964 (2.6689) Prec@1 34.375 (35.801) Prec@5 67.500 (66.184) Epoch: [6][5350/11272] Time 0.874 (1.101) Data 0.001 (0.003) Loss 2.6509 (2.6687) Prec@1 31.250 (35.803) Prec@5 68.750 (66.188) Epoch: [6][5360/11272] Time 0.898 (1.100) Data 0.006 (0.003) Loss 2.8635 (2.6688) Prec@1 32.500 (35.799) Prec@5 65.000 (66.183) Epoch: [6][5370/11272] Time 0.774 (1.100) Data 0.001 (0.003) Loss 2.5877 (2.6690) Prec@1 38.750 (35.796) Prec@5 65.625 (66.181) Epoch: [6][5380/11272] Time 0.845 (1.099) Data 0.001 (0.003) Loss 2.6916 (2.6689) Prec@1 36.875 (35.798) Prec@5 66.250 (66.181) Epoch: [6][5390/11272] Time 0.904 (1.099) Data 0.001 (0.003) Loss 2.6798 (2.6689) Prec@1 33.125 (35.797) Prec@5 63.750 (66.181) Epoch: [6][5400/11272] Time 0.922 (1.098) Data 0.001 (0.003) Loss 2.6270 (2.6688) Prec@1 36.875 (35.800) Prec@5 63.750 (66.183) Epoch: [6][5410/11272] Time 0.770 (1.098) Data 0.001 (0.002) Loss 2.5168 (2.6687) Prec@1 38.750 (35.801) Prec@5 70.000 (66.186) Epoch: [6][5420/11272] Time 0.740 (1.097) Data 0.001 (0.002) Loss 2.6871 (2.6689) Prec@1 38.750 (35.799) Prec@5 65.625 (66.180) Epoch: [6][5430/11272] Time 0.983 (1.097) Data 0.001 (0.002) Loss 2.5658 (2.6688) Prec@1 36.250 (35.799) Prec@5 68.125 (66.182) Epoch: [6][5440/11272] Time 0.889 (1.096) Data 0.001 (0.002) Loss 2.2576 (2.6687) Prec@1 43.125 (35.799) Prec@5 74.375 (66.184) Epoch: [6][5450/11272] Time 0.755 (1.096) Data 0.002 (0.002) Loss 2.5568 (2.6686) Prec@1 33.750 (35.799) Prec@5 68.750 (66.186) Epoch: [6][5460/11272] Time 0.830 (1.095) Data 0.001 (0.002) Loss 2.6175 (2.6686) Prec@1 40.625 (35.800) Prec@5 64.375 (66.188) Epoch: [6][5470/11272] Time 0.926 (1.095) Data 0.001 (0.002) Loss 2.7986 (2.6686) Prec@1 32.500 (35.796) Prec@5 65.000 (66.190) Epoch: [6][5480/11272] Time 0.772 (1.094) Data 0.001 (0.002) Loss 2.7619 (2.6686) Prec@1 36.250 (35.798) Prec@5 66.875 (66.192) Epoch: [6][5490/11272] Time 0.763 (1.094) Data 0.001 (0.002) Loss 2.8137 (2.6687) Prec@1 33.125 (35.796) Prec@5 64.375 (66.190) Epoch: [6][5500/11272] Time 0.880 (1.093) Data 0.001 (0.002) Loss 2.5433 (2.6687) Prec@1 43.750 (35.798) Prec@5 67.500 (66.191) Epoch: [6][5510/11272] Time 0.846 (1.093) Data 0.001 (0.002) Loss 2.5452 (2.6686) Prec@1 38.125 (35.798) Prec@5 69.375 (66.191) Epoch: [6][5520/11272] Time 0.767 (1.092) Data 0.001 (0.002) Loss 2.7528 (2.6686) Prec@1 33.125 (35.799) Prec@5 65.625 (66.189) Epoch: [6][5530/11272] Time 0.739 (1.092) Data 0.001 (0.002) Loss 2.7128 (2.6687) Prec@1 39.375 (35.797) Prec@5 61.875 (66.187) Epoch: [6][5540/11272] Time 0.865 (1.091) Data 0.001 (0.002) Loss 2.6002 (2.6686) Prec@1 38.125 (35.796) Prec@5 66.250 (66.189) Epoch: [6][5550/11272] Time 0.874 (1.091) Data 0.001 (0.002) Loss 2.7782 (2.6687) Prec@1 31.875 (35.796) Prec@5 64.375 (66.188) Epoch: [6][5560/11272] Time 0.756 (1.090) Data 0.001 (0.002) Loss 2.7644 (2.6687) Prec@1 36.875 (35.796) Prec@5 67.500 (66.190) Epoch: [6][5570/11272] Time 0.768 (1.090) Data 0.001 (0.002) Loss 3.0792 (2.6688) Prec@1 30.000 (35.793) Prec@5 58.125 (66.188) Epoch: [6][5580/11272] Time 0.888 (1.089) Data 0.001 (0.002) Loss 2.7999 (2.6689) Prec@1 36.250 (35.793) Prec@5 64.375 (66.186) Epoch: [6][5590/11272] Time 0.809 (1.089) Data 0.001 (0.002) Loss 2.8948 (2.6689) Prec@1 35.000 (35.794) Prec@5 60.625 (66.185) Epoch: [6][5600/11272] Time 0.771 (1.088) Data 0.001 (0.002) Loss 2.7524 (2.6690) Prec@1 37.500 (35.795) Prec@5 66.250 (66.185) Epoch: [6][5610/11272] Time 0.871 (1.088) Data 0.001 (0.002) Loss 2.7629 (2.6688) Prec@1 36.875 (35.800) Prec@5 65.000 (66.187) Epoch: [6][5620/11272] Time 0.871 (1.088) Data 0.001 (0.002) Loss 2.8629 (2.6689) Prec@1 33.125 (35.798) Prec@5 57.500 (66.185) Epoch: [6][5630/11272] Time 0.749 (1.087) Data 0.001 (0.002) Loss 2.4014 (2.6688) Prec@1 40.000 (35.799) Prec@5 73.750 (66.188) Epoch: [6][5640/11272] Time 0.766 (1.087) Data 0.002 (0.002) Loss 2.5868 (2.6688) Prec@1 36.250 (35.800) Prec@5 66.250 (66.189) Epoch: [6][5650/11272] Time 0.811 (1.086) Data 0.001 (0.002) Loss 2.5032 (2.6687) Prec@1 42.500 (35.803) Prec@5 71.875 (66.189) Epoch: [6][5660/11272] Time 0.935 (1.086) Data 0.001 (0.002) Loss 2.9136 (2.6687) Prec@1 29.375 (35.801) Prec@5 62.500 (66.191) Epoch: [6][5670/11272] Time 0.739 (1.085) Data 0.001 (0.002) Loss 2.7000 (2.6688) Prec@1 35.625 (35.797) Prec@5 63.125 (66.188) Epoch: [6][5680/11272] Time 0.776 (1.085) Data 0.001 (0.002) Loss 2.7805 (2.6689) Prec@1 36.875 (35.794) Prec@5 63.750 (66.186) Epoch: [6][5690/11272] Time 0.871 (1.084) Data 0.001 (0.002) Loss 2.4722 (2.6689) Prec@1 36.875 (35.791) Prec@5 68.750 (66.187) Epoch: [6][5700/11272] Time 0.853 (1.084) Data 0.001 (0.002) Loss 2.6252 (2.6689) Prec@1 35.000 (35.791) Prec@5 71.250 (66.185) Epoch: [6][5710/11272] Time 0.745 (1.083) Data 0.001 (0.002) Loss 2.6686 (2.6689) Prec@1 40.625 (35.791) Prec@5 63.750 (66.185) Epoch: [6][5720/11272] Time 0.904 (1.083) Data 0.001 (0.002) Loss 2.7422 (2.6688) Prec@1 35.625 (35.791) Prec@5 65.625 (66.188) Epoch: [6][5730/11272] Time 0.877 (1.083) Data 0.001 (0.002) Loss 2.8146 (2.6690) Prec@1 33.750 (35.788) Prec@5 67.500 (66.186) Epoch: [6][5740/11272] Time 0.722 (1.082) Data 0.001 (0.002) Loss 2.6202 (2.6689) Prec@1 39.375 (35.790) Prec@5 65.625 (66.188) Epoch: [6][5750/11272] Time 0.775 (1.082) Data 0.001 (0.002) Loss 2.7394 (2.6691) Prec@1 33.125 (35.789) Prec@5 65.625 (66.185) Epoch: [6][5760/11272] Time 0.955 (1.081) Data 0.001 (0.002) Loss 2.8132 (2.6691) Prec@1 37.500 (35.789) Prec@5 61.250 (66.184) Epoch: [6][5770/11272] Time 0.857 (1.081) Data 0.001 (0.002) Loss 2.5340 (2.6690) Prec@1 41.875 (35.789) Prec@5 63.750 (66.186) Epoch: [6][5780/11272] Time 0.769 (1.080) Data 0.001 (0.002) Loss 2.6674 (2.6690) Prec@1 36.250 (35.788) Prec@5 68.125 (66.186) Epoch: [6][5790/11272] Time 0.745 (1.080) Data 0.001 (0.002) Loss 2.6634 (2.6690) Prec@1 38.750 (35.789) Prec@5 65.000 (66.187) Epoch: [6][5800/11272] Time 0.889 (1.079) Data 0.001 (0.002) Loss 2.7959 (2.6690) Prec@1 31.875 (35.789) Prec@5 62.500 (66.186) Epoch: [6][5810/11272] Time 0.928 (1.079) Data 0.002 (0.002) Loss 2.5677 (2.6688) Prec@1 38.125 (35.790) Prec@5 68.125 (66.191) Epoch: [6][5820/11272] Time 0.755 (1.079) Data 0.001 (0.002) Loss 2.8403 (2.6688) Prec@1 33.125 (35.791) Prec@5 56.250 (66.188) Epoch: [6][5830/11272] Time 0.744 (1.078) Data 0.001 (0.002) Loss 2.6811 (2.6687) Prec@1 31.875 (35.792) Prec@5 63.750 (66.191) Epoch: [6][5840/11272] Time 0.896 (1.078) Data 0.001 (0.002) Loss 2.8358 (2.6688) Prec@1 32.500 (35.791) Prec@5 59.375 (66.190) Epoch: [6][5850/11272] Time 0.755 (1.077) Data 0.003 (0.002) Loss 2.6751 (2.6689) Prec@1 35.625 (35.790) Prec@5 68.125 (66.188) Epoch: [6][5860/11272] Time 0.765 (1.077) Data 0.001 (0.002) Loss 2.6488 (2.6689) Prec@1 36.250 (35.789) Prec@5 66.875 (66.186) Epoch: [6][5870/11272] Time 0.916 (1.076) Data 0.001 (0.002) Loss 3.1868 (2.6690) Prec@1 26.875 (35.786) Prec@5 56.250 (66.188) Epoch: [6][5880/11272] Time 0.872 (1.076) Data 0.001 (0.002) Loss 2.7934 (2.6690) Prec@1 31.250 (35.787) Prec@5 63.750 (66.189) Epoch: [6][5890/11272] Time 0.756 (1.076) Data 0.001 (0.002) Loss 2.6645 (2.6689) Prec@1 40.625 (35.788) Prec@5 69.375 (66.189) Epoch: [6][5900/11272] Time 0.761 (1.075) Data 0.001 (0.002) Loss 2.5840 (2.6690) Prec@1 39.375 (35.788) Prec@5 64.375 (66.186) Epoch: [6][5910/11272] Time 0.879 (1.075) Data 0.001 (0.002) Loss 2.4964 (2.6691) Prec@1 43.750 (35.789) Prec@5 65.625 (66.184) Epoch: [6][5920/11272] Time 0.896 (1.074) Data 0.001 (0.002) Loss 2.7779 (2.6691) Prec@1 35.000 (35.786) Prec@5 62.500 (66.185) Epoch: [6][5930/11272] Time 0.748 (1.074) Data 0.002 (0.002) Loss 2.6029 (2.6691) Prec@1 37.500 (35.786) Prec@5 69.375 (66.182) Epoch: [6][5940/11272] Time 0.757 (1.073) Data 0.001 (0.002) Loss 2.5253 (2.6692) Prec@1 36.875 (35.787) Prec@5 70.000 (66.180) Epoch: [6][5950/11272] Time 0.845 (1.073) Data 0.001 (0.002) Loss 2.5504 (2.6692) Prec@1 36.250 (35.788) Prec@5 67.500 (66.180) Epoch: [6][5960/11272] Time 0.873 (1.073) Data 0.001 (0.002) Loss 2.5637 (2.6693) Prec@1 37.500 (35.787) Prec@5 66.250 (66.179) Epoch: [6][5970/11272] Time 0.755 (1.072) Data 0.001 (0.002) Loss 2.6227 (2.6694) Prec@1 36.875 (35.787) Prec@5 61.875 (66.176) Epoch: [6][5980/11272] Time 0.955 (1.072) Data 0.001 (0.002) Loss 3.0062 (2.6696) Prec@1 30.000 (35.784) Prec@5 61.875 (66.175) Epoch: [6][5990/11272] Time 0.863 (1.071) Data 0.001 (0.002) Loss 2.7809 (2.6696) Prec@1 35.000 (35.783) Prec@5 60.000 (66.174) Epoch: [6][6000/11272] Time 0.806 (1.071) Data 0.001 (0.002) Loss 2.5300 (2.6695) Prec@1 35.000 (35.786) Prec@5 69.375 (66.175) Epoch: [6][6010/11272] Time 0.731 (1.071) Data 0.001 (0.002) Loss 2.5244 (2.6695) Prec@1 38.125 (35.785) Prec@5 69.375 (66.175) Epoch: [6][6020/11272] Time 0.878 (1.070) Data 0.001 (0.002) Loss 2.7786 (2.6697) Prec@1 33.125 (35.781) Prec@5 61.875 (66.173) Epoch: [6][6030/11272] Time 0.850 (1.070) Data 0.002 (0.002) Loss 2.5564 (2.6695) Prec@1 36.250 (35.784) Prec@5 67.500 (66.174) Epoch: [6][6040/11272] Time 0.720 (1.069) Data 0.001 (0.002) Loss 2.7158 (2.6696) Prec@1 34.375 (35.784) Prec@5 65.000 (66.170) Epoch: [6][6050/11272] Time 0.745 (1.069) Data 0.001 (0.002) Loss 2.7956 (2.6696) Prec@1 33.125 (35.784) Prec@5 68.750 (66.174) Epoch: [6][6060/11272] Time 0.907 (1.068) Data 0.001 (0.002) Loss 2.8523 (2.6697) Prec@1 31.250 (35.783) Prec@5 62.500 (66.169) Epoch: [6][6070/11272] Time 0.882 (1.068) Data 0.001 (0.002) Loss 2.6185 (2.6697) Prec@1 39.375 (35.784) Prec@5 70.000 (66.167) Epoch: [6][6080/11272] Time 0.753 (1.068) Data 0.001 (0.002) Loss 2.8810 (2.6698) Prec@1 26.875 (35.782) Prec@5 60.625 (66.165) Epoch: [6][6090/11272] Time 0.758 (1.067) Data 0.001 (0.002) Loss 2.7712 (2.6697) Prec@1 38.125 (35.784) Prec@5 64.375 (66.166) Epoch: [6][6100/11272] Time 0.848 (1.067) Data 0.001 (0.002) Loss 2.9022 (2.6697) Prec@1 30.625 (35.785) Prec@5 61.875 (66.166) Epoch: [6][6110/11272] Time 0.737 (1.066) Data 0.003 (0.002) Loss 2.8827 (2.6697) Prec@1 31.250 (35.785) Prec@5 64.375 (66.166) Epoch: [6][6120/11272] Time 0.743 (1.066) Data 0.001 (0.002) Loss 2.7099 (2.6697) Prec@1 33.750 (35.784) Prec@5 66.250 (66.168) Epoch: [6][6130/11272] Time 0.898 (1.066) Data 0.001 (0.002) Loss 2.9669 (2.6697) Prec@1 35.000 (35.784) Prec@5 63.125 (66.168) Epoch: [6][6140/11272] Time 0.861 (1.065) Data 0.001 (0.002) Loss 2.6354 (2.6697) Prec@1 38.125 (35.785) Prec@5 66.875 (66.169) Epoch: [6][6150/11272] Time 0.745 (1.065) Data 0.001 (0.002) Loss 2.5962 (2.6695) Prec@1 36.250 (35.790) Prec@5 68.750 (66.171) Epoch: [6][6160/11272] Time 0.740 (1.064) Data 0.001 (0.002) Loss 2.5218 (2.6694) Prec@1 38.125 (35.792) Prec@5 68.125 (66.172) Epoch: [6][6170/11272] Time 0.882 (1.064) Data 0.001 (0.002) Loss 2.4823 (2.6694) Prec@1 36.875 (35.792) Prec@5 68.750 (66.172) Epoch: [6][6180/11272] Time 0.842 (1.064) Data 0.001 (0.002) Loss 2.4050 (2.6693) Prec@1 38.125 (35.792) Prec@5 71.875 (66.175) Epoch: [6][6190/11272] Time 0.779 (1.063) Data 0.001 (0.002) Loss 2.9446 (2.6694) Prec@1 29.375 (35.791) Prec@5 58.750 (66.173) Epoch: [6][6200/11272] Time 0.753 (1.063) Data 0.001 (0.002) Loss 2.7663 (2.6695) Prec@1 32.500 (35.790) Prec@5 61.875 (66.172) Epoch: [6][6210/11272] Time 0.908 (1.062) Data 0.001 (0.002) Loss 2.6072 (2.6694) Prec@1 35.625 (35.791) Prec@5 66.875 (66.171) Epoch: [6][6220/11272] Time 0.868 (1.062) Data 0.001 (0.002) Loss 2.6865 (2.6694) Prec@1 38.750 (35.792) Prec@5 65.625 (66.171) Epoch: [6][6230/11272] Time 0.756 (1.062) Data 0.001 (0.002) Loss 2.6707 (2.6693) Prec@1 35.625 (35.795) Prec@5 63.750 (66.173) Epoch: [6][6240/11272] Time 0.876 (1.061) Data 0.001 (0.002) Loss 2.8685 (2.6695) Prec@1 30.625 (35.791) Prec@5 61.875 (66.167) Epoch: [6][6250/11272] Time 0.893 (1.061) Data 0.001 (0.002) Loss 2.4295 (2.6694) Prec@1 36.250 (35.791) Prec@5 71.875 (66.168) Epoch: [6][6260/11272] Time 0.777 (1.060) Data 0.001 (0.002) Loss 2.6890 (2.6694) Prec@1 35.625 (35.793) Prec@5 65.000 (66.168) Epoch: [6][6270/11272] Time 0.758 (1.060) Data 0.001 (0.002) Loss 2.0992 (2.6693) Prec@1 46.875 (35.797) Prec@5 77.500 (66.169) Epoch: [6][6280/11272] Time 0.890 (1.060) Data 0.001 (0.002) Loss 2.7894 (2.6694) Prec@1 30.625 (35.794) Prec@5 68.750 (66.165) Epoch: [6][6290/11272] Time 0.897 (1.059) Data 0.001 (0.002) Loss 2.9370 (2.6693) Prec@1 30.625 (35.798) Prec@5 60.000 (66.166) Epoch: [6][6300/11272] Time 0.757 (1.059) Data 0.001 (0.002) Loss 2.9538 (2.6693) Prec@1 27.500 (35.797) Prec@5 65.625 (66.166) Epoch: [6][6310/11272] Time 0.777 (1.059) Data 0.002 (0.002) Loss 2.7039 (2.6692) Prec@1 34.375 (35.801) Prec@5 64.375 (66.166) Epoch: [6][6320/11272] Time 0.856 (1.058) Data 0.001 (0.002) Loss 2.7458 (2.6691) Prec@1 37.500 (35.806) Prec@5 63.125 (66.167) Epoch: [6][6330/11272] Time 0.891 (1.058) Data 0.001 (0.002) Loss 2.6472 (2.6692) Prec@1 38.125 (35.805) Prec@5 67.500 (66.168) Epoch: [6][6340/11272] Time 0.780 (1.057) Data 0.002 (0.002) Loss 2.7488 (2.6693) Prec@1 33.750 (35.804) Prec@5 66.875 (66.165) Epoch: [6][6350/11272] Time 0.738 (1.057) Data 0.001 (0.002) Loss 2.6401 (2.6693) Prec@1 41.250 (35.805) Prec@5 70.000 (66.165) Epoch: [6][6360/11272] Time 0.894 (1.057) Data 0.001 (0.002) Loss 2.6115 (2.6692) Prec@1 34.375 (35.808) Prec@5 67.500 (66.168) Epoch: [6][6370/11272] Time 0.854 (1.056) Data 0.001 (0.002) Loss 2.5261 (2.6690) Prec@1 38.125 (35.808) Prec@5 66.875 (66.173) Epoch: [6][6380/11272] Time 0.720 (1.056) Data 0.001 (0.002) Loss 2.6824 (2.6690) Prec@1 36.250 (35.805) Prec@5 61.250 (66.169) Epoch: [6][6390/11272] Time 0.863 (1.055) Data 0.001 (0.002) Loss 2.6181 (2.6689) Prec@1 36.875 (35.809) Prec@5 70.000 (66.172) Epoch: [6][6400/11272] Time 0.876 (1.055) Data 0.001 (0.002) Loss 2.5900 (2.6688) Prec@1 35.625 (35.810) Prec@5 69.375 (66.175) Epoch: [6][6410/11272] Time 0.777 (1.055) Data 0.001 (0.002) Loss 2.8558 (2.6688) Prec@1 35.625 (35.812) Prec@5 65.625 (66.173) Epoch: [6][6420/11272] Time 0.754 (1.054) Data 0.001 (0.002) Loss 2.4995 (2.6688) Prec@1 33.125 (35.812) Prec@5 73.125 (66.173) Epoch: [6][6430/11272] Time 0.916 (1.054) Data 0.001 (0.002) Loss 2.6725 (2.6688) Prec@1 35.625 (35.814) Prec@5 66.250 (66.175) Epoch: [6][6440/11272] Time 0.928 (1.054) Data 0.001 (0.002) Loss 2.5493 (2.6688) Prec@1 41.250 (35.813) Prec@5 72.500 (66.174) Epoch: [6][6450/11272] Time 0.729 (1.053) Data 0.001 (0.002) Loss 2.8955 (2.6688) Prec@1 36.875 (35.813) Prec@5 62.500 (66.175) Epoch: [6][6460/11272] Time 0.733 (1.053) Data 0.001 (0.002) Loss 2.6375 (2.6688) Prec@1 36.250 (35.812) Prec@5 70.625 (66.177) Epoch: [6][6470/11272] Time 0.892 (1.053) Data 0.002 (0.002) Loss 2.3875 (2.6687) Prec@1 45.000 (35.814) Prec@5 70.000 (66.177) Epoch: [6][6480/11272] Time 0.894 (1.052) Data 0.001 (0.002) Loss 2.7207 (2.6689) Prec@1 36.250 (35.813) Prec@5 63.750 (66.175) Epoch: [6][6490/11272] Time 0.782 (1.052) Data 0.001 (0.002) Loss 2.7982 (2.6689) Prec@1 33.125 (35.815) Prec@5 63.750 (66.175) Epoch: [6][6500/11272] Time 0.723 (1.052) Data 0.001 (0.002) Loss 2.7192 (2.6688) Prec@1 35.000 (35.814) Prec@5 68.125 (66.177) Epoch: [6][6510/11272] Time 0.931 (1.051) Data 0.002 (0.002) Loss 2.8506 (2.6690) Prec@1 33.750 (35.809) Prec@5 64.375 (66.175) Epoch: [6][6520/11272] Time 0.790 (1.051) Data 0.001 (0.002) Loss 2.9724 (2.6690) Prec@1 30.625 (35.807) Prec@5 60.625 (66.173) Epoch: [6][6530/11272] Time 0.739 (1.050) Data 0.002 (0.002) Loss 2.7896 (2.6690) Prec@1 32.500 (35.809) Prec@5 61.875 (66.173) Epoch: [6][6540/11272] Time 0.876 (1.050) Data 0.001 (0.002) Loss 2.7205 (2.6691) Prec@1 33.125 (35.808) Prec@5 65.000 (66.173) Epoch: [6][6550/11272] Time 0.945 (1.050) Data 0.002 (0.002) Loss 2.6179 (2.6691) Prec@1 35.000 (35.805) Prec@5 68.125 (66.171) Epoch: [6][6560/11272] Time 0.747 (1.049) Data 0.001 (0.002) Loss 2.3909 (2.6691) Prec@1 41.250 (35.806) Prec@5 70.625 (66.171) Epoch: [6][6570/11272] Time 0.787 (1.049) Data 0.002 (0.002) Loss 2.6317 (2.6690) Prec@1 34.375 (35.807) Prec@5 66.875 (66.173) Epoch: [6][6580/11272] Time 0.881 (1.049) Data 0.001 (0.002) Loss 2.5808 (2.6691) Prec@1 38.125 (35.807) Prec@5 68.125 (66.173) Epoch: [6][6590/11272] Time 0.893 (1.048) Data 0.001 (0.002) Loss 2.5247 (2.6691) Prec@1 36.250 (35.806) Prec@5 64.375 (66.172) Epoch: [6][6600/11272] Time 0.746 (1.048) Data 0.001 (0.002) Loss 2.7049 (2.6691) Prec@1 37.500 (35.806) Prec@5 69.375 (66.171) Epoch: [6][6610/11272] Time 0.767 (1.048) Data 0.001 (0.002) Loss 2.7630 (2.6691) Prec@1 33.125 (35.804) Prec@5 63.125 (66.168) Epoch: [6][6620/11272] Time 0.870 (1.047) Data 0.001 (0.002) Loss 2.8150 (2.6692) Prec@1 33.125 (35.802) Prec@5 63.125 (66.168) Epoch: [6][6630/11272] Time 0.843 (1.047) Data 0.002 (0.002) Loss 2.8234 (2.6691) Prec@1 27.500 (35.801) Prec@5 61.875 (66.169) Epoch: [6][6640/11272] Time 0.759 (1.047) Data 0.001 (0.002) Loss 2.4636 (2.6690) Prec@1 42.500 (35.804) Prec@5 69.375 (66.172) Epoch: [6][6650/11272] Time 0.884 (1.046) Data 0.001 (0.002) Loss 2.7872 (2.6692) Prec@1 36.250 (35.800) Prec@5 61.250 (66.168) Epoch: [6][6660/11272] Time 0.888 (1.046) Data 0.001 (0.002) Loss 2.6083 (2.6693) Prec@1 36.875 (35.800) Prec@5 70.625 (66.170) Epoch: [6][6670/11272] Time 0.776 (1.046) Data 0.001 (0.002) Loss 2.4186 (2.6692) Prec@1 37.500 (35.802) Prec@5 72.500 (66.173) Epoch: [6][6680/11272] Time 0.747 (1.045) Data 0.001 (0.002) Loss 2.7011 (2.6692) Prec@1 33.125 (35.801) Prec@5 65.625 (66.173) Epoch: [6][6690/11272] Time 0.832 (1.045) Data 0.001 (0.002) Loss 2.3963 (2.6692) Prec@1 45.000 (35.800) Prec@5 70.000 (66.173) Epoch: [6][6700/11272] Time 0.900 (1.045) Data 0.001 (0.002) Loss 2.7686 (2.6691) Prec@1 32.500 (35.799) Prec@5 62.500 (66.175) Epoch: [6][6710/11272] Time 0.744 (1.044) Data 0.002 (0.002) Loss 2.7866 (2.6691) Prec@1 33.125 (35.799) Prec@5 60.000 (66.175) Epoch: [6][6720/11272] Time 0.752 (1.044) Data 0.001 (0.002) Loss 2.7756 (2.6692) Prec@1 32.500 (35.798) Prec@5 67.500 (66.172) Epoch: [6][6730/11272] Time 0.934 (1.044) Data 0.001 (0.002) Loss 2.5484 (2.6692) Prec@1 36.875 (35.798) Prec@5 66.875 (66.172) Epoch: [6][6740/11272] Time 0.890 (1.043) Data 0.001 (0.002) Loss 2.3878 (2.6691) Prec@1 41.875 (35.801) Prec@5 68.750 (66.174) Epoch: [6][6750/11272] Time 0.743 (1.043) Data 0.001 (0.002) Loss 2.5143 (2.6691) Prec@1 41.875 (35.800) Prec@5 66.875 (66.173) Epoch: [6][6760/11272] Time 0.741 (1.043) Data 0.001 (0.002) Loss 2.8298 (2.6692) Prec@1 31.875 (35.796) Prec@5 63.125 (66.171) Epoch: [6][6770/11272] Time 0.895 (1.042) Data 0.002 (0.002) Loss 2.7831 (2.6693) Prec@1 35.625 (35.794) Prec@5 66.250 (66.169) Epoch: [6][6780/11272] Time 0.785 (1.042) Data 0.004 (0.002) Loss 2.4323 (2.6693) Prec@1 38.125 (35.795) Prec@5 68.750 (66.169) Epoch: [6][6790/11272] Time 0.752 (1.042) Data 0.001 (0.002) Loss 2.6126 (2.6692) Prec@1 40.000 (35.796) Prec@5 68.750 (66.172) Epoch: [6][6800/11272] Time 0.862 (1.041) Data 0.001 (0.002) Loss 2.7232 (2.6692) Prec@1 37.500 (35.796) Prec@5 66.250 (66.172) Epoch: [6][6810/11272] Time 0.910 (1.041) Data 0.002 (0.002) Loss 2.7257 (2.6693) Prec@1 37.500 (35.795) Prec@5 63.750 (66.169) Epoch: [6][6820/11272] Time 0.776 (1.041) Data 0.001 (0.002) Loss 2.6512 (2.6691) Prec@1 37.500 (35.798) Prec@5 63.750 (66.172) Epoch: [6][6830/11272] Time 0.760 (1.041) Data 0.001 (0.002) Loss 2.4195 (2.6691) Prec@1 41.250 (35.796) Prec@5 68.750 (66.171) Epoch: [6][6840/11272] Time 0.868 (1.040) Data 0.002 (0.002) Loss 2.5826 (2.6692) Prec@1 41.875 (35.798) Prec@5 68.125 (66.172) Epoch: [6][6850/11272] Time 0.908 (1.040) Data 0.001 (0.002) Loss 2.6289 (2.6692) Prec@1 33.125 (35.798) Prec@5 68.750 (66.171) Epoch: [6][6860/11272] Time 0.761 (1.040) Data 0.001 (0.002) Loss 2.6236 (2.6693) Prec@1 33.750 (35.796) Prec@5 66.875 (66.167) Epoch: [6][6870/11272] Time 0.743 (1.039) Data 0.001 (0.002) Loss 2.4246 (2.6692) Prec@1 39.375 (35.799) Prec@5 70.000 (66.167) Epoch: [6][6880/11272] Time 0.867 (1.039) Data 0.001 (0.002) Loss 2.7576 (2.6692) Prec@1 34.375 (35.800) Prec@5 65.000 (66.168) Epoch: [6][6890/11272] Time 0.877 (1.039) Data 0.001 (0.002) Loss 2.9138 (2.6692) Prec@1 29.375 (35.801) Prec@5 65.625 (66.168) Epoch: [6][6900/11272] Time 0.759 (1.038) Data 0.001 (0.002) Loss 2.5616 (2.6693) Prec@1 38.125 (35.802) Prec@5 70.000 (66.168) Epoch: [6][6910/11272] Time 0.884 (1.038) Data 0.001 (0.002) Loss 2.8821 (2.6695) Prec@1 31.250 (35.799) Prec@5 63.125 (66.163) Epoch: [6][6920/11272] Time 0.869 (1.038) Data 0.001 (0.002) Loss 2.8019 (2.6694) Prec@1 35.000 (35.801) Prec@5 62.500 (66.163) Epoch: [6][6930/11272] Time 0.807 (1.037) Data 0.001 (0.002) Loss 2.8099 (2.6693) Prec@1 28.125 (35.800) Prec@5 68.750 (66.166) Epoch: [6][6940/11272] Time 0.790 (1.037) Data 0.001 (0.002) Loss 2.5403 (2.6692) Prec@1 31.250 (35.800) Prec@5 71.250 (66.167) Epoch: [6][6950/11272] Time 0.881 (1.037) Data 0.001 (0.002) Loss 2.7599 (2.6692) Prec@1 36.875 (35.801) Prec@5 66.250 (66.167) Epoch: [6][6960/11272] Time 0.912 (1.036) Data 0.001 (0.002) Loss 2.8200 (2.6692) Prec@1 36.875 (35.801) Prec@5 61.250 (66.169) Epoch: [6][6970/11272] Time 0.737 (1.036) Data 0.001 (0.002) Loss 2.6412 (2.6691) Prec@1 32.500 (35.800) Prec@5 68.125 (66.170) Epoch: [6][6980/11272] Time 0.757 (1.036) Data 0.001 (0.002) Loss 2.6085 (2.6691) Prec@1 36.875 (35.801) Prec@5 67.500 (66.170) Epoch: [6][6990/11272] Time 0.976 (1.036) Data 0.001 (0.002) Loss 2.7652 (2.6691) Prec@1 34.375 (35.802) Prec@5 63.125 (66.170) Epoch: [6][7000/11272] Time 0.881 (1.035) Data 0.001 (0.002) Loss 2.4356 (2.6690) Prec@1 39.375 (35.802) Prec@5 68.125 (66.172) Epoch: [6][7010/11272] Time 0.783 (1.035) Data 0.001 (0.002) Loss 2.6190 (2.6691) Prec@1 41.250 (35.801) Prec@5 68.750 (66.170) Epoch: [6][7020/11272] Time 0.743 (1.035) Data 0.001 (0.002) Loss 2.5216 (2.6692) Prec@1 38.125 (35.798) Prec@5 68.750 (66.168) Epoch: [6][7030/11272] Time 0.894 (1.034) Data 0.002 (0.002) Loss 3.1273 (2.6692) Prec@1 27.500 (35.799) Prec@5 57.500 (66.169) Epoch: [6][7040/11272] Time 0.753 (1.034) Data 0.003 (0.002) Loss 2.5933 (2.6692) Prec@1 34.375 (35.799) Prec@5 71.875 (66.169) Epoch: [6][7050/11272] Time 0.783 (1.034) Data 0.002 (0.002) Loss 2.6674 (2.6693) Prec@1 40.000 (35.798) Prec@5 65.000 (66.168) Epoch: [6][7060/11272] Time 0.870 (1.033) Data 0.001 (0.002) Loss 2.4448 (2.6692) Prec@1 41.250 (35.801) Prec@5 71.250 (66.169) Epoch: [6][7070/11272] Time 0.876 (1.033) Data 0.001 (0.002) Loss 2.5555 (2.6692) Prec@1 41.250 (35.802) Prec@5 65.625 (66.169) Epoch: [6][7080/11272] Time 0.725 (1.033) Data 0.001 (0.002) Loss 2.6141 (2.6692) Prec@1 36.250 (35.802) Prec@5 66.250 (66.169) Epoch: [6][7090/11272] Time 0.731 (1.033) Data 0.001 (0.002) Loss 2.6203 (2.6691) Prec@1 36.875 (35.802) Prec@5 67.500 (66.170) Epoch: [6][7100/11272] Time 0.878 (1.032) Data 0.001 (0.002) Loss 2.4337 (2.6691) Prec@1 41.250 (35.803) Prec@5 75.000 (66.172) Epoch: [6][7110/11272] Time 0.866 (1.032) Data 0.001 (0.002) Loss 2.5502 (2.6690) Prec@1 42.500 (35.804) Prec@5 66.250 (66.171) Epoch: [6][7120/11272] Time 0.742 (1.032) Data 0.002 (0.002) Loss 2.8169 (2.6691) Prec@1 30.625 (35.800) Prec@5 62.500 (66.171) Epoch: [6][7130/11272] Time 0.800 (1.031) Data 0.001 (0.002) Loss 2.6840 (2.6691) Prec@1 31.875 (35.800) Prec@5 65.625 (66.172) Epoch: [6][7140/11272] Time 0.911 (1.031) Data 0.001 (0.002) Loss 2.6934 (2.6691) Prec@1 37.500 (35.801) Prec@5 64.375 (66.172) Epoch: [6][7150/11272] Time 0.891 (1.031) Data 0.001 (0.002) Loss 2.8012 (2.6692) Prec@1 33.125 (35.802) Prec@5 66.875 (66.172) Epoch: [6][7160/11272] Time 0.716 (1.031) Data 0.001 (0.002) Loss 2.7659 (2.6692) Prec@1 36.250 (35.802) Prec@5 64.375 (66.173) Epoch: [6][7170/11272] Time 0.894 (1.030) Data 0.001 (0.002) Loss 2.7520 (2.6692) Prec@1 30.000 (35.801) Prec@5 61.875 (66.174) Epoch: [6][7180/11272] Time 0.872 (1.030) Data 0.001 (0.002) Loss 2.6203 (2.6692) Prec@1 37.500 (35.801) Prec@5 70.625 (66.174) Epoch: [6][7190/11272] Time 0.778 (1.030) Data 0.001 (0.002) Loss 2.6838 (2.6691) Prec@1 32.500 (35.803) Prec@5 63.750 (66.176) Epoch: [6][7200/11272] Time 0.746 (1.029) Data 0.002 (0.002) Loss 2.6028 (2.6691) Prec@1 33.750 (35.804) Prec@5 66.875 (66.175) Epoch: [6][7210/11272] Time 0.945 (1.029) Data 0.001 (0.002) Loss 2.6829 (2.6691) Prec@1 30.000 (35.805) Prec@5 69.375 (66.176) Epoch: [6][7220/11272] Time 0.895 (1.029) Data 0.001 (0.002) Loss 2.7148 (2.6691) Prec@1 33.750 (35.808) Prec@5 67.500 (66.175) Epoch: [6][7230/11272] Time 0.763 (1.028) Data 0.001 (0.002) Loss 2.5871 (2.6690) Prec@1 37.500 (35.810) Prec@5 68.125 (66.179) Epoch: [6][7240/11272] Time 0.750 (1.028) Data 0.002 (0.002) Loss 2.3600 (2.6689) Prec@1 40.625 (35.812) Prec@5 71.250 (66.182) Epoch: [6][7250/11272] Time 0.904 (1.028) Data 0.001 (0.002) Loss 2.7408 (2.6690) Prec@1 40.000 (35.811) Prec@5 66.250 (66.181) Epoch: [6][7260/11272] Time 0.856 (1.028) Data 0.001 (0.002) Loss 2.8002 (2.6691) Prec@1 30.000 (35.808) Prec@5 65.625 (66.179) Epoch: [6][7270/11272] Time 0.747 (1.027) Data 0.002 (0.002) Loss 2.6791 (2.6690) Prec@1 29.375 (35.810) Prec@5 65.000 (66.181) Epoch: [6][7280/11272] Time 0.723 (1.027) Data 0.001 (0.002) Loss 2.6425 (2.6690) Prec@1 40.000 (35.812) Prec@5 68.750 (66.181) Epoch: [6][7290/11272] Time 0.864 (1.027) Data 0.002 (0.002) Loss 2.7725 (2.6689) Prec@1 36.250 (35.812) Prec@5 60.000 (66.184) Epoch: [6][7300/11272] Time 0.908 (1.027) Data 0.001 (0.002) Loss 2.8871 (2.6691) Prec@1 31.250 (35.809) Prec@5 60.625 (66.180) Epoch: [6][7310/11272] Time 0.770 (1.026) Data 0.002 (0.002) Loss 2.5349 (2.6691) Prec@1 38.750 (35.811) Prec@5 65.000 (66.180) Epoch: [6][7320/11272] Time 0.863 (1.026) Data 0.002 (0.002) Loss 2.8788 (2.6691) Prec@1 35.625 (35.812) Prec@5 61.875 (66.180) Epoch: [6][7330/11272] Time 0.874 (1.026) Data 0.001 (0.002) Loss 2.7072 (2.6690) Prec@1 38.125 (35.815) Prec@5 65.625 (66.181) Epoch: [6][7340/11272] Time 0.760 (1.025) Data 0.001 (0.002) Loss 2.6312 (2.6689) Prec@1 35.625 (35.815) Prec@5 64.375 (66.182) Epoch: [6][7350/11272] Time 0.789 (1.025) Data 0.001 (0.002) Loss 2.6473 (2.6690) Prec@1 39.375 (35.815) Prec@5 65.625 (66.181) Epoch: [6][7360/11272] Time 0.901 (1.025) Data 0.001 (0.002) Loss 2.9199 (2.6691) Prec@1 33.750 (35.813) Prec@5 61.250 (66.179) Epoch: [6][7370/11272] Time 0.873 (1.025) Data 0.001 (0.002) Loss 2.7341 (2.6693) Prec@1 40.625 (35.811) Prec@5 65.000 (66.177) Epoch: [6][7380/11272] Time 0.749 (1.024) Data 0.002 (0.002) Loss 2.8135 (2.6693) Prec@1 34.375 (35.809) Prec@5 63.750 (66.175) Epoch: [6][7390/11272] Time 0.774 (1.024) Data 0.002 (0.002) Loss 2.7130 (2.6693) Prec@1 30.000 (35.807) Prec@5 63.125 (66.176) Epoch: [6][7400/11272] Time 0.840 (1.024) Data 0.001 (0.002) Loss 2.5834 (2.6693) Prec@1 31.875 (35.807) Prec@5 66.250 (66.173) Epoch: [6][7410/11272] Time 0.839 (1.024) Data 0.001 (0.002) Loss 2.6216 (2.6692) Prec@1 33.750 (35.810) Prec@5 64.375 (66.176) Epoch: [6][7420/11272] Time 0.728 (1.023) Data 0.001 (0.002) Loss 2.6647 (2.6693) Prec@1 34.375 (35.807) Prec@5 64.375 (66.173) Epoch: [6][7430/11272] Time 0.759 (1.023) Data 0.001 (0.002) Loss 2.6281 (2.6692) Prec@1 38.750 (35.809) Prec@5 66.250 (66.175) Epoch: [6][7440/11272] Time 0.880 (1.023) Data 0.001 (0.002) Loss 2.5876 (2.6692) Prec@1 36.875 (35.808) Prec@5 64.375 (66.176) Epoch: [6][7450/11272] Time 0.792 (1.022) Data 0.001 (0.002) Loss 2.9382 (2.6692) Prec@1 33.750 (35.809) Prec@5 62.500 (66.178) Epoch: [6][7460/11272] Time 0.738 (1.022) Data 0.001 (0.002) Loss 2.6947 (2.6692) Prec@1 38.125 (35.810) Prec@5 70.625 (66.176) Epoch: [6][7470/11272] Time 0.881 (1.022) Data 0.002 (0.002) Loss 2.6748 (2.6691) Prec@1 35.625 (35.814) Prec@5 66.875 (66.178) Epoch: [6][7480/11272] Time 0.937 (1.022) Data 0.001 (0.002) Loss 2.7302 (2.6692) Prec@1 31.250 (35.812) Prec@5 65.000 (66.174) Epoch: [6][7490/11272] Time 0.731 (1.021) Data 0.001 (0.002) Loss 2.6572 (2.6692) Prec@1 33.125 (35.811) Prec@5 66.250 (66.173) Epoch: [6][7500/11272] Time 0.778 (1.021) Data 0.002 (0.002) Loss 2.5122 (2.6692) Prec@1 44.375 (35.812) Prec@5 72.500 (66.174) Epoch: [6][7510/11272] Time 0.877 (1.021) Data 0.001 (0.002) Loss 2.7540 (2.6693) Prec@1 31.250 (35.809) Prec@5 63.125 (66.172) Epoch: [6][7520/11272] Time 0.867 (1.021) Data 0.001 (0.002) Loss 2.6066 (2.6693) Prec@1 36.250 (35.807) Prec@5 68.125 (66.173) Epoch: [6][7530/11272] Time 0.760 (1.020) Data 0.001 (0.002) Loss 2.4525 (2.6693) Prec@1 42.500 (35.809) Prec@5 71.250 (66.174) Epoch: [6][7540/11272] Time 0.749 (1.020) Data 0.001 (0.002) Loss 2.7579 (2.6694) Prec@1 34.375 (35.807) Prec@5 62.500 (66.172) Epoch: [6][7550/11272] Time 0.855 (1.020) Data 0.001 (0.002) Loss 2.7794 (2.6694) Prec@1 35.625 (35.805) Prec@5 63.125 (66.172) Epoch: [6][7560/11272] Time 0.941 (1.020) Data 0.002 (0.002) Loss 2.8126 (2.6694) Prec@1 39.375 (35.808) Prec@5 66.250 (66.174) Epoch: [6][7570/11272] Time 0.748 (1.019) Data 0.001 (0.002) Loss 2.6840 (2.6694) Prec@1 33.750 (35.807) Prec@5 69.375 (66.174) Epoch: [6][7580/11272] Time 0.972 (1.019) Data 0.001 (0.002) Loss 2.7784 (2.6694) Prec@1 33.125 (35.806) Prec@5 61.250 (66.174) Epoch: [6][7590/11272] Time 0.875 (1.019) Data 0.001 (0.002) Loss 2.6733 (2.6694) Prec@1 36.250 (35.805) Prec@5 71.875 (66.175) Epoch: [6][7600/11272] Time 0.763 (1.019) Data 0.001 (0.002) Loss 2.4506 (2.6694) Prec@1 36.875 (35.805) Prec@5 73.125 (66.176) Epoch: [6][7610/11272] Time 0.755 (1.018) Data 0.001 (0.002) Loss 2.4926 (2.6692) Prec@1 40.625 (35.807) Prec@5 70.625 (66.177) Epoch: [6][7620/11272] Time 0.897 (1.018) Data 0.001 (0.002) Loss 2.6845 (2.6692) Prec@1 38.125 (35.806) Prec@5 68.750 (66.178) Epoch: [6][7630/11272] Time 0.869 (1.018) Data 0.001 (0.002) Loss 2.5124 (2.6691) Prec@1 36.250 (35.807) Prec@5 65.000 (66.180) Epoch: [6][7640/11272] Time 0.734 (1.017) Data 0.001 (0.002) Loss 2.8434 (2.6692) Prec@1 40.000 (35.807) Prec@5 62.500 (66.179) Epoch: [6][7650/11272] Time 0.757 (1.017) Data 0.001 (0.002) Loss 2.6841 (2.6691) Prec@1 33.750 (35.807) Prec@5 69.375 (66.181) Epoch: [6][7660/11272] Time 0.909 (1.017) Data 0.002 (0.002) Loss 2.3959 (2.6690) Prec@1 42.500 (35.809) Prec@5 71.875 (66.184) Epoch: [6][7670/11272] Time 0.924 (1.017) Data 0.001 (0.002) Loss 2.7685 (2.6690) Prec@1 31.250 (35.809) Prec@5 66.875 (66.185) Epoch: [6][7680/11272] Time 0.740 (1.017) Data 0.001 (0.002) Loss 2.7827 (2.6689) Prec@1 31.875 (35.809) Prec@5 65.000 (66.187) Epoch: [6][7690/11272] Time 0.755 (1.016) Data 0.001 (0.002) Loss 2.6449 (2.6690) Prec@1 40.625 (35.808) Prec@5 68.125 (66.187) Epoch: [6][7700/11272] Time 0.915 (1.016) Data 0.001 (0.002) Loss 2.6343 (2.6690) Prec@1 31.875 (35.807) Prec@5 68.750 (66.189) Epoch: [6][7710/11272] Time 0.766 (1.016) Data 0.003 (0.002) Loss 2.4797 (2.6690) Prec@1 38.750 (35.809) Prec@5 67.500 (66.188) Epoch: [6][7720/11272] Time 0.736 (1.016) Data 0.001 (0.002) Loss 2.7774 (2.6690) Prec@1 31.875 (35.809) Prec@5 64.375 (66.189) Epoch: [6][7730/11272] Time 0.920 (1.015) Data 0.001 (0.002) Loss 2.4546 (2.6690) Prec@1 40.625 (35.809) Prec@5 71.250 (66.190) Epoch: [6][7740/11272] Time 0.897 (1.015) Data 0.001 (0.002) Loss 2.5640 (2.6690) Prec@1 40.000 (35.808) Prec@5 67.500 (66.190) Epoch: [6][7750/11272] Time 0.762 (1.015) Data 0.001 (0.002) Loss 2.6586 (2.6690) Prec@1 35.000 (35.808) Prec@5 63.125 (66.192) Epoch: [6][7760/11272] Time 0.775 (1.015) Data 0.001 (0.002) Loss 2.6505 (2.6689) Prec@1 35.625 (35.810) Prec@5 65.000 (66.193) Epoch: [6][7770/11272] Time 0.888 (1.014) Data 0.001 (0.002) Loss 2.5328 (2.6689) Prec@1 43.125 (35.811) Prec@5 67.500 (66.193) Epoch: [6][7780/11272] Time 0.959 (1.014) Data 0.002 (0.002) Loss 2.5633 (2.6690) Prec@1 38.750 (35.810) Prec@5 70.625 (66.192) Epoch: [6][7790/11272] Time 0.751 (1.014) Data 0.001 (0.002) Loss 2.9841 (2.6690) Prec@1 30.000 (35.810) Prec@5 60.000 (66.192) Epoch: [6][7800/11272] Time 0.760 (1.014) Data 0.001 (0.002) Loss 2.7478 (2.6690) Prec@1 35.625 (35.810) Prec@5 66.250 (66.192) Epoch: [6][7810/11272] Time 0.883 (1.013) Data 0.001 (0.002) Loss 2.7178 (2.6690) Prec@1 36.250 (35.810) Prec@5 66.875 (66.191) Epoch: [6][7820/11272] Time 0.866 (1.013) Data 0.001 (0.002) Loss 2.6000 (2.6690) Prec@1 37.500 (35.809) Prec@5 66.875 (66.193) Epoch: [6][7830/11272] Time 0.723 (1.013) Data 0.001 (0.002) Loss 2.8146 (2.6690) Prec@1 34.375 (35.811) Prec@5 61.250 (66.192) Epoch: [6][7840/11272] Time 0.869 (1.013) Data 0.001 (0.002) Loss 2.9192 (2.6690) Prec@1 34.375 (35.810) Prec@5 61.250 (66.189) Epoch: [6][7850/11272] Time 0.857 (1.012) Data 0.001 (0.002) Loss 2.5412 (2.6690) Prec@1 38.125 (35.810) Prec@5 69.375 (66.189) Epoch: [6][7860/11272] Time 0.751 (1.012) Data 0.001 (0.002) Loss 2.6238 (2.6690) Prec@1 34.375 (35.810) Prec@5 66.250 (66.189) Epoch: [6][7870/11272] Time 0.769 (1.012) Data 0.001 (0.002) Loss 2.8764 (2.6690) Prec@1 31.250 (35.810) Prec@5 62.500 (66.187) Epoch: [6][7880/11272] Time 0.871 (1.012) Data 0.001 (0.002) Loss 2.5750 (2.6691) Prec@1 35.625 (35.807) Prec@5 71.250 (66.187) Epoch: [6][7890/11272] Time 0.886 (1.011) Data 0.001 (0.002) Loss 2.5254 (2.6691) Prec@1 37.500 (35.807) Prec@5 70.000 (66.186) Epoch: [6][7900/11272] Time 0.728 (1.011) Data 0.001 (0.002) Loss 2.6236 (2.6691) Prec@1 34.375 (35.806) Prec@5 66.875 (66.186) Epoch: [6][7910/11272] Time 0.725 (1.011) Data 0.001 (0.002) Loss 2.8157 (2.6691) Prec@1 38.125 (35.807) Prec@5 67.500 (66.187) Epoch: [6][7920/11272] Time 0.906 (1.011) Data 0.001 (0.002) Loss 2.6849 (2.6691) Prec@1 33.125 (35.804) Prec@5 67.500 (66.188) Epoch: [6][7930/11272] Time 0.899 (1.010) Data 0.001 (0.002) Loss 2.9408 (2.6691) Prec@1 30.625 (35.805) Prec@5 61.250 (66.190) Epoch: [6][7940/11272] Time 0.742 (1.010) Data 0.001 (0.002) Loss 2.6306 (2.6691) Prec@1 33.125 (35.806) Prec@5 65.000 (66.188) Epoch: [6][7950/11272] Time 0.732 (1.010) Data 0.001 (0.002) Loss 2.7943 (2.6692) Prec@1 32.500 (35.804) Prec@5 60.625 (66.188) Epoch: [6][7960/11272] Time 0.857 (1.010) Data 0.002 (0.002) Loss 2.7375 (2.6692) Prec@1 33.750 (35.803) Prec@5 66.875 (66.187) Epoch: [6][7970/11272] Time 0.745 (1.009) Data 0.003 (0.002) Loss 2.4636 (2.6693) Prec@1 39.375 (35.802) Prec@5 71.875 (66.188) Epoch: [6][7980/11272] Time 0.723 (1.009) Data 0.001 (0.002) Loss 2.8175 (2.6693) Prec@1 33.125 (35.801) Prec@5 60.625 (66.186) Epoch: [6][7990/11272] Time 0.873 (1.009) Data 0.001 (0.002) Loss 2.6449 (2.6694) Prec@1 35.000 (35.798) Prec@5 66.250 (66.185) Epoch: [6][8000/11272] Time 0.896 (1.009) Data 0.004 (0.002) Loss 2.7457 (2.6694) Prec@1 30.625 (35.797) Prec@5 66.250 (66.185) Epoch: [6][8010/11272] Time 0.734 (1.009) Data 0.001 (0.002) Loss 2.2799 (2.6693) Prec@1 43.125 (35.800) Prec@5 69.375 (66.185) Epoch: [6][8020/11272] Time 0.769 (1.008) Data 0.001 (0.002) Loss 2.8075 (2.6693) Prec@1 34.375 (35.798) Prec@5 60.625 (66.183) Epoch: [6][8030/11272] Time 0.974 (1.008) Data 0.001 (0.002) Loss 2.4770 (2.6692) Prec@1 41.875 (35.803) Prec@5 66.250 (66.186) Epoch: [6][8040/11272] Time 0.917 (1.008) Data 0.001 (0.002) Loss 2.4193 (2.6691) Prec@1 39.375 (35.803) Prec@5 70.000 (66.187) Epoch: [6][8050/11272] Time 0.756 (1.008) Data 0.001 (0.002) Loss 2.7929 (2.6689) Prec@1 33.750 (35.805) Prec@5 65.000 (66.190) Epoch: [6][8060/11272] Time 0.822 (1.007) Data 0.001 (0.002) Loss 2.5622 (2.6689) Prec@1 37.500 (35.805) Prec@5 70.625 (66.192) Epoch: [6][8070/11272] Time 0.899 (1.007) Data 0.001 (0.002) Loss 2.6306 (2.6689) Prec@1 30.000 (35.804) Prec@5 65.000 (66.192) Epoch: [6][8080/11272] Time 0.890 (1.007) Data 0.001 (0.002) Loss 2.7539 (2.6688) Prec@1 33.750 (35.802) Prec@5 62.500 (66.193) Epoch: [6][8090/11272] Time 0.756 (1.007) Data 0.001 (0.002) Loss 2.7801 (2.6688) Prec@1 30.000 (35.804) Prec@5 61.875 (66.193) Epoch: [6][8100/11272] Time 0.945 (1.006) Data 0.001 (0.002) Loss 2.7322 (2.6688) Prec@1 35.000 (35.802) Prec@5 66.875 (66.192) Epoch: [6][8110/11272] Time 0.849 (1.006) Data 0.001 (0.002) Loss 2.6934 (2.6689) Prec@1 38.125 (35.800) Prec@5 65.625 (66.190) Epoch: [6][8120/11272] Time 0.753 (1.006) Data 0.001 (0.002) Loss 2.6068 (2.6689) Prec@1 36.250 (35.802) Prec@5 70.625 (66.191) Epoch: [6][8130/11272] Time 0.756 (1.006) Data 0.001 (0.002) Loss 2.7764 (2.6689) Prec@1 26.875 (35.800) Prec@5 61.875 (66.190) Epoch: [6][8140/11272] Time 0.876 (1.006) Data 0.001 (0.002) Loss 2.5133 (2.6688) Prec@1 35.625 (35.800) Prec@5 67.500 (66.191) Epoch: [6][8150/11272] Time 0.902 (1.005) Data 0.001 (0.002) Loss 2.6649 (2.6689) Prec@1 38.750 (35.801) Prec@5 66.875 (66.189) Epoch: [6][8160/11272] Time 0.776 (1.005) Data 0.001 (0.002) Loss 2.4863 (2.6689) Prec@1 43.125 (35.800) Prec@5 68.750 (66.188) Epoch: [6][8170/11272] Time 0.757 (1.005) Data 0.001 (0.002) Loss 2.7994 (2.6690) Prec@1 35.000 (35.798) Prec@5 63.125 (66.185) Epoch: [6][8180/11272] Time 0.857 (1.005) Data 0.001 (0.002) Loss 2.9069 (2.6690) Prec@1 30.000 (35.798) Prec@5 62.500 (66.187) Epoch: [6][8190/11272] Time 0.896 (1.004) Data 0.001 (0.002) Loss 2.8069 (2.6690) Prec@1 31.250 (35.796) Prec@5 60.000 (66.185) Epoch: [6][8200/11272] Time 0.775 (1.004) Data 0.001 (0.002) Loss 2.5743 (2.6691) Prec@1 40.000 (35.796) Prec@5 69.375 (66.184) Epoch: [6][8210/11272] Time 0.796 (1.004) Data 0.002 (0.002) Loss 2.4929 (2.6691) Prec@1 42.500 (35.796) Prec@5 69.375 (66.186) Epoch: [6][8220/11272] Time 0.844 (1.004) Data 0.001 (0.002) Loss 2.5305 (2.6691) Prec@1 40.000 (35.797) Prec@5 68.125 (66.186) Epoch: [6][8230/11272] Time 0.871 (1.004) Data 0.001 (0.002) Loss 2.4161 (2.6690) Prec@1 40.000 (35.798) Prec@5 73.125 (66.187) Epoch: [6][8240/11272] Time 0.784 (1.003) Data 0.001 (0.002) Loss 2.6236 (2.6689) Prec@1 34.375 (35.798) Prec@5 67.500 (66.189) Epoch: [6][8250/11272] Time 0.925 (1.003) Data 0.001 (0.002) Loss 2.8090 (2.6689) Prec@1 35.625 (35.800) Prec@5 65.000 (66.191) Epoch: [6][8260/11272] Time 0.893 (1.003) Data 0.002 (0.002) Loss 2.7728 (2.6688) Prec@1 31.250 (35.801) Prec@5 63.125 (66.191) Epoch: [6][8270/11272] Time 0.792 (1.003) Data 0.002 (0.002) Loss 2.5413 (2.6688) Prec@1 36.875 (35.801) Prec@5 72.500 (66.193) Epoch: [6][8280/11272] Time 0.739 (1.002) Data 0.002 (0.002) Loss 2.5893 (2.6688) Prec@1 34.375 (35.799) Prec@5 70.000 (66.192) Epoch: [6][8290/11272] Time 0.914 (1.002) Data 0.001 (0.002) Loss 2.5853 (2.6688) Prec@1 40.000 (35.800) Prec@5 67.500 (66.192) Epoch: [6][8300/11272] Time 0.900 (1.002) Data 0.001 (0.002) Loss 2.5182 (2.6687) Prec@1 35.625 (35.800) Prec@5 68.125 (66.193) Epoch: [6][8310/11272] Time 0.764 (1.002) Data 0.001 (0.002) Loss 2.7337 (2.6688) Prec@1 32.500 (35.800) Prec@5 62.500 (66.192) Epoch: [6][8320/11272] Time 0.747 (1.002) Data 0.002 (0.002) Loss 2.6268 (2.6689) Prec@1 35.625 (35.797) Prec@5 66.875 (66.191) Epoch: [6][8330/11272] Time 0.868 (1.001) Data 0.001 (0.002) Loss 2.8845 (2.6690) Prec@1 31.875 (35.797) Prec@5 63.125 (66.189) Epoch: [6][8340/11272] Time 0.860 (1.001) Data 0.001 (0.002) Loss 2.3071 (2.6689) Prec@1 33.750 (35.797) Prec@5 75.000 (66.193) Epoch: [6][8350/11272] Time 0.745 (1.001) Data 0.001 (0.002) Loss 2.7373 (2.6690) Prec@1 33.125 (35.794) Prec@5 63.125 (66.192) Epoch: [6][8360/11272] Time 0.796 (1.001) Data 0.001 (0.002) Loss 2.8808 (2.6691) Prec@1 36.875 (35.791) Prec@5 64.375 (66.189) Epoch: [6][8370/11272] Time 0.863 (1.001) Data 0.001 (0.002) Loss 2.4313 (2.6691) Prec@1 40.000 (35.793) Prec@5 73.750 (66.191) Epoch: [6][8380/11272] Time 0.760 (1.000) Data 0.002 (0.002) Loss 2.7650 (2.6690) Prec@1 33.125 (35.794) Prec@5 61.250 (66.191) Epoch: [6][8390/11272] Time 0.769 (1.000) Data 0.001 (0.002) Loss 2.4313 (2.6689) Prec@1 43.750 (35.796) Prec@5 75.000 (66.194) Epoch: [6][8400/11272] Time 0.901 (1.000) Data 0.001 (0.002) Loss 2.7575 (2.6689) Prec@1 36.250 (35.798) Prec@5 68.125 (66.196) Epoch: [6][8410/11272] Time 0.879 (1.000) Data 0.001 (0.002) Loss 2.6595 (2.6690) Prec@1 40.625 (35.796) Prec@5 66.250 (66.195) Epoch: [6][8420/11272] Time 0.784 (0.999) Data 0.001 (0.002) Loss 2.8009 (2.6690) Prec@1 35.000 (35.795) Prec@5 63.750 (66.194) Epoch: [6][8430/11272] Time 0.761 (0.999) Data 0.001 (0.002) Loss 2.8028 (2.6689) Prec@1 31.250 (35.797) Prec@5 63.750 (66.195) Epoch: [6][8440/11272] Time 0.877 (0.999) Data 0.001 (0.002) Loss 2.5381 (2.6689) Prec@1 38.125 (35.795) Prec@5 68.125 (66.194) Epoch: [6][8450/11272] Time 0.926 (0.999) Data 0.002 (0.002) Loss 2.8304 (2.6690) Prec@1 34.375 (35.793) Prec@5 60.000 (66.194) Epoch: [6][8460/11272] Time 0.768 (0.999) Data 0.001 (0.002) Loss 2.6960 (2.6690) Prec@1 35.000 (35.792) Prec@5 68.750 (66.193) Epoch: [6][8470/11272] Time 0.832 (0.998) Data 0.001 (0.002) Loss 2.8997 (2.6691) Prec@1 29.375 (35.790) Prec@5 61.875 (66.192) Epoch: [6][8480/11272] Time 0.904 (0.998) Data 0.001 (0.002) Loss 2.6770 (2.6691) Prec@1 38.125 (35.789) Prec@5 66.250 (66.191) Epoch: [6][8490/11272] Time 0.858 (0.998) Data 0.001 (0.002) Loss 2.4929 (2.6692) Prec@1 38.750 (35.788) Prec@5 76.250 (66.189) Epoch: [6][8500/11272] Time 0.768 (0.998) Data 0.002 (0.002) Loss 2.9106 (2.6692) Prec@1 35.000 (35.787) Prec@5 56.250 (66.188) Epoch: [6][8510/11272] Time 0.852 (0.998) Data 0.001 (0.002) Loss 2.7930 (2.6692) Prec@1 36.875 (35.785) Prec@5 62.500 (66.187) Epoch: [6][8520/11272] Time 0.918 (0.997) Data 0.001 (0.002) Loss 2.6130 (2.6693) Prec@1 36.250 (35.785) Prec@5 64.375 (66.186) Epoch: [6][8530/11272] Time 0.766 (0.997) Data 0.001 (0.002) Loss 2.7496 (2.6694) Prec@1 26.875 (35.781) Prec@5 67.500 (66.184) Epoch: [6][8540/11272] Time 0.747 (0.997) Data 0.001 (0.002) Loss 2.6048 (2.6694) Prec@1 37.500 (35.783) Prec@5 68.750 (66.186) Epoch: [6][8550/11272] Time 0.943 (0.997) Data 0.001 (0.002) Loss 2.7328 (2.6694) Prec@1 33.750 (35.783) Prec@5 69.375 (66.186) Epoch: [6][8560/11272] Time 0.906 (0.997) Data 0.001 (0.002) Loss 2.6168 (2.6695) Prec@1 38.750 (35.782) Prec@5 65.625 (66.185) Epoch: [6][8570/11272] Time 0.740 (0.996) Data 0.001 (0.002) Loss 2.6202 (2.6694) Prec@1 37.500 (35.783) Prec@5 65.625 (66.187) Epoch: [6][8580/11272] Time 0.751 (0.996) Data 0.001 (0.002) Loss 2.4821 (2.6695) Prec@1 34.375 (35.781) Prec@5 70.625 (66.187) Epoch: [6][8590/11272] Time 0.866 (0.996) Data 0.001 (0.002) Loss 2.8250 (2.6694) Prec@1 38.750 (35.784) Prec@5 64.375 (66.189) Epoch: [6][8600/11272] Time 0.878 (0.996) Data 0.001 (0.002) Loss 2.4175 (2.6694) Prec@1 43.750 (35.783) Prec@5 69.375 (66.189) Epoch: [6][8610/11272] Time 0.738 (0.996) Data 0.001 (0.002) Loss 2.7351 (2.6694) Prec@1 35.625 (35.785) Prec@5 63.750 (66.190) Epoch: [6][8620/11272] Time 0.768 (0.995) Data 0.001 (0.002) Loss 2.4621 (2.6695) Prec@1 42.500 (35.785) Prec@5 70.625 (66.188) Epoch: [6][8630/11272] Time 0.854 (0.995) Data 0.001 (0.002) Loss 2.7094 (2.6694) Prec@1 31.875 (35.785) Prec@5 64.375 (66.189) Epoch: [6][8640/11272] Time 0.731 (0.995) Data 0.003 (0.002) Loss 2.6502 (2.6694) Prec@1 37.500 (35.784) Prec@5 68.750 (66.189) Epoch: [6][8650/11272] Time 0.751 (0.995) Data 0.001 (0.002) Loss 2.5235 (2.6694) Prec@1 40.625 (35.786) Prec@5 73.750 (66.190) Epoch: [6][8660/11272] Time 0.881 (0.995) Data 0.001 (0.002) Loss 2.4180 (2.6695) Prec@1 44.375 (35.787) Prec@5 68.125 (66.188) Epoch: [6][8670/11272] Time 0.895 (0.994) Data 0.001 (0.002) Loss 2.5786 (2.6694) Prec@1 37.500 (35.790) Prec@5 69.375 (66.190) Epoch: [6][8680/11272] Time 0.754 (0.994) Data 0.001 (0.002) Loss 2.7521 (2.6695) Prec@1 32.500 (35.787) Prec@5 66.250 (66.189) Epoch: [6][8690/11272] Time 0.749 (0.994) Data 0.001 (0.002) Loss 2.4458 (2.6694) Prec@1 38.125 (35.789) Prec@5 70.000 (66.190) Epoch: [6][8700/11272] Time 0.879 (0.994) Data 0.001 (0.002) Loss 2.7418 (2.6695) Prec@1 30.000 (35.787) Prec@5 63.750 (66.188) Epoch: [6][8710/11272] Time 0.816 (0.994) Data 0.001 (0.002) Loss 2.7468 (2.6696) Prec@1 36.250 (35.787) Prec@5 63.750 (66.185) Epoch: [6][8720/11272] Time 0.726 (0.993) Data 0.002 (0.002) Loss 2.7962 (2.6696) Prec@1 35.000 (35.788) Prec@5 62.500 (66.184) Epoch: [6][8730/11272] Time 0.741 (0.993) Data 0.001 (0.002) Loss 2.3536 (2.6695) Prec@1 45.625 (35.792) Prec@5 72.500 (66.187) Epoch: [6][8740/11272] Time 0.884 (0.993) Data 0.001 (0.002) Loss 2.8753 (2.6695) Prec@1 29.375 (35.792) Prec@5 62.500 (66.185) Epoch: [6][8750/11272] Time 0.864 (0.993) Data 0.001 (0.002) Loss 2.7571 (2.6695) Prec@1 27.500 (35.790) Prec@5 67.500 (66.186) Epoch: [6][8760/11272] Time 0.749 (0.993) Data 0.001 (0.002) Loss 2.4999 (2.6694) Prec@1 36.875 (35.791) Prec@5 67.500 (66.186) Epoch: [6][8770/11272] Time 0.901 (0.992) Data 0.001 (0.002) Loss 2.4880 (2.6695) Prec@1 40.000 (35.789) Prec@5 68.750 (66.184) Epoch: [6][8780/11272] Time 0.891 (0.992) Data 0.001 (0.002) Loss 2.7838 (2.6696) Prec@1 34.375 (35.788) Prec@5 63.125 (66.182) Epoch: [6][8790/11272] Time 0.772 (0.992) Data 0.001 (0.002) Loss 2.6209 (2.6695) Prec@1 34.375 (35.789) Prec@5 70.000 (66.183) Epoch: [6][8800/11272] Time 0.737 (0.992) Data 0.001 (0.002) Loss 2.7109 (2.6696) Prec@1 37.500 (35.787) Prec@5 65.625 (66.183) Epoch: [6][8810/11272] Time 0.852 (0.992) Data 0.001 (0.002) Loss 2.7420 (2.6696) Prec@1 32.500 (35.786) Prec@5 65.625 (66.183) Epoch: [6][8820/11272] Time 0.904 (0.991) Data 0.001 (0.002) Loss 2.5784 (2.6697) Prec@1 33.750 (35.783) Prec@5 65.000 (66.183) Epoch: [6][8830/11272] Time 0.774 (0.991) Data 0.001 (0.002) Loss 2.6333 (2.6697) Prec@1 38.125 (35.784) Prec@5 66.875 (66.182) Epoch: [6][8840/11272] Time 0.735 (0.991) Data 0.001 (0.002) Loss 2.5120 (2.6697) Prec@1 40.000 (35.783) Prec@5 68.125 (66.183) Epoch: [6][8850/11272] Time 0.876 (0.991) Data 0.002 (0.002) Loss 2.8273 (2.6698) Prec@1 33.125 (35.782) Prec@5 64.375 (66.182) Epoch: [6][8860/11272] Time 0.900 (0.991) Data 0.001 (0.002) Loss 2.7267 (2.6699) Prec@1 39.375 (35.782) Prec@5 63.750 (66.182) Epoch: [6][8870/11272] Time 0.752 (0.990) Data 0.001 (0.002) Loss 2.6951 (2.6699) Prec@1 35.625 (35.781) Prec@5 67.500 (66.182) Epoch: [6][8880/11272] Time 0.735 (0.990) Data 0.001 (0.002) Loss 2.8242 (2.6699) Prec@1 30.000 (35.781) Prec@5 66.875 (66.184) Epoch: [6][8890/11272] Time 0.862 (0.990) Data 0.001 (0.002) Loss 2.6543 (2.6700) Prec@1 36.250 (35.780) Prec@5 67.500 (66.182) Epoch: [6][8900/11272] Time 0.735 (0.990) Data 0.003 (0.002) Loss 2.5745 (2.6701) Prec@1 36.875 (35.777) Prec@5 68.750 (66.179) Epoch: [6][8910/11272] Time 0.749 (0.990) Data 0.001 (0.002) Loss 2.4050 (2.6700) Prec@1 41.250 (35.778) Prec@5 70.000 (66.180) Epoch: [6][8920/11272] Time 0.877 (0.989) Data 0.001 (0.002) Loss 2.7250 (2.6701) Prec@1 37.500 (35.778) Prec@5 68.125 (66.179) Epoch: [6][8930/11272] Time 0.900 (0.989) Data 0.002 (0.002) Loss 2.7752 (2.6701) Prec@1 30.000 (35.776) Prec@5 64.375 (66.176) Epoch: [6][8940/11272] Time 0.774 (0.989) Data 0.002 (0.002) Loss 2.6599 (2.6701) Prec@1 37.500 (35.776) Prec@5 66.875 (66.179) Epoch: [6][8950/11272] Time 0.749 (0.989) Data 0.002 (0.002) Loss 2.6255 (2.6701) Prec@1 38.125 (35.778) Prec@5 71.250 (66.179) Epoch: [6][8960/11272] Time 0.921 (0.989) Data 0.002 (0.002) Loss 2.4573 (2.6701) Prec@1 36.875 (35.777) Prec@5 69.375 (66.181) Epoch: [6][8970/11272] Time 0.875 (0.989) Data 0.001 (0.002) Loss 2.5089 (2.6701) Prec@1 34.375 (35.777) Prec@5 68.750 (66.180) Epoch: [6][8980/11272] Time 0.733 (0.988) Data 0.001 (0.002) Loss 2.6501 (2.6701) Prec@1 36.875 (35.776) Prec@5 65.625 (66.180) Epoch: [6][8990/11272] Time 0.782 (0.988) Data 0.001 (0.002) Loss 2.6639 (2.6700) Prec@1 34.375 (35.776) Prec@5 66.875 (66.180) Epoch: [6][9000/11272] Time 0.940 (0.988) Data 0.002 (0.002) Loss 2.3738 (2.6700) Prec@1 43.125 (35.777) Prec@5 73.125 (66.182) Epoch: [6][9010/11272] Time 0.853 (0.988) Data 0.001 (0.002) Loss 2.5407 (2.6699) Prec@1 38.750 (35.778) Prec@5 68.125 (66.184) Epoch: [6][9020/11272] Time 0.750 (0.988) Data 0.001 (0.002) Loss 2.7427 (2.6699) Prec@1 31.250 (35.778) Prec@5 66.250 (66.185) Epoch: [6][9030/11272] Time 0.864 (0.987) Data 0.002 (0.002) Loss 2.4403 (2.6699) Prec@1 39.375 (35.779) Prec@5 69.375 (66.186) Epoch: [6][9040/11272] Time 0.884 (0.987) Data 0.001 (0.002) Loss 2.5832 (2.6699) Prec@1 38.125 (35.778) Prec@5 68.125 (66.187) Epoch: [6][9050/11272] Time 0.748 (0.987) Data 0.001 (0.002) Loss 2.6390 (2.6698) Prec@1 34.375 (35.779) Prec@5 67.500 (66.188) Epoch: [6][9060/11272] Time 0.741 (0.987) Data 0.001 (0.002) Loss 2.3933 (2.6698) Prec@1 40.625 (35.780) Prec@5 75.000 (66.189) Epoch: [6][9070/11272] Time 0.897 (0.987) Data 0.001 (0.002) Loss 2.5392 (2.6697) Prec@1 36.250 (35.779) Prec@5 70.000 (66.190) Epoch: [6][9080/11272] Time 0.883 (0.987) Data 0.001 (0.002) Loss 2.5042 (2.6697) Prec@1 41.250 (35.779) Prec@5 69.375 (66.190) Epoch: [6][9090/11272] Time 0.749 (0.986) Data 0.001 (0.002) Loss 2.8421 (2.6697) Prec@1 31.875 (35.779) Prec@5 64.375 (66.191) Epoch: [6][9100/11272] Time 0.786 (0.986) Data 0.002 (0.002) Loss 2.7468 (2.6697) Prec@1 36.875 (35.778) Prec@5 60.625 (66.190) Epoch: [6][9110/11272] Time 0.900 (0.986) Data 0.001 (0.002) Loss 2.6193 (2.6698) Prec@1 39.375 (35.776) Prec@5 69.375 (66.189) Epoch: [6][9120/11272] Time 0.867 (0.986) Data 0.001 (0.002) Loss 2.4230 (2.6698) Prec@1 45.000 (35.777) Prec@5 70.000 (66.190) Epoch: [6][9130/11272] Time 0.774 (0.986) Data 0.001 (0.002) Loss 2.5597 (2.6697) Prec@1 38.125 (35.779) Prec@5 66.250 (66.191) Epoch: [6][9140/11272] Time 0.763 (0.986) Data 0.001 (0.002) Loss 2.6130 (2.6697) Prec@1 36.875 (35.780) Prec@5 65.000 (66.192) Epoch: [6][9150/11272] Time 0.981 (0.985) Data 0.001 (0.002) Loss 2.7772 (2.6696) Prec@1 36.875 (35.782) Prec@5 65.625 (66.195) Epoch: [6][9160/11272] Time 0.859 (0.985) Data 0.002 (0.002) Loss 2.9341 (2.6697) Prec@1 30.625 (35.780) Prec@5 62.500 (66.194) Epoch: [6][9170/11272] Time 0.762 (0.985) Data 0.002 (0.002) Loss 2.7983 (2.6697) Prec@1 36.250 (35.780) Prec@5 63.125 (66.195) Epoch: [6][9180/11272] Time 0.876 (0.985) Data 0.001 (0.002) Loss 2.3927 (2.6697) Prec@1 37.500 (35.781) Prec@5 73.750 (66.196) Epoch: [6][9190/11272] Time 0.917 (0.985) Data 0.001 (0.002) Loss 2.6280 (2.6696) Prec@1 35.625 (35.781) Prec@5 68.750 (66.196) Epoch: [6][9200/11272] Time 0.791 (0.984) Data 0.001 (0.002) Loss 2.2937 (2.6694) Prec@1 37.500 (35.784) Prec@5 73.750 (66.200) Epoch: [6][9210/11272] Time 0.752 (0.984) Data 0.001 (0.002) Loss 2.5497 (2.6694) Prec@1 33.125 (35.781) Prec@5 71.250 (66.199) Epoch: [6][9220/11272] Time 0.838 (0.984) Data 0.001 (0.002) Loss 2.6063 (2.6696) Prec@1 35.000 (35.779) Prec@5 71.250 (66.196) Epoch: [6][9230/11272] Time 0.871 (0.984) Data 0.001 (0.002) Loss 2.6221 (2.6696) Prec@1 38.750 (35.779) Prec@5 71.875 (66.195) Epoch: [6][9240/11272] Time 0.744 (0.984) Data 0.001 (0.002) Loss 2.5085 (2.6695) Prec@1 40.000 (35.781) Prec@5 73.125 (66.197) Epoch: [6][9250/11272] Time 0.738 (0.984) Data 0.001 (0.002) Loss 2.6113 (2.6696) Prec@1 33.125 (35.779) Prec@5 65.625 (66.197) Epoch: [6][9260/11272] Time 0.876 (0.984) Data 0.001 (0.002) Loss 2.8750 (2.6695) Prec@1 33.125 (35.780) Prec@5 62.500 (66.198) Epoch: [6][9270/11272] Time 0.870 (0.983) Data 0.001 (0.002) Loss 2.7284 (2.6695) Prec@1 36.875 (35.780) Prec@5 67.500 (66.199) Epoch: [6][9280/11272] Time 0.749 (0.983) Data 0.002 (0.002) Loss 2.7606 (2.6694) Prec@1 36.875 (35.783) Prec@5 60.625 (66.200) Epoch: [6][9290/11272] Time 0.751 (0.983) Data 0.001 (0.002) Loss 3.1136 (2.6695) Prec@1 30.625 (35.782) Prec@5 57.500 (66.201) Epoch: [6][9300/11272] Time 0.886 (0.983) Data 0.001 (0.002) Loss 2.2142 (2.6694) Prec@1 46.250 (35.783) Prec@5 75.000 (66.203) Epoch: [6][9310/11272] Time 0.736 (0.983) Data 0.001 (0.002) Loss 2.7691 (2.6694) Prec@1 28.125 (35.781) Prec@5 66.875 (66.202) Epoch: [6][9320/11272] Time 0.761 (0.982) Data 0.001 (0.002) Loss 2.6689 (2.6694) Prec@1 33.750 (35.782) Prec@5 65.625 (66.201) Epoch: [6][9330/11272] Time 0.850 (0.982) Data 0.001 (0.002) Loss 2.7207 (2.6695) Prec@1 33.125 (35.781) Prec@5 63.750 (66.201) Epoch: [6][9340/11272] Time 0.877 (0.982) Data 0.001 (0.002) Loss 2.3461 (2.6695) Prec@1 40.000 (35.783) Prec@5 71.875 (66.201) Epoch: [6][9350/11272] Time 0.755 (0.982) Data 0.002 (0.002) Loss 2.8183 (2.6695) Prec@1 32.500 (35.783) Prec@5 60.625 (66.199) Epoch: [6][9360/11272] Time 0.743 (0.982) Data 0.001 (0.002) Loss 2.6799 (2.6695) Prec@1 31.875 (35.783) Prec@5 70.000 (66.199) Epoch: [6][9370/11272] Time 0.906 (0.982) Data 0.002 (0.002) Loss 2.3212 (2.6695) Prec@1 39.375 (35.784) Prec@5 76.250 (66.199) Epoch: [6][9380/11272] Time 0.934 (0.981) Data 0.001 (0.002) Loss 2.8300 (2.6695) Prec@1 36.250 (35.783) Prec@5 60.000 (66.197) Epoch: [6][9390/11272] Time 0.769 (0.981) Data 0.001 (0.002) Loss 2.5736 (2.6695) Prec@1 36.250 (35.783) Prec@5 71.250 (66.197) Epoch: [6][9400/11272] Time 0.753 (0.981) Data 0.001 (0.002) Loss 2.4138 (2.6695) Prec@1 40.625 (35.784) Prec@5 71.250 (66.197) Epoch: [6][9410/11272] Time 0.872 (0.981) Data 0.001 (0.002) Loss 2.8495 (2.6695) Prec@1 27.500 (35.784) Prec@5 60.625 (66.197) Epoch: [6][9420/11272] Time 0.898 (0.981) Data 0.001 (0.002) Loss 2.8327 (2.6695) Prec@1 31.875 (35.785) Prec@5 65.000 (66.199) Epoch: [6][9430/11272] Time 0.773 (0.981) Data 0.001 (0.002) Loss 2.6995 (2.6694) Prec@1 33.750 (35.785) Prec@5 66.875 (66.200) Epoch: [6][9440/11272] Time 0.876 (0.980) Data 0.001 (0.002) Loss 2.8974 (2.6694) Prec@1 28.125 (35.784) Prec@5 60.625 (66.199) Epoch: [6][9450/11272] Time 0.875 (0.980) Data 0.001 (0.002) Loss 2.6476 (2.6694) Prec@1 36.875 (35.784) Prec@5 68.125 (66.200) Epoch: [6][9460/11272] Time 0.735 (0.980) Data 0.001 (0.002) Loss 2.5442 (2.6694) Prec@1 40.000 (35.782) Prec@5 69.375 (66.201) Epoch: [6][9470/11272] Time 0.832 (0.980) Data 0.001 (0.002) Loss 2.7437 (2.6693) Prec@1 33.125 (35.782) Prec@5 64.375 (66.202) Epoch: [6][9480/11272] Time 0.856 (0.980) Data 0.001 (0.002) Loss 2.4503 (2.6693) Prec@1 38.125 (35.782) Prec@5 73.750 (66.203) Epoch: [6][9490/11272] Time 0.870 (0.980) Data 0.001 (0.002) Loss 2.6013 (2.6694) Prec@1 32.500 (35.782) Prec@5 68.750 (66.202) Epoch: [6][9500/11272] Time 0.795 (0.979) Data 0.002 (0.002) Loss 2.8443 (2.6694) Prec@1 30.625 (35.783) Prec@5 61.250 (66.203) Epoch: [6][9510/11272] Time 0.760 (0.979) Data 0.002 (0.002) Loss 2.8076 (2.6694) Prec@1 39.375 (35.782) Prec@5 61.875 (66.201) Epoch: [6][9520/11272] Time 0.900 (0.979) Data 0.001 (0.002) Loss 2.7929 (2.6695) Prec@1 32.500 (35.780) Prec@5 64.375 (66.199) Epoch: [6][9530/11272] Time 0.881 (0.979) Data 0.001 (0.002) Loss 2.4615 (2.6695) Prec@1 45.625 (35.779) Prec@5 68.125 (66.198) Epoch: [6][9540/11272] Time 0.746 (0.979) Data 0.001 (0.002) Loss 2.7240 (2.6695) Prec@1 35.000 (35.777) Prec@5 65.000 (66.198) Epoch: [6][9550/11272] Time 0.771 (0.979) Data 0.001 (0.002) Loss 2.5915 (2.6695) Prec@1 38.125 (35.778) Prec@5 70.000 (66.198) Epoch: [6][9560/11272] Time 0.885 (0.979) Data 0.001 (0.002) Loss 2.4421 (2.6695) Prec@1 40.625 (35.776) Prec@5 70.000 (66.198) Epoch: [6][9570/11272] Time 0.736 (0.978) Data 0.003 (0.002) Loss 2.8480 (2.6695) Prec@1 31.875 (35.778) Prec@5 64.375 (66.199) Epoch: [6][9580/11272] Time 0.767 (0.978) Data 0.001 (0.002) Loss 2.5593 (2.6695) Prec@1 38.750 (35.780) Prec@5 66.875 (66.200) Epoch: [6][9590/11272] Time 0.875 (0.978) Data 0.001 (0.002) Loss 2.6233 (2.6695) Prec@1 36.875 (35.779) Prec@5 71.875 (66.201) Epoch: [6][9600/11272] Time 0.876 (0.978) Data 0.001 (0.002) Loss 2.7117 (2.6695) Prec@1 38.125 (35.780) Prec@5 65.625 (66.200) Epoch: [6][9610/11272] Time 0.732 (0.978) Data 0.001 (0.002) Loss 2.4828 (2.6694) Prec@1 34.375 (35.781) Prec@5 66.875 (66.201) Epoch: [6][9620/11272] Time 0.783 (0.978) Data 0.001 (0.002) Loss 2.4722 (2.6694) Prec@1 40.625 (35.779) Prec@5 66.875 (66.200) Epoch: [6][9630/11272] Time 0.878 (0.977) Data 0.001 (0.002) Loss 2.7706 (2.6694) Prec@1 30.000 (35.780) Prec@5 64.375 (66.199) Epoch: [6][9640/11272] Time 0.921 (0.977) Data 0.001 (0.002) Loss 2.5495 (2.6693) Prec@1 37.500 (35.782) Prec@5 68.125 (66.199) Epoch: [6][9650/11272] Time 0.789 (0.977) Data 0.002 (0.002) Loss 2.7315 (2.6693) Prec@1 40.000 (35.783) Prec@5 65.000 (66.200) Epoch: [6][9660/11272] Time 0.759 (0.977) Data 0.001 (0.002) Loss 2.5556 (2.6694) Prec@1 39.375 (35.784) Prec@5 66.250 (66.199) Epoch: [6][9670/11272] Time 0.882 (0.977) Data 0.001 (0.002) Loss 2.7264 (2.6694) Prec@1 35.000 (35.784) Prec@5 68.125 (66.199) Epoch: [6][9680/11272] Time 0.901 (0.977) Data 0.001 (0.002) Loss 2.5482 (2.6694) Prec@1 39.375 (35.782) Prec@5 70.625 (66.198) Epoch: [6][9690/11272] Time 0.726 (0.976) Data 0.001 (0.002) Loss 2.3786 (2.6694) Prec@1 41.250 (35.782) Prec@5 76.875 (66.198) Epoch: [6][9700/11272] Time 0.924 (0.976) Data 0.005 (0.002) Loss 2.4499 (2.6694) Prec@1 35.625 (35.783) Prec@5 70.000 (66.199) Epoch: [6][9710/11272] Time 0.877 (0.976) Data 0.001 (0.002) Loss 2.8207 (2.6693) Prec@1 33.125 (35.783) Prec@5 62.500 (66.199) Epoch: [6][9720/11272] Time 0.766 (0.976) Data 0.001 (0.002) Loss 2.7745 (2.6693) Prec@1 35.000 (35.784) Prec@5 61.875 (66.199) Epoch: [6][9730/11272] Time 0.746 (0.976) Data 0.001 (0.002) Loss 2.7859 (2.6693) Prec@1 31.875 (35.784) Prec@5 63.750 (66.200) Epoch: [6][9740/11272] Time 0.855 (0.976) Data 0.001 (0.002) Loss 2.8067 (2.6694) Prec@1 34.375 (35.785) Prec@5 64.375 (66.198) Epoch: [6][9750/11272] Time 0.915 (0.976) Data 0.001 (0.002) Loss 2.6335 (2.6694) Prec@1 35.000 (35.783) Prec@5 68.125 (66.197) Epoch: [6][9760/11272] Time 0.748 (0.975) Data 0.001 (0.002) Loss 2.7912 (2.6694) Prec@1 33.125 (35.784) Prec@5 65.000 (66.197) Epoch: [6][9770/11272] Time 0.740 (0.975) Data 0.001 (0.002) Loss 2.5602 (2.6694) Prec@1 40.000 (35.785) Prec@5 65.625 (66.197) Epoch: [6][9780/11272] Time 0.870 (0.975) Data 0.001 (0.002) Loss 2.8260 (2.6693) Prec@1 33.750 (35.786) Prec@5 62.500 (66.197) Epoch: [6][9790/11272] Time 0.869 (0.975) Data 0.001 (0.002) Loss 2.9250 (2.6694) Prec@1 28.125 (35.785) Prec@5 59.375 (66.196) Epoch: [6][9800/11272] Time 0.759 (0.975) Data 0.001 (0.002) Loss 2.7623 (2.6693) Prec@1 38.750 (35.785) Prec@5 63.125 (66.196) Epoch: [6][9810/11272] Time 0.780 (0.975) Data 0.001 (0.002) Loss 2.6817 (2.6693) Prec@1 35.000 (35.787) Prec@5 65.000 (66.196) Epoch: [6][9820/11272] Time 0.844 (0.974) Data 0.001 (0.002) Loss 2.5380 (2.6693) Prec@1 37.500 (35.786) Prec@5 70.625 (66.196) Epoch: [6][9830/11272] Time 0.756 (0.974) Data 0.003 (0.002) Loss 2.7135 (2.6694) Prec@1 36.875 (35.784) Prec@5 63.750 (66.194) Epoch: [6][9840/11272] Time 0.771 (0.974) Data 0.002 (0.002) Loss 2.2653 (2.6695) Prec@1 47.500 (35.783) Prec@5 73.125 (66.192) Epoch: [6][9850/11272] Time 0.872 (0.974) Data 0.001 (0.002) Loss 2.6862 (2.6695) Prec@1 35.000 (35.784) Prec@5 66.875 (66.192) Epoch: [6][9860/11272] Time 0.850 (0.974) Data 0.002 (0.002) Loss 2.8017 (2.6695) Prec@1 34.375 (35.785) Prec@5 63.750 (66.193) Epoch: [6][9870/11272] Time 0.773 (0.974) Data 0.001 (0.002) Loss 2.5688 (2.6695) Prec@1 34.375 (35.784) Prec@5 71.250 (66.193) Epoch: [6][9880/11272] Time 0.759 (0.973) Data 0.001 (0.002) Loss 2.7892 (2.6694) Prec@1 30.000 (35.785) Prec@5 64.375 (66.193) Epoch: [6][9890/11272] Time 0.947 (0.973) Data 0.002 (0.002) Loss 2.8609 (2.6694) Prec@1 32.500 (35.785) Prec@5 60.625 (66.193) Epoch: [6][9900/11272] Time 0.881 (0.973) Data 0.001 (0.002) Loss 2.6433 (2.6694) Prec@1 36.250 (35.787) Prec@5 63.750 (66.192) Epoch: [6][9910/11272] Time 0.748 (0.973) Data 0.001 (0.002) Loss 2.6888 (2.6694) Prec@1 35.000 (35.787) Prec@5 70.000 (66.193) Epoch: [6][9920/11272] Time 0.753 (0.973) Data 0.001 (0.002) Loss 2.8429 (2.6694) Prec@1 31.875 (35.786) Prec@5 61.875 (66.192) Epoch: [6][9930/11272] Time 0.885 (0.973) Data 0.001 (0.002) Loss 2.5724 (2.6694) Prec@1 34.375 (35.787) Prec@5 70.625 (66.194) Epoch: [6][9940/11272] Time 0.900 (0.973) Data 0.001 (0.002) Loss 2.7604 (2.6693) Prec@1 35.625 (35.788) Prec@5 62.500 (66.194) Epoch: [6][9950/11272] Time 0.776 (0.972) Data 0.002 (0.002) Loss 2.6401 (2.6693) Prec@1 37.500 (35.788) Prec@5 66.250 (66.195) Epoch: [6][9960/11272] Time 0.904 (0.972) Data 0.001 (0.002) Loss 2.7713 (2.6694) Prec@1 26.875 (35.785) Prec@5 67.500 (66.194) Epoch: [6][9970/11272] Time 0.849 (0.972) Data 0.001 (0.002) Loss 2.4032 (2.6694) Prec@1 39.375 (35.786) Prec@5 71.250 (66.194) Epoch: [6][9980/11272] Time 0.730 (0.972) Data 0.001 (0.002) Loss 2.4104 (2.6693) Prec@1 39.375 (35.785) Prec@5 71.250 (66.195) Epoch: [6][9990/11272] Time 0.746 (0.972) Data 0.001 (0.002) Loss 2.6742 (2.6694) Prec@1 33.125 (35.784) Prec@5 65.625 (66.195) Epoch: [6][10000/11272] Time 0.984 (0.972) Data 0.001 (0.002) Loss 2.8364 (2.6694) Prec@1 38.750 (35.783) Prec@5 62.500 (66.193) Epoch: [6][10010/11272] Time 0.889 (0.972) Data 0.001 (0.002) Loss 2.7283 (2.6693) Prec@1 36.250 (35.785) Prec@5 63.125 (66.194) Epoch: [6][10020/11272] Time 0.788 (0.971) Data 0.001 (0.002) Loss 2.6820 (2.6693) Prec@1 36.875 (35.786) Prec@5 66.250 (66.196) Epoch: [6][10030/11272] Time 0.743 (0.971) Data 0.001 (0.002) Loss 2.8126 (2.6694) Prec@1 33.125 (35.785) Prec@5 68.125 (66.195) Epoch: [6][10040/11272] Time 0.885 (0.971) Data 0.002 (0.002) Loss 2.5395 (2.6694) Prec@1 37.500 (35.784) Prec@5 66.875 (66.193) Epoch: [6][10050/11272] Time 0.888 (0.971) Data 0.001 (0.002) Loss 2.6208 (2.6695) Prec@1 32.500 (35.782) Prec@5 66.250 (66.191) Epoch: [6][10060/11272] Time 0.742 (0.971) Data 0.001 (0.002) Loss 2.7695 (2.6695) Prec@1 31.875 (35.783) Prec@5 65.000 (66.192) Epoch: [6][10070/11272] Time 0.779 (0.971) Data 0.001 (0.002) Loss 2.7938 (2.6694) Prec@1 36.875 (35.784) Prec@5 62.500 (66.193) Epoch: [6][10080/11272] Time 0.875 (0.971) Data 0.001 (0.002) Loss 2.7007 (2.6694) Prec@1 35.000 (35.784) Prec@5 64.375 (66.194) Epoch: [6][10090/11272] Time 0.907 (0.970) Data 0.002 (0.002) Loss 2.9383 (2.6695) Prec@1 31.250 (35.783) Prec@5 60.625 (66.192) Epoch: [6][10100/11272] Time 0.747 (0.970) Data 0.001 (0.002) Loss 2.7401 (2.6694) Prec@1 36.250 (35.784) Prec@5 67.500 (66.192) Epoch: [6][10110/11272] Time 0.853 (0.970) Data 0.001 (0.002) Loss 2.7657 (2.6694) Prec@1 31.875 (35.785) Prec@5 65.625 (66.192) Epoch: [6][10120/11272] Time 0.954 (0.970) Data 0.002 (0.002) Loss 2.5027 (2.6694) Prec@1 39.375 (35.786) Prec@5 70.625 (66.191) Epoch: [6][10130/11272] Time 0.742 (0.970) Data 0.001 (0.002) Loss 2.6189 (2.6693) Prec@1 40.000 (35.785) Prec@5 66.250 (66.190) Epoch: [6][10140/11272] Time 0.758 (0.970) Data 0.004 (0.002) Loss 2.8613 (2.6693) Prec@1 34.375 (35.784) Prec@5 58.125 (66.190) Epoch: [6][10150/11272] Time 0.896 (0.969) Data 0.001 (0.002) Loss 2.6504 (2.6693) Prec@1 33.750 (35.784) Prec@5 67.500 (66.191) Epoch: [6][10160/11272] Time 0.914 (0.969) Data 0.001 (0.002) Loss 2.9409 (2.6693) Prec@1 28.125 (35.783) Prec@5 59.375 (66.191) Epoch: [6][10170/11272] Time 0.745 (0.969) Data 0.001 (0.002) Loss 2.9705 (2.6694) Prec@1 30.000 (35.782) Prec@5 60.000 (66.189) Epoch: [6][10180/11272] Time 0.772 (0.969) Data 0.001 (0.002) Loss 2.3830 (2.6694) Prec@1 39.375 (35.782) Prec@5 71.250 (66.189) Epoch: [6][10190/11272] Time 0.856 (0.969) Data 0.001 (0.002) Loss 2.5904 (2.6693) Prec@1 40.000 (35.784) Prec@5 65.625 (66.190) Epoch: [6][10200/11272] Time 0.882 (0.969) Data 0.001 (0.002) Loss 2.5756 (2.6693) Prec@1 35.000 (35.784) Prec@5 69.375 (66.190) Epoch: [6][10210/11272] Time 0.748 (0.969) Data 0.001 (0.002) Loss 2.6059 (2.6694) Prec@1 36.250 (35.784) Prec@5 65.000 (66.188) Epoch: [6][10220/11272] Time 0.718 (0.968) Data 0.001 (0.002) Loss 2.7550 (2.6694) Prec@1 36.250 (35.782) Prec@5 61.875 (66.187) Epoch: [6][10230/11272] Time 0.869 (0.968) Data 0.001 (0.002) Loss 2.8707 (2.6695) Prec@1 31.250 (35.779) Prec@5 65.000 (66.185) Epoch: [6][10240/11272] Time 0.770 (0.968) Data 0.002 (0.002) Loss 2.5470 (2.6696) Prec@1 36.875 (35.777) Prec@5 68.125 (66.185) Epoch: [6][10250/11272] Time 0.766 (0.968) Data 0.001 (0.002) Loss 2.7574 (2.6695) Prec@1 36.875 (35.778) Prec@5 66.875 (66.185) Epoch: [6][10260/11272] Time 0.896 (0.968) Data 0.001 (0.002) Loss 2.6108 (2.6696) Prec@1 38.750 (35.777) Prec@5 65.625 (66.186) Epoch: [6][10270/11272] Time 0.890 (0.968) Data 0.001 (0.002) Loss 2.6912 (2.6695) Prec@1 29.375 (35.778) Prec@5 66.875 (66.189) Epoch: [6][10280/11272] Time 0.749 (0.968) Data 0.002 (0.002) Loss 2.4996 (2.6695) Prec@1 33.125 (35.778) Prec@5 72.500 (66.189) Epoch: [6][10290/11272] Time 0.789 (0.967) Data 0.001 (0.002) Loss 2.6858 (2.6695) Prec@1 34.375 (35.779) Prec@5 62.500 (66.189) Epoch: [6][10300/11272] Time 0.906 (0.967) Data 0.001 (0.002) Loss 2.4939 (2.6695) Prec@1 38.750 (35.779) Prec@5 68.125 (66.188) Epoch: [6][10310/11272] Time 0.913 (0.967) Data 0.001 (0.002) Loss 2.7135 (2.6695) Prec@1 33.125 (35.779) Prec@5 63.125 (66.188) Epoch: [6][10320/11272] Time 0.788 (0.967) Data 0.001 (0.002) Loss 2.6551 (2.6694) Prec@1 34.375 (35.778) Prec@5 68.125 (66.190) Epoch: [6][10330/11272] Time 0.784 (0.967) Data 0.001 (0.002) Loss 2.3987 (2.6693) Prec@1 43.125 (35.780) Prec@5 72.500 (66.192) Epoch: [6][10340/11272] Time 0.892 (0.967) Data 0.002 (0.002) Loss 2.5972 (2.6692) Prec@1 37.500 (35.781) Prec@5 66.250 (66.193) Epoch: [6][10350/11272] Time 0.971 (0.967) Data 0.002 (0.002) Loss 2.7096 (2.6693) Prec@1 31.250 (35.779) Prec@5 68.125 (66.193) Epoch: [6][10360/11272] Time 0.730 (0.967) Data 0.002 (0.002) Loss 2.7684 (2.6693) Prec@1 34.375 (35.778) Prec@5 64.375 (66.193) Epoch: [6][10370/11272] Time 0.887 (0.966) Data 0.001 (0.002) Loss 2.6562 (2.6691) Prec@1 36.875 (35.780) Prec@5 69.375 (66.196) Epoch: [6][10380/11272] Time 0.851 (0.966) Data 0.001 (0.002) Loss 2.7625 (2.6691) Prec@1 38.125 (35.781) Prec@5 69.375 (66.198) Epoch: [6][10390/11272] Time 0.718 (0.966) Data 0.001 (0.002) Loss 2.4738 (2.6691) Prec@1 38.125 (35.780) Prec@5 66.875 (66.196) Epoch: [6][10400/11272] Time 0.749 (0.966) Data 0.001 (0.002) Loss 2.8677 (2.6691) Prec@1 31.250 (35.780) Prec@5 58.125 (66.194) Epoch: [6][10410/11272] Time 0.894 (0.966) Data 0.002 (0.002) Loss 2.6279 (2.6691) Prec@1 33.750 (35.780) Prec@5 68.125 (66.195) Epoch: [6][10420/11272] Time 0.888 (0.966) Data 0.001 (0.002) Loss 2.6002 (2.6691) Prec@1 37.500 (35.779) Prec@5 65.625 (66.195) Epoch: [6][10430/11272] Time 0.730 (0.966) Data 0.001 (0.002) Loss 2.4985 (2.6692) Prec@1 38.750 (35.779) Prec@5 69.375 (66.194) Epoch: [6][10440/11272] Time 0.730 (0.965) Data 0.001 (0.002) Loss 2.6364 (2.6692) Prec@1 32.500 (35.779) Prec@5 71.875 (66.195) Epoch: [6][10450/11272] Time 0.879 (0.965) Data 0.001 (0.002) Loss 2.8595 (2.6693) Prec@1 26.875 (35.778) Prec@5 65.000 (66.195) Epoch: [6][10460/11272] Time 0.848 (0.965) Data 0.002 (0.002) Loss 2.7047 (2.6692) Prec@1 35.000 (35.779) Prec@5 68.750 (66.196) Epoch: [6][10470/11272] Time 0.745 (0.965) Data 0.001 (0.002) Loss 2.6148 (2.6692) Prec@1 33.750 (35.777) Prec@5 64.375 (66.197) Epoch: [6][10480/11272] Time 0.742 (0.965) Data 0.001 (0.002) Loss 2.3957 (2.6692) Prec@1 36.875 (35.777) Prec@5 72.500 (66.198) Epoch: [6][10490/11272] Time 0.851 (0.965) Data 0.001 (0.002) Loss 2.6318 (2.6692) Prec@1 34.375 (35.776) Prec@5 64.375 (66.198) Epoch: [6][10500/11272] Time 0.742 (0.965) Data 0.003 (0.002) Loss 2.4690 (2.6691) Prec@1 41.875 (35.778) Prec@5 70.625 (66.198) Epoch: [6][10510/11272] Time 0.761 (0.964) Data 0.001 (0.002) Loss 2.6210 (2.6691) Prec@1 36.875 (35.779) Prec@5 63.750 (66.200) Epoch: [6][10520/11272] Time 0.875 (0.964) Data 0.001 (0.002) Loss 2.6933 (2.6691) Prec@1 35.625 (35.778) Prec@5 63.750 (66.199) Epoch: [6][10530/11272] Time 0.882 (0.964) Data 0.002 (0.002) Loss 2.3213 (2.6691) Prec@1 43.750 (35.778) Prec@5 72.500 (66.199) Epoch: [6][10540/11272] Time 0.751 (0.964) Data 0.001 (0.002) Loss 2.3322 (2.6690) Prec@1 40.000 (35.780) Prec@5 70.000 (66.201) Epoch: [6][10550/11272] Time 0.764 (0.964) Data 0.002 (0.002) Loss 2.3159 (2.6690) Prec@1 44.375 (35.781) Prec@5 72.500 (66.201) Epoch: [6][10560/11272] Time 0.892 (0.964) Data 0.001 (0.002) Loss 2.6229 (2.6689) Prec@1 33.125 (35.781) Prec@5 71.250 (66.203) Epoch: [6][10570/11272] Time 0.883 (0.964) Data 0.001 (0.002) Loss 2.7556 (2.6689) Prec@1 36.250 (35.780) Prec@5 65.000 (66.204) Epoch: [6][10580/11272] Time 0.835 (0.963) Data 0.002 (0.002) Loss 2.4161 (2.6689) Prec@1 39.375 (35.781) Prec@5 71.875 (66.204) Epoch: [6][10590/11272] Time 0.736 (0.963) Data 0.001 (0.002) Loss 2.7739 (2.6689) Prec@1 33.125 (35.781) Prec@5 63.125 (66.205) Epoch: [6][10600/11272] Time 0.869 (0.963) Data 0.001 (0.002) Loss 2.7138 (2.6689) Prec@1 36.250 (35.780) Prec@5 62.500 (66.205) Epoch: [6][10610/11272] Time 0.890 (0.963) Data 0.001 (0.002) Loss 2.5547 (2.6689) Prec@1 38.750 (35.780) Prec@5 69.375 (66.205) Epoch: [6][10620/11272] Time 0.739 (0.963) Data 0.001 (0.002) Loss 2.8011 (2.6689) Prec@1 38.125 (35.779) Prec@5 60.625 (66.205) Epoch: [6][10630/11272] Time 0.850 (0.963) Data 0.002 (0.002) Loss 2.5828 (2.6689) Prec@1 36.875 (35.778) Prec@5 66.875 (66.205) Epoch: [6][10640/11272] Time 0.865 (0.963) Data 0.001 (0.002) Loss 2.8365 (2.6689) Prec@1 33.750 (35.778) Prec@5 61.250 (66.204) Epoch: [6][10650/11272] Time 0.784 (0.963) Data 0.002 (0.002) Loss 2.4905 (2.6688) Prec@1 41.250 (35.781) Prec@5 71.250 (66.205) Epoch: [6][10660/11272] Time 0.764 (0.962) Data 0.001 (0.002) Loss 2.6176 (2.6689) Prec@1 36.875 (35.780) Prec@5 69.375 (66.205) Epoch: [6][10670/11272] Time 0.920 (0.962) Data 0.001 (0.002) Loss 2.6491 (2.6689) Prec@1 41.250 (35.779) Prec@5 63.750 (66.204) Epoch: [6][10680/11272] Time 0.832 (0.962) Data 0.001 (0.002) Loss 2.5843 (2.6689) Prec@1 35.000 (35.779) Prec@5 67.500 (66.203) Epoch: [6][10690/11272] Time 0.796 (0.962) Data 0.002 (0.002) Loss 2.8600 (2.6689) Prec@1 30.000 (35.779) Prec@5 63.750 (66.204) Epoch: [6][10700/11272] Time 0.757 (0.962) Data 0.001 (0.002) Loss 2.7519 (2.6690) Prec@1 30.625 (35.777) Prec@5 66.875 (66.204) Epoch: [6][10710/11272] Time 0.864 (0.962) Data 0.001 (0.002) Loss 2.6489 (2.6689) Prec@1 38.750 (35.779) Prec@5 66.250 (66.206) Epoch: [6][10720/11272] Time 0.909 (0.962) Data 0.001 (0.002) Loss 2.6396 (2.6689) Prec@1 35.000 (35.779) Prec@5 66.875 (66.206) Epoch: [6][10730/11272] Time 0.788 (0.962) Data 0.001 (0.002) Loss 2.4474 (2.6689) Prec@1 37.500 (35.778) Prec@5 71.250 (66.207) Epoch: [6][10740/11272] Time 0.758 (0.961) Data 0.001 (0.002) Loss 2.6644 (2.6689) Prec@1 36.250 (35.777) Prec@5 64.375 (66.206) Epoch: [6][10750/11272] Time 0.862 (0.961) Data 0.001 (0.002) Loss 2.5257 (2.6689) Prec@1 42.500 (35.778) Prec@5 70.625 (66.206) Epoch: [6][10760/11272] Time 0.747 (0.961) Data 0.003 (0.002) Loss 2.5695 (2.6689) Prec@1 38.125 (35.778) Prec@5 65.000 (66.206) Epoch: [6][10770/11272] Time 0.759 (0.961) Data 0.001 (0.002) Loss 2.7418 (2.6689) Prec@1 35.000 (35.779) Prec@5 63.125 (66.205) Epoch: [6][10780/11272] Time 0.890 (0.961) Data 0.001 (0.002) Loss 2.5418 (2.6688) Prec@1 38.125 (35.780) Prec@5 70.000 (66.206) Epoch: [6][10790/11272] Time 0.930 (0.961) Data 0.001 (0.002) Loss 2.6791 (2.6689) Prec@1 35.625 (35.778) Prec@5 67.500 (66.205) Epoch: [6][10800/11272] Time 0.746 (0.961) Data 0.001 (0.002) Loss 2.7464 (2.6689) Prec@1 30.000 (35.777) Prec@5 61.875 (66.204) Epoch: [6][10810/11272] Time 0.768 (0.960) Data 0.001 (0.002) Loss 2.7268 (2.6689) Prec@1 33.125 (35.777) Prec@5 65.625 (66.205) Epoch: [6][10820/11272] Time 0.895 (0.960) Data 0.001 (0.002) Loss 2.7014 (2.6689) Prec@1 35.625 (35.777) Prec@5 63.750 (66.203) Epoch: [6][10830/11272] Time 0.865 (0.960) Data 0.001 (0.002) Loss 2.8779 (2.6690) Prec@1 36.875 (35.777) Prec@5 60.625 (66.202) Epoch: [6][10840/11272] Time 0.737 (0.960) Data 0.001 (0.002) Loss 2.6944 (2.6690) Prec@1 35.625 (35.776) Prec@5 63.750 (66.202) Epoch: [6][10850/11272] Time 0.737 (0.960) Data 0.001 (0.002) Loss 2.8469 (2.6690) Prec@1 31.250 (35.775) Prec@5 64.375 (66.202) Epoch: [6][10860/11272] Time 0.902 (0.960) Data 0.001 (0.002) Loss 2.4366 (2.6690) Prec@1 38.750 (35.776) Prec@5 70.000 (66.204) Epoch: [6][10870/11272] Time 0.885 (0.960) Data 0.001 (0.002) Loss 2.6837 (2.6689) Prec@1 36.250 (35.776) Prec@5 68.125 (66.204) Epoch: [6][10880/11272] Time 0.767 (0.960) Data 0.001 (0.002) Loss 2.6014 (2.6690) Prec@1 36.875 (35.777) Prec@5 69.375 (66.204) Epoch: [6][10890/11272] Time 0.949 (0.959) Data 0.001 (0.002) Loss 2.4359 (2.6689) Prec@1 41.250 (35.777) Prec@5 68.125 (66.204) Epoch: [6][10900/11272] Time 0.888 (0.959) Data 0.001 (0.002) Loss 2.6435 (2.6689) Prec@1 33.750 (35.777) Prec@5 62.500 (66.202) Epoch: [6][10910/11272] Time 0.738 (0.959) Data 0.001 (0.002) Loss 2.8112 (2.6689) Prec@1 34.375 (35.776) Prec@5 62.500 (66.201) Epoch: [6][10920/11272] Time 0.740 (0.959) Data 0.001 (0.002) Loss 2.6481 (2.6689) Prec@1 31.875 (35.776) Prec@5 65.000 (66.200) Epoch: [6][10930/11272] Time 0.982 (0.959) Data 0.002 (0.002) Loss 2.7283 (2.6689) Prec@1 30.000 (35.775) Prec@5 63.750 (66.200) Epoch: [6][10940/11272] Time 0.868 (0.959) Data 0.001 (0.002) Loss 2.5872 (2.6689) Prec@1 35.625 (35.775) Prec@5 68.125 (66.200) Epoch: [6][10950/11272] Time 0.825 (0.959) Data 0.001 (0.002) Loss 2.9872 (2.6689) Prec@1 28.750 (35.775) Prec@5 61.250 (66.200) Epoch: [6][10960/11272] Time 0.749 (0.959) Data 0.001 (0.002) Loss 2.5467 (2.6688) Prec@1 38.125 (35.775) Prec@5 68.125 (66.201) Epoch: [6][10970/11272] Time 0.924 (0.959) Data 0.002 (0.002) Loss 2.4160 (2.6687) Prec@1 35.000 (35.776) Prec@5 71.875 (66.203) Epoch: [6][10980/11272] Time 0.853 (0.958) Data 0.001 (0.002) Loss 2.6824 (2.6688) Prec@1 36.250 (35.776) Prec@5 65.625 (66.202) Epoch: [6][10990/11272] Time 0.767 (0.958) Data 0.001 (0.002) Loss 2.5373 (2.6688) Prec@1 31.875 (35.775) Prec@5 67.500 (66.202) Epoch: [6][11000/11272] Time 0.758 (0.958) Data 0.001 (0.002) Loss 2.8745 (2.6688) Prec@1 30.625 (35.774) Prec@5 60.000 (66.201) Epoch: [6][11010/11272] Time 0.873 (0.958) Data 0.001 (0.002) Loss 2.7661 (2.6689) Prec@1 32.500 (35.772) Prec@5 60.000 (66.198) Epoch: [6][11020/11272] Time 0.938 (0.958) Data 0.001 (0.002) Loss 2.8970 (2.6688) Prec@1 30.625 (35.773) Prec@5 68.125 (66.199) Epoch: [6][11030/11272] Time 0.758 (0.958) Data 0.001 (0.002) Loss 2.6268 (2.6688) Prec@1 40.625 (35.773) Prec@5 64.375 (66.199) Epoch: [6][11040/11272] Time 0.932 (0.958) Data 0.001 (0.002) Loss 2.6475 (2.6688) Prec@1 33.750 (35.774) Prec@5 70.000 (66.201) Epoch: [6][11050/11272] Time 0.877 (0.958) Data 0.001 (0.002) Loss 2.5920 (2.6688) Prec@1 36.250 (35.773) Prec@5 69.375 (66.199) Epoch: [6][11060/11272] Time 0.760 (0.957) Data 0.002 (0.002) Loss 2.8554 (2.6688) Prec@1 35.625 (35.773) Prec@5 66.250 (66.200) Epoch: [6][11070/11272] Time 0.771 (0.957) Data 0.001 (0.002) Loss 2.5770 (2.6688) Prec@1 35.000 (35.773) Prec@5 65.000 (66.200) Epoch: [6][11080/11272] Time 0.946 (0.957) Data 0.001 (0.002) Loss 2.5418 (2.6688) Prec@1 38.750 (35.774) Prec@5 68.125 (66.201) Epoch: [6][11090/11272] Time 0.913 (0.957) Data 0.002 (0.002) Loss 2.8582 (2.6687) Prec@1 35.625 (35.773) Prec@5 60.625 (66.202) Epoch: [6][11100/11272] Time 0.747 (0.957) Data 0.001 (0.002) Loss 2.6565 (2.6687) Prec@1 35.625 (35.773) Prec@5 63.750 (66.201) Epoch: [6][11110/11272] Time 0.755 (0.957) Data 0.001 (0.002) Loss 2.5770 (2.6688) Prec@1 35.625 (35.771) Prec@5 69.375 (66.201) Epoch: [6][11120/11272] Time 0.868 (0.957) Data 0.002 (0.002) Loss 2.5823 (2.6687) Prec@1 38.125 (35.772) Prec@5 70.625 (66.201) Epoch: [6][11130/11272] Time 0.884 (0.957) Data 0.001 (0.002) Loss 2.7836 (2.6687) Prec@1 30.625 (35.775) Prec@5 60.000 (66.202) Epoch: [6][11140/11272] Time 0.749 (0.957) Data 0.001 (0.002) Loss 2.5032 (2.6688) Prec@1 35.000 (35.773) Prec@5 71.250 (66.199) Epoch: [6][11150/11272] Time 0.750 (0.956) Data 0.002 (0.002) Loss 2.6259 (2.6687) Prec@1 35.000 (35.774) Prec@5 70.000 (66.201) Epoch: [6][11160/11272] Time 0.870 (0.956) Data 0.001 (0.002) Loss 2.8067 (2.6687) Prec@1 31.875 (35.774) Prec@5 62.500 (66.201) Epoch: [6][11170/11272] Time 0.722 (0.956) Data 0.001 (0.002) Loss 2.5176 (2.6687) Prec@1 33.750 (35.772) Prec@5 68.750 (66.199) Epoch: [6][11180/11272] Time 0.731 (0.956) Data 0.001 (0.002) Loss 2.5662 (2.6687) Prec@1 36.875 (35.774) Prec@5 70.000 (66.199) Epoch: [6][11190/11272] Time 0.876 (0.956) Data 0.001 (0.002) Loss 2.7179 (2.6686) Prec@1 35.000 (35.775) Prec@5 67.500 (66.201) Epoch: [6][11200/11272] Time 0.889 (0.956) Data 0.002 (0.002) Loss 2.6017 (2.6686) Prec@1 38.125 (35.775) Prec@5 68.750 (66.200) Epoch: [6][11210/11272] Time 0.771 (0.956) Data 0.002 (0.002) Loss 2.7012 (2.6686) Prec@1 36.250 (35.775) Prec@5 63.750 (66.200) Epoch: [6][11220/11272] Time 0.773 (0.956) Data 0.001 (0.002) Loss 2.7151 (2.6687) Prec@1 34.375 (35.773) Prec@5 67.500 (66.198) Epoch: [6][11230/11272] Time 0.894 (0.955) Data 0.001 (0.002) Loss 2.4269 (2.6688) Prec@1 36.875 (35.773) Prec@5 71.250 (66.198) Epoch: [6][11240/11272] Time 0.919 (0.955) Data 0.002 (0.002) Loss 2.4605 (2.6687) Prec@1 38.125 (35.774) Prec@5 71.875 (66.200) Epoch: [6][11250/11272] Time 0.744 (0.955) Data 0.001 (0.002) Loss 2.7276 (2.6687) Prec@1 33.750 (35.774) Prec@5 61.250 (66.201) Epoch: [6][11260/11272] Time 0.734 (0.955) Data 0.001 (0.002) Loss 2.5092 (2.6687) Prec@1 34.375 (35.774) Prec@5 68.750 (66.202) Epoch: [6][11270/11272] Time 0.863 (0.955) Data 0.000 (0.002) Loss 2.7706 (2.6687) Prec@1 33.750 (35.773) Prec@5 61.250 (66.201) Test: [0/229] Time 3.483 (3.483) Loss 1.5614 (1.5614) Prec@1 48.125 (48.125) Prec@5 93.125 (93.125) Test: [10/229] Time 0.348 (0.682) Loss 1.6174 (2.0518) Prec@1 51.250 (46.705) Prec@5 89.375 (79.886) Test: [20/229] Time 0.452 (0.556) Loss 2.6635 (2.2657) Prec@1 37.500 (42.679) Prec@5 70.000 (75.685) Test: [30/229] Time 0.356 (0.523) Loss 2.7382 (2.2324) Prec@1 21.250 (43.730) Prec@5 66.875 (75.242) Test: [40/229] Time 0.461 (0.544) Loss 0.8085 (2.2793) Prec@1 81.250 (42.500) Prec@5 90.625 (74.177) Test: [50/229] Time 0.454 (0.554) Loss 2.7810 (2.3010) Prec@1 30.625 (42.022) Prec@5 63.750 (73.529) Test: [60/229] Time 0.403 (0.540) Loss 2.7941 (2.3032) Prec@1 30.000 (42.162) Prec@5 64.375 (73.381) Test: [70/229] Time 0.591 (0.535) Loss 2.2082 (2.3031) Prec@1 48.750 (42.324) Prec@5 73.125 (73.345) Test: [80/229] Time 0.351 (0.525) Loss 2.8598 (2.3491) Prec@1 24.375 (40.741) Prec@5 62.500 (72.778) Test: [90/229] Time 1.236 (0.530) Loss 2.0154 (2.3442) Prec@1 50.625 (40.659) Prec@5 75.625 (73.166) Test: [100/229] Time 0.457 (0.521) Loss 2.2533 (2.3348) Prec@1 50.000 (41.021) Prec@5 79.375 (73.484) Test: [110/229] Time 0.437 (0.520) Loss 2.3852 (2.3181) Prec@1 30.625 (41.391) Prec@5 71.875 (73.818) Test: [120/229] Time 0.468 (0.515) Loss 3.2273 (2.3445) Prec@1 23.750 (40.620) Prec@5 58.750 (73.538) Test: [130/229] Time 1.094 (0.515) Loss 2.0311 (2.3300) Prec@1 45.625 (40.992) Prec@5 79.375 (73.807) Test: [140/229] Time 0.395 (0.508) Loss 2.6169 (2.3473) Prec@1 33.125 (40.709) Prec@5 75.000 (73.488) Test: [150/229] Time 0.507 (0.503) Loss 1.6490 (2.3702) Prec@1 65.000 (40.306) Prec@5 79.375 (73.125) Test: [160/229] Time 0.375 (0.503) Loss 2.0232 (2.3660) Prec@1 59.375 (40.470) Prec@5 79.375 (73.218) Test: [170/229] Time 0.821 (0.504) Loss 2.4144 (2.3900) Prec@1 36.250 (39.861) Prec@5 72.500 (72.716) Test: [180/229] Time 0.346 (0.499) Loss 2.7759 (2.3940) Prec@1 30.000 (39.952) Prec@5 60.625 (72.614) Test: [190/229] Time 0.454 (0.498) Loss 1.9552 (2.3722) Prec@1 48.125 (40.576) Prec@5 88.750 (72.988) Test: [200/229] Time 0.363 (0.494) Loss 2.3164 (2.3652) Prec@1 40.000 (40.566) Prec@5 68.125 (73.203) Test: [210/229] Time 0.711 (0.492) Loss 1.3637 (2.3505) Prec@1 60.000 (40.889) Prec@5 90.000 (73.472) Test: [220/229] Time 0.390 (0.491) Loss 2.0349 (2.3368) Prec@1 50.000 (41.338) Prec@5 77.500 (73.600) * Prec@1 41.832 Prec@5 73.861 Epoch: [7][0/11272] Time 3.350 (3.350) Data 2.326 (2.326) Loss 2.4922 (2.4922) Prec@1 35.625 (35.625) Prec@5 73.125 (73.125) Epoch: [7][10/11272] Time 0.886 (1.078) Data 0.002 (0.213) Loss 2.5499 (2.6529) Prec@1 37.500 (35.568) Prec@5 68.125 (66.875) Epoch: [7][20/11272] Time 0.736 (0.954) Data 0.002 (0.112) Loss 2.5762 (2.6586) Prec@1 33.750 (35.744) Prec@5 68.125 (66.696) Epoch: [7][30/11272] Time 0.834 (0.915) Data 0.001 (0.077) Loss 2.9142 (2.6206) Prec@1 27.500 (36.008) Prec@5 60.000 (67.238) Epoch: [7][40/11272] Time 0.926 (0.904) Data 0.002 (0.058) Loss 2.5364 (2.6124) Prec@1 38.125 (36.570) Prec@5 68.125 (67.424) Epoch: [7][50/11272] Time 0.915 (0.891) Data 0.001 (0.047) Loss 2.5502 (2.6223) Prec@1 41.875 (36.556) Prec@5 68.750 (67.243) Epoch: [7][60/11272] Time 0.732 (0.881) Data 0.002 (0.040) Loss 2.7014 (2.6417) Prec@1 33.750 (36.270) Prec@5 65.000 (66.885) Epoch: [7][70/11272] Time 0.816 (0.874) Data 0.001 (0.034) Loss 2.6902 (2.6330) Prec@1 35.000 (36.224) Prec@5 64.375 (67.007) Epoch: [7][80/11272] Time 0.939 (0.871) Data 0.004 (0.030) Loss 2.7464 (2.6344) Prec@1 37.500 (36.026) Prec@5 68.750 (67.145) Epoch: [7][90/11272] Time 0.794 (0.866) Data 0.004 (0.027) Loss 2.4947 (2.6408) Prec@1 36.875 (35.783) Prec@5 68.125 (66.909) Epoch: [7][100/11272] Time 0.768 (0.862) Data 0.002 (0.025) Loss 2.7647 (2.6440) Prec@1 30.625 (35.545) Prec@5 63.125 (66.875) Epoch: [7][110/11272] Time 0.870 (0.859) Data 0.001 (0.023) Loss 2.5701 (2.6413) Prec@1 38.750 (35.563) Prec@5 68.125 (66.903) Epoch: [7][120/11272] Time 0.926 (0.856) Data 0.002 (0.021) Loss 2.7276 (2.6377) Prec@1 40.000 (35.806) Prec@5 63.125 (67.004) Epoch: [7][130/11272] Time 0.811 (0.855) Data 0.001 (0.019) Loss 2.6338 (2.6397) Prec@1 38.750 (35.720) Prec@5 66.875 (67.028) Epoch: [7][140/11272] Time 0.736 (0.852) Data 0.002 (0.018) Loss 2.7365 (2.6406) Prec@1 33.750 (35.887) Prec@5 64.375 (66.964) Epoch: [7][150/11272] Time 0.879 (0.852) Data 0.001 (0.017) Loss 2.5576 (2.6447) Prec@1 36.875 (35.844) Prec@5 70.000 (66.916) Epoch: [7][160/11272] Time 0.909 (0.851) Data 0.002 (0.016) Loss 2.6467 (2.6450) Prec@1 36.250 (35.835) Prec@5 67.500 (66.844) Epoch: [7][170/11272] Time 0.784 (0.850) Data 0.001 (0.015) Loss 2.6537 (2.6433) Prec@1 33.750 (35.844) Prec@5 63.750 (66.864) Epoch: [7][180/11272] Time 0.752 (0.849) Data 0.002 (0.015) Loss 2.4228 (2.6399) Prec@1 38.750 (35.891) Prec@5 74.375 (66.954) Epoch: [7][190/11272] Time 0.918 (0.849) Data 0.001 (0.014) Loss 2.5476 (2.6399) Prec@1 40.000 (35.995) Prec@5 67.500 (66.875) Epoch: [7][200/11272] Time 0.921 (0.849) Data 0.002 (0.013) Loss 2.8973 (2.6407) Prec@1 34.375 (36.098) Prec@5 61.250 (66.887) Epoch: [7][210/11272] Time 0.744 (0.846) Data 0.001 (0.013) Loss 2.7306 (2.6405) Prec@1 36.250 (36.078) Prec@5 60.625 (66.789) Epoch: [7][220/11272] Time 0.911 (0.847) Data 0.002 (0.012) Loss 2.6425 (2.6399) Prec@1 38.125 (36.100) Prec@5 65.625 (66.787) Epoch: [7][230/11272] Time 0.823 (0.846) Data 0.001 (0.012) Loss 2.8770 (2.6386) Prec@1 34.375 (36.134) Prec@5 61.875 (66.848) Epoch: [7][240/11272] Time 0.742 (0.845) Data 0.002 (0.011) Loss 2.8991 (2.6380) Prec@1 32.500 (36.170) Prec@5 63.750 (66.911) Epoch: [7][250/11272] Time 0.790 (0.844) Data 0.001 (0.011) Loss 2.4949 (2.6381) Prec@1 33.125 (36.188) Prec@5 69.375 (66.900) Epoch: [7][260/11272] Time 0.915 (0.845) Data 0.002 (0.011) Loss 2.9204 (2.6367) Prec@1 30.625 (36.221) Prec@5 64.375 (66.976) Epoch: [7][270/11272] Time 0.930 (0.844) Data 0.001 (0.010) Loss 2.8530 (2.6406) Prec@1 30.000 (36.165) Prec@5 62.500 (66.900) Epoch: [7][280/11272] Time 0.751 (0.844) Data 0.002 (0.010) Loss 2.8567 (2.6410) Prec@1 31.875 (36.143) Prec@5 61.875 (66.882) Epoch: [7][290/11272] Time 0.744 (0.843) Data 0.001 (0.010) Loss 2.7185 (2.6438) Prec@1 33.750 (36.102) Prec@5 65.625 (66.828) Epoch: [7][300/11272] Time 0.905 (0.843) Data 0.002 (0.009) Loss 2.6675 (2.6432) Prec@1 33.750 (36.105) Prec@5 66.250 (66.852) Epoch: [7][310/11272] Time 0.890 (0.843) Data 0.002 (0.009) Loss 2.5320 (2.6420) Prec@1 38.125 (36.150) Prec@5 68.125 (66.871) Epoch: [7][320/11272] Time 0.749 (0.842) Data 0.002 (0.009) Loss 2.8931 (2.6428) Prec@1 31.875 (36.133) Prec@5 61.875 (66.852) Epoch: [7][330/11272] Time 0.826 (0.842) Data 0.002 (0.009) Loss 2.6589 (2.6426) Prec@1 32.500 (36.137) Prec@5 61.875 (66.852) Epoch: [7][340/11272] Time 0.894 (0.843) Data 0.002 (0.009) Loss 2.7058 (2.6437) Prec@1 33.125 (36.103) Prec@5 65.000 (66.837) Epoch: [7][350/11272] Time 0.896 (0.843) Data 0.001 (0.008) Loss 2.8602 (2.6471) Prec@1 31.250 (36.006) Prec@5 63.125 (66.793) Epoch: [7][360/11272] Time 0.747 (0.843) Data 0.002 (0.008) Loss 2.4519 (2.6480) Prec@1 43.125 (35.997) Prec@5 72.500 (66.764) Epoch: [7][370/11272] Time 0.934 (0.842) Data 0.001 (0.008) Loss 2.4584 (2.6472) Prec@1 41.875 (36.004) Prec@5 71.250 (66.749) Epoch: [7][380/11272] Time 0.957 (0.842) Data 0.001 (0.008) Loss 2.3964 (2.6492) Prec@1 45.625 (36.002) Prec@5 65.625 (66.703) Epoch: [7][390/11272] Time 0.783 (0.841) Data 0.003 (0.008) Loss 2.5656 (2.6460) Prec@1 36.875 (36.055) Prec@5 68.750 (66.777) Epoch: [7][400/11272] Time 0.809 (0.841) Data 0.002 (0.008) Loss 2.4798 (2.6460) Prec@1 41.250 (36.057) Prec@5 69.375 (66.797) Epoch: [7][410/11272] Time 0.886 (0.841) Data 0.001 (0.007) Loss 2.5998 (2.6447) Prec@1 38.125 (36.087) Prec@5 65.625 (66.788) Epoch: [7][420/11272] Time 0.919 (0.841) Data 0.002 (0.007) Loss 3.1933 (2.6451) Prec@1 25.625 (36.090) Prec@5 60.000 (66.805) Epoch: [7][430/11272] Time 0.786 (0.841) Data 0.001 (0.007) Loss 2.4986 (2.6449) Prec@1 41.875 (36.076) Prec@5 67.500 (66.811) Epoch: [7][440/11272] Time 0.790 (0.841) Data 0.002 (0.007) Loss 2.3062 (2.6461) Prec@1 42.500 (36.046) Prec@5 67.500 (66.783) Epoch: [7][450/11272] Time 0.899 (0.841) Data 0.001 (0.007) Loss 2.5158 (2.6462) Prec@1 40.625 (36.060) Prec@5 68.750 (66.784) Epoch: [7][460/11272] Time 0.866 (0.841) Data 0.002 (0.007) Loss 2.6636 (2.6467) Prec@1 33.125 (36.051) Prec@5 63.125 (66.752) Epoch: [7][470/11272] Time 0.741 (0.841) Data 0.001 (0.007) Loss 2.7967 (2.6492) Prec@1 32.500 (35.999) Prec@5 63.125 (66.687) Epoch: [7][480/11272] Time 0.735 (0.840) Data 0.002 (0.007) Loss 2.6344 (2.6497) Prec@1 38.750 (36.003) Prec@5 66.250 (66.662) Epoch: [7][490/11272] Time 0.906 (0.840) Data 0.001 (0.006) Loss 2.4578 (2.6507) Prec@1 38.125 (35.990) Prec@5 73.125 (66.636) Epoch: [7][500/11272] Time 0.700 (0.840) Data 0.001 (0.006) Loss 2.6458 (2.6509) Prec@1 40.625 (35.984) Prec@5 67.500 (66.635) Epoch: [7][510/11272] Time 0.788 (0.839) Data 0.001 (0.006) Loss 2.7093 (2.6506) Prec@1 36.250 (35.972) Prec@5 61.250 (66.633) Epoch: [7][520/11272] Time 0.934 (0.840) Data 0.002 (0.006) Loss 2.6206 (2.6499) Prec@1 35.625 (35.998) Prec@5 68.750 (66.622) Epoch: [7][530/11272] Time 0.910 (0.840) Data 0.001 (0.006) Loss 2.5069 (2.6504) Prec@1 37.500 (35.995) Prec@5 69.375 (66.605) Epoch: [7][540/11272] Time 0.785 (0.840) Data 0.002 (0.006) Loss 2.6944 (2.6506) Prec@1 33.750 (35.997) Prec@5 66.250 (66.597) Epoch: [7][550/11272] Time 0.789 (0.840) Data 0.002 (0.006) Loss 2.6385 (2.6505) Prec@1 35.625 (35.995) Prec@5 62.500 (66.599) Epoch: [7][560/11272] Time 0.921 (0.840) Data 0.002 (0.006) Loss 2.7628 (2.6499) Prec@1 35.625 (36.012) Prec@5 65.000 (66.613) Epoch: [7][570/11272] Time 0.906 (0.840) Data 0.001 (0.006) Loss 2.6718 (2.6503) Prec@1 38.125 (36.008) Prec@5 67.500 (66.608) Epoch: [7][580/11272] Time 0.735 (0.839) Data 0.002 (0.006) Loss 2.7717 (2.6510) Prec@1 36.875 (35.999) Prec@5 66.250 (66.587) Epoch: [7][590/11272] Time 0.807 (0.839) Data 0.001 (0.006) Loss 2.6254 (2.6514) Prec@1 41.250 (36.044) Prec@5 66.250 (66.566) Epoch: [7][600/11272] Time 0.902 (0.840) Data 0.002 (0.006) Loss 2.4576 (2.6516) Prec@1 37.500 (36.023) Prec@5 71.250 (66.564) Epoch: [7][610/11272] Time 0.944 (0.840) Data 0.001 (0.005) Loss 2.7222 (2.6527) Prec@1 37.500 (36.001) Prec@5 63.750 (66.541) Epoch: [7][620/11272] Time 0.774 (0.840) Data 0.002 (0.005) Loss 2.5791 (2.6524) Prec@1 41.250 (36.026) Prec@5 72.500 (66.526) Epoch: [7][630/11272] Time 0.890 (0.840) Data 0.001 (0.005) Loss 2.6587 (2.6515) Prec@1 31.875 (36.041) Prec@5 66.875 (66.537) Epoch: [7][640/11272] Time 0.907 (0.840) Data 0.002 (0.005) Loss 2.8631 (2.6526) Prec@1 33.750 (36.004) Prec@5 63.125 (66.523) Epoch: [7][650/11272] Time 0.823 (0.840) Data 0.001 (0.005) Loss 2.5619 (2.6543) Prec@1 36.875 (35.992) Prec@5 70.000 (66.500) Epoch: [7][660/11272] Time 0.746 (0.840) Data 0.002 (0.005) Loss 2.6643 (2.6544) Prec@1 30.000 (35.976) Prec@5 70.000 (66.481) Epoch: [7][670/11272] Time 0.894 (0.840) Data 0.001 (0.005) Loss 2.7080 (2.6543) Prec@1 31.875 (35.995) Prec@5 67.500 (66.494) Epoch: [7][680/11272] Time 0.847 (0.839) Data 0.002 (0.005) Loss 2.4484 (2.6545) Prec@1 44.375 (35.998) Prec@5 68.750 (66.485) Epoch: [7][690/11272] Time 0.795 (0.839) Data 0.001 (0.005) Loss 2.6878 (2.6559) Prec@1 36.875 (35.972) Prec@5 63.750 (66.442) Epoch: [7][700/11272] Time 0.742 (0.839) Data 0.002 (0.005) Loss 2.5871 (2.6556) Prec@1 40.625 (35.963) Prec@5 66.875 (66.456) Epoch: [7][710/11272] Time 0.880 (0.839) Data 0.002 (0.005) Loss 2.7263 (2.6553) Prec@1 35.625 (35.963) Prec@5 67.500 (66.461) Epoch: [7][720/11272] Time 0.876 (0.839) Data 0.002 (0.005) Loss 2.6849 (2.6550) Prec@1 33.125 (35.940) Prec@5 64.375 (66.462) Epoch: [7][730/11272] Time 0.798 (0.840) Data 0.001 (0.005) Loss 2.6929 (2.6556) Prec@1 33.125 (35.935) Prec@5 66.250 (66.436) Epoch: [7][740/11272] Time 0.730 (0.840) Data 0.002 (0.005) Loss 2.6956 (2.6561) Prec@1 31.250 (35.925) Prec@5 63.125 (66.427) Epoch: [7][750/11272] Time 0.914 (0.840) Data 0.001 (0.005) Loss 2.5515 (2.6550) Prec@1 37.500 (35.948) Prec@5 68.750 (66.434) Epoch: [7][760/11272] Time 0.758 (0.839) Data 0.004 (0.005) Loss 2.9259 (2.6554) Prec@1 29.375 (35.934) Prec@5 61.250 (66.418) Epoch: [7][770/11272] Time 0.801 (0.839) Data 0.001 (0.005) Loss 2.5633 (2.6551) Prec@1 33.750 (35.927) Prec@5 69.375 (66.420) Epoch: [7][780/11272] Time 0.932 (0.839) Data 0.002 (0.005) Loss 2.7691 (2.6556) Prec@1 36.875 (35.914) Prec@5 65.000 (66.416) Epoch: [7][790/11272] Time 0.970 (0.839) Data 0.002 (0.005) Loss 2.4711 (2.6557) Prec@1 37.500 (35.909) Prec@5 73.750 (66.418) Epoch: [7][800/11272] Time 0.817 (0.839) Data 0.002 (0.005) Loss 2.5777 (2.6556) Prec@1 38.750 (35.919) Prec@5 67.500 (66.426) Epoch: [7][810/11272] Time 0.769 (0.839) Data 0.005 (0.005) Loss 2.6968 (2.6546) Prec@1 33.750 (35.929) Prec@5 63.750 (66.427) Epoch: [7][820/11272] Time 0.908 (0.840) Data 0.002 (0.005) Loss 2.7473 (2.6544) Prec@1 35.000 (35.948) Prec@5 62.500 (66.430) Epoch: [7][830/11272] Time 0.888 (0.839) Data 0.001 (0.004) Loss 2.8972 (2.6549) Prec@1 33.125 (35.943) Prec@5 61.250 (66.410) Epoch: [7][840/11272] Time 0.770 (0.839) Data 0.002 (0.004) Loss 2.5411 (2.6553) Prec@1 38.125 (35.942) Prec@5 67.500 (66.396) Epoch: [7][850/11272] Time 0.779 (0.839) Data 0.001 (0.004) Loss 2.6210 (2.6555) Prec@1 38.750 (35.943) Prec@5 61.250 (66.379) Epoch: [7][860/11272] Time 0.882 (0.839) Data 0.002 (0.004) Loss 2.9200 (2.6563) Prec@1 33.125 (35.928) Prec@5 63.125 (66.351) Epoch: [7][870/11272] Time 0.914 (0.839) Data 0.002 (0.004) Loss 2.6913 (2.6552) Prec@1 34.375 (35.941) Prec@5 68.125 (66.378) Epoch: [7][880/11272] Time 0.757 (0.839) Data 0.001 (0.004) Loss 3.0415 (2.6559) Prec@1 27.500 (35.934) Prec@5 59.375 (66.361) Epoch: [7][890/11272] Time 0.893 (0.839) Data 0.002 (0.004) Loss 2.4831 (2.6549) Prec@1 37.500 (35.948) Prec@5 71.250 (66.385) Epoch: [7][900/11272] Time 0.875 (0.839) Data 0.002 (0.004) Loss 2.6080 (2.6549) Prec@1 36.250 (35.950) Prec@5 67.500 (66.396) Epoch: [7][910/11272] Time 0.772 (0.839) Data 0.001 (0.004) Loss 2.5310 (2.6551) Prec@1 36.875 (35.969) Prec@5 66.875 (66.401) Epoch: [7][920/11272] Time 0.755 (0.839) Data 0.002 (0.004) Loss 2.9208 (2.6555) Prec@1 26.250 (35.941) Prec@5 60.000 (66.403) Epoch: [7][930/11272] Time 0.942 (0.838) Data 0.001 (0.004) Loss 2.7021 (2.6569) Prec@1 36.875 (35.910) Prec@5 65.000 (66.369) Epoch: [7][940/11272] Time 0.873 (0.838) Data 0.002 (0.004) Loss 2.5229 (2.6564) Prec@1 36.875 (35.929) Prec@5 68.750 (66.380) Epoch: [7][950/11272] Time 0.775 (0.838) Data 0.001 (0.004) Loss 2.6971 (2.6559) Prec@1 35.625 (35.917) Prec@5 63.750 (66.385) Epoch: [7][960/11272] Time 0.751 (0.838) Data 0.002 (0.004) Loss 2.3503 (2.6553) Prec@1 45.000 (35.917) Prec@5 73.125 (66.388) Epoch: [7][970/11272] Time 0.852 (0.838) Data 0.001 (0.004) Loss 2.4771 (2.6553) Prec@1 39.375 (35.924) Prec@5 71.250 (66.390) Epoch: [7][980/11272] Time 0.932 (0.838) Data 0.002 (0.004) Loss 2.7702 (2.6556) Prec@1 34.375 (35.933) Prec@5 64.375 (66.394) Epoch: [7][990/11272] Time 0.748 (0.838) Data 0.001 (0.004) Loss 2.5590 (2.6562) Prec@1 32.500 (35.905) Prec@5 71.250 (66.397) Epoch: [7][1000/11272] Time 0.769 (0.838) Data 0.002 (0.004) Loss 2.2450 (2.6558) Prec@1 43.125 (35.914) Prec@5 72.500 (66.396) Epoch: [7][1010/11272] Time 0.870 (0.838) Data 0.001 (0.004) Loss 2.5278 (2.6550) Prec@1 34.375 (35.917) Prec@5 66.250 (66.416) Epoch: [7][1020/11272] Time 0.765 (0.838) Data 0.004 (0.004) Loss 2.5830 (2.6548) Prec@1 39.375 (35.915) Prec@5 70.000 (66.429) Epoch: [7][1030/11272] Time 0.783 (0.838) Data 0.001 (0.004) Loss 2.6298 (2.6554) Prec@1 36.875 (35.909) Prec@5 70.625 (66.422) Epoch: [7][1040/11272] Time 0.911 (0.838) Data 0.002 (0.004) Loss 2.6822 (2.6553) Prec@1 36.250 (35.910) Prec@5 66.875 (66.427) Epoch: [7][1050/11272] Time 0.964 (0.838) Data 0.001 (0.004) Loss 2.8260 (2.6559) Prec@1 33.750 (35.918) Prec@5 63.125 (66.409) Epoch: [7][1060/11272] Time 0.813 (0.838) Data 0.002 (0.004) Loss 2.4997 (2.6555) Prec@1 40.000 (35.926) Prec@5 68.125 (66.410) Epoch: [7][1070/11272] Time 0.756 (0.838) Data 0.002 (0.004) Loss 2.5472 (2.6559) Prec@1 36.875 (35.907) Prec@5 68.750 (66.394) Epoch: [7][1080/11272] Time 0.920 (0.838) Data 0.002 (0.004) Loss 2.3764 (2.6553) Prec@1 42.500 (35.914) Prec@5 69.375 (66.402) Epoch: [7][1090/11272] Time 0.914 (0.838) Data 0.001 (0.004) Loss 2.7861 (2.6559) Prec@1 34.375 (35.907) Prec@5 63.750 (66.398) Epoch: [7][1100/11272] Time 0.730 (0.838) Data 0.002 (0.004) Loss 2.7882 (2.6558) Prec@1 31.250 (35.907) Prec@5 63.125 (66.398) Epoch: [7][1110/11272] Time 0.757 (0.838) Data 0.001 (0.004) Loss 2.8969 (2.6564) Prec@1 30.000 (35.894) Prec@5 58.125 (66.384) Epoch: [7][1120/11272] Time 0.887 (0.838) Data 0.002 (0.004) Loss 2.6749 (2.6565) Prec@1 36.250 (35.899) Prec@5 67.500 (66.393) Epoch: [7][1130/11272] Time 0.931 (0.838) Data 0.001 (0.004) Loss 2.6345 (2.6560) Prec@1 38.125 (35.886) Prec@5 64.375 (66.398) Epoch: [7][1140/11272] Time 0.755 (0.838) Data 0.001 (0.004) Loss 2.2862 (2.6557) Prec@1 44.375 (35.895) Prec@5 72.500 (66.395) Epoch: [7][1150/11272] Time 0.911 (0.838) Data 0.002 (0.004) Loss 2.5420 (2.6563) Prec@1 35.625 (35.885) Prec@5 72.500 (66.388) Epoch: [7][1160/11272] Time 0.914 (0.838) Data 0.002 (0.004) Loss 2.7049 (2.6568) Prec@1 35.000 (35.873) Prec@5 63.125 (66.372) Epoch: [7][1170/11272] Time 0.829 (0.838) Data 0.001 (0.004) Loss 2.5866 (2.6564) Prec@1 38.750 (35.863) Prec@5 65.000 (66.378) Epoch: [7][1180/11272] Time 0.736 (0.838) Data 0.001 (0.004) Loss 2.5188 (2.6565) Prec@1 36.875 (35.858) Prec@5 70.000 (66.373) Epoch: [7][1190/11272] Time 0.880 (0.838) Data 0.001 (0.004) Loss 2.7254 (2.6557) Prec@1 33.125 (35.874) Prec@5 65.625 (66.390) Epoch: [7][1200/11272] Time 0.916 (0.838) Data 0.002 (0.004) Loss 2.6492 (2.6561) Prec@1 38.750 (35.865) Prec@5 66.250 (66.390) Epoch: [7][1210/11272] Time 0.741 (0.838) Data 0.001 (0.004) Loss 2.5636 (2.6558) Prec@1 35.625 (35.874) Prec@5 68.750 (66.400) Epoch: [7][1220/11272] Time 0.731 (0.838) Data 0.002 (0.004) Loss 2.4933 (2.6558) Prec@1 38.750 (35.879) Prec@5 73.125 (66.403) Epoch: [7][1230/11272] Time 0.900 (0.838) Data 0.001 (0.004) Loss 2.6054 (2.6557) Prec@1 33.750 (35.872) Prec@5 66.875 (66.408) Epoch: [7][1240/11272] Time 0.906 (0.838) Data 0.002 (0.004) Loss 2.4832 (2.6561) Prec@1 38.750 (35.863) Prec@5 66.875 (66.408) Epoch: [7][1250/11272] Time 0.812 (0.838) Data 0.001 (0.004) Loss 2.6771 (2.6562) Prec@1 36.250 (35.852) Prec@5 68.750 (66.414) Epoch: [7][1260/11272] Time 0.739 (0.838) Data 0.002 (0.004) Loss 3.0502 (2.6558) Prec@1 29.375 (35.869) Prec@5 56.250 (66.417) Epoch: [7][1270/11272] Time 0.892 (0.838) Data 0.002 (0.004) Loss 2.5787 (2.6551) Prec@1 36.250 (35.873) Prec@5 66.250 (66.433) Epoch: [7][1280/11272] Time 0.923 (0.838) Data 0.002 (0.004) Loss 2.7144 (2.6554) Prec@1 35.625 (35.869) Prec@5 62.500 (66.433) Epoch: [7][1290/11272] Time 0.783 (0.838) Data 0.001 (0.003) Loss 2.9286 (2.6555) Prec@1 30.625 (35.867) Prec@5 59.375 (66.439) Epoch: [7][1300/11272] Time 0.942 (0.838) Data 0.001 (0.003) Loss 2.7284 (2.6557) Prec@1 36.250 (35.871) Prec@5 60.625 (66.434) Epoch: [7][1310/11272] Time 0.965 (0.838) Data 0.001 (0.003) Loss 2.7388 (2.6555) Prec@1 33.125 (35.864) Prec@5 64.375 (66.440) Epoch: [7][1320/11272] Time 0.727 (0.838) Data 0.002 (0.003) Loss 2.8630 (2.6557) Prec@1 33.125 (35.869) Prec@5 65.000 (66.432) Epoch: [7][1330/11272] Time 0.746 (0.838) Data 0.001 (0.003) Loss 2.4486 (2.6556) Prec@1 45.000 (35.873) Prec@5 68.125 (66.435) Epoch: [7][1340/11272] Time 0.878 (0.838) Data 0.002 (0.003) Loss 2.5837 (2.6555) Prec@1 34.375 (35.880) Prec@5 71.250 (66.442) Epoch: [7][1350/11272] Time 0.893 (0.838) Data 0.001 (0.003) Loss 2.4065 (2.6557) Prec@1 43.750 (35.885) Prec@5 69.375 (66.437) Epoch: [7][1360/11272] Time 0.789 (0.838) Data 0.002 (0.003) Loss 2.6841 (2.6554) Prec@1 34.375 (35.890) Prec@5 67.500 (66.441) Epoch: [7][1370/11272] Time 0.800 (0.838) Data 0.001 (0.003) Loss 3.0744 (2.6561) Prec@1 28.750 (35.883) Prec@5 55.000 (66.430) Epoch: [7][1380/11272] Time 0.911 (0.838) Data 0.002 (0.003) Loss 2.6934 (2.6562) Prec@1 32.500 (35.885) Prec@5 66.250 (66.429) Epoch: [7][1390/11272] Time 0.940 (0.838) Data 0.001 (0.003) Loss 2.5646 (2.6566) Prec@1 39.375 (35.880) Prec@5 75.000 (66.421) Epoch: [7][1400/11272] Time 0.755 (0.838) Data 0.002 (0.003) Loss 2.5376 (2.6561) Prec@1 38.125 (35.893) Prec@5 71.250 (66.441) Epoch: [7][1410/11272] Time 0.780 (0.838) Data 0.002 (0.003) Loss 2.8208 (2.6563) Prec@1 36.250 (35.904) Prec@5 65.625 (66.432) Epoch: [7][1420/11272] Time 0.925 (0.838) Data 0.002 (0.003) Loss 2.4689 (2.6561) Prec@1 40.000 (35.899) Prec@5 70.625 (66.444) Epoch: [7][1430/11272] Time 0.762 (0.838) Data 0.001 (0.003) Loss 3.0017 (2.6565) Prec@1 32.500 (35.894) Prec@5 60.000 (66.436) Epoch: [7][1440/11272] Time 0.774 (0.838) Data 0.002 (0.003) Loss 2.5397 (2.6561) Prec@1 40.625 (35.914) Prec@5 70.625 (66.435) Epoch: [7][1450/11272] Time 0.871 (0.838) Data 0.001 (0.003) Loss 2.6785 (2.6561) Prec@1 32.500 (35.910) Prec@5 68.125 (66.443) Epoch: [7][1460/11272] Time 0.879 (0.838) Data 0.002 (0.003) Loss 2.6651 (2.6557) Prec@1 34.375 (35.919) Prec@5 64.375 (66.455) Epoch: [7][1470/11272] Time 0.748 (0.838) Data 0.001 (0.003) Loss 2.5135 (2.6552) Prec@1 37.500 (35.933) Prec@5 69.375 (66.474) Epoch: [7][1480/11272] Time 0.743 (0.838) Data 0.002 (0.003) Loss 2.6940 (2.6557) Prec@1 33.125 (35.924) Prec@5 63.750 (66.470) Epoch: [7][1490/11272] Time 0.894 (0.838) Data 0.001 (0.003) Loss 2.7141 (2.6556) Prec@1 33.750 (35.919) Prec@5 65.000 (66.468) Epoch: [7][1500/11272] Time 0.849 (0.838) Data 0.002 (0.003) Loss 2.7014 (2.6557) Prec@1 37.500 (35.914) Prec@5 65.000 (66.461) Epoch: [7][1510/11272] Time 0.746 (0.838) Data 0.002 (0.003) Loss 2.7003 (2.6560) Prec@1 37.500 (35.903) Prec@5 62.500 (66.460) Epoch: [7][1520/11272] Time 0.749 (0.838) Data 0.002 (0.003) Loss 2.7188 (2.6556) Prec@1 35.625 (35.909) Prec@5 64.375 (66.467) Epoch: [7][1530/11272] Time 0.886 (0.838) Data 0.001 (0.003) Loss 2.6813 (2.6558) Prec@1 32.500 (35.903) Prec@5 63.125 (66.458) Epoch: [7][1540/11272] Time 0.925 (0.838) Data 0.002 (0.003) Loss 2.5408 (2.6554) Prec@1 34.375 (35.912) Prec@5 67.500 (66.455) Epoch: [7][1550/11272] Time 0.762 (0.838) Data 0.002 (0.003) Loss 2.5826 (2.6556) Prec@1 40.625 (35.916) Prec@5 68.125 (66.447) Epoch: [7][1560/11272] Time 0.841 (0.838) Data 0.002 (0.003) Loss 2.7217 (2.6552) Prec@1 32.500 (35.919) Prec@5 69.375 (66.461) Epoch: [7][1570/11272] Time 0.896 (0.838) Data 0.001 (0.003) Loss 2.5595 (2.6553) Prec@1 39.375 (35.914) Prec@5 65.625 (66.455) Epoch: [7][1580/11272] Time 0.752 (0.838) Data 0.002 (0.003) Loss 2.5529 (2.6558) Prec@1 36.250 (35.903) Prec@5 69.375 (66.449) Epoch: [7][1590/11272] Time 0.741 (0.838) Data 0.001 (0.003) Loss 2.5559 (2.6558) Prec@1 36.875 (35.906) Prec@5 66.875 (66.447) Epoch: [7][1600/11272] Time 0.902 (0.838) Data 0.002 (0.003) Loss 2.6878 (2.6557) Prec@1 38.125 (35.907) Prec@5 67.500 (66.454) Epoch: [7][1610/11272] Time 0.887 (0.838) Data 0.001 (0.003) Loss 2.8966 (2.6562) Prec@1 31.875 (35.901) Prec@5 60.625 (66.441) Epoch: [7][1620/11272] Time 0.753 (0.838) Data 0.002 (0.003) Loss 3.2204 (2.6569) Prec@1 25.625 (35.901) Prec@5 54.375 (66.423) Epoch: [7][1630/11272] Time 0.769 (0.838) Data 0.001 (0.003) Loss 2.5252 (2.6570) Prec@1 37.500 (35.902) Prec@5 69.375 (66.419) Epoch: [7][1640/11272] Time 0.916 (0.838) Data 0.002 (0.003) Loss 2.9086 (2.6574) Prec@1 31.875 (35.892) Prec@5 63.750 (66.416) Epoch: [7][1650/11272] Time 0.945 (0.838) Data 0.002 (0.003) Loss 2.6784 (2.6576) Prec@1 36.875 (35.883) Prec@5 63.125 (66.414) Epoch: [7][1660/11272] Time 0.739 (0.838) Data 0.002 (0.003) Loss 2.6396 (2.6582) Prec@1 34.375 (35.862) Prec@5 65.625 (66.410) Epoch: [7][1670/11272] Time 0.764 (0.837) Data 0.001 (0.003) Loss 2.5968 (2.6584) Prec@1 39.375 (35.863) Prec@5 68.750 (66.409) Epoch: [7][1680/11272] Time 0.926 (0.838) Data 0.002 (0.003) Loss 2.4454 (2.6573) Prec@1 43.750 (35.891) Prec@5 71.250 (66.431) Epoch: [7][1690/11272] Time 0.767 (0.837) Data 0.004 (0.003) Loss 2.6380 (2.6570) Prec@1 31.875 (35.896) Prec@5 67.500 (66.438) Epoch: [7][1700/11272] Time 0.785 (0.837) Data 0.002 (0.003) Loss 2.5859 (2.6569) Prec@1 42.500 (35.903) Prec@5 68.750 (66.447) Epoch: [7][1710/11272] Time 0.899 (0.838) Data 0.001 (0.003) Loss 2.4862 (2.6565) Prec@1 40.625 (35.907) Prec@5 69.375 (66.462) Epoch: [7][1720/11272] Time 0.869 (0.838) Data 0.002 (0.003) Loss 2.3761 (2.6563) Prec@1 38.750 (35.911) Prec@5 72.500 (66.472) Epoch: [7][1730/11272] Time 0.734 (0.838) Data 0.001 (0.003) Loss 2.5374 (2.6562) Prec@1 45.000 (35.902) Prec@5 71.875 (66.477) Epoch: [7][1740/11272] Time 0.760 (0.838) Data 0.002 (0.003) Loss 2.7258 (2.6562) Prec@1 33.125 (35.897) Prec@5 66.875 (66.477) Epoch: [7][1750/11272] Time 0.898 (0.838) Data 0.001 (0.003) Loss 2.9791 (2.6566) Prec@1 25.625 (35.881) Prec@5 61.250 (66.467) Epoch: [7][1760/11272] Time 0.906 (0.838) Data 0.002 (0.003) Loss 2.6191 (2.6565) Prec@1 38.125 (35.882) Prec@5 70.000 (66.470) Epoch: [7][1770/11272] Time 0.727 (0.837) Data 0.001 (0.003) Loss 2.5506 (2.6567) Prec@1 37.500 (35.873) Prec@5 63.125 (66.457) Epoch: [7][1780/11272] Time 0.757 (0.837) Data 0.001 (0.003) Loss 2.7423 (2.6571) Prec@1 30.625 (35.856) Prec@5 63.125 (66.448) Epoch: [7][1790/11272] Time 0.875 (0.837) Data 0.001 (0.003) Loss 2.6263 (2.6575) Prec@1 38.750 (35.847) Prec@5 65.625 (66.439) Epoch: [7][1800/11272] Time 0.867 (0.837) Data 0.002 (0.003) Loss 2.7712 (2.6572) Prec@1 38.750 (35.850) Prec@5 60.625 (66.438) Epoch: [7][1810/11272] Time 0.800 (0.837) Data 0.001 (0.003) Loss 2.4956 (2.6576) Prec@1 41.250 (35.840) Prec@5 69.375 (66.433) Epoch: [7][1820/11272] Time 0.900 (0.837) Data 0.002 (0.003) Loss 2.7733 (2.6572) Prec@1 33.750 (35.843) Prec@5 63.125 (66.443) Epoch: [7][1830/11272] Time 0.905 (0.837) Data 0.001 (0.003) Loss 2.6710 (2.6575) Prec@1 38.125 (35.844) Prec@5 63.125 (66.433) Epoch: [7][1840/11272] Time 0.740 (0.837) Data 0.002 (0.003) Loss 2.4969 (2.6573) Prec@1 38.125 (35.841) Prec@5 70.000 (66.439) Epoch: [7][1850/11272] Time 0.724 (0.837) Data 0.001 (0.003) Loss 2.8554 (2.6577) Prec@1 28.750 (35.832) Prec@5 61.875 (66.435) Epoch: [7][1860/11272] Time 0.923 (0.837) Data 0.002 (0.003) Loss 2.6671 (2.6575) Prec@1 35.625 (35.835) Prec@5 65.000 (66.439) Epoch: [7][1870/11272] Time 0.882 (0.837) Data 0.001 (0.003) Loss 2.9840 (2.6575) Prec@1 33.125 (35.836) Prec@5 57.500 (66.440) Epoch: [7][1880/11272] Time 0.742 (0.837) Data 0.002 (0.003) Loss 2.7696 (2.6570) Prec@1 31.250 (35.848) Prec@5 63.750 (66.450) Epoch: [7][1890/11272] Time 0.793 (0.837) Data 0.001 (0.003) Loss 2.6800 (2.6565) Prec@1 29.375 (35.860) Prec@5 63.750 (66.466) Epoch: [7][1900/11272] Time 0.885 (0.837) Data 0.002 (0.003) Loss 2.5755 (2.6566) Prec@1 36.250 (35.859) Prec@5 65.000 (66.468) Epoch: [7][1910/11272] Time 0.927 (0.837) Data 0.001 (0.003) Loss 2.7832 (2.6568) Prec@1 35.000 (35.853) Prec@5 65.000 (66.461) Epoch: [7][1920/11272] Time 0.738 (0.837) Data 0.002 (0.003) Loss 2.5087 (2.6565) Prec@1 36.250 (35.850) Prec@5 71.250 (66.469) Epoch: [7][1930/11272] Time 0.760 (0.837) Data 0.001 (0.003) Loss 2.6312 (2.6562) Prec@1 35.000 (35.854) Prec@5 68.125 (66.476) Epoch: [7][1940/11272] Time 0.909 (0.837) Data 0.002 (0.003) Loss 2.5353 (2.6560) Prec@1 36.875 (35.860) Prec@5 70.000 (66.487) Epoch: [7][1950/11272] Time 0.795 (0.837) Data 0.003 (0.003) Loss 2.7259 (2.6559) Prec@1 35.625 (35.858) Prec@5 66.250 (66.501) Epoch: [7][1960/11272] Time 0.737 (0.837) Data 0.002 (0.003) Loss 2.7318 (2.6559) Prec@1 31.250 (35.851) Prec@5 63.750 (66.496) Epoch: [7][1970/11272] Time 0.917 (0.837) Data 0.001 (0.003) Loss 2.6903 (2.6565) Prec@1 39.375 (35.844) Prec@5 61.250 (66.482) Epoch: [7][1980/11272] Time 0.889 (0.837) Data 0.002 (0.003) Loss 2.6918 (2.6568) Prec@1 33.125 (35.832) Prec@5 67.500 (66.476) Epoch: [7][1990/11272] Time 0.775 (0.837) Data 0.001 (0.003) Loss 2.6332 (2.6566) Prec@1 33.125 (35.826) Prec@5 66.250 (66.479) Epoch: [7][2000/11272] Time 0.741 (0.837) Data 0.003 (0.003) Loss 2.8945 (2.6568) Prec@1 38.750 (35.831) Prec@5 60.625 (66.476) Epoch: [7][2010/11272] Time 0.956 (0.837) Data 0.001 (0.003) Loss 2.6660 (2.6568) Prec@1 37.500 (35.829) Prec@5 63.125 (66.482) Epoch: [7][2020/11272] Time 0.905 (0.837) Data 0.002 (0.003) Loss 2.5173 (2.6568) Prec@1 41.875 (35.829) Prec@5 68.750 (66.491) Epoch: [7][2030/11272] Time 0.746 (0.837) Data 0.002 (0.003) Loss 2.5432 (2.6568) Prec@1 38.750 (35.825) Prec@5 68.750 (66.490) Epoch: [7][2040/11272] Time 0.733 (0.837) Data 0.002 (0.003) Loss 2.8786 (2.6567) Prec@1 30.000 (35.828) Prec@5 70.625 (66.500) Epoch: [7][2050/11272] Time 0.908 (0.837) Data 0.001 (0.003) Loss 2.6337 (2.6565) Prec@1 37.500 (35.825) Prec@5 66.875 (66.508) Epoch: [7][2060/11272] Time 0.927 (0.837) Data 0.002 (0.003) Loss 2.7100 (2.6561) Prec@1 35.625 (35.828) Prec@5 65.000 (66.518) Epoch: [7][2070/11272] Time 0.752 (0.837) Data 0.001 (0.003) Loss 2.5335 (2.6561) Prec@1 38.125 (35.830) Prec@5 66.875 (66.516) Epoch: [7][2080/11272] Time 0.863 (0.837) Data 0.002 (0.003) Loss 2.6164 (2.6560) Prec@1 42.500 (35.839) Prec@5 65.625 (66.519) Epoch: [7][2090/11272] Time 0.872 (0.837) Data 0.001 (0.003) Loss 2.6449 (2.6559) Prec@1 40.625 (35.839) Prec@5 68.750 (66.512) Epoch: [7][2100/11272] Time 0.784 (0.837) Data 0.002 (0.003) Loss 2.7541 (2.6557) Prec@1 35.000 (35.840) Prec@5 66.250 (66.513) Epoch: [7][2110/11272] Time 0.764 (0.837) Data 0.002 (0.003) Loss 2.9770 (2.6561) Prec@1 31.250 (35.831) Prec@5 55.000 (66.499) Epoch: [7][2120/11272] Time 0.868 (0.837) Data 0.002 (0.003) Loss 2.7985 (2.6563) Prec@1 36.250 (35.829) Prec@5 61.875 (66.491) Epoch: [7][2130/11272] Time 0.922 (0.837) Data 0.001 (0.003) Loss 2.6511 (2.6566) Prec@1 36.250 (35.823) Prec@5 69.375 (66.483) Epoch: [7][2140/11272] Time 0.717 (0.837) Data 0.002 (0.003) Loss 2.8309 (2.6569) Prec@1 35.625 (35.833) Prec@5 64.375 (66.477) Epoch: [7][2150/11272] Time 0.732 (0.837) Data 0.002 (0.003) Loss 2.6923 (2.6572) Prec@1 34.375 (35.830) Prec@5 65.625 (66.473) Epoch: [7][2160/11272] Time 0.875 (0.837) Data 0.002 (0.003) Loss 2.8088 (2.6574) Prec@1 29.375 (35.822) Prec@5 64.375 (66.465) Epoch: [7][2170/11272] Time 0.911 (0.837) Data 0.001 (0.003) Loss 2.5757 (2.6570) Prec@1 36.250 (35.832) Prec@5 68.750 (66.474) Epoch: [7][2180/11272] Time 0.796 (0.837) Data 0.002 (0.003) Loss 2.6481 (2.6567) Prec@1 34.375 (35.840) Prec@5 69.375 (66.479) Epoch: [7][2190/11272] Time 0.755 (0.837) Data 0.001 (0.003) Loss 2.6192 (2.6570) Prec@1 38.125 (35.834) Prec@5 65.625 (66.473) Epoch: [7][2200/11272] Time 0.930 (0.837) Data 0.002 (0.003) Loss 2.7903 (2.6572) Prec@1 28.750 (35.829) Prec@5 63.750 (66.471) Epoch: [7][2210/11272] Time 0.915 (0.837) Data 0.001 (0.003) Loss 2.7741 (2.6572) Prec@1 37.500 (35.829) Prec@5 61.875 (66.469) Epoch: [7][2220/11272] Time 0.814 (0.837) Data 0.002 (0.003) Loss 2.7850 (2.6572) Prec@1 35.625 (35.833) Prec@5 61.875 (66.470) Epoch: [7][2230/11272] Time 0.960 (0.837) Data 0.001 (0.003) Loss 2.6798 (2.6576) Prec@1 33.125 (35.826) Prec@5 69.375 (66.460) Epoch: [7][2240/11272] Time 0.950 (0.837) Data 0.002 (0.003) Loss 2.6845 (2.6578) Prec@1 40.625 (35.825) Prec@5 66.250 (66.456) Epoch: [7][2250/11272] Time 0.797 (0.837) Data 0.001 (0.003) Loss 2.7250 (2.6578) Prec@1 38.125 (35.827) Prec@5 62.500 (66.455) Epoch: [7][2260/11272] Time 0.796 (0.837) Data 0.002 (0.003) Loss 2.6374 (2.6577) Prec@1 36.250 (35.834) Prec@5 70.000 (66.458) Epoch: [7][2270/11272] Time 0.900 (0.837) Data 0.002 (0.003) Loss 2.6331 (2.6575) Prec@1 36.875 (35.832) Prec@5 67.500 (66.463) Epoch: [7][2280/11272] Time 0.917 (0.837) Data 0.002 (0.003) Loss 2.6163 (2.6577) Prec@1 35.625 (35.825) Prec@5 65.000 (66.458) Epoch: [7][2290/11272] Time 0.709 (0.837) Data 0.001 (0.003) Loss 2.7265 (2.6574) Prec@1 37.500 (35.828) Prec@5 60.625 (66.460) Epoch: [7][2300/11272] Time 0.798 (0.837) Data 0.002 (0.003) Loss 2.8768 (2.6573) Prec@1 32.500 (35.824) Prec@5 61.875 (66.460) Epoch: [7][2310/11272] Time 0.938 (0.837) Data 0.001 (0.003) Loss 2.6081 (2.6576) Prec@1 36.250 (35.818) Prec@5 66.875 (66.454) Epoch: [7][2320/11272] Time 0.923 (0.837) Data 0.002 (0.003) Loss 2.8629 (2.6575) Prec@1 33.750 (35.818) Prec@5 58.750 (66.451) Epoch: [7][2330/11272] Time 0.750 (0.837) Data 0.002 (0.003) Loss 2.8189 (2.6576) Prec@1 34.375 (35.823) Prec@5 61.250 (66.449) Epoch: [7][2340/11272] Time 0.773 (0.837) Data 0.002 (0.003) Loss 2.6305 (2.6574) Prec@1 36.875 (35.829) Prec@5 64.375 (66.452) Epoch: [7][2350/11272] Time 0.986 (0.837) Data 0.001 (0.003) Loss 2.5322 (2.6571) Prec@1 37.500 (35.832) Prec@5 68.125 (66.462) Epoch: [7][2360/11272] Time 0.747 (0.837) Data 0.002 (0.003) Loss 2.5632 (2.6569) Prec@1 36.250 (35.833) Prec@5 69.375 (66.468) Epoch: [7][2370/11272] Time 0.770 (0.837) Data 0.001 (0.003) Loss 2.5088 (2.6569) Prec@1 40.000 (35.832) Prec@5 70.000 (66.465) Epoch: [7][2380/11272] Time 0.945 (0.837) Data 0.002 (0.003) Loss 2.5718 (2.6572) Prec@1 35.625 (35.829) Prec@5 68.750 (66.457) Epoch: [7][2390/11272] Time 0.892 (0.837) Data 0.002 (0.003) Loss 2.8975 (2.6577) Prec@1 31.250 (35.825) Prec@5 61.875 (66.445) Epoch: [7][2400/11272] Time 0.764 (0.837) Data 0.003 (0.003) Loss 2.7438 (2.6576) Prec@1 35.000 (35.829) Prec@5 63.125 (66.442) Epoch: [7][2410/11272] Time 0.821 (0.837) Data 0.001 (0.003) Loss 2.6928 (2.6574) Prec@1 38.750 (35.832) Prec@5 63.750 (66.446) Epoch: [7][2420/11272] Time 0.915 (0.837) Data 0.002 (0.003) Loss 2.7462 (2.6577) Prec@1 33.125 (35.831) Prec@5 66.250 (66.445) Epoch: [7][2430/11272] Time 0.888 (0.837) Data 0.001 (0.003) Loss 2.5340 (2.6579) Prec@1 36.250 (35.825) Prec@5 68.750 (66.438) Epoch: [7][2440/11272] Time 0.738 (0.837) Data 0.002 (0.003) Loss 2.4931 (2.6581) Prec@1 38.125 (35.821) Prec@5 73.750 (66.434) Epoch: [7][2450/11272] Time 0.777 (0.837) Data 0.001 (0.003) Loss 2.3235 (2.6583) Prec@1 41.875 (35.817) Prec@5 72.500 (66.428) Epoch: [7][2460/11272] Time 0.886 (0.837) Data 0.002 (0.003) Loss 2.4900 (2.6582) Prec@1 40.625 (35.820) Prec@5 68.125 (66.430) Epoch: [7][2470/11272] Time 0.927 (0.837) Data 0.002 (0.003) Loss 2.4709 (2.6584) Prec@1 41.875 (35.820) Prec@5 70.000 (66.422) Epoch: [7][2480/11272] Time 0.805 (0.837) Data 0.002 (0.003) Loss 2.8068 (2.6584) Prec@1 30.625 (35.823) Prec@5 60.625 (66.421) Epoch: [7][2490/11272] Time 0.902 (0.837) Data 0.002 (0.003) Loss 2.6279 (2.6586) Prec@1 40.625 (35.822) Prec@5 70.625 (66.420) Epoch: [7][2500/11272] Time 0.874 (0.837) Data 0.002 (0.003) Loss 2.4412 (2.6584) Prec@1 40.625 (35.821) Prec@5 73.750 (66.425) Epoch: [7][2510/11272] Time 0.788 (0.837) Data 0.001 (0.003) Loss 2.5450 (2.6584) Prec@1 38.750 (35.825) Prec@5 66.250 (66.422) Epoch: [7][2520/11272] Time 0.770 (0.837) Data 0.002 (0.003) Loss 2.8998 (2.6581) Prec@1 30.625 (35.828) Prec@5 60.000 (66.428) Epoch: [7][2530/11272] Time 0.886 (0.837) Data 0.001 (0.003) Loss 2.7070 (2.6583) Prec@1 40.000 (35.825) Prec@5 62.500 (66.426) Epoch: [7][2540/11272] Time 0.897 (0.837) Data 0.002 (0.003) Loss 2.4932 (2.6586) Prec@1 37.500 (35.818) Prec@5 71.875 (66.418) Epoch: [7][2550/11272] Time 0.821 (0.837) Data 0.001 (0.003) Loss 2.4998 (2.6587) Prec@1 38.125 (35.814) Prec@5 68.125 (66.415) Epoch: [7][2560/11272] Time 0.767 (0.837) Data 0.002 (0.003) Loss 2.4878 (2.6584) Prec@1 38.125 (35.820) Prec@5 72.500 (66.423) Epoch: [7][2570/11272] Time 0.917 (0.837) Data 0.001 (0.003) Loss 2.4558 (2.6583) Prec@1 44.375 (35.818) Prec@5 71.875 (66.427) Epoch: [7][2580/11272] Time 0.925 (0.837) Data 0.002 (0.003) Loss 2.4462 (2.6582) Prec@1 37.500 (35.821) Prec@5 70.625 (66.429) Epoch: [7][2590/11272] Time 0.738 (0.837) Data 0.001 (0.003) Loss 2.5510 (2.6582) Prec@1 34.375 (35.811) Prec@5 72.500 (66.432) Epoch: [7][2600/11272] Time 0.745 (0.837) Data 0.002 (0.003) Loss 2.2981 (2.6581) Prec@1 36.875 (35.810) Prec@5 71.875 (66.437) Epoch: [7][2610/11272] Time 0.946 (0.837) Data 0.001 (0.003) Loss 2.3669 (2.6579) Prec@1 41.875 (35.809) Prec@5 71.250 (66.442) Epoch: [7][2620/11272] Time 0.730 (0.837) Data 0.004 (0.003) Loss 2.6069 (2.6578) Prec@1 38.125 (35.809) Prec@5 68.750 (66.442) Epoch: [7][2630/11272] Time 0.765 (0.836) Data 0.001 (0.003) Loss 2.5636 (2.6580) Prec@1 36.875 (35.806) Prec@5 67.500 (66.433) Epoch: [7][2640/11272] Time 0.875 (0.837) Data 0.002 (0.003) Loss 2.6992 (2.6582) Prec@1 38.750 (35.801) Prec@5 67.500 (66.431) Epoch: [7][2650/11272] Time 0.957 (0.837) Data 0.002 (0.003) Loss 2.7313 (2.6582) Prec@1 34.375 (35.805) Prec@5 64.375 (66.432) Epoch: [7][2660/11272] Time 0.765 (0.837) Data 0.001 (0.003) Loss 2.7491 (2.6581) Prec@1 36.250 (35.802) Prec@5 65.625 (66.437) Epoch: [7][2670/11272] Time 0.764 (0.837) Data 0.002 (0.003) Loss 2.7607 (2.6582) Prec@1 31.875 (35.799) Prec@5 68.750 (66.439) Epoch: [7][2680/11272] Time 0.933 (0.837) Data 0.002 (0.003) Loss 2.8760 (2.6583) Prec@1 35.000 (35.803) Prec@5 61.875 (66.439) Epoch: [7][2690/11272] Time 0.913 (0.837) Data 0.001 (0.003) Loss 2.4135 (2.6586) Prec@1 37.500 (35.795) Prec@5 69.375 (66.434) Epoch: [7][2700/11272] Time 0.776 (0.837) Data 0.002 (0.003) Loss 2.5582 (2.6586) Prec@1 36.875 (35.793) Prec@5 68.750 (66.437) Epoch: [7][2710/11272] Time 0.740 (0.837) Data 0.001 (0.003) Loss 2.6775 (2.6588) Prec@1 30.000 (35.785) Prec@5 66.250 (66.433) Epoch: [7][2720/11272] Time 0.912 (0.837) Data 0.002 (0.003) Loss 2.5681 (2.6589) Prec@1 37.500 (35.785) Prec@5 66.875 (66.431) Epoch: [7][2730/11272] Time 0.913 (0.837) Data 0.001 (0.003) Loss 2.7260 (2.6592) Prec@1 33.125 (35.776) Prec@5 64.375 (66.421) Epoch: [7][2740/11272] Time 0.783 (0.837) Data 0.002 (0.003) Loss 2.5869 (2.6595) Prec@1 39.375 (35.769) Prec@5 66.250 (66.417) Epoch: [7][2750/11272] Time 0.905 (0.837) Data 0.001 (0.003) Loss 2.6182 (2.6595) Prec@1 36.250 (35.769) Prec@5 66.875 (66.418) Epoch: [7][2760/11272] Time 0.896 (0.837) Data 0.001 (0.003) Loss 2.7122 (2.6598) Prec@1 34.375 (35.761) Prec@5 63.750 (66.410) Epoch: [7][2770/11272] Time 0.775 (0.837) Data 0.001 (0.003) Loss 2.4206 (2.6598) Prec@1 36.875 (35.764) Prec@5 71.250 (66.409) Epoch: [7][2780/11272] Time 0.749 (0.837) Data 0.002 (0.003) Loss 2.6082 (2.6599) Prec@1 32.500 (35.762) Prec@5 71.250 (66.408) Epoch: [7][2790/11272] Time 0.858 (0.837) Data 0.001 (0.003) Loss 2.7283 (2.6599) Prec@1 33.750 (35.763) Prec@5 64.375 (66.406) Epoch: [7][2800/11272] Time 0.921 (0.837) Data 0.002 (0.003) Loss 2.8224 (2.6599) Prec@1 35.625 (35.760) Prec@5 65.625 (66.408) Epoch: [7][2810/11272] Time 0.777 (0.837) Data 0.001 (0.003) Loss 2.5244 (2.6599) Prec@1 39.375 (35.762) Prec@5 70.000 (66.412) Epoch: [7][2820/11272] Time 0.817 (0.837) Data 0.002 (0.003) Loss 2.8120 (2.6598) Prec@1 35.000 (35.769) Prec@5 66.250 (66.413) Epoch: [7][2830/11272] Time 0.903 (0.837) Data 0.002 (0.003) Loss 2.6817 (2.6598) Prec@1 31.875 (35.768) Prec@5 66.875 (66.415) Epoch: [7][2840/11272] Time 0.926 (0.837) Data 0.001 (0.003) Loss 2.7545 (2.6600) Prec@1 35.625 (35.768) Prec@5 64.375 (66.412) Epoch: [7][2850/11272] Time 0.824 (0.837) Data 0.001 (0.003) Loss 2.7530 (2.6598) Prec@1 34.375 (35.776) Prec@5 65.625 (66.412) Epoch: [7][2860/11272] Time 0.790 (0.837) Data 0.003 (0.003) Loss 2.6819 (2.6599) Prec@1 34.375 (35.775) Prec@5 67.500 (66.413) Epoch: [7][2870/11272] Time 0.931 (0.837) Data 0.001 (0.003) Loss 2.6110 (2.6597) Prec@1 35.000 (35.776) Prec@5 71.250 (66.416) Epoch: [7][2880/11272] Time 0.772 (0.837) Data 0.004 (0.003) Loss 2.5528 (2.6596) Prec@1 35.000 (35.778) Prec@5 68.125 (66.417) Epoch: [7][2890/11272] Time 0.770 (0.837) Data 0.001 (0.003) Loss 2.3857 (2.6597) Prec@1 36.250 (35.773) Prec@5 70.625 (66.411) Epoch: [7][2900/11272] Time 0.843 (0.837) Data 0.002 (0.003) Loss 2.6483 (2.6597) Prec@1 36.250 (35.773) Prec@5 66.250 (66.414) Epoch: [7][2910/11272] Time 0.866 (0.837) Data 0.001 (0.002) Loss 2.4904 (2.6593) Prec@1 36.875 (35.778) Prec@5 68.750 (66.425) Epoch: [7][2920/11272] Time 0.734 (0.837) Data 0.002 (0.002) Loss 2.5514 (2.6594) Prec@1 34.375 (35.776) Prec@5 72.500 (66.420) Epoch: [7][2930/11272] Time 0.783 (0.837) Data 0.002 (0.002) Loss 2.6592 (2.6592) Prec@1 35.625 (35.784) Prec@5 66.250 (66.422) Epoch: [7][2940/11272] Time 0.884 (0.837) Data 0.002 (0.002) Loss 2.7331 (2.6590) Prec@1 33.750 (35.785) Prec@5 67.500 (66.427) Epoch: [7][2950/11272] Time 0.896 (0.837) Data 0.001 (0.002) Loss 2.5749 (2.6590) Prec@1 38.750 (35.784) Prec@5 69.375 (66.426) Epoch: [7][2960/11272] Time 0.735 (0.837) Data 0.002 (0.002) Loss 2.5113 (2.6590) Prec@1 31.875 (35.781) Prec@5 68.750 (66.426) Epoch: [7][2970/11272] Time 0.735 (0.837) Data 0.002 (0.002) Loss 2.7303 (2.6592) Prec@1 35.625 (35.781) Prec@5 63.125 (66.422) Epoch: [7][2980/11272] Time 0.928 (0.837) Data 0.001 (0.002) Loss 2.4133 (2.6592) Prec@1 38.125 (35.778) Prec@5 70.625 (66.418) Epoch: [7][2990/11272] Time 0.924 (0.837) Data 0.002 (0.002) Loss 2.8163 (2.6590) Prec@1 34.375 (35.783) Prec@5 64.375 (66.424) Epoch: [7][3000/11272] Time 0.748 (0.837) Data 0.001 (0.002) Loss 2.5366 (2.6588) Prec@1 37.500 (35.790) Prec@5 65.000 (66.426) Epoch: [7][3010/11272] Time 0.863 (0.837) Data 0.002 (0.002) Loss 2.4772 (2.6585) Prec@1 40.000 (35.792) Prec@5 70.000 (66.428) Epoch: [7][3020/11272] Time 0.872 (0.837) Data 0.001 (0.002) Loss 2.6341 (2.6587) Prec@1 36.875 (35.787) Prec@5 61.875 (66.423) Epoch: [7][3030/11272] Time 0.787 (0.837) Data 0.002 (0.002) Loss 2.8325 (2.6587) Prec@1 36.250 (35.788) Prec@5 66.250 (66.424) Epoch: [7][3040/11272] Time 0.752 (0.837) Data 0.002 (0.002) Loss 2.6736 (2.6587) Prec@1 34.375 (35.788) Prec@5 66.875 (66.426) Epoch: [7][3050/11272] Time 0.953 (0.837) Data 0.002 (0.002) Loss 2.6226 (2.6589) Prec@1 42.500 (35.786) Prec@5 66.250 (66.421) Epoch: [7][3060/11272] Time 1.030 (0.837) Data 0.001 (0.002) Loss 2.7954 (2.6590) Prec@1 38.750 (35.789) Prec@5 65.625 (66.420) Epoch: [7][3070/11272] Time 0.736 (0.837) Data 0.002 (0.002) Loss 2.4702 (2.6590) Prec@1 37.500 (35.788) Prec@5 73.125 (66.423) Epoch: [7][3080/11272] Time 0.752 (0.837) Data 0.001 (0.002) Loss 2.6045 (2.6591) Prec@1 38.125 (35.785) Prec@5 66.875 (66.421) Epoch: [7][3090/11272] Time 0.931 (0.837) Data 0.002 (0.002) Loss 2.7585 (2.6592) Prec@1 34.375 (35.784) Prec@5 67.500 (66.421) Epoch: [7][3100/11272] Time 0.937 (0.837) Data 0.001 (0.002) Loss 2.5979 (2.6590) Prec@1 36.875 (35.789) Prec@5 66.250 (66.422) Epoch: [7][3110/11272] Time 0.775 (0.837) Data 0.001 (0.002) Loss 2.8945 (2.6589) Prec@1 37.500 (35.796) Prec@5 64.375 (66.422) Epoch: [7][3120/11272] Time 0.768 (0.837) Data 0.002 (0.002) Loss 2.5342 (2.6590) Prec@1 38.125 (35.790) Prec@5 65.625 (66.419) Epoch: [7][3130/11272] Time 0.885 (0.837) Data 0.002 (0.002) Loss 2.6429 (2.6590) Prec@1 35.000 (35.790) Prec@5 67.500 (66.419) Epoch: [7][3140/11272] Time 0.836 (0.837) Data 0.003 (0.002) Loss 2.5650 (2.6591) Prec@1 40.000 (35.790) Prec@5 68.125 (66.420) Epoch: [7][3150/11272] Time 0.744 (0.837) Data 0.002 (0.002) Loss 2.8267 (2.6591) Prec@1 32.500 (35.788) Prec@5 61.875 (66.421) Epoch: [7][3160/11272] Time 0.888 (0.837) Data 0.001 (0.002) Loss 2.8222 (2.6594) Prec@1 31.250 (35.785) Prec@5 61.250 (66.414) Epoch: [7][3170/11272] Time 0.975 (0.837) Data 0.002 (0.002) Loss 3.0259 (2.6594) Prec@1 28.750 (35.784) Prec@5 60.000 (66.416) Epoch: [7][3180/11272] Time 0.740 (0.837) Data 0.002 (0.002) Loss 2.8816 (2.6595) Prec@1 31.875 (35.780) Prec@5 64.375 (66.413) Epoch: [7][3190/11272] Time 0.755 (0.837) Data 0.002 (0.002) Loss 2.7273 (2.6591) Prec@1 33.750 (35.781) Prec@5 65.000 (66.417) Epoch: [7][3200/11272] Time 0.960 (0.837) Data 0.001 (0.002) Loss 2.5729 (2.6591) Prec@1 37.500 (35.782) Prec@5 71.250 (66.421) Epoch: [7][3210/11272] Time 0.866 (0.837) Data 0.002 (0.002) Loss 2.5657 (2.6593) Prec@1 40.625 (35.781) Prec@5 68.125 (66.418) Epoch: [7][3220/11272] Time 0.762 (0.837) Data 0.002 (0.002) Loss 2.7435 (2.6593) Prec@1 36.250 (35.779) Prec@5 65.625 (66.416) Epoch: [7][3230/11272] Time 0.754 (0.837) Data 0.002 (0.002) Loss 2.7215 (2.6592) Prec@1 35.000 (35.781) Prec@5 63.125 (66.417) Epoch: [7][3240/11272] Time 0.919 (0.837) Data 0.003 (0.002) Loss 2.4103 (2.6591) Prec@1 41.875 (35.781) Prec@5 75.625 (66.420) Epoch: [7][3250/11272] Time 0.948 (0.837) Data 0.002 (0.002) Loss 2.6410 (2.6591) Prec@1 38.750 (35.782) Prec@5 67.500 (66.420) Epoch: [7][3260/11272] Time 0.739 (0.837) Data 0.001 (0.002) Loss 2.7494 (2.6590) Prec@1 35.625 (35.783) Prec@5 68.125 (66.424) Epoch: [7][3270/11272] Time 0.718 (0.837) Data 0.002 (0.002) Loss 2.5760 (2.6588) Prec@1 40.625 (35.788) Prec@5 70.000 (66.428) Epoch: [7][3280/11272] Time 0.920 (0.837) Data 0.002 (0.002) Loss 2.7749 (2.6591) Prec@1 35.000 (35.790) Prec@5 61.875 (66.419) Epoch: [7][3290/11272] Time 0.742 (0.837) Data 0.002 (0.002) Loss 2.6614 (2.6590) Prec@1 37.500 (35.794) Prec@5 66.250 (66.421) Epoch: [7][3300/11272] Time 0.752 (0.837) Data 0.001 (0.002) Loss 2.7292 (2.6589) Prec@1 31.875 (35.796) Prec@5 66.250 (66.426) Epoch: [7][3310/11272] Time 0.942 (0.837) Data 0.002 (0.002) Loss 2.4167 (2.6587) Prec@1 45.000 (35.807) Prec@5 68.125 (66.430) Epoch: [7][3320/11272] Time 0.889 (0.837) Data 0.001 (0.002) Loss 2.6275 (2.6585) Prec@1 36.250 (35.806) Prec@5 68.125 (66.433) Epoch: [7][3330/11272] Time 0.804 (0.837) Data 0.002 (0.002) Loss 2.9003 (2.6586) Prec@1 30.625 (35.805) Prec@5 58.750 (66.427) Epoch: [7][3340/11272] Time 0.786 (0.837) Data 0.001 (0.002) Loss 2.3351 (2.6588) Prec@1 41.875 (35.801) Prec@5 75.000 (66.423) Epoch: [7][3350/11272] Time 0.903 (0.837) Data 0.002 (0.002) Loss 2.6266 (2.6588) Prec@1 35.625 (35.797) Prec@5 65.000 (66.424) Epoch: [7][3360/11272] Time 0.896 (0.837) Data 0.003 (0.002) Loss 2.7961 (2.6589) Prec@1 37.500 (35.800) Prec@5 63.125 (66.423) Epoch: [7][3370/11272] Time 0.744 (0.837) Data 0.002 (0.002) Loss 2.4396 (2.6590) Prec@1 43.750 (35.802) Prec@5 70.000 (66.422) Epoch: [7][3380/11272] Time 0.758 (0.837) Data 0.001 (0.002) Loss 2.5070 (2.6588) Prec@1 35.625 (35.807) Prec@5 65.625 (66.426) Epoch: [7][3390/11272] Time 0.944 (0.837) Data 0.002 (0.002) Loss 2.7009 (2.6589) Prec@1 33.750 (35.805) Prec@5 66.875 (66.423) Epoch: [7][3400/11272] Time 0.878 (0.837) Data 0.001 (0.002) Loss 2.6895 (2.6589) Prec@1 38.750 (35.807) Prec@5 68.125 (66.424) Epoch: [7][3410/11272] Time 0.769 (0.837) Data 0.002 (0.002) Loss 2.8655 (2.6589) Prec@1 33.125 (35.809) Prec@5 59.375 (66.423) Epoch: [7][3420/11272] Time 0.920 (0.837) Data 0.001 (0.002) Loss 2.9135 (2.6587) Prec@1 31.250 (35.816) Prec@5 58.125 (66.424) Epoch: [7][3430/11272] Time 0.919 (0.837) Data 0.002 (0.002) Loss 2.7692 (2.6587) Prec@1 36.250 (35.818) Prec@5 64.375 (66.423) Epoch: [7][3440/11272] Time 0.775 (0.837) Data 0.002 (0.002) Loss 2.6605 (2.6586) Prec@1 35.000 (35.818) Prec@5 68.125 (66.425) Epoch: [7][3450/11272] Time 0.744 (0.837) Data 0.002 (0.002) Loss 2.7509 (2.6587) Prec@1 35.625 (35.817) Prec@5 66.250 (66.428) Epoch: [7][3460/11272] Time 0.826 (0.837) Data 0.001 (0.002) Loss 2.7220 (2.6585) Prec@1 35.625 (35.821) Prec@5 65.000 (66.431) Epoch: [7][3470/11272] Time 0.906 (0.837) Data 0.001 (0.002) Loss 2.6540 (2.6584) Prec@1 39.375 (35.822) Prec@5 70.000 (66.432) Epoch: [7][3480/11272] Time 0.776 (0.837) Data 0.001 (0.002) Loss 2.7257 (2.6585) Prec@1 36.250 (35.820) Prec@5 66.875 (66.433) Epoch: [7][3490/11272] Time 0.748 (0.837) Data 0.002 (0.002) Loss 2.7948 (2.6585) Prec@1 33.125 (35.819) Prec@5 65.000 (66.434) Epoch: [7][3500/11272] Time 0.889 (0.837) Data 0.002 (0.002) Loss 2.6575 (2.6587) Prec@1 31.250 (35.814) Prec@5 63.750 (66.429) Epoch: [7][3510/11272] Time 0.901 (0.837) Data 0.002 (0.002) Loss 2.5538 (2.6587) Prec@1 37.500 (35.817) Prec@5 66.875 (66.426) Epoch: [7][3520/11272] Time 0.740 (0.837) Data 0.001 (0.002) Loss 2.8498 (2.6588) Prec@1 32.500 (35.815) Prec@5 59.375 (66.421) Epoch: [7][3530/11272] Time 0.743 (0.837) Data 0.002 (0.002) Loss 2.8672 (2.6589) Prec@1 29.375 (35.814) Prec@5 65.000 (66.418) Epoch: [7][3540/11272] Time 0.902 (0.837) Data 0.002 (0.002) Loss 2.6424 (2.6591) Prec@1 36.875 (35.809) Prec@5 66.250 (66.415) Epoch: [7][3550/11272] Time 0.749 (0.837) Data 0.002 (0.002) Loss 2.6709 (2.6593) Prec@1 31.875 (35.804) Prec@5 68.750 (66.413) Epoch: [7][3560/11272] Time 0.786 (0.837) Data 0.001 (0.002) Loss 3.1199 (2.6593) Prec@1 28.750 (35.800) Prec@5 56.875 (66.410) Epoch: [7][3570/11272] Time 0.924 (0.837) Data 0.002 (0.002) Loss 2.5731 (2.6593) Prec@1 36.250 (35.805) Prec@5 68.750 (66.410) Epoch: [7][3580/11272] Time 0.939 (0.837) Data 0.001 (0.002) Loss 2.6936 (2.6595) Prec@1 36.875 (35.801) Prec@5 67.500 (66.410) Epoch: [7][3590/11272] Time 0.756 (0.837) Data 0.002 (0.002) Loss 2.5921 (2.6595) Prec@1 39.375 (35.800) Prec@5 66.250 (66.411) Epoch: [7][3600/11272] Time 0.768 (0.837) Data 0.001 (0.002) Loss 2.5568 (2.6594) Prec@1 36.250 (35.801) Prec@5 65.625 (66.411) Epoch: [7][3610/11272] Time 0.936 (0.837) Data 0.002 (0.002) Loss 2.6148 (2.6596) Prec@1 34.375 (35.798) Prec@5 69.375 (66.405) Epoch: [7][3620/11272] Time 0.942 (0.837) Data 0.001 (0.002) Loss 2.4352 (2.6594) Prec@1 43.750 (35.801) Prec@5 68.750 (66.407) Epoch: [7][3630/11272] Time 0.727 (0.837) Data 0.002 (0.002) Loss 2.5622 (2.6593) Prec@1 31.250 (35.803) Prec@5 66.250 (66.408) Epoch: [7][3640/11272] Time 0.747 (0.837) Data 0.002 (0.002) Loss 2.6051 (2.6591) Prec@1 35.000 (35.805) Prec@5 63.750 (66.408) Epoch: [7][3650/11272] Time 0.923 (0.837) Data 0.002 (0.002) Loss 2.8732 (2.6591) Prec@1 35.000 (35.806) Prec@5 63.125 (66.410) Epoch: [7][3660/11272] Time 0.864 (0.837) Data 0.002 (0.002) Loss 2.6228 (2.6589) Prec@1 33.125 (35.812) Prec@5 65.000 (66.415) Epoch: [7][3670/11272] Time 0.757 (0.837) Data 0.002 (0.002) Loss 2.4266 (2.6586) Prec@1 36.250 (35.815) Prec@5 70.625 (66.423) Epoch: [7][3680/11272] Time 0.932 (0.837) Data 0.001 (0.002) Loss 2.5302 (2.6584) Prec@1 35.625 (35.816) Prec@5 66.250 (66.424) Epoch: [7][3690/11272] Time 0.894 (0.837) Data 0.002 (0.002) Loss 2.6224 (2.6584) Prec@1 37.500 (35.818) Prec@5 68.125 (66.427) Epoch: [7][3700/11272] Time 0.731 (0.837) Data 0.001 (0.002) Loss 2.6857 (2.6581) Prec@1 33.750 (35.824) Prec@5 67.500 (66.432) Epoch: [7][3710/11272] Time 0.762 (0.837) Data 0.002 (0.002) Loss 2.7570 (2.6579) Prec@1 34.375 (35.825) Prec@5 63.750 (66.436) Epoch: [7][3720/11272] Time 0.847 (0.837) Data 0.001 (0.002) Loss 2.7472 (2.6579) Prec@1 30.000 (35.827) Prec@5 69.375 (66.437) Epoch: [7][3730/11272] Time 0.888 (0.837) Data 0.002 (0.002) Loss 2.7343 (2.6579) Prec@1 31.250 (35.824) Prec@5 66.875 (66.440) Epoch: [7][3740/11272] Time 0.760 (0.837) Data 0.001 (0.002) Loss 2.3693 (2.6578) Prec@1 43.750 (35.827) Prec@5 71.250 (66.445) Epoch: [7][3750/11272] Time 0.797 (0.837) Data 0.002 (0.002) Loss 2.7170 (2.6579) Prec@1 35.000 (35.827) Prec@5 64.375 (66.447) Epoch: [7][3760/11272] Time 0.884 (0.837) Data 0.001 (0.002) Loss 2.3470 (2.6578) Prec@1 43.750 (35.826) Prec@5 70.000 (66.446) Epoch: [7][3770/11272] Time 0.872 (0.837) Data 0.001 (0.002) Loss 2.5309 (2.6578) Prec@1 40.625 (35.821) Prec@5 73.750 (66.449) Epoch: [7][3780/11272] Time 0.770 (0.837) Data 0.001 (0.002) Loss 2.7740 (2.6579) Prec@1 33.125 (35.816) Prec@5 61.250 (66.445) Epoch: [7][3790/11272] Time 0.761 (0.837) Data 0.002 (0.002) Loss 2.7004 (2.6579) Prec@1 38.125 (35.818) Prec@5 66.875 (66.443) Epoch: [7][3800/11272] Time 0.876 (0.837) Data 0.001 (0.002) Loss 2.6872 (2.6576) Prec@1 30.625 (35.823) Prec@5 68.125 (66.449) Epoch: [7][3810/11272] Time 0.751 (0.837) Data 0.004 (0.002) Loss 2.6602 (2.6574) Prec@1 34.375 (35.833) Prec@5 63.750 (66.456) Epoch: [7][3820/11272] Time 0.761 (0.837) Data 0.001 (0.002) Loss 2.5337 (2.6573) Prec@1 38.750 (35.835) Prec@5 70.000 (66.459) Epoch: [7][3830/11272] Time 0.940 (0.837) Data 0.001 (0.002) Loss 2.4426 (2.6572) Prec@1 40.000 (35.835) Prec@5 70.625 (66.464) Epoch: [7][3840/11272] Time 0.881 (0.837) Data 0.001 (0.002) Loss 2.4745 (2.6571) Prec@1 43.125 (35.837) Prec@5 71.875 (66.466) Epoch: [7][3850/11272] Time 0.728 (0.837) Data 0.002 (0.002) Loss 2.4322 (2.6570) Prec@1 40.000 (35.838) Prec@5 70.000 (66.468) Epoch: [7][3860/11272] Time 0.788 (0.837) Data 0.002 (0.002) Loss 2.5918 (2.6570) Prec@1 40.000 (35.839) Prec@5 71.250 (66.468) Epoch: [7][3870/11272] Time 0.966 (0.837) Data 0.002 (0.002) Loss 2.6263 (2.6569) Prec@1 35.625 (35.838) Prec@5 68.750 (66.473) Epoch: [7][3880/11272] Time 0.906 (0.837) Data 0.002 (0.002) Loss 2.6415 (2.6569) Prec@1 35.000 (35.838) Prec@5 67.500 (66.474) Epoch: [7][3890/11272] Time 0.789 (0.837) Data 0.002 (0.002) Loss 2.8024 (2.6569) Prec@1 34.375 (35.838) Prec@5 63.750 (66.473) Epoch: [7][3900/11272] Time 0.784 (0.837) Data 0.002 (0.002) Loss 2.5413 (2.6569) Prec@1 36.250 (35.837) Prec@5 69.375 (66.477) Epoch: [7][3910/11272] Time 0.947 (0.837) Data 0.001 (0.002) Loss 2.7531 (2.6569) Prec@1 35.625 (35.835) Prec@5 66.250 (66.473) Epoch: [7][3920/11272] Time 0.859 (0.837) Data 0.002 (0.002) Loss 2.6308 (2.6567) Prec@1 31.250 (35.837) Prec@5 65.625 (66.476) Epoch: [7][3930/11272] Time 0.753 (0.837) Data 0.001 (0.002) Loss 2.6204 (2.6565) Prec@1 38.750 (35.837) Prec@5 68.750 (66.481) Epoch: [7][3940/11272] Time 0.933 (0.837) Data 0.002 (0.002) Loss 2.7901 (2.6565) Prec@1 25.625 (35.836) Prec@5 61.250 (66.479) Epoch: [7][3950/11272] Time 0.909 (0.837) Data 0.001 (0.002) Loss 2.4635 (2.6563) Prec@1 40.625 (35.838) Prec@5 68.125 (66.481) Epoch: [7][3960/11272] Time 0.795 (0.837) Data 0.002 (0.002) Loss 2.6169 (2.6563) Prec@1 37.500 (35.842) Prec@5 67.500 (66.481) Epoch: [7][3970/11272] Time 0.778 (0.837) Data 0.001 (0.002) Loss 2.7479 (2.6563) Prec@1 30.625 (35.845) Prec@5 63.125 (66.481) Epoch: [7][3980/11272] Time 0.910 (0.837) Data 0.002 (0.002) Loss 2.7420 (2.6563) Prec@1 36.250 (35.847) Prec@5 62.500 (66.478) Epoch: [7][3990/11272] Time 0.933 (0.837) Data 0.001 (0.002) Loss 2.6447 (2.6565) Prec@1 35.625 (35.844) Prec@5 66.875 (66.476) Epoch: [7][4000/11272] Time 0.737 (0.837) Data 0.001 (0.002) Loss 2.5918 (2.6567) Prec@1 35.625 (35.841) Prec@5 68.125 (66.473) Epoch: [7][4010/11272] Time 0.755 (0.837) Data 0.001 (0.002) Loss 2.9182 (2.6567) Prec@1 31.250 (35.841) Prec@5 59.375 (66.470) Epoch: [7][4020/11272] Time 0.916 (0.837) Data 0.002 (0.002) Loss 2.9098 (2.6571) Prec@1 34.375 (35.833) Prec@5 58.750 (66.463) Epoch: [7][4030/11272] Time 0.920 (0.837) Data 0.002 (0.002) Loss 2.5030 (2.6570) Prec@1 37.500 (35.837) Prec@5 68.750 (66.463) Epoch: [7][4040/11272] Time 0.771 (0.837) Data 0.002 (0.002) Loss 2.5910 (2.6568) Prec@1 35.000 (35.840) Prec@5 70.625 (66.466) Epoch: [7][4050/11272] Time 0.744 (0.837) Data 0.001 (0.002) Loss 2.5349 (2.6571) Prec@1 38.750 (35.831) Prec@5 70.625 (66.462) Epoch: [7][4060/11272] Time 0.866 (0.837) Data 0.002 (0.002) Loss 2.5877 (2.6572) Prec@1 35.625 (35.827) Prec@5 68.125 (66.455) Epoch: [7][4070/11272] Time 0.896 (0.837) Data 0.001 (0.002) Loss 2.7528 (2.6574) Prec@1 31.250 (35.820) Prec@5 65.625 (66.450) Epoch: [7][4080/11272] Time 0.800 (0.837) Data 0.002 (0.002) Loss 2.5717 (2.6574) Prec@1 38.750 (35.822) Prec@5 66.250 (66.450) Epoch: [7][4090/11272] Time 0.921 (0.837) Data 0.001 (0.002) Loss 2.6334 (2.6574) Prec@1 34.375 (35.819) Prec@5 64.375 (66.451) Epoch: [7][4100/11272] Time 0.879 (0.837) Data 0.002 (0.002) Loss 3.0086 (2.6576) Prec@1 35.000 (35.818) Prec@5 58.750 (66.445) Epoch: [7][4110/11272] Time 0.769 (0.837) Data 0.001 (0.002) Loss 2.6961 (2.6576) Prec@1 33.750 (35.820) Prec@5 64.375 (66.445) Epoch: [7][4120/11272] Time 0.747 (0.837) Data 0.002 (0.002) Loss 2.7139 (2.6577) Prec@1 32.500 (35.816) Prec@5 65.625 (66.440) Epoch: [7][4130/11272] Time 0.876 (0.837) Data 0.001 (0.002) Loss 2.4919 (2.6577) Prec@1 39.375 (35.817) Prec@5 68.125 (66.442) Epoch: [7][4140/11272] Time 0.909 (0.837) Data 0.002 (0.002) Loss 3.0095 (2.6576) Prec@1 34.375 (35.821) Prec@5 61.875 (66.442) Epoch: [7][4150/11272] Time 0.781 (0.837) Data 0.001 (0.002) Loss 2.8494 (2.6577) Prec@1 33.125 (35.820) Prec@5 60.000 (66.438) Epoch: [7][4160/11272] Time 0.789 (0.837) Data 0.002 (0.002) Loss 2.5916 (2.6577) Prec@1 38.750 (35.818) Prec@5 71.875 (66.442) Epoch: [7][4170/11272] Time 0.899 (0.837) Data 0.001 (0.002) Loss 2.8738 (2.6578) Prec@1 35.000 (35.820) Prec@5 65.000 (66.442) Epoch: [7][4180/11272] Time 0.927 (0.837) Data 0.002 (0.002) Loss 2.5389 (2.6579) Prec@1 36.250 (35.818) Prec@5 68.750 (66.442) Epoch: [7][4190/11272] Time 0.790 (0.837) Data 0.001 (0.002) Loss 2.6175 (2.6579) Prec@1 38.125 (35.819) Prec@5 66.875 (66.444) Epoch: [7][4200/11272] Time 0.810 (0.837) Data 0.002 (0.002) Loss 3.1148 (2.6580) Prec@1 30.000 (35.816) Prec@5 57.500 (66.442) Epoch: [7][4210/11272] Time 0.895 (0.837) Data 0.001 (0.002) Loss 2.7522 (2.6581) Prec@1 28.750 (35.812) Prec@5 63.125 (66.439) Epoch: [7][4220/11272] Time 0.775 (0.837) Data 0.002 (0.002) Loss 2.4523 (2.6579) Prec@1 40.000 (35.819) Prec@5 68.125 (66.440) Epoch: [7][4230/11272] Time 0.776 (0.837) Data 0.001 (0.002) Loss 2.7662 (2.6581) Prec@1 31.875 (35.815) Prec@5 66.875 (66.436) Epoch: [7][4240/11272] Time 0.928 (0.837) Data 0.002 (0.002) Loss 2.3879 (2.6579) Prec@1 43.125 (35.819) Prec@5 66.250 (66.435) Epoch: [7][4250/11272] Time 0.909 (0.837) Data 0.001 (0.002) Loss 2.7502 (2.6580) Prec@1 38.750 (35.818) Prec@5 66.875 (66.433) Epoch: [7][4260/11272] Time 0.779 (0.837) Data 0.002 (0.002) Loss 2.5681 (2.6580) Prec@1 35.625 (35.817) Prec@5 69.375 (66.432) Epoch: [7][4270/11272] Time 0.752 (0.837) Data 0.004 (0.002) Loss 2.6056 (2.6580) Prec@1 38.750 (35.816) Prec@5 64.375 (66.433) Epoch: [7][4280/11272] Time 0.896 (0.837) Data 0.002 (0.002) Loss 2.3288 (2.6579) Prec@1 40.625 (35.818) Prec@5 73.125 (66.435) Epoch: [7][4290/11272] Time 0.920 (0.837) Data 0.001 (0.002) Loss 2.8507 (2.6580) Prec@1 31.250 (35.819) Prec@5 61.875 (66.435) Epoch: [7][4300/11272] Time 0.787 (0.837) Data 0.002 (0.002) Loss 2.4385 (2.6579) Prec@1 44.375 (35.817) Prec@5 71.250 (66.436) Epoch: [7][4310/11272] Time 0.763 (0.837) Data 0.001 (0.002) Loss 2.3658 (2.6579) Prec@1 43.125 (35.819) Prec@5 69.375 (66.435) Epoch: [7][4320/11272] Time 0.922 (0.837) Data 0.002 (0.002) Loss 2.7258 (2.6579) Prec@1 34.375 (35.817) Prec@5 70.000 (66.435) Epoch: [7][4330/11272] Time 0.861 (0.837) Data 0.001 (0.002) Loss 2.5027 (2.6578) Prec@1 37.500 (35.819) Prec@5 68.750 (66.435) Epoch: [7][4340/11272] Time 0.783 (0.837) Data 0.002 (0.002) Loss 2.7135 (2.6580) Prec@1 35.000 (35.815) Prec@5 65.000 (66.434) Epoch: [7][4350/11272] Time 0.921 (0.837) Data 0.001 (0.002) Loss 2.4507 (2.6580) Prec@1 45.000 (35.816) Prec@5 68.750 (66.432) Epoch: [7][4360/11272] Time 0.904 (0.837) Data 0.002 (0.002) Loss 2.6931 (2.6581) Prec@1 36.250 (35.814) Prec@5 63.125 (66.428) Epoch: [7][4370/11272] Time 0.763 (0.837) Data 0.001 (0.002) Loss 2.9476 (2.6581) Prec@1 33.125 (35.814) Prec@5 60.625 (66.427) Epoch: [7][4380/11272] Time 0.806 (0.837) Data 0.001 (0.002) Loss 2.5292 (2.6581) Prec@1 36.250 (35.816) Prec@5 68.125 (66.428) Epoch: [7][4390/11272] Time 0.883 (0.837) Data 0.001 (0.002) Loss 2.3541 (2.6579) Prec@1 37.500 (35.820) Prec@5 71.875 (66.429) Epoch: [7][4400/11272] Time 0.911 (0.837) Data 0.002 (0.002) Loss 2.2985 (2.6577) Prec@1 40.625 (35.821) Prec@5 75.000 (66.433) Epoch: [7][4410/11272] Time 0.753 (0.837) Data 0.001 (0.002) Loss 2.3950 (2.6576) Prec@1 40.000 (35.822) Prec@5 73.750 (66.440) Epoch: [7][4420/11272] Time 0.774 (0.837) Data 0.002 (0.002) Loss 2.5003 (2.6573) Prec@1 35.625 (35.826) Prec@5 68.125 (66.445) Epoch: [7][4430/11272] Time 0.893 (0.837) Data 0.001 (0.002) Loss 2.6016 (2.6574) Prec@1 37.500 (35.825) Prec@5 66.875 (66.444) Epoch: [7][4440/11272] Time 0.940 (0.837) Data 0.002 (0.002) Loss 2.6872 (2.6575) Prec@1 34.375 (35.822) Prec@5 66.875 (66.441) Epoch: [7][4450/11272] Time 0.798 (0.837) Data 0.001 (0.002) Loss 2.4135 (2.6575) Prec@1 43.125 (35.820) Prec@5 70.625 (66.443) Epoch: [7][4460/11272] Time 0.725 (0.837) Data 0.002 (0.002) Loss 2.6486 (2.6576) Prec@1 36.250 (35.817) Prec@5 68.125 (66.442) Epoch: [7][4470/11272] Time 0.895 (0.837) Data 0.001 (0.002) Loss 2.8505 (2.6575) Prec@1 33.125 (35.822) Prec@5 62.500 (66.444) Epoch: [7][4480/11272] Time 0.771 (0.837) Data 0.004 (0.002) Loss 2.8129 (2.6575) Prec@1 28.750 (35.821) Prec@5 66.250 (66.444) Epoch: [7][4490/11272] Time 0.754 (0.837) Data 0.001 (0.002) Loss 2.5837 (2.6574) Prec@1 36.250 (35.820) Prec@5 70.625 (66.447) Epoch: [7][4500/11272] Time 0.912 (0.837) Data 0.002 (0.002) Loss 2.3532 (2.6573) Prec@1 44.375 (35.823) Prec@5 70.625 (66.449) Epoch: [7][4510/11272] Time 0.938 (0.837) Data 0.002 (0.002) Loss 2.3218 (2.6573) Prec@1 41.875 (35.824) Prec@5 74.375 (66.448) Epoch: [7][4520/11272] Time 0.727 (0.837) Data 0.002 (0.002) Loss 2.9005 (2.6573) Prec@1 26.875 (35.823) Prec@5 60.625 (66.444) Epoch: [7][4530/11272] Time 0.774 (0.837) Data 0.001 (0.002) Loss 2.8231 (2.6572) Prec@1 33.750 (35.824) Prec@5 65.000 (66.447) Epoch: [7][4540/11272] Time 0.863 (0.837) Data 0.001 (0.002) Loss 2.3192 (2.6571) Prec@1 43.125 (35.828) Prec@5 71.250 (66.450) Epoch: [7][4550/11272] Time 0.918 (0.837) Data 0.001 (0.002) Loss 2.5694 (2.6570) Prec@1 35.625 (35.827) Prec@5 66.875 (66.449) Epoch: [7][4560/11272] Time 0.763 (0.837) Data 0.002 (0.002) Loss 2.6423 (2.6570) Prec@1 40.000 (35.828) Prec@5 63.125 (66.450) Epoch: [7][4570/11272] Time 0.731 (0.837) Data 0.001 (0.002) Loss 2.2793 (2.6569) Prec@1 42.500 (35.828) Prec@5 75.625 (66.453) Epoch: [7][4580/11272] Time 0.878 (0.837) Data 0.002 (0.002) Loss 2.5739 (2.6566) Prec@1 35.000 (35.834) Prec@5 68.125 (66.458) Epoch: [7][4590/11272] Time 0.898 (0.837) Data 0.001 (0.002) Loss 2.6965 (2.6567) Prec@1 33.125 (35.833) Prec@5 64.375 (66.457) Epoch: [7][4600/11272] Time 0.764 (0.837) Data 0.002 (0.002) Loss 2.3582 (2.6567) Prec@1 43.750 (35.835) Prec@5 71.250 (66.457) Epoch: [7][4610/11272] Time 0.894 (0.837) Data 0.002 (0.002) Loss 2.3214 (2.6568) Prec@1 41.875 (35.833) Prec@5 73.750 (66.454) Epoch: [7][4620/11272] Time 0.890 (0.837) Data 0.001 (0.002) Loss 2.6750 (2.6566) Prec@1 36.250 (35.835) Prec@5 64.375 (66.457) Epoch: [7][4630/11272] Time 0.823 (0.837) Data 0.002 (0.002) Loss 2.7577 (2.6564) Prec@1 35.625 (35.837) Prec@5 61.875 (66.459) Epoch: [7][4640/11272] Time 0.721 (0.837) Data 0.001 (0.002) Loss 2.8108 (2.6563) Prec@1 35.000 (35.839) Prec@5 65.625 (66.467) Epoch: [7][4650/11272] Time 0.900 (0.837) Data 0.002 (0.002) Loss 2.6959 (2.6563) Prec@1 40.625 (35.842) Prec@5 65.000 (66.471) Epoch: [7][4660/11272] Time 0.851 (0.837) Data 0.001 (0.002) Loss 2.5188 (2.6565) Prec@1 36.875 (35.840) Prec@5 68.125 (66.467) Epoch: [7][4670/11272] Time 0.735 (0.837) Data 0.002 (0.002) Loss 2.5101 (2.6564) Prec@1 39.375 (35.842) Prec@5 71.875 (66.468) Epoch: [7][4680/11272] Time 0.816 (0.837) Data 0.001 (0.002) Loss 2.5694 (2.6564) Prec@1 35.000 (35.842) Prec@5 65.000 (66.468) Epoch: [7][4690/11272] Time 0.899 (0.837) Data 0.002 (0.002) Loss 2.4287 (2.6565) Prec@1 43.125 (35.839) Prec@5 74.375 (66.468) Epoch: [7][4700/11272] Time 0.892 (0.837) Data 0.002 (0.002) Loss 2.4707 (2.6564) Prec@1 40.000 (35.839) Prec@5 70.000 (66.469) Epoch: [7][4710/11272] Time 0.757 (0.837) Data 0.002 (0.002) Loss 2.7650 (2.6564) Prec@1 32.500 (35.839) Prec@5 64.375 (66.467) Epoch: [7][4720/11272] Time 0.744 (0.837) Data 0.002 (0.002) Loss 2.5406 (2.6563) Prec@1 40.000 (35.842) Prec@5 68.125 (66.468) Epoch: [7][4730/11272] Time 0.900 (0.837) Data 0.002 (0.002) Loss 2.5261 (2.6562) Prec@1 30.625 (35.841) Prec@5 71.875 (66.470) Epoch: [7][4740/11272] Time 0.755 (0.837) Data 0.004 (0.002) Loss 2.6337 (2.6562) Prec@1 33.125 (35.839) Prec@5 65.000 (66.469) Epoch: [7][4750/11272] Time 0.782 (0.837) Data 0.002 (0.002) Loss 2.4932 (2.6563) Prec@1 40.000 (35.834) Prec@5 67.500 (66.467) Epoch: [7][4760/11272] Time 0.851 (0.837) Data 0.001 (0.002) Loss 2.7438 (2.6563) Prec@1 30.625 (35.832) Prec@5 63.125 (66.465) Epoch: [7][4770/11272] Time 0.909 (0.837) Data 0.002 (0.002) Loss 2.6004 (2.6562) Prec@1 37.500 (35.833) Prec@5 66.875 (66.468) Epoch: [7][4780/11272] Time 0.790 (0.837) Data 0.001 (0.002) Loss 2.8339 (2.6564) Prec@1 30.000 (35.826) Prec@5 63.750 (66.464) Epoch: [7][4790/11272] Time 0.823 (0.837) Data 0.002 (0.002) Loss 2.8231 (2.6564) Prec@1 30.000 (35.822) Prec@5 61.250 (66.463) Epoch: [7][4800/11272] Time 0.890 (0.837) Data 0.001 (0.002) Loss 2.5356 (2.6565) Prec@1 35.625 (35.820) Prec@5 65.625 (66.462) Epoch: [7][4810/11272] Time 0.891 (0.837) Data 0.002 (0.002) Loss 2.5958 (2.6567) Prec@1 36.875 (35.818) Prec@5 64.375 (66.457) Epoch: [7][4820/11272] Time 0.766 (0.837) Data 0.001 (0.002) Loss 2.5508 (2.6564) Prec@1 38.125 (35.826) Prec@5 70.000 (66.463) Epoch: [7][4830/11272] Time 0.783 (0.837) Data 0.002 (0.002) Loss 2.6186 (2.6564) Prec@1 33.750 (35.826) Prec@5 69.375 (66.463) Epoch: [7][4840/11272] Time 0.871 (0.837) Data 0.001 (0.002) Loss 2.7097 (2.6564) Prec@1 32.500 (35.829) Prec@5 66.250 (66.464) Epoch: [7][4850/11272] Time 0.907 (0.837) Data 0.002 (0.002) Loss 2.7515 (2.6565) Prec@1 31.875 (35.830) Prec@5 60.625 (66.461) Epoch: [7][4860/11272] Time 0.762 (0.837) Data 0.002 (0.002) Loss 2.6574 (2.6567) Prec@1 36.875 (35.827) Prec@5 66.250 (66.456) Epoch: [7][4870/11272] Time 0.958 (0.837) Data 0.002 (0.002) Loss 2.7100 (2.6567) Prec@1 35.625 (35.827) Prec@5 65.625 (66.455) Epoch: [7][4880/11272] Time 0.904 (0.837) Data 0.002 (0.002) Loss 2.6830 (2.6570) Prec@1 35.625 (35.824) Prec@5 67.500 (66.452) Epoch: [7][4890/11272] Time 0.781 (0.837) Data 0.002 (0.002) Loss 2.7062 (2.6569) Prec@1 33.750 (35.824) Prec@5 60.625 (66.451) Epoch: [7][4900/11272] Time 0.757 (0.837) Data 0.001 (0.002) Loss 2.8546 (2.6568) Prec@1 26.875 (35.826) Prec@5 63.750 (66.452) Epoch: [7][4910/11272] Time 0.984 (0.837) Data 0.002 (0.002) Loss 2.4583 (2.6567) Prec@1 40.000 (35.827) Prec@5 71.250 (66.453) Epoch: [7][4920/11272] Time 0.900 (0.837) Data 0.001 (0.002) Loss 2.8149 (2.6569) Prec@1 30.625 (35.821) Prec@5 63.125 (66.450) Epoch: [7][4930/11272] Time 0.749 (0.837) Data 0.002 (0.002) Loss 2.9399 (2.6569) Prec@1 30.000 (35.822) Prec@5 61.250 (66.450) Epoch: [7][4940/11272] Time 0.763 (0.837) Data 0.002 (0.002) Loss 2.8106 (2.6569) Prec@1 33.125 (35.819) Prec@5 62.500 (66.450) Epoch: [7][4950/11272] Time 0.931 (0.837) Data 0.002 (0.002) Loss 2.5301 (2.6568) Prec@1 36.250 (35.820) Prec@5 68.750 (66.454) Epoch: [7][4960/11272] Time 0.834 (0.837) Data 0.001 (0.002) Loss 2.4358 (2.6567) Prec@1 40.625 (35.823) Prec@5 71.875 (66.455) Epoch: [7][4970/11272] Time 0.761 (0.837) Data 0.002 (0.002) Loss 2.6719 (2.6567) Prec@1 33.125 (35.822) Prec@5 70.000 (66.457) Epoch: [7][4980/11272] Time 0.756 (0.837) Data 0.002 (0.002) Loss 2.7140 (2.6566) Prec@1 35.625 (35.826) Prec@5 65.000 (66.457) Epoch: [7][4990/11272] Time 0.898 (0.837) Data 0.002 (0.002) Loss 2.5970 (2.6566) Prec@1 36.250 (35.825) Prec@5 68.125 (66.456) Epoch: [7][5000/11272] Time 0.910 (0.837) Data 0.001 (0.002) Loss 2.5753 (2.6566) Prec@1 36.875 (35.824) Prec@5 70.000 (66.459) Epoch: [7][5010/11272] Time 0.694 (0.837) Data 0.001 (0.002) Loss 2.9091 (2.6565) Prec@1 37.500 (35.827) Prec@5 63.750 (66.459) Epoch: [7][5020/11272] Time 0.856 (0.837) Data 0.001 (0.002) Loss 3.0113 (2.6567) Prec@1 34.375 (35.828) Prec@5 57.500 (66.454) Epoch: [7][5030/11272] Time 0.886 (0.837) Data 0.003 (0.002) Loss 2.7045 (2.6568) Prec@1 33.125 (35.829) Prec@5 60.625 (66.453) Epoch: [7][5040/11272] Time 0.744 (0.837) Data 0.002 (0.002) Loss 2.4877 (2.6566) Prec@1 39.375 (35.832) Prec@5 71.875 (66.456) Epoch: [7][5050/11272] Time 0.819 (0.837) Data 0.002 (0.002) Loss 2.6962 (2.6566) Prec@1 35.625 (35.835) Prec@5 65.625 (66.459) Epoch: [7][5060/11272] Time 0.885 (0.837) Data 0.001 (0.002) Loss 2.5833 (2.6563) Prec@1 41.250 (35.842) Prec@5 66.250 (66.462) Epoch: [7][5070/11272] Time 0.989 (0.837) Data 0.002 (0.002) Loss 2.5298 (2.6563) Prec@1 40.625 (35.845) Prec@5 65.000 (66.464) Epoch: [7][5080/11272] Time 0.725 (0.837) Data 0.002 (0.002) Loss 2.7216 (2.6562) Prec@1 37.500 (35.846) Prec@5 67.500 (66.466) Epoch: [7][5090/11272] Time 0.793 (0.837) Data 0.002 (0.002) Loss 2.5673 (2.6561) Prec@1 35.000 (35.846) Prec@5 68.750 (66.469) Epoch: [7][5100/11272] Time 0.899 (0.837) Data 0.002 (0.002) Loss 2.6473 (2.6562) Prec@1 34.375 (35.843) Prec@5 63.750 (66.468) Epoch: [7][5110/11272] Time 0.949 (0.837) Data 0.002 (0.002) Loss 2.9380 (2.6562) Prec@1 34.375 (35.846) Prec@5 56.875 (66.469) Epoch: [7][5120/11272] Time 0.756 (0.837) Data 0.002 (0.002) Loss 2.7750 (2.6562) Prec@1 32.500 (35.848) Prec@5 63.750 (66.469) Epoch: [7][5130/11272] Time 0.872 (0.837) Data 0.001 (0.002) Loss 2.6563 (2.6563) Prec@1 34.375 (35.849) Prec@5 66.875 (66.468) Epoch: [7][5140/11272] Time 0.915 (0.837) Data 0.002 (0.002) Loss 2.6860 (2.6562) Prec@1 35.625 (35.853) Prec@5 66.250 (66.472) Epoch: [7][5150/11272] Time 0.804 (0.837) Data 0.002 (0.002) Loss 2.8893 (2.6564) Prec@1 30.625 (35.851) Prec@5 61.875 (66.466) Epoch: [7][5160/11272] Time 0.768 (0.837) Data 0.001 (0.002) Loss 2.9782 (2.6564) Prec@1 28.750 (35.850) Prec@5 59.375 (66.467) Epoch: [7][5170/11272] Time 0.849 (0.837) Data 0.002 (0.002) Loss 2.6463 (2.6565) Prec@1 40.625 (35.851) Prec@5 70.625 (66.467) Epoch: [7][5180/11272] Time 0.882 (0.837) Data 0.001 (0.002) Loss 2.6175 (2.6565) Prec@1 35.625 (35.850) Prec@5 63.750 (66.468) Epoch: [7][5190/11272] Time 0.767 (0.837) Data 0.002 (0.002) Loss 2.7680 (2.6565) Prec@1 35.000 (35.853) Prec@5 60.625 (66.469) Epoch: [7][5200/11272] Time 0.728 (0.837) Data 0.002 (0.002) Loss 2.4289 (2.6565) Prec@1 37.500 (35.852) Prec@5 68.750 (66.468) Epoch: [7][5210/11272] Time 0.936 (0.837) Data 0.002 (0.002) Loss 2.5921 (2.6565) Prec@1 40.625 (35.853) Prec@5 70.000 (66.469) Epoch: [7][5220/11272] Time 0.848 (0.837) Data 0.001 (0.002) Loss 2.8433 (2.6564) Prec@1 35.625 (35.854) Prec@5 61.250 (66.470) Epoch: [7][5230/11272] Time 0.778 (0.837) Data 0.002 (0.002) Loss 2.5793 (2.6563) Prec@1 37.500 (35.856) Prec@5 65.000 (66.471) Epoch: [7][5240/11272] Time 0.724 (0.837) Data 0.001 (0.002) Loss 2.7054 (2.6563) Prec@1 35.000 (35.858) Prec@5 67.500 (66.474) Epoch: [7][5250/11272] Time 0.946 (0.837) Data 0.002 (0.002) Loss 2.5388 (2.6562) Prec@1 40.625 (35.861) Prec@5 66.250 (66.479) Epoch: [7][5260/11272] Time 0.904 (0.837) Data 0.001 (0.002) Loss 2.8454 (2.6563) Prec@1 32.500 (35.863) Prec@5 65.000 (66.476) Epoch: [7][5270/11272] Time 0.811 (0.837) Data 0.002 (0.002) Loss 2.6695 (2.6563) Prec@1 41.250 (35.865) Prec@5 62.500 (66.474) Epoch: [7][5280/11272] Time 0.863 (0.837) Data 0.002 (0.002) Loss 2.7154 (2.6561) Prec@1 36.250 (35.871) Prec@5 63.750 (66.478) Epoch: [7][5290/11272] Time 0.931 (0.837) Data 0.002 (0.002) Loss 2.2045 (2.6561) Prec@1 41.250 (35.868) Prec@5 79.375 (66.478) Epoch: [7][5300/11272] Time 0.785 (0.837) Data 0.001 (0.002) Loss 2.6663 (2.6561) Prec@1 31.875 (35.865) Prec@5 66.250 (66.476) Epoch: [7][5310/11272] Time 0.787 (0.837) Data 0.002 (0.002) Loss 2.4997 (2.6559) Prec@1 38.750 (35.869) Prec@5 69.375 (66.478) Epoch: [7][5320/11272] Time 0.883 (0.837) Data 0.001 (0.002) Loss 2.7825 (2.6559) Prec@1 32.500 (35.868) Prec@5 65.000 (66.479) Epoch: [7][5330/11272] Time 0.931 (0.837) Data 0.002 (0.002) Loss 2.7705 (2.6559) Prec@1 30.625 (35.869) Prec@5 64.375 (66.480) Epoch: [7][5340/11272] Time 0.773 (0.837) Data 0.001 (0.002) Loss 2.5133 (2.6557) Prec@1 36.250 (35.874) Prec@5 68.750 (66.482) Epoch: [7][5350/11272] Time 0.780 (0.837) Data 0.002 (0.002) Loss 2.7321 (2.6558) Prec@1 33.750 (35.871) Prec@5 61.875 (66.480) Epoch: [7][5360/11272] Time 0.870 (0.837) Data 0.002 (0.002) Loss 2.5624 (2.6558) Prec@1 39.375 (35.873) Prec@5 69.375 (66.481) Epoch: [7][5370/11272] Time 0.880 (0.837) Data 0.002 (0.002) Loss 2.7842 (2.6559) Prec@1 33.125 (35.873) Prec@5 66.250 (66.479) Epoch: [7][5380/11272] Time 0.742 (0.837) Data 0.002 (0.002) Loss 2.5412 (2.6559) Prec@1 35.000 (35.871) Prec@5 68.750 (66.478) Epoch: [7][5390/11272] Time 0.743 (0.837) Data 0.002 (0.002) Loss 2.7482 (2.6558) Prec@1 36.875 (35.875) Prec@5 65.000 (66.479) Epoch: [7][5400/11272] Time 0.927 (0.837) Data 0.001 (0.002) Loss 2.4884 (2.6559) Prec@1 39.375 (35.873) Prec@5 66.875 (66.477) Epoch: [7][5410/11272] Time 0.760 (0.837) Data 0.004 (0.002) Loss 2.9054 (2.6559) Prec@1 37.500 (35.875) Prec@5 62.500 (66.477) Epoch: [7][5420/11272] Time 0.738 (0.837) Data 0.002 (0.002) Loss 2.4856 (2.6560) Prec@1 37.500 (35.875) Prec@5 70.625 (66.475) Epoch: [7][5430/11272] Time 0.933 (0.837) Data 0.002 (0.002) Loss 2.6033 (2.6560) Prec@1 33.750 (35.875) Prec@5 67.500 (66.471) Epoch: [7][5440/11272] Time 0.930 (0.837) Data 0.001 (0.002) Loss 2.5532 (2.6559) Prec@1 43.125 (35.877) Prec@5 69.375 (66.474) Epoch: [7][5450/11272] Time 0.804 (0.837) Data 0.001 (0.002) Loss 2.5158 (2.6559) Prec@1 34.375 (35.879) Prec@5 71.250 (66.475) Epoch: [7][5460/11272] Time 0.762 (0.837) Data 0.001 (0.002) Loss 2.6615 (2.6557) Prec@1 40.000 (35.883) Prec@5 61.250 (66.478) Epoch: [7][5470/11272] Time 0.882 (0.837) Data 0.002 (0.002) Loss 2.7985 (2.6558) Prec@1 30.000 (35.879) Prec@5 66.875 (66.478) Epoch: [7][5480/11272] Time 0.902 (0.837) Data 0.001 (0.002) Loss 2.6472 (2.6557) Prec@1 31.875 (35.881) Prec@5 69.375 (66.478) Epoch: [7][5490/11272] Time 0.804 (0.837) Data 0.001 (0.002) Loss 2.4179 (2.6558) Prec@1 45.625 (35.884) Prec@5 68.750 (66.475) Epoch: [7][5500/11272] Time 0.770 (0.837) Data 0.002 (0.002) Loss 2.6983 (2.6560) Prec@1 33.750 (35.881) Prec@5 66.875 (66.472) Epoch: [7][5510/11272] Time 0.901 (0.837) Data 0.002 (0.002) Loss 2.5438 (2.6561) Prec@1 40.000 (35.877) Prec@5 69.375 (66.471) Epoch: [7][5520/11272] Time 0.883 (0.837) Data 0.001 (0.002) Loss 2.7221 (2.6561) Prec@1 33.750 (35.877) Prec@5 62.500 (66.473) Epoch: [7][5530/11272] Time 0.781 (0.837) Data 0.002 (0.002) Loss 2.8512 (2.6560) Prec@1 32.500 (35.878) Prec@5 58.750 (66.472) Epoch: [7][5540/11272] Time 0.866 (0.837) Data 0.001 (0.002) Loss 2.8753 (2.6563) Prec@1 29.375 (35.877) Prec@5 60.625 (66.468) Epoch: [7][5550/11272] Time 0.930 (0.837) Data 0.002 (0.002) Loss 2.6243 (2.6562) Prec@1 35.625 (35.876) Prec@5 67.500 (66.470) Epoch: [7][5560/11272] Time 0.761 (0.837) Data 0.002 (0.002) Loss 2.7191 (2.6560) Prec@1 35.000 (35.880) Prec@5 64.375 (66.473) Epoch: [7][5570/11272] Time 0.780 (0.837) Data 0.002 (0.002) Loss 2.5768 (2.6560) Prec@1 36.250 (35.881) Prec@5 65.000 (66.473) Epoch: [7][5580/11272] Time 0.872 (0.837) Data 0.002 (0.002) Loss 2.7344 (2.6561) Prec@1 31.875 (35.878) Prec@5 65.625 (66.471) Epoch: [7][5590/11272] Time 0.958 (0.837) Data 0.002 (0.002) Loss 3.0564 (2.6563) Prec@1 30.625 (35.876) Prec@5 59.375 (66.468) Epoch: [7][5600/11272] Time 0.758 (0.837) Data 0.002 (0.002) Loss 2.4130 (2.6562) Prec@1 40.625 (35.875) Prec@5 71.250 (66.468) Epoch: [7][5610/11272] Time 0.737 (0.837) Data 0.002 (0.002) Loss 2.5296 (2.6562) Prec@1 36.875 (35.876) Prec@5 73.125 (66.468) Epoch: [7][5620/11272] Time 0.904 (0.837) Data 0.001 (0.002) Loss 2.6507 (2.6563) Prec@1 37.500 (35.878) Prec@5 68.750 (66.469) Epoch: [7][5630/11272] Time 0.963 (0.837) Data 0.002 (0.002) Loss 2.8977 (2.6562) Prec@1 34.375 (35.880) Prec@5 62.500 (66.471) Epoch: [7][5640/11272] Time 0.745 (0.837) Data 0.002 (0.002) Loss 2.5990 (2.6562) Prec@1 36.250 (35.880) Prec@5 65.000 (66.471) Epoch: [7][5650/11272] Time 0.764 (0.837) Data 0.002 (0.002) Loss 2.5338 (2.6563) Prec@1 36.875 (35.878) Prec@5 68.750 (66.468) Epoch: [7][5660/11272] Time 0.940 (0.837) Data 0.001 (0.002) Loss 2.5884 (2.6563) Prec@1 37.500 (35.877) Prec@5 67.500 (66.468) Epoch: [7][5670/11272] Time 0.786 (0.837) Data 0.004 (0.002) Loss 2.4682 (2.6562) Prec@1 41.250 (35.877) Prec@5 72.500 (66.471) Epoch: [7][5680/11272] Time 0.726 (0.837) Data 0.001 (0.002) Loss 2.5028 (2.6562) Prec@1 32.500 (35.877) Prec@5 69.375 (66.473) Epoch: [7][5690/11272] Time 0.899 (0.837) Data 0.002 (0.002) Loss 2.7041 (2.6562) Prec@1 34.375 (35.877) Prec@5 65.000 (66.473) Epoch: [7][5700/11272] Time 0.886 (0.837) Data 0.001 (0.002) Loss 2.7251 (2.6562) Prec@1 35.625 (35.878) Prec@5 60.625 (66.472) Epoch: [7][5710/11272] Time 0.785 (0.837) Data 0.002 (0.002) Loss 2.4434 (2.6563) Prec@1 43.750 (35.879) Prec@5 70.625 (66.471) Epoch: [7][5720/11272] Time 0.792 (0.837) Data 0.001 (0.002) Loss 2.4758 (2.6562) Prec@1 36.250 (35.881) Prec@5 72.500 (66.472) Epoch: [7][5730/11272] Time 0.921 (0.837) Data 0.001 (0.002) Loss 2.8906 (2.6561) Prec@1 33.125 (35.880) Prec@5 61.250 (66.474) Epoch: [7][5740/11272] Time 0.912 (0.837) Data 0.001 (0.002) Loss 2.3604 (2.6561) Prec@1 43.750 (35.883) Prec@5 72.500 (66.476) Epoch: [7][5750/11272] Time 0.834 (0.837) Data 0.002 (0.002) Loss 2.5289 (2.6561) Prec@1 42.500 (35.885) Prec@5 70.000 (66.478) Epoch: [7][5760/11272] Time 0.790 (0.837) Data 0.001 (0.002) Loss 2.8777 (2.6561) Prec@1 31.875 (35.884) Prec@5 60.625 (66.476) Epoch: [7][5770/11272] Time 0.821 (0.837) Data 0.002 (0.002) Loss 2.7411 (2.6561) Prec@1 35.625 (35.884) Prec@5 63.750 (66.476) Epoch: [7][5780/11272] Time 0.885 (0.837) Data 0.001 (0.002) Loss 2.6451 (2.6561) Prec@1 35.625 (35.888) Prec@5 66.875 (66.476) Epoch: [7][5790/11272] Time 0.803 (0.837) Data 0.001 (0.002) Loss 2.9446 (2.6562) Prec@1 30.000 (35.884) Prec@5 63.750 (66.473) Epoch: [7][5800/11272] Time 0.885 (0.837) Data 0.002 (0.002) Loss 2.3852 (2.6564) Prec@1 43.125 (35.883) Prec@5 73.125 (66.470) Epoch: [7][5810/11272] Time 0.908 (0.837) Data 0.002 (0.002) Loss 2.7581 (2.6565) Prec@1 30.625 (35.882) Prec@5 70.000 (66.467) Epoch: [7][5820/11272] Time 0.776 (0.837) Data 0.001 (0.002) Loss 2.6111 (2.6565) Prec@1 42.500 (35.884) Prec@5 68.125 (66.467) Epoch: [7][5830/11272] Time 0.751 (0.837) Data 0.002 (0.002) Loss 2.7379 (2.6565) Prec@1 34.375 (35.883) Prec@5 63.125 (66.464) Epoch: [7][5840/11272] Time 0.884 (0.837) Data 0.001 (0.002) Loss 2.7947 (2.6565) Prec@1 36.875 (35.885) Prec@5 60.625 (66.464) Epoch: [7][5850/11272] Time 0.902 (0.837) Data 0.002 (0.002) Loss 2.4208 (2.6566) Prec@1 38.125 (35.884) Prec@5 71.250 (66.460) Epoch: [7][5860/11272] Time 0.776 (0.837) Data 0.002 (0.002) Loss 2.8009 (2.6566) Prec@1 34.375 (35.883) Prec@5 66.250 (66.460) Epoch: [7][5870/11272] Time 0.835 (0.837) Data 0.002 (0.002) Loss 2.7084 (2.6568) Prec@1 33.125 (35.878) Prec@5 61.875 (66.456) Epoch: [7][5880/11272] Time 0.879 (0.837) Data 0.001 (0.002) Loss 2.6157 (2.6569) Prec@1 30.625 (35.878) Prec@5 66.875 (66.454) Epoch: [7][5890/11272] Time 0.915 (0.837) Data 0.002 (0.002) Loss 2.4122 (2.6568) Prec@1 41.875 (35.880) Prec@5 75.000 (66.456) Epoch: [7][5900/11272] Time 0.783 (0.837) Data 0.001 (0.002) Loss 2.7248 (2.6569) Prec@1 33.750 (35.880) Prec@5 61.250 (66.453) Epoch: [7][5910/11272] Time 0.755 (0.837) Data 0.002 (0.002) Loss 2.6955 (2.6569) Prec@1 36.875 (35.880) Prec@5 63.125 (66.453) Epoch: [7][5920/11272] Time 0.941 (0.837) Data 0.002 (0.002) Loss 2.6202 (2.6570) Prec@1 33.750 (35.878) Prec@5 69.375 (66.452) Epoch: [7][5930/11272] Time 0.944 (0.837) Data 0.002 (0.002) Loss 2.8748 (2.6569) Prec@1 31.250 (35.882) Prec@5 62.500 (66.453) Epoch: [7][5940/11272] Time 0.758 (0.837) Data 0.002 (0.002) Loss 2.7447 (2.6568) Prec@1 33.750 (35.884) Prec@5 63.750 (66.455) Epoch: [7][5950/11272] Time 0.866 (0.837) Data 0.001 (0.002) Loss 2.6516 (2.6568) Prec@1 38.125 (35.884) Prec@5 66.875 (66.456) Epoch: [7][5960/11272] Time 0.901 (0.837) Data 0.001 (0.002) Loss 2.5650 (2.6568) Prec@1 41.250 (35.884) Prec@5 68.750 (66.456) Epoch: [7][5970/11272] Time 0.793 (0.837) Data 0.001 (0.002) Loss 2.7097 (2.6569) Prec@1 31.250 (35.880) Prec@5 68.750 (66.456) Epoch: [7][5980/11272] Time 0.755 (0.837) Data 0.001 (0.002) Loss 2.7336 (2.6570) Prec@1 37.500 (35.880) Prec@5 68.125 (66.454) Epoch: [7][5990/11272] Time 0.876 (0.837) Data 0.002 (0.002) Loss 2.7413 (2.6571) Prec@1 32.500 (35.877) Prec@5 66.250 (66.452) Epoch: [7][6000/11272] Time 0.893 (0.837) Data 0.001 (0.002) Loss 2.9727 (2.6570) Prec@1 28.125 (35.879) Prec@5 60.625 (66.454) Epoch: [7][6010/11272] Time 0.754 (0.837) Data 0.002 (0.002) Loss 2.8234 (2.6570) Prec@1 33.125 (35.877) Prec@5 66.250 (66.453) Epoch: [7][6020/11272] Time 0.761 (0.837) Data 0.002 (0.002) Loss 2.7747 (2.6570) Prec@1 35.625 (35.877) Prec@5 65.000 (66.452) Epoch: [7][6030/11272] Time 0.883 (0.837) Data 0.003 (0.002) Loss 2.6498 (2.6569) Prec@1 37.500 (35.881) Prec@5 68.125 (66.452) Epoch: [7][6040/11272] Time 0.955 (0.837) Data 0.001 (0.002) Loss 2.8122 (2.6570) Prec@1 35.000 (35.880) Prec@5 61.875 (66.451) Epoch: [7][6050/11272] Time 0.787 (0.837) Data 0.002 (0.002) Loss 2.7323 (2.6571) Prec@1 32.500 (35.881) Prec@5 66.250 (66.446) Epoch: [7][6060/11272] Time 0.771 (0.837) Data 0.001 (0.002) Loss 2.6237 (2.6571) Prec@1 28.750 (35.878) Prec@5 68.125 (66.447) Epoch: [7][6070/11272] Time 0.936 (0.837) Data 0.002 (0.002) Loss 2.6316 (2.6572) Prec@1 41.250 (35.878) Prec@5 66.875 (66.447) Epoch: [7][6080/11272] Time 0.751 (0.837) Data 0.002 (0.002) Loss 2.5735 (2.6572) Prec@1 40.000 (35.880) Prec@5 68.125 (66.448) Epoch: [7][6090/11272] Time 0.757 (0.837) Data 0.002 (0.002) Loss 2.7414 (2.6572) Prec@1 33.750 (35.878) Prec@5 62.500 (66.445) Epoch: [7][6100/11272] Time 0.897 (0.837) Data 0.002 (0.002) Loss 2.7226 (2.6573) Prec@1 30.625 (35.879) Prec@5 64.375 (66.442) Epoch: [7][6110/11272] Time 0.952 (0.837) Data 0.002 (0.002) Loss 2.6556 (2.6573) Prec@1 36.875 (35.881) Prec@5 67.500 (66.443) Epoch: [7][6120/11272] Time 0.785 (0.837) Data 0.001 (0.002) Loss 2.6253 (2.6573) Prec@1 31.875 (35.881) Prec@5 68.750 (66.442) Epoch: [7][6130/11272] Time 0.796 (0.837) Data 0.003 (0.002) Loss 2.5077 (2.6572) Prec@1 33.750 (35.883) Prec@5 71.250 (66.444) Epoch: [7][6140/11272] Time 0.882 (0.837) Data 0.001 (0.002) Loss 2.6529 (2.6571) Prec@1 32.500 (35.884) Prec@5 65.000 (66.444) Epoch: [7][6150/11272] Time 0.944 (0.837) Data 0.003 (0.002) Loss 2.5314 (2.6571) Prec@1 36.875 (35.885) Prec@5 69.375 (66.444) Epoch: [7][6160/11272] Time 0.755 (0.837) Data 0.001 (0.002) Loss 2.7164 (2.6571) Prec@1 37.500 (35.886) Prec@5 63.750 (66.445) Epoch: [7][6170/11272] Time 0.802 (0.837) Data 0.002 (0.002) Loss 2.3524 (2.6571) Prec@1 41.875 (35.884) Prec@5 72.500 (66.442) Epoch: [7][6180/11272] Time 0.892 (0.837) Data 0.002 (0.002) Loss 2.8004 (2.6570) Prec@1 35.625 (35.886) Prec@5 64.375 (66.445) Epoch: [7][6190/11272] Time 0.857 (0.837) Data 0.002 (0.002) Loss 2.5255 (2.6569) Prec@1 35.000 (35.887) Prec@5 68.750 (66.447) Epoch: [7][6200/11272] Time 0.734 (0.837) Data 0.002 (0.002) Loss 2.7367 (2.6569) Prec@1 37.500 (35.888) Prec@5 65.000 (66.448) Epoch: [7][6210/11272] Time 0.971 (0.837) Data 0.002 (0.002) Loss 2.0627 (2.6567) Prec@1 48.125 (35.891) Prec@5 76.875 (66.449) Epoch: [7][6220/11272] Time 0.917 (0.837) Data 0.001 (0.002) Loss 2.8397 (2.6567) Prec@1 35.000 (35.892) Prec@5 60.625 (66.450) Epoch: [7][6230/11272] Time 0.757 (0.837) Data 0.002 (0.002) Loss 2.9252 (2.6566) Prec@1 28.125 (35.893) Prec@5 61.875 (66.451) Epoch: [7][6240/11272] Time 0.728 (0.837) Data 0.002 (0.002) Loss 2.7617 (2.6566) Prec@1 33.750 (35.892) Prec@5 62.500 (66.451) Epoch: [7][6250/11272] Time 0.919 (0.837) Data 0.001 (0.002) Loss 2.5535 (2.6567) Prec@1 36.250 (35.891) Prec@5 70.000 (66.452) Epoch: [7][6260/11272] Time 0.934 (0.837) Data 0.002 (0.002) Loss 2.4605 (2.6567) Prec@1 38.750 (35.892) Prec@5 71.250 (66.451) Epoch: [7][6270/11272] Time 0.768 (0.837) Data 0.002 (0.002) Loss 2.4002 (2.6568) Prec@1 39.375 (35.889) Prec@5 71.875 (66.451) Epoch: [7][6280/11272] Time 0.743 (0.837) Data 0.002 (0.002) Loss 2.6970 (2.6567) Prec@1 38.125 (35.892) Prec@5 65.625 (66.454) Epoch: [7][6290/11272] Time 0.919 (0.837) Data 0.002 (0.002) Loss 2.6974 (2.6567) Prec@1 34.375 (35.891) Prec@5 65.000 (66.453) Epoch: [7][6300/11272] Time 0.895 (0.837) Data 0.001 (0.002) Loss 2.6584 (2.6568) Prec@1 38.750 (35.893) Prec@5 68.750 (66.452) Epoch: [7][6310/11272] Time 0.791 (0.837) Data 0.002 (0.002) Loss 2.7523 (2.6568) Prec@1 33.125 (35.893) Prec@5 64.375 (66.450) Epoch: [7][6320/11272] Time 0.717 (0.837) Data 0.001 (0.002) Loss 2.8437 (2.6569) Prec@1 30.625 (35.892) Prec@5 67.500 (66.451) Epoch: [7][6330/11272] Time 0.933 (0.837) Data 0.002 (0.002) Loss 2.8488 (2.6570) Prec@1 28.750 (35.891) Prec@5 63.125 (66.448) Epoch: [7][6340/11272] Time 0.760 (0.837) Data 0.003 (0.002) Loss 2.3971 (2.6570) Prec@1 38.125 (35.894) Prec@5 66.875 (66.447) Epoch: [7][6350/11272] Time 0.788 (0.837) Data 0.002 (0.002) Loss 2.8092 (2.6570) Prec@1 33.750 (35.896) Prec@5 63.125 (66.449) Epoch: [7][6360/11272] Time 0.919 (0.837) Data 0.001 (0.002) Loss 2.8251 (2.6569) Prec@1 38.125 (35.899) Prec@5 64.375 (66.453) Epoch: [7][6370/11272] Time 0.920 (0.837) Data 0.002 (0.002) Loss 2.5961 (2.6569) Prec@1 37.500 (35.901) Prec@5 68.125 (66.452) Epoch: [7][6380/11272] Time 0.799 (0.837) Data 0.001 (0.002) Loss 2.6299 (2.6570) Prec@1 35.625 (35.898) Prec@5 67.500 (66.451) Epoch: [7][6390/11272] Time 0.845 (0.837) Data 0.002 (0.002) Loss 2.5082 (2.6570) Prec@1 40.000 (35.900) Prec@5 70.000 (66.452) Epoch: [7][6400/11272] Time 0.921 (0.837) Data 0.001 (0.002) Loss 2.5535 (2.6570) Prec@1 38.125 (35.900) Prec@5 66.250 (66.451) Epoch: [7][6410/11272] Time 0.850 (0.837) Data 0.002 (0.002) Loss 2.5209 (2.6570) Prec@1 36.250 (35.899) Prec@5 71.250 (66.453) Epoch: [7][6420/11272] Time 0.771 (0.837) Data 0.001 (0.002) Loss 2.4753 (2.6568) Prec@1 40.000 (35.901) Prec@5 68.750 (66.456) Epoch: [7][6430/11272] Time 0.702 (0.837) Data 0.001 (0.002) Loss 2.8751 (2.6568) Prec@1 33.750 (35.901) Prec@5 65.000 (66.459) Epoch: [7][6440/11272] Time 0.872 (0.837) Data 0.001 (0.002) Loss 2.5480 (2.6568) Prec@1 35.625 (35.902) Prec@5 67.500 (66.459) Epoch: [7][6450/11272] Time 0.878 (0.837) Data 0.001 (0.002) Loss 2.8009 (2.6568) Prec@1 34.375 (35.902) Prec@5 61.250 (66.459) Epoch: [7][6460/11272] Time 0.738 (0.837) Data 0.001 (0.002) Loss 2.8100 (2.6569) Prec@1 33.750 (35.898) Prec@5 64.375 (66.456) Epoch: [7][6470/11272] Time 0.937 (0.837) Data 0.001 (0.002) Loss 2.5391 (2.6569) Prec@1 40.000 (35.898) Prec@5 70.000 (66.457) Epoch: [7][6480/11272] Time 0.896 (0.837) Data 0.001 (0.002) Loss 2.8081 (2.6568) Prec@1 34.375 (35.897) Prec@5 63.750 (66.459) Epoch: [7][6490/11272] Time 0.719 (0.837) Data 0.001 (0.002) Loss 2.6284 (2.6568) Prec@1 37.500 (35.900) Prec@5 65.000 (66.461) Epoch: [7][6500/11272] Time 0.756 (0.837) Data 0.002 (0.002) Loss 2.5797 (2.6568) Prec@1 40.000 (35.898) Prec@5 66.875 (66.458) Epoch: [7][6510/11272] Time 0.942 (0.837) Data 0.002 (0.002) Loss 2.7113 (2.6569) Prec@1 30.625 (35.896) Prec@5 68.750 (66.457) Epoch: [7][6520/11272] Time 0.924 (0.837) Data 0.002 (0.002) Loss 2.6893 (2.6568) Prec@1 35.000 (35.900) Prec@5 62.500 (66.458) Epoch: [7][6530/11272] Time 0.789 (0.837) Data 0.002 (0.002) Loss 2.8975 (2.6569) Prec@1 31.250 (35.897) Prec@5 58.750 (66.454) Epoch: [7][6540/11272] Time 0.730 (0.837) Data 0.001 (0.002) Loss 2.5024 (2.6569) Prec@1 36.875 (35.898) Prec@5 69.375 (66.455) Epoch: [7][6550/11272] Time 0.875 (0.837) Data 0.001 (0.002) Loss 2.9340 (2.6570) Prec@1 30.000 (35.896) Prec@5 61.250 (66.455) Epoch: [7][6560/11272] Time 0.864 (0.837) Data 0.001 (0.002) Loss 2.7139 (2.6569) Prec@1 35.000 (35.899) Prec@5 62.500 (66.456) Epoch: [7][6570/11272] Time 0.762 (0.837) Data 0.002 (0.002) Loss 2.4737 (2.6569) Prec@1 40.625 (35.900) Prec@5 66.875 (66.455) Epoch: [7][6580/11272] Time 0.784 (0.837) Data 0.001 (0.002) Loss 2.9144 (2.6570) Prec@1 31.875 (35.898) Prec@5 65.000 (66.453) Epoch: [7][6590/11272] Time 0.926 (0.837) Data 0.002 (0.002) Loss 2.5675 (2.6570) Prec@1 37.500 (35.901) Prec@5 69.375 (66.454) Epoch: [7][6600/11272] Time 0.718 (0.837) Data 0.004 (0.002) Loss 2.6730 (2.6570) Prec@1 36.250 (35.901) Prec@5 66.875 (66.457) Epoch: [7][6610/11272] Time 0.824 (0.837) Data 0.002 (0.002) Loss 2.9435 (2.6570) Prec@1 35.000 (35.901) Prec@5 63.125 (66.458) Epoch: [7][6620/11272] Time 0.872 (0.837) Data 0.001 (0.002) Loss 2.6070 (2.6570) Prec@1 30.625 (35.898) Prec@5 68.125 (66.456) Epoch: [7][6630/11272] Time 0.910 (0.837) Data 0.002 (0.002) Loss 2.8575 (2.6569) Prec@1 32.500 (35.899) Prec@5 61.250 (66.455) Epoch: [7][6640/11272] Time 0.710 (0.837) Data 0.001 (0.002) Loss 2.4866 (2.6570) Prec@1 40.625 (35.897) Prec@5 71.875 (66.455) Epoch: [7][6650/11272] Time 0.849 (0.837) Data 0.002 (0.002) Loss 2.6762 (2.6569) Prec@1 36.875 (35.898) Prec@5 65.625 (66.457) Epoch: [7][6660/11272] Time 0.870 (0.837) Data 0.002 (0.002) Loss 2.3206 (2.6569) Prec@1 40.625 (35.898) Prec@5 73.125 (66.457) Epoch: [7][6670/11272] Time 0.947 (0.837) Data 0.002 (0.002) Loss 2.4603 (2.6569) Prec@1 40.000 (35.898) Prec@5 71.250 (66.459) Epoch: [7][6680/11272] Time 0.733 (0.837) Data 0.002 (0.002) Loss 2.6536 (2.6569) Prec@1 38.750 (35.896) Prec@5 68.125 (66.458) Epoch: [7][6690/11272] Time 0.783 (0.837) Data 0.002 (0.002) Loss 2.7241 (2.6570) Prec@1 38.125 (35.895) Prec@5 66.250 (66.456) Epoch: [7][6700/11272] Time 0.898 (0.837) Data 0.001 (0.002) Loss 2.6236 (2.6570) Prec@1 39.375 (35.896) Prec@5 70.625 (66.456) Epoch: [7][6710/11272] Time 1.053 (0.837) Data 0.002 (0.002) Loss 2.6108 (2.6569) Prec@1 40.000 (35.900) Prec@5 65.625 (66.458) Epoch: [7][6720/11272] Time 0.758 (0.837) Data 0.003 (0.002) Loss 2.7912 (2.6570) Prec@1 31.875 (35.898) Prec@5 66.875 (66.458) Epoch: [7][6730/11272] Time 0.954 (0.837) Data 0.001 (0.002) Loss 2.6612 (2.6570) Prec@1 31.875 (35.898) Prec@5 69.375 (66.459) Epoch: [7][6740/11272] Time 0.858 (0.837) Data 0.001 (0.002) Loss 2.9281 (2.6572) Prec@1 30.625 (35.895) Prec@5 61.875 (66.456) Epoch: [7][6750/11272] Time 0.812 (0.837) Data 0.001 (0.002) Loss 2.7531 (2.6573) Prec@1 30.000 (35.893) Prec@5 68.125 (66.455) Epoch: [7][6760/11272] Time 0.773 (0.837) Data 0.002 (0.002) Loss 2.5620 (2.6572) Prec@1 36.875 (35.893) Prec@5 68.750 (66.455) Epoch: [7][6770/11272] Time 0.906 (0.837) Data 0.002 (0.002) Loss 2.5149 (2.6572) Prec@1 38.125 (35.893) Prec@5 70.625 (66.457) Epoch: [7][6780/11272] Time 0.852 (0.837) Data 0.001 (0.002) Loss 2.3554 (2.6572) Prec@1 41.250 (35.894) Prec@5 73.125 (66.457) Epoch: [7][6790/11272] Time 0.756 (0.837) Data 0.002 (0.002) Loss 2.6668 (2.6572) Prec@1 34.375 (35.893) Prec@5 65.000 (66.455) Epoch: [7][6800/11272] Time 0.762 (0.837) Data 0.001 (0.002) Loss 2.6448 (2.6572) Prec@1 37.500 (35.894) Prec@5 66.875 (66.455) Epoch: [7][6810/11272] Time 0.931 (0.837) Data 0.002 (0.002) Loss 2.6221 (2.6572) Prec@1 34.375 (35.892) Prec@5 66.875 (66.454) Epoch: [7][6820/11272] Time 0.864 (0.837) Data 0.002 (0.002) Loss 2.8208 (2.6573) Prec@1 37.500 (35.891) Prec@5 58.750 (66.453) Epoch: [7][6830/11272] Time 0.770 (0.837) Data 0.002 (0.002) Loss 2.6492 (2.6572) Prec@1 33.125 (35.892) Prec@5 64.375 (66.455) Epoch: [7][6840/11272] Time 0.729 (0.837) Data 0.004 (0.002) Loss 2.7186 (2.6571) Prec@1 28.125 (35.891) Prec@5 66.875 (66.456) Epoch: [7][6850/11272] Time 0.971 (0.837) Data 0.002 (0.002) Loss 2.7996 (2.6572) Prec@1 36.875 (35.890) Prec@5 65.000 (66.456) Epoch: [7][6860/11272] Time 0.923 (0.837) Data 0.002 (0.002) Loss 2.3993 (2.6571) Prec@1 43.750 (35.893) Prec@5 73.750 (66.460) Epoch: [7][6870/11272] Time 0.770 (0.837) Data 0.002 (0.002) Loss 2.5481 (2.6570) Prec@1 40.625 (35.896) Prec@5 68.125 (66.464) Epoch: [7][6880/11272] Time 0.868 (0.837) Data 0.001 (0.002) Loss 2.2800 (2.6570) Prec@1 46.250 (35.896) Prec@5 71.875 (66.463) Epoch: [7][6890/11272] Time 0.926 (0.837) Data 0.002 (0.002) Loss 2.4826 (2.6568) Prec@1 35.625 (35.899) Prec@5 71.250 (66.466) Epoch: [7][6900/11272] Time 0.758 (0.837) Data 0.001 (0.002) Loss 2.6429 (2.6568) Prec@1 36.250 (35.901) Prec@5 65.625 (66.466) Epoch: [7][6910/11272] Time 0.819 (0.837) Data 0.002 (0.002) Loss 2.7395 (2.6568) Prec@1 34.375 (35.901) Prec@5 66.250 (66.467) Epoch: [7][6920/11272] Time 0.875 (0.837) Data 0.001 (0.002) Loss 2.7736 (2.6568) Prec@1 28.750 (35.901) Prec@5 68.125 (66.467) Epoch: [7][6930/11272] Time 0.877 (0.837) Data 0.002 (0.002) Loss 2.6175 (2.6569) Prec@1 40.625 (35.901) Prec@5 63.750 (66.465) Epoch: [7][6940/11272] Time 0.746 (0.837) Data 0.002 (0.002) Loss 2.7763 (2.6569) Prec@1 33.125 (35.901) Prec@5 61.250 (66.463) Epoch: [7][6950/11272] Time 0.806 (0.837) Data 0.002 (0.002) Loss 2.7001 (2.6568) Prec@1 35.000 (35.901) Prec@5 68.125 (66.467) Epoch: [7][6960/11272] Time 0.867 (0.837) Data 0.001 (0.002) Loss 2.7617 (2.6569) Prec@1 34.375 (35.898) Prec@5 64.375 (66.467) Epoch: [7][6970/11272] Time 0.897 (0.837) Data 0.002 (0.002) Loss 2.6830 (2.6570) Prec@1 33.125 (35.897) Prec@5 61.250 (66.464) Epoch: [7][6980/11272] Time 0.746 (0.837) Data 0.001 (0.002) Loss 3.1120 (2.6571) Prec@1 25.000 (35.893) Prec@5 61.875 (66.463) Epoch: [7][6990/11272] Time 0.805 (0.837) Data 0.002 (0.002) Loss 2.6815 (2.6571) Prec@1 31.875 (35.892) Prec@5 67.500 (66.462) Epoch: [7][7000/11272] Time 0.870 (0.837) Data 0.001 (0.002) Loss 2.6659 (2.6571) Prec@1 39.375 (35.893) Prec@5 69.375 (66.462) Epoch: [7][7010/11272] Time 0.773 (0.837) Data 0.002 (0.002) Loss 2.5925 (2.6572) Prec@1 33.750 (35.891) Prec@5 69.375 (66.462) Epoch: [7][7020/11272] Time 0.747 (0.837) Data 0.002 (0.002) Loss 2.4882 (2.6572) Prec@1 31.875 (35.889) Prec@5 70.000 (66.463) Epoch: [7][7030/11272] Time 0.944 (0.837) Data 0.002 (0.002) Loss 2.9901 (2.6571) Prec@1 30.000 (35.890) Prec@5 59.375 (66.462) Epoch: [7][7040/11272] Time 0.889 (0.837) Data 0.002 (0.002) Loss 2.6652 (2.6572) Prec@1 37.500 (35.888) Prec@5 59.375 (66.459) Epoch: [7][7050/11272] Time 0.764 (0.837) Data 0.002 (0.002) Loss 2.4411 (2.6572) Prec@1 44.375 (35.888) Prec@5 73.125 (66.460) Epoch: [7][7060/11272] Time 0.748 (0.837) Data 0.001 (0.002) Loss 2.4507 (2.6572) Prec@1 42.500 (35.888) Prec@5 69.375 (66.460) Epoch: [7][7070/11272] Time 0.892 (0.837) Data 0.002 (0.002) Loss 2.5419 (2.6571) Prec@1 43.125 (35.890) Prec@5 67.500 (66.463) Epoch: [7][7080/11272] Time 0.859 (0.837) Data 0.002 (0.002) Loss 2.6789 (2.6571) Prec@1 36.250 (35.890) Prec@5 62.500 (66.463) Epoch: [7][7090/11272] Time 0.723 (0.837) Data 0.002 (0.002) Loss 2.6302 (2.6571) Prec@1 38.750 (35.890) Prec@5 68.750 (66.464) Epoch: [7][7100/11272] Time 0.804 (0.837) Data 0.001 (0.002) Loss 2.6282 (2.6570) Prec@1 33.125 (35.887) Prec@5 71.875 (66.467) Epoch: [7][7110/11272] Time 0.913 (0.837) Data 0.002 (0.002) Loss 2.9029 (2.6570) Prec@1 28.125 (35.886) Prec@5 63.750 (66.466) Epoch: [7][7120/11272] Time 0.847 (0.837) Data 0.001 (0.002) Loss 2.4010 (2.6570) Prec@1 42.500 (35.888) Prec@5 71.875 (66.467) Epoch: [7][7130/11272] Time 0.781 (0.837) Data 0.002 (0.002) Loss 2.5817 (2.6568) Prec@1 38.125 (35.889) Prec@5 65.000 (66.468) Epoch: [7][7140/11272] Time 0.869 (0.837) Data 0.001 (0.002) Loss 2.5583 (2.6568) Prec@1 37.500 (35.890) Prec@5 71.250 (66.469) Epoch: [7][7150/11272] Time 0.906 (0.837) Data 0.002 (0.002) Loss 2.5865 (2.6568) Prec@1 36.250 (35.892) Prec@5 64.375 (66.470) Epoch: [7][7160/11272] Time 0.757 (0.837) Data 0.001 (0.002) Loss 2.6734 (2.6568) Prec@1 34.375 (35.891) Prec@5 64.375 (66.470) Epoch: [7][7170/11272] Time 0.778 (0.837) Data 0.004 (0.002) Loss 2.5170 (2.6567) Prec@1 40.000 (35.891) Prec@5 71.875 (66.471) Epoch: [7][7180/11272] Time 0.849 (0.837) Data 0.001 (0.002) Loss 2.5961 (2.6567) Prec@1 37.500 (35.893) Prec@5 70.000 (66.473) Epoch: [7][7190/11272] Time 0.929 (0.837) Data 0.002 (0.002) Loss 2.7389 (2.6567) Prec@1 31.250 (35.893) Prec@5 66.875 (66.473) Epoch: [7][7200/11272] Time 0.764 (0.837) Data 0.001 (0.002) Loss 2.4663 (2.6566) Prec@1 40.000 (35.895) Prec@5 68.125 (66.474) Epoch: [7][7210/11272] Time 0.794 (0.837) Data 0.002 (0.002) Loss 2.8517 (2.6567) Prec@1 31.250 (35.894) Prec@5 61.250 (66.473) Epoch: [7][7220/11272] Time 0.977 (0.837) Data 0.001 (0.002) Loss 2.7722 (2.6567) Prec@1 31.250 (35.893) Prec@5 66.875 (66.472) Epoch: [7][7230/11272] Time 0.934 (0.837) Data 0.002 (0.002) Loss 2.7714 (2.6566) Prec@1 32.500 (35.896) Prec@5 67.500 (66.475) Epoch: [7][7240/11272] Time 0.757 (0.837) Data 0.001 (0.002) Loss 2.7547 (2.6567) Prec@1 33.750 (35.894) Prec@5 65.000 (66.474) Epoch: [7][7250/11272] Time 0.804 (0.837) Data 0.002 (0.002) Loss 2.7689 (2.6566) Prec@1 31.875 (35.894) Prec@5 67.500 (66.476) Epoch: [7][7260/11272] Time 0.870 (0.837) Data 0.002 (0.002) Loss 2.7166 (2.6566) Prec@1 36.250 (35.894) Prec@5 63.125 (66.474) Epoch: [7][7270/11272] Time 0.800 (0.837) Data 0.003 (0.002) Loss 2.4296 (2.6566) Prec@1 35.625 (35.895) Prec@5 71.250 (66.475) Epoch: [7][7280/11272] Time 0.759 (0.837) Data 0.001 (0.002) Loss 2.8332 (2.6566) Prec@1 31.875 (35.895) Prec@5 66.875 (66.475) Epoch: [7][7290/11272] Time 0.928 (0.837) Data 0.002 (0.002) Loss 2.8517 (2.6567) Prec@1 28.125 (35.894) Prec@5 63.125 (66.475) Epoch: [7][7300/11272] Time 0.869 (0.837) Data 0.001 (0.002) Loss 2.6734 (2.6566) Prec@1 38.750 (35.895) Prec@5 66.875 (66.476) Epoch: [7][7310/11272] Time 0.764 (0.837) Data 0.002 (0.002) Loss 2.6668 (2.6567) Prec@1 39.375 (35.895) Prec@5 69.375 (66.477) Epoch: [7][7320/11272] Time 0.741 (0.837) Data 0.002 (0.002) Loss 2.5103 (2.6566) Prec@1 41.875 (35.894) Prec@5 65.625 (66.477) Epoch: [7][7330/11272] Time 0.957 (0.837) Data 0.002 (0.002) Loss 3.0189 (2.6568) Prec@1 34.375 (35.894) Prec@5 56.250 (66.475) Epoch: [7][7340/11272] Time 0.910 (0.837) Data 0.001 (0.002) Loss 2.3992 (2.6567) Prec@1 40.625 (35.894) Prec@5 68.125 (66.475) Epoch: [7][7350/11272] Time 0.761 (0.837) Data 0.003 (0.002) Loss 2.6410 (2.6568) Prec@1 41.250 (35.892) Prec@5 65.625 (66.475) Epoch: [7][7360/11272] Time 0.728 (0.837) Data 0.002 (0.002) Loss 2.5262 (2.6568) Prec@1 38.750 (35.892) Prec@5 66.875 (66.474) Epoch: [7][7370/11272] Time 0.922 (0.837) Data 0.002 (0.002) Loss 2.4335 (2.6568) Prec@1 38.750 (35.893) Prec@5 74.375 (66.477) Epoch: [7][7380/11272] Time 0.841 (0.837) Data 0.001 (0.002) Loss 2.5071 (2.6567) Prec@1 36.250 (35.896) Prec@5 71.250 (66.480) Epoch: [7][7390/11272] Time 0.762 (0.837) Data 0.002 (0.002) Loss 3.0224 (2.6568) Prec@1 28.750 (35.895) Prec@5 61.250 (66.477) Epoch: [7][7400/11272] Time 0.853 (0.837) Data 0.001 (0.002) Loss 2.3434 (2.6569) Prec@1 43.125 (35.892) Prec@5 69.375 (66.476) Epoch: [7][7410/11272] Time 0.922 (0.837) Data 0.002 (0.002) Loss 2.5333 (2.6569) Prec@1 39.375 (35.892) Prec@5 70.000 (66.477) Epoch: [7][7420/11272] Time 0.742 (0.837) Data 0.001 (0.002) Loss 2.4454 (2.6569) Prec@1 41.875 (35.892) Prec@5 71.875 (66.478) Epoch: [7][7430/11272] Time 0.804 (0.837) Data 0.004 (0.002) Loss 2.7691 (2.6569) Prec@1 32.500 (35.891) Prec@5 66.250 (66.478) Epoch: [7][7440/11272] Time 0.919 (0.837) Data 0.001 (0.002) Loss 2.6651 (2.6569) Prec@1 37.500 (35.892) Prec@5 66.875 (66.479) Epoch: [7][7450/11272] Time 0.900 (0.837) Data 0.003 (0.002) Loss 2.5302 (2.6568) Prec@1 43.750 (35.895) Prec@5 68.125 (66.481) Epoch: [7][7460/11272] Time 0.739 (0.837) Data 0.001 (0.002) Loss 2.5310 (2.6567) Prec@1 40.000 (35.897) Prec@5 68.125 (66.482) Epoch: [7][7470/11272] Time 0.751 (0.837) Data 0.002 (0.002) Loss 2.5444 (2.6567) Prec@1 32.500 (35.897) Prec@5 67.500 (66.481) Epoch: [7][7480/11272] Time 0.882 (0.837) Data 0.001 (0.002) Loss 2.6808 (2.6567) Prec@1 36.875 (35.898) Prec@5 68.125 (66.482) Epoch: [7][7490/11272] Time 0.896 (0.837) Data 0.002 (0.002) Loss 2.6878 (2.6567) Prec@1 30.625 (35.897) Prec@5 67.500 (66.481) Epoch: [7][7500/11272] Time 0.748 (0.837) Data 0.002 (0.002) Loss 2.6984 (2.6566) Prec@1 33.750 (35.900) Prec@5 65.000 (66.481) Epoch: [7][7510/11272] Time 0.775 (0.837) Data 0.002 (0.002) Loss 2.6916 (2.6567) Prec@1 33.750 (35.901) Prec@5 66.250 (66.482) Epoch: [7][7520/11272] Time 0.912 (0.837) Data 0.001 (0.002) Loss 2.4947 (2.6568) Prec@1 34.375 (35.900) Prec@5 70.625 (66.480) Epoch: [7][7530/11272] Time 0.740 (0.837) Data 0.004 (0.002) Loss 2.5230 (2.6568) Prec@1 41.875 (35.902) Prec@5 69.375 (66.479) Epoch: [7][7540/11272] Time 0.804 (0.837) Data 0.001 (0.002) Loss 2.3310 (2.6567) Prec@1 45.000 (35.904) Prec@5 73.125 (66.482) Epoch: [7][7550/11272] Time 0.903 (0.837) Data 0.002 (0.002) Loss 2.9015 (2.6568) Prec@1 35.000 (35.903) Prec@5 61.250 (66.481) Epoch: [7][7560/11272] Time 0.793 (0.837) Data 0.001 (0.002) Loss 2.6400 (2.6567) Prec@1 35.625 (35.905) Prec@5 68.750 (66.483) Epoch: [7][7570/11272] Time 0.730 (0.837) Data 0.002 (0.002) Loss 2.5977 (2.6568) Prec@1 41.875 (35.904) Prec@5 70.000 (66.481) Epoch: [7][7580/11272] Time 0.782 (0.837) Data 0.001 (0.002) Loss 2.8354 (2.6569) Prec@1 33.750 (35.902) Prec@5 59.375 (66.478) Epoch: [7][7590/11272] Time 0.878 (0.837) Data 0.002 (0.002) Loss 2.5280 (2.6569) Prec@1 38.750 (35.903) Prec@5 70.000 (66.480) Epoch: [7][7600/11272] Time 0.873 (0.837) Data 0.001 (0.002) Loss 2.6700 (2.6569) Prec@1 41.250 (35.903) Prec@5 68.750 (66.481) Epoch: [7][7610/11272] Time 0.765 (0.837) Data 0.002 (0.002) Loss 2.8216 (2.6569) Prec@1 30.625 (35.903) Prec@5 63.125 (66.481) Epoch: [7][7620/11272] Time 0.725 (0.837) Data 0.001 (0.002) Loss 2.7826 (2.6569) Prec@1 41.875 (35.904) Prec@5 60.625 (66.480) Epoch: [7][7630/11272] Time 0.914 (0.837) Data 0.002 (0.002) Loss 2.6215 (2.6569) Prec@1 33.750 (35.905) Prec@5 65.625 (66.480) Epoch: [7][7640/11272] Time 0.864 (0.837) Data 0.001 (0.002) Loss 2.6923 (2.6569) Prec@1 31.875 (35.904) Prec@5 64.375 (66.480) Epoch: [7][7650/11272] Time 0.789 (0.837) Data 0.002 (0.002) Loss 2.8477 (2.6570) Prec@1 33.750 (35.902) Prec@5 65.625 (66.479) Epoch: [7][7660/11272] Time 0.910 (0.837) Data 0.002 (0.002) Loss 2.6362 (2.6571) Prec@1 35.625 (35.901) Prec@5 68.750 (66.476) Epoch: [7][7670/11272] Time 0.922 (0.837) Data 0.002 (0.002) Loss 2.4185 (2.6570) Prec@1 38.750 (35.901) Prec@5 72.500 (66.477) Epoch: [7][7680/11272] Time 0.737 (0.837) Data 0.001 (0.002) Loss 2.6926 (2.6570) Prec@1 33.125 (35.902) Prec@5 66.250 (66.478) Epoch: [7][7690/11272] Time 0.725 (0.837) Data 0.002 (0.002) Loss 2.6677 (2.6570) Prec@1 39.375 (35.903) Prec@5 68.125 (66.477) Epoch: [7][7700/11272] Time 0.885 (0.837) Data 0.002 (0.002) Loss 2.7204 (2.6570) Prec@1 33.750 (35.905) Prec@5 64.375 (66.478) Epoch: [7][7710/11272] Time 0.918 (0.837) Data 0.002 (0.002) Loss 2.7280 (2.6569) Prec@1 30.000 (35.905) Prec@5 68.125 (66.479) Epoch: [7][7720/11272] Time 0.751 (0.837) Data 0.001 (0.002) Loss 2.6942 (2.6568) Prec@1 36.875 (35.906) Prec@5 63.750 (66.480) Epoch: [7][7730/11272] Time 0.764 (0.837) Data 0.002 (0.002) Loss 3.0086 (2.6569) Prec@1 25.000 (35.904) Prec@5 65.000 (66.479) Epoch: [7][7740/11272] Time 0.893 (0.837) Data 0.001 (0.002) Loss 2.7854 (2.6569) Prec@1 33.750 (35.904) Prec@5 62.500 (66.479) Epoch: [7][7750/11272] Time 0.929 (0.837) Data 0.002 (0.002) Loss 2.9609 (2.6569) Prec@1 37.500 (35.906) Prec@5 62.500 (66.479) Epoch: [7][7760/11272] Time 0.784 (0.837) Data 0.001 (0.002) Loss 2.3845 (2.6569) Prec@1 41.875 (35.907) Prec@5 72.500 (66.480) Epoch: [7][7770/11272] Time 0.772 (0.837) Data 0.002 (0.002) Loss 2.4734 (2.6568) Prec@1 38.750 (35.911) Prec@5 71.250 (66.480) Epoch: [7][7780/11272] Time 0.885 (0.837) Data 0.001 (0.002) Loss 2.7719 (2.6568) Prec@1 33.750 (35.909) Prec@5 63.125 (66.479) Epoch: [7][7790/11272] Time 0.931 (0.837) Data 0.002 (0.002) Loss 2.6876 (2.6569) Prec@1 36.875 (35.909) Prec@5 66.250 (66.477) Epoch: [7][7800/11272] Time 0.760 (0.837) Data 0.001 (0.002) Loss 2.5068 (2.6569) Prec@1 37.500 (35.911) Prec@5 67.500 (66.476) Epoch: [7][7810/11272] Time 0.919 (0.837) Data 0.002 (0.002) Loss 2.5417 (2.6568) Prec@1 40.000 (35.912) Prec@5 63.125 (66.476) Epoch: [7][7820/11272] Time 0.883 (0.837) Data 0.001 (0.002) Loss 2.7005 (2.6568) Prec@1 34.375 (35.911) Prec@5 65.625 (66.476) Epoch: [7][7830/11272] Time 0.823 (0.837) Data 0.002 (0.002) Loss 2.6771 (2.6569) Prec@1 31.875 (35.907) Prec@5 63.125 (66.473) Epoch: [7][7840/11272] Time 0.771 (0.837) Data 0.003 (0.002) Loss 2.4352 (2.6570) Prec@1 45.625 (35.908) Prec@5 73.125 (66.472) Epoch: [7][7850/11272] Time 0.923 (0.837) Data 0.002 (0.002) Loss 2.6580 (2.6571) Prec@1 34.375 (35.908) Prec@5 66.250 (66.470) Epoch: [7][7860/11272] Time 0.883 (0.837) Data 0.001 (0.002) Loss 2.3427 (2.6571) Prec@1 43.750 (35.907) Prec@5 74.375 (66.470) Epoch: [7][7870/11272] Time 0.772 (0.837) Data 0.002 (0.002) Loss 2.5213 (2.6570) Prec@1 36.875 (35.907) Prec@5 72.500 (66.474) Epoch: [7][7880/11272] Time 0.770 (0.837) Data 0.002 (0.002) Loss 2.4286 (2.6570) Prec@1 37.500 (35.906) Prec@5 73.125 (66.475) Epoch: [7][7890/11272] Time 0.887 (0.837) Data 0.002 (0.002) Loss 2.9097 (2.6570) Prec@1 32.500 (35.904) Prec@5 61.250 (66.474) Epoch: [7][7900/11272] Time 0.853 (0.837) Data 0.001 (0.002) Loss 2.4787 (2.6570) Prec@1 41.250 (35.905) Prec@5 66.250 (66.474) Epoch: [7][7910/11272] Time 0.804 (0.837) Data 0.002 (0.002) Loss 2.6234 (2.6570) Prec@1 36.875 (35.904) Prec@5 69.375 (66.475) Epoch: [7][7920/11272] Time 0.736 (0.837) Data 0.002 (0.002) Loss 2.5946 (2.6569) Prec@1 38.750 (35.907) Prec@5 68.750 (66.476) Epoch: [7][7930/11272] Time 0.889 (0.837) Data 0.001 (0.002) Loss 2.6045 (2.6569) Prec@1 34.375 (35.908) Prec@5 68.750 (66.476) Epoch: [7][7940/11272] Time 0.779 (0.837) Data 0.001 (0.002) Loss 2.6533 (2.6568) Prec@1 35.000 (35.912) Prec@5 67.500 (66.478) Epoch: [7][7950/11272] Time 0.789 (0.837) Data 0.002 (0.002) Loss 2.4568 (2.6568) Prec@1 38.750 (35.912) Prec@5 68.125 (66.479) Epoch: [7][7960/11272] Time 0.875 (0.837) Data 0.001 (0.002) Loss 2.6917 (2.6568) Prec@1 30.625 (35.913) Prec@5 63.125 (66.478) Epoch: [7][7970/11272] Time 1.021 (0.837) Data 0.002 (0.002) Loss 2.4465 (2.6568) Prec@1 42.500 (35.913) Prec@5 71.875 (66.479) Epoch: [7][7980/11272] Time 0.796 (0.837) Data 0.001 (0.002) Loss 2.4451 (2.6568) Prec@1 40.625 (35.914) Prec@5 69.375 (66.478) Epoch: [7][7990/11272] Time 0.813 (0.837) Data 0.002 (0.002) Loss 2.5002 (2.6568) Prec@1 38.125 (35.913) Prec@5 68.750 (66.478) Epoch: [7][8000/11272] Time 0.915 (0.837) Data 0.001 (0.002) Loss 2.7537 (2.6568) Prec@1 32.500 (35.911) Prec@5 68.750 (66.478) Epoch: [7][8010/11272] Time 0.965 (0.837) Data 0.002 (0.002) Loss 2.5262 (2.6568) Prec@1 32.500 (35.910) Prec@5 71.875 (66.479) Epoch: [7][8020/11272] Time 0.747 (0.837) Data 0.001 (0.002) Loss 2.6016 (2.6568) Prec@1 41.875 (35.911) Prec@5 69.375 (66.478) Epoch: [7][8030/11272] Time 0.761 (0.837) Data 0.002 (0.002) Loss 2.8022 (2.6568) Prec@1 36.250 (35.911) Prec@5 65.000 (66.476) Epoch: [7][8040/11272] Time 0.865 (0.837) Data 0.001 (0.002) Loss 3.0370 (2.6569) Prec@1 31.875 (35.912) Prec@5 60.000 (66.476) Epoch: [7][8050/11272] Time 0.921 (0.837) Data 0.002 (0.002) Loss 2.5864 (2.6569) Prec@1 34.375 (35.911) Prec@5 65.625 (66.476) Epoch: [7][8060/11272] Time 0.746 (0.837) Data 0.001 (0.002) Loss 2.6887 (2.6569) Prec@1 39.375 (35.909) Prec@5 65.000 (66.475) Epoch: [7][8070/11272] Time 0.911 (0.837) Data 0.002 (0.002) Loss 2.7761 (2.6569) Prec@1 35.625 (35.910) Prec@5 61.875 (66.473) Epoch: [7][8080/11272] Time 0.890 (0.837) Data 0.002 (0.002) Loss 2.6157 (2.6569) Prec@1 33.125 (35.908) Prec@5 66.250 (66.474) Epoch: [7][8090/11272] Time 0.780 (0.837) Data 0.001 (0.002) Loss 2.6000 (2.6570) Prec@1 36.250 (35.907) Prec@5 68.750 (66.475) Epoch: [7][8100/11272] Time 0.774 (0.837) Data 0.001 (0.002) Loss 2.4428 (2.6569) Prec@1 40.625 (35.908) Prec@5 69.375 (66.474) Epoch: [7][8110/11272] Time 0.959 (0.837) Data 0.002 (0.002) Loss 2.7595 (2.6570) Prec@1 34.375 (35.905) Prec@5 60.625 (66.473) Epoch: [7][8120/11272] Time 0.835 (0.837) Data 0.001 (0.002) Loss 2.6655 (2.6570) Prec@1 30.625 (35.904) Prec@5 66.250 (66.473) Epoch: [7][8130/11272] Time 0.777 (0.837) Data 0.002 (0.002) Loss 2.7779 (2.6570) Prec@1 36.250 (35.905) Prec@5 64.375 (66.473) Epoch: [7][8140/11272] Time 0.739 (0.837) Data 0.001 (0.002) Loss 2.7771 (2.6570) Prec@1 30.625 (35.901) Prec@5 66.250 (66.471) Epoch: [7][8150/11272] Time 0.892 (0.837) Data 0.003 (0.002) Loss 2.8971 (2.6570) Prec@1 34.375 (35.902) Prec@5 63.125 (66.474) Epoch: [7][8160/11272] Time 0.930 (0.837) Data 0.001 (0.002) Loss 2.9988 (2.6569) Prec@1 31.250 (35.903) Prec@5 60.000 (66.476) Epoch: [7][8170/11272] Time 0.771 (0.837) Data 0.002 (0.002) Loss 2.5338 (2.6570) Prec@1 35.625 (35.903) Prec@5 70.000 (66.476) Epoch: [7][8180/11272] Time 0.750 (0.837) Data 0.002 (0.002) Loss 2.7358 (2.6569) Prec@1 35.000 (35.904) Prec@5 66.875 (66.479) Epoch: [7][8190/11272] Time 0.892 (0.837) Data 0.002 (0.002) Loss 3.0092 (2.6570) Prec@1 28.750 (35.903) Prec@5 58.750 (66.477) Epoch: [7][8200/11272] Time 0.727 (0.837) Data 0.003 (0.002) Loss 2.8220 (2.6571) Prec@1 31.875 (35.899) Prec@5 60.625 (66.477) Epoch: [7][8210/11272] Time 0.757 (0.837) Data 0.002 (0.002) Loss 2.6915 (2.6571) Prec@1 34.375 (35.898) Prec@5 64.375 (66.477) Epoch: [7][8220/11272] Time 0.903 (0.837) Data 0.001 (0.002) Loss 2.6781 (2.6571) Prec@1 31.250 (35.898) Prec@5 65.000 (66.477) Epoch: [7][8230/11272] Time 0.911 (0.837) Data 0.002 (0.002) Loss 2.7016 (2.6571) Prec@1 36.250 (35.898) Prec@5 68.750 (66.475) Epoch: [7][8240/11272] Time 0.733 (0.837) Data 0.001 (0.002) Loss 2.4349 (2.6570) Prec@1 41.875 (35.901) Prec@5 68.750 (66.477) Epoch: [7][8250/11272] Time 0.755 (0.837) Data 0.002 (0.002) Loss 2.4930 (2.6571) Prec@1 41.250 (35.898) Prec@5 64.375 (66.477) Epoch: [7][8260/11272] Time 0.834 (0.837) Data 0.001 (0.002) Loss 2.4719 (2.6570) Prec@1 41.250 (35.899) Prec@5 68.750 (66.479) Epoch: [7][8270/11272] Time 0.917 (0.837) Data 0.002 (0.002) Loss 2.7336 (2.6569) Prec@1 35.625 (35.901) Prec@5 65.000 (66.481) Epoch: [7][8280/11272] Time 0.786 (0.837) Data 0.002 (0.002) Loss 2.7437 (2.6570) Prec@1 36.250 (35.899) Prec@5 60.000 (66.478) Epoch: [7][8290/11272] Time 0.774 (0.837) Data 0.002 (0.002) Loss 2.7841 (2.6570) Prec@1 36.250 (35.901) Prec@5 63.750 (66.478) Epoch: [7][8300/11272] Time 0.913 (0.837) Data 0.001 (0.002) Loss 2.8474 (2.6571) Prec@1 31.875 (35.899) Prec@5 64.375 (66.478) Epoch: [7][8310/11272] Time 0.906 (0.837) Data 0.002 (0.002) Loss 2.6370 (2.6570) Prec@1 38.125 (35.900) Prec@5 66.250 (66.479) Epoch: [7][8320/11272] Time 0.763 (0.837) Data 0.001 (0.002) Loss 2.8161 (2.6570) Prec@1 37.500 (35.901) Prec@5 66.250 (66.478) Epoch: [7][8330/11272] Time 0.900 (0.837) Data 0.002 (0.002) Loss 2.9092 (2.6570) Prec@1 33.125 (35.900) Prec@5 60.000 (66.478) Epoch: [7][8340/11272] Time 0.917 (0.837) Data 0.002 (0.002) Loss 2.5188 (2.6569) Prec@1 36.875 (35.901) Prec@5 71.250 (66.478) Epoch: [7][8350/11272] Time 0.774 (0.837) Data 0.002 (0.002) Loss 2.8440 (2.6570) Prec@1 31.875 (35.900) Prec@5 64.375 (66.477) Epoch: [7][8360/11272] Time 0.774 (0.837) Data 0.001 (0.002) Loss 2.4472 (2.6570) Prec@1 40.625 (35.900) Prec@5 67.500 (66.477) Epoch: [7][8370/11272] Time 0.905 (0.837) Data 0.002 (0.002) Loss 2.2843 (2.6569) Prec@1 41.875 (35.901) Prec@5 71.875 (66.478) Epoch: [7][8380/11272] Time 0.906 (0.837) Data 0.002 (0.002) Loss 2.5051 (2.6568) Prec@1 37.500 (35.902) Prec@5 68.125 (66.479) Epoch: [7][8390/11272] Time 0.751 (0.837) Data 0.001 (0.002) Loss 2.9554 (2.6568) Prec@1 28.750 (35.904) Prec@5 60.000 (66.479) Epoch: [7][8400/11272] Time 0.741 (0.837) Data 0.002 (0.002) Loss 2.8310 (2.6569) Prec@1 31.250 (35.903) Prec@5 65.625 (66.476) Epoch: [7][8410/11272] Time 0.881 (0.837) Data 0.002 (0.002) Loss 2.5132 (2.6568) Prec@1 41.250 (35.904) Prec@5 71.875 (66.478) Epoch: [7][8420/11272] Time 0.856 (0.837) Data 0.001 (0.002) Loss 2.3899 (2.6569) Prec@1 40.625 (35.904) Prec@5 76.875 (66.478) Epoch: [7][8430/11272] Time 0.766 (0.837) Data 0.002 (0.002) Loss 2.8986 (2.6570) Prec@1 33.750 (35.902) Prec@5 65.000 (66.475) Epoch: [7][8440/11272] Time 0.748 (0.837) Data 0.002 (0.002) Loss 2.8412 (2.6572) Prec@1 32.500 (35.899) Prec@5 63.750 (66.473) Epoch: [7][8450/11272] Time 0.871 (0.837) Data 0.002 (0.002) Loss 2.5738 (2.6571) Prec@1 37.500 (35.900) Prec@5 66.875 (66.475) Epoch: [7][8460/11272] Time 0.736 (0.837) Data 0.004 (0.002) Loss 2.8232 (2.6571) Prec@1 31.250 (35.901) Prec@5 64.375 (66.472) Epoch: [7][8470/11272] Time 0.790 (0.837) Data 0.002 (0.002) Loss 2.9294 (2.6572) Prec@1 31.875 (35.900) Prec@5 61.875 (66.472) Epoch: [7][8480/11272] Time 0.841 (0.837) Data 0.001 (0.002) Loss 2.9777 (2.6573) Prec@1 34.375 (35.900) Prec@5 60.625 (66.470) Epoch: [7][8490/11272] Time 0.933 (0.837) Data 0.001 (0.002) Loss 2.7592 (2.6573) Prec@1 35.000 (35.896) Prec@5 66.875 (66.470) Epoch: [7][8500/11272] Time 0.725 (0.837) Data 0.002 (0.002) Loss 2.5198 (2.6573) Prec@1 41.875 (35.898) Prec@5 67.500 (66.469) Epoch: [7][8510/11272] Time 0.818 (0.837) Data 0.001 (0.002) Loss 2.7200 (2.6574) Prec@1 34.375 (35.896) Prec@5 65.000 (66.468) Epoch: [7][8520/11272] Time 0.878 (0.837) Data 0.001 (0.002) Loss 2.5379 (2.6575) Prec@1 41.250 (35.893) Prec@5 67.500 (66.466) Epoch: [7][8530/11272] Time 0.946 (0.837) Data 0.002 (0.002) Loss 2.6119 (2.6575) Prec@1 31.875 (35.891) Prec@5 66.875 (66.466) Epoch: [7][8540/11272] Time 0.794 (0.837) Data 0.001 (0.002) Loss 2.8046 (2.6576) Prec@1 35.000 (35.890) Prec@5 64.375 (66.465) Epoch: [7][8550/11272] Time 0.810 (0.837) Data 0.002 (0.002) Loss 2.8116 (2.6576) Prec@1 28.125 (35.890) Prec@5 61.250 (66.465) Epoch: [7][8560/11272] Time 0.902 (0.837) Data 0.001 (0.002) Loss 2.6711 (2.6576) Prec@1 35.000 (35.890) Prec@5 65.625 (66.464) Epoch: [7][8570/11272] Time 0.877 (0.837) Data 0.002 (0.002) Loss 2.4888 (2.6577) Prec@1 39.375 (35.889) Prec@5 68.750 (66.463) Epoch: [7][8580/11272] Time 0.756 (0.837) Data 0.002 (0.002) Loss 2.7108 (2.6576) Prec@1 35.000 (35.889) Prec@5 65.000 (66.463) Epoch: [7][8590/11272] Time 0.953 (0.837) Data 0.003 (0.002) Loss 2.7289 (2.6576) Prec@1 31.250 (35.888) Prec@5 68.750 (66.464) Epoch: [7][8600/11272] Time 0.833 (0.837) Data 0.001 (0.002) Loss 2.6476 (2.6576) Prec@1 36.250 (35.889) Prec@5 66.875 (66.466) Epoch: [7][8610/11272] Time 0.777 (0.837) Data 0.002 (0.002) Loss 2.7028 (2.6576) Prec@1 38.125 (35.888) Prec@5 66.875 (66.466) Epoch: [7][8620/11272] Time 0.736 (0.837) Data 0.001 (0.002) Loss 2.6244 (2.6575) Prec@1 34.375 (35.889) Prec@5 65.625 (66.467) Epoch: [7][8630/11272] Time 0.867 (0.837) Data 0.003 (0.002) Loss 2.7293 (2.6575) Prec@1 33.125 (35.890) Prec@5 67.500 (66.468) Epoch: [7][8640/11272] Time 0.931 (0.837) Data 0.001 (0.002) Loss 2.9026 (2.6575) Prec@1 31.250 (35.887) Prec@5 61.875 (66.467) Epoch: [7][8650/11272] Time 0.815 (0.837) Data 0.002 (0.002) Loss 2.6586 (2.6575) Prec@1 35.625 (35.887) Prec@5 65.625 (66.467) Epoch: [7][8660/11272] Time 0.757 (0.837) Data 0.001 (0.002) Loss 2.8675 (2.6576) Prec@1 30.000 (35.886) Prec@5 60.625 (66.466) Epoch: [7][8670/11272] Time 0.904 (0.837) Data 0.001 (0.002) Loss 2.1380 (2.6576) Prec@1 46.250 (35.887) Prec@5 76.875 (66.467) Epoch: [7][8680/11272] Time 0.886 (0.837) Data 0.001 (0.002) Loss 2.5557 (2.6575) Prec@1 35.625 (35.889) Prec@5 68.750 (66.468) Epoch: [7][8690/11272] Time 0.777 (0.837) Data 0.002 (0.002) Loss 2.6860 (2.6575) Prec@1 34.375 (35.890) Prec@5 66.250 (66.469) Epoch: [7][8700/11272] Time 0.777 (0.837) Data 0.001 (0.002) Loss 2.3093 (2.6574) Prec@1 40.625 (35.892) Prec@5 75.625 (66.470) Epoch: [7][8710/11272] Time 0.831 (0.837) Data 0.002 (0.002) Loss 3.2046 (2.6574) Prec@1 28.750 (35.891) Prec@5 54.375 (66.472) Epoch: [7][8720/11272] Time 0.869 (0.837) Data 0.002 (0.002) Loss 2.2922 (2.6573) Prec@1 42.500 (35.894) Prec@5 74.375 (66.474) Epoch: [7][8730/11272] Time 0.780 (0.837) Data 0.002 (0.002) Loss 2.6494 (2.6573) Prec@1 34.375 (35.893) Prec@5 68.750 (66.474) Epoch: [7][8740/11272] Time 0.856 (0.837) Data 0.001 (0.002) Loss 2.7310 (2.6574) Prec@1 32.500 (35.893) Prec@5 63.750 (66.473) Epoch: [7][8750/11272] Time 0.907 (0.837) Data 0.001 (0.002) Loss 2.4175 (2.6574) Prec@1 40.000 (35.893) Prec@5 69.375 (66.473) Epoch: [7][8760/11272] Time 0.777 (0.837) Data 0.002 (0.002) Loss 2.4084 (2.6573) Prec@1 35.625 (35.894) Prec@5 69.375 (66.475) Epoch: [7][8770/11272] Time 0.756 (0.837) Data 0.002 (0.002) Loss 2.6527 (2.6572) Prec@1 40.000 (35.893) Prec@5 67.500 (66.476) Epoch: [7][8780/11272] Time 0.881 (0.837) Data 0.001 (0.002) Loss 2.6411 (2.6572) Prec@1 34.375 (35.894) Prec@5 65.000 (66.476) Epoch: [7][8790/11272] Time 0.961 (0.837) Data 0.002 (0.002) Loss 2.6884 (2.6572) Prec@1 37.500 (35.894) Prec@5 68.125 (66.476) Epoch: [7][8800/11272] Time 0.795 (0.837) Data 0.001 (0.002) Loss 2.8245 (2.6572) Prec@1 36.250 (35.894) Prec@5 64.375 (66.476) Epoch: [7][8810/11272] Time 0.789 (0.837) Data 0.002 (0.002) Loss 2.6508 (2.6572) Prec@1 33.125 (35.893) Prec@5 72.500 (66.477) Epoch: [7][8820/11272] Time 0.851 (0.837) Data 0.001 (0.002) Loss 2.6332 (2.6571) Prec@1 36.250 (35.893) Prec@5 65.625 (66.480) Epoch: [7][8830/11272] Time 0.911 (0.837) Data 0.004 (0.002) Loss 2.3795 (2.6571) Prec@1 41.875 (35.893) Prec@5 72.500 (66.480) Epoch: [7][8840/11272] Time 0.742 (0.837) Data 0.002 (0.002) Loss 2.9307 (2.6571) Prec@1 28.125 (35.891) Prec@5 61.250 (66.481) Epoch: [7][8850/11272] Time 0.786 (0.837) Data 0.002 (0.002) Loss 2.6675 (2.6571) Prec@1 35.625 (35.891) Prec@5 68.125 (66.481) Epoch: [7][8860/11272] Time 0.910 (0.837) Data 0.001 (0.002) Loss 2.3543 (2.6571) Prec@1 43.750 (35.893) Prec@5 73.750 (66.482) Epoch: [7][8870/11272] Time 0.767 (0.837) Data 0.001 (0.002) Loss 2.6429 (2.6570) Prec@1 36.250 (35.895) Prec@5 69.375 (66.482) Epoch: [7][8880/11272] Time 0.758 (0.837) Data 0.002 (0.002) Loss 2.9383 (2.6571) Prec@1 30.625 (35.895) Prec@5 60.625 (66.482) Epoch: [7][8890/11272] Time 0.911 (0.837) Data 0.002 (0.002) Loss 2.6012 (2.6571) Prec@1 34.375 (35.894) Prec@5 64.375 (66.481) Epoch: [7][8900/11272] Time 0.859 (0.837) Data 0.001 (0.002) Loss 2.7489 (2.6572) Prec@1 35.625 (35.891) Prec@5 67.500 (66.479) Epoch: [7][8910/11272] Time 0.805 (0.837) Data 0.002 (0.002) Loss 2.8162 (2.6572) Prec@1 28.750 (35.890) Prec@5 63.125 (66.479) Epoch: [7][8920/11272] Time 0.757 (0.837) Data 0.001 (0.002) Loss 2.6948 (2.6572) Prec@1 36.250 (35.890) Prec@5 64.375 (66.478) Epoch: [7][8930/11272] Time 0.897 (0.837) Data 0.002 (0.002) Loss 2.8141 (2.6572) Prec@1 38.750 (35.891) Prec@5 65.625 (66.479) Epoch: [7][8940/11272] Time 0.864 (0.837) Data 0.001 (0.002) Loss 2.8022 (2.6572) Prec@1 38.750 (35.891) Prec@5 65.625 (66.479) Epoch: [7][8950/11272] Time 0.768 (0.837) Data 0.002 (0.002) Loss 2.7867 (2.6572) Prec@1 33.125 (35.890) Prec@5 65.000 (66.480) Epoch: [7][8960/11272] Time 0.783 (0.837) Data 0.001 (0.002) Loss 2.6313 (2.6572) Prec@1 36.875 (35.890) Prec@5 68.750 (66.481) Epoch: [7][8970/11272] Time 0.920 (0.837) Data 0.002 (0.002) Loss 2.7238 (2.6572) Prec@1 34.375 (35.892) Prec@5 63.750 (66.483) Epoch: [7][8980/11272] Time 0.867 (0.837) Data 0.001 (0.002) Loss 2.7387 (2.6573) Prec@1 25.625 (35.889) Prec@5 68.125 (66.482) Epoch: [7][8990/11272] Time 0.789 (0.837) Data 0.002 (0.002) Loss 2.4786 (2.6573) Prec@1 36.875 (35.889) Prec@5 74.375 (66.482) Epoch: [7][9000/11272] Time 0.922 (0.837) Data 0.001 (0.002) Loss 2.4777 (2.6572) Prec@1 43.125 (35.892) Prec@5 68.750 (66.484) Epoch: [7][9010/11272] Time 0.912 (0.837) Data 0.002 (0.002) Loss 2.3466 (2.6571) Prec@1 41.875 (35.894) Prec@5 72.500 (66.487) Epoch: [7][9020/11272] Time 0.771 (0.837) Data 0.002 (0.002) Loss 2.6447 (2.6571) Prec@1 40.625 (35.895) Prec@5 65.000 (66.487) Epoch: [7][9030/11272] Time 0.775 (0.837) Data 0.002 (0.002) Loss 2.5292 (2.6571) Prec@1 40.625 (35.896) Prec@5 66.875 (66.488) Epoch: [7][9040/11272] Time 0.865 (0.837) Data 0.001 (0.002) Loss 2.7134 (2.6571) Prec@1 36.875 (35.896) Prec@5 65.000 (66.487) Epoch: [7][9050/11272] Time 0.904 (0.837) Data 0.002 (0.002) Loss 2.9615 (2.6571) Prec@1 26.875 (35.895) Prec@5 59.375 (66.484) Epoch: [7][9060/11272] Time 0.758 (0.837) Data 0.001 (0.002) Loss 2.8315 (2.6571) Prec@1 36.250 (35.896) Prec@5 63.125 (66.485) Epoch: [7][9070/11272] Time 0.816 (0.837) Data 0.002 (0.002) Loss 2.8588 (2.6571) Prec@1 35.000 (35.896) Prec@5 65.000 (66.483) Epoch: [7][9080/11272] Time 0.864 (0.837) Data 0.001 (0.002) Loss 2.2965 (2.6571) Prec@1 42.500 (35.897) Prec@5 74.375 (66.485) Epoch: [7][9090/11272] Time 0.975 (0.837) Data 0.002 (0.002) Loss 2.6384 (2.6571) Prec@1 31.875 (35.898) Prec@5 65.000 (66.484) Epoch: [7][9100/11272] Time 0.757 (0.837) Data 0.002 (0.002) Loss 2.7535 (2.6571) Prec@1 33.125 (35.898) Prec@5 64.375 (66.485) Epoch: [7][9110/11272] Time 0.769 (0.837) Data 0.002 (0.002) Loss 2.5299 (2.6571) Prec@1 38.125 (35.897) Prec@5 66.875 (66.485) Epoch: [7][9120/11272] Time 0.907 (0.837) Data 0.001 (0.002) Loss 2.8761 (2.6571) Prec@1 28.750 (35.897) Prec@5 64.375 (66.485) Epoch: [7][9130/11272] Time 0.762 (0.837) Data 0.004 (0.002) Loss 2.7089 (2.6571) Prec@1 33.750 (35.896) Prec@5 61.250 (66.484) Epoch: [7][9140/11272] Time 0.776 (0.837) Data 0.001 (0.002) Loss 2.8472 (2.6571) Prec@1 31.875 (35.897) Prec@5 63.125 (66.485) Epoch: [7][9150/11272] Time 0.923 (0.837) Data 0.002 (0.002) Loss 2.6747 (2.6571) Prec@1 36.875 (35.898) Prec@5 66.875 (66.485) Epoch: [7][9160/11272] Time 0.885 (0.837) Data 0.001 (0.002) Loss 2.6288 (2.6570) Prec@1 39.375 (35.899) Prec@5 65.625 (66.487) Epoch: [7][9170/11272] Time 0.773 (0.837) Data 0.001 (0.002) Loss 2.8105 (2.6570) Prec@1 37.500 (35.901) Prec@5 60.000 (66.488) Epoch: [7][9180/11272] Time 0.728 (0.837) Data 0.002 (0.002) Loss 2.7208 (2.6570) Prec@1 34.375 (35.901) Prec@5 65.625 (66.489) Epoch: [7][9190/11272] Time 0.906 (0.837) Data 0.002 (0.002) Loss 2.7379 (2.6570) Prec@1 32.500 (35.900) Prec@5 64.375 (66.490) Epoch: [7][9200/11272] Time 0.909 (0.837) Data 0.001 (0.002) Loss 2.6152 (2.6570) Prec@1 37.500 (35.901) Prec@5 63.125 (66.490) Epoch: [7][9210/11272] Time 0.808 (0.837) Data 0.001 (0.002) Loss 2.3706 (2.6570) Prec@1 42.500 (35.901) Prec@5 76.250 (66.490) Epoch: [7][9220/11272] Time 0.741 (0.837) Data 0.001 (0.002) Loss 2.6849 (2.6570) Prec@1 31.875 (35.900) Prec@5 68.750 (66.491) Epoch: [7][9230/11272] Time 0.909 (0.837) Data 0.002 (0.002) Loss 2.5170 (2.6571) Prec@1 38.750 (35.899) Prec@5 69.375 (66.488) Epoch: [7][9240/11272] Time 0.887 (0.837) Data 0.001 (0.002) Loss 2.9760 (2.6571) Prec@1 27.500 (35.898) Prec@5 58.750 (66.486) Epoch: [7][9250/11272] Time 0.816 (0.837) Data 0.002 (0.002) Loss 2.5808 (2.6570) Prec@1 33.125 (35.899) Prec@5 67.500 (66.490) Epoch: [7][9260/11272] Time 0.860 (0.837) Data 0.001 (0.002) Loss 2.7000 (2.6570) Prec@1 36.250 (35.898) Prec@5 65.625 (66.489) Epoch: [7][9270/11272] Time 0.931 (0.837) Data 0.003 (0.002) Loss 2.6363 (2.6570) Prec@1 34.375 (35.899) Prec@5 67.500 (66.488) Epoch: [7][9280/11272] Time 0.760 (0.837) Data 0.001 (0.002) Loss 2.9467 (2.6570) Prec@1 31.875 (35.898) Prec@5 57.500 (66.489) Epoch: [7][9290/11272] Time 0.791 (0.837) Data 0.002 (0.002) Loss 2.5262 (2.6570) Prec@1 36.875 (35.897) Prec@5 69.375 (66.490) Epoch: [7][9300/11272] Time 0.902 (0.837) Data 0.001 (0.002) Loss 2.7669 (2.6570) Prec@1 32.500 (35.898) Prec@5 65.625 (66.490) Epoch: [7][9310/11272] Time 0.887 (0.837) Data 0.002 (0.002) Loss 2.7169 (2.6570) Prec@1 31.250 (35.898) Prec@5 63.750 (66.490) Epoch: [7][9320/11272] Time 0.755 (0.837) Data 0.002 (0.002) Loss 2.7866 (2.6571) Prec@1 33.125 (35.897) Prec@5 64.375 (66.490) Epoch: [7][9330/11272] Time 0.764 (0.837) Data 0.003 (0.002) Loss 2.7708 (2.6571) Prec@1 34.375 (35.897) Prec@5 66.250 (66.492) Epoch: [7][9340/11272] Time 0.865 (0.837) Data 0.001 (0.002) Loss 2.7255 (2.6571) Prec@1 38.750 (35.896) Prec@5 64.375 (66.491) Epoch: [7][9350/11272] Time 0.923 (0.837) Data 0.002 (0.002) Loss 2.6486 (2.6571) Prec@1 36.875 (35.896) Prec@5 68.125 (66.491) Epoch: [7][9360/11272] Time 0.759 (0.837) Data 0.001 (0.002) Loss 2.4035 (2.6572) Prec@1 42.500 (35.896) Prec@5 73.125 (66.489) Epoch: [7][9370/11272] Time 0.822 (0.837) Data 0.002 (0.002) Loss 2.6052 (2.6571) Prec@1 36.250 (35.898) Prec@5 70.625 (66.489) Epoch: [7][9380/11272] Time 0.861 (0.837) Data 0.002 (0.002) Loss 2.8173 (2.6572) Prec@1 30.625 (35.894) Prec@5 61.250 (66.489) Epoch: [7][9390/11272] Time 0.779 (0.837) Data 0.004 (0.002) Loss 2.7956 (2.6572) Prec@1 33.750 (35.894) Prec@5 64.375 (66.489) Epoch: [7][9400/11272] Time 0.762 (0.837) Data 0.001 (0.002) Loss 2.5319 (2.6572) Prec@1 41.250 (35.895) Prec@5 69.375 (66.489) Epoch: [7][9410/11272] Time 0.897 (0.837) Data 0.003 (0.002) Loss 2.7922 (2.6571) Prec@1 36.875 (35.896) Prec@5 65.000 (66.491) Epoch: [7][9420/11272] Time 0.891 (0.837) Data 0.001 (0.002) Loss 2.6088 (2.6571) Prec@1 36.875 (35.896) Prec@5 68.750 (66.491) Epoch: [7][9430/11272] Time 0.793 (0.837) Data 0.002 (0.002) Loss 2.4919 (2.6570) Prec@1 40.000 (35.898) Prec@5 68.125 (66.491) Epoch: [7][9440/11272] Time 0.757 (0.837) Data 0.002 (0.002) Loss 2.4125 (2.6571) Prec@1 42.500 (35.898) Prec@5 71.875 (66.490) Epoch: [7][9450/11272] Time 0.928 (0.837) Data 0.002 (0.002) Loss 2.3111 (2.6571) Prec@1 42.500 (35.897) Prec@5 76.875 (66.491) Epoch: [7][9460/11272] Time 0.942 (0.837) Data 0.001 (0.002) Loss 2.3418 (2.6570) Prec@1 39.375 (35.897) Prec@5 71.250 (66.491) Epoch: [7][9470/11272] Time 0.801 (0.837) Data 0.002 (0.002) Loss 2.5888 (2.6569) Prec@1 33.125 (35.899) Prec@5 67.500 (66.493) Epoch: [7][9480/11272] Time 0.753 (0.837) Data 0.001 (0.002) Loss 2.8562 (2.6569) Prec@1 33.125 (35.900) Prec@5 63.125 (66.492) Epoch: [7][9490/11272] Time 0.864 (0.837) Data 0.002 (0.002) Loss 2.4072 (2.6568) Prec@1 45.625 (35.903) Prec@5 70.625 (66.495) Epoch: [7][9500/11272] Time 0.905 (0.837) Data 0.001 (0.002) Loss 2.8186 (2.6568) Prec@1 30.000 (35.901) Prec@5 64.375 (66.494) Epoch: [7][9510/11272] Time 0.801 (0.837) Data 0.002 (0.002) Loss 2.6504 (2.6568) Prec@1 37.500 (35.901) Prec@5 66.250 (66.493) Epoch: [7][9520/11272] Time 0.900 (0.837) Data 0.002 (0.002) Loss 2.6200 (2.6569) Prec@1 31.250 (35.901) Prec@5 68.125 (66.491) Epoch: [7][9530/11272] Time 0.954 (0.837) Data 0.002 (0.002) Loss 2.7715 (2.6568) Prec@1 35.000 (35.901) Prec@5 66.250 (66.490) Epoch: [7][9540/11272] Time 0.782 (0.837) Data 0.001 (0.002) Loss 2.6410 (2.6568) Prec@1 34.375 (35.901) Prec@5 66.250 (66.490) Epoch: [7][9550/11272] Time 0.801 (0.837) Data 0.002 (0.002) Loss 2.6680 (2.6569) Prec@1 37.500 (35.901) Prec@5 69.375 (66.490) Epoch: [7][9560/11272] Time 0.909 (0.837) Data 0.001 (0.002) Loss 2.4817 (2.6569) Prec@1 38.125 (35.902) Prec@5 71.250 (66.491) Epoch: [7][9570/11272] Time 0.921 (0.837) Data 0.002 (0.002) Loss 2.6760 (2.6569) Prec@1 37.500 (35.900) Prec@5 65.000 (66.490) Epoch: [7][9580/11272] Time 0.748 (0.837) Data 0.002 (0.002) Loss 2.6303 (2.6569) Prec@1 33.125 (35.900) Prec@5 66.875 (66.489) Epoch: [7][9590/11272] Time 0.763 (0.837) Data 0.002 (0.002) Loss 2.6269 (2.6569) Prec@1 35.000 (35.899) Prec@5 68.750 (66.490) Epoch: [7][9600/11272] Time 0.886 (0.837) Data 0.001 (0.002) Loss 2.8251 (2.6570) Prec@1 31.875 (35.899) Prec@5 63.750 (66.490) Epoch: [7][9610/11272] Time 0.905 (0.837) Data 0.002 (0.002) Loss 2.7147 (2.6571) Prec@1 28.750 (35.895) Prec@5 67.500 (66.487) Epoch: [7][9620/11272] Time 0.767 (0.837) Data 0.002 (0.002) Loss 2.7644 (2.6571) Prec@1 35.625 (35.895) Prec@5 63.125 (66.487) Epoch: [7][9630/11272] Time 0.807 (0.837) Data 0.002 (0.002) Loss 2.9425 (2.6572) Prec@1 30.000 (35.892) Prec@5 61.250 (66.483) Epoch: [7][9640/11272] Time 0.941 (0.837) Data 0.002 (0.002) Loss 2.4298 (2.6572) Prec@1 40.000 (35.892) Prec@5 67.500 (66.483) Epoch: [7][9650/11272] Time 0.886 (0.837) Data 0.002 (0.002) Loss 2.5068 (2.6572) Prec@1 39.375 (35.892) Prec@5 69.375 (66.484) Epoch: [7][9660/11272] Time 0.774 (0.837) Data 0.002 (0.002) Loss 2.5550 (2.6572) Prec@1 39.375 (35.893) Prec@5 70.000 (66.485) Epoch: [7][9670/11272] Time 0.881 (0.837) Data 0.002 (0.002) Loss 2.6098 (2.6571) Prec@1 33.750 (35.894) Prec@5 71.875 (66.485) Epoch: [7][9680/11272] Time 0.928 (0.837) Data 0.001 (0.002) Loss 2.5042 (2.6571) Prec@1 36.250 (35.895) Prec@5 68.125 (66.486) Epoch: [7][9690/11272] Time 0.801 (0.837) Data 0.002 (0.002) Loss 2.4656 (2.6570) Prec@1 38.125 (35.896) Prec@5 71.250 (66.488) Epoch: [7][9700/11272] Time 0.725 (0.837) Data 0.001 (0.002) Loss 2.6465 (2.6569) Prec@1 35.000 (35.897) Prec@5 68.125 (66.488) Epoch: [7][9710/11272] Time 0.937 (0.837) Data 0.001 (0.002) Loss 2.5965 (2.6569) Prec@1 33.750 (35.899) Prec@5 68.125 (66.489) Epoch: [7][9720/11272] Time 0.857 (0.837) Data 0.001 (0.002) Loss 2.4020 (2.6569) Prec@1 36.875 (35.897) Prec@5 76.250 (66.490) Epoch: [7][9730/11272] Time 0.782 (0.837) Data 0.002 (0.002) Loss 2.6153 (2.6569) Prec@1 35.000 (35.897) Prec@5 66.875 (66.489) Epoch: [7][9740/11272] Time 0.795 (0.837) Data 0.002 (0.002) Loss 2.5558 (2.6570) Prec@1 37.500 (35.896) Prec@5 67.500 (66.487) Epoch: [7][9750/11272] Time 0.925 (0.837) Data 0.002 (0.002) Loss 2.5349 (2.6570) Prec@1 39.375 (35.896) Prec@5 66.875 (66.486) Epoch: [7][9760/11272] Time 0.919 (0.837) Data 0.001 (0.002) Loss 2.7873 (2.6570) Prec@1 38.125 (35.898) Prec@5 65.625 (66.486) Epoch: [7][9770/11272] Time 0.777 (0.837) Data 0.002 (0.002) Loss 2.5057 (2.6570) Prec@1 40.000 (35.897) Prec@5 66.250 (66.486) Epoch: [7][9780/11272] Time 0.791 (0.837) Data 0.002 (0.002) Loss 2.8366 (2.6570) Prec@1 31.250 (35.896) Prec@5 61.875 (66.486) Epoch: [7][9790/11272] Time 0.923 (0.837) Data 0.002 (0.002) Loss 2.6851 (2.6570) Prec@1 31.250 (35.896) Prec@5 69.375 (66.486) Epoch: [7][9800/11272] Time 0.734 (0.837) Data 0.002 (0.002) Loss 2.6572 (2.6571) Prec@1 33.750 (35.894) Prec@5 69.375 (66.485) Epoch: [7][9810/11272] Time 0.759 (0.837) Data 0.002 (0.002) Loss 2.7385 (2.6570) Prec@1 33.750 (35.894) Prec@5 61.875 (66.486) Epoch: [7][9820/11272] Time 0.870 (0.837) Data 0.001 (0.002) Loss 2.8639 (2.6571) Prec@1 32.500 (35.893) Prec@5 65.000 (66.485) Epoch: [7][9830/11272] Time 0.826 (0.837) Data 0.002 (0.002) Loss 2.8125 (2.6571) Prec@1 33.125 (35.892) Prec@5 68.125 (66.485) Epoch: [7][9840/11272] Time 0.741 (0.837) Data 0.004 (0.002) Loss 2.7264 (2.6571) Prec@1 37.500 (35.892) Prec@5 63.750 (66.485) Epoch: [7][9850/11272] Time 0.792 (0.837) Data 0.002 (0.002) Loss 2.5078 (2.6570) Prec@1 39.375 (35.892) Prec@5 70.625 (66.487) Epoch: [7][9860/11272] Time 0.892 (0.837) Data 0.001 (0.002) Loss 2.8497 (2.6571) Prec@1 31.250 (35.892) Prec@5 66.875 (66.485) Epoch: [7][9870/11272] Time 0.937 (0.837) Data 0.002 (0.002) Loss 2.7919 (2.6571) Prec@1 32.500 (35.890) Prec@5 60.000 (66.483) Epoch: [7][9880/11272] Time 0.773 (0.837) Data 0.001 (0.002) Loss 2.4535 (2.6572) Prec@1 43.125 (35.889) Prec@5 73.125 (66.482) Epoch: [7][9890/11272] Time 0.808 (0.837) Data 0.002 (0.002) Loss 2.4439 (2.6572) Prec@1 38.125 (35.889) Prec@5 73.125 (66.483) Epoch: [7][9900/11272] Time 0.896 (0.837) Data 0.001 (0.002) Loss 2.8774 (2.6572) Prec@1 30.000 (35.888) Prec@5 58.750 (66.484) Epoch: [7][9910/11272] Time 0.915 (0.837) Data 0.002 (0.002) Loss 2.6955 (2.6572) Prec@1 39.375 (35.889) Prec@5 66.250 (66.484) Epoch: [7][9920/11272] Time 0.757 (0.837) Data 0.001 (0.002) Loss 2.7464 (2.6572) Prec@1 33.125 (35.888) Prec@5 61.250 (66.482) Epoch: [7][9930/11272] Time 0.817 (0.837) Data 0.001 (0.002) Loss 2.4121 (2.6572) Prec@1 39.375 (35.887) Prec@5 70.625 (66.483) Epoch: [7][9940/11272] Time 0.871 (0.837) Data 0.001 (0.002) Loss 2.8914 (2.6572) Prec@1 33.125 (35.888) Prec@5 62.500 (66.483) Epoch: [7][9950/11272] Time 0.764 (0.837) Data 0.002 (0.002) Loss 2.5676 (2.6571) Prec@1 40.000 (35.891) Prec@5 66.875 (66.485) Epoch: [7][9960/11272] Time 0.743 (0.837) Data 0.002 (0.002) Loss 2.8905 (2.6570) Prec@1 29.375 (35.891) Prec@5 64.375 (66.487) Epoch: [7][9970/11272] Time 0.908 (0.837) Data 0.002 (0.002) Loss 2.6974 (2.6569) Prec@1 28.125 (35.891) Prec@5 67.500 (66.489) Epoch: [7][9980/11272] Time 0.864 (0.837) Data 0.001 (0.002) Loss 2.7794 (2.6569) Prec@1 35.625 (35.893) Prec@5 60.000 (66.489) Epoch: [7][9990/11272] Time 0.787 (0.837) Data 0.002 (0.002) Loss 2.6775 (2.6570) Prec@1 33.125 (35.893) Prec@5 66.250 (66.488) Epoch: [7][10000/11272] Time 0.731 (0.837) Data 0.002 (0.002) Loss 2.4268 (2.6569) Prec@1 40.625 (35.894) Prec@5 73.750 (66.492) Epoch: [7][10010/11272] Time 0.884 (0.837) Data 0.002 (0.002) Loss 2.6440 (2.6569) Prec@1 36.250 (35.894) Prec@5 65.000 (66.492) Epoch: [7][10020/11272] Time 0.894 (0.837) Data 0.001 (0.002) Loss 2.7271 (2.6568) Prec@1 34.375 (35.894) Prec@5 66.250 (66.494) Epoch: [7][10030/11272] Time 0.798 (0.837) Data 0.002 (0.002) Loss 2.3922 (2.6568) Prec@1 48.125 (35.895) Prec@5 68.125 (66.493) Epoch: [7][10040/11272] Time 0.748 (0.837) Data 0.001 (0.002) Loss 2.4813 (2.6568) Prec@1 40.625 (35.896) Prec@5 73.750 (66.493) Epoch: [7][10050/11272] Time 0.865 (0.837) Data 0.002 (0.002) Loss 2.8760 (2.6568) Prec@1 32.500 (35.898) Prec@5 68.125 (66.493) Epoch: [7][10060/11272] Time 0.748 (0.837) Data 0.004 (0.002) Loss 2.8633 (2.6567) Prec@1 35.625 (35.901) Prec@5 66.250 (66.496) Epoch: [7][10070/11272] Time 0.791 (0.837) Data 0.002 (0.002) Loss 2.6086 (2.6567) Prec@1 35.000 (35.901) Prec@5 68.750 (66.497) Epoch: [7][10080/11272] Time 0.899 (0.837) Data 0.001 (0.002) Loss 2.8259 (2.6567) Prec@1 31.250 (35.902) Prec@5 66.875 (66.499) Epoch: [7][10090/11272] Time 0.914 (0.837) Data 0.002 (0.002) Loss 2.7426 (2.6567) Prec@1 35.625 (35.900) Prec@5 63.750 (66.499) Epoch: [7][10100/11272] Time 0.733 (0.837) Data 0.002 (0.002) Loss 2.6932 (2.6567) Prec@1 34.375 (35.900) Prec@5 66.250 (66.497) Epoch: [7][10110/11272] Time 0.782 (0.837) Data 0.002 (0.002) Loss 2.8735 (2.6567) Prec@1 29.375 (35.900) Prec@5 60.625 (66.499) Epoch: [7][10120/11272] Time 0.902 (0.837) Data 0.001 (0.002) Loss 2.7939 (2.6567) Prec@1 31.250 (35.900) Prec@5 63.125 (66.499) Epoch: [7][10130/11272] Time 0.932 (0.837) Data 0.002 (0.002) Loss 2.4282 (2.6567) Prec@1 39.375 (35.900) Prec@5 72.500 (66.498) Epoch: [7][10140/11272] Time 0.796 (0.837) Data 0.001 (0.002) Loss 2.9014 (2.6568) Prec@1 37.500 (35.900) Prec@5 63.750 (66.497) Epoch: [7][10150/11272] Time 0.762 (0.837) Data 0.004 (0.002) Loss 2.6316 (2.6567) Prec@1 32.500 (35.900) Prec@5 66.250 (66.498) Epoch: [7][10160/11272] Time 0.873 (0.837) Data 0.001 (0.002) Loss 2.5778 (2.6567) Prec@1 40.625 (35.901) Prec@5 68.125 (66.499) Epoch: [7][10170/11272] Time 0.842 (0.837) Data 0.002 (0.002) Loss 2.8560 (2.6567) Prec@1 29.375 (35.899) Prec@5 62.500 (66.497) Epoch: [7][10180/11272] Time 0.768 (0.837) Data 0.004 (0.002) Loss 2.5749 (2.6567) Prec@1 36.250 (35.899) Prec@5 71.250 (66.498) Epoch: [7][10190/11272] Time 0.943 (0.837) Data 0.002 (0.002) Loss 2.5119 (2.6568) Prec@1 38.125 (35.898) Prec@5 68.750 (66.497) Epoch: [7][10200/11272] Time 0.871 (0.837) Data 0.001 (0.002) Loss 2.3692 (2.6567) Prec@1 39.375 (35.898) Prec@5 72.500 (66.498) Epoch: [7][10210/11272] Time 0.774 (0.837) Data 0.002 (0.002) Loss 2.2300 (2.6567) Prec@1 42.500 (35.899) Prec@5 74.375 (66.499) Epoch: [7][10220/11272] Time 0.748 (0.837) Data 0.002 (0.002) Loss 2.5313 (2.6566) Prec@1 39.375 (35.900) Prec@5 70.000 (66.501) Epoch: [7][10230/11272] Time 0.938 (0.837) Data 0.002 (0.002) Loss 2.6867 (2.6566) Prec@1 36.875 (35.900) Prec@5 70.000 (66.501) Epoch: [7][10240/11272] Time 0.927 (0.837) Data 0.001 (0.002) Loss 2.8239 (2.6567) Prec@1 36.250 (35.899) Prec@5 60.625 (66.501) Epoch: [7][10250/11272] Time 0.787 (0.837) Data 0.002 (0.002) Loss 2.8788 (2.6567) Prec@1 31.250 (35.899) Prec@5 64.375 (66.501) Epoch: [7][10260/11272] Time 0.731 (0.837) Data 0.001 (0.002) Loss 2.3610 (2.6566) Prec@1 40.000 (35.899) Prec@5 71.875 (66.502) Epoch: [7][10270/11272] Time 0.960 (0.837) Data 0.002 (0.002) Loss 2.7831 (2.6567) Prec@1 35.625 (35.897) Prec@5 64.375 (66.500) Epoch: [7][10280/11272] Time 0.902 (0.837) Data 0.001 (0.002) Loss 2.5156 (2.6567) Prec@1 38.750 (35.899) Prec@5 68.750 (66.501) Epoch: [7][10290/11272] Time 0.772 (0.837) Data 0.002 (0.002) Loss 2.4700 (2.6566) Prec@1 41.250 (35.900) Prec@5 71.250 (66.501) Epoch: [7][10300/11272] Time 0.776 (0.837) Data 0.002 (0.002) Loss 2.5822 (2.6566) Prec@1 36.875 (35.899) Prec@5 70.625 (66.500) Epoch: [7][10310/11272] Time 0.900 (0.837) Data 0.002 (0.002) Loss 2.7462 (2.6566) Prec@1 31.875 (35.900) Prec@5 65.625 (66.501) Epoch: [7][10320/11272] Time 0.741 (0.837) Data 0.003 (0.002) Loss 2.5311 (2.6566) Prec@1 38.750 (35.901) Prec@5 69.375 (66.502) Epoch: [7][10330/11272] Time 0.780 (0.837) Data 0.002 (0.002) Loss 2.6187 (2.6566) Prec@1 42.500 (35.900) Prec@5 67.500 (66.502) Epoch: [7][10340/11272] Time 0.865 (0.837) Data 0.001 (0.002) Loss 2.4602 (2.6566) Prec@1 40.625 (35.903) Prec@5 71.250 (66.503) Epoch: [7][10350/11272] Time 0.961 (0.837) Data 0.001 (0.002) Loss 2.4804 (2.6566) Prec@1 35.625 (35.902) Prec@5 68.750 (66.503) Epoch: [7][10360/11272] Time 0.769 (0.837) Data 0.002 (0.002) Loss 2.3132 (2.6565) Prec@1 43.125 (35.904) Prec@5 73.750 (66.504) Epoch: [7][10370/11272] Time 0.750 (0.837) Data 0.002 (0.002) Loss 2.8136 (2.6565) Prec@1 28.750 (35.904) Prec@5 60.625 (66.503) Epoch: [7][10380/11272] Time 0.963 (0.837) Data 0.001 (0.002) Loss 2.4833 (2.6565) Prec@1 40.625 (35.905) Prec@5 71.250 (66.503) Epoch: [7][10390/11272] Time 0.901 (0.837) Data 0.002 (0.002) Loss 2.9553 (2.6565) Prec@1 28.750 (35.905) Prec@5 63.750 (66.503) Epoch: [7][10400/11272] Time 0.764 (0.837) Data 0.001 (0.002) Loss 2.4945 (2.6565) Prec@1 36.875 (35.905) Prec@5 71.250 (66.502) Epoch: [7][10410/11272] Time 0.776 (0.837) Data 0.001 (0.002) Loss 2.8060 (2.6566) Prec@1 28.750 (35.902) Prec@5 65.625 (66.500) Epoch: [7][10420/11272] Time 0.875 (0.837) Data 0.001 (0.002) Loss 2.5740 (2.6565) Prec@1 39.375 (35.901) Prec@5 70.000 (66.499) Epoch: [7][10430/11272] Time 0.962 (0.837) Data 0.001 (0.002) Loss 2.8242 (2.6566) Prec@1 30.625 (35.902) Prec@5 65.625 (66.500) Epoch: [7][10440/11272] Time 0.814 (0.837) Data 0.002 (0.002) Loss 2.3924 (2.6565) Prec@1 43.750 (35.903) Prec@5 73.750 (66.501) Epoch: [7][10450/11272] Time 0.903 (0.837) Data 0.001 (0.002) Loss 2.7667 (2.6565) Prec@1 35.625 (35.902) Prec@5 63.125 (66.500) Epoch: [7][10460/11272] Time 0.948 (0.837) Data 0.002 (0.002) Loss 2.6810 (2.6565) Prec@1 38.125 (35.901) Prec@5 68.125 (66.499) Epoch: [7][10470/11272] Time 0.779 (0.837) Data 0.002 (0.002) Loss 3.2505 (2.6566) Prec@1 26.875 (35.900) Prec@5 58.750 (66.498) Epoch: [7][10480/11272] Time 0.837 (0.837) Data 0.001 (0.002) Loss 2.7631 (2.6566) Prec@1 37.500 (35.901) Prec@5 65.000 (66.498) Epoch: [7][10490/11272] Time 0.932 (0.837) Data 0.002 (0.002) Loss 2.7135 (2.6566) Prec@1 36.250 (35.901) Prec@5 64.375 (66.498) Epoch: [7][10500/11272] Time 0.930 (0.837) Data 0.001 (0.002) Loss 2.5723 (2.6566) Prec@1 34.375 (35.901) Prec@5 70.625 (66.497) Epoch: [7][10510/11272] Time 0.779 (0.837) Data 0.002 (0.002) Loss 2.7247 (2.6565) Prec@1 33.750 (35.903) Prec@5 68.125 (66.500) Epoch: [7][10520/11272] Time 0.737 (0.837) Data 0.002 (0.002) Loss 2.7390 (2.6565) Prec@1 28.750 (35.903) Prec@5 66.250 (66.499) Epoch: [7][10530/11272] Time 0.915 (0.837) Data 0.002 (0.002) Loss 2.5124 (2.6565) Prec@1 38.750 (35.903) Prec@5 70.000 (66.500) Epoch: [7][10540/11272] Time 0.936 (0.837) Data 0.002 (0.002) Loss 2.7634 (2.6564) Prec@1 34.375 (35.906) Prec@5 61.875 (66.500) Epoch: [7][10550/11272] Time 0.760 (0.837) Data 0.002 (0.002) Loss 2.5034 (2.6564) Prec@1 38.750 (35.906) Prec@5 66.875 (66.502) Epoch: [7][10560/11272] Time 0.755 (0.837) Data 0.001 (0.002) Loss 2.6532 (2.6564) Prec@1 33.750 (35.906) Prec@5 66.250 (66.502) Epoch: [7][10570/11272] Time 0.912 (0.837) Data 0.002 (0.002) Loss 2.4912 (2.6563) Prec@1 36.875 (35.907) Prec@5 66.250 (66.502) Epoch: [7][10580/11272] Time 0.872 (0.837) Data 0.002 (0.002) Loss 2.5177 (2.6564) Prec@1 38.125 (35.905) Prec@5 71.250 (66.501) Epoch: [7][10590/11272] Time 0.789 (0.837) Data 0.002 (0.002) Loss 2.5837 (2.6563) Prec@1 41.875 (35.906) Prec@5 69.375 (66.502) Epoch: [7][10600/11272] Time 0.865 (0.837) Data 0.003 (0.002) Loss 2.5254 (2.6563) Prec@1 38.750 (35.906) Prec@5 70.625 (66.501) Epoch: [7][10610/11272] Time 0.968 (0.837) Data 0.002 (0.002) Loss 2.9275 (2.6563) Prec@1 25.000 (35.903) Prec@5 63.125 (66.501) Epoch: [7][10620/11272] Time 0.775 (0.837) Data 0.001 (0.002) Loss 2.5881 (2.6563) Prec@1 35.625 (35.905) Prec@5 70.625 (66.503) Epoch: [7][10630/11272] Time 0.807 (0.837) Data 0.001 (0.002) Loss 2.8895 (2.6563) Prec@1 29.375 (35.904) Prec@5 60.625 (66.502) Epoch: [7][10640/11272] Time 0.946 (0.837) Data 0.001 (0.002) Loss 2.5556 (2.6563) Prec@1 33.750 (35.902) Prec@5 68.125 (66.503) Epoch: [7][10650/11272] Time 0.883 (0.837) Data 0.001 (0.002) Loss 2.6012 (2.6562) Prec@1 35.000 (35.904) Prec@5 64.375 (66.503) Epoch: [7][10660/11272] Time 0.701 (0.837) Data 0.002 (0.002) Loss 2.3913 (2.6562) Prec@1 43.750 (35.906) Prec@5 68.750 (66.503) Epoch: [7][10670/11272] Time 0.745 (0.837) Data 0.002 (0.002) Loss 2.6992 (2.6562) Prec@1 35.000 (35.906) Prec@5 66.875 (66.504) Epoch: [7][10680/11272] Time 0.862 (0.837) Data 0.001 (0.002) Loss 2.6068 (2.6562) Prec@1 36.250 (35.905) Prec@5 65.625 (66.504) Epoch: [7][10690/11272] Time 0.890 (0.837) Data 0.002 (0.002) Loss 2.3618 (2.6562) Prec@1 45.000 (35.905) Prec@5 74.375 (66.504) Epoch: [7][10700/11272] Time 0.749 (0.837) Data 0.002 (0.002) Loss 2.6886 (2.6563) Prec@1 36.250 (35.904) Prec@5 63.125 (66.504) Epoch: [7][10710/11272] Time 0.836 (0.837) Data 0.004 (0.002) Loss 2.9726 (2.6563) Prec@1 31.250 (35.904) Prec@5 63.125 (66.505) Epoch: [7][10720/11272] Time 0.903 (0.837) Data 0.001 (0.002) Loss 2.7616 (2.6563) Prec@1 30.000 (35.904) Prec@5 63.125 (66.505) Epoch: [7][10730/11272] Time 0.735 (0.837) Data 0.002 (0.002) Loss 2.6279 (2.6563) Prec@1 38.125 (35.904) Prec@5 65.000 (66.504) Epoch: [7][10740/11272] Time 0.742 (0.837) Data 0.001 (0.002) Loss 2.7011 (2.6564) Prec@1 31.250 (35.902) Prec@5 64.375 (66.503) Epoch: [7][10750/11272] Time 0.929 (0.837) Data 0.002 (0.002) Loss 2.4476 (2.6564) Prec@1 41.875 (35.902) Prec@5 73.125 (66.502) Epoch: [7][10760/11272] Time 0.847 (0.837) Data 0.001 (0.002) Loss 2.5409 (2.6563) Prec@1 43.125 (35.903) Prec@5 69.375 (66.504) Epoch: [7][10770/11272] Time 0.783 (0.837) Data 0.002 (0.002) Loss 2.5686 (2.6564) Prec@1 42.500 (35.902) Prec@5 70.000 (66.503) Epoch: [7][10780/11272] Time 0.778 (0.837) Data 0.001 (0.002) Loss 2.6615 (2.6563) Prec@1 36.875 (35.904) Prec@5 67.500 (66.504) Epoch: [7][10790/11272] Time 0.851 (0.837) Data 0.002 (0.002) Loss 2.4857 (2.6562) Prec@1 42.500 (35.904) Prec@5 71.250 (66.505) Epoch: [7][10800/11272] Time 0.885 (0.837) Data 0.002 (0.002) Loss 2.5444 (2.6562) Prec@1 42.500 (35.904) Prec@5 68.125 (66.505) Epoch: [7][10810/11272] Time 0.756 (0.837) Data 0.002 (0.002) Loss 2.6571 (2.6563) Prec@1 36.875 (35.905) Prec@5 63.125 (66.504) Epoch: [7][10820/11272] Time 0.737 (0.837) Data 0.002 (0.002) Loss 2.5910 (2.6564) Prec@1 36.250 (35.904) Prec@5 64.375 (66.502) Epoch: [7][10830/11272] Time 0.849 (0.837) Data 0.001 (0.002) Loss 2.5978 (2.6564) Prec@1 40.625 (35.904) Prec@5 65.625 (66.501) Epoch: [7][10840/11272] Time 0.892 (0.837) Data 0.001 (0.002) Loss 2.7168 (2.6564) Prec@1 34.375 (35.904) Prec@5 65.625 (66.500) Epoch: [7][10850/11272] Time 0.829 (0.837) Data 0.002 (0.002) Loss 2.5589 (2.6565) Prec@1 40.625 (35.903) Prec@5 70.625 (66.500) Epoch: [7][10860/11272] Time 0.948 (0.837) Data 0.001 (0.002) Loss 2.4547 (2.6564) Prec@1 37.500 (35.903) Prec@5 68.125 (66.500) Epoch: [7][10870/11272] Time 0.892 (0.837) Data 0.002 (0.002) Loss 2.9186 (2.6565) Prec@1 34.375 (35.903) Prec@5 60.625 (66.499) Epoch: [7][10880/11272] Time 0.799 (0.837) Data 0.001 (0.002) Loss 2.7369 (2.6565) Prec@1 31.875 (35.902) Prec@5 66.250 (66.498) Epoch: [7][10890/11272] Time 0.758 (0.837) Data 0.002 (0.002) Loss 2.6299 (2.6565) Prec@1 34.375 (35.902) Prec@5 68.750 (66.498) Epoch: [7][10900/11272] Time 0.927 (0.837) Data 0.001 (0.002) Loss 2.8899 (2.6565) Prec@1 33.750 (35.903) Prec@5 62.500 (66.498) Epoch: [7][10910/11272] Time 0.893 (0.837) Data 0.002 (0.002) Loss 2.4085 (2.6564) Prec@1 45.625 (35.905) Prec@5 68.750 (66.499) Epoch: [7][10920/11272] Time 0.749 (0.837) Data 0.002 (0.002) Loss 2.7042 (2.6564) Prec@1 38.750 (35.905) Prec@5 63.750 (66.499) Epoch: [7][10930/11272] Time 0.774 (0.837) Data 0.002 (0.002) Loss 2.5112 (2.6563) Prec@1 38.750 (35.906) Prec@5 71.250 (66.501) Epoch: [7][10940/11272] Time 0.884 (0.837) Data 0.001 (0.002) Loss 2.4547 (2.6563) Prec@1 38.750 (35.907) Prec@5 68.750 (66.500) Epoch: [7][10950/11272] Time 0.900 (0.837) Data 0.002 (0.002) Loss 2.6965 (2.6563) Prec@1 35.625 (35.908) Prec@5 67.500 (66.501) Epoch: [7][10960/11272] Time 0.807 (0.837) Data 0.001 (0.002) Loss 2.5682 (2.6563) Prec@1 40.625 (35.907) Prec@5 64.375 (66.500) Epoch: [7][10970/11272] Time 0.751 (0.837) Data 0.002 (0.002) Loss 2.5728 (2.6563) Prec@1 38.125 (35.908) Prec@5 68.125 (66.501) Epoch: [7][10980/11272] Time 0.872 (0.837) Data 0.001 (0.002) Loss 2.6944 (2.6563) Prec@1 36.875 (35.909) Prec@5 66.250 (66.502) Epoch: [7][10990/11272] Time 0.788 (0.837) Data 0.004 (0.002) Loss 2.7233 (2.6563) Prec@1 34.375 (35.908) Prec@5 63.750 (66.503) Epoch: [7][11000/11272] Time 0.735 (0.837) Data 0.001 (0.002) Loss 2.6675 (2.6563) Prec@1 34.375 (35.908) Prec@5 68.750 (66.502) Epoch: [7][11010/11272] Time 0.952 (0.837) Data 0.002 (0.002) Loss 2.5949 (2.6562) Prec@1 36.250 (35.909) Prec@5 68.750 (66.503) Epoch: [7][11020/11272] Time 0.896 (0.837) Data 0.001 (0.002) Loss 2.5915 (2.6562) Prec@1 36.875 (35.910) Prec@5 63.125 (66.503) Epoch: [7][11030/11272] Time 0.746 (0.837) Data 0.002 (0.002) Loss 2.7994 (2.6563) Prec@1 35.625 (35.909) Prec@5 64.375 (66.501) Epoch: [7][11040/11272] Time 0.781 (0.837) Data 0.001 (0.002) Loss 2.5897 (2.6564) Prec@1 34.375 (35.906) Prec@5 66.875 (66.500) Epoch: [7][11050/11272] Time 0.947 (0.837) Data 0.001 (0.002) Loss 2.7362 (2.6563) Prec@1 28.750 (35.906) Prec@5 65.000 (66.499) Epoch: [7][11060/11272] Time 0.895 (0.837) Data 0.001 (0.002) Loss 2.7836 (2.6563) Prec@1 33.125 (35.908) Prec@5 63.125 (66.500) Epoch: [7][11070/11272] Time 0.733 (0.837) Data 0.002 (0.002) Loss 2.5486 (2.6562) Prec@1 40.625 (35.909) Prec@5 66.875 (66.502) Epoch: [7][11080/11272] Time 0.725 (0.837) Data 0.002 (0.002) Loss 2.8353 (2.6562) Prec@1 33.125 (35.908) Prec@5 64.375 (66.501) Epoch: [7][11090/11272] Time 0.894 (0.837) Data 0.002 (0.002) Loss 2.5020 (2.6562) Prec@1 31.250 (35.909) Prec@5 70.625 (66.502) Epoch: [7][11100/11272] Time 0.921 (0.837) Data 0.002 (0.002) Loss 2.9227 (2.6562) Prec@1 30.000 (35.908) Prec@5 64.375 (66.503) Epoch: [7][11110/11272] Time 0.773 (0.837) Data 0.002 (0.002) Loss 2.8122 (2.6562) Prec@1 30.625 (35.908) Prec@5 63.750 (66.502) Epoch: [7][11120/11272] Time 0.856 (0.837) Data 0.002 (0.002) Loss 2.8757 (2.6563) Prec@1 33.125 (35.905) Prec@5 65.000 (66.501) Epoch: [7][11130/11272] Time 0.911 (0.837) Data 0.002 (0.002) Loss 2.5868 (2.6562) Prec@1 38.125 (35.907) Prec@5 63.750 (66.502) Epoch: [7][11140/11272] Time 0.790 (0.837) Data 0.001 (0.002) Loss 2.5820 (2.6561) Prec@1 38.125 (35.908) Prec@5 68.125 (66.504) Epoch: [7][11150/11272] Time 0.773 (0.837) Data 0.002 (0.002) Loss 2.9577 (2.6562) Prec@1 28.750 (35.906) Prec@5 58.125 (66.503) Epoch: [7][11160/11272] Time 0.841 (0.837) Data 0.001 (0.002) Loss 2.7933 (2.6563) Prec@1 35.000 (35.905) Prec@5 61.250 (66.501) Epoch: [7][11170/11272] Time 0.928 (0.837) Data 0.002 (0.002) Loss 2.4644 (2.6562) Prec@1 35.625 (35.906) Prec@5 66.875 (66.502) Epoch: [7][11180/11272] Time 0.723 (0.837) Data 0.001 (0.002) Loss 2.5991 (2.6562) Prec@1 39.375 (35.907) Prec@5 68.750 (66.501) Epoch: [7][11190/11272] Time 0.784 (0.837) Data 0.002 (0.002) Loss 2.7016 (2.6562) Prec@1 35.625 (35.908) Prec@5 65.000 (66.502) Epoch: [7][11200/11272] Time 0.836 (0.837) Data 0.001 (0.002) Loss 2.4278 (2.6561) Prec@1 39.375 (35.909) Prec@5 70.625 (66.502) Epoch: [7][11210/11272] Time 0.916 (0.837) Data 0.002 (0.002) Loss 2.7784 (2.6562) Prec@1 29.375 (35.908) Prec@5 63.125 (66.502) Epoch: [7][11220/11272] Time 0.741 (0.837) Data 0.001 (0.002) Loss 2.4430 (2.6562) Prec@1 42.500 (35.908) Prec@5 68.750 (66.503) Epoch: [7][11230/11272] Time 0.749 (0.837) Data 0.002 (0.002) Loss 2.6304 (2.6562) Prec@1 34.375 (35.907) Prec@5 60.625 (66.502) Epoch: [7][11240/11272] Time 0.892 (0.837) Data 0.001 (0.002) Loss 2.9745 (2.6562) Prec@1 29.375 (35.907) Prec@5 60.625 (66.503) Epoch: [7][11250/11272] Time 0.754 (0.837) Data 0.004 (0.002) Loss 2.6550 (2.6562) Prec@1 33.750 (35.905) Prec@5 61.875 (66.501) Epoch: [7][11260/11272] Time 0.766 (0.837) Data 0.002 (0.002) Loss 2.6246 (2.6562) Prec@1 33.125 (35.904) Prec@5 68.750 (66.500) Epoch: [7][11270/11272] Time 0.822 (0.837) Data 0.000 (0.002) Loss 2.5436 (2.6563) Prec@1 34.375 (35.904) Prec@5 70.000 (66.501) Test: [0/229] Time 1.971 (1.971) Loss 1.4202 (1.4202) Prec@1 57.500 (57.500) Prec@5 91.875 (91.875) Test: [10/229] Time 0.368 (0.551) Loss 1.7821 (2.1603) Prec@1 49.375 (45.341) Prec@5 90.000 (77.898) Test: [20/229] Time 0.454 (0.480) Loss 2.7860 (2.3552) Prec@1 32.500 (41.131) Prec@5 60.000 (73.661) Test: [30/229] Time 0.348 (0.456) Loss 2.7221 (2.2226) Prec@1 28.750 (44.093) Prec@5 66.250 (75.504) Test: [40/229] Time 0.500 (0.445) Loss 0.9232 (2.2167) Prec@1 80.000 (44.024) Prec@5 91.250 (75.351) Test: [50/229] Time 0.367 (0.436) Loss 2.0595 (2.2417) Prec@1 45.625 (43.787) Prec@5 82.500 (74.718) Test: [60/229] Time 0.374 (0.434) Loss 1.9774 (2.2534) Prec@1 45.625 (43.207) Prec@5 79.375 (74.027) Test: [70/229] Time 0.451 (0.432) Loss 1.7610 (2.2810) Prec@1 53.750 (42.342) Prec@5 78.750 (73.671) Test: [80/229] Time 0.364 (0.428) Loss 2.4683 (2.2965) Prec@1 31.250 (41.505) Prec@5 76.250 (73.966) Test: [90/229] Time 0.468 (0.426) Loss 2.0142 (2.3320) Prec@1 57.500 (40.872) Prec@5 77.500 (73.503) Test: [100/229] Time 0.369 (0.424) Loss 2.5003 (2.3148) Prec@1 36.875 (41.380) Prec@5 77.500 (73.892) Test: [110/229] Time 0.415 (0.421) Loss 2.7701 (2.3032) Prec@1 30.625 (41.588) Prec@5 67.500 (74.054) Test: [120/229] Time 0.464 (0.419) Loss 3.6853 (2.3238) Prec@1 14.375 (40.852) Prec@5 49.375 (73.714) Test: [130/229] Time 0.352 (0.417) Loss 2.2782 (2.3090) Prec@1 42.500 (41.288) Prec@5 76.875 (73.989) Test: [140/229] Time 0.509 (0.417) Loss 2.3251 (2.3239) Prec@1 37.500 (41.011) Prec@5 80.000 (73.688) Test: [150/229] Time 0.377 (0.416) Loss 1.3184 (2.3324) Prec@1 67.500 (40.960) Prec@5 88.125 (73.580) Test: [160/229] Time 0.421 (0.414) Loss 2.7512 (2.3295) Prec@1 40.625 (41.211) Prec@5 70.000 (73.630) Test: [170/229] Time 0.335 (0.413) Loss 2.3024 (2.3435) Prec@1 40.625 (40.822) Prec@5 82.500 (73.425) Test: [180/229] Time 0.392 (0.413) Loss 3.2402 (2.3512) Prec@1 24.375 (40.825) Prec@5 45.000 (73.118) Test: [190/229] Time 0.417 (0.412) Loss 1.5884 (2.3451) Prec@1 58.750 (41.005) Prec@5 92.500 (73.253) Test: [200/229] Time 0.340 (0.410) Loss 2.0488 (2.3362) Prec@1 46.250 (41.020) Prec@5 75.625 (73.545) Test: [210/229] Time 0.458 (0.410) Loss 2.0059 (2.3280) Prec@1 34.375 (41.176) Prec@5 86.875 (73.691) Test: [220/229] Time 0.389 (0.410) Loss 2.3108 (2.3230) Prec@1 39.375 (41.369) Prec@5 75.625 (73.753) * Prec@1 41.752 Prec@5 73.880 Epoch: [8][0/11272] Time 3.262 (3.262) Data 2.426 (2.426) Loss 2.5480 (2.5480) Prec@1 40.000 (40.000) Prec@5 68.125 (68.125) Epoch: [8][10/11272] Time 0.866 (1.043) Data 0.002 (0.222) Loss 2.6245 (2.6630) Prec@1 36.250 (36.250) Prec@5 70.625 (67.386) Epoch: [8][20/11272] Time 0.924 (0.939) Data 0.001 (0.117) Loss 2.7119 (2.6720) Prec@1 32.500 (35.327) Prec@5 63.750 (66.488) Epoch: [8][30/11272] Time 0.805 (0.905) Data 0.001 (0.080) Loss 2.6864 (2.6515) Prec@1 38.750 (36.069) Prec@5 63.125 (66.310) Epoch: [8][40/11272] Time 0.716 (0.884) Data 0.002 (0.061) Loss 2.3511 (2.6297) Prec@1 48.125 (36.814) Prec@5 74.375 (66.799) Epoch: [8][50/11272] Time 0.856 (0.872) Data 0.001 (0.049) Loss 2.8049 (2.6323) Prec@1 35.625 (36.642) Prec@5 62.500 (66.900) Epoch: [8][60/11272] Time 0.773 (0.861) Data 0.004 (0.042) Loss 2.2519 (2.6177) Prec@1 43.125 (36.824) Prec@5 75.000 (67.039) Epoch: [8][70/11272] Time 0.752 (0.856) Data 0.002 (0.036) Loss 2.8628 (2.6314) Prec@1 38.125 (36.523) Prec@5 66.250 (66.857) Epoch: [8][80/11272] Time 0.898 (0.853) Data 0.002 (0.032) Loss 2.3564 (2.6269) Prec@1 42.500 (36.597) Prec@5 71.875 (66.914) Epoch: [8][90/11272] Time 0.931 (0.851) Data 0.001 (0.028) Loss 2.5802 (2.6260) Prec@1 37.500 (36.552) Prec@5 66.250 (66.896) Epoch: [8][100/11272] Time 0.724 (0.848) Data 0.001 (0.026) Loss 2.3915 (2.6232) Prec@1 43.125 (36.652) Prec@5 69.375 (66.968) Epoch: [8][110/11272] Time 0.771 (0.846) Data 0.001 (0.024) Loss 2.8012 (2.6275) Prec@1 30.000 (36.616) Prec@5 66.875 (67.089) Epoch: [8][120/11272] Time 0.880 (0.845) Data 0.001 (0.022) Loss 2.3057 (2.6221) Prec@1 41.250 (36.601) Prec@5 71.875 (67.133) Epoch: [8][130/11272] Time 0.931 (0.844) Data 0.001 (0.020) Loss 2.5815 (2.6194) Prec@1 35.625 (36.732) Prec@5 66.250 (67.199) Epoch: [8][140/11272] Time 0.740 (0.842) Data 0.002 (0.019) Loss 2.7525 (2.6222) Prec@1 35.000 (36.671) Prec@5 64.375 (67.097) Epoch: [8][150/11272] Time 0.774 (0.841) Data 0.001 (0.018) Loss 2.5031 (2.6258) Prec@1 40.000 (36.647) Prec@5 68.125 (66.974) Epoch: [8][160/11272] Time 0.905 (0.841) Data 0.002 (0.017) Loss 2.4890 (2.6277) Prec@1 41.250 (36.584) Prec@5 70.000 (66.937) Epoch: [8][170/11272] Time 0.910 (0.839) Data 0.001 (0.016) Loss 2.5230 (2.6278) Prec@1 38.750 (36.517) Prec@5 67.500 (66.944) Epoch: [8][180/11272] Time 0.766 (0.839) Data 0.002 (0.015) Loss 2.5915 (2.6299) Prec@1 40.000 (36.471) Prec@5 68.750 (66.865) Epoch: [8][190/11272] Time 0.907 (0.840) Data 0.001 (0.014) Loss 2.4122 (2.6287) Prec@1 34.375 (36.420) Prec@5 75.000 (66.950) Epoch: [8][200/11272] Time 0.883 (0.839) Data 0.002 (0.014) Loss 2.5126 (2.6304) Prec@1 39.375 (36.396) Prec@5 66.875 (66.903) Epoch: [8][210/11272] Time 0.733 (0.838) Data 0.001 (0.013) Loss 2.4950 (2.6298) Prec@1 38.125 (36.395) Prec@5 70.000 (66.922) Epoch: [8][220/11272] Time 0.744 (0.838) Data 0.001 (0.013) Loss 2.5524 (2.6305) Prec@1 37.500 (36.380) Prec@5 70.000 (66.929) Epoch: [8][230/11272] Time 0.873 (0.838) Data 0.001 (0.012) Loss 2.8315 (2.6301) Prec@1 35.625 (36.399) Prec@5 65.625 (66.937) Epoch: [8][240/11272] Time 0.896 (0.837) Data 0.002 (0.012) Loss 2.4749 (2.6239) Prec@1 42.500 (36.540) Prec@5 68.125 (67.057) Epoch: [8][250/11272] Time 0.757 (0.836) Data 0.001 (0.011) Loss 2.6164 (2.6235) Prec@1 36.875 (36.549) Prec@5 68.125 (67.049) Epoch: [8][260/11272] Time 0.736 (0.836) Data 0.003 (0.011) Loss 2.1232 (2.6178) Prec@1 46.250 (36.626) Prec@5 75.000 (67.179) Epoch: [8][270/11272] Time 0.901 (0.836) Data 0.002 (0.011) Loss 2.7071 (2.6149) Prec@1 36.250 (36.741) Prec@5 65.625 (67.274) Epoch: [8][280/11272] Time 0.898 (0.836) Data 0.002 (0.010) Loss 2.3071 (2.6185) Prec@1 38.125 (36.601) Prec@5 73.750 (67.231) Epoch: [8][290/11272] Time 0.784 (0.836) Data 0.001 (0.010) Loss 2.6385 (2.6192) Prec@1 40.000 (36.561) Prec@5 66.250 (67.204) Epoch: [8][300/11272] Time 0.738 (0.836) Data 0.002 (0.010) Loss 2.7606 (2.6184) Prec@1 32.500 (36.582) Prec@5 64.375 (67.205) Epoch: [8][310/11272] Time 0.921 (0.836) Data 0.001 (0.009) Loss 2.7356 (2.6183) Prec@1 30.625 (36.578) Prec@5 65.000 (67.193) Epoch: [8][320/11272] Time 0.770 (0.836) Data 0.004 (0.009) Loss 2.5018 (2.6160) Prec@1 42.500 (36.618) Prec@5 65.625 (67.227) Epoch: [8][330/11272] Time 0.742 (0.836) Data 0.001 (0.009) Loss 2.8455 (2.6185) Prec@1 35.000 (36.594) Prec@5 60.625 (67.124) Epoch: [8][340/11272] Time 0.895 (0.835) Data 0.002 (0.009) Loss 2.6359 (2.6186) Prec@1 31.875 (36.600) Prec@5 66.250 (67.143) Epoch: [8][350/11272] Time 0.911 (0.835) Data 0.001 (0.009) Loss 2.6984 (2.6170) Prec@1 36.875 (36.608) Prec@5 66.250 (67.190) Epoch: [8][360/11272] Time 0.768 (0.835) Data 0.002 (0.008) Loss 2.7412 (2.6167) Prec@1 38.750 (36.615) Prec@5 60.625 (67.200) Epoch: [8][370/11272] Time 0.761 (0.835) Data 0.002 (0.008) Loss 2.8030 (2.6191) Prec@1 28.750 (36.555) Prec@5 68.750 (67.185) Epoch: [8][380/11272] Time 0.924 (0.836) Data 0.002 (0.008) Loss 2.5250 (2.6193) Prec@1 41.875 (36.537) Prec@5 70.625 (67.198) Epoch: [8][390/11272] Time 0.908 (0.836) Data 0.001 (0.008) Loss 2.5426 (2.6183) Prec@1 44.375 (36.555) Prec@5 71.875 (67.217) Epoch: [8][400/11272] Time 0.745 (0.835) Data 0.002 (0.008) Loss 2.7523 (2.6184) Prec@1 36.250 (36.540) Prec@5 65.625 (67.227) Epoch: [8][410/11272] Time 0.794 (0.836) Data 0.003 (0.008) Loss 2.7012 (2.6184) Prec@1 32.500 (36.533) Prec@5 66.875 (67.241) Epoch: [8][420/11272] Time 0.899 (0.836) Data 0.002 (0.007) Loss 2.4335 (2.6193) Prec@1 42.500 (36.556) Prec@5 70.000 (67.225) Epoch: [8][430/11272] Time 0.899 (0.837) Data 0.001 (0.007) Loss 2.5676 (2.6179) Prec@1 36.250 (36.586) Prec@5 67.500 (67.233) Epoch: [8][440/11272] Time 0.728 (0.837) Data 0.001 (0.007) Loss 2.7594 (2.6177) Prec@1 35.625 (36.548) Prec@5 61.875 (67.232) Epoch: [8][450/11272] Time 0.914 (0.837) Data 0.001 (0.007) Loss 2.5904 (2.6199) Prec@1 34.375 (36.499) Prec@5 75.000 (67.176) Epoch: [8][460/11272] Time 0.948 (0.837) Data 0.001 (0.007) Loss 2.9833 (2.6228) Prec@1 27.500 (36.483) Prec@5 62.500 (67.137) Epoch: [8][470/11272] Time 0.781 (0.837) Data 0.001 (0.007) Loss 2.6441 (2.6219) Prec@1 31.250 (36.486) Prec@5 65.625 (67.158) Epoch: [8][480/11272] Time 0.784 (0.837) Data 0.001 (0.007) Loss 2.8913 (2.6234) Prec@1 34.375 (36.444) Prec@5 60.625 (67.139) Epoch: [8][490/11272] Time 0.872 (0.837) Data 0.001 (0.007) Loss 2.5886 (2.6228) Prec@1 36.250 (36.478) Prec@5 65.000 (67.159) Epoch: [8][500/11272] Time 0.919 (0.837) Data 0.002 (0.007) Loss 2.3360 (2.6222) Prec@1 46.250 (36.471) Prec@5 70.625 (67.152) Epoch: [8][510/11272] Time 0.775 (0.837) Data 0.002 (0.006) Loss 2.5793 (2.6232) Prec@1 43.125 (36.490) Prec@5 67.500 (67.121) Epoch: [8][520/11272] Time 0.764 (0.837) Data 0.001 (0.006) Loss 2.4605 (2.6227) Prec@1 43.125 (36.490) Prec@5 71.875 (67.131) Epoch: [8][530/11272] Time 0.914 (0.838) Data 0.001 (0.006) Loss 2.6920 (2.6231) Prec@1 33.125 (36.483) Prec@5 66.875 (67.142) Epoch: [8][540/11272] Time 0.911 (0.838) Data 0.001 (0.006) Loss 2.7454 (2.6225) Prec@1 30.000 (36.480) Prec@5 67.500 (67.172) Epoch: [8][550/11272] Time 0.767 (0.838) Data 0.001 (0.006) Loss 2.8745 (2.6227) Prec@1 33.125 (36.467) Prec@5 63.750 (67.165) Epoch: [8][560/11272] Time 0.751 (0.838) Data 0.001 (0.006) Loss 2.6885 (2.6239) Prec@1 35.625 (36.437) Prec@5 61.250 (67.140) Epoch: [8][570/11272] Time 0.918 (0.838) Data 0.002 (0.006) Loss 2.5079 (2.6249) Prec@1 38.125 (36.431) Prec@5 71.250 (67.120) Epoch: [8][580/11272] Time 0.893 (0.838) Data 0.001 (0.006) Loss 2.5755 (2.6249) Prec@1 38.125 (36.414) Prec@5 67.500 (67.121) Epoch: [8][590/11272] Time 0.752 (0.838) Data 0.002 (0.006) Loss 2.6931 (2.6241) Prec@1 34.375 (36.398) Prec@5 64.375 (67.134) Epoch: [8][600/11272] Time 0.922 (0.838) Data 0.002 (0.006) Loss 2.5400 (2.6239) Prec@1 40.625 (36.409) Prec@5 69.375 (67.147) Epoch: [8][610/11272] Time 0.892 (0.837) Data 0.002 (0.006) Loss 2.6749 (2.6248) Prec@1 36.875 (36.386) Prec@5 63.125 (67.118) Epoch: [8][620/11272] Time 0.765 (0.837) Data 0.002 (0.006) Loss 2.6839 (2.6240) Prec@1 40.625 (36.423) Prec@5 63.125 (67.134) Epoch: [8][630/11272] Time 0.763 (0.837) Data 0.002 (0.006) Loss 2.6839 (2.6244) Prec@1 34.375 (36.435) Prec@5 67.500 (67.129) Epoch: [8][640/11272] Time 0.888 (0.837) Data 0.001 (0.005) Loss 2.5904 (2.6249) Prec@1 36.250 (36.441) Prec@5 68.125 (67.109) Epoch: [8][650/11272] Time 0.922 (0.837) Data 0.001 (0.005) Loss 2.7538 (2.6256) Prec@1 36.875 (36.454) Prec@5 61.875 (67.084) Epoch: [8][660/11272] Time 0.768 (0.837) Data 0.002 (0.005) Loss 2.5441 (2.6253) Prec@1 42.500 (36.493) Prec@5 67.500 (67.096) Epoch: [8][670/11272] Time 0.752 (0.837) Data 0.002 (0.005) Loss 2.5944 (2.6260) Prec@1 38.750 (36.486) Prec@5 66.250 (67.072) Epoch: [8][680/11272] Time 0.902 (0.837) Data 0.001 (0.005) Loss 2.2512 (2.6251) Prec@1 45.000 (36.494) Prec@5 76.250 (67.078) Epoch: [8][690/11272] Time 0.909 (0.837) Data 0.001 (0.005) Loss 2.7472 (2.6255) Prec@1 34.375 (36.507) Prec@5 62.500 (67.057) Epoch: [8][700/11272] Time 0.783 (0.836) Data 0.002 (0.005) Loss 2.8072 (2.6267) Prec@1 33.125 (36.501) Prec@5 59.375 (67.027) Epoch: [8][710/11272] Time 0.756 (0.836) Data 0.002 (0.005) Loss 2.7618 (2.6274) Prec@1 31.250 (36.493) Prec@5 65.000 (67.017) Epoch: [8][720/11272] Time 0.869 (0.836) Data 0.001 (0.005) Loss 2.8293 (2.6274) Prec@1 28.750 (36.487) Prec@5 63.750 (67.022) Epoch: [8][730/11272] Time 0.722 (0.836) Data 0.002 (0.005) Loss 2.6990 (2.6266) Prec@1 36.875 (36.489) Prec@5 65.000 (67.030) Epoch: [8][740/11272] Time 0.744 (0.836) Data 0.001 (0.005) Loss 2.3138 (2.6269) Prec@1 41.250 (36.489) Prec@5 73.125 (67.021) Epoch: [8][750/11272] Time 0.877 (0.836) Data 0.002 (0.005) Loss 2.9269 (2.6275) Prec@1 26.250 (36.472) Prec@5 55.000 (67.008) Epoch: [8][760/11272] Time 0.935 (0.836) Data 0.001 (0.005) Loss 2.6621 (2.6271) Prec@1 36.875 (36.466) Prec@5 65.625 (67.015) Epoch: [8][770/11272] Time 0.754 (0.836) Data 0.002 (0.005) Loss 2.9593 (2.6270) Prec@1 30.000 (36.469) Prec@5 59.375 (67.012) Epoch: [8][780/11272] Time 0.720 (0.835) Data 0.001 (0.005) Loss 2.5744 (2.6277) Prec@1 37.500 (36.456) Prec@5 67.500 (66.989) Epoch: [8][790/11272] Time 0.872 (0.835) Data 0.001 (0.005) Loss 2.5359 (2.6280) Prec@1 40.625 (36.454) Prec@5 66.875 (66.988) Epoch: [8][800/11272] Time 0.919 (0.836) Data 0.001 (0.005) Loss 2.9450 (2.6280) Prec@1 28.750 (36.433) Prec@5 60.625 (66.978) Epoch: [8][810/11272] Time 0.761 (0.835) Data 0.002 (0.005) Loss 2.6507 (2.6279) Prec@1 36.250 (36.430) Prec@5 68.125 (66.967) Epoch: [8][820/11272] Time 0.773 (0.835) Data 0.002 (0.005) Loss 2.8060 (2.6274) Prec@1 30.625 (36.439) Prec@5 62.500 (66.966) Epoch: [8][830/11272] Time 0.898 (0.835) Data 0.002 (0.005) Loss 2.5597 (2.6270) Prec@1 40.000 (36.439) Prec@5 65.000 (66.956) Epoch: [8][840/11272] Time 0.888 (0.836) Data 0.001 (0.005) Loss 2.6979 (2.6267) Prec@1 35.000 (36.448) Prec@5 61.250 (66.958) Epoch: [8][850/11272] Time 0.724 (0.835) Data 0.002 (0.005) Loss 2.6243 (2.6277) Prec@1 37.500 (36.447) Prec@5 68.750 (66.930) Epoch: [8][860/11272] Time 0.911 (0.836) Data 0.001 (0.004) Loss 2.5881 (2.6272) Prec@1 34.375 (36.445) Prec@5 70.000 (66.945) Epoch: [8][870/11272] Time 0.927 (0.836) Data 0.002 (0.004) Loss 2.8273 (2.6267) Prec@1 30.625 (36.453) Prec@5 57.500 (66.940) Epoch: [8][880/11272] Time 0.746 (0.835) Data 0.001 (0.004) Loss 2.6193 (2.6261) Prec@1 36.250 (36.464) Prec@5 64.375 (66.942) Epoch: [8][890/11272] Time 0.750 (0.835) Data 0.004 (0.004) Loss 2.3251 (2.6259) Prec@1 40.625 (36.470) Prec@5 73.125 (66.963) Epoch: [8][900/11272] Time 0.883 (0.835) Data 0.001 (0.004) Loss 2.4696 (2.6260) Prec@1 31.250 (36.480) Prec@5 68.750 (66.954) Epoch: [8][910/11272] Time 0.889 (0.835) Data 0.002 (0.004) Loss 2.5839 (2.6263) Prec@1 34.375 (36.470) Prec@5 66.875 (66.950) Epoch: [8][920/11272] Time 0.800 (0.835) Data 0.001 (0.004) Loss 2.7018 (2.6260) Prec@1 35.000 (36.479) Prec@5 62.500 (66.965) Epoch: [8][930/11272] Time 0.759 (0.835) Data 0.002 (0.004) Loss 2.6607 (2.6257) Prec@1 36.250 (36.485) Prec@5 65.000 (66.964) Epoch: [8][940/11272] Time 0.919 (0.835) Data 0.001 (0.004) Loss 2.5496 (2.6255) Prec@1 38.125 (36.482) Prec@5 70.000 (66.986) Epoch: [8][950/11272] Time 0.854 (0.835) Data 0.002 (0.004) Loss 2.8070 (2.6265) Prec@1 34.375 (36.454) Prec@5 60.000 (66.962) Epoch: [8][960/11272] Time 0.747 (0.835) Data 0.002 (0.004) Loss 2.6878 (2.6267) Prec@1 34.375 (36.447) Prec@5 63.750 (66.958) Epoch: [8][970/11272] Time 0.793 (0.835) Data 0.002 (0.004) Loss 2.5163 (2.6264) Prec@1 37.500 (36.441) Prec@5 70.000 (66.954) Epoch: [8][980/11272] Time 0.857 (0.835) Data 0.002 (0.004) Loss 2.6975 (2.6260) Prec@1 34.375 (36.460) Prec@5 65.625 (66.962) Epoch: [8][990/11272] Time 0.800 (0.835) Data 0.002 (0.004) Loss 2.7040 (2.6262) Prec@1 35.000 (36.447) Prec@5 65.625 (66.958) Epoch: [8][1000/11272] Time 0.764 (0.835) Data 0.002 (0.004) Loss 2.6537 (2.6271) Prec@1 38.125 (36.436) Prec@5 65.000 (66.936) Epoch: [8][1010/11272] Time 0.910 (0.835) Data 0.002 (0.004) Loss 2.6231 (2.6279) Prec@1 36.250 (36.419) Prec@5 66.875 (66.923) Epoch: [8][1020/11272] Time 0.897 (0.835) Data 0.001 (0.004) Loss 2.6732 (2.6278) Prec@1 36.250 (36.413) Prec@5 62.500 (66.920) Epoch: [8][1030/11272] Time 0.770 (0.835) Data 0.005 (0.004) Loss 2.4506 (2.6276) Prec@1 36.875 (36.420) Prec@5 70.625 (66.931) Epoch: [8][1040/11272] Time 0.769 (0.835) Data 0.002 (0.004) Loss 2.5920 (2.6277) Prec@1 35.000 (36.423) Prec@5 71.250 (66.941) Epoch: [8][1050/11272] Time 0.891 (0.835) Data 0.002 (0.004) Loss 2.6958 (2.6280) Prec@1 33.750 (36.417) Prec@5 60.625 (66.923) Epoch: [8][1060/11272] Time 0.915 (0.835) Data 0.001 (0.004) Loss 3.0362 (2.6287) Prec@1 26.250 (36.403) Prec@5 61.250 (66.911) Epoch: [8][1070/11272] Time 0.752 (0.835) Data 0.002 (0.004) Loss 2.5857 (2.6286) Prec@1 43.125 (36.426) Prec@5 68.125 (66.922) Epoch: [8][1080/11272] Time 0.810 (0.835) Data 0.001 (0.004) Loss 2.4251 (2.6286) Prec@1 40.000 (36.433) Prec@5 71.250 (66.915) Epoch: [8][1090/11272] Time 0.939 (0.835) Data 0.001 (0.004) Loss 2.3024 (2.6289) Prec@1 40.000 (36.425) Prec@5 74.375 (66.907) Epoch: [8][1100/11272] Time 0.909 (0.835) Data 0.001 (0.004) Loss 1.9900 (2.6291) Prec@1 50.625 (36.424) Prec@5 76.250 (66.908) Epoch: [8][1110/11272] Time 0.819 (0.835) Data 0.002 (0.004) Loss 2.8268 (2.6285) Prec@1 34.375 (36.436) Prec@5 65.625 (66.917) Epoch: [8][1120/11272] Time 0.935 (0.835) Data 0.002 (0.004) Loss 3.0073 (2.6290) Prec@1 29.375 (36.437) Prec@5 62.500 (66.915) Epoch: [8][1130/11272] Time 0.866 (0.835) Data 0.002 (0.004) Loss 2.5617 (2.6286) Prec@1 38.125 (36.440) Prec@5 73.125 (66.925) Epoch: [8][1140/11272] Time 0.825 (0.835) Data 0.001 (0.004) Loss 2.6360 (2.6287) Prec@1 36.875 (36.445) Prec@5 63.125 (66.926) Epoch: [8][1150/11272] Time 0.744 (0.835) Data 0.001 (0.004) Loss 2.6878 (2.6290) Prec@1 40.000 (36.432) Prec@5 62.500 (66.905) Epoch: [8][1160/11272] Time 0.956 (0.835) Data 0.001 (0.004) Loss 2.6921 (2.6299) Prec@1 41.875 (36.434) Prec@5 67.500 (66.885) Epoch: [8][1170/11272] Time 0.929 (0.835) Data 0.002 (0.004) Loss 2.4514 (2.6297) Prec@1 37.500 (36.434) Prec@5 68.750 (66.885) Epoch: [8][1180/11272] Time 0.747 (0.835) Data 0.001 (0.004) Loss 2.5961 (2.6294) Prec@1 35.625 (36.430) Prec@5 67.500 (66.891) Epoch: [8][1190/11272] Time 0.751 (0.835) Data 0.002 (0.004) Loss 2.3959 (2.6296) Prec@1 38.125 (36.416) Prec@5 72.500 (66.900) Epoch: [8][1200/11272] Time 0.896 (0.835) Data 0.002 (0.004) Loss 2.3572 (2.6291) Prec@1 45.625 (36.424) Prec@5 74.375 (66.912) Epoch: [8][1210/11272] Time 0.928 (0.835) Data 0.002 (0.004) Loss 2.7363 (2.6294) Prec@1 32.500 (36.431) Prec@5 66.250 (66.906) Epoch: [8][1220/11272] Time 0.757 (0.835) Data 0.001 (0.004) Loss 2.5111 (2.6295) Prec@1 40.000 (36.434) Prec@5 71.875 (66.915) Epoch: [8][1230/11272] Time 0.718 (0.835) Data 0.001 (0.004) Loss 2.6990 (2.6291) Prec@1 31.875 (36.435) Prec@5 66.875 (66.923) Epoch: [8][1240/11272] Time 0.866 (0.835) Data 0.001 (0.004) Loss 2.8910 (2.6295) Prec@1 33.750 (36.432) Prec@5 60.000 (66.908) Epoch: [8][1250/11272] Time 0.748 (0.835) Data 0.004 (0.004) Loss 2.7407 (2.6286) Prec@1 36.250 (36.447) Prec@5 63.125 (66.919) Epoch: [8][1260/11272] Time 0.721 (0.835) Data 0.001 (0.004) Loss 2.7704 (2.6286) Prec@1 32.500 (36.450) Prec@5 69.375 (66.929) Epoch: [8][1270/11272] Time 0.898 (0.835) Data 0.002 (0.004) Loss 2.6058 (2.6296) Prec@1 38.125 (36.442) Prec@5 66.875 (66.920) Epoch: [8][1280/11272] Time 0.869 (0.835) Data 0.001 (0.004) Loss 2.7085 (2.6297) Prec@1 30.625 (36.432) Prec@5 66.875 (66.918) Epoch: [8][1290/11272] Time 0.748 (0.835) Data 0.002 (0.004) Loss 3.0047 (2.6302) Prec@1 31.250 (36.414) Prec@5 58.125 (66.901) Epoch: [8][1300/11272] Time 0.752 (0.835) Data 0.002 (0.004) Loss 2.7324 (2.6305) Prec@1 34.375 (36.400) Prec@5 63.125 (66.903) Epoch: [8][1310/11272] Time 0.913 (0.835) Data 0.001 (0.004) Loss 2.4227 (2.6306) Prec@1 40.625 (36.399) Prec@5 68.750 (66.904) Epoch: [8][1320/11272] Time 0.879 (0.835) Data 0.001 (0.004) Loss 2.6342 (2.6302) Prec@1 34.375 (36.398) Prec@5 67.500 (66.921) Epoch: [8][1330/11272] Time 0.708 (0.835) Data 0.002 (0.003) Loss 2.8842 (2.6294) Prec@1 33.750 (36.410) Prec@5 58.125 (66.931) Epoch: [8][1340/11272] Time 0.811 (0.835) Data 0.001 (0.003) Loss 2.8518 (2.6296) Prec@1 29.375 (36.404) Prec@5 61.250 (66.923) Epoch: [8][1350/11272] Time 0.916 (0.835) Data 0.002 (0.003) Loss 2.9122 (2.6295) Prec@1 32.500 (36.404) Prec@5 61.250 (66.927) Epoch: [8][1360/11272] Time 0.916 (0.835) Data 0.002 (0.003) Loss 2.7627 (2.6292) Prec@1 30.000 (36.406) Prec@5 63.750 (66.938) Epoch: [8][1370/11272] Time 0.751 (0.834) Data 0.002 (0.003) Loss 2.9001 (2.6292) Prec@1 32.500 (36.395) Prec@5 61.875 (66.940) Epoch: [8][1380/11272] Time 0.918 (0.834) Data 0.002 (0.003) Loss 2.6577 (2.6296) Prec@1 35.000 (36.397) Prec@5 60.000 (66.932) Epoch: [8][1390/11272] Time 0.912 (0.834) Data 0.002 (0.003) Loss 2.6908 (2.6290) Prec@1 36.875 (36.418) Prec@5 68.750 (66.949) Epoch: [8][1400/11272] Time 0.778 (0.834) Data 0.001 (0.003) Loss 2.5296 (2.6293) Prec@1 41.875 (36.420) Prec@5 68.750 (66.951) Epoch: [8][1410/11272] Time 0.816 (0.834) Data 0.002 (0.003) Loss 2.5449 (2.6298) Prec@1 39.375 (36.406) Prec@5 71.250 (66.943) Epoch: [8][1420/11272] Time 0.897 (0.834) Data 0.001 (0.003) Loss 2.5401 (2.6301) Prec@1 34.375 (36.402) Prec@5 67.500 (66.933) Epoch: [8][1430/11272] Time 0.939 (0.834) Data 0.002 (0.003) Loss 2.5221 (2.6301) Prec@1 31.875 (36.392) Prec@5 67.500 (66.931) Epoch: [8][1440/11272] Time 0.740 (0.834) Data 0.001 (0.003) Loss 2.7305 (2.6300) Prec@1 36.250 (36.387) Prec@5 65.000 (66.921) Epoch: [8][1450/11272] Time 0.735 (0.834) Data 0.002 (0.003) Loss 2.8812 (2.6300) Prec@1 35.000 (36.389) Prec@5 61.875 (66.918) Epoch: [8][1460/11272] Time 0.858 (0.834) Data 0.001 (0.003) Loss 2.6035 (2.6301) Prec@1 39.375 (36.388) Prec@5 66.875 (66.915) Epoch: [8][1470/11272] Time 0.929 (0.834) Data 0.002 (0.003) Loss 2.5683 (2.6299) Prec@1 33.750 (36.394) Prec@5 70.625 (66.915) Epoch: [8][1480/11272] Time 0.742 (0.834) Data 0.001 (0.003) Loss 2.6108 (2.6295) Prec@1 37.500 (36.407) Prec@5 65.000 (66.921) Epoch: [8][1490/11272] Time 0.769 (0.834) Data 0.003 (0.003) Loss 2.7507 (2.6299) Prec@1 36.250 (36.409) Prec@5 61.875 (66.914) Epoch: [8][1500/11272] Time 0.865 (0.834) Data 0.001 (0.003) Loss 2.7050 (2.6300) Prec@1 35.000 (36.412) Prec@5 65.000 (66.911) Epoch: [8][1510/11272] Time 0.868 (0.834) Data 0.002 (0.003) Loss 2.3285 (2.6296) Prec@1 43.750 (36.417) Prec@5 71.875 (66.918) Epoch: [8][1520/11272] Time 0.774 (0.834) Data 0.001 (0.003) Loss 2.7552 (2.6299) Prec@1 39.375 (36.409) Prec@5 65.000 (66.905) Epoch: [8][1530/11272] Time 0.922 (0.834) Data 0.001 (0.003) Loss 2.8273 (2.6301) Prec@1 31.250 (36.403) Prec@5 64.375 (66.904) Epoch: [8][1540/11272] Time 0.896 (0.834) Data 0.002 (0.003) Loss 2.7534 (2.6301) Prec@1 30.625 (36.400) Prec@5 67.500 (66.902) Epoch: [8][1550/11272] Time 0.777 (0.834) Data 0.002 (0.003) Loss 3.0481 (2.6309) Prec@1 26.250 (36.380) Prec@5 60.625 (66.882) Epoch: [8][1560/11272] Time 0.751 (0.834) Data 0.001 (0.003) Loss 2.4834 (2.6305) Prec@1 38.750 (36.388) Prec@5 71.250 (66.885) Epoch: [8][1570/11272] Time 0.905 (0.834) Data 0.002 (0.003) Loss 2.5866 (2.6302) Prec@1 35.625 (36.390) Prec@5 66.875 (66.894) Epoch: [8][1580/11272] Time 0.894 (0.834) Data 0.001 (0.003) Loss 2.6603 (2.6300) Prec@1 37.500 (36.389) Prec@5 66.250 (66.899) Epoch: [8][1590/11272] Time 0.740 (0.834) Data 0.002 (0.003) Loss 2.5300 (2.6305) Prec@1 33.750 (36.383) Prec@5 71.250 (66.893) Epoch: [8][1600/11272] Time 0.797 (0.833) Data 0.001 (0.003) Loss 2.5151 (2.6304) Prec@1 43.750 (36.384) Prec@5 66.250 (66.891) Epoch: [8][1610/11272] Time 0.881 (0.833) Data 0.002 (0.003) Loss 2.4381 (2.6303) Prec@1 40.625 (36.393) Prec@5 71.250 (66.902) Epoch: [8][1620/11272] Time 0.900 (0.833) Data 0.001 (0.003) Loss 2.8688 (2.6302) Prec@1 29.375 (36.384) Prec@5 59.375 (66.902) Epoch: [8][1630/11272] Time 0.786 (0.833) Data 0.002 (0.003) Loss 2.5240 (2.6303) Prec@1 34.375 (36.380) Prec@5 70.000 (66.901) Epoch: [8][1640/11272] Time 0.784 (0.833) Data 0.001 (0.003) Loss 2.8102 (2.6308) Prec@1 30.000 (36.367) Prec@5 63.125 (66.890) Epoch: [8][1650/11272] Time 0.909 (0.833) Data 0.001 (0.003) Loss 2.6656 (2.6305) Prec@1 36.875 (36.376) Prec@5 68.125 (66.901) Epoch: [8][1660/11272] Time 0.734 (0.833) Data 0.001 (0.003) Loss 2.6724 (2.6303) Prec@1 36.875 (36.388) Prec@5 66.875 (66.908) Epoch: [8][1670/11272] Time 0.740 (0.833) Data 0.002 (0.003) Loss 2.6442 (2.6302) Prec@1 38.125 (36.396) Prec@5 63.125 (66.905) Epoch: [8][1680/11272] Time 0.866 (0.833) Data 0.001 (0.003) Loss 2.7881 (2.6305) Prec@1 29.375 (36.386) Prec@5 61.250 (66.900) Epoch: [8][1690/11272] Time 0.922 (0.833) Data 0.002 (0.003) Loss 2.7529 (2.6306) Prec@1 31.875 (36.382) Prec@5 63.750 (66.900) Epoch: [8][1700/11272] Time 0.790 (0.833) Data 0.001 (0.003) Loss 2.8681 (2.6307) Prec@1 33.750 (36.376) Prec@5 60.625 (66.897) Epoch: [8][1710/11272] Time 0.745 (0.833) Data 0.002 (0.003) Loss 2.5525 (2.6306) Prec@1 38.750 (36.379) Prec@5 70.000 (66.896) Epoch: [8][1720/11272] Time 0.895 (0.833) Data 0.001 (0.003) Loss 2.7024 (2.6308) Prec@1 34.375 (36.375) Prec@5 62.500 (66.891) Epoch: [8][1730/11272] Time 0.938 (0.833) Data 0.002 (0.003) Loss 2.4781 (2.6313) Prec@1 39.375 (36.358) Prec@5 70.000 (66.879) Epoch: [8][1740/11272] Time 0.784 (0.833) Data 0.001 (0.003) Loss 2.9109 (2.6318) Prec@1 35.000 (36.341) Prec@5 64.375 (66.862) Epoch: [8][1750/11272] Time 0.799 (0.833) Data 0.003 (0.003) Loss 2.4910 (2.6324) Prec@1 38.750 (36.332) Prec@5 71.875 (66.857) Epoch: [8][1760/11272] Time 0.842 (0.833) Data 0.001 (0.003) Loss 2.7042 (2.6323) Prec@1 36.875 (36.337) Prec@5 62.500 (66.860) Epoch: [8][1770/11272] Time 0.906 (0.833) Data 0.002 (0.003) Loss 2.5085 (2.6322) Prec@1 33.750 (36.334) Prec@5 68.750 (66.867) Epoch: [8][1780/11272] Time 0.782 (0.833) Data 0.002 (0.003) Loss 2.6091 (2.6324) Prec@1 38.750 (36.338) Prec@5 65.000 (66.858) Epoch: [8][1790/11272] Time 0.932 (0.833) Data 0.002 (0.003) Loss 2.5254 (2.6327) Prec@1 36.250 (36.320) Prec@5 66.875 (66.847) Epoch: [8][1800/11272] Time 0.918 (0.833) Data 0.001 (0.003) Loss 2.5536 (2.6327) Prec@1 43.125 (36.319) Prec@5 68.750 (66.847) Epoch: [8][1810/11272] Time 0.755 (0.833) Data 0.002 (0.003) Loss 2.5403 (2.6328) Prec@1 39.375 (36.318) Prec@5 68.750 (66.846) Epoch: [8][1820/11272] Time 0.745 (0.833) Data 0.001 (0.003) Loss 2.5735 (2.6326) Prec@1 36.250 (36.322) Prec@5 66.875 (66.853) Epoch: [8][1830/11272] Time 0.921 (0.833) Data 0.002 (0.003) Loss 2.4083 (2.6317) Prec@1 40.000 (36.335) Prec@5 76.250 (66.874) Epoch: [8][1840/11272] Time 0.911 (0.833) Data 0.001 (0.003) Loss 2.6518 (2.6319) Prec@1 36.250 (36.328) Prec@5 67.500 (66.874) Epoch: [8][1850/11272] Time 0.757 (0.833) Data 0.002 (0.003) Loss 2.6669 (2.6322) Prec@1 35.000 (36.321) Prec@5 67.500 (66.869) Epoch: [8][1860/11272] Time 0.773 (0.833) Data 0.002 (0.003) Loss 2.6227 (2.6331) Prec@1 30.625 (36.305) Prec@5 66.875 (66.855) Epoch: [8][1870/11272] Time 0.920 (0.833) Data 0.002 (0.003) Loss 2.4776 (2.6330) Prec@1 38.750 (36.300) Prec@5 70.625 (66.859) Epoch: [8][1880/11272] Time 0.878 (0.833) Data 0.002 (0.003) Loss 2.5705 (2.6332) Prec@1 37.500 (36.291) Prec@5 66.250 (66.853) Epoch: [8][1890/11272] Time 0.745 (0.833) Data 0.002 (0.003) Loss 2.6436 (2.6332) Prec@1 37.500 (36.292) Prec@5 65.625 (66.854) Epoch: [8][1900/11272] Time 0.755 (0.833) Data 0.001 (0.003) Loss 2.8576 (2.6334) Prec@1 33.125 (36.286) Prec@5 60.000 (66.849) Epoch: [8][1910/11272] Time 0.872 (0.833) Data 0.002 (0.003) Loss 2.6126 (2.6337) Prec@1 39.375 (36.277) Prec@5 69.375 (66.847) Epoch: [8][1920/11272] Time 0.761 (0.833) Data 0.004 (0.003) Loss 2.4943 (2.6337) Prec@1 30.625 (36.268) Prec@5 70.625 (66.848) Epoch: [8][1930/11272] Time 0.774 (0.833) Data 0.002 (0.003) Loss 2.5129 (2.6338) Prec@1 35.000 (36.258) Prec@5 67.500 (66.841) Epoch: [8][1940/11272] Time 0.897 (0.833) Data 0.001 (0.003) Loss 2.8376 (2.6337) Prec@1 32.500 (36.264) Prec@5 65.625 (66.848) Epoch: [8][1950/11272] Time 0.925 (0.833) Data 0.002 (0.003) Loss 2.6059 (2.6338) Prec@1 37.500 (36.260) Prec@5 68.125 (66.847) Epoch: [8][1960/11272] Time 0.741 (0.833) Data 0.002 (0.003) Loss 2.6510 (2.6337) Prec@1 35.625 (36.262) Prec@5 67.500 (66.848) Epoch: [8][1970/11272] Time 0.785 (0.833) Data 0.002 (0.003) Loss 2.8109 (2.6339) Prec@1 30.625 (36.256) Prec@5 63.750 (66.843) Epoch: [8][1980/11272] Time 0.882 (0.833) Data 0.001 (0.003) Loss 2.5963 (2.6339) Prec@1 34.375 (36.260) Prec@5 70.000 (66.848) Epoch: [8][1990/11272] Time 0.872 (0.833) Data 0.002 (0.003) Loss 2.7405 (2.6342) Prec@1 34.375 (36.259) Prec@5 66.875 (66.848) Epoch: [8][2000/11272] Time 0.758 (0.833) Data 0.002 (0.003) Loss 2.6501 (2.6344) Prec@1 33.125 (36.251) Prec@5 69.375 (66.843) Epoch: [8][2010/11272] Time 0.745 (0.833) Data 0.002 (0.003) Loss 2.6910 (2.6348) Prec@1 35.000 (36.247) Prec@5 69.375 (66.837) Epoch: [8][2020/11272] Time 0.867 (0.833) Data 0.001 (0.003) Loss 2.4058 (2.6349) Prec@1 40.000 (36.252) Prec@5 69.375 (66.834) Epoch: [8][2030/11272] Time 0.955 (0.833) Data 0.002 (0.003) Loss 2.3623 (2.6351) Prec@1 41.250 (36.252) Prec@5 72.500 (66.829) Epoch: [8][2040/11272] Time 0.778 (0.833) Data 0.001 (0.003) Loss 2.7967 (2.6350) Prec@1 34.375 (36.251) Prec@5 65.625 (66.825) Epoch: [8][2050/11272] Time 0.933 (0.833) Data 0.002 (0.003) Loss 2.6307 (2.6356) Prec@1 39.375 (36.247) Prec@5 67.500 (66.818) Epoch: [8][2060/11272] Time 0.841 (0.833) Data 0.001 (0.003) Loss 2.6822 (2.6354) Prec@1 36.250 (36.251) Prec@5 65.000 (66.823) Epoch: [8][2070/11272] Time 0.745 (0.833) Data 0.002 (0.003) Loss 2.6624 (2.6355) Prec@1 40.625 (36.258) Prec@5 66.875 (66.819) Epoch: [8][2080/11272] Time 0.763 (0.833) Data 0.002 (0.003) Loss 2.8476 (2.6354) Prec@1 36.250 (36.270) Prec@5 63.125 (66.824) Epoch: [8][2090/11272] Time 0.861 (0.833) Data 0.001 (0.003) Loss 2.4241 (2.6353) Prec@1 40.625 (36.276) Prec@5 73.125 (66.827) Epoch: [8][2100/11272] Time 0.921 (0.833) Data 0.001 (0.003) Loss 2.4872 (2.6355) Prec@1 39.375 (36.273) Prec@5 70.625 (66.825) Epoch: [8][2110/11272] Time 0.743 (0.832) Data 0.002 (0.003) Loss 2.5333 (2.6360) Prec@1 35.625 (36.267) Prec@5 70.000 (66.818) Epoch: [8][2120/11272] Time 0.796 (0.832) Data 0.001 (0.003) Loss 2.4751 (2.6363) Prec@1 41.250 (36.267) Prec@5 67.500 (66.813) Epoch: [8][2130/11272] Time 0.852 (0.833) Data 0.002 (0.003) Loss 2.6988 (2.6366) Prec@1 35.000 (36.267) Prec@5 65.625 (66.807) Epoch: [8][2140/11272] Time 0.921 (0.832) Data 0.001 (0.003) Loss 2.5165 (2.6363) Prec@1 33.125 (36.263) Prec@5 72.500 (66.817) Epoch: [8][2150/11272] Time 0.738 (0.833) Data 0.002 (0.003) Loss 2.7675 (2.6367) Prec@1 36.875 (36.255) Prec@5 64.375 (66.817) Epoch: [8][2160/11272] Time 0.780 (0.832) Data 0.002 (0.003) Loss 2.4642 (2.6371) Prec@1 33.125 (36.245) Prec@5 71.250 (66.805) Epoch: [8][2170/11272] Time 0.909 (0.832) Data 0.002 (0.003) Loss 2.7326 (2.6370) Prec@1 37.500 (36.243) Prec@5 68.750 (66.811) Epoch: [8][2180/11272] Time 0.800 (0.832) Data 0.004 (0.003) Loss 2.6204 (2.6369) Prec@1 33.125 (36.242) Prec@5 67.500 (66.816) Epoch: [8][2190/11272] Time 0.800 (0.832) Data 0.002 (0.003) Loss 2.9365 (2.6367) Prec@1 31.875 (36.246) Prec@5 59.375 (66.819) Epoch: [8][2200/11272] Time 0.944 (0.832) Data 0.001 (0.003) Loss 2.6110 (2.6368) Prec@1 33.750 (36.248) Prec@5 66.875 (66.812) Epoch: [8][2210/11272] Time 0.933 (0.833) Data 0.002 (0.003) Loss 2.8020 (2.6370) Prec@1 32.500 (36.241) Prec@5 64.375 (66.811) Epoch: [8][2220/11272] Time 0.777 (0.833) Data 0.001 (0.003) Loss 2.5450 (2.6372) Prec@1 40.000 (36.245) Prec@5 67.500 (66.810) Epoch: [8][2230/11272] Time 0.734 (0.833) Data 0.002 (0.003) Loss 2.6480 (2.6368) Prec@1 37.500 (36.251) Prec@5 66.250 (66.822) Epoch: [8][2240/11272] Time 0.872 (0.833) Data 0.001 (0.003) Loss 2.4729 (2.6368) Prec@1 38.750 (36.247) Prec@5 70.625 (66.817) Epoch: [8][2250/11272] Time 0.916 (0.833) Data 0.002 (0.003) Loss 2.5363 (2.6367) Prec@1 36.250 (36.246) Prec@5 64.375 (66.816) Epoch: [8][2260/11272] Time 0.754 (0.833) Data 0.002 (0.003) Loss 2.6045 (2.6365) Prec@1 36.875 (36.247) Prec@5 66.875 (66.822) Epoch: [8][2270/11272] Time 0.745 (0.833) Data 0.002 (0.003) Loss 2.7284 (2.6364) Prec@1 41.875 (36.258) Prec@5 66.250 (66.818) Epoch: [8][2280/11272] Time 0.884 (0.833) Data 0.001 (0.003) Loss 2.4938 (2.6362) Prec@1 38.750 (36.260) Prec@5 71.250 (66.824) Epoch: [8][2290/11272] Time 0.920 (0.833) Data 0.002 (0.003) Loss 2.7372 (2.6364) Prec@1 31.250 (36.250) Prec@5 64.375 (66.819) Epoch: [8][2300/11272] Time 0.790 (0.832) Data 0.001 (0.003) Loss 2.5408 (2.6364) Prec@1 37.500 (36.245) Prec@5 70.000 (66.821) Epoch: [8][2310/11272] Time 0.902 (0.833) Data 0.002 (0.003) Loss 2.5805 (2.6361) Prec@1 41.875 (36.246) Prec@5 66.875 (66.830) Epoch: [8][2320/11272] Time 0.904 (0.833) Data 0.001 (0.003) Loss 2.5724 (2.6361) Prec@1 33.750 (36.241) Prec@5 70.625 (66.835) Epoch: [8][2330/11272] Time 0.731 (0.833) Data 0.002 (0.003) Loss 2.4360 (2.6361) Prec@1 36.250 (36.240) Prec@5 70.625 (66.837) Epoch: [8][2340/11272] Time 0.748 (0.833) Data 0.001 (0.003) Loss 2.7502 (2.6363) Prec@1 30.000 (36.238) Prec@5 62.500 (66.838) Epoch: [8][2350/11272] Time 0.899 (0.833) Data 0.002 (0.003) Loss 2.6511 (2.6362) Prec@1 38.750 (36.245) Prec@5 63.125 (66.838) Epoch: [8][2360/11272] Time 0.889 (0.833) Data 0.001 (0.003) Loss 2.5691 (2.6362) Prec@1 37.500 (36.242) Prec@5 71.250 (66.843) Epoch: [8][2370/11272] Time 0.752 (0.832) Data 0.002 (0.003) Loss 2.5825 (2.6361) Prec@1 38.125 (36.241) Prec@5 68.125 (66.838) Epoch: [8][2380/11272] Time 0.734 (0.832) Data 0.001 (0.003) Loss 2.6272 (2.6362) Prec@1 32.500 (36.241) Prec@5 68.125 (66.838) Epoch: [8][2390/11272] Time 0.934 (0.832) Data 0.002 (0.003) Loss 2.5312 (2.6361) Prec@1 36.875 (36.243) Prec@5 69.375 (66.839) Epoch: [8][2400/11272] Time 0.911 (0.832) Data 0.001 (0.003) Loss 2.6950 (2.6360) Prec@1 34.375 (36.246) Prec@5 65.625 (66.840) Epoch: [8][2410/11272] Time 0.755 (0.832) Data 0.002 (0.003) Loss 2.2131 (2.6359) Prec@1 40.625 (36.247) Prec@5 76.875 (66.847) Epoch: [8][2420/11272] Time 0.748 (0.832) Data 0.001 (0.003) Loss 2.8400 (2.6358) Prec@1 33.125 (36.248) Prec@5 61.875 (66.845) Epoch: [8][2430/11272] Time 0.906 (0.832) Data 0.002 (0.003) Loss 2.5157 (2.6357) Prec@1 36.875 (36.255) Prec@5 68.750 (66.849) Epoch: [8][2440/11272] Time 0.881 (0.832) Data 0.001 (0.003) Loss 2.8023 (2.6358) Prec@1 36.250 (36.254) Prec@5 70.625 (66.852) Epoch: [8][2450/11272] Time 0.797 (0.832) Data 0.002 (0.003) Loss 2.8259 (2.6357) Prec@1 33.750 (36.250) Prec@5 61.250 (66.854) Epoch: [8][2460/11272] Time 0.943 (0.832) Data 0.001 (0.003) Loss 2.7921 (2.6359) Prec@1 33.125 (36.249) Prec@5 63.125 (66.851) Epoch: [8][2470/11272] Time 0.901 (0.832) Data 0.002 (0.003) Loss 2.5706 (2.6358) Prec@1 38.125 (36.248) Prec@5 66.875 (66.853) Epoch: [8][2480/11272] Time 0.769 (0.832) Data 0.001 (0.003) Loss 2.5680 (2.6354) Prec@1 36.875 (36.253) Prec@5 68.125 (66.860) Epoch: [8][2490/11272] Time 0.791 (0.832) Data 0.002 (0.003) Loss 2.7369 (2.6354) Prec@1 36.875 (36.256) Prec@5 64.375 (66.860) Epoch: [8][2500/11272] Time 0.909 (0.832) Data 0.002 (0.003) Loss 2.4808 (2.6355) Prec@1 36.250 (36.251) Prec@5 71.875 (66.858) Epoch: [8][2510/11272] Time 0.859 (0.832) Data 0.003 (0.003) Loss 2.5526 (2.6356) Prec@1 36.875 (36.247) Prec@5 70.000 (66.854) Epoch: [8][2520/11272] Time 0.723 (0.832) Data 0.002 (0.003) Loss 2.6126 (2.6357) Prec@1 35.000 (36.242) Prec@5 68.750 (66.850) Epoch: [8][2530/11272] Time 0.731 (0.832) Data 0.002 (0.003) Loss 2.5813 (2.6355) Prec@1 38.125 (36.244) Prec@5 63.750 (66.855) Epoch: [8][2540/11272] Time 0.891 (0.832) Data 0.001 (0.003) Loss 2.4478 (2.6355) Prec@1 44.375 (36.243) Prec@5 71.250 (66.858) Epoch: [8][2550/11272] Time 0.902 (0.832) Data 0.001 (0.003) Loss 2.4619 (2.6356) Prec@1 40.000 (36.243) Prec@5 70.625 (66.857) Epoch: [8][2560/11272] Time 0.795 (0.832) Data 0.001 (0.003) Loss 2.4326 (2.6355) Prec@1 36.250 (36.240) Prec@5 73.125 (66.857) Epoch: [8][2570/11272] Time 0.745 (0.832) Data 0.002 (0.003) Loss 2.7852 (2.6355) Prec@1 37.500 (36.243) Prec@5 66.250 (66.861) Epoch: [8][2580/11272] Time 0.885 (0.832) Data 0.002 (0.003) Loss 2.8560 (2.6354) Prec@1 30.000 (36.246) Prec@5 61.250 (66.861) Epoch: [8][2590/11272] Time 0.737 (0.832) Data 0.002 (0.003) Loss 2.4127 (2.6353) Prec@1 39.375 (36.248) Prec@5 73.125 (66.861) Epoch: [8][2600/11272] Time 0.818 (0.832) Data 0.001 (0.003) Loss 2.5515 (2.6352) Prec@1 31.250 (36.249) Prec@5 66.250 (66.861) Epoch: [8][2610/11272] Time 0.913 (0.832) Data 0.001 (0.003) Loss 2.7772 (2.6356) Prec@1 38.750 (36.248) Prec@5 60.625 (66.852) Epoch: [8][2620/11272] Time 0.930 (0.832) Data 0.002 (0.003) Loss 2.7897 (2.6355) Prec@1 38.750 (36.253) Prec@5 64.375 (66.858) Epoch: [8][2630/11272] Time 0.741 (0.833) Data 0.002 (0.003) Loss 2.6990 (2.6355) Prec@1 36.250 (36.257) Prec@5 69.375 (66.859) Epoch: [8][2640/11272] Time 0.783 (0.833) Data 0.002 (0.003) Loss 2.5650 (2.6355) Prec@1 41.875 (36.257) Prec@5 64.375 (66.859) Epoch: [8][2650/11272] Time 0.886 (0.833) Data 0.002 (0.003) Loss 2.7689 (2.6355) Prec@1 28.750 (36.257) Prec@5 65.000 (66.857) Epoch: [8][2660/11272] Time 0.920 (0.833) Data 0.001 (0.003) Loss 2.8217 (2.6357) Prec@1 31.875 (36.256) Prec@5 60.000 (66.851) Epoch: [8][2670/11272] Time 0.742 (0.832) Data 0.002 (0.003) Loss 2.2769 (2.6354) Prec@1 45.000 (36.261) Prec@5 72.500 (66.858) Epoch: [8][2680/11272] Time 0.741 (0.832) Data 0.001 (0.003) Loss 2.5481 (2.6354) Prec@1 36.250 (36.264) Prec@5 68.125 (66.860) Epoch: [8][2690/11272] Time 0.885 (0.832) Data 0.002 (0.003) Loss 2.4823 (2.6355) Prec@1 41.250 (36.265) Prec@5 68.750 (66.856) Epoch: [8][2700/11272] Time 0.909 (0.833) Data 0.001 (0.003) Loss 2.7562 (2.6359) Prec@1 30.000 (36.258) Prec@5 60.625 (66.846) Epoch: [8][2710/11272] Time 0.749 (0.832) Data 0.002 (0.003) Loss 2.6705 (2.6359) Prec@1 33.125 (36.256) Prec@5 64.375 (66.845) Epoch: [8][2720/11272] Time 0.964 (0.833) Data 0.001 (0.003) Loss 2.5437 (2.6361) Prec@1 35.000 (36.253) Prec@5 66.250 (66.839) Epoch: [8][2730/11272] Time 0.913 (0.833) Data 0.002 (0.003) Loss 2.5528 (2.6360) Prec@1 36.875 (36.250) Prec@5 66.875 (66.836) Epoch: [8][2740/11272] Time 0.750 (0.833) Data 0.001 (0.003) Loss 2.7728 (2.6359) Prec@1 33.750 (36.254) Prec@5 58.750 (66.834) Epoch: [8][2750/11272] Time 0.749 (0.832) Data 0.002 (0.003) Loss 2.6682 (2.6362) Prec@1 33.125 (36.258) Prec@5 61.875 (66.828) Epoch: [8][2760/11272] Time 0.889 (0.832) Data 0.001 (0.003) Loss 2.6783 (2.6364) Prec@1 36.875 (36.255) Prec@5 65.000 (66.823) Epoch: [8][2770/11272] Time 0.920 (0.832) Data 0.001 (0.003) Loss 2.5946 (2.6365) Prec@1 35.625 (36.255) Prec@5 68.750 (66.825) Epoch: [8][2780/11272] Time 0.746 (0.832) Data 0.001 (0.003) Loss 2.8389 (2.6367) Prec@1 31.875 (36.252) Prec@5 63.750 (66.818) Epoch: [8][2790/11272] Time 0.766 (0.832) Data 0.002 (0.003) Loss 2.6324 (2.6366) Prec@1 33.125 (36.252) Prec@5 68.750 (66.818) Epoch: [8][2800/11272] Time 0.873 (0.832) Data 0.001 (0.003) Loss 2.6804 (2.6366) Prec@1 36.875 (36.252) Prec@5 65.625 (66.817) Epoch: [8][2810/11272] Time 0.879 (0.832) Data 0.003 (0.003) Loss 2.8059 (2.6367) Prec@1 33.125 (36.257) Prec@5 65.000 (66.819) Epoch: [8][2820/11272] Time 0.814 (0.832) Data 0.001 (0.003) Loss 2.5486 (2.6366) Prec@1 33.125 (36.255) Prec@5 67.500 (66.819) Epoch: [8][2830/11272] Time 0.720 (0.833) Data 0.002 (0.003) Loss 2.6363 (2.6366) Prec@1 35.000 (36.252) Prec@5 66.250 (66.817) Epoch: [8][2840/11272] Time 0.875 (0.832) Data 0.001 (0.003) Loss 2.7849 (2.6368) Prec@1 34.375 (36.247) Prec@5 65.625 (66.814) Epoch: [8][2850/11272] Time 0.755 (0.832) Data 0.004 (0.003) Loss 2.5092 (2.6369) Prec@1 41.250 (36.248) Prec@5 68.125 (66.813) Epoch: [8][2860/11272] Time 0.763 (0.832) Data 0.001 (0.003) Loss 2.7364 (2.6370) Prec@1 30.625 (36.246) Prec@5 64.375 (66.808) Epoch: [8][2870/11272] Time 0.902 (0.832) Data 0.002 (0.003) Loss 2.5020 (2.6370) Prec@1 36.875 (36.240) Prec@5 67.500 (66.810) Epoch: [8][2880/11272] Time 0.883 (0.832) Data 0.001 (0.003) Loss 2.7162 (2.6369) Prec@1 36.250 (36.246) Prec@5 66.875 (66.815) Epoch: [8][2890/11272] Time 0.762 (0.832) Data 0.002 (0.003) Loss 2.5215 (2.6367) Prec@1 38.750 (36.250) Prec@5 73.125 (66.820) Epoch: [8][2900/11272] Time 0.790 (0.832) Data 0.002 (0.003) Loss 2.6239 (2.6369) Prec@1 38.750 (36.246) Prec@5 62.500 (66.820) Epoch: [8][2910/11272] Time 0.900 (0.832) Data 0.002 (0.003) Loss 2.6635 (2.6369) Prec@1 35.625 (36.243) Prec@5 67.500 (66.822) Epoch: [8][2920/11272] Time 0.947 (0.832) Data 0.001 (0.003) Loss 2.7745 (2.6372) Prec@1 36.250 (36.236) Prec@5 66.875 (66.817) Epoch: [8][2930/11272] Time 0.734 (0.832) Data 0.002 (0.003) Loss 2.7737 (2.6374) Prec@1 32.500 (36.232) Prec@5 65.625 (66.811) Epoch: [8][2940/11272] Time 0.780 (0.832) Data 0.002 (0.003) Loss 2.8562 (2.6375) Prec@1 28.125 (36.230) Prec@5 66.250 (66.810) Epoch: [8][2950/11272] Time 0.940 (0.832) Data 0.001 (0.003) Loss 2.5280 (2.6376) Prec@1 38.125 (36.229) Prec@5 70.625 (66.811) Epoch: [8][2960/11272] Time 0.920 (0.832) Data 0.001 (0.003) Loss 2.7384 (2.6377) Prec@1 31.875 (36.224) Prec@5 65.625 (66.812) Epoch: [8][2970/11272] Time 0.764 (0.832) Data 0.002 (0.003) Loss 2.2844 (2.6377) Prec@1 41.250 (36.216) Prec@5 73.125 (66.813) Epoch: [8][2980/11272] Time 0.890 (0.832) Data 0.002 (0.002) Loss 2.7635 (2.6379) Prec@1 33.750 (36.216) Prec@5 65.625 (66.807) Epoch: [8][2990/11272] Time 0.925 (0.832) Data 0.002 (0.002) Loss 2.8718 (2.6380) Prec@1 36.875 (36.216) Prec@5 59.375 (66.806) Epoch: [8][3000/11272] Time 0.742 (0.832) Data 0.001 (0.002) Loss 2.5986 (2.6377) Prec@1 39.375 (36.219) Prec@5 70.000 (66.810) Epoch: [8][3010/11272] Time 0.825 (0.832) Data 0.002 (0.002) Loss 2.4637 (2.6379) Prec@1 43.750 (36.220) Prec@5 68.750 (66.806) Epoch: [8][3020/11272] Time 0.877 (0.832) Data 0.001 (0.002) Loss 2.6353 (2.6380) Prec@1 41.875 (36.218) Prec@5 70.000 (66.804) Epoch: [8][3030/11272] Time 0.906 (0.833) Data 0.002 (0.002) Loss 2.8025 (2.6381) Prec@1 34.375 (36.219) Prec@5 66.875 (66.802) Epoch: [8][3040/11272] Time 0.747 (0.832) Data 0.001 (0.002) Loss 2.7479 (2.6380) Prec@1 36.250 (36.220) Prec@5 63.750 (66.801) Epoch: [8][3050/11272] Time 0.745 (0.832) Data 0.003 (0.002) Loss 3.0852 (2.6381) Prec@1 28.125 (36.216) Prec@5 63.125 (66.802) Epoch: [8][3060/11272] Time 0.946 (0.833) Data 0.001 (0.002) Loss 2.6654 (2.6382) Prec@1 35.000 (36.215) Prec@5 68.750 (66.803) Epoch: [8][3070/11272] Time 0.925 (0.833) Data 0.002 (0.002) Loss 2.6719 (2.6381) Prec@1 36.250 (36.218) Prec@5 66.875 (66.804) Epoch: [8][3080/11272] Time 0.756 (0.833) Data 0.003 (0.002) Loss 2.7419 (2.6378) Prec@1 38.125 (36.227) Prec@5 61.875 (66.809) Epoch: [8][3090/11272] Time 0.731 (0.833) Data 0.002 (0.002) Loss 2.6492 (2.6378) Prec@1 45.625 (36.225) Prec@5 65.625 (66.811) Epoch: [8][3100/11272] Time 0.956 (0.833) Data 0.001 (0.002) Loss 2.7837 (2.6376) Prec@1 33.750 (36.232) Prec@5 63.125 (66.819) Epoch: [8][3110/11272] Time 0.732 (0.832) Data 0.003 (0.002) Loss 2.7928 (2.6377) Prec@1 32.500 (36.224) Prec@5 64.375 (66.817) Epoch: [8][3120/11272] Time 0.782 (0.832) Data 0.002 (0.002) Loss 2.5961 (2.6378) Prec@1 38.750 (36.226) Prec@5 70.000 (66.819) Epoch: [8][3130/11272] Time 0.934 (0.833) Data 0.001 (0.002) Loss 2.7963 (2.6379) Prec@1 36.875 (36.227) Prec@5 61.875 (66.815) Epoch: [8][3140/11272] Time 0.894 (0.833) Data 0.001 (0.002) Loss 2.6790 (2.6379) Prec@1 38.750 (36.229) Prec@5 65.000 (66.818) Epoch: [8][3150/11272] Time 0.783 (0.833) Data 0.002 (0.002) Loss 2.4104 (2.6377) Prec@1 45.000 (36.236) Prec@5 70.000 (66.826) Epoch: [8][3160/11272] Time 0.812 (0.833) Data 0.001 (0.002) Loss 2.7877 (2.6376) Prec@1 34.375 (36.242) Prec@5 65.625 (66.830) Epoch: [8][3170/11272] Time 0.910 (0.833) Data 0.002 (0.002) Loss 2.4337 (2.6375) Prec@1 38.750 (36.242) Prec@5 71.875 (66.831) Epoch: [8][3180/11272] Time 0.891 (0.833) Data 0.001 (0.002) Loss 2.7309 (2.6376) Prec@1 33.750 (36.239) Prec@5 63.125 (66.829) Epoch: [8][3190/11272] Time 0.733 (0.833) Data 0.002 (0.002) Loss 2.5504 (2.6379) Prec@1 38.125 (36.229) Prec@5 66.875 (66.822) Epoch: [8][3200/11272] Time 0.758 (0.833) Data 0.002 (0.002) Loss 2.4897 (2.6377) Prec@1 36.875 (36.231) Prec@5 70.000 (66.827) Epoch: [8][3210/11272] Time 0.941 (0.833) Data 0.001 (0.002) Loss 2.8941 (2.6378) Prec@1 32.500 (36.229) Prec@5 59.375 (66.828) Epoch: [8][3220/11272] Time 1.073 (0.833) Data 0.002 (0.002) Loss 2.7215 (2.6377) Prec@1 32.500 (36.230) Prec@5 68.750 (66.829) Epoch: [8][3230/11272] Time 0.749 (0.833) Data 0.002 (0.002) Loss 2.7456 (2.6377) Prec@1 40.000 (36.232) Prec@5 61.250 (66.828) Epoch: [8][3240/11272] Time 0.882 (0.833) Data 0.002 (0.002) Loss 2.8152 (2.6378) Prec@1 31.250 (36.231) Prec@5 66.250 (66.829) Epoch: [8][3250/11272] Time 0.891 (0.833) Data 0.002 (0.002) Loss 2.7717 (2.6379) Prec@1 35.625 (36.228) Prec@5 61.875 (66.828) Epoch: [8][3260/11272] Time 0.801 (0.833) Data 0.001 (0.002) Loss 2.4873 (2.6381) Prec@1 38.125 (36.224) Prec@5 71.875 (66.824) Epoch: [8][3270/11272] Time 0.747 (0.833) Data 0.002 (0.002) Loss 2.5971 (2.6381) Prec@1 31.875 (36.219) Prec@5 69.375 (66.822) Epoch: [8][3280/11272] Time 0.911 (0.833) Data 0.001 (0.002) Loss 2.6313 (2.6380) Prec@1 37.500 (36.219) Prec@5 66.875 (66.821) Epoch: [8][3290/11272] Time 0.922 (0.833) Data 0.002 (0.002) Loss 2.7670 (2.6381) Prec@1 31.250 (36.215) Prec@5 70.000 (66.821) Epoch: [8][3300/11272] Time 0.738 (0.833) Data 0.002 (0.002) Loss 2.8455 (2.6383) Prec@1 35.625 (36.215) Prec@5 63.750 (66.821) Epoch: [8][3310/11272] Time 0.722 (0.833) Data 0.002 (0.002) Loss 2.6951 (2.6383) Prec@1 31.250 (36.214) Prec@5 65.625 (66.819) Epoch: [8][3320/11272] Time 0.883 (0.833) Data 0.001 (0.002) Loss 2.9036 (2.6385) Prec@1 30.625 (36.213) Prec@5 63.125 (66.814) Epoch: [8][3330/11272] Time 0.942 (0.833) Data 0.001 (0.002) Loss 2.7392 (2.6386) Prec@1 35.000 (36.212) Prec@5 62.500 (66.811) Epoch: [8][3340/11272] Time 0.770 (0.833) Data 0.001 (0.002) Loss 2.5966 (2.6387) Prec@1 36.250 (36.212) Prec@5 70.000 (66.809) Epoch: [8][3350/11272] Time 0.778 (0.833) Data 0.002 (0.002) Loss 2.5958 (2.6386) Prec@1 33.125 (36.213) Prec@5 68.750 (66.808) Epoch: [8][3360/11272] Time 0.887 (0.833) Data 0.001 (0.002) Loss 2.5108 (2.6388) Prec@1 41.875 (36.212) Prec@5 71.250 (66.807) Epoch: [8][3370/11272] Time 0.855 (0.833) Data 0.002 (0.002) Loss 2.5032 (2.6389) Prec@1 41.250 (36.214) Prec@5 65.000 (66.802) Epoch: [8][3380/11272] Time 0.772 (0.833) Data 0.001 (0.002) Loss 2.6754 (2.6388) Prec@1 35.625 (36.213) Prec@5 62.500 (66.803) Epoch: [8][3390/11272] Time 0.932 (0.833) Data 0.002 (0.002) Loss 2.7795 (2.6391) Prec@1 31.875 (36.208) Prec@5 61.875 (66.794) Epoch: [8][3400/11272] Time 0.914 (0.833) Data 0.001 (0.002) Loss 2.7890 (2.6390) Prec@1 32.500 (36.213) Prec@5 68.750 (66.800) Epoch: [8][3410/11272] Time 0.721 (0.833) Data 0.002 (0.002) Loss 2.6184 (2.6391) Prec@1 35.000 (36.209) Prec@5 65.625 (66.799) Epoch: [8][3420/11272] Time 0.752 (0.833) Data 0.001 (0.002) Loss 2.7772 (2.6392) Prec@1 26.875 (36.203) Prec@5 63.125 (66.796) Epoch: [8][3430/11272] Time 0.927 (0.833) Data 0.002 (0.002) Loss 2.8851 (2.6392) Prec@1 29.375 (36.203) Prec@5 59.375 (66.796) Epoch: [8][3440/11272] Time 0.907 (0.833) Data 0.001 (0.002) Loss 2.6240 (2.6391) Prec@1 37.500 (36.201) Prec@5 65.625 (66.801) Epoch: [8][3450/11272] Time 0.765 (0.833) Data 0.002 (0.002) Loss 2.4695 (2.6392) Prec@1 37.500 (36.199) Prec@5 69.375 (66.800) Epoch: [8][3460/11272] Time 0.775 (0.833) Data 0.002 (0.002) Loss 2.8224 (2.6391) Prec@1 33.125 (36.197) Prec@5 67.500 (66.804) Epoch: [8][3470/11272] Time 0.893 (0.833) Data 0.002 (0.002) Loss 2.4906 (2.6392) Prec@1 37.500 (36.200) Prec@5 68.125 (66.800) Epoch: [8][3480/11272] Time 0.859 (0.833) Data 0.001 (0.002) Loss 2.6314 (2.6393) Prec@1 38.750 (36.200) Prec@5 68.750 (66.797) Epoch: [8][3490/11272] Time 0.745 (0.833) Data 0.002 (0.002) Loss 2.6133 (2.6394) Prec@1 33.750 (36.199) Prec@5 70.625 (66.796) Epoch: [8][3500/11272] Time 0.747 (0.833) Data 0.001 (0.002) Loss 2.3200 (2.6395) Prec@1 43.750 (36.198) Prec@5 69.375 (66.793) Epoch: [8][3510/11272] Time 0.877 (0.833) Data 0.002 (0.002) Loss 2.4483 (2.6396) Prec@1 38.750 (36.199) Prec@5 73.750 (66.792) Epoch: [8][3520/11272] Time 0.744 (0.832) Data 0.002 (0.002) Loss 2.5353 (2.6397) Prec@1 41.875 (36.197) Prec@5 67.500 (66.791) Epoch: [8][3530/11272] Time 0.759 (0.832) Data 0.003 (0.002) Loss 2.6540 (2.6398) Prec@1 36.250 (36.197) Prec@5 63.750 (66.789) Epoch: [8][3540/11272] Time 0.875 (0.832) Data 0.001 (0.002) Loss 2.5376 (2.6398) Prec@1 36.875 (36.198) Prec@5 70.625 (66.791) Epoch: [8][3550/11272] Time 0.861 (0.832) Data 0.002 (0.002) Loss 2.6482 (2.6399) Prec@1 36.875 (36.193) Prec@5 68.750 (66.792) Epoch: [8][3560/11272] Time 0.805 (0.832) Data 0.001 (0.002) Loss 2.8881 (2.6400) Prec@1 30.625 (36.187) Prec@5 61.875 (66.789) Epoch: [8][3570/11272] Time 0.757 (0.832) Data 0.001 (0.002) Loss 2.3723 (2.6400) Prec@1 41.875 (36.187) Prec@5 73.750 (66.790) Epoch: [8][3580/11272] Time 0.901 (0.832) Data 0.001 (0.002) Loss 2.8459 (2.6401) Prec@1 34.375 (36.184) Prec@5 61.875 (66.790) Epoch: [8][3590/11272] Time 0.926 (0.832) Data 0.002 (0.002) Loss 2.6800 (2.6403) Prec@1 36.250 (36.182) Prec@5 65.625 (66.784) Epoch: [8][3600/11272] Time 0.738 (0.832) Data 0.001 (0.002) Loss 2.8413 (2.6406) Prec@1 34.375 (36.173) Prec@5 68.125 (66.779) Epoch: [8][3610/11272] Time 0.739 (0.832) Data 0.002 (0.002) Loss 2.6011 (2.6406) Prec@1 34.375 (36.170) Prec@5 66.250 (66.777) Epoch: [8][3620/11272] Time 0.883 (0.832) Data 0.001 (0.002) Loss 2.6175 (2.6406) Prec@1 34.375 (36.171) Prec@5 70.625 (66.780) Epoch: [8][3630/11272] Time 0.933 (0.832) Data 0.002 (0.002) Loss 2.6061 (2.6406) Prec@1 36.875 (36.171) Prec@5 69.375 (66.777) Epoch: [8][3640/11272] Time 0.802 (0.832) Data 0.002 (0.002) Loss 2.6130 (2.6407) Prec@1 36.250 (36.169) Prec@5 64.375 (66.776) Epoch: [8][3650/11272] Time 0.861 (0.832) Data 0.002 (0.002) Loss 2.7273 (2.6409) Prec@1 35.625 (36.166) Prec@5 65.625 (66.770) Epoch: [8][3660/11272] Time 0.926 (0.832) Data 0.001 (0.002) Loss 2.4531 (2.6410) Prec@1 39.375 (36.167) Prec@5 65.625 (66.770) Epoch: [8][3670/11272] Time 0.754 (0.832) Data 0.002 (0.002) Loss 2.6343 (2.6410) Prec@1 41.875 (36.173) Prec@5 65.625 (66.771) Epoch: [8][3680/11272] Time 0.758 (0.832) Data 0.002 (0.002) Loss 2.5844 (2.6413) Prec@1 37.500 (36.170) Prec@5 67.500 (66.766) Epoch: [8][3690/11272] Time 0.908 (0.832) Data 0.002 (0.002) Loss 2.5920 (2.6414) Prec@1 38.750 (36.170) Prec@5 66.875 (66.765) Epoch: [8][3700/11272] Time 0.871 (0.832) Data 0.001 (0.002) Loss 2.6428 (2.6414) Prec@1 30.625 (36.169) Prec@5 65.625 (66.767) Epoch: [8][3710/11272] Time 0.746 (0.832) Data 0.002 (0.002) Loss 2.8101 (2.6416) Prec@1 36.875 (36.169) Prec@5 62.500 (66.767) Epoch: [8][3720/11272] Time 0.745 (0.832) Data 0.002 (0.002) Loss 2.6654 (2.6418) Prec@1 37.500 (36.167) Prec@5 66.875 (66.760) Epoch: [8][3730/11272] Time 0.894 (0.832) Data 0.002 (0.002) Loss 2.7624 (2.6418) Prec@1 33.125 (36.167) Prec@5 64.375 (66.757) Epoch: [8][3740/11272] Time 0.908 (0.832) Data 0.001 (0.002) Loss 2.5146 (2.6417) Prec@1 37.500 (36.167) Prec@5 70.000 (66.758) Epoch: [8][3750/11272] Time 0.733 (0.832) Data 0.002 (0.002) Loss 2.4338 (2.6415) Prec@1 40.625 (36.170) Prec@5 70.625 (66.763) Epoch: [8][3760/11272] Time 0.753 (0.832) Data 0.001 (0.002) Loss 2.6446 (2.6416) Prec@1 38.750 (36.166) Prec@5 65.000 (66.760) Epoch: [8][3770/11272] Time 0.908 (0.832) Data 0.002 (0.002) Loss 2.6758 (2.6415) Prec@1 36.250 (36.165) Prec@5 64.375 (66.760) Epoch: [8][3780/11272] Time 0.773 (0.832) Data 0.004 (0.002) Loss 2.2429 (2.6413) Prec@1 48.750 (36.176) Prec@5 75.000 (66.762) Epoch: [8][3790/11272] Time 0.730 (0.832) Data 0.002 (0.002) Loss 2.7400 (2.6412) Prec@1 31.875 (36.176) Prec@5 63.125 (66.765) Epoch: [8][3800/11272] Time 0.897 (0.832) Data 0.001 (0.002) Loss 2.4580 (2.6413) Prec@1 40.625 (36.177) Prec@5 66.875 (66.764) Epoch: [8][3810/11272] Time 0.926 (0.832) Data 0.003 (0.002) Loss 2.7458 (2.6414) Prec@1 34.375 (36.175) Prec@5 62.500 (66.761) Epoch: [8][3820/11272] Time 0.732 (0.832) Data 0.002 (0.002) Loss 2.5831 (2.6415) Prec@1 36.250 (36.172) Prec@5 68.750 (66.760) Epoch: [8][3830/11272] Time 0.776 (0.832) Data 0.003 (0.002) Loss 2.6778 (2.6417) Prec@1 36.250 (36.169) Prec@5 66.875 (66.759) Epoch: [8][3840/11272] Time 0.858 (0.832) Data 0.002 (0.002) Loss 2.5179 (2.6418) Prec@1 35.000 (36.167) Prec@5 70.000 (66.753) Epoch: [8][3850/11272] Time 0.914 (0.832) Data 0.002 (0.002) Loss 2.7368 (2.6417) Prec@1 35.625 (36.164) Prec@5 65.000 (66.756) Epoch: [8][3860/11272] Time 0.778 (0.832) Data 0.001 (0.002) Loss 2.8813 (2.6417) Prec@1 31.250 (36.162) Prec@5 66.250 (66.759) Epoch: [8][3870/11272] Time 0.748 (0.832) Data 0.002 (0.002) Loss 2.3919 (2.6417) Prec@1 37.500 (36.161) Prec@5 68.125 (66.758) Epoch: [8][3880/11272] Time 0.910 (0.832) Data 0.001 (0.002) Loss 2.3963 (2.6418) Prec@1 40.000 (36.159) Prec@5 70.625 (66.754) Epoch: [8][3890/11272] Time 0.899 (0.832) Data 0.002 (0.002) Loss 2.7555 (2.6420) Prec@1 30.625 (36.152) Prec@5 66.250 (66.751) Epoch: [8][3900/11272] Time 0.746 (0.832) Data 0.001 (0.002) Loss 2.6043 (2.6420) Prec@1 35.000 (36.150) Prec@5 68.750 (66.752) Epoch: [8][3910/11272] Time 0.896 (0.832) Data 0.002 (0.002) Loss 2.7551 (2.6420) Prec@1 30.000 (36.151) Prec@5 70.000 (66.753) Epoch: [8][3920/11272] Time 0.866 (0.832) Data 0.001 (0.002) Loss 2.8282 (2.6419) Prec@1 33.750 (36.153) Prec@5 62.500 (66.754) Epoch: [8][3930/11272] Time 0.773 (0.832) Data 0.002 (0.002) Loss 2.5593 (2.6419) Prec@1 40.000 (36.157) Prec@5 67.500 (66.753) Epoch: [8][3940/11272] Time 0.767 (0.832) Data 0.001 (0.002) Loss 2.6643 (2.6420) Prec@1 37.500 (36.155) Prec@5 63.750 (66.749) Epoch: [8][3950/11272] Time 0.848 (0.832) Data 0.002 (0.002) Loss 2.6274 (2.6420) Prec@1 35.625 (36.158) Prec@5 69.375 (66.751) Epoch: [8][3960/11272] Time 0.862 (0.832) Data 0.001 (0.002) Loss 2.5966 (2.6422) Prec@1 35.625 (36.156) Prec@5 67.500 (66.745) Epoch: [8][3970/11272] Time 0.815 (0.832) Data 0.002 (0.002) Loss 2.9162 (2.6423) Prec@1 31.875 (36.151) Prec@5 59.375 (66.741) Epoch: [8][3980/11272] Time 0.755 (0.832) Data 0.002 (0.002) Loss 2.7263 (2.6424) Prec@1 28.750 (36.148) Prec@5 65.625 (66.740) Epoch: [8][3990/11272] Time 0.896 (0.832) Data 0.002 (0.002) Loss 2.3273 (2.6423) Prec@1 38.125 (36.150) Prec@5 74.375 (66.744) Epoch: [8][4000/11272] Time 0.918 (0.832) Data 0.001 (0.002) Loss 2.5696 (2.6424) Prec@1 35.625 (36.151) Prec@5 61.875 (66.741) Epoch: [8][4010/11272] Time 0.712 (0.832) Data 0.002 (0.002) Loss 2.8385 (2.6425) Prec@1 33.125 (36.148) Prec@5 63.750 (66.740) Epoch: [8][4020/11272] Time 0.752 (0.832) Data 0.001 (0.002) Loss 2.7323 (2.6423) Prec@1 36.875 (36.151) Prec@5 66.875 (66.742) Epoch: [8][4030/11272] Time 0.864 (0.832) Data 0.002 (0.002) Loss 2.3595 (2.6422) Prec@1 37.500 (36.152) Prec@5 71.875 (66.743) Epoch: [8][4040/11272] Time 0.746 (0.832) Data 0.003 (0.002) Loss 2.5104 (2.6422) Prec@1 36.250 (36.154) Prec@5 71.875 (66.743) Epoch: [8][4050/11272] Time 0.802 (0.832) Data 0.001 (0.002) Loss 2.6961 (2.6423) Prec@1 36.250 (36.152) Prec@5 69.375 (66.741) Epoch: [8][4060/11272] Time 0.868 (0.832) Data 0.001 (0.002) Loss 2.4922 (2.6423) Prec@1 34.375 (36.152) Prec@5 66.875 (66.740) Epoch: [8][4070/11272] Time 0.924 (0.832) Data 0.002 (0.002) Loss 2.7428 (2.6424) Prec@1 32.500 (36.147) Prec@5 63.125 (66.737) Epoch: [8][4080/11272] Time 0.766 (0.832) Data 0.002 (0.002) Loss 2.3919 (2.6422) Prec@1 41.250 (36.153) Prec@5 73.750 (66.742) Epoch: [8][4090/11272] Time 0.807 (0.832) Data 0.001 (0.002) Loss 2.5441 (2.6422) Prec@1 34.375 (36.153) Prec@5 66.875 (66.746) Epoch: [8][4100/11272] Time 0.890 (0.832) Data 0.001 (0.002) Loss 2.4920 (2.6422) Prec@1 41.875 (36.155) Prec@5 66.875 (66.745) Epoch: [8][4110/11272] Time 0.943 (0.832) Data 0.001 (0.002) Loss 2.8785 (2.6421) Prec@1 35.625 (36.156) Prec@5 61.250 (66.745) Epoch: [8][4120/11272] Time 0.729 (0.832) Data 0.001 (0.002) Loss 2.7108 (2.6421) Prec@1 30.000 (36.156) Prec@5 62.500 (66.739) Epoch: [8][4130/11272] Time 0.753 (0.832) Data 0.002 (0.002) Loss 2.3760 (2.6420) Prec@1 42.500 (36.159) Prec@5 71.250 (66.741) Epoch: [8][4140/11272] Time 0.922 (0.832) Data 0.001 (0.002) Loss 2.5751 (2.6420) Prec@1 38.750 (36.158) Prec@5 68.750 (66.744) Epoch: [8][4150/11272] Time 0.923 (0.832) Data 0.002 (0.002) Loss 2.4088 (2.6419) Prec@1 40.000 (36.162) Prec@5 74.375 (66.749) Epoch: [8][4160/11272] Time 0.792 (0.832) Data 0.001 (0.002) Loss 2.6658 (2.6419) Prec@1 35.625 (36.159) Prec@5 68.750 (66.748) Epoch: [8][4170/11272] Time 0.904 (0.832) Data 0.002 (0.002) Loss 2.7086 (2.6419) Prec@1 38.125 (36.158) Prec@5 65.000 (66.747) Epoch: [8][4180/11272] Time 0.896 (0.832) Data 0.001 (0.002) Loss 2.7596 (2.6420) Prec@1 33.125 (36.159) Prec@5 62.500 (66.746) Epoch: [8][4190/11272] Time 0.770 (0.832) Data 0.002 (0.002) Loss 2.5883 (2.6421) Prec@1 31.250 (36.156) Prec@5 66.875 (66.744) Epoch: [8][4200/11272] Time 0.761 (0.832) Data 0.002 (0.002) Loss 2.7676 (2.6423) Prec@1 36.250 (36.154) Prec@5 63.750 (66.741) Epoch: [8][4210/11272] Time 0.851 (0.832) Data 0.002 (0.002) Loss 2.7256 (2.6424) Prec@1 33.750 (36.151) Prec@5 63.750 (66.739) Epoch: [8][4220/11272] Time 0.899 (0.832) Data 0.001 (0.002) Loss 2.5896 (2.6425) Prec@1 36.875 (36.154) Prec@5 68.125 (66.739) Epoch: [8][4230/11272] Time 0.731 (0.832) Data 0.002 (0.002) Loss 2.4210 (2.6425) Prec@1 39.375 (36.154) Prec@5 73.750 (66.739) Epoch: [8][4240/11272] Time 0.773 (0.832) Data 0.001 (0.002) Loss 2.8996 (2.6427) Prec@1 33.750 (36.148) Prec@5 63.125 (66.736) Epoch: [8][4250/11272] Time 0.885 (0.832) Data 0.002 (0.002) Loss 2.6637 (2.6428) Prec@1 37.500 (36.146) Prec@5 60.625 (66.731) Epoch: [8][4260/11272] Time 0.924 (0.832) Data 0.001 (0.002) Loss 2.6477 (2.6428) Prec@1 33.750 (36.143) Prec@5 67.500 (66.731) Epoch: [8][4270/11272] Time 0.800 (0.832) Data 0.002 (0.002) Loss 2.6702 (2.6427) Prec@1 35.000 (36.139) Prec@5 65.000 (66.730) Epoch: [8][4280/11272] Time 0.814 (0.832) Data 0.002 (0.002) Loss 2.8185 (2.6429) Prec@1 33.750 (36.135) Prec@5 63.750 (66.726) Epoch: [8][4290/11272] Time 0.881 (0.832) Data 0.002 (0.002) Loss 2.7189 (2.6429) Prec@1 30.625 (36.137) Prec@5 62.500 (66.728) Epoch: [8][4300/11272] Time 0.913 (0.832) Data 0.001 (0.002) Loss 2.9099 (2.6428) Prec@1 28.750 (36.139) Prec@5 59.375 (66.727) Epoch: [8][4310/11272] Time 0.731 (0.832) Data 0.002 (0.002) Loss 2.5862 (2.6428) Prec@1 38.750 (36.140) Prec@5 70.625 (66.728) Epoch: [8][4320/11272] Time 0.865 (0.832) Data 0.001 (0.002) Loss 2.7814 (2.6429) Prec@1 32.500 (36.137) Prec@5 63.125 (66.726) Epoch: [8][4330/11272] Time 0.885 (0.832) Data 0.003 (0.002) Loss 2.6703 (2.6429) Prec@1 35.625 (36.135) Prec@5 65.000 (66.726) Epoch: [8][4340/11272] Time 0.744 (0.832) Data 0.002 (0.002) Loss 2.5083 (2.6430) Prec@1 39.375 (36.132) Prec@5 70.000 (66.726) Epoch: [8][4350/11272] Time 0.793 (0.832) Data 0.002 (0.002) Loss 2.5269 (2.6427) Prec@1 42.500 (36.138) Prec@5 65.625 (66.727) Epoch: [8][4360/11272] Time 0.883 (0.832) Data 0.001 (0.002) Loss 2.4874 (2.6428) Prec@1 38.750 (36.139) Prec@5 69.375 (66.726) Epoch: [8][4370/11272] Time 0.934 (0.832) Data 0.001 (0.002) Loss 2.5901 (2.6427) Prec@1 35.625 (36.134) Prec@5 67.500 (66.728) Epoch: [8][4380/11272] Time 0.753 (0.832) Data 0.001 (0.002) Loss 2.5553 (2.6426) Prec@1 36.250 (36.138) Prec@5 68.750 (66.731) Epoch: [8][4390/11272] Time 0.729 (0.832) Data 0.002 (0.002) Loss 2.5766 (2.6427) Prec@1 36.875 (36.139) Prec@5 66.875 (66.731) Epoch: [8][4400/11272] Time 0.932 (0.832) Data 0.001 (0.002) Loss 2.4981 (2.6426) Prec@1 41.875 (36.139) Prec@5 71.250 (66.734) Epoch: [8][4410/11272] Time 0.920 (0.832) Data 0.001 (0.002) Loss 2.7672 (2.6425) Prec@1 28.125 (36.137) Prec@5 63.125 (66.738) Epoch: [8][4420/11272] Time 0.745 (0.832) Data 0.001 (0.002) Loss 2.6488 (2.6426) Prec@1 31.875 (36.135) Prec@5 66.875 (66.735) Epoch: [8][4430/11272] Time 0.695 (0.832) Data 0.001 (0.002) Loss 2.4804 (2.6428) Prec@1 36.250 (36.128) Prec@5 73.125 (66.735) Epoch: [8][4440/11272] Time 0.940 (0.832) Data 0.001 (0.002) Loss 2.6372 (2.6428) Prec@1 32.500 (36.129) Prec@5 65.000 (66.735) Epoch: [8][4450/11272] Time 0.757 (0.832) Data 0.001 (0.002) Loss 2.7680 (2.6428) Prec@1 32.500 (36.129) Prec@5 68.125 (66.734) Epoch: [8][4460/11272] Time 0.751 (0.832) Data 0.002 (0.002) Loss 2.4270 (2.6430) Prec@1 43.750 (36.125) Prec@5 71.875 (66.727) Epoch: [8][4470/11272] Time 0.863 (0.832) Data 0.001 (0.002) Loss 2.5642 (2.6430) Prec@1 34.375 (36.128) Prec@5 67.500 (66.727) Epoch: [8][4480/11272] Time 0.880 (0.832) Data 0.001 (0.002) Loss 2.5122 (2.6430) Prec@1 41.875 (36.129) Prec@5 70.625 (66.727) Epoch: [8][4490/11272] Time 0.743 (0.832) Data 0.002 (0.002) Loss 2.6427 (2.6429) Prec@1 38.125 (36.132) Prec@5 68.125 (66.730) Epoch: [8][4500/11272] Time 0.795 (0.832) Data 0.001 (0.002) Loss 2.4295 (2.6429) Prec@1 41.875 (36.132) Prec@5 73.125 (66.730) Epoch: [8][4510/11272] Time 0.876 (0.832) Data 0.002 (0.002) Loss 2.6976 (2.6429) Prec@1 39.375 (36.134) Prec@5 63.750 (66.728) Epoch: [8][4520/11272] Time 0.885 (0.832) Data 0.001 (0.002) Loss 2.7128 (2.6428) Prec@1 36.875 (36.137) Prec@5 68.125 (66.732) Epoch: [8][4530/11272] Time 0.792 (0.832) Data 0.002 (0.002) Loss 2.6803 (2.6429) Prec@1 40.000 (36.136) Prec@5 69.375 (66.732) Epoch: [8][4540/11272] Time 0.777 (0.832) Data 0.001 (0.002) Loss 2.8199 (2.6430) Prec@1 36.250 (36.139) Prec@5 62.500 (66.730) Epoch: [8][4550/11272] Time 0.929 (0.832) Data 0.002 (0.002) Loss 2.8404 (2.6429) Prec@1 35.625 (36.139) Prec@5 60.000 (66.731) Epoch: [8][4560/11272] Time 0.920 (0.832) Data 0.001 (0.002) Loss 2.4148 (2.6429) Prec@1 40.000 (36.141) Prec@5 71.875 (66.732) Epoch: [8][4570/11272] Time 0.743 (0.832) Data 0.002 (0.002) Loss 2.7420 (2.6431) Prec@1 35.000 (36.138) Prec@5 66.875 (66.727) Epoch: [8][4580/11272] Time 0.871 (0.832) Data 0.002 (0.002) Loss 2.6669 (2.6432) Prec@1 35.625 (36.139) Prec@5 64.375 (66.726) Epoch: [8][4590/11272] Time 0.915 (0.832) Data 0.001 (0.002) Loss 2.5023 (2.6432) Prec@1 40.625 (36.139) Prec@5 70.625 (66.723) Epoch: [8][4600/11272] Time 0.733 (0.832) Data 0.001 (0.002) Loss 2.6780 (2.6434) Prec@1 31.875 (36.134) Prec@5 69.375 (66.721) Epoch: [8][4610/11272] Time 0.760 (0.832) Data 0.005 (0.002) Loss 2.4958 (2.6433) Prec@1 40.625 (36.136) Prec@5 70.625 (66.725) Epoch: [8][4620/11272] Time 0.911 (0.832) Data 0.001 (0.002) Loss 2.9234 (2.6432) Prec@1 33.750 (36.133) Prec@5 60.625 (66.727) Epoch: [8][4630/11272] Time 0.884 (0.832) Data 0.001 (0.002) Loss 2.5876 (2.6433) Prec@1 37.500 (36.137) Prec@5 70.625 (66.729) Epoch: [8][4640/11272] Time 0.780 (0.832) Data 0.001 (0.002) Loss 2.6720 (2.6434) Prec@1 28.125 (36.131) Prec@5 64.375 (66.724) Epoch: [8][4650/11272] Time 0.761 (0.832) Data 0.002 (0.002) Loss 2.7092 (2.6433) Prec@1 35.000 (36.131) Prec@5 68.125 (66.726) Epoch: [8][4660/11272] Time 0.911 (0.832) Data 0.002 (0.002) Loss 2.6478 (2.6432) Prec@1 32.500 (36.131) Prec@5 66.250 (66.727) Epoch: [8][4670/11272] Time 0.887 (0.832) Data 0.001 (0.002) Loss 2.7536 (2.6432) Prec@1 31.250 (36.131) Prec@5 63.750 (66.727) Epoch: [8][4680/11272] Time 0.769 (0.832) Data 0.002 (0.002) Loss 2.6067 (2.6431) Prec@1 36.250 (36.132) Prec@5 65.000 (66.728) Epoch: [8][4690/11272] Time 0.726 (0.832) Data 0.001 (0.002) Loss 2.4455 (2.6430) Prec@1 38.125 (36.134) Prec@5 70.625 (66.731) Epoch: [8][4700/11272] Time 0.854 (0.832) Data 0.002 (0.002) Loss 2.4916 (2.6431) Prec@1 45.000 (36.136) Prec@5 72.500 (66.734) Epoch: [8][4710/11272] Time 0.765 (0.832) Data 0.003 (0.002) Loss 2.6816 (2.6431) Prec@1 33.125 (36.134) Prec@5 65.625 (66.735) Epoch: [8][4720/11272] Time 0.761 (0.832) Data 0.002 (0.002) Loss 2.5735 (2.6430) Prec@1 36.250 (36.137) Prec@5 66.875 (66.737) Epoch: [8][4730/11272] Time 0.901 (0.832) Data 0.002 (0.002) Loss 2.6033 (2.6430) Prec@1 36.875 (36.140) Prec@5 68.750 (66.738) Epoch: [8][4740/11272] Time 0.872 (0.832) Data 0.001 (0.002) Loss 2.6256 (2.6429) Prec@1 38.750 (36.142) Prec@5 65.000 (66.737) Epoch: [8][4750/11272] Time 0.777 (0.832) Data 0.001 (0.002) Loss 2.6410 (2.6429) Prec@1 33.750 (36.141) Prec@5 68.750 (66.739) Epoch: [8][4760/11272] Time 0.765 (0.832) Data 0.002 (0.002) Loss 2.7302 (2.6429) Prec@1 35.625 (36.142) Prec@5 66.250 (66.740) Epoch: [8][4770/11272] Time 0.900 (0.832) Data 0.001 (0.002) Loss 2.5787 (2.6429) Prec@1 37.500 (36.138) Prec@5 70.625 (66.740) Epoch: [8][4780/11272] Time 0.895 (0.832) Data 0.001 (0.002) Loss 2.3556 (2.6427) Prec@1 42.500 (36.139) Prec@5 74.375 (66.743) Epoch: [8][4790/11272] Time 0.781 (0.832) Data 0.002 (0.002) Loss 2.7481 (2.6428) Prec@1 35.625 (36.140) Prec@5 66.875 (66.745) Epoch: [8][4800/11272] Time 0.762 (0.832) Data 0.002 (0.002) Loss 2.3533 (2.6428) Prec@1 38.750 (36.137) Prec@5 72.500 (66.744) Epoch: [8][4810/11272] Time 0.886 (0.832) Data 0.001 (0.002) Loss 2.7888 (2.6428) Prec@1 38.125 (36.140) Prec@5 63.750 (66.744) Epoch: [8][4820/11272] Time 0.921 (0.832) Data 0.001 (0.002) Loss 2.5471 (2.6427) Prec@1 35.625 (36.140) Prec@5 68.125 (66.742) Epoch: [8][4830/11272] Time 0.747 (0.832) Data 0.001 (0.002) Loss 2.6324 (2.6427) Prec@1 41.250 (36.142) Prec@5 68.125 (66.744) Epoch: [8][4840/11272] Time 1.018 (0.832) Data 0.002 (0.002) Loss 2.4266 (2.6427) Prec@1 37.500 (36.139) Prec@5 71.250 (66.746) Epoch: [8][4850/11272] Time 0.904 (0.832) Data 0.001 (0.002) Loss 2.9449 (2.6430) Prec@1 38.125 (36.135) Prec@5 63.750 (66.741) Epoch: [8][4860/11272] Time 0.749 (0.832) Data 0.002 (0.002) Loss 2.6039 (2.6430) Prec@1 31.875 (36.137) Prec@5 66.875 (66.742) Epoch: [8][4870/11272] Time 0.784 (0.832) Data 0.002 (0.002) Loss 2.4868 (2.6428) Prec@1 36.875 (36.139) Prec@5 70.625 (66.744) Epoch: [8][4880/11272] Time 0.927 (0.832) Data 0.001 (0.002) Loss 2.5294 (2.6429) Prec@1 41.250 (36.140) Prec@5 69.375 (66.744) Epoch: [8][4890/11272] Time 0.909 (0.832) Data 0.001 (0.002) Loss 2.5996 (2.6429) Prec@1 36.875 (36.139) Prec@5 70.000 (66.746) Epoch: [8][4900/11272] Time 0.762 (0.832) Data 0.001 (0.002) Loss 2.7284 (2.6431) Prec@1 31.875 (36.135) Prec@5 65.000 (66.743) Epoch: [8][4910/11272] Time 0.791 (0.832) Data 0.001 (0.002) Loss 2.6364 (2.6431) Prec@1 31.250 (36.133) Prec@5 68.125 (66.744) Epoch: [8][4920/11272] Time 0.890 (0.832) Data 0.006 (0.002) Loss 2.8386 (2.6430) Prec@1 37.500 (36.134) Prec@5 64.375 (66.747) Epoch: [8][4930/11272] Time 0.907 (0.832) Data 0.002 (0.002) Loss 2.7276 (2.6430) Prec@1 36.875 (36.134) Prec@5 63.750 (66.748) Epoch: [8][4940/11272] Time 0.751 (0.832) Data 0.002 (0.002) Loss 2.8698 (2.6430) Prec@1 32.500 (36.132) Prec@5 60.625 (66.748) Epoch: [8][4950/11272] Time 0.733 (0.832) Data 0.001 (0.002) Loss 2.5690 (2.6430) Prec@1 37.500 (36.130) Prec@5 63.750 (66.746) Epoch: [8][4960/11272] Time 0.913 (0.832) Data 0.001 (0.002) Loss 2.7652 (2.6431) Prec@1 30.000 (36.127) Prec@5 63.750 (66.745) Epoch: [8][4970/11272] Time 0.758 (0.832) Data 0.005 (0.002) Loss 2.5719 (2.6431) Prec@1 36.875 (36.128) Prec@5 68.750 (66.744) Epoch: [8][4980/11272] Time 0.749 (0.832) Data 0.002 (0.002) Loss 2.5536 (2.6431) Prec@1 39.375 (36.124) Prec@5 65.000 (66.741) Epoch: [8][4990/11272] Time 0.850 (0.832) Data 0.001 (0.002) Loss 2.4859 (2.6431) Prec@1 43.750 (36.123) Prec@5 67.500 (66.739) Epoch: [8][5000/11272] Time 0.912 (0.832) Data 0.002 (0.002) Loss 2.7846 (2.6433) Prec@1 31.875 (36.116) Prec@5 66.875 (66.733) Epoch: [8][5010/11272] Time 0.746 (0.832) Data 0.001 (0.002) Loss 2.9107 (2.6434) Prec@1 30.625 (36.115) Prec@5 61.250 (66.733) Epoch: [8][5020/11272] Time 0.766 (0.832) Data 0.002 (0.002) Loss 2.7015 (2.6433) Prec@1 41.250 (36.118) Prec@5 63.750 (66.735) Epoch: [8][5030/11272] Time 0.901 (0.832) Data 0.002 (0.002) Loss 2.7375 (2.6432) Prec@1 36.250 (36.120) Prec@5 61.250 (66.737) Epoch: [8][5040/11272] Time 0.861 (0.832) Data 0.002 (0.002) Loss 2.7571 (2.6432) Prec@1 35.625 (36.118) Prec@5 64.375 (66.737) Epoch: [8][5050/11272] Time 0.757 (0.832) Data 0.001 (0.002) Loss 2.7217 (2.6430) Prec@1 39.375 (36.126) Prec@5 68.750 (66.742) Epoch: [8][5060/11272] Time 0.732 (0.832) Data 0.002 (0.002) Loss 2.8778 (2.6431) Prec@1 28.750 (36.126) Prec@5 64.375 (66.742) Epoch: [8][5070/11272] Time 0.892 (0.832) Data 0.001 (0.002) Loss 2.7321 (2.6431) Prec@1 38.750 (36.124) Prec@5 64.375 (66.740) Epoch: [8][5080/11272] Time 0.840 (0.832) Data 0.002 (0.002) Loss 2.6895 (2.6432) Prec@1 31.250 (36.123) Prec@5 69.375 (66.739) Epoch: [8][5090/11272] Time 0.791 (0.832) Data 0.001 (0.002) Loss 2.7365 (2.6433) Prec@1 33.750 (36.118) Prec@5 65.625 (66.735) Epoch: [8][5100/11272] Time 0.890 (0.832) Data 0.002 (0.002) Loss 2.5387 (2.6432) Prec@1 36.250 (36.119) Prec@5 67.500 (66.735) Epoch: [8][5110/11272] Time 0.886 (0.832) Data 0.001 (0.002) Loss 2.4165 (2.6432) Prec@1 41.250 (36.116) Prec@5 70.625 (66.737) Epoch: [8][5120/11272] Time 0.735 (0.832) Data 0.002 (0.002) Loss 2.7949 (2.6433) Prec@1 34.375 (36.113) Prec@5 63.750 (66.734) Epoch: [8][5130/11272] Time 0.843 (0.832) Data 0.001 (0.002) Loss 2.8001 (2.6433) Prec@1 31.875 (36.109) Prec@5 66.875 (66.735) Epoch: [8][5140/11272] Time 0.886 (0.832) Data 0.001 (0.002) Loss 2.7404 (2.6432) Prec@1 35.625 (36.111) Prec@5 62.500 (66.736) Epoch: [8][5150/11272] Time 0.916 (0.832) Data 0.002 (0.002) Loss 2.5573 (2.6432) Prec@1 41.250 (36.112) Prec@5 68.125 (66.735) Epoch: [8][5160/11272] Time 0.755 (0.832) Data 0.002 (0.002) Loss 2.6263 (2.6433) Prec@1 39.375 (36.113) Prec@5 66.250 (66.733) Epoch: [8][5170/11272] Time 0.792 (0.832) Data 0.001 (0.002) Loss 2.8401 (2.6431) Prec@1 36.875 (36.117) Prec@5 63.125 (66.734) Epoch: [8][5180/11272] Time 0.866 (0.832) Data 0.002 (0.002) Loss 2.8375 (2.6432) Prec@1 32.500 (36.114) Prec@5 60.000 (66.731) Epoch: [8][5190/11272] Time 0.916 (0.832) Data 0.001 (0.002) Loss 2.5480 (2.6431) Prec@1 37.500 (36.114) Prec@5 68.125 (66.733) Epoch: [8][5200/11272] Time 0.820 (0.832) Data 0.001 (0.002) Loss 2.3821 (2.6431) Prec@1 43.125 (36.116) Prec@5 68.750 (66.733) Epoch: [8][5210/11272] Time 0.771 (0.832) Data 0.001 (0.002) Loss 2.7709 (2.6431) Prec@1 29.375 (36.116) Prec@5 62.500 (66.734) Epoch: [8][5220/11272] Time 0.881 (0.832) Data 0.002 (0.002) Loss 2.5150 (2.6432) Prec@1 35.625 (36.112) Prec@5 69.375 (66.736) Epoch: [8][5230/11272] Time 0.888 (0.832) Data 0.001 (0.002) Loss 2.6554 (2.6433) Prec@1 36.250 (36.109) Prec@5 68.125 (66.735) Epoch: [8][5240/11272] Time 0.734 (0.832) Data 0.002 (0.002) Loss 2.8274 (2.6433) Prec@1 35.000 (36.110) Prec@5 63.125 (66.732) Epoch: [8][5250/11272] Time 0.942 (0.832) Data 0.001 (0.002) Loss 2.8819 (2.6432) Prec@1 33.750 (36.115) Prec@5 58.750 (66.733) Epoch: [8][5260/11272] Time 0.876 (0.832) Data 0.002 (0.002) Loss 2.3606 (2.6432) Prec@1 40.625 (36.113) Prec@5 74.375 (66.733) Epoch: [8][5270/11272] Time 0.774 (0.832) Data 0.001 (0.002) Loss 2.7150 (2.6430) Prec@1 34.375 (36.115) Prec@5 66.250 (66.737) Epoch: [8][5280/11272] Time 0.770 (0.832) Data 0.002 (0.002) Loss 2.4767 (2.6429) Prec@1 40.000 (36.119) Prec@5 70.000 (66.739) Epoch: [8][5290/11272] Time 0.892 (0.832) Data 0.001 (0.002) Loss 2.6492 (2.6428) Prec@1 35.000 (36.117) Prec@5 66.250 (66.742) Epoch: [8][5300/11272] Time 0.885 (0.832) Data 0.002 (0.002) Loss 2.5441 (2.6429) Prec@1 36.875 (36.114) Prec@5 65.625 (66.740) Epoch: [8][5310/11272] Time 0.747 (0.832) Data 0.001 (0.002) Loss 2.5554 (2.6430) Prec@1 42.500 (36.114) Prec@5 66.250 (66.740) Epoch: [8][5320/11272] Time 0.768 (0.832) Data 0.002 (0.002) Loss 2.4678 (2.6430) Prec@1 35.625 (36.115) Prec@5 76.875 (66.737) Epoch: [8][5330/11272] Time 0.894 (0.832) Data 0.002 (0.002) Loss 2.8395 (2.6431) Prec@1 35.000 (36.114) Prec@5 62.500 (66.737) Epoch: [8][5340/11272] Time 0.888 (0.832) Data 0.002 (0.002) Loss 2.6479 (2.6430) Prec@1 35.625 (36.117) Prec@5 68.750 (66.739) Epoch: [8][5350/11272] Time 0.799 (0.832) Data 0.001 (0.002) Loss 2.5201 (2.6429) Prec@1 36.875 (36.116) Prec@5 69.375 (66.741) Epoch: [8][5360/11272] Time 0.745 (0.832) Data 0.002 (0.002) Loss 2.5543 (2.6430) Prec@1 38.125 (36.114) Prec@5 67.500 (66.738) Epoch: [8][5370/11272] Time 0.907 (0.832) Data 0.001 (0.002) Loss 2.3569 (2.6428) Prec@1 45.625 (36.119) Prec@5 71.875 (66.743) Epoch: [8][5380/11272] Time 0.747 (0.832) Data 0.001 (0.002) Loss 2.5853 (2.6428) Prec@1 36.875 (36.119) Prec@5 70.625 (66.741) Epoch: [8][5390/11272] Time 0.731 (0.832) Data 0.001 (0.002) Loss 2.8728 (2.6428) Prec@1 28.125 (36.117) Prec@5 60.000 (66.741) Epoch: [8][5400/11272] Time 0.891 (0.832) Data 0.002 (0.002) Loss 2.7965 (2.6428) Prec@1 35.625 (36.118) Prec@5 58.125 (66.740) Epoch: [8][5410/11272] Time 0.907 (0.832) Data 0.002 (0.002) Loss 2.6534 (2.6429) Prec@1 34.375 (36.117) Prec@5 64.375 (66.738) Epoch: [8][5420/11272] Time 0.792 (0.832) Data 0.002 (0.002) Loss 2.8460 (2.6430) Prec@1 33.125 (36.114) Prec@5 60.625 (66.736) Epoch: [8][5430/11272] Time 0.799 (0.832) Data 0.004 (0.002) Loss 2.5133 (2.6430) Prec@1 35.625 (36.116) Prec@5 70.000 (66.735) Epoch: [8][5440/11272] Time 0.941 (0.832) Data 0.002 (0.002) Loss 2.7126 (2.6431) Prec@1 35.000 (36.113) Prec@5 65.000 (66.733) Epoch: [8][5450/11272] Time 0.868 (0.832) Data 0.001 (0.002) Loss 2.7275 (2.6430) Prec@1 39.375 (36.115) Prec@5 65.625 (66.732) Epoch: [8][5460/11272] Time 0.781 (0.832) Data 0.002 (0.002) Loss 2.4167 (2.6428) Prec@1 37.500 (36.119) Prec@5 76.250 (66.739) Epoch: [8][5470/11272] Time 0.766 (0.832) Data 0.002 (0.002) Loss 2.7291 (2.6428) Prec@1 33.750 (36.119) Prec@5 67.500 (66.739) Epoch: [8][5480/11272] Time 0.886 (0.832) Data 0.002 (0.002) Loss 2.3327 (2.6427) Prec@1 46.875 (36.124) Prec@5 71.875 (66.740) Epoch: [8][5490/11272] Time 0.911 (0.832) Data 0.001 (0.002) Loss 2.7607 (2.6427) Prec@1 35.000 (36.123) Prec@5 67.500 (66.742) Epoch: [8][5500/11272] Time 0.785 (0.832) Data 0.002 (0.002) Loss 2.8018 (2.6427) Prec@1 31.250 (36.122) Prec@5 60.000 (66.742) Epoch: [8][5510/11272] Time 0.958 (0.832) Data 0.001 (0.002) Loss 2.6247 (2.6426) Prec@1 31.250 (36.124) Prec@5 63.750 (66.744) Epoch: [8][5520/11272] Time 0.890 (0.832) Data 0.006 (0.002) Loss 2.7315 (2.6426) Prec@1 36.250 (36.123) Prec@5 65.000 (66.743) Epoch: [8][5530/11272] Time 0.761 (0.832) Data 0.002 (0.002) Loss 2.7329 (2.6427) Prec@1 33.750 (36.123) Prec@5 68.750 (66.739) Epoch: [8][5540/11272] Time 0.800 (0.832) Data 0.001 (0.002) Loss 2.6494 (2.6426) Prec@1 33.125 (36.121) Prec@5 66.250 (66.740) Epoch: [8][5550/11272] Time 0.896 (0.832) Data 0.002 (0.002) Loss 2.5521 (2.6426) Prec@1 37.500 (36.122) Prec@5 66.875 (66.740) Epoch: [8][5560/11272] Time 0.895 (0.832) Data 0.002 (0.002) Loss 2.4876 (2.6427) Prec@1 38.750 (36.123) Prec@5 70.625 (66.740) Epoch: [8][5570/11272] Time 0.752 (0.832) Data 0.002 (0.002) Loss 2.8357 (2.6427) Prec@1 35.000 (36.124) Prec@5 60.000 (66.738) Epoch: [8][5580/11272] Time 0.751 (0.832) Data 0.001 (0.002) Loss 2.7452 (2.6429) Prec@1 39.375 (36.119) Prec@5 61.875 (66.736) Epoch: [8][5590/11272] Time 0.907 (0.832) Data 0.002 (0.002) Loss 2.7491 (2.6430) Prec@1 32.500 (36.117) Prec@5 61.875 (66.732) Epoch: [8][5600/11272] Time 0.891 (0.832) Data 0.002 (0.002) Loss 2.4860 (2.6429) Prec@1 36.875 (36.120) Prec@5 66.875 (66.732) Epoch: [8][5610/11272] Time 0.778 (0.832) Data 0.002 (0.002) Loss 2.7196 (2.6429) Prec@1 35.625 (36.120) Prec@5 63.750 (66.733) Epoch: [8][5620/11272] Time 0.706 (0.832) Data 0.003 (0.002) Loss 2.9669 (2.6429) Prec@1 27.500 (36.119) Prec@5 58.125 (66.735) Epoch: [8][5630/11272] Time 0.926 (0.832) Data 0.001 (0.002) Loss 2.5970 (2.6430) Prec@1 41.250 (36.118) Prec@5 70.625 (66.735) Epoch: [8][5640/11272] Time 0.745 (0.832) Data 0.004 (0.002) Loss 2.7919 (2.6431) Prec@1 36.875 (36.117) Prec@5 64.375 (66.735) Epoch: [8][5650/11272] Time 0.802 (0.832) Data 0.005 (0.002) Loss 2.6030 (2.6431) Prec@1 33.750 (36.117) Prec@5 68.125 (66.736) Epoch: [8][5660/11272] Time 0.925 (0.832) Data 0.001 (0.002) Loss 2.7566 (2.6431) Prec@1 35.625 (36.118) Prec@5 67.500 (66.735) Epoch: [8][5670/11272] Time 0.905 (0.832) Data 0.002 (0.002) Loss 2.6845 (2.6430) Prec@1 30.625 (36.119) Prec@5 65.000 (66.735) Epoch: [8][5680/11272] Time 0.757 (0.832) Data 0.002 (0.002) Loss 2.7316 (2.6431) Prec@1 32.500 (36.119) Prec@5 66.250 (66.732) Epoch: [8][5690/11272] Time 0.798 (0.832) Data 0.002 (0.002) Loss 2.8858 (2.6435) Prec@1 35.000 (36.113) Prec@5 58.750 (66.724) Epoch: [8][5700/11272] Time 0.870 (0.832) Data 0.002 (0.002) Loss 2.8620 (2.6436) Prec@1 31.250 (36.110) Prec@5 64.375 (66.724) Epoch: [8][5710/11272] Time 0.892 (0.832) Data 0.001 (0.002) Loss 2.5825 (2.6437) Prec@1 39.375 (36.107) Prec@5 71.250 (66.723) Epoch: [8][5720/11272] Time 0.743 (0.832) Data 0.002 (0.002) Loss 2.7028 (2.6437) Prec@1 33.750 (36.107) Prec@5 68.750 (66.724) Epoch: [8][5730/11272] Time 0.725 (0.832) Data 0.001 (0.002) Loss 2.7531 (2.6437) Prec@1 33.750 (36.104) Prec@5 68.125 (66.724) Epoch: [8][5740/11272] Time 1.006 (0.832) Data 0.002 (0.002) Loss 2.8849 (2.6438) Prec@1 36.250 (36.102) Prec@5 61.875 (66.722) Epoch: [8][5750/11272] Time 0.938 (0.832) Data 0.002 (0.002) Loss 2.9222 (2.6438) Prec@1 37.500 (36.101) Prec@5 61.875 (66.723) Epoch: [8][5760/11272] Time 0.774 (0.832) Data 0.002 (0.002) Loss 2.7206 (2.6438) Prec@1 37.500 (36.100) Prec@5 63.125 (66.721) Epoch: [8][5770/11272] Time 0.916 (0.832) Data 0.001 (0.002) Loss 2.6384 (2.6438) Prec@1 35.000 (36.099) Prec@5 63.750 (66.722) Epoch: [8][5780/11272] Time 0.908 (0.832) Data 0.002 (0.002) Loss 2.6216 (2.6439) Prec@1 42.500 (36.097) Prec@5 67.500 (66.721) Epoch: [8][5790/11272] Time 0.759 (0.832) Data 0.002 (0.002) Loss 2.3515 (2.6437) Prec@1 44.375 (36.100) Prec@5 71.875 (66.722) Epoch: [8][5800/11272] Time 0.795 (0.832) Data 0.002 (0.002) Loss 2.6851 (2.6437) Prec@1 36.250 (36.103) Prec@5 66.250 (66.722) Epoch: [8][5810/11272] Time 1.034 (0.832) Data 0.001 (0.002) Loss 2.7857 (2.6436) Prec@1 30.000 (36.104) Prec@5 65.000 (66.723) Epoch: [8][5820/11272] Time 1.022 (0.832) Data 0.002 (0.002) Loss 2.7236 (2.6435) Prec@1 33.125 (36.106) Prec@5 68.750 (66.724) Epoch: [8][5830/11272] Time 0.732 (0.832) Data 0.001 (0.002) Loss 2.5110 (2.6433) Prec@1 43.750 (36.110) Prec@5 67.500 (66.728) Epoch: [8][5840/11272] Time 0.740 (0.832) Data 0.001 (0.002) Loss 2.5054 (2.6434) Prec@1 40.000 (36.111) Prec@5 73.125 (66.726) Epoch: [8][5850/11272] Time 0.887 (0.832) Data 0.001 (0.002) Loss 2.7207 (2.6435) Prec@1 36.875 (36.107) Prec@5 61.875 (66.721) Epoch: [8][5860/11272] Time 0.935 (0.832) Data 0.002 (0.002) Loss 2.4638 (2.6435) Prec@1 45.625 (36.107) Prec@5 70.000 (66.723) Epoch: [8][5870/11272] Time 0.748 (0.832) Data 0.001 (0.002) Loss 2.9277 (2.6436) Prec@1 31.250 (36.102) Prec@5 59.375 (66.719) Epoch: [8][5880/11272] Time 0.754 (0.832) Data 0.002 (0.002) Loss 2.5537 (2.6436) Prec@1 40.625 (36.101) Prec@5 70.625 (66.721) Epoch: [8][5890/11272] Time 0.927 (0.832) Data 0.001 (0.002) Loss 2.5189 (2.6435) Prec@1 36.875 (36.103) Prec@5 65.625 (66.721) Epoch: [8][5900/11272] Time 0.782 (0.832) Data 0.004 (0.002) Loss 2.6550 (2.6434) Prec@1 36.250 (36.103) Prec@5 67.500 (66.724) Epoch: [8][5910/11272] Time 0.754 (0.832) Data 0.001 (0.002) Loss 2.4856 (2.6433) Prec@1 39.375 (36.104) Prec@5 66.875 (66.725) Epoch: [8][5920/11272] Time 0.932 (0.832) Data 0.002 (0.002) Loss 2.6217 (2.6432) Prec@1 36.875 (36.107) Prec@5 67.500 (66.728) Epoch: [8][5930/11272] Time 0.945 (0.832) Data 0.002 (0.002) Loss 2.6206 (2.6430) Prec@1 39.375 (36.112) Prec@5 65.625 (66.732) Epoch: [8][5940/11272] Time 0.766 (0.832) Data 0.002 (0.002) Loss 2.7537 (2.6429) Prec@1 35.000 (36.113) Prec@5 64.375 (66.732) Epoch: [8][5950/11272] Time 0.738 (0.832) Data 0.001 (0.002) Loss 2.6519 (2.6431) Prec@1 35.625 (36.114) Prec@5 65.625 (66.730) Epoch: [8][5960/11272] Time 0.924 (0.832) Data 0.002 (0.002) Loss 2.3754 (2.6431) Prec@1 42.500 (36.113) Prec@5 72.500 (66.730) Epoch: [8][5970/11272] Time 0.959 (0.833) Data 0.001 (0.002) Loss 2.9591 (2.6432) Prec@1 25.000 (36.111) Prec@5 63.125 (66.727) Epoch: [8][5980/11272] Time 0.733 (0.832) Data 0.002 (0.002) Loss 2.4425 (2.6434) Prec@1 43.750 (36.110) Prec@5 68.125 (66.724) Epoch: [8][5990/11272] Time 0.766 (0.833) Data 0.001 (0.002) Loss 2.8646 (2.6435) Prec@1 35.625 (36.109) Prec@5 60.625 (66.721) Epoch: [8][6000/11272] Time 0.921 (0.833) Data 0.001 (0.002) Loss 2.5299 (2.6435) Prec@1 36.875 (36.109) Prec@5 71.875 (66.721) Epoch: [8][6010/11272] Time 0.941 (0.833) Data 0.001 (0.002) Loss 2.5967 (2.6433) Prec@1 35.000 (36.113) Prec@5 66.250 (66.722) Epoch: [8][6020/11272] Time 0.824 (0.833) Data 0.002 (0.002) Loss 2.8036 (2.6434) Prec@1 32.500 (36.112) Prec@5 65.000 (66.723) Epoch: [8][6030/11272] Time 0.900 (0.833) Data 0.001 (0.002) Loss 2.5259 (2.6433) Prec@1 35.625 (36.112) Prec@5 70.000 (66.722) Epoch: [8][6040/11272] Time 0.900 (0.833) Data 0.002 (0.002) Loss 2.5377 (2.6435) Prec@1 37.500 (36.107) Prec@5 64.375 (66.720) Epoch: [8][6050/11272] Time 0.709 (0.833) Data 0.001 (0.002) Loss 2.7333 (2.6436) Prec@1 35.000 (36.105) Prec@5 66.875 (66.717) Epoch: [8][6060/11272] Time 0.743 (0.833) Data 0.002 (0.002) Loss 2.6986 (2.6437) Prec@1 30.000 (36.101) Prec@5 66.250 (66.717) Epoch: [8][6070/11272] Time 0.927 (0.833) Data 0.001 (0.002) Loss 2.5096 (2.6437) Prec@1 41.250 (36.099) Prec@5 65.000 (66.712) Epoch: [8][6080/11272] Time 0.877 (0.833) Data 0.002 (0.002) Loss 2.6500 (2.6437) Prec@1 38.125 (36.099) Prec@5 65.625 (66.709) Epoch: [8][6090/11272] Time 0.772 (0.833) Data 0.002 (0.002) Loss 2.4595 (2.6436) Prec@1 41.250 (36.102) Prec@5 66.875 (66.709) Epoch: [8][6100/11272] Time 0.738 (0.833) Data 0.002 (0.002) Loss 2.5413 (2.6436) Prec@1 33.750 (36.101) Prec@5 65.625 (66.707) Epoch: [8][6110/11272] Time 0.900 (0.833) Data 0.002 (0.002) Loss 2.7504 (2.6437) Prec@1 32.500 (36.101) Prec@5 61.250 (66.707) Epoch: [8][6120/11272] Time 0.962 (0.833) Data 0.002 (0.002) Loss 2.6109 (2.6439) Prec@1 35.625 (36.096) Prec@5 63.750 (66.703) Epoch: [8][6130/11272] Time 0.795 (0.833) Data 0.002 (0.002) Loss 2.6801 (2.6438) Prec@1 35.000 (36.099) Prec@5 68.125 (66.706) Epoch: [8][6140/11272] Time 0.759 (0.833) Data 0.002 (0.002) Loss 2.6972 (2.6438) Prec@1 33.750 (36.101) Prec@5 67.500 (66.707) Epoch: [8][6150/11272] Time 0.945 (0.833) Data 0.001 (0.002) Loss 2.6728 (2.6437) Prec@1 35.625 (36.104) Prec@5 60.000 (66.707) Epoch: [8][6160/11272] Time 0.900 (0.833) Data 0.001 (0.002) Loss 2.7188 (2.6436) Prec@1 36.250 (36.105) Prec@5 65.000 (66.708) Epoch: [8][6170/11272] Time 0.793 (0.833) Data 0.001 (0.002) Loss 2.4207 (2.6437) Prec@1 45.000 (36.105) Prec@5 71.250 (66.710) Epoch: [8][6180/11272] Time 0.893 (0.833) Data 0.002 (0.002) Loss 2.7963 (2.6438) Prec@1 31.875 (36.105) Prec@5 66.875 (66.709) Epoch: [8][6190/11272] Time 0.887 (0.833) Data 0.002 (0.002) Loss 2.3470 (2.6438) Prec@1 43.125 (36.106) Prec@5 70.000 (66.709) Epoch: [8][6200/11272] Time 0.807 (0.833) Data 0.002 (0.002) Loss 2.6564 (2.6437) Prec@1 33.125 (36.107) Prec@5 63.125 (66.711) Epoch: [8][6210/11272] Time 0.756 (0.833) Data 0.002 (0.002) Loss 2.4870 (2.6437) Prec@1 37.500 (36.107) Prec@5 71.250 (66.711) Epoch: [8][6220/11272] Time 0.904 (0.833) Data 0.002 (0.002) Loss 2.6086 (2.6436) Prec@1 33.125 (36.107) Prec@5 67.500 (66.711) Epoch: [8][6230/11272] Time 0.942 (0.833) Data 0.001 (0.002) Loss 2.5673 (2.6437) Prec@1 38.125 (36.105) Prec@5 73.750 (66.714) Epoch: [8][6240/11272] Time 0.811 (0.833) Data 0.002 (0.002) Loss 2.7133 (2.6437) Prec@1 37.500 (36.107) Prec@5 63.125 (66.714) Epoch: [8][6250/11272] Time 0.755 (0.833) Data 0.001 (0.002) Loss 2.8766 (2.6438) Prec@1 38.750 (36.107) Prec@5 63.750 (66.711) Epoch: [8][6260/11272] Time 0.924 (0.833) Data 0.002 (0.002) Loss 3.1540 (2.6439) Prec@1 26.250 (36.102) Prec@5 56.875 (66.709) Epoch: [8][6270/11272] Time 0.844 (0.833) Data 0.001 (0.002) Loss 2.5571 (2.6439) Prec@1 38.750 (36.106) Prec@5 68.750 (66.710) Epoch: [8][6280/11272] Time 0.737 (0.833) Data 0.002 (0.002) Loss 2.7024 (2.6439) Prec@1 36.875 (36.105) Prec@5 66.250 (66.710) Epoch: [8][6290/11272] Time 0.772 (0.833) Data 0.001 (0.002) Loss 2.7548 (2.6439) Prec@1 31.875 (36.105) Prec@5 71.250 (66.711) Epoch: [8][6300/11272] Time 0.872 (0.833) Data 0.002 (0.002) Loss 2.7076 (2.6440) Prec@1 35.000 (36.103) Prec@5 70.625 (66.710) Epoch: [8][6310/11272] Time 0.794 (0.833) Data 0.001 (0.002) Loss 2.4405 (2.6440) Prec@1 40.000 (36.103) Prec@5 68.750 (66.711) Epoch: [8][6320/11272] Time 0.775 (0.833) Data 0.002 (0.002) Loss 2.4858 (2.6440) Prec@1 43.125 (36.105) Prec@5 68.750 (66.710) Epoch: [8][6330/11272] Time 0.934 (0.833) Data 0.001 (0.002) Loss 2.5075 (2.6439) Prec@1 44.375 (36.107) Prec@5 67.500 (66.711) Epoch: [8][6340/11272] Time 0.927 (0.833) Data 0.002 (0.002) Loss 2.6871 (2.6439) Prec@1 38.125 (36.108) Prec@5 67.500 (66.713) Epoch: [8][6350/11272] Time 0.754 (0.833) Data 0.002 (0.002) Loss 2.5584 (2.6438) Prec@1 37.500 (36.111) Prec@5 66.875 (66.713) Epoch: [8][6360/11272] Time 0.756 (0.833) Data 0.002 (0.002) Loss 2.5680 (2.6437) Prec@1 37.500 (36.112) Prec@5 66.250 (66.713) Epoch: [8][6370/11272] Time 0.907 (0.833) Data 0.002 (0.002) Loss 2.8082 (2.6438) Prec@1 33.750 (36.112) Prec@5 62.500 (66.710) Epoch: [8][6380/11272] Time 0.964 (0.833) Data 0.001 (0.002) Loss 2.3914 (2.6437) Prec@1 40.625 (36.113) Prec@5 74.375 (66.713) Epoch: [8][6390/11272] Time 0.757 (0.833) Data 0.003 (0.002) Loss 2.6071 (2.6436) Prec@1 36.875 (36.113) Prec@5 70.625 (66.714) Epoch: [8][6400/11272] Time 0.760 (0.833) Data 0.002 (0.002) Loss 2.7177 (2.6438) Prec@1 35.625 (36.110) Prec@5 67.500 (66.712) Epoch: [8][6410/11272] Time 0.891 (0.833) Data 0.001 (0.002) Loss 2.3457 (2.6438) Prec@1 41.875 (36.109) Prec@5 75.000 (66.712) Epoch: [8][6420/11272] Time 0.918 (0.833) Data 0.002 (0.002) Loss 2.8058 (2.6438) Prec@1 34.375 (36.111) Prec@5 65.000 (66.712) Epoch: [8][6430/11272] Time 0.724 (0.833) Data 0.001 (0.002) Loss 2.6956 (2.6438) Prec@1 37.500 (36.111) Prec@5 61.250 (66.711) Epoch: [8][6440/11272] Time 0.886 (0.833) Data 0.003 (0.002) Loss 2.3548 (2.6438) Prec@1 41.250 (36.113) Prec@5 73.750 (66.711) Epoch: [8][6450/11272] Time 0.924 (0.833) Data 0.001 (0.002) Loss 2.5680 (2.6438) Prec@1 35.625 (36.113) Prec@5 65.625 (66.709) Epoch: [8][6460/11272] Time 0.746 (0.833) Data 0.002 (0.002) Loss 2.5720 (2.6438) Prec@1 36.875 (36.113) Prec@5 66.875 (66.711) Epoch: [8][6470/11272] Time 0.842 (0.833) Data 0.002 (0.002) Loss 2.7740 (2.6438) Prec@1 36.875 (36.111) Prec@5 66.250 (66.710) Epoch: [8][6480/11272] Time 0.918 (0.833) Data 0.002 (0.002) Loss 2.7224 (2.6439) Prec@1 33.125 (36.109) Prec@5 64.375 (66.709) Epoch: [8][6490/11272] Time 0.918 (0.833) Data 0.001 (0.002) Loss 2.8210 (2.6439) Prec@1 34.375 (36.110) Prec@5 60.000 (66.709) Epoch: [8][6500/11272] Time 0.747 (0.833) Data 0.003 (0.002) Loss 2.4146 (2.6440) Prec@1 40.000 (36.108) Prec@5 68.125 (66.707) Epoch: [8][6510/11272] Time 0.762 (0.833) Data 0.002 (0.002) Loss 2.7243 (2.6440) Prec@1 32.500 (36.109) Prec@5 61.875 (66.706) Epoch: [8][6520/11272] Time 0.948 (0.833) Data 0.002 (0.002) Loss 2.5908 (2.6440) Prec@1 38.750 (36.111) Prec@5 67.500 (66.705) Epoch: [8][6530/11272] Time 0.878 (0.833) Data 0.001 (0.002) Loss 2.4583 (2.6439) Prec@1 39.375 (36.113) Prec@5 70.625 (66.707) Epoch: [8][6540/11272] Time 0.749 (0.833) Data 0.002 (0.002) Loss 2.7249 (2.6440) Prec@1 31.875 (36.110) Prec@5 66.250 (66.706) Epoch: [8][6550/11272] Time 0.748 (0.833) Data 0.002 (0.002) Loss 2.6161 (2.6439) Prec@1 36.875 (36.111) Prec@5 69.375 (66.707) Epoch: [8][6560/11272] Time 0.878 (0.833) Data 0.001 (0.002) Loss 2.6101 (2.6439) Prec@1 36.875 (36.109) Prec@5 64.375 (66.707) Epoch: [8][6570/11272] Time 0.756 (0.833) Data 0.003 (0.002) Loss 2.7293 (2.6442) Prec@1 36.250 (36.106) Prec@5 63.125 (66.703) Epoch: [8][6580/11272] Time 0.764 (0.833) Data 0.002 (0.002) Loss 2.5375 (2.6441) Prec@1 40.000 (36.107) Prec@5 67.500 (66.704) Epoch: [8][6590/11272] Time 0.876 (0.833) Data 0.001 (0.002) Loss 2.6072 (2.6442) Prec@1 34.375 (36.104) Prec@5 68.125 (66.702) Epoch: [8][6600/11272] Time 0.912 (0.833) Data 0.002 (0.002) Loss 2.5242 (2.6442) Prec@1 40.000 (36.105) Prec@5 66.250 (66.702) Epoch: [8][6610/11272] Time 0.757 (0.833) Data 0.002 (0.002) Loss 2.8999 (2.6441) Prec@1 35.625 (36.108) Prec@5 58.125 (66.704) Epoch: [8][6620/11272] Time 0.718 (0.833) Data 0.002 (0.002) Loss 2.6537 (2.6441) Prec@1 35.000 (36.105) Prec@5 70.000 (66.706) Epoch: [8][6630/11272] Time 0.912 (0.833) Data 0.001 (0.002) Loss 2.4728 (2.6440) Prec@1 35.625 (36.104) Prec@5 70.000 (66.707) Epoch: [8][6640/11272] Time 0.949 (0.833) Data 0.001 (0.002) Loss 2.4816 (2.6440) Prec@1 38.125 (36.106) Prec@5 71.875 (66.707) Epoch: [8][6650/11272] Time 0.795 (0.833) Data 0.001 (0.002) Loss 2.5417 (2.6439) Prec@1 35.000 (36.107) Prec@5 68.125 (66.707) Epoch: [8][6660/11272] Time 0.750 (0.833) Data 0.002 (0.002) Loss 2.6929 (2.6440) Prec@1 33.750 (36.107) Prec@5 66.875 (66.706) Epoch: [8][6670/11272] Time 0.901 (0.833) Data 0.001 (0.002) Loss 2.7862 (2.6440) Prec@1 36.875 (36.106) Prec@5 67.500 (66.709) Epoch: [8][6680/11272] Time 0.869 (0.833) Data 0.002 (0.002) Loss 2.6854 (2.6440) Prec@1 33.125 (36.104) Prec@5 64.375 (66.709) Epoch: [8][6690/11272] Time 0.754 (0.833) Data 0.002 (0.002) Loss 2.5392 (2.6439) Prec@1 36.875 (36.109) Prec@5 71.875 (66.712) Epoch: [8][6700/11272] Time 0.888 (0.833) Data 0.002 (0.002) Loss 2.6534 (2.6440) Prec@1 38.750 (36.109) Prec@5 64.375 (66.711) Epoch: [8][6710/11272] Time 0.904 (0.833) Data 0.002 (0.002) Loss 2.3516 (2.6439) Prec@1 40.000 (36.109) Prec@5 71.250 (66.711) Epoch: [8][6720/11272] Time 0.793 (0.833) Data 0.002 (0.002) Loss 2.6810 (2.6439) Prec@1 41.250 (36.109) Prec@5 61.875 (66.711) Epoch: [8][6730/11272] Time 0.802 (0.833) Data 0.002 (0.002) Loss 2.7502 (2.6439) Prec@1 39.375 (36.111) Prec@5 63.125 (66.710) Epoch: [8][6740/11272] Time 0.924 (0.833) Data 0.001 (0.002) Loss 2.7562 (2.6438) Prec@1 33.125 (36.112) Prec@5 66.250 (66.712) Epoch: [8][6750/11272] Time 0.929 (0.833) Data 0.001 (0.002) Loss 2.6119 (2.6439) Prec@1 40.625 (36.112) Prec@5 70.000 (66.713) Epoch: [8][6760/11272] Time 0.742 (0.833) Data 0.002 (0.002) Loss 2.6593 (2.6439) Prec@1 32.500 (36.109) Prec@5 68.125 (66.712) Epoch: [8][6770/11272] Time 0.740 (0.833) Data 0.001 (0.002) Loss 2.8619 (2.6440) Prec@1 29.375 (36.108) Prec@5 57.500 (66.710) Epoch: [8][6780/11272] Time 0.888 (0.833) Data 0.002 (0.002) Loss 2.6052 (2.6441) Prec@1 35.000 (36.105) Prec@5 68.125 (66.707) Epoch: [8][6790/11272] Time 0.912 (0.833) Data 0.002 (0.002) Loss 2.6320 (2.6442) Prec@1 33.750 (36.102) Prec@5 68.125 (66.703) Epoch: [8][6800/11272] Time 0.745 (0.833) Data 0.002 (0.002) Loss 2.7031 (2.6443) Prec@1 29.375 (36.100) Prec@5 66.250 (66.704) Epoch: [8][6810/11272] Time 0.756 (0.833) Data 0.001 (0.002) Loss 2.8091 (2.6442) Prec@1 31.250 (36.098) Prec@5 63.125 (66.705) Epoch: [8][6820/11272] Time 0.937 (0.833) Data 0.001 (0.002) Loss 2.5788 (2.6442) Prec@1 34.375 (36.099) Prec@5 66.875 (66.706) Epoch: [8][6830/11272] Time 0.765 (0.833) Data 0.004 (0.002) Loss 2.5924 (2.6442) Prec@1 36.875 (36.099) Prec@5 66.875 (66.704) Epoch: [8][6840/11272] Time 0.763 (0.833) Data 0.002 (0.002) Loss 2.6377 (2.6442) Prec@1 38.750 (36.102) Prec@5 65.625 (66.703) Epoch: [8][6850/11272] Time 0.871 (0.833) Data 0.001 (0.002) Loss 2.7429 (2.6441) Prec@1 35.625 (36.101) Prec@5 61.875 (66.704) Epoch: [8][6860/11272] Time 0.894 (0.833) Data 0.002 (0.002) Loss 2.5530 (2.6441) Prec@1 39.375 (36.101) Prec@5 72.500 (66.702) Epoch: [8][6870/11272] Time 0.747 (0.833) Data 0.002 (0.002) Loss 2.5521 (2.6441) Prec@1 36.875 (36.101) Prec@5 68.750 (66.703) Epoch: [8][6880/11272] Time 0.833 (0.833) Data 0.001 (0.002) Loss 2.7582 (2.6441) Prec@1 30.000 (36.101) Prec@5 67.500 (66.706) Epoch: [8][6890/11272] Time 0.898 (0.833) Data 0.001 (0.002) Loss 2.7614 (2.6442) Prec@1 34.375 (36.100) Prec@5 63.750 (66.704) Epoch: [8][6900/11272] Time 0.951 (0.833) Data 0.002 (0.002) Loss 2.6611 (2.6442) Prec@1 41.250 (36.100) Prec@5 65.625 (66.704) Epoch: [8][6910/11272] Time 0.756 (0.833) Data 0.002 (0.002) Loss 2.8031 (2.6442) Prec@1 36.250 (36.102) Prec@5 65.000 (66.706) Epoch: [8][6920/11272] Time 0.732 (0.833) Data 0.001 (0.002) Loss 2.5440 (2.6441) Prec@1 38.125 (36.103) Prec@5 68.750 (66.707) Epoch: [8][6930/11272] Time 0.907 (0.833) Data 0.001 (0.002) Loss 2.7099 (2.6441) Prec@1 31.250 (36.102) Prec@5 64.375 (66.709) Epoch: [8][6940/11272] Time 0.917 (0.833) Data 0.003 (0.002) Loss 2.5247 (2.6441) Prec@1 38.125 (36.103) Prec@5 68.125 (66.709) Epoch: [8][6950/11272] Time 0.761 (0.833) Data 0.002 (0.002) Loss 2.6261 (2.6441) Prec@1 36.250 (36.102) Prec@5 70.625 (66.709) Epoch: [8][6960/11272] Time 0.890 (0.833) Data 0.001 (0.002) Loss 2.5496 (2.6442) Prec@1 36.250 (36.099) Prec@5 69.375 (66.707) Epoch: [8][6970/11272] Time 0.932 (0.833) Data 0.002 (0.002) Loss 2.4692 (2.6442) Prec@1 44.375 (36.099) Prec@5 70.000 (66.706) Epoch: [8][6980/11272] Time 0.735 (0.833) Data 0.001 (0.002) Loss 2.5783 (2.6443) Prec@1 35.000 (36.097) Prec@5 69.375 (66.707) Epoch: [8][6990/11272] Time 0.752 (0.833) Data 0.002 (0.002) Loss 2.4448 (2.6442) Prec@1 35.625 (36.096) Prec@5 73.750 (66.708) Epoch: [8][7000/11272] Time 0.892 (0.833) Data 0.001 (0.002) Loss 2.7325 (2.6442) Prec@1 30.625 (36.095) Prec@5 60.625 (66.705) Epoch: [8][7010/11272] Time 0.902 (0.833) Data 0.001 (0.002) Loss 2.4239 (2.6440) Prec@1 42.500 (36.097) Prec@5 71.875 (66.708) Epoch: [8][7020/11272] Time 0.752 (0.833) Data 0.002 (0.002) Loss 2.6547 (2.6441) Prec@1 31.875 (36.096) Prec@5 67.500 (66.708) Epoch: [8][7030/11272] Time 0.739 (0.833) Data 0.002 (0.002) Loss 2.6571 (2.6441) Prec@1 34.375 (36.097) Prec@5 68.125 (66.706) Epoch: [8][7040/11272] Time 0.878 (0.833) Data 0.001 (0.002) Loss 2.8453 (2.6443) Prec@1 33.125 (36.095) Prec@5 63.125 (66.704) Epoch: [8][7050/11272] Time 0.922 (0.833) Data 0.002 (0.002) Loss 2.6546 (2.6444) Prec@1 38.125 (36.094) Prec@5 68.125 (66.701) Epoch: [8][7060/11272] Time 0.795 (0.833) Data 0.001 (0.002) Loss 2.4801 (2.6443) Prec@1 37.500 (36.096) Prec@5 68.750 (66.702) Epoch: [8][7070/11272] Time 0.789 (0.833) Data 0.002 (0.002) Loss 2.7348 (2.6444) Prec@1 39.375 (36.096) Prec@5 68.750 (66.701) Epoch: [8][7080/11272] Time 0.894 (0.833) Data 0.002 (0.002) Loss 2.6378 (2.6446) Prec@1 40.000 (36.094) Prec@5 61.250 (66.696) Epoch: [8][7090/11272] Time 0.904 (0.833) Data 0.002 (0.002) Loss 2.4755 (2.6446) Prec@1 39.375 (36.094) Prec@5 69.375 (66.695) Epoch: [8][7100/11272] Time 0.733 (0.833) Data 0.001 (0.002) Loss 2.5494 (2.6446) Prec@1 33.750 (36.093) Prec@5 72.500 (66.696) Epoch: [8][7110/11272] Time 0.918 (0.833) Data 0.002 (0.002) Loss 2.9636 (2.6447) Prec@1 30.625 (36.091) Prec@5 53.750 (66.694) Epoch: [8][7120/11272] Time 0.875 (0.833) Data 0.002 (0.002) Loss 2.6002 (2.6446) Prec@1 38.125 (36.091) Prec@5 70.000 (66.696) Epoch: [8][7130/11272] Time 0.794 (0.833) Data 0.002 (0.002) Loss 2.8867 (2.6446) Prec@1 31.875 (36.091) Prec@5 63.125 (66.696) Epoch: [8][7140/11272] Time 0.767 (0.833) Data 0.001 (0.002) Loss 2.6221 (2.6447) Prec@1 36.250 (36.088) Prec@5 65.000 (66.694) Epoch: [8][7150/11272] Time 0.925 (0.833) Data 0.002 (0.002) Loss 2.7330 (2.6448) Prec@1 32.500 (36.086) Prec@5 64.375 (66.692) Epoch: [8][7160/11272] Time 0.915 (0.833) Data 0.001 (0.002) Loss 2.7939 (2.6448) Prec@1 32.500 (36.086) Prec@5 61.875 (66.691) Epoch: [8][7170/11272] Time 0.735 (0.833) Data 0.002 (0.002) Loss 2.7919 (2.6448) Prec@1 33.750 (36.085) Prec@5 62.500 (66.693) Epoch: [8][7180/11272] Time 0.728 (0.833) Data 0.002 (0.002) Loss 2.8518 (2.6448) Prec@1 30.625 (36.082) Prec@5 61.250 (66.691) Epoch: [8][7190/11272] Time 0.932 (0.833) Data 0.001 (0.002) Loss 2.5992 (2.6448) Prec@1 32.500 (36.079) Prec@5 66.250 (66.689) Epoch: [8][7200/11272] Time 0.933 (0.833) Data 0.001 (0.002) Loss 2.6507 (2.6449) Prec@1 36.250 (36.077) Prec@5 68.750 (66.688) Epoch: [8][7210/11272] Time 0.729 (0.833) Data 0.002 (0.002) Loss 2.9774 (2.6450) Prec@1 28.125 (36.077) Prec@5 64.375 (66.685) Epoch: [8][7220/11272] Time 0.742 (0.833) Data 0.001 (0.002) Loss 2.7834 (2.6450) Prec@1 33.125 (36.078) Prec@5 60.625 (66.684) Epoch: [8][7230/11272] Time 0.955 (0.833) Data 0.002 (0.002) Loss 2.5882 (2.6450) Prec@1 43.125 (36.079) Prec@5 67.500 (66.682) Epoch: [8][7240/11272] Time 0.793 (0.833) Data 0.001 (0.002) Loss 2.6342 (2.6450) Prec@1 37.500 (36.080) Prec@5 63.125 (66.682) Epoch: [8][7250/11272] Time 0.738 (0.833) Data 0.002 (0.002) Loss 2.6539 (2.6450) Prec@1 36.875 (36.082) Prec@5 68.125 (66.681) Epoch: [8][7260/11272] Time 0.857 (0.833) Data 0.001 (0.002) Loss 2.5882 (2.6448) Prec@1 38.125 (36.085) Prec@5 69.375 (66.683) Epoch: [8][7270/11272] Time 0.889 (0.833) Data 0.002 (0.002) Loss 2.3959 (2.6447) Prec@1 34.375 (36.088) Prec@5 72.500 (66.684) Epoch: [8][7280/11272] Time 0.778 (0.833) Data 0.001 (0.002) Loss 2.4548 (2.6446) Prec@1 43.125 (36.089) Prec@5 70.625 (66.688) Epoch: [8][7290/11272] Time 0.759 (0.833) Data 0.003 (0.002) Loss 2.7036 (2.6445) Prec@1 36.875 (36.090) Prec@5 64.375 (66.690) Epoch: [8][7300/11272] Time 0.857 (0.833) Data 0.001 (0.002) Loss 2.7244 (2.6446) Prec@1 38.125 (36.089) Prec@5 65.625 (66.690) Epoch: [8][7310/11272] Time 0.923 (0.833) Data 0.002 (0.002) Loss 2.6987 (2.6446) Prec@1 35.625 (36.088) Prec@5 67.500 (66.689) Epoch: [8][7320/11272] Time 0.785 (0.833) Data 0.001 (0.002) Loss 2.7351 (2.6447) Prec@1 29.375 (36.085) Prec@5 65.625 (66.687) Epoch: [8][7330/11272] Time 0.750 (0.833) Data 0.002 (0.002) Loss 2.4743 (2.6447) Prec@1 43.750 (36.084) Prec@5 67.500 (66.688) Epoch: [8][7340/11272] Time 0.901 (0.833) Data 0.003 (0.002) Loss 2.7216 (2.6447) Prec@1 33.750 (36.086) Prec@5 65.625 (66.688) Epoch: [8][7350/11272] Time 0.907 (0.833) Data 0.002 (0.002) Loss 2.7283 (2.6447) Prec@1 33.125 (36.086) Prec@5 64.375 (66.689) Epoch: [8][7360/11272] Time 0.788 (0.833) Data 0.001 (0.002) Loss 2.9095 (2.6447) Prec@1 29.375 (36.086) Prec@5 60.000 (66.689) Epoch: [8][7370/11272] Time 0.863 (0.833) Data 0.002 (0.002) Loss 2.5935 (2.6448) Prec@1 39.375 (36.086) Prec@5 69.375 (66.688) Epoch: [8][7380/11272] Time 0.923 (0.833) Data 0.001 (0.002) Loss 2.7074 (2.6449) Prec@1 37.500 (36.085) Prec@5 63.125 (66.686) Epoch: [8][7390/11272] Time 0.754 (0.833) Data 0.001 (0.002) Loss 2.4681 (2.6450) Prec@1 39.375 (36.085) Prec@5 71.875 (66.685) Epoch: [8][7400/11272] Time 0.799 (0.833) Data 0.001 (0.002) Loss 2.6476 (2.6450) Prec@1 36.250 (36.085) Prec@5 66.875 (66.684) Epoch: [8][7410/11272] Time 0.901 (0.833) Data 0.002 (0.002) Loss 2.8317 (2.6449) Prec@1 33.750 (36.088) Prec@5 63.750 (66.685) Epoch: [8][7420/11272] Time 0.908 (0.833) Data 0.002 (0.002) Loss 2.6522 (2.6451) Prec@1 35.000 (36.084) Prec@5 68.125 (66.681) Epoch: [8][7430/11272] Time 0.769 (0.833) Data 0.002 (0.002) Loss 2.6258 (2.6451) Prec@1 36.250 (36.081) Prec@5 65.625 (66.679) Epoch: [8][7440/11272] Time 0.768 (0.833) Data 0.002 (0.002) Loss 2.6543 (2.6450) Prec@1 31.875 (36.082) Prec@5 65.625 (66.682) Epoch: [8][7450/11272] Time 0.905 (0.833) Data 0.002 (0.002) Loss 2.6584 (2.6450) Prec@1 39.375 (36.082) Prec@5 63.750 (66.682) Epoch: [8][7460/11272] Time 0.930 (0.833) Data 0.001 (0.002) Loss 2.5210 (2.6450) Prec@1 31.250 (36.079) Prec@5 75.625 (66.682) Epoch: [8][7470/11272] Time 0.742 (0.833) Data 0.002 (0.002) Loss 2.5691 (2.6450) Prec@1 33.125 (36.079) Prec@5 71.250 (66.682) Epoch: [8][7480/11272] Time 0.769 (0.833) Data 0.001 (0.002) Loss 2.8492 (2.6451) Prec@1 28.750 (36.078) Prec@5 61.875 (66.681) Epoch: [8][7490/11272] Time 0.935 (0.833) Data 0.001 (0.002) Loss 2.5110 (2.6450) Prec@1 41.250 (36.078) Prec@5 69.375 (66.682) Epoch: [8][7500/11272] Time 0.764 (0.833) Data 0.003 (0.002) Loss 2.7529 (2.6450) Prec@1 30.000 (36.077) Prec@5 68.125 (66.683) Epoch: [8][7510/11272] Time 0.774 (0.833) Data 0.002 (0.002) Loss 2.6086 (2.6449) Prec@1 34.375 (36.079) Prec@5 68.750 (66.684) Epoch: [8][7520/11272] Time 0.951 (0.833) Data 0.001 (0.002) Loss 2.7167 (2.6450) Prec@1 33.750 (36.077) Prec@5 66.250 (66.683) Epoch: [8][7530/11272] Time 0.810 (0.833) Data 0.002 (0.002) Loss 2.6324 (2.6449) Prec@1 36.250 (36.078) Prec@5 65.000 (66.684) Epoch: [8][7540/11272] Time 0.778 (0.833) Data 0.002 (0.002) Loss 2.6747 (2.6451) Prec@1 36.250 (36.077) Prec@5 70.625 (66.681) Epoch: [8][7550/11272] Time 0.747 (0.833) Data 0.002 (0.002) Loss 2.6012 (2.6451) Prec@1 36.250 (36.077) Prec@5 65.625 (66.681) Epoch: [8][7560/11272] Time 0.888 (0.833) Data 0.001 (0.002) Loss 2.6781 (2.6450) Prec@1 31.875 (36.077) Prec@5 64.375 (66.682) Epoch: [8][7570/11272] Time 0.962 (0.833) Data 0.002 (0.002) Loss 2.7785 (2.6450) Prec@1 35.625 (36.079) Prec@5 65.000 (66.682) Epoch: [8][7580/11272] Time 0.739 (0.833) Data 0.001 (0.002) Loss 2.6792 (2.6450) Prec@1 35.000 (36.077) Prec@5 65.625 (66.682) Epoch: [8][7590/11272] Time 0.701 (0.833) Data 0.002 (0.002) Loss 2.3500 (2.6449) Prec@1 43.750 (36.078) Prec@5 67.500 (66.682) Epoch: [8][7600/11272] Time 0.889 (0.833) Data 0.001 (0.002) Loss 2.6874 (2.6449) Prec@1 38.125 (36.080) Prec@5 66.250 (66.681) Epoch: [8][7610/11272] Time 0.909 (0.833) Data 0.002 (0.002) Loss 2.7971 (2.6449) Prec@1 33.750 (36.080) Prec@5 65.000 (66.681) Epoch: [8][7620/11272] Time 0.738 (0.833) Data 0.001 (0.002) Loss 2.6492 (2.6450) Prec@1 36.250 (36.078) Prec@5 68.125 (66.680) Epoch: [8][7630/11272] Time 0.936 (0.833) Data 0.002 (0.002) Loss 2.7030 (2.6450) Prec@1 37.500 (36.079) Prec@5 64.375 (66.679) Epoch: [8][7640/11272] Time 0.954 (0.833) Data 0.001 (0.002) Loss 2.5763 (2.6450) Prec@1 38.125 (36.081) Prec@5 68.750 (66.679) Epoch: [8][7650/11272] Time 0.750 (0.833) Data 0.002 (0.002) Loss 2.4915 (2.6451) Prec@1 36.875 (36.078) Prec@5 70.000 (66.678) Epoch: [8][7660/11272] Time 0.731 (0.833) Data 0.001 (0.002) Loss 2.6824 (2.6451) Prec@1 33.125 (36.078) Prec@5 65.000 (66.678) Epoch: [8][7670/11272] Time 0.879 (0.833) Data 0.002 (0.002) Loss 2.4480 (2.6451) Prec@1 40.000 (36.077) Prec@5 71.250 (66.679) Epoch: [8][7680/11272] Time 0.906 (0.833) Data 0.001 (0.002) Loss 2.5546 (2.6451) Prec@1 36.250 (36.078) Prec@5 70.000 (66.678) Epoch: [8][7690/11272] Time 0.737 (0.833) Data 0.002 (0.002) Loss 2.7807 (2.6451) Prec@1 30.625 (36.078) Prec@5 61.875 (66.677) Epoch: [8][7700/11272] Time 0.749 (0.833) Data 0.001 (0.002) Loss 2.5736 (2.6451) Prec@1 33.750 (36.077) Prec@5 66.250 (66.676) Epoch: [8][7710/11272] Time 0.920 (0.833) Data 0.005 (0.002) Loss 2.7066 (2.6453) Prec@1 39.375 (36.075) Prec@5 68.125 (66.674) Epoch: [8][7720/11272] Time 0.938 (0.833) Data 0.001 (0.002) Loss 2.5313 (2.6452) Prec@1 37.500 (36.074) Prec@5 61.875 (66.676) Epoch: [8][7730/11272] Time 0.748 (0.833) Data 0.002 (0.002) Loss 2.6103 (2.6452) Prec@1 40.625 (36.074) Prec@5 70.000 (66.674) Epoch: [8][7740/11272] Time 0.745 (0.833) Data 0.002 (0.002) Loss 2.3994 (2.6452) Prec@1 40.000 (36.075) Prec@5 69.375 (66.674) Epoch: [8][7750/11272] Time 0.939 (0.833) Data 0.002 (0.002) Loss 2.7157 (2.6452) Prec@1 35.000 (36.075) Prec@5 66.875 (66.673) Epoch: [8][7760/11272] Time 0.764 (0.833) Data 0.004 (0.002) Loss 2.9796 (2.6452) Prec@1 25.000 (36.073) Prec@5 63.750 (66.673) Epoch: [8][7770/11272] Time 0.763 (0.833) Data 0.002 (0.002) Loss 2.5351 (2.6453) Prec@1 38.125 (36.071) Prec@5 68.125 (66.672) Epoch: [8][7780/11272] Time 0.876 (0.833) Data 0.001 (0.002) Loss 2.3960 (2.6453) Prec@1 38.125 (36.074) Prec@5 73.750 (66.671) Epoch: [8][7790/11272] Time 0.895 (0.833) Data 0.002 (0.002) Loss 2.4460 (2.6454) Prec@1 44.375 (36.074) Prec@5 71.875 (66.669) Epoch: [8][7800/11272] Time 0.811 (0.833) Data 0.002 (0.002) Loss 2.7158 (2.6453) Prec@1 33.125 (36.075) Prec@5 65.625 (66.671) Epoch: [8][7810/11272] Time 0.824 (0.833) Data 0.002 (0.002) Loss 2.5572 (2.6453) Prec@1 38.125 (36.075) Prec@5 73.125 (66.671) Epoch: [8][7820/11272] Time 0.935 (0.833) Data 0.001 (0.002) Loss 2.4502 (2.6452) Prec@1 36.250 (36.075) Prec@5 67.500 (66.672) Epoch: [8][7830/11272] Time 0.931 (0.833) Data 0.002 (0.002) Loss 2.7120 (2.6452) Prec@1 35.000 (36.075) Prec@5 66.250 (66.673) Epoch: [8][7840/11272] Time 0.777 (0.833) Data 0.001 (0.002) Loss 2.6653 (2.6452) Prec@1 37.500 (36.076) Prec@5 66.250 (66.672) Epoch: [8][7850/11272] Time 0.757 (0.833) Data 0.002 (0.002) Loss 2.6908 (2.6452) Prec@1 33.125 (36.078) Prec@5 63.125 (66.672) Epoch: [8][7860/11272] Time 0.877 (0.833) Data 0.001 (0.002) Loss 2.5232 (2.6452) Prec@1 38.750 (36.078) Prec@5 68.125 (66.671) Epoch: [8][7870/11272] Time 0.898 (0.833) Data 0.002 (0.002) Loss 2.4456 (2.6452) Prec@1 42.500 (36.082) Prec@5 70.625 (66.672) Epoch: [8][7880/11272] Time 0.782 (0.833) Data 0.002 (0.002) Loss 2.5536 (2.6452) Prec@1 38.125 (36.082) Prec@5 66.250 (66.673) Epoch: [8][7890/11272] Time 0.870 (0.833) Data 0.002 (0.002) Loss 2.8282 (2.6451) Prec@1 34.375 (36.081) Prec@5 60.000 (66.674) Epoch: [8][7900/11272] Time 0.886 (0.833) Data 0.001 (0.002) Loss 2.6665 (2.6452) Prec@1 35.000 (36.082) Prec@5 68.750 (66.672) Epoch: [8][7910/11272] Time 0.763 (0.833) Data 0.002 (0.002) Loss 2.6316 (2.6451) Prec@1 40.625 (36.086) Prec@5 65.000 (66.674) Epoch: [8][7920/11272] Time 0.761 (0.833) Data 0.002 (0.002) Loss 2.6334 (2.6451) Prec@1 36.250 (36.086) Prec@5 68.750 (66.677) Epoch: [8][7930/11272] Time 0.894 (0.833) Data 0.001 (0.002) Loss 2.3823 (2.6450) Prec@1 40.000 (36.087) Prec@5 70.625 (66.677) Epoch: [8][7940/11272] Time 0.942 (0.833) Data 0.001 (0.002) Loss 2.5015 (2.6450) Prec@1 40.625 (36.087) Prec@5 71.250 (66.678) Epoch: [8][7950/11272] Time 0.740 (0.833) Data 0.001 (0.002) Loss 2.4693 (2.6450) Prec@1 38.750 (36.088) Prec@5 75.000 (66.678) Epoch: [8][7960/11272] Time 0.759 (0.833) Data 0.001 (0.002) Loss 2.5823 (2.6449) Prec@1 35.625 (36.090) Prec@5 68.125 (66.681) Epoch: [8][7970/11272] Time 0.916 (0.833) Data 0.001 (0.002) Loss 2.7212 (2.6450) Prec@1 31.875 (36.088) Prec@5 68.125 (66.680) Epoch: [8][7980/11272] Time 0.933 (0.833) Data 0.001 (0.002) Loss 2.6106 (2.6450) Prec@1 38.125 (36.089) Prec@5 65.625 (66.679) Epoch: [8][7990/11272] Time 0.734 (0.833) Data 0.002 (0.002) Loss 2.6416 (2.6449) Prec@1 35.000 (36.091) Prec@5 70.000 (66.681) Epoch: [8][8000/11272] Time 0.740 (0.833) Data 0.001 (0.002) Loss 2.4086 (2.6449) Prec@1 38.125 (36.091) Prec@5 70.000 (66.680) Epoch: [8][8010/11272] Time 0.917 (0.833) Data 0.002 (0.002) Loss 2.6844 (2.6450) Prec@1 35.625 (36.090) Prec@5 65.000 (66.679) Epoch: [8][8020/11272] Time 0.892 (0.833) Data 0.001 (0.002) Loss 2.5766 (2.6450) Prec@1 33.750 (36.088) Prec@5 71.875 (66.679) Epoch: [8][8030/11272] Time 0.740 (0.833) Data 0.002 (0.002) Loss 2.5393 (2.6450) Prec@1 36.250 (36.087) Prec@5 65.625 (66.678) Epoch: [8][8040/11272] Time 0.982 (0.833) Data 0.001 (0.002) Loss 2.6998 (2.6451) Prec@1 35.625 (36.085) Prec@5 63.750 (66.678) Epoch: [8][8050/11272] Time 0.904 (0.833) Data 0.002 (0.002) Loss 2.5941 (2.6450) Prec@1 37.500 (36.086) Prec@5 65.000 (66.680) Epoch: [8][8060/11272] Time 0.779 (0.833) Data 0.001 (0.002) Loss 2.6367 (2.6450) Prec@1 33.125 (36.086) Prec@5 63.125 (66.678) Epoch: [8][8070/11272] Time 0.773 (0.833) Data 0.002 (0.002) Loss 2.4649 (2.6450) Prec@1 39.375 (36.088) Prec@5 71.875 (66.680) Epoch: [8][8080/11272] Time 0.897 (0.833) Data 0.002 (0.002) Loss 2.5832 (2.6449) Prec@1 35.000 (36.090) Prec@5 63.750 (66.681) Epoch: [8][8090/11272] Time 0.886 (0.833) Data 0.001 (0.002) Loss 2.8400 (2.6449) Prec@1 35.625 (36.090) Prec@5 62.500 (66.679) Epoch: [8][8100/11272] Time 0.766 (0.833) Data 0.001 (0.002) Loss 2.7849 (2.6449) Prec@1 34.375 (36.092) Prec@5 62.500 (66.678) Epoch: [8][8110/11272] Time 0.691 (0.833) Data 0.001 (0.002) Loss 2.6073 (2.6448) Prec@1 37.500 (36.091) Prec@5 66.250 (66.681) Epoch: [8][8120/11272] Time 0.932 (0.833) Data 0.001 (0.002) Loss 2.6089 (2.6447) Prec@1 32.500 (36.090) Prec@5 65.000 (66.682) Epoch: [8][8130/11272] Time 0.917 (0.833) Data 0.003 (0.002) Loss 2.7474 (2.6447) Prec@1 34.375 (36.091) Prec@5 64.375 (66.684) Epoch: [8][8140/11272] Time 0.778 (0.833) Data 0.001 (0.002) Loss 2.5460 (2.6447) Prec@1 35.000 (36.093) Prec@5 66.875 (66.683) Epoch: [8][8150/11272] Time 0.734 (0.833) Data 0.002 (0.002) Loss 2.5716 (2.6447) Prec@1 38.750 (36.093) Prec@5 66.875 (66.684) Epoch: [8][8160/11272] Time 0.931 (0.833) Data 0.001 (0.002) Loss 2.5363 (2.6447) Prec@1 34.375 (36.092) Prec@5 66.875 (66.684) Epoch: [8][8170/11272] Time 0.727 (0.833) Data 0.001 (0.002) Loss 2.5988 (2.6446) Prec@1 37.500 (36.096) Prec@5 69.375 (66.686) Epoch: [8][8180/11272] Time 0.786 (0.833) Data 0.002 (0.002) Loss 2.8489 (2.6447) Prec@1 31.875 (36.095) Prec@5 60.625 (66.686) Epoch: [8][8190/11272] Time 0.904 (0.833) Data 0.001 (0.002) Loss 2.6424 (2.6447) Prec@1 34.375 (36.095) Prec@5 67.500 (66.686) Epoch: [8][8200/11272] Time 0.966 (0.833) Data 0.002 (0.002) Loss 2.6443 (2.6446) Prec@1 36.250 (36.098) Prec@5 68.125 (66.689) Epoch: [8][8210/11272] Time 0.761 (0.833) Data 0.002 (0.002) Loss 2.6339 (2.6446) Prec@1 43.750 (36.101) Prec@5 63.750 (66.688) Epoch: [8][8220/11272] Time 0.735 (0.833) Data 0.002 (0.002) Loss 2.4699 (2.6447) Prec@1 35.000 (36.100) Prec@5 73.750 (66.687) Epoch: [8][8230/11272] Time 0.930 (0.833) Data 0.002 (0.002) Loss 2.8419 (2.6447) Prec@1 35.000 (36.100) Prec@5 61.250 (66.684) Epoch: [8][8240/11272] Time 0.869 (0.833) Data 0.001 (0.002) Loss 2.8506 (2.6447) Prec@1 30.625 (36.100) Prec@5 58.750 (66.684) Epoch: [8][8250/11272] Time 0.745 (0.833) Data 0.002 (0.002) Loss 2.6740 (2.6447) Prec@1 38.125 (36.101) Prec@5 66.875 (66.683) Epoch: [8][8260/11272] Time 0.735 (0.833) Data 0.001 (0.002) Loss 2.7509 (2.6448) Prec@1 36.250 (36.100) Prec@5 67.500 (66.682) Epoch: [8][8270/11272] Time 0.911 (0.833) Data 0.002 (0.002) Loss 2.4873 (2.6447) Prec@1 41.875 (36.100) Prec@5 74.375 (66.683) Epoch: [8][8280/11272] Time 0.873 (0.833) Data 0.001 (0.002) Loss 2.5935 (2.6447) Prec@1 38.125 (36.101) Prec@5 70.000 (66.684) Epoch: [8][8290/11272] Time 0.766 (0.833) Data 0.002 (0.002) Loss 2.4570 (2.6447) Prec@1 41.250 (36.101) Prec@5 69.375 (66.684) Epoch: [8][8300/11272] Time 0.909 (0.833) Data 0.001 (0.002) Loss 2.7017 (2.6447) Prec@1 36.875 (36.102) Prec@5 66.875 (66.683) Epoch: [8][8310/11272] Time 0.890 (0.833) Data 0.002 (0.002) Loss 2.6271 (2.6447) Prec@1 30.625 (36.103) Prec@5 70.625 (66.684) Epoch: [8][8320/11272] Time 0.761 (0.833) Data 0.002 (0.002) Loss 2.9624 (2.6448) Prec@1 34.375 (36.101) Prec@5 61.250 (66.682) Epoch: [8][8330/11272] Time 0.781 (0.833) Data 0.002 (0.002) Loss 2.6745 (2.6449) Prec@1 35.000 (36.098) Prec@5 67.500 (66.681) Epoch: [8][8340/11272] Time 0.880 (0.833) Data 0.001 (0.002) Loss 2.5968 (2.6448) Prec@1 36.875 (36.100) Prec@5 71.250 (66.683) Epoch: [8][8350/11272] Time 0.881 (0.833) Data 0.002 (0.002) Loss 2.9621 (2.6448) Prec@1 40.000 (36.102) Prec@5 61.875 (66.682) Epoch: [8][8360/11272] Time 0.740 (0.833) Data 0.001 (0.002) Loss 2.8565 (2.6449) Prec@1 26.250 (36.099) Prec@5 61.250 (66.680) Epoch: [8][8370/11272] Time 0.757 (0.833) Data 0.002 (0.002) Loss 2.8352 (2.6448) Prec@1 31.875 (36.101) Prec@5 63.125 (66.683) Epoch: [8][8380/11272] Time 0.869 (0.833) Data 0.001 (0.002) Loss 2.4623 (2.6448) Prec@1 40.625 (36.104) Prec@5 69.375 (66.683) Epoch: [8][8390/11272] Time 0.903 (0.833) Data 0.002 (0.002) Loss 2.4445 (2.6448) Prec@1 43.125 (36.102) Prec@5 71.250 (66.683) Epoch: [8][8400/11272] Time 0.753 (0.833) Data 0.002 (0.002) Loss 2.7708 (2.6450) Prec@1 37.500 (36.099) Prec@5 66.250 (66.679) Epoch: [8][8410/11272] Time 0.762 (0.833) Data 0.002 (0.002) Loss 2.9019 (2.6449) Prec@1 31.250 (36.098) Prec@5 65.000 (66.679) Epoch: [8][8420/11272] Time 0.909 (0.833) Data 0.001 (0.002) Loss 2.8267 (2.6449) Prec@1 35.000 (36.097) Prec@5 63.750 (66.680) Epoch: [8][8430/11272] Time 0.747 (0.833) Data 0.002 (0.002) Loss 2.4782 (2.6449) Prec@1 38.125 (36.097) Prec@5 70.000 (66.680) Epoch: [8][8440/11272] Time 0.795 (0.833) Data 0.002 (0.002) Loss 2.5300 (2.6449) Prec@1 40.000 (36.098) Prec@5 64.375 (66.680) Epoch: [8][8450/11272] Time 0.923 (0.833) Data 0.001 (0.002) Loss 2.9235 (2.6450) Prec@1 32.500 (36.095) Prec@5 65.625 (66.679) Epoch: [8][8460/11272] Time 0.956 (0.833) Data 0.001 (0.002) Loss 2.6383 (2.6451) Prec@1 34.375 (36.094) Prec@5 67.500 (66.677) Epoch: [8][8470/11272] Time 0.773 (0.833) Data 0.002 (0.002) Loss 2.5947 (2.6451) Prec@1 37.500 (36.093) Prec@5 67.500 (66.678) Epoch: [8][8480/11272] Time 0.774 (0.833) Data 0.001 (0.002) Loss 2.4926 (2.6450) Prec@1 40.000 (36.096) Prec@5 64.375 (66.678) Epoch: [8][8490/11272] Time 0.870 (0.833) Data 0.001 (0.002) Loss 2.6925 (2.6450) Prec@1 35.625 (36.097) Prec@5 64.375 (66.677) Epoch: [8][8500/11272] Time 0.900 (0.833) Data 0.001 (0.002) Loss 2.4798 (2.6451) Prec@1 37.500 (36.096) Prec@5 70.625 (66.675) Epoch: [8][8510/11272] Time 0.733 (0.833) Data 0.002 (0.002) Loss 2.6555 (2.6451) Prec@1 38.750 (36.097) Prec@5 68.125 (66.674) Epoch: [8][8520/11272] Time 0.775 (0.833) Data 0.001 (0.002) Loss 2.7064 (2.6451) Prec@1 35.625 (36.095) Prec@5 65.625 (66.672) Epoch: [8][8530/11272] Time 0.924 (0.833) Data 0.002 (0.002) Loss 2.5782 (2.6452) Prec@1 38.125 (36.095) Prec@5 65.625 (66.672) Epoch: [8][8540/11272] Time 0.929 (0.833) Data 0.001 (0.002) Loss 2.4571 (2.6452) Prec@1 41.875 (36.096) Prec@5 71.875 (66.670) Epoch: [8][8550/11272] Time 0.748 (0.833) Data 0.002 (0.002) Loss 2.7500 (2.6452) Prec@1 35.625 (36.095) Prec@5 61.875 (66.669) Epoch: [8][8560/11272] Time 0.904 (0.833) Data 0.001 (0.002) Loss 2.4639 (2.6453) Prec@1 40.000 (36.093) Prec@5 74.375 (66.668) Epoch: [8][8570/11272] Time 0.921 (0.833) Data 0.002 (0.002) Loss 2.9007 (2.6453) Prec@1 29.375 (36.093) Prec@5 61.250 (66.669) Epoch: [8][8580/11272] Time 0.789 (0.833) Data 0.001 (0.002) Loss 2.5661 (2.6453) Prec@1 36.250 (36.095) Prec@5 70.625 (66.670) Epoch: [8][8590/11272] Time 0.787 (0.833) Data 0.002 (0.002) Loss 2.6277 (2.6453) Prec@1 37.500 (36.095) Prec@5 66.250 (66.670) Epoch: [8][8600/11272] Time 0.885 (0.833) Data 0.001 (0.002) Loss 2.7098 (2.6454) Prec@1 35.000 (36.095) Prec@5 61.875 (66.668) Epoch: [8][8610/11272] Time 0.908 (0.833) Data 0.002 (0.002) Loss 2.7245 (2.6454) Prec@1 29.375 (36.093) Prec@5 68.125 (66.668) Epoch: [8][8620/11272] Time 0.782 (0.833) Data 0.001 (0.002) Loss 2.5550 (2.6454) Prec@1 42.500 (36.093) Prec@5 65.625 (66.667) Epoch: [8][8630/11272] Time 0.787 (0.833) Data 0.002 (0.002) Loss 2.6265 (2.6453) Prec@1 37.500 (36.094) Prec@5 67.500 (66.669) Epoch: [8][8640/11272] Time 0.938 (0.833) Data 0.001 (0.002) Loss 2.4462 (2.6453) Prec@1 40.625 (36.095) Prec@5 70.625 (66.668) Epoch: [8][8650/11272] Time 0.941 (0.833) Data 0.001 (0.002) Loss 2.6300 (2.6453) Prec@1 36.250 (36.097) Prec@5 67.500 (66.670) Epoch: [8][8660/11272] Time 0.791 (0.833) Data 0.001 (0.002) Loss 2.6858 (2.6452) Prec@1 37.500 (36.097) Prec@5 62.500 (66.672) Epoch: [8][8670/11272] Time 0.770 (0.833) Data 0.002 (0.002) Loss 2.4540 (2.6452) Prec@1 38.125 (36.096) Prec@5 72.500 (66.672) Epoch: [8][8680/11272] Time 0.956 (0.833) Data 0.001 (0.002) Loss 2.7636 (2.6454) Prec@1 35.000 (36.092) Prec@5 65.000 (66.669) Epoch: [8][8690/11272] Time 0.781 (0.833) Data 0.004 (0.002) Loss 2.6028 (2.6453) Prec@1 31.250 (36.092) Prec@5 66.250 (66.670) Epoch: [8][8700/11272] Time 0.755 (0.833) Data 0.001 (0.002) Loss 2.5696 (2.6452) Prec@1 38.125 (36.095) Prec@5 71.875 (66.673) Epoch: [8][8710/11272] Time 0.917 (0.833) Data 0.002 (0.002) Loss 2.7830 (2.6452) Prec@1 36.250 (36.096) Prec@5 67.500 (66.675) Epoch: [8][8720/11272] Time 0.841 (0.833) Data 0.001 (0.002) Loss 2.7850 (2.6452) Prec@1 29.375 (36.096) Prec@5 63.750 (66.676) Epoch: [8][8730/11272] Time 0.736 (0.833) Data 0.002 (0.002) Loss 2.5461 (2.6451) Prec@1 36.250 (36.097) Prec@5 67.500 (66.676) Epoch: [8][8740/11272] Time 0.772 (0.833) Data 0.001 (0.002) Loss 2.5756 (2.6451) Prec@1 39.375 (36.097) Prec@5 68.125 (66.676) Epoch: [8][8750/11272] Time 0.903 (0.833) Data 0.003 (0.002) Loss 2.8312 (2.6452) Prec@1 30.000 (36.097) Prec@5 66.875 (66.675) Epoch: [8][8760/11272] Time 0.912 (0.833) Data 0.001 (0.002) Loss 2.6674 (2.6453) Prec@1 37.500 (36.096) Prec@5 64.375 (66.675) Epoch: [8][8770/11272] Time 0.748 (0.833) Data 0.001 (0.002) Loss 2.6228 (2.6452) Prec@1 41.875 (36.098) Prec@5 67.500 (66.676) Epoch: [8][8780/11272] Time 0.753 (0.833) Data 0.001 (0.002) Loss 2.8717 (2.6452) Prec@1 31.250 (36.097) Prec@5 62.500 (66.675) Epoch: [8][8790/11272] Time 0.893 (0.833) Data 0.002 (0.002) Loss 2.6274 (2.6452) Prec@1 39.375 (36.099) Prec@5 64.375 (66.676) Epoch: [8][8800/11272] Time 0.905 (0.833) Data 0.001 (0.002) Loss 2.4316 (2.6452) Prec@1 38.125 (36.100) Prec@5 70.625 (66.676) Epoch: [8][8810/11272] Time 0.752 (0.833) Data 0.002 (0.002) Loss 2.5097 (2.6453) Prec@1 38.750 (36.100) Prec@5 68.750 (66.675) Epoch: [8][8820/11272] Time 0.921 (0.833) Data 0.003 (0.002) Loss 2.9017 (2.6452) Prec@1 31.250 (36.101) Prec@5 63.750 (66.677) Epoch: [8][8830/11272] Time 0.926 (0.833) Data 0.002 (0.002) Loss 2.6228 (2.6452) Prec@1 35.000 (36.099) Prec@5 68.125 (66.677) Epoch: [8][8840/11272] Time 0.811 (0.833) Data 0.001 (0.002) Loss 2.7832 (2.6452) Prec@1 34.375 (36.099) Prec@5 60.625 (66.676) Epoch: [8][8850/11272] Time 0.726 (0.833) Data 0.002 (0.002) Loss 2.5469 (2.6452) Prec@1 36.250 (36.098) Prec@5 70.000 (66.677) Epoch: [8][8860/11272] Time 0.869 (0.833) Data 0.001 (0.002) Loss 2.5302 (2.6452) Prec@1 36.875 (36.098) Prec@5 70.625 (66.678) Epoch: [8][8870/11272] Time 0.856 (0.833) Data 0.002 (0.002) Loss 2.6751 (2.6451) Prec@1 30.625 (36.099) Prec@5 64.375 (66.678) Epoch: [8][8880/11272] Time 0.745 (0.833) Data 0.002 (0.002) Loss 2.9771 (2.6452) Prec@1 33.125 (36.100) Prec@5 58.125 (66.677) Epoch: [8][8890/11272] Time 0.767 (0.833) Data 0.001 (0.002) Loss 2.7102 (2.6452) Prec@1 34.375 (36.099) Prec@5 63.125 (66.675) Epoch: [8][8900/11272] Time 0.889 (0.833) Data 0.001 (0.002) Loss 2.5694 (2.6452) Prec@1 38.750 (36.100) Prec@5 66.250 (66.675) Epoch: [8][8910/11272] Time 0.951 (0.833) Data 0.001 (0.002) Loss 2.5300 (2.6452) Prec@1 35.625 (36.100) Prec@5 72.500 (66.676) Epoch: [8][8920/11272] Time 0.782 (0.833) Data 0.002 (0.002) Loss 2.4771 (2.6451) Prec@1 38.750 (36.100) Prec@5 68.125 (66.676) Epoch: [8][8930/11272] Time 0.735 (0.833) Data 0.002 (0.002) Loss 2.3924 (2.6450) Prec@1 42.500 (36.102) Prec@5 70.625 (66.678) Epoch: [8][8940/11272] Time 0.909 (0.833) Data 0.002 (0.002) Loss 2.5007 (2.6449) Prec@1 40.000 (36.104) Prec@5 71.250 (66.680) Epoch: [8][8950/11272] Time 0.929 (0.833) Data 0.001 (0.002) Loss 2.7268 (2.6450) Prec@1 40.625 (36.103) Prec@5 66.875 (66.680) Epoch: [8][8960/11272] Time 0.764 (0.833) Data 0.001 (0.002) Loss 2.4123 (2.6449) Prec@1 40.000 (36.105) Prec@5 72.500 (66.683) Epoch: [8][8970/11272] Time 0.892 (0.833) Data 0.001 (0.002) Loss 2.6491 (2.6450) Prec@1 42.500 (36.105) Prec@5 70.000 (66.683) Epoch: [8][8980/11272] Time 0.966 (0.833) Data 0.001 (0.002) Loss 2.8354 (2.6450) Prec@1 32.500 (36.104) Prec@5 65.000 (66.683) Epoch: [8][8990/11272] Time 0.835 (0.833) Data 0.002 (0.002) Loss 2.6568 (2.6450) Prec@1 31.875 (36.106) Prec@5 67.500 (66.685) Epoch: [8][9000/11272] Time 0.764 (0.833) Data 0.001 (0.002) Loss 2.7031 (2.6450) Prec@1 34.375 (36.106) Prec@5 63.750 (66.685) Epoch: [8][9010/11272] Time 0.926 (0.833) Data 0.001 (0.002) Loss 2.4478 (2.6450) Prec@1 41.250 (36.105) Prec@5 70.000 (66.685) Epoch: [8][9020/11272] Time 0.919 (0.833) Data 0.001 (0.002) Loss 2.6697 (2.6450) Prec@1 35.625 (36.105) Prec@5 64.375 (66.686) Epoch: [8][9030/11272] Time 0.760 (0.833) Data 0.002 (0.002) Loss 2.5970 (2.6450) Prec@1 35.000 (36.106) Prec@5 68.750 (66.686) Epoch: [8][9040/11272] Time 0.734 (0.833) Data 0.001 (0.002) Loss 2.7101 (2.6450) Prec@1 33.750 (36.105) Prec@5 63.750 (66.687) Epoch: [8][9050/11272] Time 0.908 (0.833) Data 0.002 (0.002) Loss 2.5687 (2.6450) Prec@1 33.125 (36.102) Prec@5 66.250 (66.685) Epoch: [8][9060/11272] Time 0.937 (0.833) Data 0.002 (0.002) Loss 2.4360 (2.6450) Prec@1 40.000 (36.102) Prec@5 70.625 (66.687) Epoch: [8][9070/11272] Time 0.780 (0.833) Data 0.002 (0.002) Loss 2.5478 (2.6449) Prec@1 35.000 (36.102) Prec@5 65.625 (66.687) Epoch: [8][9080/11272] Time 0.774 (0.833) Data 0.001 (0.002) Loss 2.7468 (2.6450) Prec@1 33.750 (36.102) Prec@5 65.625 (66.685) Epoch: [8][9090/11272] Time 0.903 (0.833) Data 0.002 (0.002) Loss 2.8038 (2.6450) Prec@1 33.750 (36.102) Prec@5 63.750 (66.686) Epoch: [8][9100/11272] Time 0.776 (0.833) Data 0.002 (0.002) Loss 2.7524 (2.6451) Prec@1 36.875 (36.101) Prec@5 65.625 (66.683) Epoch: [8][9110/11272] Time 0.753 (0.833) Data 0.002 (0.002) Loss 2.7648 (2.6451) Prec@1 35.625 (36.102) Prec@5 63.750 (66.684) Epoch: [8][9120/11272] Time 0.948 (0.833) Data 0.003 (0.002) Loss 2.6750 (2.6451) Prec@1 30.625 (36.102) Prec@5 63.125 (66.683) Epoch: [8][9130/11272] Time 0.906 (0.833) Data 0.002 (0.002) Loss 2.6602 (2.6451) Prec@1 40.000 (36.103) Prec@5 64.375 (66.684) Epoch: [8][9140/11272] Time 0.740 (0.833) Data 0.001 (0.002) Loss 2.5398 (2.6452) Prec@1 35.625 (36.099) Prec@5 66.875 (66.683) Epoch: [8][9150/11272] Time 0.779 (0.833) Data 0.002 (0.002) Loss 2.8803 (2.6452) Prec@1 32.500 (36.100) Prec@5 64.375 (66.683) Epoch: [8][9160/11272] Time 0.948 (0.833) Data 0.001 (0.002) Loss 2.6744 (2.6452) Prec@1 35.625 (36.098) Prec@5 70.000 (66.682) Epoch: [8][9170/11272] Time 0.913 (0.833) Data 0.002 (0.002) Loss 2.6125 (2.6452) Prec@1 36.250 (36.098) Prec@5 70.625 (66.683) Epoch: [8][9180/11272] Time 0.742 (0.833) Data 0.002 (0.002) Loss 2.5978 (2.6452) Prec@1 40.625 (36.098) Prec@5 65.000 (66.682) Epoch: [8][9190/11272] Time 0.737 (0.833) Data 0.002 (0.002) Loss 2.4494 (2.6451) Prec@1 43.750 (36.100) Prec@5 71.875 (66.683) Epoch: [8][9200/11272] Time 0.900 (0.833) Data 0.001 (0.002) Loss 2.3871 (2.6452) Prec@1 40.625 (36.099) Prec@5 75.000 (66.683) Epoch: [8][9210/11272] Time 0.966 (0.833) Data 0.002 (0.002) Loss 2.7976 (2.6452) Prec@1 31.875 (36.098) Prec@5 65.000 (66.683) Epoch: [8][9220/11272] Time 0.749 (0.833) Data 0.002 (0.002) Loss 2.5952 (2.6453) Prec@1 33.125 (36.096) Prec@5 67.500 (66.680) Epoch: [8][9230/11272] Time 0.946 (0.833) Data 0.001 (0.002) Loss 2.7766 (2.6452) Prec@1 31.250 (36.097) Prec@5 67.500 (66.682) Epoch: [8][9240/11272] Time 0.886 (0.833) Data 0.001 (0.002) Loss 2.8503 (2.6453) Prec@1 36.875 (36.097) Prec@5 60.625 (66.682) Epoch: [8][9250/11272] Time 0.773 (0.833) Data 0.001 (0.002) Loss 2.6614 (2.6452) Prec@1 36.250 (36.098) Prec@5 65.625 (66.682) Epoch: [8][9260/11272] Time 0.725 (0.833) Data 0.002 (0.002) Loss 2.6956 (2.6453) Prec@1 37.500 (36.097) Prec@5 66.875 (66.682) Epoch: [8][9270/11272] Time 0.872 (0.833) Data 0.001 (0.002) Loss 2.9102 (2.6452) Prec@1 28.125 (36.098) Prec@5 63.750 (66.684) Epoch: [8][9280/11272] Time 0.954 (0.833) Data 0.002 (0.002) Loss 2.6915 (2.6452) Prec@1 35.000 (36.100) Prec@5 66.875 (66.685) Epoch: [8][9290/11272] Time 0.803 (0.833) Data 0.001 (0.002) Loss 2.5904 (2.6452) Prec@1 34.375 (36.100) Prec@5 70.000 (66.684) Epoch: [8][9300/11272] Time 0.783 (0.833) Data 0.003 (0.002) Loss 2.5224 (2.6451) Prec@1 36.250 (36.100) Prec@5 66.875 (66.685) Epoch: [8][9310/11272] Time 0.882 (0.833) Data 0.002 (0.002) Loss 2.6703 (2.6452) Prec@1 32.500 (36.099) Prec@5 62.500 (66.683) Epoch: [8][9320/11272] Time 0.965 (0.833) Data 0.002 (0.002) Loss 2.5586 (2.6451) Prec@1 37.500 (36.102) Prec@5 70.625 (66.685) Epoch: [8][9330/11272] Time 0.778 (0.833) Data 0.001 (0.002) Loss 2.6861 (2.6450) Prec@1 33.125 (36.103) Prec@5 67.500 (66.686) Epoch: [8][9340/11272] Time 0.791 (0.833) Data 0.001 (0.002) Loss 2.9394 (2.6451) Prec@1 30.625 (36.102) Prec@5 60.000 (66.684) Epoch: [8][9350/11272] Time 0.930 (0.833) Data 0.001 (0.002) Loss 2.9638 (2.6453) Prec@1 31.875 (36.100) Prec@5 61.250 (66.679) Epoch: [8][9360/11272] Time 0.778 (0.833) Data 0.004 (0.002) Loss 2.5167 (2.6452) Prec@1 38.750 (36.102) Prec@5 70.000 (66.681) Epoch: [8][9370/11272] Time 0.773 (0.833) Data 0.001 (0.002) Loss 2.6841 (2.6451) Prec@1 35.000 (36.102) Prec@5 68.125 (66.683) Epoch: [8][9380/11272] Time 0.898 (0.833) Data 0.002 (0.002) Loss 2.6294 (2.6453) Prec@1 33.125 (36.098) Prec@5 63.750 (66.681) Epoch: [8][9390/11272] Time 0.914 (0.833) Data 0.001 (0.002) Loss 2.6606 (2.6453) Prec@1 36.250 (36.097) Prec@5 65.000 (66.681) Epoch: [8][9400/11272] Time 0.703 (0.833) Data 0.001 (0.002) Loss 2.5522 (2.6453) Prec@1 41.250 (36.097) Prec@5 70.000 (66.680) Epoch: [8][9410/11272] Time 0.748 (0.833) Data 0.002 (0.002) Loss 2.8454 (2.6454) Prec@1 31.250 (36.095) Prec@5 62.500 (66.679) Epoch: [8][9420/11272] Time 0.908 (0.833) Data 0.002 (0.002) Loss 2.7577 (2.6454) Prec@1 33.125 (36.097) Prec@5 61.875 (66.679) Epoch: [8][9430/11272] Time 0.866 (0.833) Data 0.002 (0.002) Loss 2.7984 (2.6454) Prec@1 32.500 (36.096) Prec@5 63.750 (66.677) Epoch: [8][9440/11272] Time 0.705 (0.833) Data 0.002 (0.002) Loss 2.7219 (2.6454) Prec@1 35.625 (36.096) Prec@5 63.125 (66.678) Epoch: [8][9450/11272] Time 0.795 (0.833) Data 0.001 (0.002) Loss 2.8449 (2.6455) Prec@1 35.000 (36.095) Prec@5 65.625 (66.679) Epoch: [8][9460/11272] Time 0.884 (0.833) Data 0.001 (0.002) Loss 2.8499 (2.6455) Prec@1 31.250 (36.094) Prec@5 62.500 (66.676) Epoch: [8][9470/11272] Time 0.887 (0.833) Data 0.001 (0.002) Loss 2.5939 (2.6455) Prec@1 39.375 (36.094) Prec@5 69.375 (66.677) Epoch: [8][9480/11272] Time 0.765 (0.833) Data 0.002 (0.002) Loss 2.7437 (2.6455) Prec@1 30.000 (36.095) Prec@5 68.750 (66.680) Epoch: [8][9490/11272] Time 0.943 (0.833) Data 0.001 (0.002) Loss 2.8303 (2.6454) Prec@1 36.250 (36.096) Prec@5 60.625 (66.682) Epoch: [8][9500/11272] Time 0.820 (0.833) Data 0.002 (0.002) Loss 2.6175 (2.6454) Prec@1 35.000 (36.096) Prec@5 68.750 (66.682) Epoch: [8][9510/11272] Time 0.743 (0.833) Data 0.002 (0.002) Loss 2.5398 (2.6454) Prec@1 38.125 (36.096) Prec@5 70.625 (66.683) Epoch: [8][9520/11272] Time 0.766 (0.833) Data 0.002 (0.002) Loss 2.5860 (2.6455) Prec@1 34.375 (36.094) Prec@5 63.750 (66.680) Epoch: [8][9530/11272] Time 0.885 (0.833) Data 0.001 (0.002) Loss 2.7090 (2.6455) Prec@1 38.750 (36.095) Prec@5 66.875 (66.681) Epoch: [8][9540/11272] Time 0.901 (0.833) Data 0.002 (0.002) Loss 2.4991 (2.6455) Prec@1 37.500 (36.098) Prec@5 73.125 (66.682) Epoch: [8][9550/11272] Time 0.753 (0.833) Data 0.002 (0.002) Loss 2.8445 (2.6454) Prec@1 32.500 (36.098) Prec@5 61.250 (66.681) Epoch: [8][9560/11272] Time 0.769 (0.833) Data 0.002 (0.002) Loss 2.3729 (2.6454) Prec@1 41.250 (36.098) Prec@5 74.375 (66.680) Epoch: [8][9570/11272] Time 0.895 (0.833) Data 0.002 (0.002) Loss 2.6087 (2.6456) Prec@1 37.500 (36.097) Prec@5 65.625 (66.678) Epoch: [8][9580/11272] Time 0.945 (0.833) Data 0.002 (0.002) Loss 2.5796 (2.6456) Prec@1 40.625 (36.098) Prec@5 66.875 (66.677) Epoch: [8][9590/11272] Time 0.748 (0.833) Data 0.001 (0.002) Loss 2.9060 (2.6456) Prec@1 26.250 (36.097) Prec@5 58.125 (66.677) Epoch: [8][9600/11272] Time 0.741 (0.833) Data 0.002 (0.002) Loss 2.7007 (2.6457) Prec@1 38.125 (36.096) Prec@5 61.250 (66.677) Epoch: [8][9610/11272] Time 0.886 (0.833) Data 0.001 (0.002) Loss 2.7485 (2.6457) Prec@1 38.125 (36.096) Prec@5 64.375 (66.677) Epoch: [8][9620/11272] Time 0.735 (0.833) Data 0.004 (0.002) Loss 2.6216 (2.6458) Prec@1 34.375 (36.094) Prec@5 68.750 (66.676) Epoch: [8][9630/11272] Time 0.794 (0.833) Data 0.001 (0.002) Loss 2.8271 (2.6458) Prec@1 33.125 (36.091) Prec@5 67.500 (66.675) Epoch: [8][9640/11272] Time 0.880 (0.833) Data 0.002 (0.002) Loss 2.3227 (2.6457) Prec@1 40.000 (36.093) Prec@5 75.000 (66.677) Epoch: [8][9650/11272] Time 0.886 (0.833) Data 0.002 (0.002) Loss 2.5496 (2.6458) Prec@1 35.625 (36.092) Prec@5 70.000 (66.677) Epoch: [8][9660/11272] Time 0.754 (0.833) Data 0.002 (0.002) Loss 2.6858 (2.6458) Prec@1 34.375 (36.091) Prec@5 62.500 (66.677) Epoch: [8][9670/11272] Time 0.743 (0.833) Data 0.002 (0.002) Loss 2.8804 (2.6458) Prec@1 28.750 (36.091) Prec@5 58.125 (66.678) Epoch: [8][9680/11272] Time 0.901 (0.833) Data 0.002 (0.002) Loss 2.8611 (2.6457) Prec@1 31.875 (36.091) Prec@5 63.125 (66.678) Epoch: [8][9690/11272] Time 0.915 (0.833) Data 0.001 (0.002) Loss 2.7054 (2.6457) Prec@1 30.625 (36.092) Prec@5 65.625 (66.678) Epoch: [8][9700/11272] Time 0.739 (0.833) Data 0.002 (0.002) Loss 2.4239 (2.6457) Prec@1 40.000 (36.092) Prec@5 68.750 (66.677) Epoch: [8][9710/11272] Time 0.769 (0.833) Data 0.001 (0.002) Loss 2.8024 (2.6458) Prec@1 33.125 (36.090) Prec@5 63.750 (66.676) Epoch: [8][9720/11272] Time 0.885 (0.833) Data 0.002 (0.002) Loss 2.6847 (2.6458) Prec@1 33.750 (36.089) Prec@5 66.250 (66.677) Epoch: [8][9730/11272] Time 0.858 (0.833) Data 0.001 (0.002) Loss 2.6412 (2.6458) Prec@1 37.500 (36.090) Prec@5 69.375 (66.677) Epoch: [8][9740/11272] Time 0.733 (0.833) Data 0.002 (0.002) Loss 2.5697 (2.6458) Prec@1 35.000 (36.089) Prec@5 70.625 (66.676) Epoch: [8][9750/11272] Time 0.897 (0.833) Data 0.001 (0.002) Loss 2.7338 (2.6460) Prec@1 34.375 (36.089) Prec@5 63.125 (66.673) Epoch: [8][9760/11272] Time 0.940 (0.833) Data 0.002 (0.002) Loss 2.6363 (2.6460) Prec@1 39.375 (36.088) Prec@5 66.250 (66.672) Epoch: [8][9770/11272] Time 0.744 (0.833) Data 0.001 (0.002) Loss 2.8748 (2.6461) Prec@1 31.875 (36.087) Prec@5 65.000 (66.670) Epoch: [8][9780/11272] Time 0.741 (0.833) Data 0.002 (0.002) Loss 2.5883 (2.6460) Prec@1 37.500 (36.090) Prec@5 63.750 (66.673) Epoch: [8][9790/11272] Time 0.900 (0.833) Data 0.001 (0.002) Loss 2.7130 (2.6460) Prec@1 40.000 (36.090) Prec@5 60.625 (66.673) Epoch: [8][9800/11272] Time 0.906 (0.833) Data 0.002 (0.002) Loss 2.7483 (2.6460) Prec@1 32.500 (36.088) Prec@5 66.250 (66.671) Epoch: [8][9810/11272] Time 0.766 (0.833) Data 0.002 (0.002) Loss 2.6022 (2.6460) Prec@1 34.375 (36.089) Prec@5 69.375 (66.670) Epoch: [8][9820/11272] Time 0.769 (0.833) Data 0.004 (0.002) Loss 2.8877 (2.6460) Prec@1 32.500 (36.088) Prec@5 60.000 (66.671) Epoch: [8][9830/11272] Time 0.885 (0.833) Data 0.001 (0.002) Loss 2.4823 (2.6460) Prec@1 33.125 (36.089) Prec@5 74.375 (66.671) Epoch: [8][9840/11272] Time 0.949 (0.833) Data 0.002 (0.002) Loss 2.6961 (2.6460) Prec@1 35.000 (36.089) Prec@5 67.500 (66.671) Epoch: [8][9850/11272] Time 0.752 (0.833) Data 0.002 (0.002) Loss 2.4999 (2.6460) Prec@1 34.375 (36.088) Prec@5 67.500 (66.672) Epoch: [8][9860/11272] Time 0.773 (0.833) Data 0.002 (0.002) Loss 2.4336 (2.6460) Prec@1 40.625 (36.088) Prec@5 69.375 (66.672) Epoch: [8][9870/11272] Time 0.900 (0.833) Data 0.002 (0.002) Loss 2.9109 (2.6460) Prec@1 32.500 (36.087) Prec@5 61.250 (66.671) Epoch: [8][9880/11272] Time 0.883 (0.833) Data 0.001 (0.002) Loss 2.6019 (2.6461) Prec@1 41.250 (36.086) Prec@5 67.500 (66.669) Epoch: [8][9890/11272] Time 0.725 (0.833) Data 0.001 (0.002) Loss 2.5425 (2.6461) Prec@1 35.625 (36.087) Prec@5 65.625 (66.669) Epoch: [8][9900/11272] Time 0.923 (0.833) Data 0.002 (0.002) Loss 2.5932 (2.6460) Prec@1 35.000 (36.087) Prec@5 63.125 (66.669) Epoch: [8][9910/11272] Time 0.866 (0.833) Data 0.001 (0.002) Loss 2.6543 (2.6460) Prec@1 32.500 (36.087) Prec@5 63.750 (66.669) Epoch: [8][9920/11272] Time 0.741 (0.833) Data 0.002 (0.002) Loss 2.7203 (2.6461) Prec@1 37.500 (36.084) Prec@5 61.875 (66.665) Epoch: [8][9930/11272] Time 0.744 (0.833) Data 0.002 (0.002) Loss 2.4163 (2.6460) Prec@1 35.625 (36.086) Prec@5 72.500 (66.668) Epoch: [8][9940/11272] Time 0.884 (0.833) Data 0.002 (0.002) Loss 2.9940 (2.6460) Prec@1 28.750 (36.085) Prec@5 61.250 (66.667) Epoch: [8][9950/11272] Time 0.893 (0.833) Data 0.001 (0.002) Loss 2.6695 (2.6460) Prec@1 38.750 (36.086) Prec@5 66.875 (66.669) Epoch: [8][9960/11272] Time 0.770 (0.833) Data 0.002 (0.002) Loss 2.6452 (2.6460) Prec@1 38.750 (36.085) Prec@5 70.625 (66.669) Epoch: [8][9970/11272] Time 0.753 (0.833) Data 0.001 (0.002) Loss 2.6308 (2.6460) Prec@1 35.000 (36.085) Prec@5 66.875 (66.668) Epoch: [8][9980/11272] Time 0.925 (0.833) Data 0.001 (0.002) Loss 2.5395 (2.6460) Prec@1 38.125 (36.085) Prec@5 70.000 (66.670) Epoch: [8][9990/11272] Time 0.979 (0.833) Data 0.002 (0.002) Loss 2.7382 (2.6460) Prec@1 33.750 (36.083) Prec@5 66.250 (66.670) Epoch: [8][10000/11272] Time 0.734 (0.833) Data 0.001 (0.002) Loss 2.8335 (2.6460) Prec@1 34.375 (36.085) Prec@5 61.875 (66.669) Epoch: [8][10010/11272] Time 0.798 (0.833) Data 0.002 (0.002) Loss 2.8524 (2.6461) Prec@1 31.250 (36.085) Prec@5 63.750 (66.669) Epoch: [8][10020/11272] Time 0.920 (0.833) Data 0.002 (0.002) Loss 2.8061 (2.6461) Prec@1 34.375 (36.081) Prec@5 66.250 (66.669) Epoch: [8][10030/11272] Time 0.758 (0.833) Data 0.001 (0.002) Loss 2.3965 (2.6460) Prec@1 41.875 (36.081) Prec@5 70.000 (66.670) Epoch: [8][10040/11272] Time 0.752 (0.833) Data 0.001 (0.002) Loss 2.6734 (2.6460) Prec@1 36.875 (36.082) Prec@5 65.000 (66.669) Epoch: [8][10050/11272] Time 0.918 (0.833) Data 0.002 (0.002) Loss 2.9645 (2.6461) Prec@1 27.500 (36.079) Prec@5 61.875 (66.667) Epoch: [8][10060/11272] Time 0.915 (0.833) Data 0.002 (0.002) Loss 2.7278 (2.6461) Prec@1 33.750 (36.080) Prec@5 68.125 (66.666) Epoch: [8][10070/11272] Time 0.743 (0.833) Data 0.002 (0.002) Loss 2.6387 (2.6460) Prec@1 31.250 (36.081) Prec@5 67.500 (66.668) Epoch: [8][10080/11272] Time 0.775 (0.833) Data 0.002 (0.002) Loss 2.5525 (2.6460) Prec@1 38.750 (36.082) Prec@5 67.500 (66.670) Epoch: [8][10090/11272] Time 0.878 (0.833) Data 0.001 (0.002) Loss 2.6248 (2.6460) Prec@1 38.125 (36.083) Prec@5 66.250 (66.668) Epoch: [8][10100/11272] Time 0.929 (0.833) Data 0.002 (0.002) Loss 2.6997 (2.6461) Prec@1 33.750 (36.084) Prec@5 66.875 (66.668) Epoch: [8][10110/11272] Time 0.781 (0.833) Data 0.002 (0.002) Loss 2.5105 (2.6461) Prec@1 37.500 (36.081) Prec@5 70.625 (66.668) Epoch: [8][10120/11272] Time 0.729 (0.833) Data 0.002 (0.002) Loss 2.9613 (2.6461) Prec@1 31.875 (36.081) Prec@5 64.375 (66.669) Epoch: [8][10130/11272] Time 0.910 (0.833) Data 0.001 (0.002) Loss 2.7146 (2.6461) Prec@1 35.625 (36.080) Prec@5 65.000 (66.670) Epoch: [8][10140/11272] Time 0.920 (0.833) Data 0.002 (0.002) Loss 2.9175 (2.6462) Prec@1 36.875 (36.080) Prec@5 62.500 (66.668) Epoch: [8][10150/11272] Time 0.772 (0.833) Data 0.001 (0.002) Loss 2.7712 (2.6462) Prec@1 33.750 (36.079) Prec@5 62.500 (66.669) Epoch: [8][10160/11272] Time 0.896 (0.833) Data 0.002 (0.002) Loss 2.5306 (2.6463) Prec@1 36.875 (36.078) Prec@5 66.250 (66.669) Epoch: [8][10170/11272] Time 0.915 (0.833) Data 0.001 (0.002) Loss 2.5768 (2.6463) Prec@1 38.125 (36.078) Prec@5 72.500 (66.667) Epoch: [8][10180/11272] Time 0.755 (0.833) Data 0.001 (0.002) Loss 2.5788 (2.6462) Prec@1 38.125 (36.080) Prec@5 65.625 (66.668) Epoch: [8][10190/11272] Time 0.754 (0.833) Data 0.002 (0.002) Loss 2.7372 (2.6462) Prec@1 34.375 (36.079) Prec@5 68.125 (66.669) Epoch: [8][10200/11272] Time 0.959 (0.833) Data 0.001 (0.002) Loss 2.6141 (2.6461) Prec@1 38.750 (36.082) Prec@5 66.875 (66.671) Epoch: [8][10210/11272] Time 0.889 (0.833) Data 0.001 (0.002) Loss 2.4196 (2.6461) Prec@1 37.500 (36.083) Prec@5 69.375 (66.672) Epoch: [8][10220/11272] Time 0.735 (0.833) Data 0.002 (0.002) Loss 2.6576 (2.6461) Prec@1 33.125 (36.082) Prec@5 69.375 (66.671) Epoch: [8][10230/11272] Time 0.720 (0.833) Data 0.002 (0.002) Loss 2.5049 (2.6462) Prec@1 33.125 (36.081) Prec@5 70.625 (66.670) Epoch: [8][10240/11272] Time 0.892 (0.833) Data 0.002 (0.002) Loss 2.5170 (2.6461) Prec@1 35.625 (36.082) Prec@5 67.500 (66.671) Epoch: [8][10250/11272] Time 0.969 (0.833) Data 0.001 (0.002) Loss 2.7921 (2.6461) Prec@1 31.875 (36.082) Prec@5 68.750 (66.672) Epoch: [8][10260/11272] Time 0.779 (0.833) Data 0.001 (0.002) Loss 2.3826 (2.6460) Prec@1 40.625 (36.083) Prec@5 70.000 (66.671) Epoch: [8][10270/11272] Time 0.726 (0.833) Data 0.001 (0.002) Loss 2.4793 (2.6460) Prec@1 35.000 (36.085) Prec@5 72.500 (66.672) Epoch: [8][10280/11272] Time 0.887 (0.833) Data 0.002 (0.002) Loss 2.6488 (2.6460) Prec@1 33.125 (36.085) Prec@5 67.500 (66.672) Epoch: [8][10290/11272] Time 0.746 (0.833) Data 0.004 (0.002) Loss 2.6814 (2.6459) Prec@1 36.875 (36.086) Prec@5 67.500 (66.673) Epoch: [8][10300/11272] Time 0.753 (0.833) Data 0.001 (0.002) Loss 2.6490 (2.6459) Prec@1 37.500 (36.087) Prec@5 68.750 (66.673) Epoch: [8][10310/11272] Time 0.858 (0.833) Data 0.002 (0.002) Loss 2.5601 (2.6459) Prec@1 30.000 (36.086) Prec@5 66.875 (66.672) Epoch: [8][10320/11272] Time 0.880 (0.833) Data 0.001 (0.002) Loss 2.5410 (2.6459) Prec@1 36.250 (36.087) Prec@5 68.125 (66.671) Epoch: [8][10330/11272] Time 0.774 (0.833) Data 0.002 (0.002) Loss 2.6925 (2.6459) Prec@1 35.625 (36.088) Prec@5 66.875 (66.670) Epoch: [8][10340/11272] Time 0.731 (0.833) Data 0.002 (0.002) Loss 2.3811 (2.6459) Prec@1 37.500 (36.090) Prec@5 75.000 (66.671) Epoch: [8][10350/11272] Time 0.929 (0.833) Data 0.002 (0.002) Loss 2.6128 (2.6459) Prec@1 38.750 (36.091) Prec@5 68.750 (66.671) Epoch: [8][10360/11272] Time 0.927 (0.833) Data 0.001 (0.002) Loss 2.8580 (2.6460) Prec@1 33.750 (36.090) Prec@5 62.500 (66.668) Epoch: [8][10370/11272] Time 0.784 (0.833) Data 0.002 (0.002) Loss 2.7381 (2.6461) Prec@1 34.375 (36.089) Prec@5 65.000 (66.667) Epoch: [8][10380/11272] Time 0.812 (0.833) Data 0.001 (0.002) Loss 2.8301 (2.6460) Prec@1 30.625 (36.088) Prec@5 65.000 (66.668) Epoch: [8][10390/11272] Time 0.895 (0.833) Data 0.002 (0.002) Loss 2.5408 (2.6460) Prec@1 38.750 (36.088) Prec@5 71.875 (66.667) Epoch: [8][10400/11272] Time 0.847 (0.833) Data 0.001 (0.002) Loss 2.8539 (2.6460) Prec@1 30.000 (36.089) Prec@5 63.750 (66.668) Epoch: [8][10410/11272] Time 0.773 (0.833) Data 0.002 (0.002) Loss 2.3179 (2.6459) Prec@1 38.125 (36.089) Prec@5 73.125 (66.669) Epoch: [8][10420/11272] Time 0.832 (0.833) Data 0.002 (0.002) Loss 2.7583 (2.6459) Prec@1 32.500 (36.090) Prec@5 64.375 (66.668) Epoch: [8][10430/11272] Time 0.975 (0.833) Data 0.002 (0.002) Loss 2.5796 (2.6458) Prec@1 40.625 (36.092) Prec@5 71.875 (66.670) Epoch: [8][10440/11272] Time 0.732 (0.833) Data 0.002 (0.002) Loss 2.5395 (2.6459) Prec@1 36.250 (36.089) Prec@5 71.250 (66.669) Epoch: [8][10450/11272] Time 0.825 (0.833) Data 0.002 (0.002) Loss 2.7208 (2.6459) Prec@1 35.000 (36.090) Prec@5 70.625 (66.669) Epoch: [8][10460/11272] Time 0.853 (0.833) Data 0.001 (0.002) Loss 2.6484 (2.6459) Prec@1 35.625 (36.091) Prec@5 66.875 (66.671) Epoch: [8][10470/11272] Time 0.929 (0.833) Data 0.002 (0.002) Loss 2.6073 (2.6458) Prec@1 40.000 (36.092) Prec@5 70.000 (66.673) Epoch: [8][10480/11272] Time 0.759 (0.833) Data 0.001 (0.002) Loss 2.6415 (2.6458) Prec@1 31.875 (36.093) Prec@5 66.875 (66.671) Epoch: [8][10490/11272] Time 0.774 (0.833) Data 0.002 (0.002) Loss 2.4861 (2.6459) Prec@1 38.750 (36.093) Prec@5 69.375 (66.669) Epoch: [8][10500/11272] Time 0.908 (0.833) Data 0.001 (0.002) Loss 2.5558 (2.6459) Prec@1 32.500 (36.093) Prec@5 74.375 (66.669) Epoch: [8][10510/11272] Time 0.877 (0.833) Data 0.002 (0.002) Loss 2.4422 (2.6459) Prec@1 38.750 (36.092) Prec@5 71.250 (66.669) Epoch: [8][10520/11272] Time 0.781 (0.833) Data 0.002 (0.002) Loss 2.9261 (2.6459) Prec@1 32.500 (36.094) Prec@5 60.625 (66.669) Epoch: [8][10530/11272] Time 0.740 (0.833) Data 0.002 (0.002) Loss 2.6862 (2.6458) Prec@1 30.625 (36.094) Prec@5 68.125 (66.671) Epoch: [8][10540/11272] Time 0.890 (0.833) Data 0.001 (0.002) Loss 2.6431 (2.6458) Prec@1 36.875 (36.092) Prec@5 64.375 (66.669) Epoch: [8][10550/11272] Time 0.772 (0.833) Data 0.005 (0.002) Loss 2.7779 (2.6458) Prec@1 31.875 (36.093) Prec@5 61.875 (66.669) Epoch: [8][10560/11272] Time 0.730 (0.833) Data 0.002 (0.002) Loss 2.6009 (2.6457) Prec@1 35.625 (36.093) Prec@5 68.750 (66.672) Epoch: [8][10570/11272] Time 0.904 (0.833) Data 0.001 (0.002) Loss 2.6484 (2.6457) Prec@1 34.375 (36.092) Prec@5 68.750 (66.671) Epoch: [8][10580/11272] Time 0.867 (0.833) Data 0.001 (0.002) Loss 2.5895 (2.6458) Prec@1 36.250 (36.090) Prec@5 68.125 (66.670) Epoch: [8][10590/11272] Time 0.809 (0.833) Data 0.002 (0.002) Loss 2.5197 (2.6457) Prec@1 34.375 (36.091) Prec@5 66.875 (66.672) Epoch: [8][10600/11272] Time 0.737 (0.833) Data 0.002 (0.002) Loss 2.5023 (2.6455) Prec@1 42.500 (36.093) Prec@5 67.500 (66.675) Epoch: [8][10610/11272] Time 0.890 (0.833) Data 0.002 (0.002) Loss 2.4548 (2.6455) Prec@1 40.625 (36.092) Prec@5 70.625 (66.676) Epoch: [8][10620/11272] Time 0.878 (0.833) Data 0.001 (0.002) Loss 2.6336 (2.6455) Prec@1 42.500 (36.093) Prec@5 71.250 (66.677) Epoch: [8][10630/11272] Time 0.753 (0.833) Data 0.002 (0.002) Loss 2.8103 (2.6455) Prec@1 33.125 (36.093) Prec@5 66.875 (66.677) Epoch: [8][10640/11272] Time 0.732 (0.833) Data 0.002 (0.002) Loss 2.8447 (2.6456) Prec@1 33.750 (36.091) Prec@5 61.875 (66.675) Epoch: [8][10650/11272] Time 0.968 (0.833) Data 0.002 (0.002) Loss 2.4966 (2.6455) Prec@1 37.500 (36.094) Prec@5 71.250 (66.677) Epoch: [8][10660/11272] Time 0.886 (0.833) Data 0.001 (0.002) Loss 2.7983 (2.6456) Prec@1 34.375 (36.093) Prec@5 63.750 (66.675) Epoch: [8][10670/11272] Time 0.802 (0.833) Data 0.002 (0.002) Loss 2.7298 (2.6456) Prec@1 33.125 (36.092) Prec@5 66.250 (66.675) Epoch: [8][10680/11272] Time 0.843 (0.833) Data 0.001 (0.002) Loss 2.6695 (2.6456) Prec@1 38.125 (36.092) Prec@5 70.625 (66.676) Epoch: [8][10690/11272] Time 0.938 (0.833) Data 0.002 (0.002) Loss 2.6973 (2.6456) Prec@1 32.500 (36.092) Prec@5 66.250 (66.675) Epoch: [8][10700/11272] Time 0.731 (0.833) Data 0.002 (0.002) Loss 2.6230 (2.6456) Prec@1 36.875 (36.092) Prec@5 68.750 (66.676) Epoch: [8][10710/11272] Time 0.762 (0.833) Data 0.002 (0.002) Loss 2.6656 (2.6457) Prec@1 38.750 (36.092) Prec@5 65.625 (66.676) Epoch: [8][10720/11272] Time 0.938 (0.833) Data 0.001 (0.002) Loss 2.6721 (2.6457) Prec@1 30.000 (36.091) Prec@5 68.750 (66.676) Epoch: [8][10730/11272] Time 0.913 (0.833) Data 0.002 (0.002) Loss 2.8548 (2.6457) Prec@1 31.875 (36.089) Prec@5 63.750 (66.675) Epoch: [8][10740/11272] Time 0.743 (0.833) Data 0.002 (0.002) Loss 2.5682 (2.6457) Prec@1 37.500 (36.088) Prec@5 67.500 (66.674) Epoch: [8][10750/11272] Time 0.802 (0.833) Data 0.002 (0.002) Loss 2.9618 (2.6458) Prec@1 27.500 (36.087) Prec@5 62.500 (66.673) Epoch: [8][10760/11272] Time 0.904 (0.833) Data 0.001 (0.002) Loss 2.7277 (2.6458) Prec@1 34.375 (36.087) Prec@5 65.000 (66.673) Epoch: [8][10770/11272] Time 0.919 (0.833) Data 0.002 (0.002) Loss 2.7863 (2.6458) Prec@1 38.125 (36.087) Prec@5 60.000 (66.671) Epoch: [8][10780/11272] Time 0.789 (0.833) Data 0.001 (0.002) Loss 2.6573 (2.6458) Prec@1 34.375 (36.087) Prec@5 63.750 (66.671) Epoch: [8][10790/11272] Time 0.790 (0.833) Data 0.002 (0.002) Loss 2.7181 (2.6458) Prec@1 36.250 (36.087) Prec@5 66.250 (66.672) Epoch: [8][10800/11272] Time 0.888 (0.833) Data 0.001 (0.002) Loss 2.5716 (2.6457) Prec@1 43.750 (36.087) Prec@5 70.000 (66.672) Epoch: [8][10810/11272] Time 0.943 (0.833) Data 0.002 (0.002) Loss 2.7914 (2.6458) Prec@1 39.375 (36.087) Prec@5 60.000 (66.671) Epoch: [8][10820/11272] Time 0.746 (0.833) Data 0.001 (0.002) Loss 2.4657 (2.6457) Prec@1 38.125 (36.088) Prec@5 71.250 (66.674) Epoch: [8][10830/11272] Time 0.948 (0.833) Data 0.002 (0.002) Loss 2.8327 (2.6458) Prec@1 25.000 (36.086) Prec@5 64.375 (66.673) Epoch: [8][10840/11272] Time 0.875 (0.833) Data 0.001 (0.002) Loss 2.9361 (2.6458) Prec@1 28.125 (36.085) Prec@5 61.875 (66.672) Epoch: [8][10850/11272] Time 0.771 (0.833) Data 0.001 (0.002) Loss 2.6934 (2.6458) Prec@1 34.375 (36.084) Prec@5 68.125 (66.672) Epoch: [8][10860/11272] Time 0.764 (0.833) Data 0.002 (0.002) Loss 2.6816 (2.6458) Prec@1 34.375 (36.082) Prec@5 66.875 (66.672) Epoch: [8][10870/11272] Time 0.985 (0.833) Data 0.002 (0.002) Loss 2.7429 (2.6458) Prec@1 31.875 (36.082) Prec@5 62.500 (66.673) Epoch: [8][10880/11272] Time 0.867 (0.833) Data 0.003 (0.002) Loss 2.8047 (2.6458) Prec@1 31.875 (36.081) Prec@5 63.125 (66.672) Epoch: [8][10890/11272] Time 0.782 (0.833) Data 0.002 (0.002) Loss 2.6630 (2.6458) Prec@1 39.375 (36.082) Prec@5 62.500 (66.673) Epoch: [8][10900/11272] Time 0.723 (0.833) Data 0.001 (0.002) Loss 2.6066 (2.6458) Prec@1 36.250 (36.082) Prec@5 68.125 (66.672) Epoch: [8][10910/11272] Time 0.892 (0.833) Data 0.002 (0.002) Loss 2.7055 (2.6458) Prec@1 34.375 (36.082) Prec@5 68.125 (66.673) Epoch: [8][10920/11272] Time 0.877 (0.833) Data 0.001 (0.002) Loss 2.6160 (2.6457) Prec@1 38.125 (36.082) Prec@5 66.875 (66.674) Epoch: [8][10930/11272] Time 0.851 (0.833) Data 0.002 (0.002) Loss 2.3581 (2.6457) Prec@1 42.500 (36.084) Prec@5 69.375 (66.675) Epoch: [8][10940/11272] Time 0.773 (0.833) Data 0.002 (0.002) Loss 2.6150 (2.6457) Prec@1 35.625 (36.085) Prec@5 65.000 (66.673) Epoch: [8][10950/11272] Time 0.966 (0.833) Data 0.003 (0.002) Loss 2.4814 (2.6457) Prec@1 35.625 (36.086) Prec@5 71.875 (66.673) Epoch: [8][10960/11272] Time 0.727 (0.833) Data 0.002 (0.002) Loss 2.5423 (2.6457) Prec@1 38.750 (36.086) Prec@5 68.750 (66.675) Epoch: [8][10970/11272] Time 0.768 (0.833) Data 0.002 (0.002) Loss 2.6143 (2.6458) Prec@1 35.625 (36.084) Prec@5 66.875 (66.673) Epoch: [8][10980/11272] Time 0.932 (0.833) Data 0.001 (0.002) Loss 2.7065 (2.6458) Prec@1 34.375 (36.084) Prec@5 64.375 (66.672) Epoch: [8][10990/11272] Time 0.947 (0.833) Data 0.002 (0.002) Loss 2.9088 (2.6459) Prec@1 32.500 (36.082) Prec@5 58.125 (66.670) Epoch: [8][11000/11272] Time 0.767 (0.833) Data 0.001 (0.002) Loss 2.6525 (2.6459) Prec@1 33.125 (36.081) Prec@5 61.875 (66.669) Epoch: [8][11010/11272] Time 0.758 (0.833) Data 0.002 (0.002) Loss 2.7452 (2.6460) Prec@1 38.125 (36.079) Prec@5 62.500 (66.667) Epoch: [8][11020/11272] Time 0.907 (0.833) Data 0.001 (0.002) Loss 2.2657 (2.6459) Prec@1 43.750 (36.080) Prec@5 74.375 (66.670) Epoch: [8][11030/11272] Time 0.922 (0.833) Data 0.002 (0.002) Loss 2.7554 (2.6459) Prec@1 38.125 (36.080) Prec@5 61.250 (66.671) Epoch: [8][11040/11272] Time 0.732 (0.833) Data 0.001 (0.002) Loss 2.4032 (2.6459) Prec@1 43.125 (36.080) Prec@5 69.375 (66.671) Epoch: [8][11050/11272] Time 0.773 (0.833) Data 0.002 (0.002) Loss 2.6220 (2.6458) Prec@1 38.125 (36.082) Prec@5 66.875 (66.672) Epoch: [8][11060/11272] Time 0.865 (0.833) Data 0.001 (0.002) Loss 2.6660 (2.6458) Prec@1 36.250 (36.082) Prec@5 67.500 (66.671) Epoch: [8][11070/11272] Time 0.932 (0.833) Data 0.002 (0.002) Loss 2.6509 (2.6458) Prec@1 33.750 (36.083) Prec@5 66.875 (66.672) Epoch: [8][11080/11272] Time 0.728 (0.833) Data 0.001 (0.002) Loss 2.2612 (2.6457) Prec@1 42.500 (36.086) Prec@5 72.500 (66.674) Epoch: [8][11090/11272] Time 0.955 (0.833) Data 0.002 (0.002) Loss 2.4906 (2.6457) Prec@1 41.875 (36.087) Prec@5 66.250 (66.675) Epoch: [8][11100/11272] Time 0.870 (0.833) Data 0.001 (0.002) Loss 2.5217 (2.6457) Prec@1 40.625 (36.087) Prec@5 70.000 (66.676) Epoch: [8][11110/11272] Time 0.803 (0.833) Data 0.002 (0.002) Loss 2.5434 (2.6456) Prec@1 35.625 (36.086) Prec@5 73.750 (66.677) Epoch: [8][11120/11272] Time 0.719 (0.833) Data 0.001 (0.002) Loss 2.7721 (2.6457) Prec@1 35.625 (36.084) Prec@5 65.000 (66.676) Epoch: [8][11130/11272] Time 0.898 (0.833) Data 0.002 (0.002) Loss 2.4887 (2.6457) Prec@1 44.375 (36.084) Prec@5 70.000 (66.677) Epoch: [8][11140/11272] Time 0.873 (0.833) Data 0.001 (0.002) Loss 2.7785 (2.6457) Prec@1 33.125 (36.081) Prec@5 66.250 (66.677) Epoch: [8][11150/11272] Time 0.792 (0.833) Data 0.002 (0.002) Loss 2.7331 (2.6457) Prec@1 39.375 (36.083) Prec@5 62.500 (66.678) Epoch: [8][11160/11272] Time 0.734 (0.833) Data 0.001 (0.002) Loss 2.4256 (2.6456) Prec@1 41.875 (36.084) Prec@5 67.500 (66.680) Epoch: [8][11170/11272] Time 0.937 (0.833) Data 0.002 (0.002) Loss 2.6172 (2.6457) Prec@1 37.500 (36.082) Prec@5 64.375 (66.678) Epoch: [8][11180/11272] Time 0.841 (0.833) Data 0.001 (0.002) Loss 2.8759 (2.6458) Prec@1 28.125 (36.081) Prec@5 62.500 (66.677) Epoch: [8][11190/11272] Time 0.823 (0.833) Data 0.002 (0.002) Loss 2.2611 (2.6458) Prec@1 42.500 (36.081) Prec@5 75.625 (66.677) Epoch: [8][11200/11272] Time 0.722 (0.833) Data 0.001 (0.002) Loss 2.5712 (2.6458) Prec@1 40.000 (36.083) Prec@5 70.000 (66.678) Epoch: [8][11210/11272] Time 0.932 (0.833) Data 0.002 (0.002) Loss 2.6991 (2.6459) Prec@1 33.750 (36.081) Prec@5 65.000 (66.676) Epoch: [8][11220/11272] Time 0.743 (0.833) Data 0.004 (0.002) Loss 2.6843 (2.6459) Prec@1 36.875 (36.080) Prec@5 63.750 (66.677) Epoch: [8][11230/11272] Time 0.794 (0.833) Data 0.002 (0.002) Loss 2.5654 (2.6459) Prec@1 39.375 (36.081) Prec@5 71.875 (66.677) Epoch: [8][11240/11272] Time 0.918 (0.833) Data 0.001 (0.002) Loss 2.6922 (2.6459) Prec@1 32.500 (36.080) Prec@5 69.375 (66.677) Epoch: [8][11250/11272] Time 0.902 (0.833) Data 0.002 (0.002) Loss 2.7175 (2.6459) Prec@1 30.625 (36.079) Prec@5 65.000 (66.677) Epoch: [8][11260/11272] Time 0.751 (0.833) Data 0.001 (0.002) Loss 2.4254 (2.6459) Prec@1 39.375 (36.077) Prec@5 68.125 (66.677) Epoch: [8][11270/11272] Time 0.792 (0.833) Data 0.000 (0.002) Loss 2.4848 (2.6459) Prec@1 40.000 (36.078) Prec@5 67.500 (66.677) Test: [0/229] Time 1.848 (1.848) Loss 1.4495 (1.4495) Prec@1 58.125 (58.125) Prec@5 90.625 (90.625) Test: [10/229] Time 0.394 (0.530) Loss 1.3775 (2.2317) Prec@1 58.750 (44.773) Prec@5 91.250 (75.852) Test: [20/229] Time 0.436 (0.474) Loss 2.5206 (2.3354) Prec@1 37.500 (41.488) Prec@5 71.250 (73.542) Test: [30/229] Time 0.363 (0.451) Loss 2.3059 (2.2202) Prec@1 30.000 (44.456) Prec@5 78.750 (74.839) Test: [40/229] Time 0.474 (0.442) Loss 0.8035 (2.1948) Prec@1 83.125 (44.665) Prec@5 90.000 (75.091) Test: [50/229] Time 0.363 (0.431) Loss 2.7112 (2.2085) Prec@1 23.125 (44.718) Prec@5 66.875 (74.608) Test: [60/229] Time 0.443 (0.427) Loss 2.9510 (2.2450) Prec@1 20.625 (43.945) Prec@5 60.000 (73.924) Test: [70/229] Time 0.365 (0.421) Loss 2.1814 (2.2645) Prec@1 40.000 (43.099) Prec@5 76.250 (73.829) Test: [80/229] Time 0.346 (0.420) Loss 2.3541 (2.2923) Prec@1 38.125 (42.153) Prec@5 77.500 (73.873) Test: [90/229] Time 0.472 (0.418) Loss 2.0372 (2.2907) Prec@1 53.750 (42.232) Prec@5 76.875 (73.935) Test: [100/229] Time 0.355 (0.416) Loss 2.3389 (2.2826) Prec@1 43.125 (42.525) Prec@5 76.250 (74.208) Test: [110/229] Time 0.425 (0.415) Loss 2.0433 (2.2707) Prec@1 44.375 (42.691) Prec@5 77.500 (74.279) Test: [120/229] Time 0.361 (0.414) Loss 3.5300 (2.2949) Prec@1 15.625 (42.076) Prec@5 51.250 (73.802) Test: [130/229] Time 0.467 (0.414) Loss 1.5476 (2.2763) Prec@1 58.750 (42.428) Prec@5 89.375 (74.251) Test: [140/229] Time 0.360 (0.413) Loss 2.6241 (2.2923) Prec@1 23.750 (41.848) Prec@5 73.750 (74.060) Test: [150/229] Time 0.343 (0.412) Loss 1.3915 (2.2976) Prec@1 66.250 (41.796) Prec@5 86.875 (74.056) Test: [160/229] Time 0.444 (0.411) Loss 2.4100 (2.2984) Prec@1 44.375 (41.766) Prec@5 76.875 (74.053) Test: [170/229] Time 0.383 (0.409) Loss 2.7865 (2.3110) Prec@1 30.000 (41.352) Prec@5 65.000 (73.882) Test: [180/229] Time 0.437 (0.408) Loss 2.4675 (2.3210) Prec@1 25.000 (41.271) Prec@5 71.875 (73.629) Test: [190/229] Time 0.354 (0.408) Loss 2.3913 (2.3256) Prec@1 33.750 (41.214) Prec@5 83.125 (73.609) Test: [200/229] Time 0.476 (0.408) Loss 1.5334 (2.3269) Prec@1 58.125 (41.014) Prec@5 86.250 (73.797) Test: [210/229] Time 0.449 (0.408) Loss 1.9733 (2.3097) Prec@1 50.625 (41.466) Prec@5 80.000 (74.064) Test: [220/229] Time 0.446 (0.408) Loss 1.9803 (2.2947) Prec@1 46.875 (41.810) Prec@5 81.875 (74.228) * Prec@1 42.051 Prec@5 74.274 Epoch: [9][0/11272] Time 3.091 (3.091) Data 2.241 (2.241) Loss 2.6175 (2.6175) Prec@1 36.875 (36.875) Prec@5 66.875 (66.875) Epoch: [9][10/11272] Time 0.751 (1.042) Data 0.004 (0.206) Loss 2.7032 (2.6759) Prec@1 30.000 (35.455) Prec@5 63.750 (65.170) Epoch: [9][20/11272] Time 0.921 (0.947) Data 0.001 (0.108) Loss 2.6114 (2.6799) Prec@1 41.250 (35.863) Prec@5 68.750 (65.387) Epoch: [9][30/11272] Time 0.758 (0.912) Data 0.001 (0.074) Loss 2.3991 (2.6586) Prec@1 42.500 (36.129) Prec@5 71.250 (65.968) Epoch: [9][40/11272] Time 0.751 (0.892) Data 0.002 (0.056) Loss 2.8115 (2.6637) Prec@1 38.125 (35.671) Prec@5 62.500 (66.006) Epoch: [9][50/11272] Time 0.895 (0.887) Data 0.001 (0.046) Loss 2.8719 (2.6689) Prec@1 28.125 (35.588) Prec@5 66.250 (66.066) Epoch: [9][60/11272] Time 0.873 (0.877) Data 0.002 (0.038) Loss 2.5985 (2.6514) Prec@1 35.625 (35.922) Prec@5 67.500 (66.281) Epoch: [9][70/11272] Time 0.779 (0.871) Data 0.001 (0.033) Loss 2.8197 (2.6544) Prec@1 32.500 (35.968) Prec@5 63.125 (66.224) Epoch: [9][80/11272] Time 0.773 (0.869) Data 0.002 (0.029) Loss 2.3363 (2.6344) Prec@1 43.125 (36.366) Prec@5 73.125 (66.767) Epoch: [9][90/11272] Time 0.929 (0.866) Data 0.001 (0.026) Loss 2.6737 (2.6245) Prec@1 36.250 (36.662) Prec@5 68.125 (66.944) Epoch: [9][100/11272] Time 0.925 (0.863) Data 0.002 (0.024) Loss 2.7337 (2.6211) Prec@1 33.125 (36.696) Prec@5 65.625 (66.999) Epoch: [9][110/11272] Time 0.770 (0.859) Data 0.001 (0.022) Loss 2.6364 (2.6235) Prec@1 35.000 (36.734) Prec@5 63.750 (66.937) Epoch: [9][120/11272] Time 0.754 (0.857) Data 0.002 (0.020) Loss 2.5040 (2.6226) Prec@1 39.375 (36.622) Prec@5 73.125 (67.076) Epoch: [9][130/11272] Time 0.908 (0.857) Data 0.002 (0.019) Loss 2.8432 (2.6253) Prec@1 33.125 (36.646) Prec@5 65.625 (67.047) Epoch: [9][140/11272] Time 0.810 (0.855) Data 0.001 (0.018) Loss 2.6606 (2.6291) Prec@1 35.000 (36.538) Prec@5 68.125 (67.026) Epoch: [9][150/11272] Time 0.754 (0.854) Data 0.002 (0.017) Loss 2.8564 (2.6300) Prec@1 36.875 (36.598) Prec@5 65.000 (66.978) Epoch: [9][160/11272] Time 0.917 (0.854) Data 0.001 (0.016) Loss 2.7252 (2.6315) Prec@1 33.750 (36.572) Prec@5 64.375 (66.922) Epoch: [9][170/11272] Time 0.893 (0.853) Data 0.002 (0.015) Loss 2.7043 (2.6331) Prec@1 31.250 (36.458) Prec@5 60.000 (66.842) Epoch: [9][180/11272] Time 0.784 (0.851) Data 0.001 (0.014) Loss 2.5724 (2.6327) Prec@1 35.000 (36.447) Prec@5 68.750 (66.847) Epoch: [9][190/11272] Time 0.794 (0.849) Data 0.003 (0.013) Loss 2.7581 (2.6347) Prec@1 35.000 (36.378) Prec@5 62.500 (66.800) Epoch: [9][200/11272] Time 0.895 (0.848) Data 0.001 (0.013) Loss 2.6728 (2.6374) Prec@1 30.000 (36.297) Prec@5 66.250 (66.791) Epoch: [9][210/11272] Time 0.952 (0.848) Data 0.002 (0.012) Loss 2.7190 (2.6373) Prec@1 34.375 (36.383) Prec@5 65.000 (66.798) Epoch: [9][220/11272] Time 0.733 (0.846) Data 0.002 (0.012) Loss 2.6387 (2.6346) Prec@1 38.125 (36.530) Prec@5 67.500 (66.821) Epoch: [9][230/11272] Time 0.772 (0.845) Data 0.002 (0.011) Loss 2.6608 (2.6339) Prec@1 34.375 (36.548) Prec@5 66.250 (66.843) Epoch: [9][240/11272] Time 0.882 (0.845) Data 0.001 (0.011) Loss 2.5513 (2.6292) Prec@1 38.750 (36.598) Prec@5 66.875 (66.919) Epoch: [9][250/11272] Time 0.934 (0.844) Data 0.002 (0.011) Loss 2.6697 (2.6281) Prec@1 36.875 (36.594) Prec@5 66.875 (66.962) Epoch: [9][260/11272] Time 0.765 (0.844) Data 0.001 (0.010) Loss 2.7907 (2.6301) Prec@1 35.000 (36.523) Prec@5 62.500 (66.906) Epoch: [9][270/11272] Time 0.823 (0.843) Data 0.002 (0.010) Loss 2.5400 (2.6295) Prec@1 36.250 (36.515) Prec@5 70.000 (66.986) Epoch: [9][280/11272] Time 0.899 (0.843) Data 0.001 (0.010) Loss 2.6664 (2.6279) Prec@1 38.125 (36.530) Prec@5 66.250 (67.044) Epoch: [9][290/11272] Time 0.791 (0.842) Data 0.004 (0.009) Loss 2.7025 (2.6281) Prec@1 33.125 (36.495) Prec@5 68.750 (67.038) Epoch: [9][300/11272] Time 0.729 (0.841) Data 0.002 (0.009) Loss 2.6583 (2.6294) Prec@1 26.875 (36.470) Prec@5 63.125 (66.977) Epoch: [9][310/11272] Time 0.935 (0.841) Data 0.002 (0.009) Loss 2.6371 (2.6291) Prec@1 34.375 (36.479) Prec@5 65.625 (66.982) Epoch: [9][320/11272] Time 0.885 (0.841) Data 0.002 (0.009) Loss 2.5671 (2.6293) Prec@1 38.750 (36.460) Prec@5 71.875 (67.002) Epoch: [9][330/11272] Time 0.799 (0.841) Data 0.001 (0.008) Loss 2.6270 (2.6317) Prec@1 39.375 (36.384) Prec@5 65.000 (66.968) Epoch: [9][340/11272] Time 0.735 (0.840) Data 0.001 (0.008) Loss 2.6738 (2.6315) Prec@1 34.375 (36.387) Prec@5 65.000 (66.948) Epoch: [9][350/11272] Time 0.873 (0.840) Data 0.002 (0.008) Loss 2.8038 (2.6315) Prec@1 34.375 (36.359) Prec@5 62.500 (66.948) Epoch: [9][360/11272] Time 0.862 (0.840) Data 0.001 (0.008) Loss 2.8229 (2.6315) Prec@1 29.375 (36.338) Prec@5 60.625 (66.951) Epoch: [9][370/11272] Time 0.751 (0.840) Data 0.002 (0.008) Loss 2.5385 (2.6294) Prec@1 39.375 (36.386) Prec@5 70.000 (66.998) Epoch: [9][380/11272] Time 0.762 (0.840) Data 0.003 (0.008) Loss 2.7492 (2.6282) Prec@1 32.500 (36.437) Prec@5 63.750 (67.006) Epoch: [9][390/11272] Time 0.909 (0.839) Data 0.002 (0.007) Loss 2.7696 (2.6300) Prec@1 31.250 (36.402) Prec@5 65.000 (66.979) Epoch: [9][400/11272] Time 0.910 (0.839) Data 0.001 (0.007) Loss 2.6662 (2.6305) Prec@1 31.875 (36.351) Prec@5 68.750 (67.014) Epoch: [9][410/11272] Time 0.787 (0.839) Data 0.002 (0.007) Loss 2.3950 (2.6296) Prec@1 39.375 (36.364) Prec@5 66.250 (67.045) Epoch: [9][420/11272] Time 0.809 (0.839) Data 0.001 (0.007) Loss 2.6871 (2.6280) Prec@1 29.375 (36.360) Prec@5 60.000 (67.044) Epoch: [9][430/11272] Time 0.911 (0.838) Data 0.002 (0.007) Loss 2.6568 (2.6280) Prec@1 38.125 (36.394) Prec@5 64.375 (67.040) Epoch: [9][440/11272] Time 0.756 (0.838) Data 0.002 (0.007) Loss 2.5600 (2.6271) Prec@1 37.500 (36.385) Prec@5 67.500 (67.049) Epoch: [9][450/11272] Time 0.740 (0.838) Data 0.001 (0.007) Loss 2.6345 (2.6294) Prec@1 38.750 (36.355) Prec@5 70.000 (67.014) Epoch: [9][460/11272] Time 0.863 (0.838) Data 0.001 (0.007) Loss 2.5930 (2.6290) Prec@1 33.125 (36.356) Prec@5 63.750 (66.998) Epoch: [9][470/11272] Time 0.887 (0.837) Data 0.002 (0.006) Loss 2.6578 (2.6292) Prec@1 35.625 (36.361) Prec@5 66.875 (66.993) Epoch: [9][480/11272] Time 0.749 (0.837) Data 0.002 (0.006) Loss 2.8367 (2.6313) Prec@1 35.625 (36.337) Prec@5 63.125 (66.962) Epoch: [9][490/11272] Time 0.750 (0.837) Data 0.002 (0.006) Loss 2.6572 (2.6305) Prec@1 35.625 (36.337) Prec@5 66.875 (66.987) Epoch: [9][500/11272] Time 0.892 (0.837) Data 0.002 (0.006) Loss 2.5555 (2.6297) Prec@1 38.125 (36.365) Prec@5 66.250 (66.990) Epoch: [9][510/11272] Time 0.914 (0.837) Data 0.002 (0.006) Loss 2.6570 (2.6311) Prec@1 29.375 (36.328) Prec@5 68.125 (66.948) Epoch: [9][520/11272] Time 0.778 (0.837) Data 0.001 (0.006) Loss 2.5067 (2.6302) Prec@1 39.375 (36.359) Prec@5 70.000 (66.985) Epoch: [9][530/11272] Time 0.734 (0.837) Data 0.002 (0.006) Loss 2.7061 (2.6314) Prec@1 38.750 (36.343) Prec@5 68.750 (66.969) Epoch: [9][540/11272] Time 0.868 (0.837) Data 0.001 (0.006) Loss 2.5637 (2.6317) Prec@1 40.625 (36.329) Prec@5 70.625 (66.982) Epoch: [9][550/11272] Time 0.734 (0.837) Data 0.004 (0.006) Loss 2.7452 (2.6327) Prec@1 34.375 (36.321) Prec@5 67.500 (66.974) Epoch: [9][560/11272] Time 0.806 (0.837) Data 0.001 (0.006) Loss 2.6829 (2.6335) Prec@1 35.000 (36.307) Prec@5 60.625 (66.960) Epoch: [9][570/11272] Time 0.880 (0.837) Data 0.002 (0.006) Loss 2.5510 (2.6334) Prec@1 35.625 (36.307) Prec@5 66.875 (66.968) Epoch: [9][580/11272] Time 0.907 (0.837) Data 0.001 (0.005) Loss 2.3472 (2.6327) Prec@1 41.250 (36.316) Prec@5 71.875 (66.952) Epoch: [9][590/11272] Time 0.738 (0.837) Data 0.002 (0.005) Loss 2.6373 (2.6341) Prec@1 36.250 (36.272) Prec@5 65.000 (66.931) Epoch: [9][600/11272] Time 0.724 (0.836) Data 0.002 (0.005) Loss 2.6412 (2.6337) Prec@1 35.625 (36.248) Prec@5 65.625 (66.913) Epoch: [9][610/11272] Time 0.983 (0.836) Data 0.002 (0.005) Loss 2.5303 (2.6321) Prec@1 35.625 (36.289) Prec@5 72.500 (66.953) Epoch: [9][620/11272] Time 0.910 (0.836) Data 0.001 (0.005) Loss 2.6737 (2.6315) Prec@1 35.625 (36.294) Prec@5 72.500 (66.974) Epoch: [9][630/11272] Time 0.749 (0.836) Data 0.002 (0.005) Loss 2.6226 (2.6312) Prec@1 39.375 (36.307) Prec@5 70.625 (66.998) Epoch: [9][640/11272] Time 0.777 (0.836) Data 0.001 (0.005) Loss 2.3609 (2.6309) Prec@1 42.500 (36.311) Prec@5 73.125 (66.995) Epoch: [9][650/11272] Time 0.901 (0.836) Data 0.002 (0.005) Loss 2.6064 (2.6296) Prec@1 38.125 (36.349) Prec@5 68.750 (67.031) Epoch: [9][660/11272] Time 0.919 (0.836) Data 0.001 (0.005) Loss 2.4856 (2.6293) Prec@1 41.250 (36.358) Prec@5 73.750 (67.050) Epoch: [9][670/11272] Time 0.755 (0.836) Data 0.002 (0.005) Loss 2.3066 (2.6291) Prec@1 42.500 (36.359) Prec@5 76.250 (67.078) Epoch: [9][680/11272] Time 0.893 (0.836) Data 0.001 (0.005) Loss 2.6483 (2.6287) Prec@1 31.250 (36.373) Prec@5 66.250 (67.087) Epoch: [9][690/11272] Time 0.927 (0.836) Data 0.002 (0.005) Loss 2.6315 (2.6276) Prec@1 35.625 (36.400) Prec@5 65.000 (67.110) Epoch: [9][700/11272] Time 0.759 (0.836) Data 0.001 (0.005) Loss 2.6350 (2.6265) Prec@1 37.500 (36.415) Prec@5 65.625 (67.133) Epoch: [9][710/11272] Time 0.751 (0.835) Data 0.002 (0.005) Loss 2.6981 (2.6264) Prec@1 33.125 (36.421) Prec@5 66.875 (67.132) Epoch: [9][720/11272] Time 0.850 (0.835) Data 0.001 (0.005) Loss 2.4866 (2.6274) Prec@1 41.875 (36.411) Prec@5 71.875 (67.126) Epoch: [9][730/11272] Time 0.891 (0.835) Data 0.001 (0.005) Loss 2.5782 (2.6279) Prec@1 38.750 (36.387) Prec@5 66.250 (67.132) Epoch: [9][740/11272] Time 0.727 (0.835) Data 0.001 (0.005) Loss 2.4244 (2.6273) Prec@1 39.375 (36.384) Prec@5 73.750 (67.161) Epoch: [9][750/11272] Time 0.781 (0.835) Data 0.002 (0.005) Loss 2.5036 (2.6271) Prec@1 42.500 (36.378) Prec@5 66.875 (67.143) Epoch: [9][760/11272] Time 0.865 (0.835) Data 0.001 (0.005) Loss 2.5463 (2.6268) Prec@1 42.500 (36.386) Prec@5 66.875 (67.139) Epoch: [9][770/11272] Time 0.947 (0.835) Data 0.002 (0.005) Loss 2.5169 (2.6282) Prec@1 44.375 (36.368) Prec@5 67.500 (67.125) Epoch: [9][780/11272] Time 0.774 (0.834) Data 0.001 (0.004) Loss 2.6952 (2.6277) Prec@1 39.375 (36.391) Prec@5 71.250 (67.158) Epoch: [9][790/11272] Time 0.790 (0.834) Data 0.002 (0.004) Loss 2.6129 (2.6269) Prec@1 40.625 (36.425) Prec@5 65.625 (67.163) Epoch: [9][800/11272] Time 0.855 (0.834) Data 0.002 (0.004) Loss 2.4410 (2.6279) Prec@1 38.750 (36.406) Prec@5 68.125 (67.131) Epoch: [9][810/11272] Time 0.904 (0.834) Data 0.002 (0.004) Loss 2.9543 (2.6295) Prec@1 33.125 (36.369) Prec@5 58.750 (67.095) Epoch: [9][820/11272] Time 0.739 (0.834) Data 0.002 (0.004) Loss 2.5602 (2.6293) Prec@1 42.500 (36.385) Prec@5 69.375 (67.097) Epoch: [9][830/11272] Time 0.896 (0.834) Data 0.002 (0.004) Loss 2.8679 (2.6286) Prec@1 35.000 (36.406) Prec@5 61.875 (67.104) Epoch: [9][840/11272] Time 0.894 (0.834) Data 0.001 (0.004) Loss 2.7808 (2.6281) Prec@1 32.500 (36.413) Prec@5 61.875 (67.108) Epoch: [9][850/11272] Time 0.719 (0.834) Data 0.001 (0.004) Loss 2.4865 (2.6283) Prec@1 37.500 (36.420) Prec@5 73.125 (67.110) Epoch: [9][860/11272] Time 0.725 (0.834) Data 0.002 (0.004) Loss 2.7434 (2.6275) Prec@1 35.625 (36.444) Prec@5 65.625 (67.131) Epoch: [9][870/11272] Time 0.916 (0.834) Data 0.002 (0.004) Loss 2.5240 (2.6271) Prec@1 33.750 (36.435) Prec@5 68.125 (67.127) Epoch: [9][880/11272] Time 0.864 (0.834) Data 0.001 (0.004) Loss 2.9687 (2.6282) Prec@1 27.500 (36.427) Prec@5 58.750 (67.103) Epoch: [9][890/11272] Time 0.800 (0.834) Data 0.002 (0.004) Loss 2.5573 (2.6293) Prec@1 38.750 (36.413) Prec@5 68.125 (67.061) Epoch: [9][900/11272] Time 0.826 (0.834) Data 0.001 (0.004) Loss 2.7999 (2.6293) Prec@1 31.875 (36.413) Prec@5 62.500 (67.054) Epoch: [9][910/11272] Time 0.917 (0.834) Data 0.002 (0.004) Loss 2.6793 (2.6289) Prec@1 35.000 (36.416) Prec@5 66.875 (67.055) Epoch: [9][920/11272] Time 0.822 (0.834) Data 0.001 (0.004) Loss 2.6574 (2.6289) Prec@1 36.250 (36.422) Prec@5 63.750 (67.053) Epoch: [9][930/11272] Time 0.737 (0.833) Data 0.002 (0.004) Loss 2.6713 (2.6287) Prec@1 35.625 (36.412) Prec@5 68.125 (67.057) Epoch: [9][940/11272] Time 0.760 (0.833) Data 0.001 (0.004) Loss 2.5692 (2.6289) Prec@1 40.625 (36.419) Prec@5 70.625 (67.058) Epoch: [9][950/11272] Time 0.918 (0.833) Data 0.002 (0.004) Loss 2.5673 (2.6296) Prec@1 36.250 (36.397) Prec@5 69.375 (67.050) Epoch: [9][960/11272] Time 0.755 (0.833) Data 0.001 (0.004) Loss 2.5930 (2.6300) Prec@1 35.625 (36.385) Prec@5 65.000 (67.043) Epoch: [9][970/11272] Time 0.750 (0.833) Data 0.001 (0.004) Loss 2.4924 (2.6296) Prec@1 40.000 (36.403) Prec@5 71.875 (67.057) Epoch: [9][980/11272] Time 0.880 (0.833) Data 0.001 (0.004) Loss 2.6336 (2.6310) Prec@1 38.125 (36.380) Prec@5 66.250 (67.028) Epoch: [9][990/11272] Time 0.883 (0.833) Data 0.002 (0.004) Loss 2.5583 (2.6312) Prec@1 37.500 (36.391) Prec@5 66.250 (67.028) Epoch: [9][1000/11272] Time 0.771 (0.833) Data 0.001 (0.004) Loss 2.6811 (2.6311) Prec@1 32.500 (36.403) Prec@5 64.375 (67.020) Epoch: [9][1010/11272] Time 0.753 (0.833) Data 0.002 (0.004) Loss 2.8020 (2.6318) Prec@1 31.250 (36.388) Prec@5 63.125 (67.003) Epoch: [9][1020/11272] Time 0.815 (0.833) Data 0.001 (0.004) Loss 2.4627 (2.6323) Prec@1 38.125 (36.371) Prec@5 71.875 (66.993) Epoch: [9][1030/11272] Time 0.920 (0.833) Data 0.002 (0.004) Loss 2.4390 (2.6326) Prec@1 39.375 (36.373) Prec@5 74.375 (66.988) Epoch: [9][1040/11272] Time 0.785 (0.833) Data 0.002 (0.004) Loss 2.7274 (2.6322) Prec@1 36.875 (36.370) Prec@5 65.000 (66.997) Epoch: [9][1050/11272] Time 0.696 (0.833) Data 0.001 (0.004) Loss 2.7608 (2.6328) Prec@1 35.625 (36.353) Prec@5 64.375 (66.986) Epoch: [9][1060/11272] Time 0.857 (0.833) Data 0.001 (0.004) Loss 2.6715 (2.6332) Prec@1 35.000 (36.349) Prec@5 63.125 (66.973) Epoch: [9][1070/11272] Time 0.906 (0.833) Data 0.002 (0.004) Loss 2.8513 (2.6335) Prec@1 31.875 (36.341) Prec@5 60.625 (66.961) Epoch: [9][1080/11272] Time 0.743 (0.833) Data 0.002 (0.004) Loss 2.6969 (2.6339) Prec@1 36.875 (36.337) Prec@5 61.875 (66.946) Epoch: [9][1090/11272] Time 0.868 (0.833) Data 0.002 (0.004) Loss 2.5419 (2.6343) Prec@1 33.750 (36.331) Prec@5 70.625 (66.944) Epoch: [9][1100/11272] Time 0.865 (0.833) Data 0.001 (0.004) Loss 2.3526 (2.6339) Prec@1 43.750 (36.334) Prec@5 73.750 (66.945) Epoch: [9][1110/11272] Time 0.772 (0.833) Data 0.002 (0.004) Loss 3.0720 (2.6345) Prec@1 29.375 (36.328) Prec@5 61.875 (66.933) Epoch: [9][1120/11272] Time 0.700 (0.832) Data 0.001 (0.004) Loss 2.5795 (2.6340) Prec@1 39.375 (36.339) Prec@5 70.000 (66.941) Epoch: [9][1130/11272] Time 0.889 (0.832) Data 0.002 (0.004) Loss 2.8470 (2.6344) Prec@1 30.000 (36.320) Prec@5 66.250 (66.921) Epoch: [9][1140/11272] Time 0.864 (0.832) Data 0.001 (0.004) Loss 2.1648 (2.6340) Prec@1 48.750 (36.326) Prec@5 75.000 (66.941) Epoch: [9][1150/11272] Time 0.738 (0.832) Data 0.002 (0.004) Loss 2.5412 (2.6336) Prec@1 35.625 (36.336) Prec@5 73.750 (66.958) Epoch: [9][1160/11272] Time 0.745 (0.832) Data 0.002 (0.004) Loss 2.6760 (2.6331) Prec@1 35.000 (36.336) Prec@5 64.375 (66.970) Epoch: [9][1170/11272] Time 0.890 (0.832) Data 0.002 (0.003) Loss 2.4723 (2.6336) Prec@1 39.375 (36.321) Prec@5 71.875 (66.962) Epoch: [9][1180/11272] Time 0.906 (0.832) Data 0.001 (0.003) Loss 2.6599 (2.6339) Prec@1 31.875 (36.314) Prec@5 65.000 (66.961) Epoch: [9][1190/11272] Time 0.760 (0.832) Data 0.002 (0.003) Loss 2.7300 (2.6337) Prec@1 36.875 (36.315) Prec@5 66.250 (66.971) Epoch: [9][1200/11272] Time 0.769 (0.832) Data 0.001 (0.003) Loss 2.2925 (2.6331) Prec@1 42.500 (36.323) Prec@5 75.000 (66.986) Epoch: [9][1210/11272] Time 0.974 (0.832) Data 0.002 (0.003) Loss 2.6746 (2.6322) Prec@1 35.000 (36.334) Prec@5 65.000 (67.005) Epoch: [9][1220/11272] Time 0.729 (0.832) Data 0.003 (0.003) Loss 2.3326 (2.6318) Prec@1 44.375 (36.331) Prec@5 73.125 (67.018) Epoch: [9][1230/11272] Time 0.750 (0.832) Data 0.002 (0.003) Loss 2.5254 (2.6313) Prec@1 38.125 (36.345) Prec@5 69.375 (67.032) Epoch: [9][1240/11272] Time 0.891 (0.832) Data 0.001 (0.003) Loss 2.8249 (2.6316) Prec@1 36.875 (36.342) Prec@5 63.750 (67.033) Epoch: [9][1250/11272] Time 0.912 (0.832) Data 0.002 (0.003) Loss 2.8407 (2.6322) Prec@1 35.625 (36.322) Prec@5 65.000 (67.027) Epoch: [9][1260/11272] Time 0.746 (0.832) Data 0.001 (0.003) Loss 2.6115 (2.6322) Prec@1 40.000 (36.320) Prec@5 67.500 (67.032) Epoch: [9][1270/11272] Time 0.759 (0.832) Data 0.002 (0.003) Loss 2.6665 (2.6325) Prec@1 31.875 (36.319) Prec@5 66.250 (67.030) Epoch: [9][1280/11272] Time 0.906 (0.832) Data 0.003 (0.003) Loss 2.7142 (2.6324) Prec@1 35.625 (36.325) Prec@5 65.625 (67.030) Epoch: [9][1290/11272] Time 0.953 (0.832) Data 0.002 (0.003) Loss 2.8613 (2.6327) Prec@1 30.000 (36.308) Prec@5 62.500 (67.029) Epoch: [9][1300/11272] Time 0.751 (0.832) Data 0.004 (0.003) Loss 2.5832 (2.6326) Prec@1 37.500 (36.303) Prec@5 65.000 (67.028) Epoch: [9][1310/11272] Time 0.780 (0.832) Data 0.002 (0.003) Loss 2.6264 (2.6325) Prec@1 36.250 (36.304) Prec@5 66.875 (67.042) Epoch: [9][1320/11272] Time 0.871 (0.832) Data 0.001 (0.003) Loss 2.4539 (2.6321) Prec@1 39.375 (36.310) Prec@5 72.500 (67.050) Epoch: [9][1330/11272] Time 0.893 (0.832) Data 0.002 (0.003) Loss 2.5440 (2.6321) Prec@1 33.750 (36.306) Prec@5 68.125 (67.049) Epoch: [9][1340/11272] Time 0.804 (0.832) Data 0.001 (0.003) Loss 2.3849 (2.6321) Prec@1 43.750 (36.317) Prec@5 74.375 (67.052) Epoch: [9][1350/11272] Time 0.929 (0.832) Data 0.002 (0.003) Loss 2.7264 (2.6320) Prec@1 38.125 (36.320) Prec@5 66.875 (67.055) Epoch: [9][1360/11272] Time 0.849 (0.832) Data 0.002 (0.003) Loss 2.3493 (2.6320) Prec@1 46.875 (36.320) Prec@5 66.875 (67.048) Epoch: [9][1370/11272] Time 0.773 (0.832) Data 0.002 (0.003) Loss 2.5504 (2.6323) Prec@1 34.375 (36.316) Prec@5 72.500 (67.051) Epoch: [9][1380/11272] Time 0.776 (0.832) Data 0.001 (0.003) Loss 2.6488 (2.6324) Prec@1 37.500 (36.312) Prec@5 66.250 (67.055) Epoch: [9][1390/11272] Time 0.911 (0.832) Data 0.002 (0.003) Loss 2.3979 (2.6324) Prec@1 40.000 (36.303) Prec@5 73.125 (67.050) Epoch: [9][1400/11272] Time 0.871 (0.832) Data 0.001 (0.003) Loss 2.3529 (2.6321) Prec@1 42.500 (36.309) Prec@5 73.750 (67.056) Epoch: [9][1410/11272] Time 0.753 (0.832) Data 0.002 (0.003) Loss 2.4428 (2.6319) Prec@1 34.375 (36.316) Prec@5 75.000 (67.059) Epoch: [9][1420/11272] Time 0.745 (0.832) Data 0.001 (0.003) Loss 2.5658 (2.6321) Prec@1 33.750 (36.301) Prec@5 67.500 (67.058) Epoch: [9][1430/11272] Time 0.892 (0.832) Data 0.002 (0.003) Loss 2.6983 (2.6323) Prec@1 34.375 (36.301) Prec@5 68.750 (67.050) Epoch: [9][1440/11272] Time 0.886 (0.832) Data 0.001 (0.003) Loss 2.6313 (2.6324) Prec@1 33.750 (36.292) Prec@5 66.875 (67.046) Epoch: [9][1450/11272] Time 0.731 (0.832) Data 0.002 (0.003) Loss 2.3240 (2.6326) Prec@1 40.000 (36.297) Prec@5 75.000 (67.035) Epoch: [9][1460/11272] Time 0.748 (0.832) Data 0.001 (0.003) Loss 2.6043 (2.6323) Prec@1 33.750 (36.297) Prec@5 67.500 (67.047) Epoch: [9][1470/11272] Time 0.904 (0.832) Data 0.002 (0.003) Loss 2.6795 (2.6322) Prec@1 38.750 (36.308) Prec@5 68.125 (67.051) Epoch: [9][1480/11272] Time 0.766 (0.832) Data 0.003 (0.003) Loss 2.6982 (2.6323) Prec@1 37.500 (36.310) Prec@5 66.875 (67.051) Epoch: [9][1490/11272] Time 0.767 (0.832) Data 0.002 (0.003) Loss 2.5288 (2.6327) Prec@1 44.375 (36.301) Prec@5 69.375 (67.044) Epoch: [9][1500/11272] Time 0.849 (0.832) Data 0.001 (0.003) Loss 2.6813 (2.6327) Prec@1 37.500 (36.298) Prec@5 63.750 (67.044) Epoch: [9][1510/11272] Time 0.899 (0.832) Data 0.001 (0.003) Loss 2.6134 (2.6325) Prec@1 38.125 (36.312) Prec@5 66.250 (67.041) Epoch: [9][1520/11272] Time 0.760 (0.832) Data 0.001 (0.003) Loss 2.6670 (2.6328) Prec@1 35.000 (36.305) Prec@5 65.000 (67.035) Epoch: [9][1530/11272] Time 0.735 (0.832) Data 0.002 (0.003) Loss 2.5661 (2.6327) Prec@1 41.875 (36.310) Prec@5 67.500 (67.031) Epoch: [9][1540/11272] Time 0.874 (0.832) Data 0.001 (0.003) Loss 2.5161 (2.6322) Prec@1 41.250 (36.325) Prec@5 68.125 (67.044) Epoch: [9][1550/11272] Time 0.991 (0.832) Data 0.002 (0.003) Loss 2.3619 (2.6323) Prec@1 44.375 (36.327) Prec@5 69.375 (67.037) Epoch: [9][1560/11272] Time 0.762 (0.832) Data 0.001 (0.003) Loss 2.7429 (2.6319) Prec@1 32.500 (36.342) Prec@5 60.625 (67.046) Epoch: [9][1570/11272] Time 0.775 (0.832) Data 0.002 (0.003) Loss 2.3823 (2.6318) Prec@1 43.125 (36.351) Prec@5 71.250 (67.051) Epoch: [9][1580/11272] Time 0.854 (0.832) Data 0.001 (0.003) Loss 2.7583 (2.6316) Prec@1 34.375 (36.352) Prec@5 65.000 (67.060) Epoch: [9][1590/11272] Time 0.811 (0.832) Data 0.002 (0.003) Loss 2.2978 (2.6318) Prec@1 42.500 (36.347) Prec@5 70.625 (67.054) Epoch: [9][1600/11272] Time 0.702 (0.832) Data 0.001 (0.003) Loss 2.3855 (2.6316) Prec@1 37.500 (36.346) Prec@5 73.125 (67.063) Epoch: [9][1610/11272] Time 0.892 (0.832) Data 0.002 (0.003) Loss 2.7013 (2.6312) Prec@1 38.125 (36.357) Prec@5 65.000 (67.071) Epoch: [9][1620/11272] Time 0.880 (0.832) Data 0.001 (0.003) Loss 2.8368 (2.6314) Prec@1 41.250 (36.356) Prec@5 66.250 (67.063) Epoch: [9][1630/11272] Time 0.751 (0.832) Data 0.002 (0.003) Loss 2.7999 (2.6317) Prec@1 36.875 (36.356) Prec@5 66.250 (67.057) Epoch: [9][1640/11272] Time 0.752 (0.832) Data 0.001 (0.003) Loss 2.6015 (2.6320) Prec@1 35.625 (36.338) Prec@5 70.000 (67.053) Epoch: [9][1650/11272] Time 0.894 (0.832) Data 0.002 (0.003) Loss 2.6753 (2.6323) Prec@1 36.875 (36.333) Prec@5 68.125 (67.049) Epoch: [9][1660/11272] Time 0.911 (0.832) Data 0.001 (0.003) Loss 2.4546 (2.6321) Prec@1 41.875 (36.341) Prec@5 71.875 (67.052) Epoch: [9][1670/11272] Time 0.749 (0.832) Data 0.002 (0.003) Loss 2.6211 (2.6322) Prec@1 37.500 (36.337) Prec@5 66.875 (67.043) Epoch: [9][1680/11272] Time 0.761 (0.832) Data 0.001 (0.003) Loss 2.7027 (2.6327) Prec@1 30.000 (36.331) Prec@5 62.500 (67.034) Epoch: [9][1690/11272] Time 0.938 (0.832) Data 0.003 (0.003) Loss 2.5851 (2.6330) Prec@1 36.250 (36.320) Prec@5 65.625 (67.033) Epoch: [9][1700/11272] Time 0.852 (0.832) Data 0.001 (0.003) Loss 2.8057 (2.6329) Prec@1 32.500 (36.322) Prec@5 63.125 (67.036) Epoch: [9][1710/11272] Time 0.824 (0.832) Data 0.002 (0.003) Loss 2.7355 (2.6332) Prec@1 31.250 (36.313) Prec@5 66.875 (67.035) Epoch: [9][1720/11272] Time 0.789 (0.832) Data 0.001 (0.003) Loss 2.5513 (2.6331) Prec@1 40.000 (36.315) Prec@5 68.750 (67.036) Epoch: [9][1730/11272] Time 0.901 (0.832) Data 0.003 (0.003) Loss 2.9171 (2.6333) Prec@1 31.875 (36.307) Prec@5 63.125 (67.037) Epoch: [9][1740/11272] Time 0.870 (0.832) Data 0.001 (0.003) Loss 2.5889 (2.6332) Prec@1 34.375 (36.305) Prec@5 62.500 (67.038) Epoch: [9][1750/11272] Time 0.777 (0.832) Data 0.002 (0.003) Loss 2.6468 (2.6331) Prec@1 34.375 (36.310) Prec@5 68.125 (67.038) Epoch: [9][1760/11272] Time 0.892 (0.832) Data 0.002 (0.003) Loss 2.5620 (2.6334) Prec@1 36.250 (36.296) Prec@5 72.500 (67.026) Epoch: [9][1770/11272] Time 0.968 (0.832) Data 0.002 (0.003) Loss 2.5677 (2.6334) Prec@1 35.625 (36.291) Prec@5 68.125 (67.026) Epoch: [9][1780/11272] Time 0.775 (0.832) Data 0.002 (0.003) Loss 2.6452 (2.6334) Prec@1 36.875 (36.291) Prec@5 66.250 (67.020) Epoch: [9][1790/11272] Time 0.769 (0.832) Data 0.001 (0.003) Loss 2.8463 (2.6335) Prec@1 35.625 (36.290) Prec@5 65.625 (67.021) Epoch: [9][1800/11272] Time 0.932 (0.832) Data 0.002 (0.003) Loss 2.7022 (2.6337) Prec@1 35.625 (36.294) Prec@5 63.125 (67.019) Epoch: [9][1810/11272] Time 0.904 (0.832) Data 0.001 (0.003) Loss 2.8197 (2.6339) Prec@1 36.250 (36.295) Prec@5 62.500 (67.017) Epoch: [9][1820/11272] Time 0.786 (0.832) Data 0.002 (0.003) Loss 2.6064 (2.6338) Prec@1 39.375 (36.301) Prec@5 65.000 (67.017) Epoch: [9][1830/11272] Time 0.739 (0.832) Data 0.001 (0.003) Loss 2.6480 (2.6337) Prec@1 33.750 (36.296) Prec@5 64.375 (67.021) Epoch: [9][1840/11272] Time 0.884 (0.832) Data 0.002 (0.003) Loss 2.6194 (2.6341) Prec@1 40.000 (36.292) Prec@5 65.625 (67.016) Epoch: [9][1850/11272] Time 0.920 (0.832) Data 0.001 (0.003) Loss 2.8266 (2.6342) Prec@1 32.500 (36.291) Prec@5 66.250 (67.016) Epoch: [9][1860/11272] Time 0.771 (0.832) Data 0.002 (0.003) Loss 2.4391 (2.6337) Prec@1 37.500 (36.289) Prec@5 68.125 (67.021) Epoch: [9][1870/11272] Time 0.811 (0.832) Data 0.003 (0.003) Loss 2.7401 (2.6335) Prec@1 37.500 (36.298) Prec@5 58.750 (67.025) Epoch: [9][1880/11272] Time 0.921 (0.832) Data 0.002 (0.003) Loss 2.4637 (2.6335) Prec@1 43.125 (36.297) Prec@5 67.500 (67.023) Epoch: [9][1890/11272] Time 0.723 (0.832) Data 0.001 (0.003) Loss 2.4768 (2.6338) Prec@1 39.375 (36.287) Prec@5 68.750 (67.021) Epoch: [9][1900/11272] Time 0.780 (0.832) Data 0.002 (0.003) Loss 2.4550 (2.6336) Prec@1 36.250 (36.286) Prec@5 73.750 (67.029) Epoch: [9][1910/11272] Time 0.907 (0.833) Data 0.001 (0.003) Loss 2.8726 (2.6337) Prec@1 36.250 (36.292) Prec@5 60.000 (67.023) Epoch: [9][1920/11272] Time 0.897 (0.833) Data 0.002 (0.003) Loss 2.6077 (2.6340) Prec@1 41.875 (36.290) Prec@5 65.625 (67.025) Epoch: [9][1930/11272] Time 0.770 (0.833) Data 0.002 (0.003) Loss 2.5647 (2.6338) Prec@1 41.875 (36.295) Prec@5 67.500 (67.028) Epoch: [9][1940/11272] Time 0.759 (0.833) Data 0.002 (0.003) Loss 2.3693 (2.6340) Prec@1 42.500 (36.291) Prec@5 72.500 (67.027) Epoch: [9][1950/11272] Time 0.857 (0.833) Data 0.001 (0.003) Loss 2.6926 (2.6343) Prec@1 32.500 (36.287) Prec@5 66.250 (67.024) Epoch: [9][1960/11272] Time 0.950 (0.833) Data 0.001 (0.003) Loss 3.0182 (2.6350) Prec@1 26.250 (36.274) Prec@5 62.500 (67.011) Epoch: [9][1970/11272] Time 0.787 (0.833) Data 0.001 (0.003) Loss 2.5627 (2.6352) Prec@1 36.250 (36.274) Prec@5 68.750 (67.007) Epoch: [9][1980/11272] Time 0.735 (0.833) Data 0.002 (0.003) Loss 2.5299 (2.6353) Prec@1 40.000 (36.280) Prec@5 70.000 (67.003) Epoch: [9][1990/11272] Time 0.912 (0.833) Data 0.001 (0.003) Loss 2.6937 (2.6350) Prec@1 30.625 (36.277) Prec@5 69.375 (67.011) Epoch: [9][2000/11272] Time 0.965 (0.833) Data 0.002 (0.003) Loss 2.6614 (2.6353) Prec@1 40.625 (36.271) Prec@5 65.000 (66.999) Epoch: [9][2010/11272] Time 0.760 (0.833) Data 0.002 (0.003) Loss 2.5274 (2.6351) Prec@1 37.500 (36.279) Prec@5 70.625 (67.003) Epoch: [9][2020/11272] Time 0.940 (0.833) Data 0.002 (0.003) Loss 2.7266 (2.6352) Prec@1 36.875 (36.275) Prec@5 64.375 (66.997) Epoch: [9][2030/11272] Time 0.920 (0.833) Data 0.001 (0.003) Loss 2.3666 (2.6350) Prec@1 37.500 (36.279) Prec@5 71.875 (67.005) Epoch: [9][2040/11272] Time 0.793 (0.833) Data 0.002 (0.003) Loss 2.7462 (2.6351) Prec@1 32.500 (36.279) Prec@5 62.500 (67.000) Epoch: [9][2050/11272] Time 0.774 (0.833) Data 0.002 (0.003) Loss 2.5311 (2.6350) Prec@1 36.875 (36.286) Prec@5 70.625 (67.002) Epoch: [9][2060/11272] Time 0.941 (0.833) Data 0.002 (0.003) Loss 2.6175 (2.6352) Prec@1 34.375 (36.282) Prec@5 66.875 (66.997) Epoch: [9][2070/11272] Time 0.903 (0.833) Data 0.001 (0.003) Loss 2.4392 (2.6351) Prec@1 37.500 (36.283) Prec@5 69.375 (66.994) Epoch: [9][2080/11272] Time 0.724 (0.833) Data 0.001 (0.003) Loss 2.5914 (2.6350) Prec@1 36.875 (36.279) Prec@5 65.000 (66.996) Epoch: [9][2090/11272] Time 0.758 (0.833) Data 0.001 (0.003) Loss 2.6591 (2.6350) Prec@1 31.875 (36.279) Prec@5 67.500 (66.998) Epoch: [9][2100/11272] Time 0.879 (0.833) Data 0.002 (0.003) Loss 2.5153 (2.6345) Prec@1 33.125 (36.276) Prec@5 68.125 (67.003) Epoch: [9][2110/11272] Time 0.896 (0.833) Data 0.004 (0.003) Loss 2.8510 (2.6348) Prec@1 33.750 (36.278) Prec@5 61.250 (66.995) Epoch: [9][2120/11272] Time 0.804 (0.833) Data 0.002 (0.003) Loss 2.3592 (2.6345) Prec@1 41.250 (36.284) Prec@5 71.250 (67.001) Epoch: [9][2130/11272] Time 0.784 (0.833) Data 0.001 (0.003) Loss 2.8188 (2.6344) Prec@1 35.625 (36.285) Prec@5 65.000 (67.000) Epoch: [9][2140/11272] Time 0.898 (0.833) Data 0.002 (0.003) Loss 2.3056 (2.6342) Prec@1 40.625 (36.287) Prec@5 75.625 (67.003) Epoch: [9][2150/11272] Time 0.701 (0.833) Data 0.003 (0.003) Loss 2.6042 (2.6339) Prec@1 31.250 (36.290) Prec@5 66.875 (67.007) Epoch: [9][2160/11272] Time 0.774 (0.832) Data 0.002 (0.003) Loss 2.2644 (2.6339) Prec@1 43.125 (36.294) Prec@5 77.500 (67.005) Epoch: [9][2170/11272] Time 0.875 (0.832) Data 0.001 (0.003) Loss 2.6880 (2.6337) Prec@1 38.125 (36.304) Prec@5 63.125 (67.001) Epoch: [9][2180/11272] Time 0.906 (0.832) Data 0.002 (0.003) Loss 2.6922 (2.6342) Prec@1 36.250 (36.302) Prec@5 62.500 (66.985) Epoch: [9][2190/11272] Time 0.754 (0.832) Data 0.002 (0.003) Loss 2.5757 (2.6345) Prec@1 33.750 (36.298) Prec@5 65.000 (66.973) Epoch: [9][2200/11272] Time 0.762 (0.832) Data 0.002 (0.003) Loss 2.9245 (2.6346) Prec@1 33.125 (36.297) Prec@5 58.125 (66.976) Epoch: [9][2210/11272] Time 0.891 (0.832) Data 0.001 (0.003) Loss 2.5623 (2.6347) Prec@1 43.125 (36.301) Prec@5 68.750 (66.971) Epoch: [9][2220/11272] Time 0.896 (0.832) Data 0.002 (0.003) Loss 2.7056 (2.6346) Prec@1 35.000 (36.303) Prec@5 67.500 (66.975) Epoch: [9][2230/11272] Time 0.803 (0.832) Data 0.001 (0.003) Loss 2.7708 (2.6347) Prec@1 31.875 (36.302) Prec@5 61.250 (66.972) Epoch: [9][2240/11272] Time 0.750 (0.832) Data 0.002 (0.003) Loss 2.9856 (2.6354) Prec@1 26.875 (36.285) Prec@5 60.625 (66.956) Epoch: [9][2250/11272] Time 0.900 (0.832) Data 0.001 (0.003) Loss 2.7309 (2.6356) Prec@1 33.125 (36.279) Prec@5 66.250 (66.954) Epoch: [9][2260/11272] Time 0.899 (0.832) Data 0.002 (0.003) Loss 2.7771 (2.6360) Prec@1 29.375 (36.266) Prec@5 64.375 (66.945) Epoch: [9][2270/11272] Time 0.775 (0.832) Data 0.001 (0.003) Loss 2.6844 (2.6359) Prec@1 39.375 (36.270) Prec@5 66.250 (66.949) Epoch: [9][2280/11272] Time 0.916 (0.832) Data 0.002 (0.003) Loss 2.4755 (2.6359) Prec@1 39.375 (36.266) Prec@5 70.625 (66.951) Epoch: [9][2290/11272] Time 0.855 (0.832) Data 0.001 (0.003) Loss 2.7775 (2.6364) Prec@1 33.750 (36.261) Prec@5 67.500 (66.945) Epoch: [9][2300/11272] Time 0.744 (0.832) Data 0.002 (0.003) Loss 2.7476 (2.6365) Prec@1 41.875 (36.264) Prec@5 61.250 (66.943) Epoch: [9][2310/11272] Time 0.772 (0.832) Data 0.002 (0.003) Loss 2.7483 (2.6365) Prec@1 35.625 (36.265) Prec@5 70.000 (66.947) Epoch: [9][2320/11272] Time 0.900 (0.832) Data 0.002 (0.003) Loss 2.5191 (2.6365) Prec@1 39.375 (36.267) Prec@5 66.875 (66.943) Epoch: [9][2330/11272] Time 0.879 (0.832) Data 0.001 (0.003) Loss 2.5623 (2.6364) Prec@1 33.125 (36.267) Prec@5 69.375 (66.943) Epoch: [9][2340/11272] Time 0.766 (0.832) Data 0.002 (0.003) Loss 2.5973 (2.6363) Prec@1 37.500 (36.267) Prec@5 68.750 (66.945) Epoch: [9][2350/11272] Time 0.777 (0.832) Data 0.001 (0.003) Loss 2.7493 (2.6369) Prec@1 40.625 (36.259) Prec@5 66.875 (66.935) Epoch: [9][2360/11272] Time 0.871 (0.832) Data 0.001 (0.003) Loss 2.4954 (2.6373) Prec@1 41.875 (36.259) Prec@5 68.750 (66.929) Epoch: [9][2370/11272] Time 0.875 (0.832) Data 0.001 (0.003) Loss 2.5293 (2.6373) Prec@1 34.375 (36.248) Prec@5 70.000 (66.928) Epoch: [9][2380/11272] Time 0.806 (0.832) Data 0.002 (0.003) Loss 2.5073 (2.6373) Prec@1 43.750 (36.249) Prec@5 71.875 (66.928) Epoch: [9][2390/11272] Time 0.775 (0.832) Data 0.002 (0.003) Loss 2.8847 (2.6375) Prec@1 30.000 (36.242) Prec@5 61.875 (66.925) Epoch: [9][2400/11272] Time 0.900 (0.832) Data 0.002 (0.003) Loss 2.5321 (2.6378) Prec@1 39.375 (36.240) Prec@5 66.250 (66.921) Epoch: [9][2410/11272] Time 0.736 (0.832) Data 0.003 (0.003) Loss 2.5860 (2.6379) Prec@1 39.375 (36.239) Prec@5 68.125 (66.918) Epoch: [9][2420/11272] Time 0.731 (0.832) Data 0.002 (0.003) Loss 2.5931 (2.6381) Prec@1 41.875 (36.241) Prec@5 65.000 (66.913) Epoch: [9][2430/11272] Time 0.862 (0.832) Data 0.001 (0.003) Loss 2.8069 (2.6383) Prec@1 35.000 (36.238) Prec@5 65.625 (66.908) Epoch: [9][2440/11272] Time 0.880 (0.832) Data 0.002 (0.003) Loss 3.0285 (2.6385) Prec@1 33.125 (36.237) Prec@5 55.625 (66.903) Epoch: [9][2450/11272] Time 0.752 (0.832) Data 0.001 (0.003) Loss 2.6992 (2.6385) Prec@1 33.125 (36.239) Prec@5 63.750 (66.902) Epoch: [9][2460/11272] Time 0.762 (0.832) Data 0.002 (0.003) Loss 2.5323 (2.6385) Prec@1 33.750 (36.242) Prec@5 68.125 (66.901) Epoch: [9][2470/11272] Time 0.865 (0.832) Data 0.002 (0.003) Loss 2.3957 (2.6386) Prec@1 37.500 (36.232) Prec@5 68.750 (66.897) Epoch: [9][2480/11272] Time 0.893 (0.832) Data 0.002 (0.003) Loss 2.4414 (2.6384) Prec@1 43.750 (36.240) Prec@5 71.250 (66.902) Epoch: [9][2490/11272] Time 0.728 (0.832) Data 0.001 (0.003) Loss 2.6553 (2.6383) Prec@1 36.875 (36.242) Prec@5 64.375 (66.903) Epoch: [9][2500/11272] Time 0.778 (0.832) Data 0.002 (0.003) Loss 2.5308 (2.6380) Prec@1 38.125 (36.249) Prec@5 72.500 (66.904) Epoch: [9][2510/11272] Time 0.881 (0.832) Data 0.001 (0.003) Loss 2.6327 (2.6378) Prec@1 35.625 (36.253) Prec@5 67.500 (66.910) Epoch: [9][2520/11272] Time 0.891 (0.832) Data 0.002 (0.003) Loss 2.4918 (2.6377) Prec@1 41.250 (36.259) Prec@5 67.500 (66.914) Epoch: [9][2530/11272] Time 0.789 (0.832) Data 0.002 (0.003) Loss 2.5773 (2.6377) Prec@1 36.250 (36.256) Prec@5 66.250 (66.917) Epoch: [9][2540/11272] Time 0.960 (0.832) Data 0.002 (0.003) Loss 2.5497 (2.6376) Prec@1 37.500 (36.258) Prec@5 72.500 (66.919) Epoch: [9][2550/11272] Time 0.929 (0.832) Data 0.001 (0.003) Loss 2.6042 (2.6380) Prec@1 36.250 (36.256) Prec@5 66.875 (66.915) Epoch: [9][2560/11272] Time 0.808 (0.832) Data 0.002 (0.003) Loss 2.7901 (2.6380) Prec@1 30.625 (36.257) Prec@5 60.625 (66.913) Epoch: [9][2570/11272] Time 0.752 (0.832) Data 0.001 (0.003) Loss 2.7105 (2.6381) Prec@1 40.625 (36.261) Prec@5 64.375 (66.910) Epoch: [9][2580/11272] Time 0.903 (0.832) Data 0.002 (0.003) Loss 2.5720 (2.6377) Prec@1 38.125 (36.269) Prec@5 70.625 (66.923) Epoch: [9][2590/11272] Time 0.904 (0.832) Data 0.002 (0.003) Loss 2.3959 (2.6375) Prec@1 41.250 (36.268) Prec@5 74.375 (66.929) Epoch: [9][2600/11272] Time 0.821 (0.832) Data 0.002 (0.003) Loss 2.4656 (2.6375) Prec@1 40.000 (36.268) Prec@5 68.125 (66.925) Epoch: [9][2610/11272] Time 0.809 (0.833) Data 0.001 (0.003) Loss 2.3752 (2.6375) Prec@1 39.375 (36.262) Prec@5 73.125 (66.923) Epoch: [9][2620/11272] Time 0.913 (0.833) Data 0.002 (0.003) Loss 2.5611 (2.6376) Prec@1 28.125 (36.255) Prec@5 69.375 (66.920) Epoch: [9][2630/11272] Time 0.867 (0.833) Data 0.001 (0.003) Loss 2.7720 (2.6379) Prec@1 35.000 (36.256) Prec@5 63.750 (66.910) Epoch: [9][2640/11272] Time 0.809 (0.833) Data 0.002 (0.003) Loss 2.7110 (2.6378) Prec@1 33.750 (36.256) Prec@5 70.000 (66.912) Epoch: [9][2650/11272] Time 0.777 (0.833) Data 0.002 (0.003) Loss 2.7612 (2.6377) Prec@1 32.500 (36.251) Prec@5 62.500 (66.914) Epoch: [9][2660/11272] Time 0.915 (0.833) Data 0.002 (0.002) Loss 2.7326 (2.6376) Prec@1 33.750 (36.260) Prec@5 63.750 (66.919) Epoch: [9][2670/11272] Time 0.882 (0.833) Data 0.001 (0.002) Loss 2.6492 (2.6375) Prec@1 38.750 (36.264) Prec@5 68.750 (66.923) Epoch: [9][2680/11272] Time 0.797 (0.833) Data 0.002 (0.002) Loss 2.6526 (2.6379) Prec@1 37.500 (36.262) Prec@5 65.000 (66.917) Epoch: [9][2690/11272] Time 0.859 (0.833) Data 0.002 (0.002) Loss 2.5132 (2.6379) Prec@1 35.625 (36.261) Prec@5 74.375 (66.921) Epoch: [9][2700/11272] Time 0.955 (0.833) Data 0.001 (0.002) Loss 2.6107 (2.6379) Prec@1 33.125 (36.259) Prec@5 64.375 (66.921) Epoch: [9][2710/11272] Time 0.784 (0.833) Data 0.002 (0.002) Loss 2.5366 (2.6377) Prec@1 40.625 (36.263) Prec@5 68.125 (66.922) Epoch: [9][2720/11272] Time 0.803 (0.833) Data 0.002 (0.002) Loss 2.6849 (2.6378) Prec@1 33.750 (36.259) Prec@5 65.000 (66.923) Epoch: [9][2730/11272] Time 0.885 (0.833) Data 0.001 (0.002) Loss 2.4056 (2.6379) Prec@1 35.625 (36.251) Prec@5 68.125 (66.920) Epoch: [9][2740/11272] Time 0.922 (0.833) Data 0.002 (0.002) Loss 2.5849 (2.6378) Prec@1 40.625 (36.251) Prec@5 66.250 (66.923) Epoch: [9][2750/11272] Time 0.737 (0.833) Data 0.002 (0.002) Loss 2.6221 (2.6377) Prec@1 32.500 (36.246) Prec@5 73.750 (66.929) Epoch: [9][2760/11272] Time 0.781 (0.833) Data 0.002 (0.002) Loss 2.4195 (2.6378) Prec@1 40.000 (36.242) Prec@5 72.500 (66.929) Epoch: [9][2770/11272] Time 0.927 (0.833) Data 0.002 (0.002) Loss 2.5045 (2.6380) Prec@1 40.625 (36.234) Prec@5 65.625 (66.922) Epoch: [9][2780/11272] Time 0.882 (0.833) Data 0.002 (0.002) Loss 2.6139 (2.6379) Prec@1 35.625 (36.238) Prec@5 65.625 (66.925) Epoch: [9][2790/11272] Time 0.773 (0.833) Data 0.003 (0.002) Loss 2.8517 (2.6379) Prec@1 36.250 (36.238) Prec@5 60.000 (66.931) Epoch: [9][2800/11272] Time 0.779 (0.833) Data 0.002 (0.002) Loss 2.7308 (2.6380) Prec@1 31.875 (36.231) Prec@5 65.000 (66.931) Epoch: [9][2810/11272] Time 0.863 (0.833) Data 0.001 (0.002) Loss 2.6025 (2.6381) Prec@1 36.250 (36.231) Prec@5 68.125 (66.927) Epoch: [9][2820/11272] Time 0.772 (0.833) Data 0.002 (0.002) Loss 2.5202 (2.6380) Prec@1 40.625 (36.233) Prec@5 65.000 (66.929) Epoch: [9][2830/11272] Time 0.776 (0.833) Data 0.001 (0.002) Loss 2.7302 (2.6382) Prec@1 32.500 (36.225) Prec@5 63.750 (66.923) Epoch: [9][2840/11272] Time 0.877 (0.833) Data 0.002 (0.002) Loss 2.5011 (2.6379) Prec@1 35.625 (36.232) Prec@5 71.250 (66.930) Epoch: [9][2850/11272] Time 0.912 (0.833) Data 0.001 (0.002) Loss 2.6834 (2.6381) Prec@1 31.875 (36.230) Prec@5 66.250 (66.922) Epoch: [9][2860/11272] Time 0.759 (0.833) Data 0.002 (0.002) Loss 2.9254 (2.6383) Prec@1 31.250 (36.225) Prec@5 64.375 (66.917) Epoch: [9][2870/11272] Time 0.830 (0.833) Data 0.001 (0.002) Loss 2.7542 (2.6382) Prec@1 38.750 (36.225) Prec@5 66.250 (66.922) Epoch: [9][2880/11272] Time 0.912 (0.833) Data 0.002 (0.002) Loss 2.7106 (2.6381) Prec@1 36.250 (36.229) Prec@5 61.875 (66.923) Epoch: [9][2890/11272] Time 0.901 (0.833) Data 0.001 (0.002) Loss 2.9219 (2.6378) Prec@1 35.000 (36.238) Prec@5 60.625 (66.930) Epoch: [9][2900/11272] Time 0.801 (0.833) Data 0.002 (0.002) Loss 2.6773 (2.6380) Prec@1 38.125 (36.239) Prec@5 65.000 (66.924) Epoch: [9][2910/11272] Time 0.769 (0.833) Data 0.002 (0.002) Loss 2.6189 (2.6378) Prec@1 39.375 (36.242) Prec@5 71.250 (66.927) Epoch: [9][2920/11272] Time 0.946 (0.833) Data 0.002 (0.002) Loss 2.7344 (2.6376) Prec@1 30.000 (36.245) Prec@5 63.750 (66.931) Epoch: [9][2930/11272] Time 0.933 (0.833) Data 0.001 (0.002) Loss 2.8058 (2.6375) Prec@1 36.875 (36.250) Prec@5 64.375 (66.930) Epoch: [9][2940/11272] Time 0.776 (0.833) Data 0.003 (0.002) Loss 2.5181 (2.6375) Prec@1 40.000 (36.251) Prec@5 69.375 (66.931) Epoch: [9][2950/11272] Time 0.910 (0.833) Data 0.002 (0.002) Loss 3.0975 (2.6375) Prec@1 30.000 (36.250) Prec@5 56.250 (66.929) Epoch: [9][2960/11272] Time 0.899 (0.833) Data 0.002 (0.002) Loss 2.4704 (2.6374) Prec@1 43.125 (36.249) Prec@5 70.625 (66.931) Epoch: [9][2970/11272] Time 0.767 (0.833) Data 0.001 (0.002) Loss 2.5703 (2.6371) Prec@1 40.000 (36.258) Prec@5 71.250 (66.931) Epoch: [9][2980/11272] Time 0.726 (0.833) Data 0.001 (0.002) Loss 2.6803 (2.6373) Prec@1 33.125 (36.251) Prec@5 65.000 (66.927) Epoch: [9][2990/11272] Time 0.949 (0.833) Data 0.001 (0.002) Loss 2.9834 (2.6373) Prec@1 32.500 (36.253) Prec@5 56.250 (66.925) Epoch: [9][3000/11272] Time 0.956 (0.833) Data 0.002 (0.002) Loss 2.4982 (2.6376) Prec@1 43.125 (36.251) Prec@5 70.000 (66.916) Epoch: [9][3010/11272] Time 0.772 (0.833) Data 0.002 (0.002) Loss 2.6870 (2.6375) Prec@1 36.875 (36.255) Prec@5 67.500 (66.918) Epoch: [9][3020/11272] Time 0.769 (0.833) Data 0.002 (0.002) Loss 2.6673 (2.6376) Prec@1 33.125 (36.252) Prec@5 68.125 (66.921) Epoch: [9][3030/11272] Time 0.843 (0.833) Data 0.001 (0.002) Loss 2.6498 (2.6376) Prec@1 35.000 (36.248) Prec@5 67.500 (66.921) Epoch: [9][3040/11272] Time 0.901 (0.833) Data 0.002 (0.002) Loss 2.7603 (2.6376) Prec@1 33.125 (36.251) Prec@5 66.875 (66.922) Epoch: [9][3050/11272] Time 0.779 (0.833) Data 0.001 (0.002) Loss 2.5880 (2.6375) Prec@1 35.625 (36.251) Prec@5 66.250 (66.923) Epoch: [9][3060/11272] Time 0.757 (0.833) Data 0.002 (0.002) Loss 2.8684 (2.6379) Prec@1 33.125 (36.249) Prec@5 63.125 (66.916) Epoch: [9][3070/11272] Time 0.881 (0.833) Data 0.001 (0.002) Loss 2.9452 (2.6383) Prec@1 31.875 (36.243) Prec@5 62.500 (66.909) Epoch: [9][3080/11272] Time 0.746 (0.833) Data 0.003 (0.002) Loss 2.7231 (2.6382) Prec@1 35.000 (36.242) Prec@5 65.000 (66.911) Epoch: [9][3090/11272] Time 0.764 (0.833) Data 0.002 (0.002) Loss 2.5675 (2.6381) Prec@1 40.000 (36.244) Prec@5 70.625 (66.914) Epoch: [9][3100/11272] Time 0.883 (0.833) Data 0.002 (0.002) Loss 2.4091 (2.6378) Prec@1 41.875 (36.250) Prec@5 68.750 (66.919) Epoch: [9][3110/11272] Time 0.879 (0.833) Data 0.001 (0.002) Loss 2.5396 (2.6375) Prec@1 35.625 (36.253) Prec@5 70.000 (66.921) Epoch: [9][3120/11272] Time 0.806 (0.833) Data 0.002 (0.002) Loss 2.6246 (2.6375) Prec@1 33.750 (36.251) Prec@5 69.375 (66.924) Epoch: [9][3130/11272] Time 0.745 (0.833) Data 0.001 (0.002) Loss 2.5628 (2.6376) Prec@1 38.125 (36.252) Prec@5 68.750 (66.922) Epoch: [9][3140/11272] Time 0.883 (0.833) Data 0.002 (0.002) Loss 2.5886 (2.6374) Prec@1 40.000 (36.255) Prec@5 66.250 (66.923) Epoch: [9][3150/11272] Time 0.883 (0.833) Data 0.001 (0.002) Loss 2.9932 (2.6374) Prec@1 36.875 (36.259) Prec@5 59.375 (66.925) Epoch: [9][3160/11272] Time 0.766 (0.833) Data 0.002 (0.002) Loss 2.5473 (2.6373) Prec@1 35.000 (36.259) Prec@5 66.250 (66.928) Epoch: [9][3170/11272] Time 0.743 (0.833) Data 0.001 (0.002) Loss 2.4704 (2.6374) Prec@1 38.750 (36.255) Prec@5 72.500 (66.925) Epoch: [9][3180/11272] Time 0.865 (0.833) Data 0.002 (0.002) Loss 2.7375 (2.6371) Prec@1 35.000 (36.258) Prec@5 63.750 (66.928) Epoch: [9][3190/11272] Time 0.932 (0.833) Data 0.001 (0.002) Loss 2.6773 (2.6372) Prec@1 33.750 (36.257) Prec@5 66.875 (66.926) Epoch: [9][3200/11272] Time 0.761 (0.833) Data 0.002 (0.002) Loss 2.6451 (2.6372) Prec@1 38.750 (36.260) Prec@5 68.125 (66.925) Epoch: [9][3210/11272] Time 0.851 (0.833) Data 0.002 (0.002) Loss 2.8731 (2.6372) Prec@1 28.125 (36.257) Prec@5 63.750 (66.925) Epoch: [9][3220/11272] Time 0.911 (0.833) Data 0.002 (0.002) Loss 2.5211 (2.6372) Prec@1 41.875 (36.260) Prec@5 65.000 (66.922) Epoch: [9][3230/11272] Time 0.764 (0.833) Data 0.001 (0.002) Loss 2.4838 (2.6373) Prec@1 41.250 (36.262) Prec@5 70.000 (66.920) Epoch: [9][3240/11272] Time 0.799 (0.833) Data 0.002 (0.002) Loss 2.5885 (2.6374) Prec@1 39.375 (36.260) Prec@5 71.250 (66.917) Epoch: [9][3250/11272] Time 0.909 (0.833) Data 0.001 (0.002) Loss 2.5476 (2.6373) Prec@1 37.500 (36.256) Prec@5 70.000 (66.915) Epoch: [9][3260/11272] Time 0.920 (0.833) Data 0.002 (0.002) Loss 2.7377 (2.6373) Prec@1 31.875 (36.261) Prec@5 68.750 (66.915) Epoch: [9][3270/11272] Time 0.787 (0.833) Data 0.002 (0.002) Loss 2.5347 (2.6372) Prec@1 37.500 (36.256) Prec@5 70.625 (66.913) Epoch: [9][3280/11272] Time 0.774 (0.833) Data 0.002 (0.002) Loss 2.6841 (2.6371) Prec@1 38.750 (36.256) Prec@5 68.750 (66.919) Epoch: [9][3290/11272] Time 0.859 (0.833) Data 0.001 (0.002) Loss 2.5941 (2.6372) Prec@1 36.875 (36.253) Prec@5 66.875 (66.917) Epoch: [9][3300/11272] Time 0.888 (0.833) Data 0.002 (0.002) Loss 2.8703 (2.6375) Prec@1 32.500 (36.245) Prec@5 64.375 (66.913) Epoch: [9][3310/11272] Time 0.819 (0.833) Data 0.001 (0.002) Loss 2.7095 (2.6373) Prec@1 29.375 (36.247) Prec@5 65.625 (66.916) Epoch: [9][3320/11272] Time 0.745 (0.833) Data 0.002 (0.002) Loss 2.5084 (2.6372) Prec@1 38.750 (36.247) Prec@5 69.375 (66.915) Epoch: [9][3330/11272] Time 0.922 (0.833) Data 0.001 (0.002) Loss 2.6687 (2.6373) Prec@1 29.375 (36.242) Prec@5 70.625 (66.913) Epoch: [9][3340/11272] Time 0.740 (0.833) Data 0.003 (0.002) Loss 2.7511 (2.6372) Prec@1 30.000 (36.243) Prec@5 63.750 (66.914) Epoch: [9][3350/11272] Time 0.764 (0.833) Data 0.001 (0.002) Loss 2.5543 (2.6370) Prec@1 34.375 (36.248) Prec@5 73.125 (66.921) Epoch: [9][3360/11272] Time 0.874 (0.833) Data 0.002 (0.002) Loss 2.5404 (2.6371) Prec@1 39.375 (36.246) Prec@5 68.125 (66.917) Epoch: [9][3370/11272] Time 0.878 (0.833) Data 0.001 (0.002) Loss 2.7101 (2.6371) Prec@1 38.125 (36.245) Prec@5 66.875 (66.913) Epoch: [9][3380/11272] Time 0.775 (0.833) Data 0.002 (0.002) Loss 2.6676 (2.6371) Prec@1 31.250 (36.244) Prec@5 65.625 (66.913) Epoch: [9][3390/11272] Time 0.757 (0.833) Data 0.001 (0.002) Loss 2.6219 (2.6368) Prec@1 36.250 (36.246) Prec@5 70.000 (66.918) Epoch: [9][3400/11272] Time 0.931 (0.833) Data 0.002 (0.002) Loss 2.7737 (2.6369) Prec@1 31.875 (36.241) Prec@5 63.750 (66.916) Epoch: [9][3410/11272] Time 0.914 (0.833) Data 0.002 (0.002) Loss 2.5520 (2.6370) Prec@1 38.750 (36.238) Prec@5 68.125 (66.911) Epoch: [9][3420/11272] Time 0.784 (0.833) Data 0.002 (0.002) Loss 2.7193 (2.6369) Prec@1 33.125 (36.240) Prec@5 70.625 (66.913) Epoch: [9][3430/11272] Time 0.784 (0.833) Data 0.002 (0.002) Loss 2.5837 (2.6370) Prec@1 35.000 (36.242) Prec@5 73.125 (66.912) Epoch: [9][3440/11272] Time 0.919 (0.833) Data 0.002 (0.002) Loss 2.8908 (2.6373) Prec@1 30.625 (36.238) Prec@5 62.500 (66.907) Epoch: [9][3450/11272] Time 0.929 (0.833) Data 0.001 (0.002) Loss 2.4490 (2.6371) Prec@1 37.500 (36.237) Prec@5 71.250 (66.914) Epoch: [9][3460/11272] Time 0.769 (0.833) Data 0.002 (0.002) Loss 2.7651 (2.6371) Prec@1 30.000 (36.239) Prec@5 64.375 (66.913) Epoch: [9][3470/11272] Time 0.895 (0.833) Data 0.001 (0.002) Loss 2.6232 (2.6371) Prec@1 38.750 (36.237) Prec@5 65.625 (66.912) Epoch: [9][3480/11272] Time 0.860 (0.833) Data 0.002 (0.002) Loss 2.7779 (2.6370) Prec@1 35.000 (36.240) Prec@5 63.125 (66.911) Epoch: [9][3490/11272] Time 0.802 (0.833) Data 0.001 (0.002) Loss 2.8503 (2.6373) Prec@1 28.750 (36.232) Prec@5 63.125 (66.903) Epoch: [9][3500/11272] Time 0.747 (0.833) Data 0.002 (0.002) Loss 2.7457 (2.6373) Prec@1 37.500 (36.232) Prec@5 68.125 (66.904) Epoch: [9][3510/11272] Time 0.859 (0.833) Data 0.002 (0.002) Loss 2.7203 (2.6372) Prec@1 31.250 (36.235) Prec@5 66.250 (66.902) Epoch: [9][3520/11272] Time 0.923 (0.833) Data 0.002 (0.002) Loss 2.6598 (2.6372) Prec@1 33.750 (36.235) Prec@5 67.500 (66.906) Epoch: [9][3530/11272] Time 0.773 (0.833) Data 0.002 (0.002) Loss 2.4594 (2.6371) Prec@1 38.750 (36.236) Prec@5 65.000 (66.904) Epoch: [9][3540/11272] Time 0.794 (0.833) Data 0.002 (0.002) Loss 2.4865 (2.6371) Prec@1 38.125 (36.237) Prec@5 67.500 (66.904) Epoch: [9][3550/11272] Time 0.904 (0.833) Data 0.001 (0.002) Loss 2.5307 (2.6370) Prec@1 36.875 (36.235) Prec@5 66.875 (66.904) Epoch: [9][3560/11272] Time 1.001 (0.833) Data 0.002 (0.002) Loss 2.3246 (2.6369) Prec@1 41.875 (36.238) Prec@5 72.500 (66.908) Epoch: [9][3570/11272] Time 0.807 (0.833) Data 0.001 (0.002) Loss 2.4518 (2.6367) Prec@1 39.375 (36.241) Prec@5 67.500 (66.910) Epoch: [9][3580/11272] Time 0.770 (0.834) Data 0.002 (0.002) Loss 2.5546 (2.6365) Prec@1 33.750 (36.243) Prec@5 71.250 (66.916) Epoch: [9][3590/11272] Time 0.861 (0.834) Data 0.002 (0.002) Loss 2.5518 (2.6364) Prec@1 36.250 (36.245) Prec@5 67.500 (66.918) Epoch: [9][3600/11272] Time 0.896 (0.834) Data 0.002 (0.002) Loss 2.5794 (2.6364) Prec@1 36.250 (36.249) Prec@5 68.125 (66.921) Epoch: [9][3610/11272] Time 0.757 (0.834) Data 0.002 (0.002) Loss 2.6092 (2.6362) Prec@1 38.125 (36.252) Prec@5 65.625 (66.921) Epoch: [9][3620/11272] Time 0.936 (0.834) Data 0.002 (0.002) Loss 2.9500 (2.6365) Prec@1 32.500 (36.247) Prec@5 63.750 (66.918) Epoch: [9][3630/11272] Time 0.919 (0.834) Data 0.001 (0.002) Loss 2.6014 (2.6366) Prec@1 40.000 (36.248) Prec@5 63.125 (66.914) Epoch: [9][3640/11272] Time 0.747 (0.833) Data 0.002 (0.002) Loss 2.6709 (2.6367) Prec@1 35.000 (36.243) Prec@5 66.250 (66.918) Epoch: [9][3650/11272] Time 0.822 (0.833) Data 0.002 (0.002) Loss 2.6476 (2.6368) Prec@1 38.125 (36.241) Prec@5 66.875 (66.912) Epoch: [9][3660/11272] Time 0.894 (0.834) Data 0.002 (0.002) Loss 2.6379 (2.6369) Prec@1 35.625 (36.240) Prec@5 66.875 (66.906) Epoch: [9][3670/11272] Time 0.863 (0.834) Data 0.002 (0.002) Loss 2.6340 (2.6368) Prec@1 38.750 (36.239) Prec@5 71.250 (66.912) Epoch: [9][3680/11272] Time 0.748 (0.833) Data 0.002 (0.002) Loss 2.6549 (2.6370) Prec@1 33.750 (36.237) Prec@5 65.000 (66.905) Epoch: [9][3690/11272] Time 0.773 (0.833) Data 0.002 (0.002) Loss 2.6932 (2.6369) Prec@1 37.500 (36.238) Prec@5 63.750 (66.906) Epoch: [9][3700/11272] Time 0.892 (0.834) Data 0.002 (0.002) Loss 2.6894 (2.6371) Prec@1 36.250 (36.236) Prec@5 70.625 (66.906) Epoch: [9][3710/11272] Time 0.869 (0.834) Data 0.001 (0.002) Loss 2.5884 (2.6369) Prec@1 35.625 (36.236) Prec@5 69.375 (66.907) Epoch: [9][3720/11272] Time 0.768 (0.833) Data 0.002 (0.002) Loss 2.6405 (2.6368) Prec@1 40.000 (36.236) Prec@5 68.750 (66.910) Epoch: [9][3730/11272] Time 0.809 (0.834) Data 0.001 (0.002) Loss 2.6514 (2.6368) Prec@1 33.125 (36.234) Prec@5 66.250 (66.908) Epoch: [9][3740/11272] Time 0.884 (0.834) Data 0.001 (0.002) Loss 2.9509 (2.6368) Prec@1 34.375 (36.238) Prec@5 57.500 (66.909) Epoch: [9][3750/11272] Time 0.753 (0.833) Data 0.001 (0.002) Loss 2.8101 (2.6368) Prec@1 35.000 (36.236) Prec@5 65.000 (66.910) Epoch: [9][3760/11272] Time 0.829 (0.834) Data 0.002 (0.002) Loss 2.7728 (2.6371) Prec@1 35.000 (36.232) Prec@5 63.125 (66.899) Epoch: [9][3770/11272] Time 0.873 (0.834) Data 0.001 (0.002) Loss 2.5816 (2.6371) Prec@1 36.250 (36.232) Prec@5 64.375 (66.899) Epoch: [9][3780/11272] Time 0.861 (0.834) Data 0.003 (0.002) Loss 2.8365 (2.6374) Prec@1 29.375 (36.224) Prec@5 65.625 (66.895) Epoch: [9][3790/11272] Time 0.748 (0.834) Data 0.002 (0.002) Loss 2.4661 (2.6371) Prec@1 41.250 (36.229) Prec@5 69.375 (66.901) Epoch: [9][3800/11272] Time 0.771 (0.834) Data 0.002 (0.002) Loss 2.7669 (2.6370) Prec@1 34.375 (36.234) Prec@5 65.000 (66.902) Epoch: [9][3810/11272] Time 0.865 (0.834) Data 0.001 (0.002) Loss 2.7322 (2.6372) Prec@1 36.250 (36.230) Prec@5 68.750 (66.900) Epoch: [9][3820/11272] Time 0.902 (0.834) Data 0.002 (0.002) Loss 2.8118 (2.6372) Prec@1 31.250 (36.227) Prec@5 65.625 (66.898) Epoch: [9][3830/11272] Time 0.768 (0.834) Data 0.001 (0.002) Loss 2.5724 (2.6372) Prec@1 35.625 (36.224) Prec@5 66.250 (66.897) Epoch: [9][3840/11272] Time 0.770 (0.833) Data 0.002 (0.002) Loss 2.7656 (2.6373) Prec@1 33.750 (36.217) Prec@5 63.750 (66.895) Epoch: [9][3850/11272] Time 0.874 (0.834) Data 0.001 (0.002) Loss 2.5057 (2.6371) Prec@1 35.000 (36.221) Prec@5 68.125 (66.899) Epoch: [9][3860/11272] Time 0.976 (0.834) Data 0.002 (0.002) Loss 2.5686 (2.6370) Prec@1 28.125 (36.225) Prec@5 68.125 (66.902) Epoch: [9][3870/11272] Time 0.753 (0.834) Data 0.001 (0.002) Loss 2.5028 (2.6370) Prec@1 35.000 (36.220) Prec@5 69.375 (66.901) Epoch: [9][3880/11272] Time 0.888 (0.834) Data 0.002 (0.002) Loss 2.7371 (2.6370) Prec@1 35.000 (36.222) Prec@5 65.625 (66.901) Epoch: [9][3890/11272] Time 0.937 (0.834) Data 0.002 (0.002) Loss 2.6173 (2.6368) Prec@1 31.250 (36.224) Prec@5 68.125 (66.902) Epoch: [9][3900/11272] Time 0.776 (0.834) Data 0.002 (0.002) Loss 2.5097 (2.6370) Prec@1 35.000 (36.219) Prec@5 68.125 (66.899) Epoch: [9][3910/11272] Time 0.791 (0.834) Data 0.002 (0.002) Loss 2.5053 (2.6372) Prec@1 39.375 (36.215) Prec@5 70.000 (66.894) Epoch: [9][3920/11272] Time 0.974 (0.834) Data 0.002 (0.002) Loss 2.4191 (2.6372) Prec@1 46.875 (36.215) Prec@5 74.375 (66.893) Epoch: [9][3930/11272] Time 0.842 (0.834) Data 0.001 (0.002) Loss 2.7518 (2.6373) Prec@1 32.500 (36.214) Prec@5 65.000 (66.891) Epoch: [9][3940/11272] Time 0.783 (0.834) Data 0.002 (0.002) Loss 2.8949 (2.6371) Prec@1 33.125 (36.217) Prec@5 62.500 (66.895) Epoch: [9][3950/11272] Time 0.825 (0.834) Data 0.002 (0.002) Loss 2.4964 (2.6371) Prec@1 46.875 (36.220) Prec@5 66.250 (66.896) Epoch: [9][3960/11272] Time 0.887 (0.834) Data 0.002 (0.002) Loss 2.5906 (2.6370) Prec@1 36.875 (36.220) Prec@5 65.000 (66.893) Epoch: [9][3970/11272] Time 0.955 (0.834) Data 0.001 (0.002) Loss 2.4959 (2.6371) Prec@1 39.375 (36.220) Prec@5 69.375 (66.887) Epoch: [9][3980/11272] Time 0.778 (0.834) Data 0.002 (0.002) Loss 2.4474 (2.6370) Prec@1 43.125 (36.225) Prec@5 70.000 (66.889) Epoch: [9][3990/11272] Time 0.762 (0.834) Data 0.002 (0.002) Loss 2.8161 (2.6370) Prec@1 35.000 (36.227) Prec@5 63.125 (66.887) Epoch: [9][4000/11272] Time 0.870 (0.834) Data 0.002 (0.002) Loss 2.8336 (2.6372) Prec@1 35.625 (36.222) Prec@5 64.375 (66.885) Epoch: [9][4010/11272] Time 0.755 (0.834) Data 0.004 (0.002) Loss 2.6533 (2.6370) Prec@1 34.375 (36.223) Prec@5 65.000 (66.886) Epoch: [9][4020/11272] Time 0.779 (0.834) Data 0.002 (0.002) Loss 2.8433 (2.6370) Prec@1 35.625 (36.224) Prec@5 65.000 (66.890) Epoch: [9][4030/11272] Time 0.876 (0.834) Data 0.001 (0.002) Loss 2.4869 (2.6369) Prec@1 41.250 (36.223) Prec@5 69.375 (66.893) Epoch: [9][4040/11272] Time 0.856 (0.834) Data 0.002 (0.002) Loss 2.4638 (2.6368) Prec@1 39.375 (36.222) Prec@5 66.250 (66.893) Epoch: [9][4050/11272] Time 0.753 (0.834) Data 0.001 (0.002) Loss 2.6112 (2.6366) Prec@1 36.875 (36.227) Prec@5 66.250 (66.899) Epoch: [9][4060/11272] Time 0.753 (0.834) Data 0.002 (0.002) Loss 2.4271 (2.6365) Prec@1 41.250 (36.228) Prec@5 70.625 (66.901) Epoch: [9][4070/11272] Time 0.872 (0.834) Data 0.001 (0.002) Loss 2.6669 (2.6365) Prec@1 32.500 (36.228) Prec@5 64.375 (66.898) Epoch: [9][4080/11272] Time 0.884 (0.834) Data 0.002 (0.002) Loss 2.2483 (2.6364) Prec@1 45.625 (36.230) Prec@5 76.250 (66.901) Epoch: [9][4090/11272] Time 0.799 (0.834) Data 0.001 (0.002) Loss 2.5919 (2.6364) Prec@1 36.250 (36.233) Prec@5 66.250 (66.903) Epoch: [9][4100/11272] Time 0.796 (0.834) Data 0.002 (0.002) Loss 2.5829 (2.6364) Prec@1 36.875 (36.235) Prec@5 66.875 (66.905) Epoch: [9][4110/11272] Time 0.830 (0.834) Data 0.001 (0.002) Loss 2.7301 (2.6364) Prec@1 37.500 (36.234) Prec@5 67.500 (66.907) Epoch: [9][4120/11272] Time 0.821 (0.834) Data 0.002 (0.002) Loss 2.6133 (2.6364) Prec@1 39.375 (36.233) Prec@5 70.000 (66.909) Epoch: [9][4130/11272] Time 0.769 (0.834) Data 0.001 (0.002) Loss 2.6050 (2.6364) Prec@1 32.500 (36.232) Prec@5 63.750 (66.908) Epoch: [9][4140/11272] Time 0.899 (0.834) Data 0.004 (0.002) Loss 2.2362 (2.6364) Prec@1 44.375 (36.232) Prec@5 75.000 (66.909) Epoch: [9][4150/11272] Time 0.883 (0.834) Data 0.002 (0.002) Loss 2.5947 (2.6363) Prec@1 36.875 (36.233) Prec@5 71.250 (66.909) Epoch: [9][4160/11272] Time 0.791 (0.834) Data 0.002 (0.002) Loss 2.8107 (2.6363) Prec@1 31.875 (36.236) Prec@5 63.750 (66.909) Epoch: [9][4170/11272] Time 0.739 (0.834) Data 0.001 (0.002) Loss 2.6475 (2.6363) Prec@1 33.750 (36.235) Prec@5 65.000 (66.910) Epoch: [9][4180/11272] Time 0.919 (0.834) Data 0.004 (0.002) Loss 2.5413 (2.6365) Prec@1 43.750 (36.231) Prec@5 66.250 (66.905) Epoch: [9][4190/11272] Time 0.942 (0.834) Data 0.001 (0.002) Loss 2.6354 (2.6363) Prec@1 34.375 (36.230) Prec@5 66.250 (66.908) Epoch: [9][4200/11272] Time 0.798 (0.834) Data 0.002 (0.002) Loss 2.6714 (2.6367) Prec@1 38.125 (36.224) Prec@5 66.250 (66.900) Epoch: [9][4210/11272] Time 0.761 (0.834) Data 0.002 (0.002) Loss 2.8293 (2.6368) Prec@1 29.375 (36.219) Prec@5 63.125 (66.897) Epoch: [9][4220/11272] Time 0.896 (0.834) Data 0.002 (0.002) Loss 2.5503 (2.6370) Prec@1 41.250 (36.216) Prec@5 70.625 (66.895) Epoch: [9][4230/11272] Time 0.886 (0.834) Data 0.001 (0.002) Loss 2.6821 (2.6369) Prec@1 31.250 (36.214) Prec@5 66.875 (66.896) Epoch: [9][4240/11272] Time 0.768 (0.834) Data 0.002 (0.002) Loss 2.6892 (2.6369) Prec@1 38.750 (36.214) Prec@5 61.250 (66.895) Epoch: [9][4250/11272] Time 0.778 (0.834) Data 0.001 (0.002) Loss 2.5467 (2.6369) Prec@1 38.125 (36.214) Prec@5 66.250 (66.895) Epoch: [9][4260/11272] Time 0.907 (0.834) Data 0.001 (0.002) Loss 2.4298 (2.6368) Prec@1 42.500 (36.218) Prec@5 70.000 (66.896) Epoch: [9][4270/11272] Time 0.757 (0.834) Data 0.003 (0.002) Loss 2.4414 (2.6368) Prec@1 38.750 (36.216) Prec@5 70.000 (66.897) Epoch: [9][4280/11272] Time 0.829 (0.834) Data 0.002 (0.002) Loss 3.2091 (2.6368) Prec@1 28.750 (36.219) Prec@5 53.750 (66.896) Epoch: [9][4290/11272] Time 0.884 (0.834) Data 0.001 (0.002) Loss 2.7936 (2.6367) Prec@1 36.250 (36.224) Prec@5 62.500 (66.898) Epoch: [9][4300/11272] Time 0.907 (0.834) Data 0.002 (0.002) Loss 2.7741 (2.6367) Prec@1 40.000 (36.225) Prec@5 66.250 (66.899) Epoch: [9][4310/11272] Time 0.813 (0.834) Data 0.001 (0.002) Loss 2.5609 (2.6367) Prec@1 40.625 (36.225) Prec@5 68.750 (66.900) Epoch: [9][4320/11272] Time 0.776 (0.834) Data 0.001 (0.002) Loss 2.8387 (2.6369) Prec@1 33.125 (36.221) Prec@5 64.375 (66.897) Epoch: [9][4330/11272] Time 0.891 (0.834) Data 0.002 (0.002) Loss 2.7216 (2.6368) Prec@1 35.625 (36.221) Prec@5 62.500 (66.900) Epoch: [9][4340/11272] Time 0.940 (0.834) Data 0.002 (0.002) Loss 2.5177 (2.6367) Prec@1 36.875 (36.223) Prec@5 70.000 (66.902) Epoch: [9][4350/11272] Time 0.772 (0.834) Data 0.001 (0.002) Loss 2.7890 (2.6368) Prec@1 31.875 (36.220) Prec@5 62.500 (66.897) Epoch: [9][4360/11272] Time 0.770 (0.834) Data 0.002 (0.002) Loss 2.7598 (2.6372) Prec@1 33.750 (36.214) Prec@5 65.625 (66.890) Epoch: [9][4370/11272] Time 0.870 (0.834) Data 0.001 (0.002) Loss 2.6563 (2.6373) Prec@1 34.375 (36.209) Prec@5 71.875 (66.890) Epoch: [9][4380/11272] Time 0.931 (0.834) Data 0.002 (0.002) Loss 2.5389 (2.6373) Prec@1 38.750 (36.211) Prec@5 68.125 (66.892) Epoch: [9][4390/11272] Time 0.750 (0.834) Data 0.001 (0.002) Loss 2.8740 (2.6373) Prec@1 31.875 (36.209) Prec@5 63.125 (66.891) Epoch: [9][4400/11272] Time 0.921 (0.834) Data 0.002 (0.002) Loss 2.3765 (2.6372) Prec@1 41.875 (36.211) Prec@5 73.750 (66.893) Epoch: [9][4410/11272] Time 0.902 (0.834) Data 0.001 (0.002) Loss 2.4574 (2.6371) Prec@1 40.000 (36.211) Prec@5 71.875 (66.897) Epoch: [9][4420/11272] Time 0.771 (0.834) Data 0.001 (0.002) Loss 2.8837 (2.6371) Prec@1 36.250 (36.212) Prec@5 63.125 (66.898) Epoch: [9][4430/11272] Time 0.738 (0.834) Data 0.002 (0.002) Loss 2.6301 (2.6373) Prec@1 37.500 (36.211) Prec@5 68.750 (66.897) Epoch: [9][4440/11272] Time 0.905 (0.834) Data 0.002 (0.002) Loss 2.7043 (2.6374) Prec@1 32.500 (36.207) Prec@5 65.625 (66.896) Epoch: [9][4450/11272] Time 0.859 (0.834) Data 0.001 (0.002) Loss 2.8001 (2.6373) Prec@1 35.000 (36.209) Prec@5 63.750 (66.897) Epoch: [9][4460/11272] Time 0.753 (0.834) Data 0.002 (0.002) Loss 2.6716 (2.6373) Prec@1 35.000 (36.206) Prec@5 67.500 (66.898) Epoch: [9][4470/11272] Time 0.774 (0.834) Data 0.002 (0.002) Loss 2.6102 (2.6374) Prec@1 30.000 (36.205) Prec@5 66.875 (66.897) Epoch: [9][4480/11272] Time 0.897 (0.834) Data 0.001 (0.002) Loss 2.3045 (2.6372) Prec@1 41.250 (36.211) Prec@5 70.625 (66.901) Epoch: [9][4490/11272] Time 0.882 (0.834) Data 0.001 (0.002) Loss 2.3980 (2.6370) Prec@1 38.125 (36.211) Prec@5 71.250 (66.908) Epoch: [9][4500/11272] Time 0.789 (0.834) Data 0.002 (0.002) Loss 2.8502 (2.6370) Prec@1 33.750 (36.208) Prec@5 64.375 (66.909) Epoch: [9][4510/11272] Time 0.753 (0.834) Data 0.001 (0.002) Loss 2.6946 (2.6372) Prec@1 33.750 (36.206) Prec@5 69.375 (66.908) Epoch: [9][4520/11272] Time 0.891 (0.834) Data 0.002 (0.002) Loss 2.7504 (2.6373) Prec@1 28.750 (36.204) Prec@5 61.875 (66.904) Epoch: [9][4530/11272] Time 0.834 (0.834) Data 0.001 (0.002) Loss 2.4411 (2.6372) Prec@1 38.750 (36.204) Prec@5 69.375 (66.909) Epoch: [9][4540/11272] Time 0.763 (0.834) Data 0.001 (0.002) Loss 2.6377 (2.6372) Prec@1 37.500 (36.203) Prec@5 67.500 (66.909) Epoch: [9][4550/11272] Time 0.870 (0.834) Data 0.001 (0.002) Loss 2.6235 (2.6372) Prec@1 36.250 (36.201) Prec@5 62.500 (66.907) Epoch: [9][4560/11272] Time 0.895 (0.834) Data 0.002 (0.002) Loss 2.6221 (2.6373) Prec@1 38.750 (36.197) Prec@5 66.250 (66.906) Epoch: [9][4570/11272] Time 0.740 (0.834) Data 0.001 (0.002) Loss 2.9196 (2.6374) Prec@1 34.375 (36.196) Prec@5 58.750 (66.899) Epoch: [9][4580/11272] Time 0.787 (0.834) Data 0.002 (0.002) Loss 2.6094 (2.6376) Prec@1 33.750 (36.193) Prec@5 70.000 (66.894) Epoch: [9][4590/11272] Time 0.903 (0.834) Data 0.001 (0.002) Loss 2.7858 (2.6375) Prec@1 34.375 (36.189) Prec@5 65.000 (66.896) Epoch: [9][4600/11272] Time 0.860 (0.834) Data 0.002 (0.002) Loss 2.6665 (2.6373) Prec@1 32.500 (36.190) Prec@5 65.625 (66.894) Epoch: [9][4610/11272] Time 0.763 (0.834) Data 0.001 (0.002) Loss 2.7183 (2.6375) Prec@1 36.250 (36.187) Prec@5 65.625 (66.890) Epoch: [9][4620/11272] Time 0.759 (0.834) Data 0.003 (0.002) Loss 2.4179 (2.6374) Prec@1 40.625 (36.188) Prec@5 74.375 (66.890) Epoch: [9][4630/11272] Time 0.890 (0.834) Data 0.003 (0.002) Loss 2.4861 (2.6373) Prec@1 31.875 (36.192) Prec@5 65.625 (66.894) Epoch: [9][4640/11272] Time 0.890 (0.834) Data 0.002 (0.002) Loss 2.5036 (2.6372) Prec@1 35.625 (36.194) Prec@5 68.750 (66.892) Epoch: [9][4650/11272] Time 0.767 (0.834) Data 0.001 (0.002) Loss 2.8659 (2.6373) Prec@1 28.125 (36.194) Prec@5 62.500 (66.889) Epoch: [9][4660/11272] Time 0.737 (0.834) Data 0.001 (0.002) Loss 2.3091 (2.6373) Prec@1 39.375 (36.197) Prec@5 74.375 (66.892) Epoch: [9][4670/11272] Time 0.890 (0.834) Data 0.001 (0.002) Loss 2.7935 (2.6374) Prec@1 33.125 (36.196) Prec@5 61.875 (66.887) Epoch: [9][4680/11272] Time 0.759 (0.834) Data 0.002 (0.002) Loss 2.7372 (2.6376) Prec@1 30.000 (36.189) Prec@5 61.875 (66.882) Epoch: [9][4690/11272] Time 0.728 (0.834) Data 0.001 (0.002) Loss 2.1903 (2.6376) Prec@1 45.625 (36.191) Prec@5 75.625 (66.882) Epoch: [9][4700/11272] Time 0.920 (0.834) Data 0.002 (0.002) Loss 2.6509 (2.6376) Prec@1 33.125 (36.188) Prec@5 66.875 (66.882) Epoch: [9][4710/11272] Time 0.877 (0.834) Data 0.002 (0.002) Loss 2.7264 (2.6375) Prec@1 31.250 (36.188) Prec@5 66.250 (66.885) Epoch: [9][4720/11272] Time 0.746 (0.834) Data 0.002 (0.002) Loss 2.4592 (2.6373) Prec@1 35.625 (36.192) Prec@5 66.250 (66.887) Epoch: [9][4730/11272] Time 0.773 (0.834) Data 0.002 (0.002) Loss 2.7260 (2.6375) Prec@1 35.625 (36.188) Prec@5 63.125 (66.883) Epoch: [9][4740/11272] Time 1.011 (0.834) Data 0.004 (0.002) Loss 2.4179 (2.6374) Prec@1 36.875 (36.191) Prec@5 70.625 (66.886) Epoch: [9][4750/11272] Time 0.879 (0.834) Data 0.002 (0.002) Loss 2.4587 (2.6371) Prec@1 36.875 (36.193) Prec@5 66.250 (66.890) Epoch: [9][4760/11272] Time 0.792 (0.834) Data 0.002 (0.002) Loss 2.4811 (2.6372) Prec@1 37.500 (36.195) Prec@5 70.000 (66.890) Epoch: [9][4770/11272] Time 0.763 (0.834) Data 0.002 (0.002) Loss 2.4473 (2.6369) Prec@1 35.000 (36.196) Prec@5 71.875 (66.894) Epoch: [9][4780/11272] Time 0.926 (0.834) Data 0.002 (0.002) Loss 2.7131 (2.6369) Prec@1 33.750 (36.197) Prec@5 66.250 (66.893) Epoch: [9][4790/11272] Time 0.886 (0.834) Data 0.001 (0.002) Loss 2.3761 (2.6369) Prec@1 46.250 (36.200) Prec@5 71.875 (66.891) Epoch: [9][4800/11272] Time 0.764 (0.834) Data 0.002 (0.002) Loss 2.5962 (2.6370) Prec@1 38.750 (36.204) Prec@5 63.750 (66.891) Epoch: [9][4810/11272] Time 0.968 (0.834) Data 0.002 (0.002) Loss 2.4611 (2.6369) Prec@1 36.250 (36.203) Prec@5 74.375 (66.894) Epoch: [9][4820/11272] Time 0.860 (0.834) Data 0.002 (0.002) Loss 2.7489 (2.6371) Prec@1 33.125 (36.198) Prec@5 66.875 (66.890) Epoch: [9][4830/11272] Time 0.755 (0.834) Data 0.001 (0.002) Loss 2.9141 (2.6370) Prec@1 32.500 (36.199) Prec@5 56.250 (66.890) Epoch: [9][4840/11272] Time 0.744 (0.834) Data 0.001 (0.002) Loss 2.5394 (2.6370) Prec@1 39.375 (36.200) Prec@5 69.375 (66.892) Epoch: [9][4850/11272] Time 0.871 (0.834) Data 0.001 (0.002) Loss 2.6441 (2.6371) Prec@1 40.000 (36.198) Prec@5 69.375 (66.891) Epoch: [9][4860/11272] Time 0.896 (0.834) Data 0.001 (0.002) Loss 2.7333 (2.6370) Prec@1 34.375 (36.197) Prec@5 65.000 (66.893) Epoch: [9][4870/11272] Time 0.757 (0.834) Data 0.001 (0.002) Loss 2.7971 (2.6370) Prec@1 31.875 (36.197) Prec@5 61.875 (66.892) Epoch: [9][4880/11272] Time 0.750 (0.834) Data 0.002 (0.002) Loss 2.5513 (2.6371) Prec@1 36.875 (36.197) Prec@5 66.875 (66.889) Epoch: [9][4890/11272] Time 0.920 (0.834) Data 0.002 (0.002) Loss 2.7462 (2.6372) Prec@1 36.250 (36.195) Prec@5 66.250 (66.887) Epoch: [9][4900/11272] Time 0.853 (0.834) Data 0.001 (0.002) Loss 2.9873 (2.6373) Prec@1 29.375 (36.193) Prec@5 62.500 (66.884) Epoch: [9][4910/11272] Time 0.764 (0.833) Data 0.001 (0.002) Loss 2.5387 (2.6373) Prec@1 39.375 (36.193) Prec@5 66.875 (66.882) Epoch: [9][4920/11272] Time 0.756 (0.833) Data 0.002 (0.002) Loss 2.4025 (2.6372) Prec@1 40.000 (36.194) Prec@5 71.250 (66.887) Epoch: [9][4930/11272] Time 0.953 (0.833) Data 0.002 (0.002) Loss 2.3873 (2.6372) Prec@1 40.625 (36.195) Prec@5 71.250 (66.887) Epoch: [9][4940/11272] Time 0.757 (0.833) Data 0.004 (0.002) Loss 2.5025 (2.6372) Prec@1 35.000 (36.195) Prec@5 67.500 (66.884) Epoch: [9][4950/11272] Time 0.745 (0.833) Data 0.002 (0.002) Loss 2.3404 (2.6370) Prec@1 45.000 (36.201) Prec@5 71.875 (66.888) Epoch: [9][4960/11272] Time 0.889 (0.833) Data 0.002 (0.002) Loss 2.6015 (2.6368) Prec@1 36.250 (36.204) Prec@5 67.500 (66.889) Epoch: [9][4970/11272] Time 0.891 (0.833) Data 0.002 (0.002) Loss 2.3688 (2.6367) Prec@1 35.625 (36.205) Prec@5 72.500 (66.893) Epoch: [9][4980/11272] Time 0.731 (0.833) Data 0.001 (0.002) Loss 2.5627 (2.6367) Prec@1 35.000 (36.202) Prec@5 68.750 (66.893) Epoch: [9][4990/11272] Time 0.805 (0.833) Data 0.002 (0.002) Loss 2.6529 (2.6368) Prec@1 36.250 (36.199) Prec@5 65.000 (66.894) Epoch: [9][5000/11272] Time 0.922 (0.833) Data 0.002 (0.002) Loss 2.6940 (2.6368) Prec@1 29.375 (36.200) Prec@5 66.875 (66.895) Epoch: [9][5010/11272] Time 0.847 (0.833) Data 0.001 (0.002) Loss 2.7032 (2.6367) Prec@1 32.500 (36.201) Prec@5 64.375 (66.896) Epoch: [9][5020/11272] Time 0.757 (0.833) Data 0.001 (0.002) Loss 2.6162 (2.6367) Prec@1 35.000 (36.201) Prec@5 66.250 (66.897) Epoch: [9][5030/11272] Time 0.745 (0.833) Data 0.001 (0.002) Loss 2.7787 (2.6369) Prec@1 35.625 (36.199) Prec@5 62.500 (66.891) Epoch: [9][5040/11272] Time 0.850 (0.833) Data 0.001 (0.002) Loss 2.7826 (2.6369) Prec@1 33.125 (36.199) Prec@5 65.625 (66.890) Epoch: [9][5050/11272] Time 0.861 (0.833) Data 0.002 (0.002) Loss 2.5730 (2.6371) Prec@1 39.375 (36.195) Prec@5 70.000 (66.887) Epoch: [9][5060/11272] Time 0.715 (0.833) Data 0.002 (0.002) Loss 2.6577 (2.6371) Prec@1 32.500 (36.196) Prec@5 61.875 (66.887) Epoch: [9][5070/11272] Time 0.879 (0.833) Data 0.002 (0.002) Loss 2.4682 (2.6370) Prec@1 40.625 (36.197) Prec@5 71.250 (66.891) Epoch: [9][5080/11272] Time 0.842 (0.833) Data 0.002 (0.002) Loss 2.6683 (2.6370) Prec@1 30.625 (36.196) Prec@5 65.000 (66.892) Epoch: [9][5090/11272] Time 0.758 (0.833) Data 0.002 (0.002) Loss 2.3098 (2.6369) Prec@1 43.750 (36.198) Prec@5 75.625 (66.896) Epoch: [9][5100/11272] Time 0.761 (0.833) Data 0.002 (0.002) Loss 2.5552 (2.6370) Prec@1 36.875 (36.199) Prec@5 69.375 (66.895) Epoch: [9][5110/11272] Time 0.878 (0.833) Data 0.001 (0.002) Loss 2.7928 (2.6370) Prec@1 33.750 (36.197) Prec@5 65.000 (66.894) Epoch: [9][5120/11272] Time 0.891 (0.833) Data 0.002 (0.002) Loss 2.7607 (2.6370) Prec@1 30.625 (36.196) Prec@5 62.500 (66.892) Epoch: [9][5130/11272] Time 0.772 (0.833) Data 0.002 (0.002) Loss 2.8111 (2.6372) Prec@1 35.625 (36.194) Prec@5 58.750 (66.889) Epoch: [9][5140/11272] Time 0.761 (0.833) Data 0.002 (0.002) Loss 2.5475 (2.6372) Prec@1 37.500 (36.191) Prec@5 67.500 (66.887) Epoch: [9][5150/11272] Time 0.899 (0.833) Data 0.002 (0.002) Loss 2.4660 (2.6371) Prec@1 38.750 (36.190) Prec@5 70.625 (66.889) Epoch: [9][5160/11272] Time 0.905 (0.833) Data 0.002 (0.002) Loss 2.8189 (2.6372) Prec@1 36.875 (36.188) Prec@5 65.000 (66.888) Epoch: [9][5170/11272] Time 0.763 (0.833) Data 0.002 (0.002) Loss 2.7212 (2.6373) Prec@1 34.375 (36.185) Prec@5 68.750 (66.889) Epoch: [9][5180/11272] Time 0.751 (0.833) Data 0.002 (0.002) Loss 2.8820 (2.6374) Prec@1 31.875 (36.184) Prec@5 62.500 (66.887) Epoch: [9][5190/11272] Time 1.005 (0.833) Data 0.002 (0.002) Loss 2.6926 (2.6374) Prec@1 34.375 (36.182) Prec@5 67.500 (66.887) Epoch: [9][5200/11272] Time 0.739 (0.833) Data 0.004 (0.002) Loss 2.6069 (2.6373) Prec@1 40.625 (36.188) Prec@5 66.250 (66.889) Epoch: [9][5210/11272] Time 0.726 (0.833) Data 0.002 (0.002) Loss 2.4359 (2.6374) Prec@1 36.875 (36.186) Prec@5 75.625 (66.886) Epoch: [9][5220/11272] Time 0.860 (0.833) Data 0.001 (0.002) Loss 2.8454 (2.6376) Prec@1 28.750 (36.180) Prec@5 66.875 (66.883) Epoch: [9][5230/11272] Time 0.818 (0.833) Data 0.001 (0.002) Loss 2.7113 (2.6376) Prec@1 32.500 (36.181) Prec@5 66.250 (66.882) Epoch: [9][5240/11272] Time 0.761 (0.832) Data 0.001 (0.002) Loss 2.6896 (2.6377) Prec@1 32.500 (36.176) Prec@5 66.250 (66.880) Epoch: [9][5250/11272] Time 0.775 (0.832) Data 0.002 (0.002) Loss 2.5952 (2.6377) Prec@1 38.125 (36.175) Prec@5 66.250 (66.878) Epoch: [9][5260/11272] Time 0.881 (0.832) Data 0.002 (0.002) Loss 2.6695 (2.6376) Prec@1 31.250 (36.176) Prec@5 70.000 (66.880) Epoch: [9][5270/11272] Time 0.876 (0.832) Data 0.002 (0.002) Loss 2.6833 (2.6375) Prec@1 33.125 (36.175) Prec@5 66.875 (66.882) Epoch: [9][5280/11272] Time 0.762 (0.832) Data 0.001 (0.002) Loss 2.7133 (2.6377) Prec@1 33.750 (36.173) Prec@5 63.125 (66.881) Epoch: [9][5290/11272] Time 0.745 (0.832) Data 0.001 (0.002) Loss 2.8510 (2.6377) Prec@1 28.125 (36.168) Prec@5 61.875 (66.879) Epoch: [9][5300/11272] Time 0.923 (0.832) Data 0.003 (0.002) Loss 2.6804 (2.6377) Prec@1 34.375 (36.166) Prec@5 65.000 (66.879) Epoch: [9][5310/11272] Time 0.880 (0.832) Data 0.002 (0.002) Loss 2.4245 (2.6377) Prec@1 44.375 (36.169) Prec@5 70.000 (66.880) Epoch: [9][5320/11272] Time 0.764 (0.832) Data 0.002 (0.002) Loss 2.8040 (2.6377) Prec@1 33.750 (36.170) Prec@5 65.625 (66.882) Epoch: [9][5330/11272] Time 0.901 (0.832) Data 0.002 (0.002) Loss 2.6843 (2.6376) Prec@1 36.875 (36.171) Prec@5 67.500 (66.884) Epoch: [9][5340/11272] Time 0.889 (0.832) Data 0.001 (0.002) Loss 2.8040 (2.6376) Prec@1 36.875 (36.170) Prec@5 64.375 (66.884) Epoch: [9][5350/11272] Time 0.766 (0.832) Data 0.001 (0.002) Loss 2.7659 (2.6377) Prec@1 31.875 (36.164) Prec@5 63.125 (66.883) Epoch: [9][5360/11272] Time 0.768 (0.832) Data 0.002 (0.002) Loss 2.6398 (2.6377) Prec@1 33.125 (36.164) Prec@5 68.125 (66.884) Epoch: [9][5370/11272] Time 0.906 (0.832) Data 0.002 (0.002) Loss 2.6085 (2.6377) Prec@1 33.125 (36.160) Prec@5 73.125 (66.887) Epoch: [9][5380/11272] Time 0.872 (0.832) Data 0.002 (0.002) Loss 2.4960 (2.6377) Prec@1 34.375 (36.156) Prec@5 66.875 (66.885) Epoch: [9][5390/11272] Time 0.727 (0.832) Data 0.003 (0.002) Loss 2.4978 (2.6377) Prec@1 38.750 (36.155) Prec@5 70.000 (66.887) Epoch: [9][5400/11272] Time 0.766 (0.832) Data 0.002 (0.002) Loss 2.8296 (2.6376) Prec@1 35.000 (36.158) Prec@5 63.125 (66.889) Epoch: [9][5410/11272] Time 0.841 (0.832) Data 0.001 (0.002) Loss 2.4296 (2.6376) Prec@1 40.625 (36.159) Prec@5 69.375 (66.889) Epoch: [9][5420/11272] Time 0.889 (0.832) Data 0.002 (0.002) Loss 2.3945 (2.6376) Prec@1 40.625 (36.161) Prec@5 70.625 (66.889) Epoch: [9][5430/11272] Time 0.800 (0.832) Data 0.002 (0.002) Loss 2.6642 (2.6375) Prec@1 36.250 (36.164) Prec@5 69.375 (66.890) Epoch: [9][5440/11272] Time 0.762 (0.832) Data 0.002 (0.002) Loss 2.5870 (2.6376) Prec@1 31.250 (36.162) Prec@5 73.125 (66.891) Epoch: [9][5450/11272] Time 0.867 (0.832) Data 0.001 (0.002) Loss 2.6946 (2.6376) Prec@1 36.875 (36.163) Prec@5 62.500 (66.889) Epoch: [9][5460/11272] Time 0.863 (0.832) Data 0.002 (0.002) Loss 2.8220 (2.6376) Prec@1 33.125 (36.163) Prec@5 61.250 (66.889) Epoch: [9][5470/11272] Time 0.753 (0.832) Data 0.002 (0.002) Loss 2.8222 (2.6377) Prec@1 31.875 (36.161) Prec@5 62.500 (66.886) Epoch: [9][5480/11272] Time 0.954 (0.832) Data 0.002 (0.002) Loss 2.6521 (2.6377) Prec@1 36.875 (36.163) Prec@5 64.375 (66.886) Epoch: [9][5490/11272] Time 0.878 (0.832) Data 0.001 (0.002) Loss 2.6386 (2.6376) Prec@1 35.000 (36.166) Prec@5 68.125 (66.887) Epoch: [9][5500/11272] Time 0.752 (0.832) Data 0.002 (0.002) Loss 2.9949 (2.6377) Prec@1 29.375 (36.163) Prec@5 57.500 (66.885) Epoch: [9][5510/11272] Time 0.750 (0.832) Data 0.002 (0.002) Loss 2.5943 (2.6376) Prec@1 38.125 (36.164) Prec@5 68.125 (66.889) Epoch: [9][5520/11272] Time 0.832 (0.832) Data 0.001 (0.002) Loss 2.3981 (2.6376) Prec@1 40.000 (36.164) Prec@5 70.625 (66.888) Epoch: [9][5530/11272] Time 0.880 (0.832) Data 0.002 (0.002) Loss 3.1556 (2.6375) Prec@1 26.875 (36.164) Prec@5 61.250 (66.888) Epoch: [9][5540/11272] Time 0.701 (0.832) Data 0.001 (0.002) Loss 2.6812 (2.6376) Prec@1 32.500 (36.163) Prec@5 66.250 (66.889) Epoch: [9][5550/11272] Time 0.765 (0.832) Data 0.002 (0.002) Loss 2.5038 (2.6376) Prec@1 35.625 (36.165) Prec@5 66.250 (66.890) Epoch: [9][5560/11272] Time 0.867 (0.832) Data 0.002 (0.002) Loss 2.7214 (2.6377) Prec@1 33.750 (36.165) Prec@5 64.375 (66.888) Epoch: [9][5570/11272] Time 0.885 (0.832) Data 0.002 (0.002) Loss 2.6134 (2.6377) Prec@1 36.875 (36.163) Prec@5 68.750 (66.889) Epoch: [9][5580/11272] Time 0.750 (0.832) Data 0.001 (0.002) Loss 2.6965 (2.6377) Prec@1 29.375 (36.164) Prec@5 66.875 (66.890) Epoch: [9][5590/11272] Time 0.763 (0.832) Data 0.002 (0.002) Loss 2.3282 (2.6376) Prec@1 45.625 (36.168) Prec@5 71.875 (66.893) Epoch: [9][5600/11272] Time 0.867 (0.832) Data 0.002 (0.002) Loss 2.4242 (2.6375) Prec@1 44.375 (36.170) Prec@5 71.250 (66.894) Epoch: [9][5610/11272] Time 0.747 (0.832) Data 0.001 (0.002) Loss 2.6779 (2.6374) Prec@1 35.625 (36.173) Prec@5 65.625 (66.899) Epoch: [9][5620/11272] Time 0.807 (0.832) Data 0.002 (0.002) Loss 2.4621 (2.6373) Prec@1 40.625 (36.174) Prec@5 67.500 (66.898) Epoch: [9][5630/11272] Time 0.932 (0.832) Data 0.002 (0.002) Loss 2.5236 (2.6374) Prec@1 36.875 (36.177) Prec@5 71.875 (66.899) Epoch: [9][5640/11272] Time 0.900 (0.832) Data 0.002 (0.002) Loss 2.5339 (2.6373) Prec@1 37.500 (36.180) Prec@5 66.875 (66.899) Epoch: [9][5650/11272] Time 0.746 (0.832) Data 0.002 (0.002) Loss 2.5906 (2.6373) Prec@1 33.125 (36.176) Prec@5 70.625 (66.902) Epoch: [9][5660/11272] Time 0.750 (0.831) Data 0.001 (0.002) Loss 2.6557 (2.6372) Prec@1 31.875 (36.179) Prec@5 64.375 (66.904) Epoch: [9][5670/11272] Time 0.866 (0.831) Data 0.002 (0.002) Loss 2.7874 (2.6372) Prec@1 33.750 (36.179) Prec@5 61.250 (66.901) Epoch: [9][5680/11272] Time 0.856 (0.831) Data 0.002 (0.002) Loss 2.2366 (2.6371) Prec@1 43.125 (36.180) Prec@5 74.375 (66.906) Epoch: [9][5690/11272] Time 0.758 (0.831) Data 0.002 (0.002) Loss 2.7738 (2.6370) Prec@1 33.750 (36.180) Prec@5 65.000 (66.906) Epoch: [9][5700/11272] Time 0.802 (0.831) Data 0.002 (0.002) Loss 2.8808 (2.6371) Prec@1 26.250 (36.179) Prec@5 63.125 (66.904) Epoch: [9][5710/11272] Time 0.870 (0.831) Data 0.002 (0.002) Loss 2.5242 (2.6371) Prec@1 38.750 (36.181) Prec@5 68.125 (66.903) Epoch: [9][5720/11272] Time 0.913 (0.831) Data 0.002 (0.002) Loss 2.5715 (2.6370) Prec@1 36.250 (36.186) Prec@5 68.125 (66.905) Epoch: [9][5730/11272] Time 0.740 (0.831) Data 0.002 (0.002) Loss 2.6646 (2.6370) Prec@1 37.500 (36.186) Prec@5 66.875 (66.904) Epoch: [9][5740/11272] Time 0.934 (0.831) Data 0.002 (0.002) Loss 2.7722 (2.6370) Prec@1 36.875 (36.186) Prec@5 62.500 (66.902) Epoch: [9][5750/11272] Time 0.871 (0.831) Data 0.001 (0.002) Loss 2.5497 (2.6370) Prec@1 34.375 (36.184) Prec@5 69.375 (66.901) Epoch: [9][5760/11272] Time 0.748 (0.831) Data 0.002 (0.002) Loss 2.7041 (2.6370) Prec@1 32.500 (36.184) Prec@5 62.500 (66.900) Epoch: [9][5770/11272] Time 0.777 (0.831) Data 0.001 (0.002) Loss 2.8395 (2.6370) Prec@1 28.750 (36.183) Prec@5 63.750 (66.900) Epoch: [9][5780/11272] Time 0.889 (0.831) Data 0.002 (0.002) Loss 2.4133 (2.6369) Prec@1 41.875 (36.185) Prec@5 72.500 (66.902) Epoch: [9][5790/11272] Time 0.864 (0.831) Data 0.002 (0.002) Loss 2.5363 (2.6369) Prec@1 30.625 (36.185) Prec@5 68.750 (66.903) Epoch: [9][5800/11272] Time 0.784 (0.831) Data 0.002 (0.002) Loss 2.7042 (2.6368) Prec@1 40.000 (36.188) Prec@5 61.250 (66.900) Epoch: [9][5810/11272] Time 0.778 (0.831) Data 0.002 (0.002) Loss 2.8227 (2.6368) Prec@1 35.625 (36.185) Prec@5 64.375 (66.902) Epoch: [9][5820/11272] Time 0.938 (0.831) Data 0.002 (0.002) Loss 2.6939 (2.6368) Prec@1 33.125 (36.188) Prec@5 68.125 (66.902) Epoch: [9][5830/11272] Time 0.843 (0.831) Data 0.001 (0.002) Loss 2.6140 (2.6367) Prec@1 35.000 (36.189) Prec@5 65.000 (66.901) Epoch: [9][5840/11272] Time 0.791 (0.831) Data 0.001 (0.002) Loss 2.9934 (2.6367) Prec@1 26.875 (36.188) Prec@5 58.125 (66.898) Epoch: [9][5850/11272] Time 0.758 (0.831) Data 0.001 (0.002) Loss 2.6465 (2.6366) Prec@1 30.625 (36.189) Prec@5 69.375 (66.901) Epoch: [9][5860/11272] Time 0.846 (0.831) Data 0.002 (0.002) Loss 2.5310 (2.6367) Prec@1 36.250 (36.186) Prec@5 70.000 (66.899) Epoch: [9][5870/11272] Time 0.727 (0.831) Data 0.004 (0.002) Loss 2.5877 (2.6368) Prec@1 37.500 (36.186) Prec@5 64.375 (66.898) Epoch: [9][5880/11272] Time 0.748 (0.831) Data 0.002 (0.002) Loss 2.7449 (2.6369) Prec@1 29.375 (36.184) Prec@5 61.875 (66.896) Epoch: [9][5890/11272] Time 0.827 (0.831) Data 0.002 (0.002) Loss 2.4413 (2.6369) Prec@1 41.875 (36.183) Prec@5 68.750 (66.895) Epoch: [9][5900/11272] Time 0.936 (0.831) Data 0.002 (0.002) Loss 3.0868 (2.6370) Prec@1 27.500 (36.182) Prec@5 59.375 (66.892) Epoch: [9][5910/11272] Time 0.746 (0.831) Data 0.002 (0.002) Loss 2.5459 (2.6371) Prec@1 36.250 (36.179) Prec@5 68.125 (66.890) Epoch: [9][5920/11272] Time 0.760 (0.831) Data 0.002 (0.002) Loss 2.5846 (2.6370) Prec@1 31.250 (36.181) Prec@5 70.000 (66.892) Epoch: [9][5930/11272] Time 0.829 (0.831) Data 0.002 (0.002) Loss 2.7219 (2.6370) Prec@1 33.750 (36.184) Prec@5 64.375 (66.890) Epoch: [9][5940/11272] Time 0.855 (0.831) Data 0.002 (0.002) Loss 2.7185 (2.6370) Prec@1 33.750 (36.180) Prec@5 66.250 (66.889) Epoch: [9][5950/11272] Time 0.743 (0.831) Data 0.002 (0.002) Loss 2.4496 (2.6371) Prec@1 40.000 (36.180) Prec@5 68.750 (66.887) Epoch: [9][5960/11272] Time 0.767 (0.831) Data 0.002 (0.002) Loss 2.4647 (2.6371) Prec@1 42.500 (36.180) Prec@5 70.625 (66.889) Epoch: [9][5970/11272] Time 0.841 (0.831) Data 0.002 (0.002) Loss 2.8002 (2.6372) Prec@1 32.500 (36.180) Prec@5 61.875 (66.889) Epoch: [9][5980/11272] Time 0.901 (0.831) Data 0.001 (0.002) Loss 2.8404 (2.6372) Prec@1 35.625 (36.181) Prec@5 63.125 (66.890) Epoch: [9][5990/11272] Time 0.778 (0.831) Data 0.001 (0.002) Loss 2.6541 (2.6372) Prec@1 36.250 (36.181) Prec@5 69.375 (66.892) Epoch: [9][6000/11272] Time 0.931 (0.831) Data 0.001 (0.002) Loss 2.6961 (2.6371) Prec@1 30.625 (36.179) Prec@5 69.375 (66.893) Epoch: [9][6010/11272] Time 0.898 (0.831) Data 0.002 (0.002) Loss 2.6237 (2.6371) Prec@1 35.625 (36.181) Prec@5 65.000 (66.892) Epoch: [9][6020/11272] Time 0.756 (0.831) Data 0.001 (0.002) Loss 2.6866 (2.6372) Prec@1 35.625 (36.180) Prec@5 61.250 (66.888) Epoch: [9][6030/11272] Time 0.748 (0.831) Data 0.002 (0.002) Loss 2.9589 (2.6372) Prec@1 28.125 (36.181) Prec@5 61.875 (66.889) Epoch: [9][6040/11272] Time 0.959 (0.831) Data 0.002 (0.002) Loss 2.6512 (2.6371) Prec@1 38.125 (36.182) Prec@5 67.500 (66.892) Epoch: [9][6050/11272] Time 0.901 (0.831) Data 0.002 (0.002) Loss 2.5431 (2.6371) Prec@1 38.125 (36.180) Prec@5 68.750 (66.892) Epoch: [9][6060/11272] Time 0.717 (0.831) Data 0.001 (0.002) Loss 2.6875 (2.6371) Prec@1 33.750 (36.180) Prec@5 65.000 (66.892) Epoch: [9][6070/11272] Time 0.854 (0.831) Data 0.002 (0.002) Loss 2.5223 (2.6371) Prec@1 41.250 (36.179) Prec@5 68.750 (66.891) Epoch: [9][6080/11272] Time 0.880 (0.831) Data 0.002 (0.002) Loss 2.8182 (2.6372) Prec@1 33.125 (36.180) Prec@5 65.625 (66.890) Epoch: [9][6090/11272] Time 0.909 (0.831) Data 0.002 (0.002) Loss 2.5438 (2.6371) Prec@1 41.250 (36.182) Prec@5 70.625 (66.891) Epoch: [9][6100/11272] Time 0.773 (0.831) Data 0.002 (0.002) Loss 2.6217 (2.6372) Prec@1 34.375 (36.182) Prec@5 65.000 (66.889) Epoch: [9][6110/11272] Time 0.736 (0.831) Data 0.002 (0.002) Loss 2.6244 (2.6372) Prec@1 32.500 (36.179) Prec@5 63.750 (66.886) Epoch: [9][6120/11272] Time 0.881 (0.831) Data 0.002 (0.002) Loss 2.4913 (2.6371) Prec@1 40.625 (36.183) Prec@5 66.875 (66.890) Epoch: [9][6130/11272] Time 0.759 (0.831) Data 0.004 (0.002) Loss 2.6283 (2.6371) Prec@1 36.250 (36.185) Prec@5 63.125 (66.890) Epoch: [9][6140/11272] Time 0.770 (0.831) Data 0.002 (0.002) Loss 2.6720 (2.6370) Prec@1 36.875 (36.187) Prec@5 68.125 (66.891) Epoch: [9][6150/11272] Time 0.865 (0.831) Data 0.002 (0.002) Loss 2.7289 (2.6369) Prec@1 35.625 (36.191) Prec@5 63.125 (66.893) Epoch: [9][6160/11272] Time 0.901 (0.831) Data 0.002 (0.002) Loss 2.7064 (2.6369) Prec@1 35.000 (36.191) Prec@5 66.250 (66.893) Epoch: [9][6170/11272] Time 0.776 (0.830) Data 0.002 (0.002) Loss 2.8436 (2.6369) Prec@1 36.250 (36.192) Prec@5 65.000 (66.894) Epoch: [9][6180/11272] Time 0.738 (0.830) Data 0.006 (0.002) Loss 2.4699 (2.6370) Prec@1 40.000 (36.193) Prec@5 72.500 (66.893) Epoch: [9][6190/11272] Time 0.957 (0.830) Data 0.002 (0.002) Loss 2.4629 (2.6371) Prec@1 41.250 (36.193) Prec@5 73.125 (66.890) Epoch: [9][6200/11272] Time 0.920 (0.830) Data 0.001 (0.002) Loss 2.6717 (2.6372) Prec@1 39.375 (36.194) Prec@5 65.000 (66.889) Epoch: [9][6210/11272] Time 0.744 (0.830) Data 0.002 (0.002) Loss 2.9125 (2.6373) Prec@1 29.375 (36.192) Prec@5 60.625 (66.884) Epoch: [9][6220/11272] Time 0.753 (0.830) Data 0.001 (0.002) Loss 2.6094 (2.6374) Prec@1 35.625 (36.192) Prec@5 67.500 (66.884) Epoch: [9][6230/11272] Time 0.903 (0.830) Data 0.002 (0.002) Loss 2.5581 (2.6374) Prec@1 36.875 (36.193) Prec@5 71.875 (66.886) Epoch: [9][6240/11272] Time 0.795 (0.830) Data 0.001 (0.002) Loss 2.7885 (2.6374) Prec@1 32.500 (36.191) Prec@5 71.250 (66.888) Epoch: [9][6250/11272] Time 0.774 (0.830) Data 0.002 (0.002) Loss 2.7611 (2.6375) Prec@1 40.000 (36.191) Prec@5 63.750 (66.888) Epoch: [9][6260/11272] Time 0.907 (0.830) Data 0.001 (0.002) Loss 2.4757 (2.6374) Prec@1 39.375 (36.193) Prec@5 69.375 (66.888) Epoch: [9][6270/11272] Time 0.903 (0.830) Data 0.001 (0.002) Loss 2.8204 (2.6375) Prec@1 30.000 (36.191) Prec@5 63.750 (66.884) Epoch: [9][6280/11272] Time 0.763 (0.830) Data 0.002 (0.002) Loss 2.7209 (2.6374) Prec@1 33.125 (36.190) Prec@5 65.000 (66.886) Epoch: [9][6290/11272] Time 0.757 (0.830) Data 0.001 (0.002) Loss 2.7175 (2.6375) Prec@1 35.625 (36.186) Prec@5 61.250 (66.883) Epoch: [9][6300/11272] Time 0.839 (0.830) Data 0.002 (0.002) Loss 2.5881 (2.6376) Prec@1 38.750 (36.186) Prec@5 65.000 (66.881) Epoch: [9][6310/11272] Time 0.883 (0.830) Data 0.002 (0.002) Loss 2.6172 (2.6375) Prec@1 34.375 (36.187) Prec@5 65.625 (66.883) Epoch: [9][6320/11272] Time 0.747 (0.830) Data 0.002 (0.002) Loss 2.7136 (2.6376) Prec@1 35.000 (36.187) Prec@5 63.125 (66.882) Epoch: [9][6330/11272] Time 0.755 (0.830) Data 0.002 (0.002) Loss 2.4735 (2.6375) Prec@1 41.250 (36.190) Prec@5 75.000 (66.884) Epoch: [9][6340/11272] Time 0.902 (0.830) Data 0.001 (0.002) Loss 2.6232 (2.6374) Prec@1 40.000 (36.192) Prec@5 67.500 (66.886) Epoch: [9][6350/11272] Time 0.836 (0.830) Data 0.002 (0.002) Loss 2.6191 (2.6375) Prec@1 33.125 (36.194) Prec@5 65.625 (66.884) Epoch: [9][6360/11272] Time 0.683 (0.830) Data 0.001 (0.002) Loss 2.4218 (2.6375) Prec@1 41.875 (36.195) Prec@5 70.625 (66.885) Epoch: [9][6370/11272] Time 0.764 (0.830) Data 0.001 (0.002) Loss 2.8302 (2.6375) Prec@1 30.625 (36.193) Prec@5 61.875 (66.886) Epoch: [9][6380/11272] Time 0.847 (0.830) Data 0.002 (0.002) Loss 2.9297 (2.6375) Prec@1 32.500 (36.194) Prec@5 58.125 (66.885) Epoch: [9][6390/11272] Time 0.848 (0.830) Data 0.002 (0.002) Loss 2.5530 (2.6376) Prec@1 38.750 (36.196) Prec@5 66.875 (66.885) Epoch: [9][6400/11272] Time 0.762 (0.830) Data 0.002 (0.002) Loss 2.8938 (2.6376) Prec@1 25.000 (36.195) Prec@5 65.000 (66.886) Epoch: [9][6410/11272] Time 0.828 (0.830) Data 0.001 (0.002) Loss 2.7715 (2.6375) Prec@1 34.375 (36.197) Prec@5 64.375 (66.887) Epoch: [9][6420/11272] Time 0.908 (0.830) Data 0.002 (0.002) Loss 2.5793 (2.6375) Prec@1 41.250 (36.197) Prec@5 64.375 (66.887) Epoch: [9][6430/11272] Time 0.768 (0.830) Data 0.002 (0.002) Loss 2.6245 (2.6375) Prec@1 33.125 (36.197) Prec@5 69.375 (66.887) Epoch: [9][6440/11272] Time 0.796 (0.830) Data 0.002 (0.002) Loss 2.5556 (2.6375) Prec@1 32.500 (36.195) Prec@5 66.250 (66.888) Epoch: [9][6450/11272] Time 0.870 (0.830) Data 0.001 (0.002) Loss 2.9163 (2.6375) Prec@1 31.875 (36.194) Prec@5 61.875 (66.888) Epoch: [9][6460/11272] Time 0.872 (0.830) Data 0.002 (0.002) Loss 2.6239 (2.6374) Prec@1 40.625 (36.196) Prec@5 63.750 (66.890) Epoch: [9][6470/11272] Time 0.748 (0.830) Data 0.001 (0.002) Loss 2.8955 (2.6375) Prec@1 31.250 (36.198) Prec@5 60.000 (66.891) Epoch: [9][6480/11272] Time 0.750 (0.830) Data 0.002 (0.002) Loss 2.7726 (2.6376) Prec@1 38.125 (36.198) Prec@5 62.500 (66.890) Epoch: [9][6490/11272] Time 0.913 (0.830) Data 0.002 (0.002) Loss 2.8890 (2.6377) Prec@1 30.625 (36.197) Prec@5 66.875 (66.889) Epoch: [9][6500/11272] Time 0.945 (0.830) Data 0.002 (0.002) Loss 2.5821 (2.6376) Prec@1 38.125 (36.198) Prec@5 66.875 (66.891) Epoch: [9][6510/11272] Time 0.775 (0.830) Data 0.002 (0.002) Loss 2.5435 (2.6375) Prec@1 37.500 (36.198) Prec@5 69.375 (66.893) Epoch: [9][6520/11272] Time 0.914 (0.830) Data 0.002 (0.002) Loss 2.4336 (2.6376) Prec@1 40.625 (36.199) Prec@5 69.375 (66.893) Epoch: [9][6530/11272] Time 0.877 (0.830) Data 0.002 (0.002) Loss 2.8148 (2.6376) Prec@1 36.250 (36.199) Prec@5 62.500 (66.893) Epoch: [9][6540/11272] Time 0.764 (0.830) Data 0.002 (0.002) Loss 2.7156 (2.6376) Prec@1 33.750 (36.196) Prec@5 66.250 (66.892) Epoch: [9][6550/11272] Time 0.783 (0.830) Data 0.002 (0.002) Loss 2.4651 (2.6376) Prec@1 38.125 (36.197) Prec@5 71.875 (66.893) Epoch: [9][6560/11272] Time 0.914 (0.830) Data 0.001 (0.002) Loss 2.4804 (2.6375) Prec@1 38.125 (36.199) Prec@5 67.500 (66.894) Epoch: [9][6570/11272] Time 0.923 (0.830) Data 0.002 (0.002) Loss 2.7007 (2.6375) Prec@1 31.250 (36.198) Prec@5 61.875 (66.893) Epoch: [9][6580/11272] Time 0.731 (0.830) Data 0.001 (0.002) Loss 2.6154 (2.6376) Prec@1 33.125 (36.194) Prec@5 67.500 (66.891) Epoch: [9][6590/11272] Time 0.755 (0.830) Data 0.002 (0.002) Loss 2.7829 (2.6376) Prec@1 32.500 (36.194) Prec@5 60.000 (66.892) Epoch: [9][6600/11272] Time 0.896 (0.830) Data 0.002 (0.002) Loss 2.8335 (2.6377) Prec@1 31.250 (36.194) Prec@5 63.125 (66.889) Epoch: [9][6610/11272] Time 0.856 (0.830) Data 0.002 (0.002) Loss 2.4802 (2.6378) Prec@1 39.375 (36.189) Prec@5 72.500 (66.885) Epoch: [9][6620/11272] Time 0.767 (0.830) Data 0.002 (0.002) Loss 2.8211 (2.6377) Prec@1 30.000 (36.192) Prec@5 58.750 (66.888) Epoch: [9][6630/11272] Time 0.739 (0.830) Data 0.002 (0.002) Loss 2.6070 (2.6376) Prec@1 33.750 (36.193) Prec@5 70.000 (66.888) Epoch: [9][6640/11272] Time 0.874 (0.830) Data 0.001 (0.002) Loss 2.7935 (2.6377) Prec@1 35.000 (36.193) Prec@5 67.500 (66.890) Epoch: [9][6650/11272] Time 0.869 (0.830) Data 0.001 (0.002) Loss 2.7568 (2.6378) Prec@1 30.625 (36.189) Prec@5 58.750 (66.886) Epoch: [9][6660/11272] Time 0.750 (0.830) Data 0.002 (0.002) Loss 2.8794 (2.6379) Prec@1 31.875 (36.186) Prec@5 58.750 (66.884) Epoch: [9][6670/11272] Time 0.878 (0.830) Data 0.002 (0.002) Loss 2.6986 (2.6378) Prec@1 35.000 (36.187) Prec@5 64.375 (66.887) Epoch: [9][6680/11272] Time 0.858 (0.830) Data 0.002 (0.002) Loss 2.7916 (2.6378) Prec@1 35.000 (36.185) Prec@5 64.375 (66.885) Epoch: [9][6690/11272] Time 0.751 (0.830) Data 0.002 (0.002) Loss 2.5710 (2.6379) Prec@1 34.375 (36.183) Prec@5 69.375 (66.884) Epoch: [9][6700/11272] Time 0.722 (0.830) Data 0.002 (0.002) Loss 2.5634 (2.6379) Prec@1 39.375 (36.183) Prec@5 65.000 (66.883) Epoch: [9][6710/11272] Time 0.874 (0.830) Data 0.001 (0.002) Loss 2.7598 (2.6381) Prec@1 36.250 (36.181) Prec@5 60.000 (66.878) Epoch: [9][6720/11272] Time 0.832 (0.830) Data 0.002 (0.002) Loss 2.5572 (2.6381) Prec@1 39.375 (36.182) Prec@5 68.125 (66.878) Epoch: [9][6730/11272] Time 0.763 (0.830) Data 0.002 (0.002) Loss 2.6803 (2.6381) Prec@1 35.625 (36.183) Prec@5 67.500 (66.878) Epoch: [9][6740/11272] Time 0.785 (0.830) Data 0.002 (0.002) Loss 2.6310 (2.6380) Prec@1 34.375 (36.183) Prec@5 67.500 (66.880) Epoch: [9][6750/11272] Time 0.860 (0.830) Data 0.001 (0.002) Loss 2.9110 (2.6381) Prec@1 32.500 (36.183) Prec@5 61.875 (66.878) Epoch: [9][6760/11272] Time 0.866 (0.830) Data 0.002 (0.002) Loss 2.3535 (2.6380) Prec@1 42.500 (36.183) Prec@5 69.375 (66.880) Epoch: [9][6770/11272] Time 0.769 (0.829) Data 0.001 (0.002) Loss 2.5450 (2.6379) Prec@1 37.500 (36.186) Prec@5 66.875 (66.880) Epoch: [9][6780/11272] Time 0.722 (0.829) Data 0.001 (0.002) Loss 2.6402 (2.6379) Prec@1 34.375 (36.186) Prec@5 65.625 (66.880) Epoch: [9][6790/11272] Time 0.862 (0.829) Data 0.001 (0.002) Loss 2.5872 (2.6379) Prec@1 41.875 (36.189) Prec@5 67.500 (66.880) Epoch: [9][6800/11272] Time 0.732 (0.829) Data 0.004 (0.002) Loss 2.6738 (2.6378) Prec@1 35.625 (36.188) Prec@5 65.625 (66.882) Epoch: [9][6810/11272] Time 0.731 (0.829) Data 0.001 (0.002) Loss 2.4231 (2.6378) Prec@1 36.875 (36.186) Prec@5 71.250 (66.882) Epoch: [9][6820/11272] Time 0.834 (0.829) Data 0.001 (0.002) Loss 2.6123 (2.6378) Prec@1 39.375 (36.185) Prec@5 64.375 (66.882) Epoch: [9][6830/11272] Time 0.857 (0.829) Data 0.001 (0.002) Loss 2.6565 (2.6377) Prec@1 31.250 (36.185) Prec@5 68.125 (66.884) Epoch: [9][6840/11272] Time 0.751 (0.829) Data 0.002 (0.002) Loss 2.4533 (2.6378) Prec@1 43.125 (36.183) Prec@5 66.250 (66.884) Epoch: [9][6850/11272] Time 0.720 (0.829) Data 0.001 (0.002) Loss 2.7083 (2.6378) Prec@1 35.625 (36.184) Prec@5 68.125 (66.886) Epoch: [9][6860/11272] Time 0.928 (0.829) Data 0.002 (0.002) Loss 2.9487 (2.6379) Prec@1 27.500 (36.180) Prec@5 57.500 (66.882) Epoch: [9][6870/11272] Time 0.863 (0.829) Data 0.002 (0.002) Loss 2.7560 (2.6379) Prec@1 31.875 (36.183) Prec@5 62.500 (66.883) Epoch: [9][6880/11272] Time 0.739 (0.829) Data 0.002 (0.002) Loss 2.5424 (2.6379) Prec@1 40.000 (36.183) Prec@5 68.125 (66.884) Epoch: [9][6890/11272] Time 0.773 (0.829) Data 0.002 (0.002) Loss 2.5735 (2.6377) Prec@1 40.000 (36.187) Prec@5 67.500 (66.886) Epoch: [9][6900/11272] Time 0.838 (0.829) Data 0.002 (0.002) Loss 2.4239 (2.6376) Prec@1 40.000 (36.188) Prec@5 70.625 (66.887) Epoch: [9][6910/11272] Time 0.908 (0.829) Data 0.002 (0.002) Loss 2.6626 (2.6376) Prec@1 35.000 (36.188) Prec@5 65.000 (66.888) Epoch: [9][6920/11272] Time 0.736 (0.829) Data 0.001 (0.002) Loss 2.6938 (2.6376) Prec@1 35.000 (36.186) Prec@5 66.875 (66.889) Epoch: [9][6930/11272] Time 0.896 (0.829) Data 0.002 (0.002) Loss 2.6017 (2.6375) Prec@1 37.500 (36.186) Prec@5 67.500 (66.890) Epoch: [9][6940/11272] Time 0.864 (0.829) Data 0.002 (0.002) Loss 2.4737 (2.6376) Prec@1 43.125 (36.186) Prec@5 70.625 (66.890) Epoch: [9][6950/11272] Time 0.778 (0.829) Data 0.001 (0.002) Loss 2.8412 (2.6376) Prec@1 30.000 (36.185) Prec@5 65.000 (66.891) Epoch: [9][6960/11272] Time 0.781 (0.829) Data 0.002 (0.002) Loss 2.5853 (2.6377) Prec@1 36.250 (36.186) Prec@5 67.500 (66.888) Epoch: [9][6970/11272] Time 0.883 (0.829) Data 0.001 (0.002) Loss 2.7152 (2.6378) Prec@1 31.250 (36.183) Prec@5 66.875 (66.885) Epoch: [9][6980/11272] Time 0.858 (0.829) Data 0.001 (0.002) Loss 2.5244 (2.6379) Prec@1 33.125 (36.182) Prec@5 68.750 (66.886) Epoch: [9][6990/11272] Time 0.726 (0.829) Data 0.001 (0.002) Loss 2.5093 (2.6380) Prec@1 40.000 (36.181) Prec@5 67.500 (66.885) Epoch: [9][7000/11272] Time 0.743 (0.829) Data 0.002 (0.002) Loss 2.7347 (2.6380) Prec@1 33.125 (36.180) Prec@5 64.375 (66.885) Epoch: [9][7010/11272] Time 0.867 (0.829) Data 0.002 (0.002) Loss 2.3978 (2.6379) Prec@1 40.625 (36.180) Prec@5 71.875 (66.886) Epoch: [9][7020/11272] Time 0.876 (0.829) Data 0.001 (0.002) Loss 2.6818 (2.6380) Prec@1 37.500 (36.183) Prec@5 66.250 (66.885) Epoch: [9][7030/11272] Time 0.768 (0.829) Data 0.002 (0.002) Loss 2.8013 (2.6379) Prec@1 34.375 (36.183) Prec@5 63.125 (66.884) Epoch: [9][7040/11272] Time 0.822 (0.829) Data 0.002 (0.002) Loss 2.6119 (2.6380) Prec@1 35.625 (36.181) Prec@5 70.000 (66.884) Epoch: [9][7050/11272] Time 0.869 (0.829) Data 0.002 (0.002) Loss 2.4632 (2.6381) Prec@1 43.125 (36.180) Prec@5 70.000 (66.881) Epoch: [9][7060/11272] Time 0.724 (0.829) Data 0.004 (0.002) Loss 2.6997 (2.6381) Prec@1 35.625 (36.178) Prec@5 66.875 (66.879) Epoch: [9][7070/11272] Time 0.802 (0.829) Data 0.003 (0.002) Loss 2.4999 (2.6380) Prec@1 38.750 (36.181) Prec@5 63.750 (66.880) Epoch: [9][7080/11272] Time 0.851 (0.829) Data 0.001 (0.002) Loss 2.4712 (2.6380) Prec@1 36.875 (36.181) Prec@5 71.875 (66.880) Epoch: [9][7090/11272] Time 0.899 (0.829) Data 0.002 (0.002) Loss 2.6059 (2.6380) Prec@1 36.250 (36.180) Prec@5 65.625 (66.880) Epoch: [9][7100/11272] Time 0.752 (0.829) Data 0.002 (0.002) Loss 2.9742 (2.6381) Prec@1 29.375 (36.177) Prec@5 59.375 (66.878) Epoch: [9][7110/11272] Time 0.762 (0.829) Data 0.002 (0.002) Loss 2.5038 (2.6381) Prec@1 41.250 (36.177) Prec@5 71.250 (66.879) Epoch: [9][7120/11272] Time 0.875 (0.829) Data 0.002 (0.002) Loss 2.4789 (2.6380) Prec@1 40.000 (36.181) Prec@5 69.375 (66.881) Epoch: [9][7130/11272] Time 0.883 (0.829) Data 0.002 (0.002) Loss 2.8236 (2.6380) Prec@1 34.375 (36.183) Prec@5 63.125 (66.880) Epoch: [9][7140/11272] Time 0.740 (0.829) Data 0.001 (0.002) Loss 2.5754 (2.6382) Prec@1 38.125 (36.181) Prec@5 66.250 (66.877) Epoch: [9][7150/11272] Time 0.768 (0.829) Data 0.002 (0.002) Loss 2.8068 (2.6384) Prec@1 31.875 (36.175) Prec@5 58.750 (66.873) Epoch: [9][7160/11272] Time 0.900 (0.829) Data 0.002 (0.002) Loss 2.8667 (2.6384) Prec@1 32.500 (36.174) Prec@5 60.625 (66.874) Epoch: [9][7170/11272] Time 0.867 (0.829) Data 0.002 (0.002) Loss 2.1821 (2.6383) Prec@1 41.875 (36.175) Prec@5 76.250 (66.877) Epoch: [9][7180/11272] Time 0.787 (0.829) Data 0.002 (0.002) Loss 2.6159 (2.6383) Prec@1 38.125 (36.176) Prec@5 64.375 (66.878) Epoch: [9][7190/11272] Time 0.898 (0.829) Data 0.001 (0.002) Loss 2.6186 (2.6384) Prec@1 26.250 (36.173) Prec@5 69.375 (66.876) Epoch: [9][7200/11272] Time 0.872 (0.829) Data 0.001 (0.002) Loss 2.5422 (2.6384) Prec@1 35.625 (36.173) Prec@5 66.875 (66.876) Epoch: [9][7210/11272] Time 0.745 (0.829) Data 0.001 (0.002) Loss 2.6544 (2.6384) Prec@1 39.375 (36.176) Prec@5 69.375 (66.876) Epoch: [9][7220/11272] Time 0.771 (0.829) Data 0.001 (0.002) Loss 2.4196 (2.6385) Prec@1 42.500 (36.175) Prec@5 72.500 (66.876) Epoch: [9][7230/11272] Time 0.849 (0.829) Data 0.001 (0.002) Loss 2.8387 (2.6384) Prec@1 33.750 (36.177) Prec@5 66.875 (66.878) Epoch: [9][7240/11272] Time 0.864 (0.829) Data 0.002 (0.002) Loss 2.4191 (2.6384) Prec@1 36.875 (36.175) Prec@5 65.000 (66.877) Epoch: [9][7250/11272] Time 0.780 (0.829) Data 0.001 (0.002) Loss 2.5687 (2.6384) Prec@1 38.125 (36.175) Prec@5 70.625 (66.880) Epoch: [9][7260/11272] Time 0.763 (0.829) Data 0.002 (0.002) Loss 2.6430 (2.6383) Prec@1 35.000 (36.178) Prec@5 65.625 (66.882) Epoch: [9][7270/11272] Time 0.783 (0.829) Data 0.001 (0.002) Loss 2.6083 (2.6382) Prec@1 38.750 (36.178) Prec@5 69.375 (66.883) Epoch: [9][7280/11272] Time 0.893 (0.828) Data 0.002 (0.002) Loss 2.5350 (2.6382) Prec@1 37.500 (36.178) Prec@5 70.000 (66.883) Epoch: [9][7290/11272] Time 0.759 (0.829) Data 0.002 (0.002) Loss 2.6619 (2.6381) Prec@1 41.250 (36.180) Prec@5 60.625 (66.885) Epoch: [9][7300/11272] Time 0.735 (0.828) Data 0.002 (0.002) Loss 2.4331 (2.6380) Prec@1 35.000 (36.180) Prec@5 71.250 (66.886) Epoch: [9][7310/11272] Time 0.901 (0.828) Data 0.002 (0.002) Loss 2.5889 (2.6381) Prec@1 37.500 (36.177) Prec@5 68.125 (66.884) Epoch: [9][7320/11272] Time 0.849 (0.828) Data 0.001 (0.002) Loss 2.8345 (2.6380) Prec@1 38.750 (36.181) Prec@5 66.250 (66.889) Epoch: [9][7330/11272] Time 0.731 (0.828) Data 0.001 (0.002) Loss 2.4205 (2.6379) Prec@1 40.000 (36.182) Prec@5 75.625 (66.892) Epoch: [9][7340/11272] Time 0.948 (0.828) Data 0.001 (0.002) Loss 2.7205 (2.6379) Prec@1 29.375 (36.180) Prec@5 69.375 (66.892) Epoch: [9][7350/11272] Time 0.874 (0.828) Data 0.002 (0.002) Loss 2.6334 (2.6380) Prec@1 32.500 (36.179) Prec@5 64.375 (66.891) Epoch: [9][7360/11272] Time 0.763 (0.828) Data 0.002 (0.002) Loss 2.5976 (2.6380) Prec@1 37.500 (36.179) Prec@5 64.375 (66.891) Epoch: [9][7370/11272] Time 0.719 (0.828) Data 0.001 (0.002) Loss 2.5822 (2.6379) Prec@1 36.875 (36.182) Prec@5 68.125 (66.892) Epoch: [9][7380/11272] Time 0.882 (0.828) Data 0.002 (0.002) Loss 2.6584 (2.6380) Prec@1 34.375 (36.179) Prec@5 68.750 (66.891) Epoch: [9][7390/11272] Time 0.835 (0.828) Data 0.001 (0.002) Loss 2.8794 (2.6381) Prec@1 31.250 (36.176) Prec@5 57.500 (66.888) Epoch: [9][7400/11272] Time 0.795 (0.828) Data 0.002 (0.002) Loss 2.5353 (2.6381) Prec@1 38.125 (36.178) Prec@5 70.000 (66.887) Epoch: [9][7410/11272] Time 0.774 (0.828) Data 0.002 (0.002) Loss 2.8578 (2.6381) Prec@1 31.250 (36.179) Prec@5 63.750 (66.888) Epoch: [9][7420/11272] Time 0.897 (0.828) Data 0.002 (0.002) Loss 2.6420 (2.6381) Prec@1 36.875 (36.177) Prec@5 67.500 (66.887) Epoch: [9][7430/11272] Time 0.837 (0.828) Data 0.001 (0.002) Loss 2.4423 (2.6381) Prec@1 41.250 (36.178) Prec@5 75.625 (66.887) Epoch: [9][7440/11272] Time 0.793 (0.828) Data 0.002 (0.002) Loss 2.4738 (2.6381) Prec@1 39.375 (36.177) Prec@5 71.250 (66.887) Epoch: [9][7450/11272] Time 0.763 (0.828) Data 0.002 (0.002) Loss 2.3690 (2.6381) Prec@1 40.000 (36.176) Prec@5 76.250 (66.889) Epoch: [9][7460/11272] Time 0.847 (0.828) Data 0.002 (0.002) Loss 2.6748 (2.6381) Prec@1 38.750 (36.177) Prec@5 64.375 (66.890) Epoch: [9][7470/11272] Time 0.810 (0.828) Data 0.001 (0.002) Loss 2.5358 (2.6380) Prec@1 33.125 (36.178) Prec@5 71.875 (66.894) Epoch: [9][7480/11272] Time 0.743 (0.828) Data 0.002 (0.002) Loss 2.5204 (2.6380) Prec@1 41.250 (36.180) Prec@5 68.125 (66.894) Epoch: [9][7490/11272] Time 0.862 (0.828) Data 0.001 (0.002) Loss 2.7665 (2.6380) Prec@1 35.000 (36.179) Prec@5 61.875 (66.894) Epoch: [9][7500/11272] Time 0.877 (0.828) Data 0.002 (0.002) Loss 2.7729 (2.6379) Prec@1 37.500 (36.180) Prec@5 61.875 (66.893) Epoch: [9][7510/11272] Time 0.775 (0.828) Data 0.002 (0.002) Loss 2.6019 (2.6380) Prec@1 36.875 (36.178) Prec@5 63.750 (66.892) Epoch: [9][7520/11272] Time 0.783 (0.828) Data 0.002 (0.002) Loss 2.6136 (2.6380) Prec@1 40.625 (36.179) Prec@5 66.875 (66.893) Epoch: [9][7530/11272] Time 0.910 (0.828) Data 0.002 (0.002) Loss 2.4971 (2.6379) Prec@1 37.500 (36.179) Prec@5 68.750 (66.894) Epoch: [9][7540/11272] Time 0.885 (0.828) Data 0.002 (0.002) Loss 2.6010 (2.6378) Prec@1 35.000 (36.181) Prec@5 65.000 (66.895) Epoch: [9][7550/11272] Time 0.763 (0.828) Data 0.001 (0.002) Loss 2.5852 (2.6379) Prec@1 39.375 (36.180) Prec@5 65.625 (66.896) Epoch: [9][7560/11272] Time 0.758 (0.828) Data 0.002 (0.002) Loss 2.5306 (2.6379) Prec@1 35.000 (36.179) Prec@5 69.375 (66.894) Epoch: [9][7570/11272] Time 0.853 (0.828) Data 0.002 (0.002) Loss 2.8038 (2.6379) Prec@1 35.625 (36.180) Prec@5 58.750 (66.893) Epoch: [9][7580/11272] Time 0.867 (0.828) Data 0.002 (0.002) Loss 2.4276 (2.6379) Prec@1 40.625 (36.179) Prec@5 68.750 (66.892) Epoch: [9][7590/11272] Time 0.745 (0.828) Data 0.002 (0.002) Loss 2.4613 (2.6378) Prec@1 40.625 (36.179) Prec@5 68.125 (66.893) Epoch: [9][7600/11272] Time 0.899 (0.828) Data 0.002 (0.002) Loss 2.5168 (2.6378) Prec@1 35.625 (36.180) Prec@5 68.750 (66.894) Epoch: [9][7610/11272] Time 0.924 (0.828) Data 0.001 (0.002) Loss 2.7123 (2.6377) Prec@1 32.500 (36.181) Prec@5 65.625 (66.894) Epoch: [9][7620/11272] Time 0.788 (0.828) Data 0.002 (0.002) Loss 2.4222 (2.6377) Prec@1 43.125 (36.180) Prec@5 71.250 (66.895) Epoch: [9][7630/11272] Time 0.756 (0.828) Data 0.002 (0.002) Loss 2.4254 (2.6376) Prec@1 36.875 (36.181) Prec@5 72.500 (66.899) Epoch: [9][7640/11272] Time 0.872 (0.828) Data 0.002 (0.002) Loss 2.6994 (2.6375) Prec@1 38.125 (36.184) Prec@5 64.375 (66.900) Epoch: [9][7650/11272] Time 0.869 (0.828) Data 0.002 (0.002) Loss 2.8416 (2.6375) Prec@1 31.875 (36.185) Prec@5 63.125 (66.901) Epoch: [9][7660/11272] Time 0.766 (0.828) Data 0.001 (0.002) Loss 2.6487 (2.6375) Prec@1 40.000 (36.186) Prec@5 67.500 (66.900) Epoch: [9][7670/11272] Time 0.743 (0.828) Data 0.002 (0.002) Loss 2.7884 (2.6376) Prec@1 33.750 (36.184) Prec@5 63.125 (66.897) Epoch: [9][7680/11272] Time 0.845 (0.828) Data 0.002 (0.002) Loss 2.5528 (2.6376) Prec@1 36.875 (36.184) Prec@5 68.750 (66.898) Epoch: [9][7690/11272] Time 0.808 (0.828) Data 0.001 (0.002) Loss 2.5283 (2.6377) Prec@1 40.000 (36.183) Prec@5 68.125 (66.897) Epoch: [9][7700/11272] Time 0.758 (0.828) Data 0.001 (0.002) Loss 2.3543 (2.6376) Prec@1 46.250 (36.184) Prec@5 66.250 (66.897) Epoch: [9][7710/11272] Time 0.764 (0.828) Data 0.001 (0.002) Loss 2.7231 (2.6376) Prec@1 37.500 (36.186) Prec@5 63.125 (66.896) Epoch: [9][7720/11272] Time 0.867 (0.828) Data 0.001 (0.002) Loss 2.5481 (2.6375) Prec@1 36.875 (36.187) Prec@5 66.875 (66.897) Epoch: [9][7730/11272] Time 0.750 (0.828) Data 0.003 (0.002) Loss 2.5053 (2.6375) Prec@1 37.500 (36.187) Prec@5 65.625 (66.896) Epoch: [9][7740/11272] Time 0.752 (0.828) Data 0.002 (0.002) Loss 2.8616 (2.6376) Prec@1 35.000 (36.184) Prec@5 58.750 (66.893) Epoch: [9][7750/11272] Time 0.893 (0.828) Data 0.001 (0.002) Loss 2.7568 (2.6376) Prec@1 33.125 (36.184) Prec@5 60.000 (66.893) Epoch: [9][7760/11272] Time 0.916 (0.828) Data 0.002 (0.002) Loss 2.7342 (2.6377) Prec@1 31.250 (36.183) Prec@5 63.750 (66.892) Epoch: [9][7770/11272] Time 0.764 (0.828) Data 0.002 (0.002) Loss 2.4779 (2.6376) Prec@1 36.250 (36.184) Prec@5 71.875 (66.893) Epoch: [9][7780/11272] Time 0.762 (0.828) Data 0.002 (0.002) Loss 2.6684 (2.6377) Prec@1 37.500 (36.182) Prec@5 68.750 (66.893) Epoch: [9][7790/11272] Time 0.913 (0.828) Data 0.002 (0.002) Loss 2.5364 (2.6376) Prec@1 38.750 (36.183) Prec@5 70.000 (66.894) Epoch: [9][7800/11272] Time 0.856 (0.828) Data 0.001 (0.002) Loss 2.6188 (2.6376) Prec@1 35.000 (36.182) Prec@5 66.875 (66.894) Epoch: [9][7810/11272] Time 0.757 (0.828) Data 0.002 (0.002) Loss 2.8180 (2.6376) Prec@1 31.250 (36.183) Prec@5 63.125 (66.895) Epoch: [9][7820/11272] Time 0.764 (0.828) Data 0.002 (0.002) Loss 2.2677 (2.6374) Prec@1 45.625 (36.186) Prec@5 74.375 (66.900) Epoch: [9][7830/11272] Time 0.855 (0.828) Data 0.001 (0.002) Loss 2.3929 (2.6374) Prec@1 41.250 (36.187) Prec@5 68.750 (66.901) Epoch: [9][7840/11272] Time 0.860 (0.828) Data 0.001 (0.002) Loss 2.8074 (2.6374) Prec@1 40.000 (36.186) Prec@5 61.250 (66.900) Epoch: [9][7850/11272] Time 0.757 (0.828) Data 0.002 (0.002) Loss 2.5833 (2.6375) Prec@1 36.875 (36.184) Prec@5 67.500 (66.897) Epoch: [9][7860/11272] Time 0.862 (0.828) Data 0.001 (0.002) Loss 2.6610 (2.6374) Prec@1 35.000 (36.183) Prec@5 68.125 (66.899) Epoch: [9][7870/11272] Time 0.868 (0.828) Data 0.001 (0.002) Loss 2.6402 (2.6374) Prec@1 38.750 (36.185) Prec@5 70.000 (66.901) Epoch: [9][7880/11272] Time 0.743 (0.828) Data 0.001 (0.002) Loss 2.5130 (2.6373) Prec@1 40.000 (36.185) Prec@5 70.000 (66.902) Epoch: [9][7890/11272] Time 0.796 (0.828) Data 0.002 (0.002) Loss 2.6870 (2.6374) Prec@1 37.500 (36.185) Prec@5 64.375 (66.902) Epoch: [9][7900/11272] Time 0.897 (0.828) Data 0.002 (0.002) Loss 2.6671 (2.6374) Prec@1 36.875 (36.187) Prec@5 68.750 (66.901) Epoch: [9][7910/11272] Time 0.879 (0.828) Data 0.002 (0.002) Loss 2.4022 (2.6374) Prec@1 35.625 (36.186) Prec@5 72.500 (66.903) Epoch: [9][7920/11272] Time 0.748 (0.828) Data 0.002 (0.002) Loss 2.3784 (2.6372) Prec@1 40.625 (36.187) Prec@5 75.000 (66.906) Epoch: [9][7930/11272] Time 0.743 (0.828) Data 0.001 (0.002) Loss 2.6610 (2.6372) Prec@1 35.625 (36.187) Prec@5 65.625 (66.908) Epoch: [9][7940/11272] Time 0.867 (0.828) Data 0.002 (0.002) Loss 2.8197 (2.6373) Prec@1 35.625 (36.187) Prec@5 62.500 (66.907) Epoch: [9][7950/11272] Time 0.871 (0.828) Data 0.001 (0.002) Loss 2.7606 (2.6373) Prec@1 32.500 (36.186) Prec@5 62.500 (66.907) Epoch: [9][7960/11272] Time 0.766 (0.828) Data 0.001 (0.002) Loss 2.5300 (2.6371) Prec@1 35.625 (36.191) Prec@5 68.125 (66.910) Epoch: [9][7970/11272] Time 0.754 (0.828) Data 0.003 (0.002) Loss 2.6138 (2.6370) Prec@1 35.625 (36.193) Prec@5 68.125 (66.910) Epoch: [9][7980/11272] Time 0.801 (0.828) Data 0.001 (0.002) Loss 2.7131 (2.6370) Prec@1 34.375 (36.194) Prec@5 65.625 (66.911) Epoch: [9][7990/11272] Time 0.729 (0.828) Data 0.003 (0.002) Loss 2.5436 (2.6370) Prec@1 38.750 (36.195) Prec@5 68.125 (66.911) Epoch: [9][8000/11272] Time 0.743 (0.828) Data 0.001 (0.002) Loss 2.6844 (2.6371) Prec@1 38.750 (36.193) Prec@5 61.875 (66.907) Epoch: [9][8010/11272] Time 0.852 (0.828) Data 0.001 (0.002) Loss 2.5985 (2.6371) Prec@1 39.375 (36.194) Prec@5 65.000 (66.909) Epoch: [9][8020/11272] Time 0.845 (0.828) Data 0.001 (0.002) Loss 2.7915 (2.6371) Prec@1 31.875 (36.194) Prec@5 63.125 (66.910) Epoch: [9][8030/11272] Time 0.773 (0.828) Data 0.001 (0.002) Loss 2.9136 (2.6372) Prec@1 31.875 (36.192) Prec@5 59.375 (66.908) Epoch: [9][8040/11272] Time 0.755 (0.828) Data 0.002 (0.002) Loss 2.9326 (2.6372) Prec@1 30.000 (36.192) Prec@5 61.875 (66.908) Epoch: [9][8050/11272] Time 0.832 (0.828) Data 0.002 (0.002) Loss 2.8359 (2.6372) Prec@1 35.000 (36.194) Prec@5 66.250 (66.908) Epoch: [9][8060/11272] Time 0.893 (0.827) Data 0.002 (0.002) Loss 2.5158 (2.6372) Prec@1 38.750 (36.193) Prec@5 67.500 (66.907) Epoch: [9][8070/11272] Time 0.748 (0.827) Data 0.001 (0.002) Loss 2.6701 (2.6371) Prec@1 31.250 (36.194) Prec@5 65.625 (66.909) Epoch: [9][8080/11272] Time 0.753 (0.827) Data 0.002 (0.002) Loss 2.4697 (2.6371) Prec@1 36.875 (36.194) Prec@5 69.375 (66.910) Epoch: [9][8090/11272] Time 0.884 (0.828) Data 0.001 (0.002) Loss 2.7120 (2.6370) Prec@1 32.500 (36.195) Prec@5 65.625 (66.910) Epoch: [9][8100/11272] Time 0.892 (0.827) Data 0.002 (0.002) Loss 2.8201 (2.6371) Prec@1 31.250 (36.195) Prec@5 63.750 (66.908) Epoch: [9][8110/11272] Time 0.767 (0.827) Data 0.002 (0.002) Loss 2.7855 (2.6370) Prec@1 33.750 (36.197) Prec@5 65.000 (66.909) Epoch: [9][8120/11272] Time 0.907 (0.827) Data 0.002 (0.002) Loss 2.5077 (2.6371) Prec@1 36.875 (36.197) Prec@5 63.125 (66.906) Epoch: [9][8130/11272] Time 0.889 (0.827) Data 0.002 (0.002) Loss 2.7555 (2.6371) Prec@1 30.000 (36.196) Prec@5 64.375 (66.905) Epoch: [9][8140/11272] Time 0.735 (0.827) Data 0.001 (0.002) Loss 2.9278 (2.6372) Prec@1 35.000 (36.193) Prec@5 59.375 (66.904) Epoch: [9][8150/11272] Time 0.745 (0.827) Data 0.002 (0.002) Loss 2.6714 (2.6372) Prec@1 31.250 (36.193) Prec@5 65.625 (66.902) Epoch: [9][8160/11272] Time 0.896 (0.827) Data 0.002 (0.002) Loss 2.6769 (2.6372) Prec@1 29.375 (36.193) Prec@5 66.250 (66.900) Epoch: [9][8170/11272] Time 0.837 (0.827) Data 0.002 (0.002) Loss 2.5657 (2.6372) Prec@1 33.125 (36.194) Prec@5 68.125 (66.902) Epoch: [9][8180/11272] Time 0.837 (0.827) Data 0.002 (0.002) Loss 2.5411 (2.6372) Prec@1 40.000 (36.194) Prec@5 68.125 (66.901) Epoch: [9][8190/11272] Time 0.738 (0.827) Data 0.002 (0.002) Loss 2.4356 (2.6372) Prec@1 36.250 (36.193) Prec@5 71.250 (66.901) Epoch: [9][8200/11272] Time 0.918 (0.827) Data 0.002 (0.002) Loss 2.6431 (2.6373) Prec@1 38.125 (36.195) Prec@5 69.375 (66.901) Epoch: [9][8210/11272] Time 0.837 (0.827) Data 0.001 (0.002) Loss 2.5231 (2.6373) Prec@1 37.500 (36.196) Prec@5 66.250 (66.900) Epoch: [9][8220/11272] Time 0.711 (0.827) Data 0.002 (0.002) Loss 2.7538 (2.6373) Prec@1 36.250 (36.195) Prec@5 63.125 (66.900) Epoch: [9][8230/11272] Time 0.812 (0.827) Data 0.001 (0.002) Loss 2.6670 (2.6372) Prec@1 35.000 (36.198) Prec@5 68.125 (66.901) Epoch: [9][8240/11272] Time 0.910 (0.827) Data 0.001 (0.002) Loss 2.3861 (2.6374) Prec@1 46.875 (36.197) Prec@5 71.875 (66.899) Epoch: [9][8250/11272] Time 0.859 (0.827) Data 0.002 (0.002) Loss 2.7226 (2.6374) Prec@1 33.125 (36.196) Prec@5 68.125 (66.900) Epoch: [9][8260/11272] Time 0.754 (0.827) Data 0.001 (0.002) Loss 2.4277 (2.6375) Prec@1 40.000 (36.197) Prec@5 73.125 (66.899) Epoch: [9][8270/11272] Time 0.913 (0.827) Data 0.002 (0.002) Loss 2.6477 (2.6375) Prec@1 33.125 (36.199) Prec@5 66.250 (66.900) Epoch: [9][8280/11272] Time 0.926 (0.827) Data 0.002 (0.002) Loss 2.3334 (2.6375) Prec@1 43.750 (36.199) Prec@5 74.375 (66.901) Epoch: [9][8290/11272] Time 0.808 (0.827) Data 0.002 (0.002) Loss 2.7029 (2.6375) Prec@1 38.125 (36.197) Prec@5 60.625 (66.899) Epoch: [9][8300/11272] Time 0.756 (0.827) Data 0.002 (0.002) Loss 2.6277 (2.6375) Prec@1 37.500 (36.198) Prec@5 67.500 (66.898) Epoch: [9][8310/11272] Time 0.880 (0.827) Data 0.002 (0.002) Loss 2.6221 (2.6376) Prec@1 38.125 (36.198) Prec@5 61.250 (66.895) Epoch: [9][8320/11272] Time 0.855 (0.827) Data 0.002 (0.002) Loss 2.6364 (2.6375) Prec@1 39.375 (36.201) Prec@5 64.375 (66.896) Epoch: [9][8330/11272] Time 0.754 (0.827) Data 0.001 (0.002) Loss 2.7096 (2.6375) Prec@1 36.250 (36.201) Prec@5 67.500 (66.895) Epoch: [9][8340/11272] Time 0.732 (0.827) Data 0.002 (0.002) Loss 2.7854 (2.6375) Prec@1 31.875 (36.202) Prec@5 61.250 (66.893) Epoch: [9][8350/11272] Time 0.879 (0.827) Data 0.001 (0.002) Loss 2.6461 (2.6375) Prec@1 36.875 (36.205) Prec@5 64.375 (66.894) Epoch: [9][8360/11272] Time 0.859 (0.827) Data 0.001 (0.002) Loss 2.6901 (2.6374) Prec@1 32.500 (36.205) Prec@5 68.750 (66.894) Epoch: [9][8370/11272] Time 0.774 (0.827) Data 0.002 (0.002) Loss 2.6010 (2.6375) Prec@1 38.750 (36.202) Prec@5 68.750 (66.894) Epoch: [9][8380/11272] Time 0.723 (0.827) Data 0.001 (0.002) Loss 2.7978 (2.6375) Prec@1 36.250 (36.201) Prec@5 66.875 (66.894) Epoch: [9][8390/11272] Time 0.888 (0.827) Data 0.002 (0.002) Loss 2.6997 (2.6376) Prec@1 38.750 (36.200) Prec@5 67.500 (66.893) Epoch: [9][8400/11272] Time 0.743 (0.827) Data 0.001 (0.002) Loss 2.6231 (2.6376) Prec@1 36.875 (36.199) Prec@5 68.750 (66.893) Epoch: [9][8410/11272] Time 0.795 (0.827) Data 0.001 (0.002) Loss 2.4836 (2.6376) Prec@1 41.250 (36.197) Prec@5 68.750 (66.891) Epoch: [9][8420/11272] Time 0.955 (0.827) Data 0.002 (0.002) Loss 3.0865 (2.6377) Prec@1 31.250 (36.196) Prec@5 59.375 (66.889) Epoch: [9][8430/11272] Time 0.844 (0.827) Data 0.001 (0.002) Loss 2.8241 (2.6377) Prec@1 31.250 (36.196) Prec@5 57.500 (66.888) Epoch: [9][8440/11272] Time 0.755 (0.827) Data 0.002 (0.002) Loss 2.6823 (2.6378) Prec@1 33.750 (36.195) Prec@5 71.250 (66.887) Epoch: [9][8450/11272] Time 0.767 (0.827) Data 0.001 (0.002) Loss 2.6080 (2.6378) Prec@1 36.875 (36.194) Prec@5 68.125 (66.886) Epoch: [9][8460/11272] Time 0.839 (0.827) Data 0.002 (0.002) Loss 2.5935 (2.6379) Prec@1 39.375 (36.192) Prec@5 64.375 (66.885) Epoch: [9][8470/11272] Time 0.843 (0.827) Data 0.002 (0.002) Loss 2.4629 (2.6379) Prec@1 41.875 (36.195) Prec@5 70.625 (66.884) Epoch: [9][8480/11272] Time 0.769 (0.827) Data 0.001 (0.002) Loss 2.6969 (2.6379) Prec@1 31.875 (36.194) Prec@5 68.750 (66.884) Epoch: [9][8490/11272] Time 0.771 (0.827) Data 0.001 (0.002) Loss 2.7286 (2.6379) Prec@1 35.000 (36.193) Prec@5 64.375 (66.884) Epoch: [9][8500/11272] Time 0.874 (0.827) Data 0.001 (0.002) Loss 2.6403 (2.6380) Prec@1 33.125 (36.191) Prec@5 66.250 (66.883) Epoch: [9][8510/11272] Time 0.887 (0.827) Data 0.001 (0.002) Loss 2.6733 (2.6380) Prec@1 35.000 (36.193) Prec@5 66.250 (66.883) Epoch: [9][8520/11272] Time 0.724 (0.827) Data 0.001 (0.002) Loss 2.9424 (2.6380) Prec@1 31.875 (36.192) Prec@5 60.625 (66.881) Epoch: [9][8530/11272] Time 0.869 (0.827) Data 0.001 (0.002) Loss 2.5973 (2.6381) Prec@1 32.500 (36.190) Prec@5 69.375 (66.882) Epoch: [9][8540/11272] Time 0.848 (0.827) Data 0.002 (0.002) Loss 2.7174 (2.6381) Prec@1 29.375 (36.189) Prec@5 69.375 (66.882) Epoch: [9][8550/11272] Time 0.728 (0.827) Data 0.001 (0.002) Loss 2.5941 (2.6381) Prec@1 34.375 (36.186) Prec@5 70.625 (66.882) Epoch: [9][8560/11272] Time 0.761 (0.827) Data 0.001 (0.002) Loss 2.3309 (2.6380) Prec@1 41.875 (36.187) Prec@5 74.375 (66.884) Epoch: [9][8570/11272] Time 0.911 (0.827) Data 0.002 (0.002) Loss 2.5634 (2.6380) Prec@1 35.625 (36.188) Prec@5 66.250 (66.885) Epoch: [9][8580/11272] Time 0.920 (0.827) Data 0.002 (0.002) Loss 2.4508 (2.6380) Prec@1 34.375 (36.188) Prec@5 70.000 (66.885) Epoch: [9][8590/11272] Time 0.756 (0.827) Data 0.002 (0.002) Loss 2.2665 (2.6379) Prec@1 40.625 (36.190) Prec@5 73.750 (66.886) Epoch: [9][8600/11272] Time 0.728 (0.827) Data 0.002 (0.002) Loss 2.5459 (2.6378) Prec@1 38.750 (36.191) Prec@5 69.375 (66.887) Epoch: [9][8610/11272] Time 0.914 (0.827) Data 0.001 (0.002) Loss 2.5688 (2.6377) Prec@1 36.250 (36.193) Prec@5 66.250 (66.890) Epoch: [9][8620/11272] Time 0.853 (0.827) Data 0.002 (0.002) Loss 2.7240 (2.6377) Prec@1 40.000 (36.193) Prec@5 65.625 (66.889) Epoch: [9][8630/11272] Time 0.745 (0.827) Data 0.002 (0.002) Loss 2.8662 (2.6378) Prec@1 28.750 (36.191) Prec@5 65.000 (66.887) Epoch: [9][8640/11272] Time 0.784 (0.827) Data 0.001 (0.002) Loss 2.6663 (2.6378) Prec@1 35.000 (36.192) Prec@5 66.250 (66.887) Epoch: [9][8650/11272] Time 0.906 (0.827) Data 0.001 (0.002) Loss 2.6972 (2.6378) Prec@1 36.250 (36.193) Prec@5 65.000 (66.886) Epoch: [9][8660/11272] Time 0.759 (0.827) Data 0.003 (0.002) Loss 2.6757 (2.6378) Prec@1 35.625 (36.193) Prec@5 65.000 (66.886) Epoch: [9][8670/11272] Time 0.746 (0.827) Data 0.002 (0.002) Loss 2.7022 (2.6378) Prec@1 35.000 (36.194) Prec@5 69.375 (66.886) Epoch: [9][8680/11272] Time 0.823 (0.827) Data 0.001 (0.002) Loss 2.7713 (2.6379) Prec@1 31.250 (36.193) Prec@5 65.625 (66.884) Epoch: [9][8690/11272] Time 0.857 (0.827) Data 0.002 (0.002) Loss 2.9188 (2.6379) Prec@1 30.625 (36.192) Prec@5 62.500 (66.884) Epoch: [9][8700/11272] Time 0.790 (0.827) Data 0.002 (0.002) Loss 2.8639 (2.6380) Prec@1 30.625 (36.192) Prec@5 62.500 (66.882) Epoch: [9][8710/11272] Time 0.743 (0.827) Data 0.001 (0.002) Loss 2.7096 (2.6380) Prec@1 33.125 (36.195) Prec@5 64.375 (66.883) Epoch: [9][8720/11272] Time 0.906 (0.827) Data 0.001 (0.002) Loss 2.3108 (2.6380) Prec@1 41.250 (36.195) Prec@5 73.125 (66.884) Epoch: [9][8730/11272] Time 0.924 (0.827) Data 0.002 (0.002) Loss 2.3016 (2.6380) Prec@1 42.500 (36.194) Prec@5 71.875 (66.883) Epoch: [9][8740/11272] Time 0.815 (0.827) Data 0.002 (0.002) Loss 2.4041 (2.6381) Prec@1 40.000 (36.193) Prec@5 66.875 (66.882) Epoch: [9][8750/11272] Time 0.728 (0.827) Data 0.001 (0.002) Loss 2.5948 (2.6381) Prec@1 34.375 (36.192) Prec@5 70.000 (66.880) Epoch: [9][8760/11272] Time 0.847 (0.827) Data 0.001 (0.002) Loss 2.7802 (2.6381) Prec@1 30.625 (36.193) Prec@5 60.000 (66.879) Epoch: [9][8770/11272] Time 0.854 (0.827) Data 0.002 (0.002) Loss 2.7896 (2.6383) Prec@1 32.500 (36.190) Prec@5 67.500 (66.877) Epoch: [9][8780/11272] Time 0.763 (0.827) Data 0.002 (0.002) Loss 2.6233 (2.6384) Prec@1 35.625 (36.189) Prec@5 67.500 (66.875) Epoch: [9][8790/11272] Time 0.897 (0.827) Data 0.001 (0.002) Loss 2.3803 (2.6384) Prec@1 45.625 (36.191) Prec@5 74.375 (66.876) Epoch: [9][8800/11272] Time 0.821 (0.827) Data 0.001 (0.002) Loss 2.7495 (2.6384) Prec@1 33.125 (36.191) Prec@5 63.750 (66.876) Epoch: [9][8810/11272] Time 0.791 (0.827) Data 0.002 (0.002) Loss 2.7592 (2.6384) Prec@1 35.625 (36.191) Prec@5 65.625 (66.876) Epoch: [9][8820/11272] Time 0.748 (0.827) Data 0.001 (0.002) Loss 2.6655 (2.6384) Prec@1 38.125 (36.192) Prec@5 67.500 (66.876) Epoch: [9][8830/11272] Time 0.894 (0.827) Data 0.002 (0.002) Loss 2.4535 (2.6383) Prec@1 38.125 (36.193) Prec@5 70.000 (66.876) Epoch: [9][8840/11272] Time 0.891 (0.827) Data 0.002 (0.002) Loss 2.1728 (2.6382) Prec@1 45.000 (36.195) Prec@5 73.750 (66.877) Epoch: [9][8850/11272] Time 0.769 (0.827) Data 0.002 (0.002) Loss 2.4869 (2.6382) Prec@1 38.750 (36.196) Prec@5 69.375 (66.876) Epoch: [9][8860/11272] Time 0.772 (0.827) Data 0.002 (0.002) Loss 2.6071 (2.6383) Prec@1 41.875 (36.193) Prec@5 65.000 (66.875) Epoch: [9][8870/11272] Time 0.907 (0.827) Data 0.002 (0.002) Loss 2.5451 (2.6384) Prec@1 36.875 (36.192) Prec@5 65.625 (66.873) Epoch: [9][8880/11272] Time 0.904 (0.827) Data 0.002 (0.002) Loss 2.9675 (2.6384) Prec@1 30.625 (36.190) Prec@5 58.125 (66.871) Epoch: [9][8890/11272] Time 0.768 (0.827) Data 0.001 (0.002) Loss 2.6504 (2.6385) Prec@1 34.375 (36.187) Prec@5 70.000 (66.871) Epoch: [9][8900/11272] Time 0.731 (0.827) Data 0.002 (0.002) Loss 2.6394 (2.6385) Prec@1 37.500 (36.187) Prec@5 68.125 (66.871) Epoch: [9][8910/11272] Time 0.916 (0.827) Data 0.002 (0.002) Loss 2.6020 (2.6386) Prec@1 33.750 (36.186) Prec@5 67.500 (66.871) Epoch: [9][8920/11272] Time 0.731 (0.827) Data 0.003 (0.002) Loss 2.7465 (2.6386) Prec@1 39.375 (36.185) Prec@5 63.750 (66.870) Epoch: [9][8930/11272] Time 0.738 (0.827) Data 0.002 (0.002) Loss 2.6037 (2.6386) Prec@1 40.625 (36.186) Prec@5 68.125 (66.872) Epoch: [9][8940/11272] Time 0.854 (0.827) Data 0.002 (0.002) Loss 2.8728 (2.6386) Prec@1 29.375 (36.185) Prec@5 62.500 (66.872) Epoch: [9][8950/11272] Time 0.856 (0.827) Data 0.002 (0.002) Loss 2.7614 (2.6386) Prec@1 40.625 (36.185) Prec@5 65.625 (66.872) Epoch: [9][8960/11272] Time 0.768 (0.827) Data 0.002 (0.002) Loss 2.3964 (2.6385) Prec@1 41.250 (36.185) Prec@5 67.500 (66.873) Epoch: [9][8970/11272] Time 0.735 (0.827) Data 0.002 (0.002) Loss 2.7981 (2.6385) Prec@1 36.250 (36.185) Prec@5 64.375 (66.875) Epoch: [9][8980/11272] Time 0.897 (0.827) Data 0.002 (0.002) Loss 2.4502 (2.6385) Prec@1 38.125 (36.187) Prec@5 71.875 (66.875) Epoch: [9][8990/11272] Time 0.918 (0.827) Data 0.002 (0.002) Loss 2.8260 (2.6386) Prec@1 34.375 (36.187) Prec@5 66.250 (66.876) Epoch: [9][9000/11272] Time 0.755 (0.827) Data 0.001 (0.002) Loss 2.6229 (2.6385) Prec@1 39.375 (36.189) Prec@5 64.375 (66.877) Epoch: [9][9010/11272] Time 0.788 (0.827) Data 0.002 (0.002) Loss 2.8511 (2.6385) Prec@1 30.625 (36.188) Prec@5 63.125 (66.876) Epoch: [9][9020/11272] Time 0.910 (0.827) Data 0.002 (0.002) Loss 2.5973 (2.6386) Prec@1 36.875 (36.187) Prec@5 66.875 (66.874) Epoch: [9][9030/11272] Time 0.892 (0.827) Data 0.002 (0.002) Loss 2.4363 (2.6387) Prec@1 38.750 (36.188) Prec@5 71.875 (66.874) Epoch: [9][9040/11272] Time 0.743 (0.827) Data 0.001 (0.002) Loss 2.5277 (2.6387) Prec@1 38.750 (36.189) Prec@5 69.375 (66.872) Epoch: [9][9050/11272] Time 0.887 (0.827) Data 0.001 (0.002) Loss 2.7117 (2.6388) Prec@1 38.750 (36.188) Prec@5 63.750 (66.871) Epoch: [9][9060/11272] Time 0.850 (0.827) Data 0.002 (0.002) Loss 2.5848 (2.6388) Prec@1 38.125 (36.188) Prec@5 68.125 (66.869) Epoch: [9][9070/11272] Time 0.749 (0.827) Data 0.001 (0.002) Loss 2.6600 (2.6389) Prec@1 36.875 (36.188) Prec@5 67.500 (66.868) Epoch: [9][9080/11272] Time 0.750 (0.827) Data 0.002 (0.002) Loss 2.5261 (2.6389) Prec@1 40.000 (36.186) Prec@5 68.125 (66.867) Epoch: [9][9090/11272] Time 0.908 (0.827) Data 0.002 (0.002) Loss 2.5452 (2.6389) Prec@1 35.000 (36.184) Prec@5 72.500 (66.869) Epoch: [9][9100/11272] Time 0.845 (0.827) Data 0.001 (0.002) Loss 2.5621 (2.6389) Prec@1 38.125 (36.183) Prec@5 68.750 (66.869) Epoch: [9][9110/11272] Time 0.772 (0.827) Data 0.002 (0.002) Loss 2.6607 (2.6389) Prec@1 33.125 (36.183) Prec@5 65.625 (66.867) Epoch: [9][9120/11272] Time 0.741 (0.827) Data 0.002 (0.002) Loss 2.4717 (2.6388) Prec@1 41.250 (36.186) Prec@5 68.125 (66.867) Epoch: [9][9130/11272] Time 0.944 (0.827) Data 0.002 (0.002) Loss 2.4943 (2.6388) Prec@1 41.875 (36.186) Prec@5 69.375 (66.867) Epoch: [9][9140/11272] Time 0.869 (0.826) Data 0.002 (0.002) Loss 2.4120 (2.6388) Prec@1 38.125 (36.186) Prec@5 71.250 (66.865) Epoch: [9][9150/11272] Time 0.739 (0.826) Data 0.002 (0.002) Loss 2.4907 (2.6388) Prec@1 35.000 (36.185) Prec@5 70.625 (66.867) Epoch: [9][9160/11272] Time 0.726 (0.826) Data 0.002 (0.002) Loss 2.7944 (2.6389) Prec@1 32.500 (36.183) Prec@5 64.375 (66.866) Epoch: [9][9170/11272] Time 0.877 (0.826) Data 0.001 (0.002) Loss 2.5272 (2.6389) Prec@1 37.500 (36.183) Prec@5 68.750 (66.866) Epoch: [9][9180/11272] Time 0.911 (0.826) Data 0.002 (0.002) Loss 2.7538 (2.6389) Prec@1 31.875 (36.181) Prec@5 63.125 (66.865) Epoch: [9][9190/11272] Time 0.777 (0.826) Data 0.002 (0.002) Loss 2.5593 (2.6389) Prec@1 36.250 (36.182) Prec@5 69.375 (66.866) Epoch: [9][9200/11272] Time 0.839 (0.826) Data 0.001 (0.002) Loss 2.7664 (2.6389) Prec@1 26.250 (36.181) Prec@5 62.500 (66.865) Epoch: [9][9210/11272] Time 0.920 (0.827) Data 0.002 (0.002) Loss 2.4434 (2.6388) Prec@1 39.375 (36.182) Prec@5 70.625 (66.867) Epoch: [9][9220/11272] Time 0.744 (0.826) Data 0.002 (0.002) Loss 2.8230 (2.6388) Prec@1 33.125 (36.181) Prec@5 59.375 (66.865) Epoch: [9][9230/11272] Time 0.751 (0.826) Data 0.002 (0.002) Loss 2.5065 (2.6389) Prec@1 39.375 (36.181) Prec@5 72.500 (66.867) Epoch: [9][9240/11272] Time 0.911 (0.826) Data 0.002 (0.002) Loss 2.5436 (2.6389) Prec@1 36.250 (36.181) Prec@5 70.000 (66.867) Epoch: [9][9250/11272] Time 0.880 (0.826) Data 0.002 (0.002) Loss 2.6701 (2.6388) Prec@1 34.375 (36.182) Prec@5 66.875 (66.868) Epoch: [9][9260/11272] Time 0.792 (0.826) Data 0.002 (0.002) Loss 2.6319 (2.6389) Prec@1 38.125 (36.180) Prec@5 65.000 (66.867) Epoch: [9][9270/11272] Time 0.745 (0.826) Data 0.002 (0.002) Loss 2.7867 (2.6390) Prec@1 32.500 (36.178) Prec@5 65.625 (66.864) Epoch: [9][9280/11272] Time 0.916 (0.826) Data 0.001 (0.002) Loss 2.6545 (2.6390) Prec@1 36.250 (36.180) Prec@5 68.125 (66.864) Epoch: [9][9290/11272] Time 0.852 (0.826) Data 0.002 (0.002) Loss 2.4796 (2.6390) Prec@1 36.875 (36.180) Prec@5 71.250 (66.865) Epoch: [9][9300/11272] Time 0.740 (0.826) Data 0.002 (0.002) Loss 2.5999 (2.6390) Prec@1 40.000 (36.179) Prec@5 65.625 (66.863) Epoch: [9][9310/11272] Time 0.758 (0.826) Data 0.001 (0.002) Loss 2.3510 (2.6391) Prec@1 44.375 (36.178) Prec@5 71.875 (66.862) Epoch: [9][9320/11272] Time 0.871 (0.826) Data 0.001 (0.002) Loss 2.5404 (2.6392) Prec@1 36.250 (36.177) Prec@5 71.250 (66.862) Epoch: [9][9330/11272] Time 0.764 (0.826) Data 0.002 (0.002) Loss 2.4811 (2.6393) Prec@1 36.875 (36.173) Prec@5 71.250 (66.859) Epoch: [9][9340/11272] Time 0.775 (0.826) Data 0.002 (0.002) Loss 2.5292 (2.6394) Prec@1 41.875 (36.170) Prec@5 69.375 (66.858) Epoch: [9][9350/11272] Time 0.968 (0.826) Data 0.002 (0.002) Loss 2.5045 (2.6393) Prec@1 37.500 (36.170) Prec@5 71.875 (66.857) Epoch: [9][9360/11272] Time 0.904 (0.826) Data 0.002 (0.002) Loss 2.6186 (2.6393) Prec@1 36.875 (36.170) Prec@5 65.000 (66.857) Epoch: [9][9370/11272] Time 0.827 (0.827) Data 0.002 (0.002) Loss 2.8825 (2.6392) Prec@1 34.375 (36.171) Prec@5 68.125 (66.860) Epoch: [9][9380/11272] Time 0.758 (0.827) Data 0.002 (0.002) Loss 2.6505 (2.6393) Prec@1 39.375 (36.171) Prec@5 66.250 (66.859) Epoch: [9][9390/11272] Time 0.948 (0.827) Data 0.002 (0.002) Loss 2.4873 (2.6392) Prec@1 43.125 (36.173) Prec@5 72.500 (66.861) Epoch: [9][9400/11272] Time 0.895 (0.827) Data 0.002 (0.002) Loss 2.9360 (2.6392) Prec@1 29.375 (36.174) Prec@5 61.875 (66.862) Epoch: [9][9410/11272] Time 0.795 (0.827) Data 0.001 (0.002) Loss 2.5071 (2.6392) Prec@1 38.750 (36.174) Prec@5 68.750 (66.861) Epoch: [9][9420/11272] Time 0.704 (0.827) Data 0.001 (0.002) Loss 2.6281 (2.6392) Prec@1 36.875 (36.175) Prec@5 66.250 (66.862) Epoch: [9][9430/11272] Time 0.932 (0.827) Data 0.002 (0.002) Loss 2.4382 (2.6391) Prec@1 43.125 (36.177) Prec@5 70.625 (66.863) Epoch: [9][9440/11272] Time 0.856 (0.826) Data 0.001 (0.002) Loss 2.8559 (2.6392) Prec@1 32.500 (36.176) Prec@5 59.375 (66.860) Epoch: [9][9450/11272] Time 0.728 (0.826) Data 0.002 (0.002) Loss 2.5843 (2.6392) Prec@1 34.375 (36.175) Prec@5 65.000 (66.860) Epoch: [9][9460/11272] Time 1.005 (0.827) Data 0.002 (0.002) Loss 2.8326 (2.6393) Prec@1 35.625 (36.175) Prec@5 64.375 (66.859) Epoch: [9][9470/11272] Time 0.855 (0.827) Data 0.002 (0.002) Loss 2.7700 (2.6393) Prec@1 31.250 (36.175) Prec@5 65.625 (66.859) Epoch: [9][9480/11272] Time 0.772 (0.827) Data 0.002 (0.002) Loss 2.6135 (2.6393) Prec@1 38.125 (36.174) Prec@5 69.375 (66.858) Epoch: [9][9490/11272] Time 0.814 (0.827) Data 0.001 (0.002) Loss 2.7691 (2.6393) Prec@1 36.875 (36.174) Prec@5 66.250 (66.859) Epoch: [9][9500/11272] Time 0.876 (0.826) Data 0.002 (0.002) Loss 2.9559 (2.6393) Prec@1 31.875 (36.172) Prec@5 60.000 (66.859) Epoch: [9][9510/11272] Time 0.825 (0.826) Data 0.001 (0.002) Loss 2.3714 (2.6392) Prec@1 45.000 (36.174) Prec@5 73.125 (66.860) Epoch: [9][9520/11272] Time 0.759 (0.827) Data 0.002 (0.002) Loss 2.7167 (2.6393) Prec@1 35.000 (36.174) Prec@5 66.250 (66.860) Epoch: [9][9530/11272] Time 0.749 (0.826) Data 0.002 (0.002) Loss 2.7357 (2.6393) Prec@1 31.875 (36.173) Prec@5 68.750 (66.859) Epoch: [9][9540/11272] Time 0.894 (0.827) Data 0.002 (0.002) Loss 2.4499 (2.6392) Prec@1 40.000 (36.174) Prec@5 67.500 (66.861) Epoch: [9][9550/11272] Time 0.912 (0.826) Data 0.002 (0.002) Loss 2.4693 (2.6392) Prec@1 42.500 (36.174) Prec@5 71.250 (66.861) Epoch: [9][9560/11272] Time 0.740 (0.826) Data 0.002 (0.002) Loss 2.6214 (2.6392) Prec@1 36.250 (36.174) Prec@5 66.250 (66.863) Epoch: [9][9570/11272] Time 0.750 (0.826) Data 0.002 (0.002) Loss 2.8050 (2.6391) Prec@1 35.000 (36.174) Prec@5 63.125 (66.863) Epoch: [9][9580/11272] Time 0.858 (0.826) Data 0.002 (0.002) Loss 2.7450 (2.6393) Prec@1 31.875 (36.170) Prec@5 61.875 (66.861) Epoch: [9][9590/11272] Time 0.774 (0.826) Data 0.005 (0.002) Loss 2.6443 (2.6392) Prec@1 33.750 (36.170) Prec@5 70.000 (66.861) Epoch: [9][9600/11272] Time 0.765 (0.826) Data 0.002 (0.002) Loss 2.5913 (2.6393) Prec@1 33.125 (36.169) Prec@5 67.500 (66.860) Epoch: [9][9610/11272] Time 0.848 (0.826) Data 0.002 (0.002) Loss 2.6550 (2.6392) Prec@1 35.000 (36.171) Prec@5 67.500 (66.862) Epoch: [9][9620/11272] Time 0.891 (0.826) Data 0.002 (0.002) Loss 2.5651 (2.6393) Prec@1 38.750 (36.169) Prec@5 70.000 (66.860) Epoch: [9][9630/11272] Time 0.747 (0.826) Data 0.002 (0.002) Loss 2.6810 (2.6393) Prec@1 31.875 (36.167) Prec@5 64.375 (66.858) Epoch: [9][9640/11272] Time 0.697 (0.826) Data 0.002 (0.002) Loss 2.5382 (2.6393) Prec@1 38.750 (36.167) Prec@5 66.875 (66.858) Epoch: [9][9650/11272] Time 0.925 (0.827) Data 0.004 (0.002) Loss 2.6593 (2.6392) Prec@1 35.000 (36.169) Prec@5 65.000 (66.860) Epoch: [9][9660/11272] Time 0.920 (0.827) Data 0.001 (0.002) Loss 2.8287 (2.6393) Prec@1 28.750 (36.167) Prec@5 65.000 (66.859) Epoch: [9][9670/11272] Time 0.751 (0.827) Data 0.002 (0.002) Loss 2.5054 (2.6393) Prec@1 37.500 (36.166) Prec@5 71.250 (66.858) Epoch: [9][9680/11272] Time 0.739 (0.827) Data 0.002 (0.002) Loss 2.7165 (2.6393) Prec@1 33.750 (36.167) Prec@5 66.250 (66.857) Epoch: [9][9690/11272] Time 0.890 (0.827) Data 0.001 (0.002) Loss 2.6312 (2.6393) Prec@1 38.125 (36.167) Prec@5 66.875 (66.856) Epoch: [9][9700/11272] Time 0.933 (0.827) Data 0.002 (0.002) Loss 2.6569 (2.6393) Prec@1 31.875 (36.169) Prec@5 68.750 (66.858) Epoch: [9][9710/11272] Time 0.747 (0.827) Data 0.002 (0.002) Loss 2.5745 (2.6393) Prec@1 35.000 (36.170) Prec@5 66.875 (66.860) Epoch: [9][9720/11272] Time 0.804 (0.827) Data 0.001 (0.002) Loss 2.4203 (2.6393) Prec@1 44.375 (36.169) Prec@5 67.500 (66.858) Epoch: [9][9730/11272] Time 0.864 (0.827) Data 0.002 (0.002) Loss 2.5236 (2.6393) Prec@1 35.625 (36.170) Prec@5 71.875 (66.859) Epoch: [9][9740/11272] Time 0.762 (0.827) Data 0.001 (0.002) Loss 2.4878 (2.6394) Prec@1 42.500 (36.169) Prec@5 68.750 (66.857) Epoch: [9][9750/11272] Time 0.836 (0.827) Data 0.002 (0.002) Loss 2.6056 (2.6393) Prec@1 35.000 (36.171) Prec@5 66.875 (66.858) Epoch: [9][9760/11272] Time 0.859 (0.827) Data 0.001 (0.002) Loss 2.5710 (2.6393) Prec@1 36.250 (36.171) Prec@5 66.250 (66.857) Epoch: [9][9770/11272] Time 0.907 (0.827) Data 0.001 (0.002) Loss 2.6236 (2.6392) Prec@1 36.875 (36.172) Prec@5 69.375 (66.859) Epoch: [9][9780/11272] Time 0.785 (0.827) Data 0.002 (0.002) Loss 2.6929 (2.6392) Prec@1 35.000 (36.172) Prec@5 65.000 (66.858) Epoch: [9][9790/11272] Time 0.779 (0.826) Data 0.002 (0.002) Loss 2.5028 (2.6392) Prec@1 36.875 (36.172) Prec@5 73.750 (66.859) Epoch: [9][9800/11272] Time 0.916 (0.827) Data 0.002 (0.002) Loss 2.8776 (2.6393) Prec@1 31.250 (36.170) Prec@5 61.875 (66.859) Epoch: [9][9810/11272] Time 0.878 (0.827) Data 0.001 (0.002) Loss 2.4398 (2.6392) Prec@1 43.750 (36.171) Prec@5 71.875 (66.861) Epoch: [9][9820/11272] Time 0.775 (0.827) Data 0.001 (0.002) Loss 2.7462 (2.6392) Prec@1 39.375 (36.172) Prec@5 66.250 (66.860) Epoch: [9][9830/11272] Time 0.775 (0.827) Data 0.002 (0.002) Loss 2.5103 (2.6393) Prec@1 43.125 (36.170) Prec@5 66.250 (66.860) Epoch: [9][9840/11272] Time 0.893 (0.827) Data 0.002 (0.002) Loss 2.5621 (2.6393) Prec@1 36.875 (36.170) Prec@5 71.250 (66.861) Epoch: [9][9850/11272] Time 0.747 (0.827) Data 0.004 (0.002) Loss 2.6830 (2.6392) Prec@1 36.875 (36.172) Prec@5 68.750 (66.861) Epoch: [9][9860/11272] Time 0.739 (0.827) Data 0.002 (0.002) Loss 2.7713 (2.6393) Prec@1 37.500 (36.171) Prec@5 66.875 (66.861) Epoch: [9][9870/11272] Time 0.891 (0.827) Data 0.002 (0.002) Loss 2.8452 (2.6392) Prec@1 35.625 (36.173) Prec@5 63.750 (66.862) Epoch: [9][9880/11272] Time 0.942 (0.826) Data 0.002 (0.002) Loss 2.7163 (2.6393) Prec@1 30.625 (36.171) Prec@5 67.500 (66.860) Epoch: [9][9890/11272] Time 0.778 (0.827) Data 0.002 (0.002) Loss 2.8171 (2.6393) Prec@1 30.625 (36.170) Prec@5 69.375 (66.860) Epoch: [9][9900/11272] Time 0.755 (0.827) Data 0.001 (0.002) Loss 3.0288 (2.6393) Prec@1 30.625 (36.169) Prec@5 58.750 (66.859) Epoch: [9][9910/11272] Time 0.872 (0.826) Data 0.002 (0.002) Loss 2.6708 (2.6393) Prec@1 35.000 (36.169) Prec@5 66.250 (66.859) Epoch: [9][9920/11272] Time 0.856 (0.826) Data 0.001 (0.002) Loss 2.9951 (2.6394) Prec@1 29.375 (36.168) Prec@5 62.500 (66.860) Epoch: [9][9930/11272] Time 0.798 (0.826) Data 0.002 (0.002) Loss 2.8214 (2.6393) Prec@1 36.250 (36.170) Prec@5 67.500 (66.861) Epoch: [9][9940/11272] Time 0.787 (0.826) Data 0.002 (0.002) Loss 2.5744 (2.6393) Prec@1 35.000 (36.170) Prec@5 66.875 (66.861) Epoch: [9][9950/11272] Time 0.873 (0.826) Data 0.002 (0.002) Loss 2.5947 (2.6394) Prec@1 36.250 (36.168) Prec@5 63.750 (66.860) Epoch: [9][9960/11272] Time 0.899 (0.826) Data 0.001 (0.002) Loss 2.7235 (2.6394) Prec@1 36.250 (36.169) Prec@5 70.000 (66.860) Epoch: [9][9970/11272] Time 0.761 (0.826) Data 0.001 (0.002) Loss 2.3949 (2.6393) Prec@1 40.000 (36.170) Prec@5 73.125 (66.861) Epoch: [9][9980/11272] Time 0.918 (0.826) Data 0.002 (0.002) Loss 2.4970 (2.6393) Prec@1 35.625 (36.169) Prec@5 72.500 (66.862) Epoch: [9][9990/11272] Time 0.819 (0.826) Data 0.001 (0.002) Loss 2.7528 (2.6392) Prec@1 35.625 (36.171) Prec@5 65.625 (66.864) Epoch: [9][10000/11272] Time 0.779 (0.826) Data 0.002 (0.002) Loss 2.3933 (2.6391) Prec@1 41.875 (36.173) Prec@5 71.875 (66.866) Epoch: [9][10010/11272] Time 0.759 (0.826) Data 0.001 (0.002) Loss 2.6129 (2.6392) Prec@1 38.125 (36.172) Prec@5 66.250 (66.865) Epoch: [9][10020/11272] Time 0.844 (0.826) Data 0.001 (0.002) Loss 2.5699 (2.6391) Prec@1 39.375 (36.175) Prec@5 66.250 (66.868) Epoch: [9][10030/11272] Time 0.873 (0.826) Data 0.002 (0.002) Loss 2.6859 (2.6391) Prec@1 31.250 (36.172) Prec@5 70.000 (66.867) Epoch: [9][10040/11272] Time 0.781 (0.826) Data 0.002 (0.002) Loss 2.5009 (2.6391) Prec@1 36.875 (36.172) Prec@5 71.250 (66.866) Epoch: [9][10050/11272] Time 0.764 (0.826) Data 0.002 (0.002) Loss 2.7585 (2.6391) Prec@1 33.125 (36.172) Prec@5 62.500 (66.866) Epoch: [9][10060/11272] Time 0.864 (0.826) Data 0.001 (0.002) Loss 2.6654 (2.6391) Prec@1 37.500 (36.173) Prec@5 68.750 (66.867) Epoch: [9][10070/11272] Time 0.883 (0.826) Data 0.002 (0.002) Loss 2.6051 (2.6390) Prec@1 40.000 (36.174) Prec@5 69.375 (66.869) Epoch: [9][10080/11272] Time 0.764 (0.826) Data 0.001 (0.002) Loss 2.5180 (2.6390) Prec@1 35.000 (36.173) Prec@5 70.625 (66.869) Epoch: [9][10090/11272] Time 0.753 (0.826) Data 0.002 (0.002) Loss 2.6944 (2.6389) Prec@1 35.625 (36.174) Prec@5 70.000 (66.871) Epoch: [9][10100/11272] Time 0.865 (0.826) Data 0.002 (0.002) Loss 2.7241 (2.6389) Prec@1 33.125 (36.174) Prec@5 62.500 (66.870) Epoch: [9][10110/11272] Time 0.876 (0.826) Data 0.002 (0.002) Loss 2.6010 (2.6388) Prec@1 36.875 (36.176) Prec@5 71.875 (66.872) Epoch: [9][10120/11272] Time 0.808 (0.826) Data 0.002 (0.002) Loss 2.3086 (2.6387) Prec@1 41.250 (36.180) Prec@5 71.250 (66.875) Epoch: [9][10130/11272] Time 0.914 (0.826) Data 0.002 (0.002) Loss 2.5058 (2.6387) Prec@1 36.875 (36.179) Prec@5 70.000 (66.875) Epoch: [9][10140/11272] Time 0.920 (0.826) Data 0.002 (0.002) Loss 2.7828 (2.6387) Prec@1 33.750 (36.181) Prec@5 60.000 (66.875) Epoch: [9][10150/11272] Time 0.766 (0.826) Data 0.002 (0.002) Loss 2.8759 (2.6387) Prec@1 30.625 (36.180) Prec@5 59.375 (66.875) Epoch: [9][10160/11272] Time 0.803 (0.826) Data 0.003 (0.002) Loss 2.5204 (2.6388) Prec@1 36.250 (36.178) Prec@5 67.500 (66.873) Epoch: [9][10170/11272] Time 0.891 (0.826) Data 0.001 (0.002) Loss 2.6582 (2.6388) Prec@1 35.625 (36.176) Prec@5 66.875 (66.871) Epoch: [9][10180/11272] Time 0.853 (0.826) Data 0.001 (0.002) Loss 2.6858 (2.6388) Prec@1 34.375 (36.175) Prec@5 65.625 (66.870) Epoch: [9][10190/11272] Time 0.766 (0.826) Data 0.002 (0.002) Loss 2.6826 (2.6389) Prec@1 40.000 (36.176) Prec@5 67.500 (66.870) Epoch: [9][10200/11272] Time 0.807 (0.826) Data 0.002 (0.002) Loss 2.8046 (2.6389) Prec@1 30.625 (36.175) Prec@5 68.125 (66.870) Epoch: [9][10210/11272] Time 0.869 (0.826) Data 0.002 (0.002) Loss 2.5526 (2.6389) Prec@1 41.875 (36.176) Prec@5 66.250 (66.870) Epoch: [9][10220/11272] Time 0.872 (0.826) Data 0.002 (0.002) Loss 2.8478 (2.6389) Prec@1 38.750 (36.176) Prec@5 63.125 (66.870) Epoch: [9][10230/11272] Time 0.774 (0.826) Data 0.002 (0.002) Loss 2.4445 (2.6389) Prec@1 41.250 (36.177) Prec@5 72.500 (66.871) Epoch: [9][10240/11272] Time 0.716 (0.826) Data 0.002 (0.002) Loss 2.8638 (2.6390) Prec@1 31.250 (36.177) Prec@5 61.250 (66.869) Epoch: [9][10250/11272] Time 0.899 (0.826) Data 0.001 (0.002) Loss 2.7976 (2.6389) Prec@1 32.500 (36.177) Prec@5 65.625 (66.871) Epoch: [9][10260/11272] Time 0.773 (0.826) Data 0.002 (0.002) Loss 2.3413 (2.6389) Prec@1 39.375 (36.177) Prec@5 72.500 (66.870) Epoch: [9][10270/11272] Time 0.808 (0.826) Data 0.002 (0.002) Loss 2.6614 (2.6390) Prec@1 32.500 (36.176) Prec@5 65.000 (66.869) Epoch: [9][10280/11272] Time 0.880 (0.826) Data 0.001 (0.002) Loss 2.9498 (2.6390) Prec@1 30.000 (36.175) Prec@5 56.875 (66.867) Epoch: [9][10290/11272] Time 0.875 (0.826) Data 0.002 (0.002) Loss 2.8490 (2.6391) Prec@1 33.125 (36.174) Prec@5 59.375 (66.864) Epoch: [9][10300/11272] Time 0.752 (0.826) Data 0.001 (0.002) Loss 2.6133 (2.6391) Prec@1 37.500 (36.175) Prec@5 68.750 (66.865) Epoch: [9][10310/11272] Time 0.762 (0.826) Data 0.001 (0.002) Loss 2.4332 (2.6390) Prec@1 44.375 (36.176) Prec@5 69.375 (66.866) Epoch: [9][10320/11272] Time 0.897 (0.826) Data 0.001 (0.002) Loss 2.6917 (2.6392) Prec@1 38.125 (36.174) Prec@5 62.500 (66.863) Epoch: [9][10330/11272] Time 0.859 (0.826) Data 0.002 (0.002) Loss 2.5285 (2.6392) Prec@1 42.500 (36.174) Prec@5 66.250 (66.862) Epoch: [9][10340/11272] Time 0.751 (0.826) Data 0.002 (0.002) Loss 2.4411 (2.6391) Prec@1 40.625 (36.176) Prec@5 69.375 (66.863) Epoch: [9][10350/11272] Time 0.838 (0.826) Data 0.002 (0.002) Loss 2.6847 (2.6391) Prec@1 36.250 (36.177) Prec@5 70.000 (66.863) Epoch: [9][10360/11272] Time 0.894 (0.826) Data 0.002 (0.002) Loss 2.5607 (2.6391) Prec@1 35.000 (36.177) Prec@5 67.500 (66.862) Epoch: [9][10370/11272] Time 0.920 (0.826) Data 0.001 (0.002) Loss 2.8427 (2.6392) Prec@1 33.125 (36.176) Prec@5 65.625 (66.862) Epoch: [9][10380/11272] Time 0.783 (0.826) Data 0.001 (0.002) Loss 2.5826 (2.6391) Prec@1 36.875 (36.177) Prec@5 68.750 (66.864) Epoch: [9][10390/11272] Time 0.882 (0.826) Data 0.001 (0.002) Loss 2.5906 (2.6391) Prec@1 40.625 (36.177) Prec@5 65.000 (66.864) Epoch: [9][10400/11272] Time 0.931 (0.826) Data 0.002 (0.002) Loss 2.5069 (2.6390) Prec@1 40.625 (36.176) Prec@5 73.125 (66.866) Epoch: [9][10410/11272] Time 0.774 (0.826) Data 0.001 (0.002) Loss 2.9388 (2.6391) Prec@1 28.125 (36.174) Prec@5 62.500 (66.864) Epoch: [9][10420/11272] Time 0.722 (0.826) Data 0.002 (0.002) Loss 2.8462 (2.6391) Prec@1 31.875 (36.172) Prec@5 65.000 (66.865) Epoch: [9][10430/11272] Time 0.839 (0.826) Data 0.001 (0.002) Loss 2.7179 (2.6392) Prec@1 36.250 (36.170) Prec@5 67.500 (66.863) Epoch: [9][10440/11272] Time 0.871 (0.826) Data 0.002 (0.002) Loss 2.7107 (2.6392) Prec@1 35.625 (36.171) Prec@5 66.875 (66.863) Epoch: [9][10450/11272] Time 0.750 (0.826) Data 0.001 (0.002) Loss 2.5856 (2.6392) Prec@1 35.000 (36.172) Prec@5 69.375 (66.864) Epoch: [9][10460/11272] Time 0.746 (0.826) Data 0.001 (0.002) Loss 2.7322 (2.6392) Prec@1 33.750 (36.171) Prec@5 66.875 (66.863) Epoch: [9][10470/11272] Time 0.872 (0.826) Data 0.002 (0.002) Loss 2.5992 (2.6392) Prec@1 38.750 (36.171) Prec@5 66.250 (66.862) Epoch: [9][10480/11272] Time 0.869 (0.826) Data 0.001 (0.002) Loss 2.6916 (2.6393) Prec@1 37.500 (36.170) Prec@5 66.250 (66.861) Epoch: [9][10490/11272] Time 0.753 (0.826) Data 0.001 (0.002) Loss 2.3896 (2.6393) Prec@1 38.125 (36.168) Prec@5 72.500 (66.861) Epoch: [9][10500/11272] Time 0.756 (0.826) Data 0.002 (0.002) Loss 2.8256 (2.6393) Prec@1 30.000 (36.167) Prec@5 62.500 (66.861) Epoch: [9][10510/11272] Time 0.792 (0.826) Data 0.001 (0.002) Loss 2.8415 (2.6393) Prec@1 31.875 (36.166) Prec@5 63.125 (66.861) Epoch: [9][10520/11272] Time 0.763 (0.826) Data 0.004 (0.002) Loss 2.7809 (2.6393) Prec@1 28.750 (36.165) Prec@5 63.750 (66.861) Epoch: [9][10530/11272] Time 0.723 (0.826) Data 0.001 (0.002) Loss 2.5438 (2.6393) Prec@1 40.000 (36.163) Prec@5 69.375 (66.861) Epoch: [9][10540/11272] Time 0.880 (0.826) Data 0.002 (0.002) Loss 2.6542 (2.6393) Prec@1 35.625 (36.164) Prec@5 66.875 (66.861) Epoch: [9][10550/11272] Time 0.885 (0.826) Data 0.002 (0.002) Loss 2.8447 (2.6392) Prec@1 35.000 (36.167) Prec@5 62.500 (66.861) Epoch: [9][10560/11272] Time 0.737 (0.826) Data 0.002 (0.002) Loss 2.6553 (2.6392) Prec@1 39.375 (36.168) Prec@5 68.125 (66.863) Epoch: [9][10570/11272] Time 0.770 (0.826) Data 0.001 (0.002) Loss 2.5019 (2.6392) Prec@1 34.375 (36.167) Prec@5 72.500 (66.862) Epoch: [9][10580/11272] Time 0.885 (0.826) Data 0.002 (0.002) Loss 2.7070 (2.6393) Prec@1 34.375 (36.168) Prec@5 65.000 (66.862) Epoch: [9][10590/11272] Time 0.831 (0.826) Data 0.001 (0.002) Loss 2.5593 (2.6392) Prec@1 37.500 (36.169) Prec@5 71.250 (66.862) Epoch: [9][10600/11272] Time 0.769 (0.826) Data 0.002 (0.002) Loss 2.5346 (2.6392) Prec@1 33.750 (36.168) Prec@5 66.875 (66.861) Epoch: [9][10610/11272] Time 0.782 (0.826) Data 0.002 (0.002) Loss 2.6846 (2.6391) Prec@1 38.125 (36.171) Prec@5 68.125 (66.863) Epoch: [9][10620/11272] Time 0.877 (0.826) Data 0.003 (0.002) Loss 2.5389 (2.6392) Prec@1 40.000 (36.171) Prec@5 61.250 (66.862) Epoch: [9][10630/11272] Time 0.836 (0.826) Data 0.001 (0.002) Loss 2.6937 (2.6391) Prec@1 37.500 (36.170) Prec@5 65.625 (66.864) Epoch: [9][10640/11272] Time 0.756 (0.826) Data 0.002 (0.002) Loss 2.8470 (2.6392) Prec@1 31.875 (36.169) Prec@5 61.875 (66.862) Epoch: [9][10650/11272] Time 0.940 (0.826) Data 0.003 (0.002) Loss 2.4707 (2.6392) Prec@1 40.000 (36.168) Prec@5 71.250 (66.862) Epoch: [9][10660/11272] Time 0.898 (0.826) Data 0.001 (0.002) Loss 2.7123 (2.6392) Prec@1 33.750 (36.167) Prec@5 67.500 (66.862) Epoch: [9][10670/11272] Time 0.801 (0.826) Data 0.002 (0.002) Loss 2.5799 (2.6392) Prec@1 32.500 (36.165) Prec@5 70.625 (66.862) Epoch: [9][10680/11272] Time 0.768 (0.826) Data 0.002 (0.002) Loss 2.7070 (2.6392) Prec@1 37.500 (36.164) Prec@5 61.875 (66.861) Epoch: [9][10690/11272] Time 0.850 (0.826) Data 0.002 (0.002) Loss 2.3927 (2.6391) Prec@1 39.375 (36.165) Prec@5 71.875 (66.864) Epoch: [9][10700/11272] Time 0.908 (0.826) Data 0.002 (0.002) Loss 2.5312 (2.6391) Prec@1 37.500 (36.166) Prec@5 68.125 (66.865) Epoch: [9][10710/11272] Time 0.742 (0.826) Data 0.002 (0.002) Loss 2.7399 (2.6391) Prec@1 37.500 (36.166) Prec@5 63.750 (66.865) Epoch: [9][10720/11272] Time 0.781 (0.826) Data 0.001 (0.002) Loss 2.7445 (2.6392) Prec@1 35.000 (36.166) Prec@5 66.250 (66.864) Epoch: [9][10730/11272] Time 0.898 (0.826) Data 0.002 (0.002) Loss 2.6846 (2.6391) Prec@1 33.125 (36.165) Prec@5 65.625 (66.864) Epoch: [9][10740/11272] Time 0.852 (0.826) Data 0.002 (0.002) Loss 2.8162 (2.6393) Prec@1 33.750 (36.162) Prec@5 64.375 (66.862) Epoch: [9][10750/11272] Time 0.785 (0.826) Data 0.002 (0.002) Loss 2.7660 (2.6392) Prec@1 37.500 (36.163) Prec@5 61.250 (66.861) Epoch: [9][10760/11272] Time 0.726 (0.826) Data 0.002 (0.002) Loss 2.5027 (2.6392) Prec@1 33.750 (36.163) Prec@5 68.750 (66.860) Epoch: [9][10770/11272] Time 0.872 (0.826) Data 0.002 (0.002) Loss 2.6367 (2.6392) Prec@1 40.000 (36.163) Prec@5 68.125 (66.861) Epoch: [9][10780/11272] Time 0.756 (0.826) Data 0.004 (0.002) Loss 2.9131 (2.6393) Prec@1 33.750 (36.161) Prec@5 60.000 (66.859) Epoch: [9][10790/11272] Time 0.733 (0.826) Data 0.001 (0.002) Loss 2.6832 (2.6393) Prec@1 31.875 (36.160) Prec@5 66.875 (66.858) Epoch: [9][10800/11272] Time 0.827 (0.826) Data 0.001 (0.002) Loss 2.5034 (2.6393) Prec@1 35.000 (36.160) Prec@5 72.500 (66.859) Epoch: [9][10810/11272] Time 0.869 (0.826) Data 0.001 (0.002) Loss 2.7055 (2.6393) Prec@1 39.375 (36.161) Prec@5 68.125 (66.859) Epoch: [9][10820/11272] Time 0.784 (0.826) Data 0.001 (0.002) Loss 2.8297 (2.6394) Prec@1 33.750 (36.159) Prec@5 61.250 (66.858) Epoch: [9][10830/11272] Time 0.736 (0.826) Data 0.002 (0.002) Loss 2.8374 (2.6394) Prec@1 30.000 (36.158) Prec@5 56.875 (66.856) Epoch: [9][10840/11272] Time 0.881 (0.826) Data 0.002 (0.002) Loss 2.6480 (2.6394) Prec@1 39.375 (36.158) Prec@5 65.625 (66.856) Epoch: [9][10850/11272] Time 0.891 (0.826) Data 0.001 (0.002) Loss 2.7603 (2.6395) Prec@1 35.000 (36.157) Prec@5 64.375 (66.856) Epoch: [9][10860/11272] Time 0.732 (0.826) Data 0.001 (0.002) Loss 2.4559 (2.6394) Prec@1 37.500 (36.158) Prec@5 70.625 (66.856) Epoch: [9][10870/11272] Time 0.797 (0.826) Data 0.002 (0.002) Loss 2.3689 (2.6394) Prec@1 46.250 (36.160) Prec@5 71.250 (66.855) Epoch: [9][10880/11272] Time 0.911 (0.826) Data 0.002 (0.002) Loss 2.7117 (2.6395) Prec@1 36.875 (36.159) Prec@5 64.375 (66.854) Epoch: [9][10890/11272] Time 0.921 (0.826) Data 0.001 (0.002) Loss 2.6016 (2.6395) Prec@1 41.875 (36.159) Prec@5 69.375 (66.854) Epoch: [9][10900/11272] Time 0.757 (0.826) Data 0.001 (0.002) Loss 2.4800 (2.6395) Prec@1 38.750 (36.159) Prec@5 70.625 (66.855) Epoch: [9][10910/11272] Time 0.924 (0.826) Data 0.001 (0.002) Loss 2.5444 (2.6395) Prec@1 41.875 (36.160) Prec@5 68.750 (66.854) Epoch: [9][10920/11272] Time 0.901 (0.826) Data 0.002 (0.002) Loss 2.6219 (2.6395) Prec@1 35.000 (36.160) Prec@5 66.875 (66.854) Epoch: [9][10930/11272] Time 0.764 (0.826) Data 0.002 (0.002) Loss 2.3787 (2.6395) Prec@1 40.000 (36.160) Prec@5 71.875 (66.855) Epoch: [9][10940/11272] Time 0.776 (0.826) Data 0.002 (0.002) Loss 2.4427 (2.6396) Prec@1 44.375 (36.158) Prec@5 71.875 (66.852) Epoch: [9][10950/11272] Time 0.854 (0.826) Data 0.002 (0.002) Loss 2.6128 (2.6395) Prec@1 40.000 (36.159) Prec@5 64.375 (66.852) Epoch: [9][10960/11272] Time 0.915 (0.826) Data 0.002 (0.002) Loss 2.6241 (2.6395) Prec@1 36.250 (36.157) Prec@5 65.000 (66.851) Epoch: [9][10970/11272] Time 0.799 (0.826) Data 0.002 (0.002) Loss 2.3121 (2.6394) Prec@1 43.125 (36.160) Prec@5 74.375 (66.854) Epoch: [9][10980/11272] Time 0.814 (0.826) Data 0.002 (0.002) Loss 2.5469 (2.6395) Prec@1 35.625 (36.158) Prec@5 66.875 (66.852) Epoch: [9][10990/11272] Time 0.907 (0.826) Data 0.002 (0.002) Loss 2.6399 (2.6395) Prec@1 33.750 (36.157) Prec@5 68.125 (66.852) Epoch: [9][11000/11272] Time 0.902 (0.826) Data 0.002 (0.002) Loss 2.7441 (2.6395) Prec@1 36.250 (36.158) Prec@5 65.000 (66.852) Epoch: [9][11010/11272] Time 0.773 (0.826) Data 0.002 (0.002) Loss 2.5421 (2.6394) Prec@1 38.125 (36.159) Prec@5 66.875 (66.854) Epoch: [9][11020/11272] Time 0.759 (0.826) Data 0.002 (0.002) Loss 2.8075 (2.6395) Prec@1 33.125 (36.157) Prec@5 59.375 (66.852) Epoch: [9][11030/11272] Time 0.861 (0.826) Data 0.002 (0.002) Loss 2.5857 (2.6394) Prec@1 36.250 (36.158) Prec@5 68.125 (66.853) Epoch: [9][11040/11272] Time 0.897 (0.826) Data 0.002 (0.002) Loss 2.4771 (2.6394) Prec@1 37.500 (36.159) Prec@5 73.125 (66.853) Epoch: [9][11050/11272] Time 0.776 (0.826) Data 0.002 (0.002) Loss 2.6652 (2.6394) Prec@1 39.375 (36.157) Prec@5 66.875 (66.852) Epoch: [9][11060/11272] Time 0.900 (0.826) Data 0.001 (0.002) Loss 2.7016 (2.6395) Prec@1 35.000 (36.158) Prec@5 65.625 (66.852) Epoch: [9][11070/11272] Time 0.835 (0.826) Data 0.001 (0.002) Loss 2.5251 (2.6395) Prec@1 40.625 (36.158) Prec@5 70.000 (66.852) Epoch: [9][11080/11272] Time 0.764 (0.826) Data 0.002 (0.002) Loss 2.4133 (2.6395) Prec@1 41.250 (36.157) Prec@5 68.125 (66.851) Epoch: [9][11090/11272] Time 0.727 (0.826) Data 0.001 (0.002) Loss 2.5373 (2.6395) Prec@1 34.375 (36.156) Prec@5 70.000 (66.851) Epoch: [9][11100/11272] Time 0.872 (0.826) Data 0.001 (0.002) Loss 2.8074 (2.6395) Prec@1 34.375 (36.154) Prec@5 68.125 (66.850) Epoch: [9][11110/11272] Time 0.958 (0.826) Data 0.002 (0.002) Loss 2.8610 (2.6396) Prec@1 37.500 (36.155) Prec@5 62.500 (66.850) Epoch: [9][11120/11272] Time 0.730 (0.826) Data 0.001 (0.002) Loss 2.7658 (2.6396) Prec@1 35.000 (36.153) Prec@5 63.125 (66.850) Epoch: [9][11130/11272] Time 0.756 (0.826) Data 0.001 (0.002) Loss 2.8614 (2.6396) Prec@1 32.500 (36.153) Prec@5 65.000 (66.850) Epoch: [9][11140/11272] Time 0.925 (0.826) Data 0.003 (0.002) Loss 2.3870 (2.6397) Prec@1 39.375 (36.154) Prec@5 69.375 (66.849) Epoch: [9][11150/11272] Time 0.885 (0.826) Data 0.002 (0.002) Loss 2.6681 (2.6397) Prec@1 35.000 (36.154) Prec@5 67.500 (66.850) Epoch: [9][11160/11272] Time 0.759 (0.826) Data 0.002 (0.002) Loss 2.8065 (2.6396) Prec@1 32.500 (36.156) Prec@5 64.375 (66.850) Epoch: [9][11170/11272] Time 0.741 (0.826) Data 0.002 (0.002) Loss 2.4724 (2.6395) Prec@1 43.750 (36.158) Prec@5 66.250 (66.851) Epoch: [9][11180/11272] Time 0.896 (0.826) Data 0.001 (0.002) Loss 2.6948 (2.6395) Prec@1 31.875 (36.157) Prec@5 66.250 (66.851) Epoch: [9][11190/11272] Time 0.742 (0.826) Data 0.002 (0.002) Loss 2.8098 (2.6395) Prec@1 31.250 (36.157) Prec@5 60.625 (66.852) Epoch: [9][11200/11272] Time 0.794 (0.826) Data 0.002 (0.002) Loss 2.6780 (2.6396) Prec@1 41.250 (36.157) Prec@5 67.500 (66.850) Epoch: [9][11210/11272] Time 0.939 (0.826) Data 0.001 (0.002) Loss 2.6381 (2.6396) Prec@1 35.625 (36.157) Prec@5 61.250 (66.850) Epoch: [9][11220/11272] Time 0.875 (0.826) Data 0.002 (0.002) Loss 2.5026 (2.6397) Prec@1 42.500 (36.156) Prec@5 74.375 (66.850) Epoch: [9][11230/11272] Time 0.766 (0.826) Data 0.001 (0.002) Loss 2.4472 (2.6396) Prec@1 40.000 (36.157) Prec@5 70.625 (66.851) Epoch: [9][11240/11272] Time 0.739 (0.826) Data 0.002 (0.002) Loss 2.6335 (2.6396) Prec@1 36.875 (36.155) Prec@5 68.125 (66.850) Epoch: [9][11250/11272] Time 0.873 (0.826) Data 0.001 (0.002) Loss 2.4734 (2.6396) Prec@1 43.750 (36.158) Prec@5 71.250 (66.852) Epoch: [9][11260/11272] Time 0.923 (0.826) Data 0.002 (0.002) Loss 2.6508 (2.6396) Prec@1 43.750 (36.159) Prec@5 68.125 (66.851) Epoch: [9][11270/11272] Time 0.756 (0.826) Data 0.000 (0.002) Loss 2.8403 (2.6397) Prec@1 33.125 (36.158) Prec@5 63.125 (66.849) Test: [0/229] Time 1.831 (1.831) Loss 1.6061 (1.6061) Prec@1 49.375 (49.375) Prec@5 92.500 (92.500) Test: [10/229] Time 0.467 (0.549) Loss 1.5753 (2.2741) Prec@1 54.375 (41.648) Prec@5 88.750 (76.591) Test: [20/229] Time 0.386 (0.479) Loss 1.7622 (2.3668) Prec@1 51.250 (40.357) Prec@5 85.625 (73.988) Test: [30/229] Time 0.447 (0.457) Loss 2.4048 (2.2291) Prec@1 36.875 (43.992) Prec@5 71.875 (75.685) Test: [40/229] Time 0.491 (0.448) Loss 1.1492 (2.1998) Prec@1 73.750 (44.863) Prec@5 86.875 (75.884) Test: [50/229] Time 0.358 (0.439) Loss 2.9191 (2.2614) Prec@1 21.875 (43.431) Prec@5 62.500 (74.510) Test: [60/229] Time 0.524 (0.438) Loss 2.7856 (2.2537) Prec@1 31.250 (43.371) Prec@5 57.500 (74.549) Test: [70/229] Time 0.364 (0.434) Loss 2.2691 (2.2680) Prec@1 50.625 (43.046) Prec@5 71.250 (74.322) Test: [80/229] Time 0.462 (0.429) Loss 2.7675 (2.3075) Prec@1 23.125 (41.813) Prec@5 71.250 (73.989) Test: [90/229] Time 0.374 (0.425) Loss 1.9828 (2.3172) Prec@1 55.000 (41.669) Prec@5 75.000 (73.819) Test: [100/229] Time 0.320 (0.424) Loss 2.6243 (2.2904) Prec@1 29.375 (42.252) Prec@5 77.500 (74.363) Test: [110/229] Time 0.377 (0.421) Loss 1.7598 (2.2729) Prec@1 51.875 (42.472) Prec@5 85.000 (74.809) Test: [120/229] Time 0.393 (0.421) Loss 3.2454 (2.2924) Prec@1 17.500 (41.844) Prec@5 61.250 (74.561) Test: [130/229] Time 0.463 (0.421) Loss 1.5419 (2.2753) Prec@1 58.750 (42.223) Prec@5 88.750 (74.776) Test: [140/229] Time 0.414 (0.420) Loss 2.5016 (2.2881) Prec@1 38.125 (41.809) Prec@5 69.375 (74.504) Test: [150/229] Time 0.481 (0.420) Loss 1.7297 (2.3089) Prec@1 60.000 (41.490) Prec@5 80.625 (74.197) Test: [160/229] Time 0.366 (0.419) Loss 2.1722 (2.3060) Prec@1 52.500 (41.642) Prec@5 80.000 (74.189) Test: [170/229] Time 0.369 (0.419) Loss 2.2902 (2.3205) Prec@1 41.250 (41.308) Prec@5 76.875 (73.925) Test: [180/229] Time 0.434 (0.419) Loss 2.9722 (2.3339) Prec@1 24.375 (41.164) Prec@5 56.250 (73.512) Test: [190/229] Time 0.373 (0.418) Loss 2.0755 (2.3311) Prec@1 38.750 (41.253) Prec@5 85.000 (73.541) Test: [200/229] Time 0.433 (0.417) Loss 2.2459 (2.3288) Prec@1 37.500 (41.123) Prec@5 78.750 (73.753) Test: [210/229] Time 0.366 (0.417) Loss 1.2774 (2.3183) Prec@1 67.500 (41.277) Prec@5 90.000 (73.975) Test: [220/229] Time 0.397 (0.417) Loss 2.0811 (2.3043) Prec@1 43.125 (41.728) Prec@5 81.250 (74.149) * Prec@1 41.977 Prec@5 74.195 Epoch: [10][0/11272] Time 3.505 (3.505) Data 2.659 (2.659) Loss 2.6518 (2.6518) Prec@1 34.375 (34.375) Prec@5 66.875 (66.875) Epoch: [10][10/11272] Time 0.718 (1.065) Data 0.002 (0.243) Loss 2.5680 (2.5721) Prec@1 36.250 (36.477) Prec@5 66.250 (67.216) Epoch: [10][20/11272] Time 0.852 (0.952) Data 0.002 (0.128) Loss 2.5823 (2.6389) Prec@1 37.500 (35.625) Prec@5 65.000 (65.923) Epoch: [10][30/11272] Time 0.876 (0.913) Data 0.002 (0.087) Loss 2.6899 (2.6357) Prec@1 36.250 (35.927) Prec@5 65.625 (66.310) Epoch: [10][40/11272] Time 0.758 (0.882) Data 0.002 (0.066) Loss 2.5749 (2.6241) Prec@1 38.125 (36.159) Prec@5 68.750 (66.738) Epoch: [10][50/11272] Time 0.748 (0.871) Data 0.002 (0.054) Loss 2.7448 (2.6211) Prec@1 33.750 (36.029) Prec@5 62.500 (66.826) Epoch: [10][60/11272] Time 0.902 (0.869) Data 0.001 (0.045) Loss 2.7122 (2.6308) Prec@1 35.000 (35.953) Prec@5 59.375 (66.721) Epoch: [10][70/11272] Time 0.879 (0.862) Data 0.002 (0.039) Loss 2.7770 (2.6242) Prec@1 28.125 (35.880) Prec@5 63.750 (66.813) Epoch: [10][80/11272] Time 0.776 (0.856) Data 0.002 (0.034) Loss 2.5367 (2.6174) Prec@1 39.375 (35.988) Prec@5 65.000 (66.975) Epoch: [10][90/11272] Time 0.783 (0.854) Data 0.002 (0.031) Loss 2.7203 (2.6174) Prec@1 36.250 (36.037) Prec@5 65.625 (66.964) Epoch: [10][100/11272] Time 0.875 (0.852) Data 0.002 (0.028) Loss 2.3824 (2.6146) Prec@1 39.375 (36.157) Prec@5 71.250 (67.061) Epoch: [10][110/11272] Time 0.885 (0.850) Data 0.002 (0.026) Loss 2.5305 (2.6137) Prec@1 38.750 (36.115) Prec@5 67.500 (67.078) Epoch: [10][120/11272] Time 0.768 (0.849) Data 0.002 (0.024) Loss 2.5455 (2.6104) Prec@1 31.875 (36.214) Prec@5 73.125 (67.164) Epoch: [10][130/11272] Time 0.897 (0.847) Data 0.002 (0.022) Loss 2.4205 (2.6119) Prec@1 39.375 (36.307) Prec@5 72.500 (67.118) Epoch: [10][140/11272] Time 0.936 (0.845) Data 0.002 (0.021) Loss 2.5429 (2.6111) Prec@1 39.375 (36.401) Prec@5 68.750 (67.163) Epoch: [10][150/11272] Time 0.748 (0.843) Data 0.002 (0.019) Loss 2.4027 (2.6092) Prec@1 36.875 (36.366) Prec@5 76.875 (67.223) Epoch: [10][160/11272] Time 0.744 (0.843) Data 0.002 (0.018) Loss 2.4643 (2.6084) Prec@1 36.250 (36.417) Prec@5 69.375 (67.349) Epoch: [10][170/11272] Time 0.870 (0.842) Data 0.002 (0.017) Loss 2.4894 (2.6096) Prec@1 35.625 (36.378) Prec@5 70.625 (67.343) Epoch: [10][180/11272] Time 0.851 (0.841) Data 0.001 (0.016) Loss 2.5962 (2.6082) Prec@1 39.375 (36.412) Prec@5 70.625 (67.390) Epoch: [10][190/11272] Time 0.710 (0.841) Data 0.001 (0.016) Loss 2.4980 (2.6077) Prec@1 43.125 (36.440) Prec@5 66.250 (67.389) Epoch: [10][200/11272] Time 0.724 (0.839) Data 0.002 (0.015) Loss 2.7637 (2.6087) Prec@1 34.375 (36.418) Prec@5 61.875 (67.310) Epoch: [10][210/11272] Time 0.895 (0.839) Data 0.002 (0.014) Loss 2.5088 (2.6047) Prec@1 38.750 (36.502) Prec@5 70.000 (67.340) Epoch: [10][220/11272] Time 0.861 (0.837) Data 0.002 (0.014) Loss 2.5580 (2.6045) Prec@1 39.375 (36.510) Prec@5 67.500 (67.344) Epoch: [10][230/11272] Time 0.756 (0.836) Data 0.002 (0.013) Loss 2.5933 (2.6061) Prec@1 39.375 (36.518) Prec@5 68.125 (67.273) Epoch: [10][240/11272] Time 0.837 (0.836) Data 0.002 (0.013) Loss 2.5450 (2.6041) Prec@1 37.500 (36.613) Prec@5 70.000 (67.303) Epoch: [10][250/11272] Time 0.922 (0.836) Data 0.002 (0.012) Loss 2.6616 (2.6025) Prec@1 33.125 (36.611) Prec@5 67.500 (67.383) Epoch: [10][260/11272] Time 0.790 (0.836) Data 0.001 (0.012) Loss 2.7588 (2.6042) Prec@1 34.375 (36.595) Prec@5 66.875 (67.385) Epoch: [10][270/11272] Time 0.769 (0.836) Data 0.002 (0.012) Loss 2.6537 (2.6042) Prec@1 31.875 (36.601) Prec@5 65.625 (67.343) Epoch: [10][280/11272] Time 0.848 (0.836) Data 0.002 (0.011) Loss 2.3210 (2.6037) Prec@1 45.000 (36.617) Prec@5 74.375 (67.380) Epoch: [10][290/11272] Time 0.868 (0.835) Data 0.002 (0.011) Loss 2.4922 (2.6055) Prec@1 41.250 (36.630) Prec@5 71.875 (67.345) Epoch: [10][300/11272] Time 0.806 (0.835) Data 0.001 (0.011) Loss 2.6536 (2.6077) Prec@1 41.875 (36.605) Prec@5 65.000 (67.321) Epoch: [10][310/11272] Time 0.745 (0.834) Data 0.002 (0.010) Loss 2.5587 (2.6063) Prec@1 36.875 (36.626) Prec@5 65.625 (67.343) Epoch: [10][320/11272] Time 0.892 (0.833) Data 0.002 (0.010) Loss 2.5938 (2.6062) Prec@1 37.500 (36.600) Prec@5 65.000 (67.354) Epoch: [10][330/11272] Time 0.802 (0.833) Data 0.001 (0.010) Loss 2.6062 (2.6080) Prec@1 35.000 (36.545) Prec@5 68.750 (67.336) Epoch: [10][340/11272] Time 0.767 (0.832) Data 0.001 (0.010) Loss 2.4911 (2.6072) Prec@1 38.750 (36.556) Prec@5 68.750 (67.383) Epoch: [10][350/11272] Time 0.822 (0.832) Data 0.002 (0.009) Loss 2.5558 (2.6058) Prec@1 33.125 (36.556) Prec@5 69.375 (67.434) Epoch: [10][360/11272] Time 0.865 (0.832) Data 0.001 (0.009) Loss 2.5325 (2.6070) Prec@1 30.625 (36.494) Prec@5 75.625 (67.469) Epoch: [10][370/11272] Time 0.882 (0.832) Data 0.002 (0.009) Loss 2.4564 (2.6057) Prec@1 38.750 (36.552) Prec@5 71.875 (67.503) Epoch: [10][380/11272] Time 0.768 (0.832) Data 0.002 (0.009) Loss 2.5723 (2.6066) Prec@1 38.125 (36.550) Prec@5 70.625 (67.502) Epoch: [10][390/11272] Time 0.919 (0.832) Data 0.002 (0.009) Loss 2.3282 (2.6071) Prec@1 44.375 (36.576) Prec@5 70.000 (67.470) Epoch: [10][400/11272] Time 0.905 (0.832) Data 0.001 (0.008) Loss 2.7677 (2.6052) Prec@1 36.875 (36.605) Prec@5 63.125 (67.512) Epoch: [10][410/11272] Time 0.839 (0.832) Data 0.002 (0.008) Loss 2.4945 (2.6049) Prec@1 38.750 (36.578) Prec@5 66.250 (67.485) Epoch: [10][420/11272] Time 0.758 (0.832) Data 0.001 (0.008) Loss 2.7100 (2.6073) Prec@1 32.500 (36.505) Prec@5 65.625 (67.445) Epoch: [10][430/11272] Time 0.872 (0.831) Data 0.002 (0.008) Loss 2.5600 (2.6059) Prec@1 36.250 (36.528) Prec@5 71.875 (67.468) Epoch: [10][440/11272] Time 0.846 (0.831) Data 0.002 (0.008) Loss 2.5478 (2.6063) Prec@1 36.250 (36.529) Prec@5 61.875 (67.445) Epoch: [10][450/11272] Time 0.769 (0.831) Data 0.002 (0.008) Loss 2.6557 (2.6071) Prec@1 35.000 (36.516) Prec@5 68.750 (67.446) Epoch: [10][460/11272] Time 0.743 (0.831) Data 0.001 (0.007) Loss 2.4811 (2.6074) Prec@1 41.875 (36.525) Prec@5 70.625 (67.446) Epoch: [10][470/11272] Time 0.844 (0.830) Data 0.002 (0.007) Loss 2.6742 (2.6078) Prec@1 35.000 (36.492) Prec@5 66.250 (67.416) Epoch: [10][480/11272] Time 0.878 (0.830) Data 0.001 (0.007) Loss 2.5123 (2.6078) Prec@1 40.000 (36.476) Prec@5 67.500 (67.404) Epoch: [10][490/11272] Time 0.756 (0.830) Data 0.002 (0.007) Loss 2.5904 (2.6093) Prec@1 33.125 (36.468) Prec@5 66.250 (67.373) Epoch: [10][500/11272] Time 0.724 (0.830) Data 0.001 (0.007) Loss 2.6694 (2.6098) Prec@1 37.500 (36.487) Prec@5 65.625 (67.376) Epoch: [10][510/11272] Time 0.882 (0.830) Data 0.002 (0.007) Loss 2.3110 (2.6102) Prec@1 40.000 (36.487) Prec@5 76.250 (67.378) Epoch: [10][520/11272] Time 0.768 (0.829) Data 0.004 (0.007) Loss 2.5897 (2.6089) Prec@1 35.000 (36.515) Prec@5 66.875 (67.398) Epoch: [10][530/11272] Time 0.758 (0.829) Data 0.001 (0.007) Loss 2.5897 (2.6082) Prec@1 36.875 (36.534) Prec@5 67.500 (67.415) Epoch: [10][540/11272] Time 0.880 (0.829) Data 0.002 (0.007) Loss 2.4750 (2.6086) Prec@1 36.250 (36.506) Prec@5 71.250 (67.425) Epoch: [10][550/11272] Time 0.859 (0.828) Data 0.002 (0.007) Loss 2.4572 (2.6083) Prec@1 37.500 (36.505) Prec@5 72.500 (67.424) Epoch: [10][560/11272] Time 0.731 (0.828) Data 0.002 (0.006) Loss 2.5184 (2.6092) Prec@1 38.125 (36.484) Prec@5 69.375 (67.412) Epoch: [10][570/11272] Time 0.763 (0.828) Data 0.002 (0.006) Loss 2.3877 (2.6091) Prec@1 42.500 (36.501) Prec@5 71.875 (67.414) Epoch: [10][580/11272] Time 0.874 (0.828) Data 0.001 (0.006) Loss 2.2320 (2.6091) Prec@1 45.625 (36.487) Prec@5 71.250 (67.398) Epoch: [10][590/11272] Time 0.918 (0.828) Data 0.002 (0.006) Loss 2.7120 (2.6085) Prec@1 37.500 (36.506) Prec@5 63.750 (67.394) Epoch: [10][600/11272] Time 0.768 (0.828) Data 0.001 (0.006) Loss 2.6934 (2.6097) Prec@1 41.250 (36.494) Prec@5 65.625 (67.375) Epoch: [10][610/11272] Time 0.807 (0.828) Data 0.002 (0.006) Loss 2.7711 (2.6100) Prec@1 30.000 (36.503) Prec@5 67.500 (67.358) Epoch: [10][620/11272] Time 0.883 (0.828) Data 0.001 (0.006) Loss 2.4624 (2.6104) Prec@1 40.625 (36.518) Prec@5 67.500 (67.332) Epoch: [10][630/11272] Time 0.889 (0.828) Data 0.001 (0.006) Loss 2.4110 (2.6093) Prec@1 39.375 (36.527) Prec@5 68.750 (67.334) Epoch: [10][640/11272] Time 0.781 (0.828) Data 0.001 (0.006) Loss 2.6377 (2.6097) Prec@1 31.250 (36.524) Prec@5 64.375 (67.330) Epoch: [10][650/11272] Time 0.903 (0.828) Data 0.001 (0.006) Loss 2.6794 (2.6109) Prec@1 36.875 (36.497) Prec@5 66.250 (67.296) Epoch: [10][660/11272] Time 0.842 (0.827) Data 0.001 (0.006) Loss 2.6729 (2.6107) Prec@1 35.000 (36.504) Prec@5 65.000 (67.284) Epoch: [10][670/11272] Time 0.719 (0.827) Data 0.001 (0.006) Loss 2.5489 (2.6097) Prec@1 38.125 (36.512) Prec@5 71.250 (67.309) Epoch: [10][680/11272] Time 0.742 (0.827) Data 0.001 (0.006) Loss 2.6769 (2.6097) Prec@1 38.125 (36.520) Prec@5 63.125 (67.302) Epoch: [10][690/11272] Time 0.872 (0.826) Data 0.001 (0.006) Loss 2.4753 (2.6097) Prec@1 34.375 (36.517) Prec@5 69.375 (67.309) Epoch: [10][700/11272] Time 0.933 (0.826) Data 0.001 (0.005) Loss 2.6098 (2.6110) Prec@1 36.250 (36.490) Prec@5 69.375 (67.263) Epoch: [10][710/11272] Time 0.717 (0.826) Data 0.003 (0.005) Loss 2.7266 (2.6117) Prec@1 35.000 (36.479) Prec@5 65.000 (67.271) Epoch: [10][720/11272] Time 0.739 (0.826) Data 0.002 (0.005) Loss 2.5078 (2.6127) Prec@1 35.625 (36.461) Prec@5 71.250 (67.256) Epoch: [10][730/11272] Time 0.871 (0.826) Data 0.001 (0.005) Loss 2.5348 (2.6119) Prec@1 40.625 (36.491) Prec@5 69.375 (67.265) Epoch: [10][740/11272] Time 0.888 (0.826) Data 0.002 (0.005) Loss 2.7506 (2.6124) Prec@1 36.250 (36.469) Prec@5 62.500 (67.256) Epoch: [10][750/11272] Time 0.786 (0.826) Data 0.002 (0.005) Loss 2.6065 (2.6124) Prec@1 38.125 (36.471) Prec@5 68.750 (67.260) Epoch: [10][760/11272] Time 0.761 (0.826) Data 0.002 (0.005) Loss 2.8931 (2.6122) Prec@1 30.625 (36.481) Prec@5 61.250 (67.263) Epoch: [10][770/11272] Time 0.837 (0.826) Data 0.002 (0.005) Loss 2.3460 (2.6128) Prec@1 40.625 (36.480) Prec@5 72.500 (67.255) Epoch: [10][780/11272] Time 0.750 (0.826) Data 0.003 (0.005) Loss 2.6118 (2.6130) Prec@1 40.000 (36.469) Prec@5 69.375 (67.262) Epoch: [10][790/11272] Time 0.760 (0.826) Data 0.002 (0.005) Loss 2.7099 (2.6126) Prec@1 34.375 (36.469) Prec@5 66.250 (67.281) Epoch: [10][800/11272] Time 0.870 (0.826) Data 0.001 (0.005) Loss 2.6596 (2.6131) Prec@1 35.000 (36.444) Prec@5 68.750 (67.278) Epoch: [10][810/11272] Time 0.876 (0.826) Data 0.001 (0.005) Loss 2.7292 (2.6139) Prec@1 28.750 (36.441) Prec@5 65.625 (67.262) Epoch: [10][820/11272] Time 0.809 (0.826) Data 0.002 (0.005) Loss 2.8153 (2.6142) Prec@1 30.625 (36.430) Prec@5 64.375 (67.261) Epoch: [10][830/11272] Time 0.741 (0.826) Data 0.001 (0.005) Loss 2.7457 (2.6150) Prec@1 35.000 (36.425) Prec@5 61.250 (67.233) Epoch: [10][840/11272] Time 0.883 (0.826) Data 0.002 (0.005) Loss 2.5323 (2.6157) Prec@1 33.125 (36.405) Prec@5 73.125 (67.228) Epoch: [10][850/11272] Time 0.893 (0.826) Data 0.001 (0.005) Loss 2.7492 (2.6165) Prec@1 36.250 (36.389) Prec@5 65.000 (67.208) Epoch: [10][860/11272] Time 0.741 (0.826) Data 0.002 (0.005) Loss 2.7571 (2.6163) Prec@1 33.750 (36.399) Prec@5 63.125 (67.210) Epoch: [10][870/11272] Time 0.728 (0.826) Data 0.001 (0.005) Loss 2.5794 (2.6161) Prec@1 38.125 (36.397) Prec@5 66.875 (67.219) Epoch: [10][880/11272] Time 0.926 (0.826) Data 0.002 (0.005) Loss 2.5689 (2.6158) Prec@1 33.750 (36.398) Prec@5 67.500 (67.215) Epoch: [10][890/11272] Time 0.916 (0.826) Data 0.001 (0.005) Loss 2.8888 (2.6169) Prec@1 28.750 (36.366) Prec@5 60.000 (67.200) Epoch: [10][900/11272] Time 0.769 (0.826) Data 0.002 (0.005) Loss 2.5586 (2.6171) Prec@1 38.750 (36.369) Prec@5 66.875 (67.188) Epoch: [10][910/11272] Time 0.931 (0.826) Data 0.001 (0.005) Loss 2.8930 (2.6183) Prec@1 25.000 (36.360) Prec@5 63.750 (67.174) Epoch: [10][920/11272] Time 0.924 (0.826) Data 0.002 (0.005) Loss 2.5427 (2.6177) Prec@1 38.750 (36.378) Prec@5 67.500 (67.194) Epoch: [10][930/11272] Time 0.788 (0.826) Data 0.001 (0.005) Loss 2.7968 (2.6181) Prec@1 30.000 (36.375) Prec@5 65.000 (67.184) Epoch: [10][940/11272] Time 0.746 (0.826) Data 0.002 (0.005) Loss 2.6459 (2.6172) Prec@1 34.375 (36.409) Prec@5 68.125 (67.215) Epoch: [10][950/11272] Time 0.879 (0.826) Data 0.002 (0.005) Loss 2.7016 (2.6172) Prec@1 41.875 (36.424) Prec@5 65.625 (67.221) Epoch: [10][960/11272] Time 0.873 (0.826) Data 0.001 (0.004) Loss 2.3278 (2.6174) Prec@1 33.125 (36.402) Prec@5 71.250 (67.214) Epoch: [10][970/11272] Time 0.750 (0.826) Data 0.001 (0.004) Loss 2.5994 (2.6185) Prec@1 35.000 (36.385) Prec@5 67.500 (67.211) Epoch: [10][980/11272] Time 0.735 (0.826) Data 0.001 (0.004) Loss 2.5051 (2.6188) Prec@1 43.750 (36.400) Prec@5 68.125 (67.197) Epoch: [10][990/11272] Time 0.892 (0.826) Data 0.001 (0.004) Loss 2.5424 (2.6181) Prec@1 42.500 (36.421) Prec@5 68.750 (67.203) Epoch: [10][1000/11272] Time 0.863 (0.826) Data 0.002 (0.004) Loss 2.7328 (2.6182) Prec@1 35.625 (36.432) Prec@5 63.750 (67.203) Epoch: [10][1010/11272] Time 0.725 (0.826) Data 0.002 (0.004) Loss 2.7753 (2.6182) Prec@1 34.375 (36.449) Prec@5 65.000 (67.195) Epoch: [10][1020/11272] Time 0.768 (0.826) Data 0.002 (0.004) Loss 2.4824 (2.6186) Prec@1 42.500 (36.446) Prec@5 69.375 (67.182) Epoch: [10][1030/11272] Time 0.880 (0.826) Data 0.001 (0.004) Loss 2.8053 (2.6195) Prec@1 31.875 (36.437) Prec@5 64.375 (67.170) Epoch: [10][1040/11272] Time 0.941 (0.826) Data 0.003 (0.004) Loss 2.5703 (2.6193) Prec@1 36.250 (36.431) Prec@5 69.375 (67.178) Epoch: [10][1050/11272] Time 0.809 (0.826) Data 0.002 (0.004) Loss 2.6423 (2.6189) Prec@1 38.750 (36.435) Prec@5 65.625 (67.193) Epoch: [10][1060/11272] Time 0.966 (0.826) Data 0.003 (0.004) Loss 2.5260 (2.6181) Prec@1 40.625 (36.437) Prec@5 69.375 (67.201) Epoch: [10][1070/11272] Time 0.808 (0.826) Data 0.002 (0.004) Loss 2.8593 (2.6186) Prec@1 35.000 (36.431) Prec@5 60.000 (67.184) Epoch: [10][1080/11272] Time 0.764 (0.826) Data 0.002 (0.004) Loss 2.5463 (2.6184) Prec@1 42.500 (36.429) Prec@5 68.125 (67.188) Epoch: [10][1090/11272] Time 0.722 (0.826) Data 0.001 (0.004) Loss 2.5550 (2.6188) Prec@1 36.875 (36.425) Prec@5 71.250 (67.181) Epoch: [10][1100/11272] Time 0.861 (0.826) Data 0.001 (0.004) Loss 2.4416 (2.6184) Prec@1 42.500 (36.436) Prec@5 68.750 (67.174) Epoch: [10][1110/11272] Time 0.897 (0.826) Data 0.001 (0.004) Loss 2.5708 (2.6177) Prec@1 34.375 (36.442) Prec@5 65.625 (67.188) Epoch: [10][1120/11272] Time 0.751 (0.826) Data 0.002 (0.004) Loss 2.6683 (2.6179) Prec@1 33.750 (36.435) Prec@5 62.500 (67.183) Epoch: [10][1130/11272] Time 0.757 (0.826) Data 0.002 (0.004) Loss 2.6143 (2.6180) Prec@1 36.250 (36.436) Prec@5 65.625 (67.170) Epoch: [10][1140/11272] Time 0.928 (0.826) Data 0.001 (0.004) Loss 2.8724 (2.6182) Prec@1 28.750 (36.435) Prec@5 65.000 (67.161) Epoch: [10][1150/11272] Time 0.913 (0.826) Data 0.001 (0.004) Loss 2.5493 (2.6184) Prec@1 41.875 (36.440) Prec@5 68.125 (67.163) Epoch: [10][1160/11272] Time 0.766 (0.826) Data 0.001 (0.004) Loss 2.5106 (2.6183) Prec@1 37.500 (36.435) Prec@5 73.125 (67.162) Epoch: [10][1170/11272] Time 0.767 (0.826) Data 0.001 (0.004) Loss 2.6026 (2.6178) Prec@1 38.750 (36.446) Prec@5 68.750 (67.175) Epoch: [10][1180/11272] Time 0.883 (0.826) Data 0.002 (0.004) Loss 2.6688 (2.6177) Prec@1 33.750 (36.441) Prec@5 66.250 (67.182) Epoch: [10][1190/11272] Time 0.767 (0.826) Data 0.002 (0.004) Loss 2.6994 (2.6179) Prec@1 33.750 (36.448) Prec@5 70.000 (67.189) Epoch: [10][1200/11272] Time 0.777 (0.826) Data 0.002 (0.004) Loss 2.7438 (2.6183) Prec@1 36.250 (36.443) Prec@5 61.875 (67.176) Epoch: [10][1210/11272] Time 0.866 (0.826) Data 0.002 (0.004) Loss 2.4846 (2.6184) Prec@1 39.375 (36.443) Prec@5 70.000 (67.176) Epoch: [10][1220/11272] Time 0.821 (0.826) Data 0.002 (0.004) Loss 2.7462 (2.6184) Prec@1 38.125 (36.443) Prec@5 65.625 (67.180) Epoch: [10][1230/11272] Time 0.722 (0.826) Data 0.001 (0.004) Loss 2.6993 (2.6187) Prec@1 35.000 (36.432) Prec@5 63.750 (67.167) Epoch: [10][1240/11272] Time 0.751 (0.825) Data 0.002 (0.004) Loss 2.5623 (2.6186) Prec@1 36.875 (36.443) Prec@5 69.375 (67.166) Epoch: [10][1250/11272] Time 0.860 (0.825) Data 0.001 (0.004) Loss 2.5131 (2.6183) Prec@1 40.000 (36.446) Prec@5 68.750 (67.169) Epoch: [10][1260/11272] Time 0.874 (0.825) Data 0.002 (0.004) Loss 2.6756 (2.6187) Prec@1 36.875 (36.446) Prec@5 62.500 (67.152) Epoch: [10][1270/11272] Time 0.845 (0.825) Data 0.002 (0.004) Loss 2.4059 (2.6188) Prec@1 41.875 (36.453) Prec@5 71.875 (67.149) Epoch: [10][1280/11272] Time 0.724 (0.825) Data 0.002 (0.004) Loss 2.6334 (2.6188) Prec@1 38.750 (36.458) Prec@5 65.000 (67.146) Epoch: [10][1290/11272] Time 0.888 (0.825) Data 0.002 (0.004) Loss 2.4151 (2.6188) Prec@1 36.875 (36.458) Prec@5 68.125 (67.149) Epoch: [10][1300/11272] Time 0.847 (0.825) Data 0.002 (0.004) Loss 2.5096 (2.6189) Prec@1 37.500 (36.464) Prec@5 70.625 (67.143) Epoch: [10][1310/11272] Time 0.774 (0.825) Data 0.002 (0.004) Loss 2.6865 (2.6189) Prec@1 30.625 (36.465) Prec@5 68.125 (67.148) Epoch: [10][1320/11272] Time 0.870 (0.825) Data 0.002 (0.004) Loss 2.6692 (2.6190) Prec@1 35.625 (36.468) Prec@5 70.000 (67.149) Epoch: [10][1330/11272] Time 0.929 (0.825) Data 0.001 (0.004) Loss 2.3148 (2.6196) Prec@1 41.250 (36.457) Prec@5 71.875 (67.137) Epoch: [10][1340/11272] Time 0.787 (0.825) Data 0.002 (0.004) Loss 2.7027 (2.6199) Prec@1 35.000 (36.456) Prec@5 65.000 (67.138) Epoch: [10][1350/11272] Time 0.758 (0.825) Data 0.002 (0.004) Loss 2.6688 (2.6199) Prec@1 35.000 (36.455) Prec@5 66.875 (67.145) Epoch: [10][1360/11272] Time 0.808 (0.825) Data 0.001 (0.004) Loss 2.5093 (2.6202) Prec@1 37.500 (36.442) Prec@5 64.375 (67.138) Epoch: [10][1370/11272] Time 0.882 (0.825) Data 0.002 (0.004) Loss 2.5961 (2.6201) Prec@1 43.750 (36.460) Prec@5 67.500 (67.144) Epoch: [10][1380/11272] Time 0.771 (0.825) Data 0.002 (0.004) Loss 2.6066 (2.6202) Prec@1 40.625 (36.454) Prec@5 68.125 (67.137) Epoch: [10][1390/11272] Time 0.757 (0.824) Data 0.002 (0.004) Loss 2.4985 (2.6197) Prec@1 33.125 (36.471) Prec@5 68.750 (67.141) Epoch: [10][1400/11272] Time 0.919 (0.825) Data 0.003 (0.004) Loss 2.8237 (2.6200) Prec@1 29.375 (36.456) Prec@5 66.250 (67.130) Epoch: [10][1410/11272] Time 0.944 (0.824) Data 0.002 (0.004) Loss 2.6393 (2.6204) Prec@1 34.375 (36.449) Prec@5 65.625 (67.122) Epoch: [10][1420/11272] Time 0.738 (0.824) Data 0.002 (0.004) Loss 2.5540 (2.6208) Prec@1 30.000 (36.431) Prec@5 69.375 (67.121) Epoch: [10][1430/11272] Time 0.715 (0.824) Data 0.001 (0.004) Loss 2.6810 (2.6207) Prec@1 31.875 (36.436) Prec@5 63.750 (67.122) Epoch: [10][1440/11272] Time 0.843 (0.824) Data 0.002 (0.004) Loss 2.4739 (2.6208) Prec@1 38.125 (36.433) Prec@5 70.000 (67.123) Epoch: [10][1450/11272] Time 0.743 (0.824) Data 0.003 (0.004) Loss 2.4743 (2.6208) Prec@1 37.500 (36.438) Prec@5 67.500 (67.122) Epoch: [10][1460/11272] Time 0.767 (0.824) Data 0.003 (0.004) Loss 2.4143 (2.6203) Prec@1 43.750 (36.448) Prec@5 69.375 (67.127) Epoch: [10][1470/11272] Time 0.909 (0.824) Data 0.002 (0.004) Loss 2.4910 (2.6203) Prec@1 34.375 (36.444) Prec@5 67.500 (67.126) Epoch: [10][1480/11272] Time 0.928 (0.824) Data 0.002 (0.003) Loss 2.6941 (2.6205) Prec@1 38.750 (36.454) Prec@5 64.375 (67.123) Epoch: [10][1490/11272] Time 0.778 (0.824) Data 0.001 (0.003) Loss 2.6162 (2.6205) Prec@1 38.750 (36.455) Prec@5 66.875 (67.129) Epoch: [10][1500/11272] Time 0.752 (0.824) Data 0.002 (0.003) Loss 2.5777 (2.6200) Prec@1 32.500 (36.467) Prec@5 71.875 (67.144) Epoch: [10][1510/11272] Time 0.866 (0.824) Data 0.002 (0.003) Loss 2.7020 (2.6200) Prec@1 33.750 (36.473) Prec@5 65.000 (67.146) Epoch: [10][1520/11272] Time 0.909 (0.824) Data 0.002 (0.003) Loss 2.6094 (2.6204) Prec@1 42.500 (36.463) Prec@5 65.000 (67.137) Epoch: [10][1530/11272] Time 0.756 (0.824) Data 0.002 (0.003) Loss 2.5386 (2.6205) Prec@1 37.500 (36.455) Prec@5 66.875 (67.135) Epoch: [10][1540/11272] Time 0.769 (0.824) Data 0.002 (0.003) Loss 2.6519 (2.6206) Prec@1 33.750 (36.453) Prec@5 63.750 (67.133) Epoch: [10][1550/11272] Time 1.008 (0.824) Data 0.002 (0.003) Loss 2.5803 (2.6206) Prec@1 43.125 (36.460) Prec@5 64.375 (67.130) Epoch: [10][1560/11272] Time 0.858 (0.824) Data 0.002 (0.003) Loss 2.5574 (2.6209) Prec@1 39.375 (36.447) Prec@5 66.875 (67.117) Epoch: [10][1570/11272] Time 0.757 (0.824) Data 0.002 (0.003) Loss 2.6509 (2.6213) Prec@1 31.875 (36.436) Prec@5 65.625 (67.100) Epoch: [10][1580/11272] Time 0.844 (0.824) Data 0.002 (0.003) Loss 2.6825 (2.6215) Prec@1 31.250 (36.426) Prec@5 65.625 (67.090) Epoch: [10][1590/11272] Time 0.840 (0.824) Data 0.002 (0.003) Loss 2.3947 (2.6215) Prec@1 43.125 (36.440) Prec@5 70.000 (67.084) Epoch: [10][1600/11272] Time 0.799 (0.824) Data 0.003 (0.003) Loss 2.3221 (2.6215) Prec@1 43.750 (36.436) Prec@5 77.500 (67.083) Epoch: [10][1610/11272] Time 0.771 (0.824) Data 0.001 (0.003) Loss 2.9490 (2.6215) Prec@1 33.125 (36.439) Prec@5 59.375 (67.086) Epoch: [10][1620/11272] Time 0.894 (0.824) Data 0.002 (0.003) Loss 2.8665 (2.6211) Prec@1 31.250 (36.439) Prec@5 63.750 (67.092) Epoch: [10][1630/11272] Time 0.894 (0.824) Data 0.001 (0.003) Loss 2.6015 (2.6211) Prec@1 37.500 (36.439) Prec@5 67.500 (67.091) Epoch: [10][1640/11272] Time 0.750 (0.824) Data 0.001 (0.003) Loss 2.8653 (2.6208) Prec@1 33.750 (36.445) Prec@5 65.000 (67.096) Epoch: [10][1650/11272] Time 0.721 (0.824) Data 0.002 (0.003) Loss 2.3478 (2.6205) Prec@1 43.750 (36.450) Prec@5 73.750 (67.104) Epoch: [10][1660/11272] Time 0.829 (0.824) Data 0.002 (0.003) Loss 2.6697 (2.6206) Prec@1 34.375 (36.450) Prec@5 70.625 (67.106) Epoch: [10][1670/11272] Time 0.875 (0.824) Data 0.001 (0.003) Loss 2.5864 (2.6208) Prec@1 33.750 (36.448) Prec@5 63.750 (67.099) Epoch: [10][1680/11272] Time 0.771 (0.824) Data 0.002 (0.003) Loss 2.6387 (2.6211) Prec@1 36.875 (36.439) Prec@5 63.750 (67.098) Epoch: [10][1690/11272] Time 0.744 (0.824) Data 0.002 (0.003) Loss 2.8141 (2.6211) Prec@1 32.500 (36.439) Prec@5 65.625 (67.101) Epoch: [10][1700/11272] Time 0.874 (0.824) Data 0.002 (0.003) Loss 2.6889 (2.6209) Prec@1 36.875 (36.446) Prec@5 66.875 (67.109) Epoch: [10][1710/11272] Time 0.749 (0.824) Data 0.003 (0.003) Loss 2.6352 (2.6211) Prec@1 38.750 (36.445) Prec@5 74.375 (67.108) Epoch: [10][1720/11272] Time 0.753 (0.824) Data 0.002 (0.003) Loss 2.6711 (2.6214) Prec@1 39.375 (36.440) Prec@5 64.375 (67.101) Epoch: [10][1730/11272] Time 0.860 (0.824) Data 0.002 (0.003) Loss 2.6170 (2.6219) Prec@1 36.875 (36.428) Prec@5 65.000 (67.090) Epoch: [10][1740/11272] Time 0.905 (0.824) Data 0.002 (0.003) Loss 2.5852 (2.6221) Prec@1 37.500 (36.419) Prec@5 68.125 (67.091) Epoch: [10][1750/11272] Time 0.738 (0.824) Data 0.002 (0.003) Loss 2.7724 (2.6228) Prec@1 35.000 (36.411) Prec@5 60.625 (67.071) Epoch: [10][1760/11272] Time 0.782 (0.824) Data 0.001 (0.003) Loss 2.3144 (2.6226) Prec@1 41.250 (36.413) Prec@5 75.625 (67.072) Epoch: [10][1770/11272] Time 0.926 (0.824) Data 0.001 (0.003) Loss 2.6578 (2.6226) Prec@1 31.250 (36.406) Prec@5 68.750 (67.074) Epoch: [10][1780/11272] Time 0.849 (0.824) Data 0.002 (0.003) Loss 2.5096 (2.6224) Prec@1 43.125 (36.407) Prec@5 69.375 (67.079) Epoch: [10][1790/11272] Time 0.778 (0.824) Data 0.002 (0.003) Loss 2.7614 (2.6228) Prec@1 30.625 (36.404) Prec@5 63.125 (67.068) Epoch: [10][1800/11272] Time 0.809 (0.824) Data 0.001 (0.003) Loss 2.6243 (2.6232) Prec@1 36.250 (36.406) Prec@5 61.875 (67.063) Epoch: [10][1810/11272] Time 0.883 (0.824) Data 0.001 (0.003) Loss 2.4863 (2.6232) Prec@1 36.250 (36.401) Prec@5 66.875 (67.063) Epoch: [10][1820/11272] Time 0.956 (0.824) Data 0.002 (0.003) Loss 2.5153 (2.6232) Prec@1 38.750 (36.399) Prec@5 68.125 (67.069) Epoch: [10][1830/11272] Time 0.760 (0.824) Data 0.001 (0.003) Loss 2.9023 (2.6230) Prec@1 34.375 (36.395) Prec@5 62.500 (67.075) Epoch: [10][1840/11272] Time 0.905 (0.824) Data 0.001 (0.003) Loss 2.4211 (2.6233) Prec@1 41.875 (36.391) Prec@5 70.625 (67.070) Epoch: [10][1850/11272] Time 1.007 (0.824) Data 0.002 (0.003) Loss 2.5949 (2.6231) Prec@1 39.375 (36.397) Prec@5 71.875 (67.077) Epoch: [10][1860/11272] Time 0.763 (0.824) Data 0.001 (0.003) Loss 2.6037 (2.6229) Prec@1 36.250 (36.405) Prec@5 64.375 (67.084) Epoch: [10][1870/11272] Time 0.765 (0.824) Data 0.002 (0.003) Loss 2.5436 (2.6228) Prec@1 34.375 (36.408) Prec@5 71.875 (67.085) Epoch: [10][1880/11272] Time 0.842 (0.824) Data 0.002 (0.003) Loss 2.7367 (2.6228) Prec@1 41.250 (36.411) Prec@5 63.125 (67.085) Epoch: [10][1890/11272] Time 0.871 (0.824) Data 0.001 (0.003) Loss 2.8611 (2.6234) Prec@1 31.250 (36.401) Prec@5 63.125 (67.075) Epoch: [10][1900/11272] Time 0.762 (0.824) Data 0.002 (0.003) Loss 2.8730 (2.6238) Prec@1 35.625 (36.400) Prec@5 60.625 (67.065) Epoch: [10][1910/11272] Time 0.796 (0.824) Data 0.002 (0.003) Loss 2.5782 (2.6242) Prec@1 37.500 (36.401) Prec@5 70.000 (67.060) Epoch: [10][1920/11272] Time 0.933 (0.824) Data 0.002 (0.003) Loss 2.6629 (2.6242) Prec@1 33.125 (36.393) Prec@5 65.000 (67.057) Epoch: [10][1930/11272] Time 0.898 (0.824) Data 0.002 (0.003) Loss 2.8014 (2.6238) Prec@1 36.250 (36.400) Prec@5 61.250 (67.060) Epoch: [10][1940/11272] Time 0.779 (0.824) Data 0.002 (0.003) Loss 2.5668 (2.6236) Prec@1 41.875 (36.400) Prec@5 69.375 (67.066) Epoch: [10][1950/11272] Time 0.768 (0.824) Data 0.001 (0.003) Loss 2.6023 (2.6234) Prec@1 34.375 (36.403) Prec@5 68.750 (67.081) Epoch: [10][1960/11272] Time 0.912 (0.824) Data 0.002 (0.003) Loss 2.5628 (2.6233) Prec@1 33.125 (36.395) Prec@5 66.875 (67.085) Epoch: [10][1970/11272] Time 0.856 (0.824) Data 0.002 (0.003) Loss 2.6763 (2.6234) Prec@1 36.875 (36.394) Prec@5 63.750 (67.082) Epoch: [10][1980/11272] Time 0.741 (0.824) Data 0.002 (0.003) Loss 2.6424 (2.6231) Prec@1 36.250 (36.392) Prec@5 70.000 (67.089) Epoch: [10][1990/11272] Time 0.864 (0.824) Data 0.002 (0.003) Loss 2.6251 (2.6233) Prec@1 34.375 (36.385) Prec@5 70.000 (67.089) Epoch: [10][2000/11272] Time 0.910 (0.824) Data 0.002 (0.003) Loss 2.4137 (2.6232) Prec@1 41.875 (36.383) Prec@5 73.750 (67.100) Epoch: [10][2010/11272] Time 0.726 (0.824) Data 0.001 (0.003) Loss 2.4694 (2.6233) Prec@1 40.625 (36.381) Prec@5 69.375 (67.094) Epoch: [10][2020/11272] Time 0.777 (0.824) Data 0.002 (0.003) Loss 2.5834 (2.6231) Prec@1 35.625 (36.383) Prec@5 67.500 (67.098) Epoch: [10][2030/11272] Time 0.862 (0.824) Data 0.001 (0.003) Loss 2.6779 (2.6230) Prec@1 32.500 (36.385) Prec@5 68.125 (67.095) Epoch: [10][2040/11272] Time 0.877 (0.823) Data 0.001 (0.003) Loss 2.6010 (2.6229) Prec@1 35.625 (36.394) Prec@5 70.625 (67.099) Epoch: [10][2050/11272] Time 0.780 (0.823) Data 0.002 (0.003) Loss 2.6398 (2.6228) Prec@1 35.625 (36.394) Prec@5 64.375 (67.104) Epoch: [10][2060/11272] Time 0.749 (0.823) Data 0.002 (0.003) Loss 2.6048 (2.6232) Prec@1 41.875 (36.392) Prec@5 73.125 (67.094) Epoch: [10][2070/11272] Time 0.866 (0.823) Data 0.001 (0.003) Loss 2.4903 (2.6231) Prec@1 40.625 (36.405) Prec@5 65.625 (67.096) Epoch: [10][2080/11272] Time 0.847 (0.823) Data 0.002 (0.003) Loss 2.8440 (2.6234) Prec@1 38.125 (36.407) Prec@5 63.750 (67.095) Epoch: [10][2090/11272] Time 0.718 (0.823) Data 0.002 (0.003) Loss 2.6909 (2.6238) Prec@1 34.375 (36.397) Prec@5 65.000 (67.092) Epoch: [10][2100/11272] Time 0.773 (0.823) Data 0.002 (0.003) Loss 2.6069 (2.6241) Prec@1 36.250 (36.393) Prec@5 66.250 (67.088) Epoch: [10][2110/11272] Time 0.897 (0.823) Data 0.001 (0.003) Loss 2.6842 (2.6241) Prec@1 37.500 (36.393) Prec@5 68.125 (67.084) Epoch: [10][2120/11272] Time 0.792 (0.823) Data 0.002 (0.003) Loss 2.5182 (2.6242) Prec@1 43.750 (36.394) Prec@5 69.375 (67.084) Epoch: [10][2130/11272] Time 0.722 (0.823) Data 0.001 (0.003) Loss 2.6338 (2.6242) Prec@1 35.000 (36.395) Prec@5 66.875 (67.083) Epoch: [10][2140/11272] Time 0.975 (0.823) Data 0.002 (0.003) Loss 2.6623 (2.6246) Prec@1 34.375 (36.384) Prec@5 65.625 (67.071) Epoch: [10][2150/11272] Time 0.897 (0.823) Data 0.002 (0.003) Loss 2.6632 (2.6246) Prec@1 33.750 (36.379) Prec@5 69.375 (67.069) Epoch: [10][2160/11272] Time 0.729 (0.823) Data 0.002 (0.003) Loss 2.6889 (2.6247) Prec@1 34.375 (36.376) Prec@5 70.625 (67.077) Epoch: [10][2170/11272] Time 0.734 (0.823) Data 0.001 (0.003) Loss 2.7162 (2.6248) Prec@1 38.125 (36.371) Prec@5 65.625 (67.078) Epoch: [10][2180/11272] Time 0.888 (0.823) Data 0.002 (0.003) Loss 2.5999 (2.6250) Prec@1 34.375 (36.370) Prec@5 67.500 (67.075) Epoch: [10][2190/11272] Time 0.946 (0.823) Data 0.002 (0.003) Loss 2.6276 (2.6252) Prec@1 40.625 (36.373) Prec@5 65.000 (67.064) Epoch: [10][2200/11272] Time 0.761 (0.823) Data 0.001 (0.003) Loss 2.5021 (2.6253) Prec@1 37.500 (36.369) Prec@5 70.625 (67.061) Epoch: [10][2210/11272] Time 0.751 (0.823) Data 0.003 (0.003) Loss 2.7217 (2.6258) Prec@1 36.875 (36.363) Prec@5 65.625 (67.047) Epoch: [10][2220/11272] Time 0.890 (0.823) Data 0.002 (0.003) Loss 2.5617 (2.6256) Prec@1 39.375 (36.371) Prec@5 67.500 (67.050) Epoch: [10][2230/11272] Time 0.893 (0.823) Data 0.002 (0.003) Loss 2.4893 (2.6253) Prec@1 38.125 (36.372) Prec@5 69.375 (67.052) Epoch: [10][2240/11272] Time 0.730 (0.823) Data 0.001 (0.003) Loss 2.5752 (2.6254) Prec@1 44.375 (36.377) Prec@5 69.375 (67.054) Epoch: [10][2250/11272] Time 0.928 (0.823) Data 0.002 (0.003) Loss 2.6168 (2.6258) Prec@1 35.000 (36.369) Prec@5 65.000 (67.043) Epoch: [10][2260/11272] Time 0.867 (0.823) Data 0.002 (0.003) Loss 2.5781 (2.6257) Prec@1 38.125 (36.376) Prec@5 65.625 (67.041) Epoch: [10][2270/11272] Time 0.785 (0.823) Data 0.002 (0.003) Loss 2.5741 (2.6255) Prec@1 40.625 (36.380) Prec@5 71.250 (67.047) Epoch: [10][2280/11272] Time 0.771 (0.823) Data 0.001 (0.003) Loss 2.3460 (2.6258) Prec@1 45.000 (36.376) Prec@5 68.125 (67.040) Epoch: [10][2290/11272] Time 0.910 (0.823) Data 0.002 (0.003) Loss 2.6120 (2.6261) Prec@1 38.125 (36.371) Prec@5 66.250 (67.035) Epoch: [10][2300/11272] Time 0.868 (0.823) Data 0.001 (0.003) Loss 2.8581 (2.6266) Prec@1 33.750 (36.361) Prec@5 66.875 (67.025) Epoch: [10][2310/11272] Time 0.758 (0.823) Data 0.002 (0.003) Loss 2.4812 (2.6264) Prec@1 41.875 (36.368) Prec@5 66.875 (67.024) Epoch: [10][2320/11272] Time 0.747 (0.823) Data 0.001 (0.003) Loss 2.8838 (2.6266) Prec@1 30.000 (36.366) Prec@5 59.375 (67.028) Epoch: [10][2330/11272] Time 0.898 (0.823) Data 0.002 (0.003) Loss 2.6568 (2.6265) Prec@1 38.750 (36.369) Prec@5 65.000 (67.031) Epoch: [10][2340/11272] Time 0.848 (0.823) Data 0.001 (0.003) Loss 2.6898 (2.6267) Prec@1 41.250 (36.368) Prec@5 67.500 (67.024) Epoch: [10][2350/11272] Time 0.765 (0.823) Data 0.002 (0.003) Loss 2.5782 (2.6266) Prec@1 36.250 (36.368) Prec@5 67.500 (67.025) Epoch: [10][2360/11272] Time 0.718 (0.823) Data 0.001 (0.003) Loss 2.3755 (2.6265) Prec@1 43.125 (36.370) Prec@5 71.250 (67.027) Epoch: [10][2370/11272] Time 0.897 (0.823) Data 0.002 (0.003) Loss 2.5662 (2.6264) Prec@1 33.125 (36.372) Prec@5 66.250 (67.030) Epoch: [10][2380/11272] Time 0.737 (0.823) Data 0.003 (0.003) Loss 2.6601 (2.6263) Prec@1 38.125 (36.373) Prec@5 71.250 (67.034) Epoch: [10][2390/11272] Time 0.764 (0.823) Data 0.002 (0.003) Loss 2.6616 (2.6266) Prec@1 36.250 (36.375) Prec@5 65.000 (67.027) Epoch: [10][2400/11272] Time 0.888 (0.823) Data 0.001 (0.003) Loss 2.6725 (2.6266) Prec@1 33.750 (36.374) Prec@5 65.625 (67.028) Epoch: [10][2410/11272] Time 0.951 (0.823) Data 0.002 (0.003) Loss 2.7194 (2.6265) Prec@1 30.625 (36.377) Prec@5 65.625 (67.035) Epoch: [10][2420/11272] Time 0.744 (0.823) Data 0.002 (0.003) Loss 2.3377 (2.6263) Prec@1 41.250 (36.379) Prec@5 75.000 (67.040) Epoch: [10][2430/11272] Time 0.774 (0.823) Data 0.002 (0.003) Loss 2.5846 (2.6263) Prec@1 40.625 (36.378) Prec@5 69.375 (67.044) Epoch: [10][2440/11272] Time 0.842 (0.823) Data 0.002 (0.003) Loss 2.7966 (2.6262) Prec@1 34.375 (36.378) Prec@5 66.875 (67.050) Epoch: [10][2450/11272] Time 0.888 (0.823) Data 0.002 (0.003) Loss 2.6650 (2.6262) Prec@1 33.750 (36.381) Prec@5 66.250 (67.053) Epoch: [10][2460/11272] Time 0.743 (0.823) Data 0.001 (0.003) Loss 2.5436 (2.6263) Prec@1 38.750 (36.381) Prec@5 73.750 (67.051) Epoch: [10][2470/11272] Time 0.734 (0.823) Data 0.001 (0.003) Loss 2.6496 (2.6266) Prec@1 30.625 (36.373) Prec@5 64.375 (67.043) Epoch: [10][2480/11272] Time 0.910 (0.823) Data 0.001 (0.003) Loss 2.9194 (2.6268) Prec@1 26.250 (36.368) Prec@5 58.750 (67.044) Epoch: [10][2490/11272] Time 0.850 (0.823) Data 0.002 (0.003) Loss 2.6247 (2.6269) Prec@1 37.500 (36.367) Prec@5 64.375 (67.045) Epoch: [10][2500/11272] Time 0.738 (0.823) Data 0.001 (0.003) Loss 2.6544 (2.6268) Prec@1 33.750 (36.364) Prec@5 63.750 (67.051) Epoch: [10][2510/11272] Time 0.852 (0.823) Data 0.002 (0.003) Loss 2.6119 (2.6269) Prec@1 40.000 (36.369) Prec@5 68.750 (67.050) Epoch: [10][2520/11272] Time 0.879 (0.823) Data 0.001 (0.003) Loss 2.5306 (2.6267) Prec@1 38.750 (36.375) Prec@5 69.375 (67.054) Epoch: [10][2530/11272] Time 0.719 (0.823) Data 0.001 (0.003) Loss 2.4633 (2.6264) Prec@1 41.875 (36.383) Prec@5 69.375 (67.057) Epoch: [10][2540/11272] Time 0.806 (0.823) Data 0.002 (0.003) Loss 2.6385 (2.6264) Prec@1 36.250 (36.383) Prec@5 68.125 (67.055) Epoch: [10][2550/11272] Time 0.908 (0.823) Data 0.001 (0.003) Loss 2.4766 (2.6265) Prec@1 37.500 (36.376) Prec@5 68.125 (67.049) Epoch: [10][2560/11272] Time 0.907 (0.823) Data 0.002 (0.003) Loss 2.6740 (2.6267) Prec@1 39.375 (36.373) Prec@5 68.125 (67.045) Epoch: [10][2570/11272] Time 0.752 (0.823) Data 0.002 (0.003) Loss 2.6854 (2.6267) Prec@1 31.875 (36.369) Prec@5 67.500 (67.047) Epoch: [10][2580/11272] Time 0.763 (0.823) Data 0.002 (0.003) Loss 2.8140 (2.6269) Prec@1 30.000 (36.367) Prec@5 61.875 (67.045) Epoch: [10][2590/11272] Time 0.871 (0.823) Data 0.002 (0.003) Loss 2.5770 (2.6268) Prec@1 36.875 (36.367) Prec@5 68.125 (67.042) Epoch: [10][2600/11272] Time 0.862 (0.823) Data 0.001 (0.003) Loss 2.6841 (2.6270) Prec@1 31.250 (36.360) Prec@5 68.125 (67.042) Epoch: [10][2610/11272] Time 0.758 (0.823) Data 0.002 (0.003) Loss 2.6477 (2.6270) Prec@1 36.875 (36.360) Prec@5 66.875 (67.042) Epoch: [10][2620/11272] Time 0.723 (0.823) Data 0.002 (0.003) Loss 2.4676 (2.6269) Prec@1 38.125 (36.367) Prec@5 70.000 (67.045) Epoch: [10][2630/11272] Time 0.905 (0.823) Data 0.002 (0.003) Loss 2.6749 (2.6268) Prec@1 33.125 (36.366) Prec@5 70.000 (67.050) Epoch: [10][2640/11272] Time 0.776 (0.823) Data 0.003 (0.003) Loss 2.5442 (2.6271) Prec@1 38.750 (36.360) Prec@5 71.250 (67.043) Epoch: [10][2650/11272] Time 0.770 (0.823) Data 0.002 (0.003) Loss 2.5295 (2.6270) Prec@1 39.375 (36.369) Prec@5 71.875 (67.048) Epoch: [10][2660/11272] Time 0.891 (0.823) Data 0.002 (0.003) Loss 2.6513 (2.6270) Prec@1 38.750 (36.372) Prec@5 65.000 (67.048) Epoch: [10][2670/11272] Time 0.923 (0.823) Data 0.002 (0.003) Loss 2.5864 (2.6269) Prec@1 36.250 (36.376) Prec@5 65.625 (67.047) Epoch: [10][2680/11272] Time 0.745 (0.823) Data 0.002 (0.003) Loss 2.4376 (2.6270) Prec@1 45.625 (36.378) Prec@5 65.625 (67.042) Epoch: [10][2690/11272] Time 0.749 (0.823) Data 0.002 (0.003) Loss 2.4661 (2.6270) Prec@1 39.375 (36.377) Prec@5 68.750 (67.044) Epoch: [10][2700/11272] Time 0.863 (0.823) Data 0.002 (0.003) Loss 2.3552 (2.6270) Prec@1 41.875 (36.377) Prec@5 74.375 (67.048) Epoch: [10][2710/11272] Time 0.940 (0.823) Data 0.001 (0.003) Loss 2.6829 (2.6269) Prec@1 35.000 (36.380) Prec@5 67.500 (67.048) Epoch: [10][2720/11272] Time 0.768 (0.823) Data 0.002 (0.003) Loss 2.8330 (2.6270) Prec@1 29.375 (36.373) Prec@5 65.000 (67.042) Epoch: [10][2730/11272] Time 0.738 (0.823) Data 0.001 (0.003) Loss 2.4385 (2.6269) Prec@1 39.375 (36.371) Prec@5 70.625 (67.045) Epoch: [10][2740/11272] Time 0.868 (0.823) Data 0.002 (0.003) Loss 2.5839 (2.6270) Prec@1 33.750 (36.363) Prec@5 70.000 (67.041) Epoch: [10][2750/11272] Time 0.851 (0.823) Data 0.002 (0.003) Loss 2.6722 (2.6272) Prec@1 34.375 (36.362) Prec@5 65.000 (67.034) Epoch: [10][2760/11272] Time 0.736 (0.823) Data 0.002 (0.003) Loss 2.6488 (2.6273) Prec@1 38.125 (36.360) Prec@5 68.750 (67.038) Epoch: [10][2770/11272] Time 0.860 (0.823) Data 0.001 (0.003) Loss 2.7425 (2.6271) Prec@1 35.625 (36.366) Prec@5 66.250 (67.039) Epoch: [10][2780/11272] Time 0.920 (0.823) Data 0.002 (0.003) Loss 2.7564 (2.6270) Prec@1 33.125 (36.370) Prec@5 71.250 (67.042) Epoch: [10][2790/11272] Time 0.762 (0.823) Data 0.002 (0.003) Loss 2.3853 (2.6270) Prec@1 36.875 (36.367) Prec@5 71.250 (67.048) Epoch: [10][2800/11272] Time 0.737 (0.823) Data 0.001 (0.003) Loss 2.4402 (2.6271) Prec@1 41.875 (36.365) Prec@5 70.000 (67.042) Epoch: [10][2810/11272] Time 0.885 (0.823) Data 0.001 (0.003) Loss 2.4214 (2.6269) Prec@1 39.375 (36.368) Prec@5 70.000 (67.045) Epoch: [10][2820/11272] Time 0.853 (0.823) Data 0.002 (0.003) Loss 2.7645 (2.6270) Prec@1 35.000 (36.372) Prec@5 63.750 (67.041) Epoch: [10][2830/11272] Time 0.774 (0.823) Data 0.001 (0.003) Loss 2.4354 (2.6269) Prec@1 36.875 (36.368) Prec@5 73.125 (67.049) Epoch: [10][2840/11272] Time 0.733 (0.823) Data 0.002 (0.003) Loss 2.7371 (2.6271) Prec@1 31.250 (36.366) Prec@5 67.500 (67.046) Epoch: [10][2850/11272] Time 0.887 (0.823) Data 0.002 (0.003) Loss 2.5608 (2.6269) Prec@1 35.000 (36.365) Prec@5 71.250 (67.053) Epoch: [10][2860/11272] Time 0.869 (0.823) Data 0.002 (0.003) Loss 2.5241 (2.6273) Prec@1 40.625 (36.359) Prec@5 66.875 (67.047) Epoch: [10][2870/11272] Time 0.801 (0.823) Data 0.002 (0.003) Loss 2.7248 (2.6273) Prec@1 34.375 (36.359) Prec@5 65.000 (67.049) Epoch: [10][2880/11272] Time 0.726 (0.823) Data 0.002 (0.003) Loss 2.6952 (2.6274) Prec@1 38.125 (36.361) Prec@5 66.250 (67.048) Epoch: [10][2890/11272] Time 0.844 (0.823) Data 0.002 (0.003) Loss 2.4343 (2.6273) Prec@1 43.125 (36.363) Prec@5 70.000 (67.050) Epoch: [10][2900/11272] Time 0.958 (0.823) Data 0.002 (0.003) Loss 2.5541 (2.6270) Prec@1 35.000 (36.366) Prec@5 68.750 (67.054) Epoch: [10][2910/11272] Time 0.798 (0.823) Data 0.002 (0.003) Loss 2.5109 (2.6271) Prec@1 36.250 (36.362) Prec@5 73.750 (67.055) Epoch: [10][2920/11272] Time 0.922 (0.823) Data 0.002 (0.003) Loss 2.5440 (2.6269) Prec@1 37.500 (36.367) Prec@5 67.500 (67.056) Epoch: [10][2930/11272] Time 0.884 (0.823) Data 0.001 (0.003) Loss 2.6586 (2.6272) Prec@1 35.000 (36.366) Prec@5 68.125 (67.055) Epoch: [10][2940/11272] Time 0.715 (0.823) Data 0.001 (0.003) Loss 2.8185 (2.6272) Prec@1 30.625 (36.361) Prec@5 60.625 (67.055) Epoch: [10][2950/11272] Time 0.750 (0.823) Data 0.002 (0.003) Loss 2.9464 (2.6274) Prec@1 32.500 (36.356) Prec@5 60.625 (67.045) Epoch: [10][2960/11272] Time 0.874 (0.823) Data 0.002 (0.003) Loss 2.6837 (2.6278) Prec@1 35.000 (36.348) Prec@5 69.375 (67.039) Epoch: [10][2970/11272] Time 0.901 (0.823) Data 0.001 (0.003) Loss 2.8587 (2.6279) Prec@1 28.750 (36.348) Prec@5 65.625 (67.039) Epoch: [10][2980/11272] Time 0.735 (0.823) Data 0.002 (0.003) Loss 2.6579 (2.6281) Prec@1 36.250 (36.339) Prec@5 71.250 (67.036) Epoch: [10][2990/11272] Time 0.795 (0.823) Data 0.001 (0.003) Loss 2.9319 (2.6282) Prec@1 31.875 (36.339) Prec@5 60.625 (67.035) Epoch: [10][3000/11272] Time 0.867 (0.823) Data 0.001 (0.003) Loss 2.7021 (2.6279) Prec@1 33.125 (36.341) Prec@5 63.125 (67.040) Epoch: [10][3010/11272] Time 0.862 (0.823) Data 0.002 (0.003) Loss 2.3588 (2.6277) Prec@1 41.250 (36.346) Prec@5 70.625 (67.039) Epoch: [10][3020/11272] Time 0.720 (0.823) Data 0.002 (0.003) Loss 2.7608 (2.6276) Prec@1 31.875 (36.346) Prec@5 63.125 (67.042) Epoch: [10][3030/11272] Time 0.764 (0.823) Data 0.002 (0.003) Loss 2.7080 (2.6274) Prec@1 36.875 (36.351) Prec@5 65.000 (67.040) Epoch: [10][3040/11272] Time 0.871 (0.823) Data 0.002 (0.003) Loss 2.6710 (2.6276) Prec@1 31.250 (36.349) Prec@5 65.000 (67.033) Epoch: [10][3050/11272] Time 0.772 (0.823) Data 0.002 (0.003) Loss 2.5573 (2.6276) Prec@1 37.500 (36.350) Prec@5 65.000 (67.030) Epoch: [10][3060/11272] Time 0.772 (0.823) Data 0.001 (0.003) Loss 2.6364 (2.6276) Prec@1 38.750 (36.351) Prec@5 67.500 (67.029) Epoch: [10][3070/11272] Time 0.826 (0.823) Data 0.001 (0.003) Loss 2.5284 (2.6275) Prec@1 38.125 (36.355) Prec@5 68.125 (67.028) Epoch: [10][3080/11272] Time 0.840 (0.823) Data 0.001 (0.003) Loss 2.2704 (2.6274) Prec@1 48.750 (36.360) Prec@5 73.750 (67.028) Epoch: [10][3090/11272] Time 0.790 (0.823) Data 0.002 (0.003) Loss 3.0101 (2.6277) Prec@1 30.000 (36.360) Prec@5 62.500 (67.025) Epoch: [10][3100/11272] Time 0.769 (0.823) Data 0.002 (0.003) Loss 2.4241 (2.6277) Prec@1 38.750 (36.363) Prec@5 71.250 (67.024) Epoch: [10][3110/11272] Time 0.846 (0.823) Data 0.002 (0.003) Loss 2.7491 (2.6279) Prec@1 35.625 (36.362) Prec@5 65.000 (67.019) Epoch: [10][3120/11272] Time 0.876 (0.823) Data 0.002 (0.003) Loss 2.8135 (2.6278) Prec@1 35.000 (36.364) Prec@5 63.750 (67.019) Epoch: [10][3130/11272] Time 0.773 (0.823) Data 0.002 (0.003) Loss 2.5871 (2.6279) Prec@1 35.625 (36.364) Prec@5 64.375 (67.013) Epoch: [10][3140/11272] Time 0.746 (0.822) Data 0.002 (0.003) Loss 2.8324 (2.6279) Prec@1 36.250 (36.365) Prec@5 61.875 (67.015) Epoch: [10][3150/11272] Time 0.844 (0.822) Data 0.002 (0.003) Loss 2.6088 (2.6280) Prec@1 41.875 (36.363) Prec@5 63.125 (67.012) Epoch: [10][3160/11272] Time 0.842 (0.822) Data 0.001 (0.003) Loss 2.7656 (2.6281) Prec@1 35.625 (36.361) Prec@5 64.375 (67.009) Epoch: [10][3170/11272] Time 0.760 (0.822) Data 0.002 (0.003) Loss 2.6217 (2.6278) Prec@1 40.625 (36.369) Prec@5 65.625 (67.016) Epoch: [10][3180/11272] Time 0.912 (0.822) Data 0.002 (0.003) Loss 2.4415 (2.6279) Prec@1 39.375 (36.371) Prec@5 70.625 (67.015) Epoch: [10][3190/11272] Time 0.873 (0.822) Data 0.001 (0.003) Loss 2.5621 (2.6279) Prec@1 38.125 (36.375) Prec@5 64.375 (67.013) Epoch: [10][3200/11272] Time 0.754 (0.822) Data 0.002 (0.003) Loss 2.5415 (2.6276) Prec@1 36.250 (36.379) Prec@5 67.500 (67.019) Epoch: [10][3210/11272] Time 0.756 (0.822) Data 0.002 (0.003) Loss 2.5457 (2.6273) Prec@1 37.500 (36.384) Prec@5 67.500 (67.022) Epoch: [10][3220/11272] Time 0.947 (0.822) Data 0.002 (0.003) Loss 2.7233 (2.6273) Prec@1 35.625 (36.388) Prec@5 66.875 (67.024) Epoch: [10][3230/11272] Time 0.905 (0.822) Data 0.002 (0.003) Loss 2.7379 (2.6273) Prec@1 30.625 (36.387) Prec@5 66.875 (67.029) Epoch: [10][3240/11272] Time 0.764 (0.822) Data 0.001 (0.003) Loss 2.4894 (2.6274) Prec@1 43.750 (36.386) Prec@5 70.000 (67.030) Epoch: [10][3250/11272] Time 0.782 (0.822) Data 0.001 (0.003) Loss 2.6167 (2.6272) Prec@1 36.250 (36.391) Prec@5 67.500 (67.030) Epoch: [10][3260/11272] Time 0.913 (0.822) Data 0.001 (0.003) Loss 2.6226 (2.6271) Prec@1 32.500 (36.386) Prec@5 66.875 (67.035) Epoch: [10][3270/11272] Time 0.812 (0.822) Data 0.001 (0.003) Loss 2.7943 (2.6272) Prec@1 32.500 (36.384) Prec@5 65.625 (67.033) Epoch: [10][3280/11272] Time 0.845 (0.822) Data 0.002 (0.003) Loss 2.5365 (2.6275) Prec@1 36.250 (36.380) Prec@5 71.875 (67.030) Epoch: [10][3290/11272] Time 0.789 (0.822) Data 0.001 (0.002) Loss 2.7600 (2.6276) Prec@1 36.250 (36.380) Prec@5 63.750 (67.024) Epoch: [10][3300/11272] Time 0.901 (0.822) Data 0.002 (0.002) Loss 2.4675 (2.6277) Prec@1 38.750 (36.380) Prec@5 72.500 (67.021) Epoch: [10][3310/11272] Time 0.755 (0.822) Data 0.003 (0.002) Loss 2.6314 (2.6278) Prec@1 33.750 (36.383) Prec@5 70.625 (67.023) Epoch: [10][3320/11272] Time 0.783 (0.822) Data 0.002 (0.002) Loss 2.5626 (2.6276) Prec@1 33.750 (36.382) Prec@5 64.375 (67.025) Epoch: [10][3330/11272] Time 0.848 (0.822) Data 0.002 (0.002) Loss 2.5437 (2.6275) Prec@1 39.375 (36.386) Prec@5 70.000 (67.030) Epoch: [10][3340/11272] Time 0.836 (0.822) Data 0.001 (0.002) Loss 2.9518 (2.6274) Prec@1 30.000 (36.386) Prec@5 62.500 (67.034) Epoch: [10][3350/11272] Time 0.758 (0.822) Data 0.002 (0.002) Loss 3.0598 (2.6276) Prec@1 29.375 (36.386) Prec@5 58.750 (67.029) Epoch: [10][3360/11272] Time 0.755 (0.822) Data 0.002 (0.002) Loss 2.5240 (2.6273) Prec@1 38.750 (36.386) Prec@5 67.500 (67.035) Epoch: [10][3370/11272] Time 0.865 (0.822) Data 0.002 (0.002) Loss 2.4227 (2.6271) Prec@1 40.625 (36.392) Prec@5 72.500 (67.040) Epoch: [10][3380/11272] Time 0.866 (0.822) Data 0.001 (0.002) Loss 2.7534 (2.6271) Prec@1 35.625 (36.390) Prec@5 67.500 (67.038) Epoch: [10][3390/11272] Time 0.769 (0.822) Data 0.002 (0.002) Loss 2.5330 (2.6272) Prec@1 38.750 (36.383) Prec@5 66.875 (67.037) Epoch: [10][3400/11272] Time 0.741 (0.822) Data 0.001 (0.002) Loss 2.4880 (2.6272) Prec@1 38.125 (36.387) Prec@5 68.125 (67.038) Epoch: [10][3410/11272] Time 0.875 (0.822) Data 0.001 (0.002) Loss 2.6139 (2.6271) Prec@1 36.250 (36.390) Prec@5 65.000 (67.041) Epoch: [10][3420/11272] Time 0.886 (0.822) Data 0.002 (0.002) Loss 2.7949 (2.6272) Prec@1 32.500 (36.388) Prec@5 65.000 (67.041) Epoch: [10][3430/11272] Time 0.798 (0.822) Data 0.002 (0.002) Loss 2.5815 (2.6275) Prec@1 35.625 (36.384) Prec@5 68.750 (67.038) Epoch: [10][3440/11272] Time 0.909 (0.822) Data 0.002 (0.002) Loss 2.6335 (2.6275) Prec@1 38.125 (36.385) Prec@5 65.000 (67.042) Epoch: [10][3450/11272] Time 0.849 (0.822) Data 0.001 (0.002) Loss 2.7159 (2.6275) Prec@1 33.125 (36.385) Prec@5 60.000 (67.043) Epoch: [10][3460/11272] Time 0.747 (0.822) Data 0.002 (0.002) Loss 2.7197 (2.6275) Prec@1 35.000 (36.383) Prec@5 65.000 (67.044) Epoch: [10][3470/11272] Time 0.769 (0.822) Data 0.002 (0.002) Loss 2.6129 (2.6273) Prec@1 34.375 (36.386) Prec@5 66.875 (67.048) Epoch: [10][3480/11272] Time 0.880 (0.822) Data 0.001 (0.002) Loss 2.3929 (2.6275) Prec@1 41.875 (36.384) Prec@5 73.125 (67.044) Epoch: [10][3490/11272] Time 0.884 (0.822) Data 0.001 (0.002) Loss 2.4986 (2.6275) Prec@1 37.500 (36.387) Prec@5 69.375 (67.045) Epoch: [10][3500/11272] Time 0.726 (0.822) Data 0.001 (0.002) Loss 2.5166 (2.6273) Prec@1 43.125 (36.391) Prec@5 68.125 (67.045) Epoch: [10][3510/11272] Time 0.816 (0.822) Data 0.002 (0.002) Loss 2.6686 (2.6277) Prec@1 30.625 (36.380) Prec@5 66.875 (67.041) Epoch: [10][3520/11272] Time 0.829 (0.822) Data 0.001 (0.002) Loss 2.5248 (2.6277) Prec@1 41.250 (36.382) Prec@5 68.750 (67.040) Epoch: [10][3530/11272] Time 0.900 (0.822) Data 0.002 (0.002) Loss 2.5188 (2.6278) Prec@1 42.500 (36.381) Prec@5 65.000 (67.037) Epoch: [10][3540/11272] Time 0.754 (0.822) Data 0.001 (0.002) Loss 2.6078 (2.6278) Prec@1 34.375 (36.379) Prec@5 66.250 (67.036) Epoch: [10][3550/11272] Time 0.821 (0.822) Data 0.002 (0.002) Loss 2.3618 (2.6277) Prec@1 44.375 (36.381) Prec@5 74.375 (67.039) Epoch: [10][3560/11272] Time 0.832 (0.822) Data 0.002 (0.002) Loss 2.7724 (2.6277) Prec@1 31.875 (36.382) Prec@5 63.125 (67.040) Epoch: [10][3570/11272] Time 0.792 (0.822) Data 0.004 (0.002) Loss 2.5025 (2.6278) Prec@1 38.125 (36.380) Prec@5 68.125 (67.036) Epoch: [10][3580/11272] Time 0.772 (0.822) Data 0.001 (0.002) Loss 2.3734 (2.6278) Prec@1 39.375 (36.381) Prec@5 74.375 (67.040) Epoch: [10][3590/11272] Time 0.871 (0.822) Data 0.001 (0.002) Loss 2.8367 (2.6279) Prec@1 30.625 (36.376) Prec@5 63.125 (67.038)