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='densenet161_latest.pth.tar', 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) ) ) => loading checkpoint 'densenet161_latest.pth.tar' => loaded checkpoint 'densenet161_latest.pth.tar' (epoch 10) Epoch: [10][0/11272] Time 50.284 (50.284) Data 5.616 (5.616) Loss 2.7748 (2.7748) Prec@1 32.500 (32.500) Prec@5 60.625 (60.625) Epoch: [10][10/11272] Time 0.761 (5.487) Data 0.002 (0.512) Loss 2.4231 (2.6163) Prec@1 43.750 (37.102) Prec@5 69.375 (67.045) Epoch: [10][20/11272] Time 0.771 (3.281) Data 0.001 (0.269) Loss 2.3615 (2.5789) Prec@1 42.500 (37.708) Prec@5 73.125 (68.214) Epoch: [10][30/11272] Time 0.931 (2.497) Data 0.002 (0.183) Loss 2.5420 (2.5690) Prec@1 34.375 (37.681) Prec@5 71.250 (68.024) Epoch: [10][40/11272] Time 0.869 (2.109) Data 0.001 (0.145) Loss 2.4656 (2.5531) Prec@1 35.625 (37.546) Prec@5 71.250 (68.430) Epoch: [10][50/11272] Time 0.763 (1.884) Data 0.001 (0.135) Loss 2.6423 (2.5764) Prec@1 35.625 (36.703) Prec@5 63.125 (67.708) Epoch: [10][60/11272] Time 0.888 (1.730) Data 0.002 (0.126) Loss 2.9692 (2.5792) Prec@1 31.250 (36.773) Prec@5 59.375 (67.613) Epoch: [10][70/11272] Time 0.919 (1.637) Data 0.002 (0.137) Loss 2.4687 (2.5725) Prec@1 40.625 (36.954) Prec@5 68.750 (67.702) Epoch: [10][80/11272] Time 0.787 (1.549) Data 0.002 (0.130) Loss 2.5194 (2.5746) Prec@1 39.375 (37.052) Prec@5 70.000 (67.762) Epoch: [10][90/11272] Time 0.746 (1.485) Data 0.001 (0.130) Loss 2.6280 (2.5735) Prec@1 38.750 (37.170) Prec@5 66.250 (67.891) Epoch: [10][100/11272] Time 0.963 (1.440) Data 0.002 (0.134) Loss 2.8293 (2.5784) Prec@1 30.000 (37.203) Prec@5 65.625 (67.834) Epoch: [10][110/11272] Time 0.895 (1.397) Data 0.002 (0.131) Loss 2.5149 (2.5750) Prec@1 37.500 (37.280) Prec@5 70.625 (67.950) Epoch: [10][120/11272] Time 0.787 (1.366) Data 0.002 (0.133) Loss 2.4220 (2.5710) Prec@1 35.625 (37.361) Prec@5 70.000 (68.120) Epoch: [10][130/11272] Time 0.789 (1.328) Data 0.002 (0.123) Loss 2.4814 (2.5702) Prec@1 36.875 (37.304) Prec@5 68.750 (68.230) Epoch: [10][140/11272] Time 0.939 (1.314) Data 0.002 (0.132) Loss 2.4044 (2.5726) Prec@1 41.250 (37.225) Prec@5 72.500 (68.187) Epoch: [10][150/11272] Time 0.863 (1.285) Data 0.001 (0.126) Loss 2.7765 (2.5826) Prec@1 32.500 (37.078) Prec@5 63.125 (67.889) Epoch: [10][160/11272] Time 0.843 (1.269) Data 0.002 (0.127) Loss 2.9427 (2.5861) Prec@1 33.125 (37.026) Prec@5 57.500 (67.807) Epoch: [10][170/11272] Time 0.815 (1.251) Data 0.002 (0.125) Loss 2.6112 (2.5851) Prec@1 37.500 (37.061) Prec@5 68.750 (67.811) Epoch: [10][180/11272] Time 0.886 (1.234) Data 0.002 (0.122) Loss 2.4825 (2.5834) Prec@1 37.500 (37.048) Prec@5 68.125 (67.807) Epoch: [10][190/11272] Time 0.998 (1.224) Data 0.188 (0.125) Loss 2.5534 (2.5874) Prec@1 36.875 (36.944) Prec@5 69.375 (67.749) Epoch: [10][200/11272] Time 0.783 (1.214) Data 0.002 (0.126) Loss 2.7289 (2.5890) Prec@1 39.375 (37.021) Prec@5 66.250 (67.727) Epoch: [10][210/11272] Time 1.435 (1.204) Data 0.489 (0.124) Loss 2.7256 (2.5916) Prec@1 35.000 (36.991) Prec@5 63.750 (67.660) Epoch: [10][220/11272] Time 0.924 (1.191) Data 0.002 (0.121) Loss 2.2174 (2.5877) Prec@1 43.750 (37.056) Prec@5 73.125 (67.740) Epoch: [10][230/11272] Time 0.793 (1.184) Data 0.001 (0.123) Loss 2.5044 (2.5887) Prec@1 41.875 (36.991) Prec@5 71.875 (67.765) Epoch: [10][240/11272] Time 0.819 (1.176) Data 0.002 (0.122) Loss 2.7449 (2.5898) Prec@1 31.875 (36.976) Prec@5 68.750 (67.759) Epoch: [10][250/11272] Time 0.945 (1.168) Data 0.002 (0.120) Loss 2.7382 (2.5940) Prec@1 31.250 (36.858) Prec@5 66.250 (67.644) Epoch: [10][260/11272] Time 0.889 (1.162) Data 0.001 (0.120) Loss 2.7255 (2.5930) Prec@1 33.125 (36.853) Prec@5 68.125 (67.675) Epoch: [10][270/11272] Time 1.271 (1.154) Data 0.476 (0.119) Loss 2.6873 (2.5983) Prec@1 41.250 (36.771) Prec@5 66.250 (67.555) Epoch: [10][280/11272] Time 0.800 (1.147) Data 0.002 (0.118) Loss 2.7204 (2.5975) Prec@1 35.000 (36.759) Prec@5 63.125 (67.593) Epoch: [10][290/11272] Time 1.363 (1.145) Data 0.346 (0.122) Loss 2.6744 (2.6021) Prec@1 34.375 (36.727) Prec@5 65.000 (67.474) Epoch: [10][300/11272] Time 0.959 (1.139) Data 0.001 (0.121) Loss 2.5637 (2.6057) Prec@1 38.125 (36.672) Prec@5 66.875 (67.419) Epoch: [10][310/11272] Time 0.962 (1.132) Data 0.142 (0.120) Loss 2.7469 (2.6044) Prec@1 34.375 (36.720) Prec@5 65.625 (67.428) Epoch: [10][320/11272] Time 1.013 (1.128) Data 0.002 (0.119) Loss 2.9037 (2.6041) Prec@1 30.625 (36.723) Prec@5 61.250 (67.414) Epoch: [10][330/11272] Time 1.282 (1.123) Data 0.274 (0.118) Loss 2.7081 (2.6066) Prec@1 36.250 (36.709) Prec@5 65.000 (67.349) Epoch: [10][340/11272] Time 0.802 (1.117) Data 0.001 (0.116) Loss 2.9143 (2.6093) Prec@1 26.875 (36.651) Prec@5 64.375 (67.278) Epoch: [10][350/11272] Time 0.749 (1.114) Data 0.001 (0.116) Loss 2.5820 (2.6092) Prec@1 34.375 (36.676) Prec@5 69.375 (67.288) Epoch: [10][360/11272] Time 0.916 (1.109) Data 0.002 (0.116) Loss 2.6512 (2.6105) Prec@1 39.375 (36.679) Prec@5 68.125 (67.285) Epoch: [10][370/11272] Time 0.999 (1.106) Data 0.001 (0.115) Loss 2.6392 (2.6111) Prec@1 37.500 (36.673) Prec@5 66.250 (67.278) Epoch: [10][380/11272] Time 1.153 (1.102) Data 0.396 (0.115) Loss 2.7003 (2.6112) Prec@1 33.125 (36.693) Prec@5 69.375 (67.267) Epoch: [10][390/11272] Time 0.762 (1.102) Data 0.001 (0.119) Loss 2.7462 (2.6134) Prec@1 32.500 (36.597) Prec@5 63.125 (67.217) Epoch: [10][400/11272] Time 1.384 (1.098) Data 0.464 (0.118) Loss 2.5926 (2.6133) Prec@1 38.750 (36.580) Prec@5 66.875 (67.212) Epoch: [10][410/11272] Time 0.929 (1.095) Data 0.002 (0.118) Loss 2.6630 (2.6126) Prec@1 38.750 (36.603) Prec@5 68.125 (67.211) Epoch: [10][420/11272] Time 0.758 (1.093) Data 0.002 (0.118) Loss 2.6723 (2.6123) Prec@1 32.500 (36.648) Prec@5 64.375 (67.224) Epoch: [10][430/11272] Time 0.753 (1.090) Data 0.002 (0.118) Loss 2.6663 (2.6114) Prec@1 36.875 (36.649) Prec@5 66.250 (67.267) Epoch: [10][440/11272] Time 1.616 (1.089) Data 0.709 (0.120) Loss 2.8194 (2.6121) Prec@1 32.500 (36.614) Prec@5 59.375 (67.259) Epoch: [10][450/11272] Time 0.935 (1.087) Data 0.002 (0.121) Loss 2.6788 (2.6130) Prec@1 38.125 (36.598) Prec@5 70.625 (67.245) Epoch: [10][460/11272] Time 1.303 (1.084) Data 0.479 (0.119) Loss 2.8019 (2.6150) Prec@1 36.875 (36.558) Prec@5 63.750 (67.215) Epoch: [10][470/11272] Time 0.923 (1.080) Data 0.001 (0.118) Loss 2.6068 (2.6154) Prec@1 38.125 (36.555) Prec@5 66.875 (67.239) Epoch: [10][480/11272] Time 0.906 (1.078) Data 0.013 (0.118) Loss 2.4957 (2.6152) Prec@1 40.000 (36.563) Prec@5 68.125 (67.228) Epoch: [10][490/11272] Time 0.735 (1.077) Data 0.001 (0.120) Loss 2.5645 (2.6154) Prec@1 38.750 (36.573) Prec@5 65.625 (67.215) Epoch: [10][500/11272] Time 1.378 (1.075) Data 0.575 (0.121) Loss 2.6069 (2.6151) Prec@1 31.875 (36.625) Prec@5 69.375 (67.207) Epoch: [10][510/11272] Time 1.043 (1.074) Data 0.002 (0.121) Loss 2.1783 (2.6146) Prec@1 46.875 (36.616) Prec@5 72.500 (67.198) Epoch: [10][520/11272] Time 1.530 (1.072) Data 0.596 (0.121) Loss 2.6507 (2.6145) Prec@1 40.000 (36.607) Prec@5 68.750 (67.212) Epoch: [10][530/11272] Time 0.787 (1.069) Data 0.001 (0.120) Loss 2.6049 (2.6148) Prec@1 39.375 (36.609) Prec@5 64.375 (67.210) Epoch: [10][540/11272] Time 0.791 (1.067) Data 0.002 (0.119) Loss 2.4350 (2.6146) Prec@1 36.250 (36.595) Prec@5 73.750 (67.229) Epoch: [10][550/11272] Time 0.948 (1.066) Data 0.001 (0.119) Loss 2.6734 (2.6146) Prec@1 36.250 (36.591) Prec@5 64.375 (67.223) Epoch: [10][560/11272] Time 1.039 (1.064) Data 0.128 (0.118) Loss 2.8624 (2.6154) Prec@1 31.875 (36.595) Prec@5 59.375 (67.194) Epoch: [10][570/11272] Time 1.032 (1.062) Data 0.147 (0.118) Loss 2.6174 (2.6147) Prec@1 36.250 (36.608) Prec@5 65.625 (67.210) Epoch: [10][580/11272] Time 1.123 (1.062) Data 0.348 (0.120) Loss 2.6257 (2.6157) Prec@1 35.625 (36.592) Prec@5 71.250 (67.204) Epoch: [10][590/11272] Time 0.924 (1.061) Data 0.001 (0.120) Loss 2.3976 (2.6151) Prec@1 37.500 (36.604) Prec@5 72.500 (67.232) Epoch: [10][600/11272] Time 0.824 (1.060) Data 0.002 (0.120) Loss 2.6596 (2.6146) Prec@1 38.750 (36.625) Prec@5 66.250 (67.239) Epoch: [10][610/11272] Time 0.791 (1.058) Data 0.003 (0.119) Loss 2.5804 (2.6149) Prec@1 33.750 (36.616) Prec@5 67.500 (67.241) Epoch: [10][620/11272] Time 0.925 (1.057) Data 0.002 (0.119) Loss 2.4209 (2.6140) Prec@1 40.625 (36.630) Prec@5 71.875 (67.260) Epoch: [10][630/11272] Time 0.918 (1.055) Data 0.001 (0.118) Loss 2.6021 (2.6150) Prec@1 38.750 (36.608) Prec@5 63.125 (67.215) Epoch: [10][640/11272] Time 1.152 (1.054) Data 0.329 (0.119) Loss 2.7328 (2.6163) Prec@1 30.625 (36.590) Prec@5 66.250 (67.199) Epoch: [10][650/11272] Time 0.743 (1.053) Data 0.002 (0.118) Loss 2.8026 (2.6174) Prec@1 32.500 (36.574) Prec@5 64.375 (67.184) Epoch: [10][660/11272] Time 0.931 (1.052) Data 0.002 (0.119) Loss 2.5332 (2.6176) Prec@1 38.750 (36.586) Prec@5 69.375 (67.189) Epoch: [10][670/11272] Time 0.963 (1.051) Data 0.002 (0.119) Loss 2.5192 (2.6177) Prec@1 41.250 (36.579) Prec@5 70.625 (67.187) Epoch: [10][680/11272] Time 0.771 (1.050) Data 0.001 (0.119) Loss 2.5324 (2.6169) Prec@1 35.000 (36.582) Prec@5 66.250 (67.206) Epoch: [10][690/11272] Time 1.120 (1.049) Data 0.320 (0.119) Loss 2.4430 (2.6173) Prec@1 41.875 (36.577) Prec@5 73.125 (67.202) Epoch: [10][700/11272] Time 0.932 (1.050) Data 0.001 (0.120) Loss 2.6657 (2.6185) Prec@1 37.500 (36.557) Prec@5 65.000 (67.200) Epoch: [10][710/11272] Time 0.948 (1.048) Data 0.002 (0.119) Loss 2.4447 (2.6186) Prec@1 41.250 (36.544) Prec@5 69.375 (67.195) Epoch: [10][720/11272] Time 0.780 (1.047) Data 0.002 (0.118) Loss 2.7041 (2.6185) Prec@1 33.750 (36.536) Prec@5 64.375 (67.189) Epoch: [10][730/11272] Time 0.905 (1.046) Data 0.002 (0.118) Loss 2.8133 (2.6201) Prec@1 33.750 (36.519) Prec@5 59.375 (67.159) Epoch: [10][740/11272] Time 0.984 (1.045) Data 0.002 (0.119) Loss 2.5670 (2.6212) Prec@1 38.125 (36.500) Prec@5 67.500 (67.127) Epoch: [10][750/11272] Time 0.793 (1.044) Data 0.002 (0.118) Loss 2.3434 (2.6215) Prec@1 43.125 (36.491) Prec@5 71.875 (67.118) Epoch: [10][760/11272] Time 0.760 (1.044) Data 0.001 (0.119) Loss 2.3288 (2.6205) Prec@1 44.375 (36.510) Prec@5 75.625 (67.130) Epoch: [10][770/11272] Time 1.277 (1.043) Data 0.345 (0.118) Loss 2.7372 (2.6219) Prec@1 36.250 (36.501) Prec@5 65.000 (67.086) Epoch: [10][780/11272] Time 0.873 (1.041) Data 0.001 (0.118) Loss 2.7233 (2.6215) Prec@1 34.375 (36.492) Prec@5 61.875 (67.072) Epoch: [10][790/11272] Time 0.776 (1.040) Data 0.002 (0.118) Loss 2.7744 (2.6228) Prec@1 33.125 (36.483) Prec@5 66.875 (67.032) Epoch: [10][800/11272] Time 0.787 (1.040) Data 0.001 (0.118) Loss 2.6478 (2.6239) Prec@1 35.625 (36.455) Prec@5 66.250 (67.013) Epoch: [10][810/11272] Time 1.861 (1.041) Data 0.924 (0.119) Loss 2.6702 (2.6239) Prec@1 34.375 (36.451) Prec@5 69.375 (67.019) Epoch: [10][820/11272] Time 0.932 (1.039) Data 0.002 (0.119) Loss 2.5598 (2.6240) Prec@1 36.875 (36.446) Prec@5 67.500 (67.030) Epoch: [10][830/11272] Time 1.316 (1.039) Data 0.536 (0.118) Loss 2.6799 (2.6240) Prec@1 33.125 (36.446) Prec@5 67.500 (67.034) Epoch: [10][840/11272] Time 0.786 (1.037) Data 0.002 (0.118) Loss 2.5019 (2.6239) Prec@1 38.750 (36.463) Prec@5 68.125 (67.046) Epoch: [10][850/11272] Time 0.972 (1.037) Data 0.002 (0.118) Loss 2.5932 (2.6236) Prec@1 35.625 (36.469) Prec@5 66.250 (67.055) Epoch: [10][860/11272] Time 0.803 (1.036) Data 0.004 (0.118) Loss 2.7309 (2.6241) Prec@1 40.625 (36.476) Prec@5 63.750 (67.057) Epoch: [10][870/11272] Time 1.596 (1.036) Data 0.772 (0.118) Loss 2.5895 (2.6240) Prec@1 37.500 (36.484) Prec@5 66.250 (67.055) Epoch: [10][880/11272] Time 0.903 (1.035) Data 0.002 (0.117) Loss 2.7406 (2.6252) Prec@1 37.500 (36.481) Prec@5 65.625 (67.036) Epoch: [10][890/11272] Time 1.912 (1.035) Data 1.016 (0.119) Loss 2.6628 (2.6250) Prec@1 33.750 (36.479) Prec@5 65.625 (67.053) Epoch: [10][900/11272] Time 0.772 (1.033) Data 0.001 (0.118) Loss 2.6369 (2.6250) Prec@1 37.500 (36.484) Prec@5 64.375 (67.061) Epoch: [10][910/11272] Time 1.361 (1.033) Data 0.518 (0.119) Loss 2.6493 (2.6250) Prec@1 33.750 (36.470) Prec@5 63.750 (67.061) Epoch: [10][920/11272] Time 0.871 (1.033) Data 0.001 (0.120) Loss 2.4975 (2.6251) Prec@1 36.875 (36.454) Prec@5 69.375 (67.065) Epoch: [10][930/11272] Time 1.175 (1.032) Data 0.280 (0.120) Loss 2.3461 (2.6258) Prec@1 40.625 (36.454) Prec@5 72.500 (67.062) Epoch: [10][940/11272] Time 0.783 (1.031) Data 0.002 (0.120) Loss 2.8326 (2.6267) Prec@1 31.875 (36.433) Prec@5 62.500 (67.058) Epoch: [10][950/11272] Time 1.492 (1.031) Data 0.705 (0.121) Loss 2.8143 (2.6271) Prec@1 34.375 (36.433) Prec@5 63.125 (67.046) Epoch: [10][960/11272] Time 1.743 (1.031) Data 0.842 (0.122) Loss 2.9723 (2.6274) Prec@1 30.625 (36.430) Prec@5 61.875 (67.043) Epoch: [10][970/11272] Time 0.828 (1.030) Data 0.001 (0.122) Loss 2.5924 (2.6276) Prec@1 30.625 (36.408) Prec@5 66.875 (67.044) Epoch: [10][980/11272] Time 0.951 (1.029) Data 0.163 (0.122) Loss 2.5894 (2.6275) Prec@1 36.250 (36.417) Prec@5 71.250 (67.050) Epoch: [10][990/11272] Time 0.914 (1.029) Data 0.001 (0.122) Loss 2.6778 (2.6276) Prec@1 40.625 (36.422) Prec@5 70.000 (67.052) Epoch: [10][1000/11272] Time 1.679 (1.028) Data 0.736 (0.123) Loss 2.6592 (2.6280) Prec@1 38.125 (36.417) Prec@5 68.125 (67.039) Epoch: [10][1010/11272] Time 0.750 (1.028) Data 0.001 (0.123) Loss 2.3231 (2.6273) Prec@1 39.375 (36.427) Prec@5 70.625 (67.053) Epoch: [10][1020/11272] Time 1.708 (1.027) Data 0.984 (0.123) Loss 2.7013 (2.6279) Prec@1 38.125 (36.413) Prec@5 66.250 (67.044) Epoch: [10][1030/11272] Time 0.910 (1.027) Data 0.001 (0.124) Loss 2.6676 (2.6285) Prec@1 31.875 (36.406) Prec@5 63.750 (67.028) Epoch: [10][1040/11272] Time 0.807 (1.026) Data 0.001 (0.124) Loss 2.7403 (2.6291) Prec@1 36.250 (36.398) Prec@5 65.625 (67.014) Epoch: [10][1050/11272] Time 0.756 (1.025) Data 0.001 (0.124) Loss 3.1184 (2.6288) Prec@1 31.875 (36.393) Prec@5 58.750 (67.030) Epoch: [10][1060/11272] Time 0.747 (1.025) Data 0.001 (0.125) Loss 2.6289 (2.6281) Prec@1 35.000 (36.414) Prec@5 65.000 (67.033) Epoch: [10][1070/11272] Time 0.928 (1.025) Data 0.001 (0.126) Loss 2.2955 (2.6279) Prec@1 43.750 (36.431) Prec@5 75.000 (67.037) Epoch: [10][1080/11272] Time 0.871 (1.024) Data 0.008 (0.125) Loss 2.3511 (2.6276) Prec@1 42.500 (36.436) Prec@5 70.625 (67.042) Epoch: [10][1090/11272] Time 0.736 (1.023) Data 0.001 (0.125) Loss 2.6403 (2.6274) Prec@1 38.125 (36.435) Prec@5 66.875 (67.051) Epoch: [10][1100/11272] Time 0.736 (1.023) Data 0.001 (0.125) Loss 2.4228 (2.6271) Prec@1 40.000 (36.441) Prec@5 73.125 (67.057) Epoch: [10][1110/11272] Time 0.869 (1.023) Data 0.001 (0.126) Loss 2.2971 (2.6273) Prec@1 40.000 (36.432) Prec@5 75.625 (67.053) Epoch: [10][1120/11272] Time 1.587 (1.022) Data 0.802 (0.126) Loss 2.3761 (2.6276) Prec@1 40.625 (36.432) Prec@5 70.000 (67.057) Epoch: [10][1130/11272] Time 1.820 (1.023) Data 0.966 (0.127) Loss 2.4157 (2.6274) Prec@1 41.250 (36.437) Prec@5 67.500 (67.057) Epoch: [10][1140/11272] Time 0.893 (1.021) Data 0.001 (0.127) Loss 2.6206 (2.6276) Prec@1 34.375 (36.437) Prec@5 63.125 (67.049) Epoch: [10][1150/11272] Time 1.201 (1.022) Data 0.259 (0.127) Loss 2.6071 (2.6272) Prec@1 38.750 (36.437) Prec@5 65.000 (67.055) Epoch: [10][1160/11272] Time 0.758 (1.021) Data 0.002 (0.127) Loss 2.5496 (2.6263) Prec@1 42.500 (36.468) Prec@5 67.500 (67.075) Epoch: [10][1170/11272] Time 0.767 (1.020) Data 0.001 (0.127) Loss 2.7088 (2.6255) Prec@1 36.250 (36.490) Prec@5 67.500 (67.082) Epoch: [10][1180/11272] Time 0.888 (1.020) Data 0.001 (0.127) Loss 2.5569 (2.6256) Prec@1 38.125 (36.482) Prec@5 66.250 (67.082) Epoch: [10][1190/11272] Time 1.583 (1.020) Data 0.740 (0.128) Loss 2.5281 (2.6256) Prec@1 37.500 (36.488) Prec@5 70.000 (67.081) Epoch: [10][1200/11272] Time 1.123 (1.020) Data 0.353 (0.128) Loss 2.8555 (2.6263) Prec@1 35.000 (36.482) Prec@5 65.000 (67.069) Epoch: [10][1210/11272] Time 0.859 (1.019) Data 0.086 (0.128) Loss 2.4459 (2.6263) Prec@1 40.625 (36.476) Prec@5 72.500 (67.071) Epoch: [10][1220/11272] Time 0.920 (1.019) Data 0.001 (0.128) Loss 2.5317 (2.6266) Prec@1 36.875 (36.468) Prec@5 69.375 (67.067) Epoch: [10][1230/11272] Time 1.842 (1.019) Data 0.957 (0.129) Loss 2.6610 (2.6270) Prec@1 31.250 (36.442) Prec@5 66.875 (67.058) Epoch: [10][1240/11272] Time 0.773 (1.019) Data 0.001 (0.129) Loss 2.5660 (2.6269) Prec@1 38.125 (36.429) Prec@5 67.500 (67.061) Epoch: [10][1250/11272] Time 0.862 (1.018) Data 0.002 (0.129) Loss 2.6393 (2.6274) Prec@1 38.125 (36.429) Prec@5 67.500 (67.060) Epoch: [10][1260/11272] Time 0.862 (1.018) Data 0.001 (0.129) Loss 2.6580 (2.6269) Prec@1 40.000 (36.450) Prec@5 66.875 (67.064) Epoch: [10][1270/11272] Time 1.360 (1.018) Data 0.499 (0.129) Loss 2.6921 (2.6267) Prec@1 32.500 (36.445) Prec@5 68.125 (67.071) Epoch: [10][1280/11272] Time 0.761 (1.017) Data 0.001 (0.129) Loss 2.5852 (2.6265) Prec@1 38.750 (36.445) Prec@5 67.500 (67.078) Epoch: [10][1290/11272] Time 0.858 (1.017) Data 0.001 (0.129) Loss 2.8417 (2.6268) Prec@1 36.250 (36.444) Prec@5 58.750 (67.072) Epoch: [10][1300/11272] Time 0.955 (1.016) Data 0.041 (0.129) Loss 2.6056 (2.6263) Prec@1 36.875 (36.442) Prec@5 66.250 (67.077) Epoch: [10][1310/11272] Time 1.678 (1.017) Data 0.907 (0.130) Loss 2.8855 (2.6264) Prec@1 31.250 (36.449) Prec@5 60.000 (67.074) Epoch: [10][1320/11272] Time 0.770 (1.015) Data 0.001 (0.129) Loss 2.5356 (2.6264) Prec@1 32.500 (36.440) Prec@5 67.500 (67.076) Epoch: [10][1330/11272] Time 0.914 (1.015) Data 0.002 (0.130) Loss 2.7388 (2.6267) Prec@1 35.625 (36.438) Prec@5 60.625 (67.059) Epoch: [10][1340/11272] Time 2.237 (1.016) Data 1.245 (0.131) Loss 2.5484 (2.6270) Prec@1 36.250 (36.429) Prec@5 66.875 (67.055) Epoch: [10][1350/11272] Time 1.522 (1.015) Data 0.707 (0.131) Loss 2.7397 (2.6269) Prec@1 36.875 (36.434) Prec@5 64.375 (67.053) Epoch: [10][1360/11272] Time 1.511 (1.015) Data 0.697 (0.131) Loss 2.6892 (2.6272) Prec@1 31.875 (36.421) Prec@5 66.875 (67.041) Epoch: [10][1370/11272] Time 0.896 (1.015) Data 0.001 (0.131) Loss 2.6206 (2.6272) Prec@1 30.000 (36.420) Prec@5 67.500 (67.037) Epoch: [10][1380/11272] Time 0.834 (1.015) Data 0.001 (0.131) Loss 2.6767 (2.6269) Prec@1 35.000 (36.422) Prec@5 69.375 (67.036) Epoch: [10][1390/11272] Time 0.736 (1.015) Data 0.001 (0.132) Loss 2.4869 (2.6275) Prec@1 41.875 (36.405) Prec@5 68.125 (67.023) Epoch: [10][1400/11272] Time 0.881 (1.015) Data 0.001 (0.132) Loss 2.7046 (2.6272) Prec@1 31.875 (36.402) Prec@5 64.375 (67.035) Epoch: [10][1410/11272] Time 0.855 (1.014) Data 0.001 (0.132) Loss 2.4704 (2.6275) Prec@1 36.250 (36.381) Prec@5 71.250 (67.034) Epoch: [10][1420/11272] Time 0.986 (1.014) Data 0.116 (0.132) Loss 2.7446 (2.6275) Prec@1 34.375 (36.379) Prec@5 64.375 (67.035) Epoch: [10][1430/11272] Time 0.956 (1.013) Data 0.160 (0.132) Loss 2.6583 (2.6275) Prec@1 38.750 (36.386) Prec@5 65.625 (67.025) Epoch: [10][1440/11272] Time 0.983 (1.013) Data 0.002 (0.132) Loss 2.5042 (2.6273) Prec@1 35.000 (36.394) Prec@5 69.375 (67.032) Epoch: [10][1450/11272] Time 0.897 (1.013) Data 0.002 (0.133) Loss 2.5637 (2.6268) Prec@1 38.750 (36.405) Prec@5 66.250 (67.055) Epoch: [10][1460/11272] Time 0.738 (1.013) Data 0.002 (0.133) Loss 2.6176 (2.6269) Prec@1 36.875 (36.392) Prec@5 66.875 (67.050) Epoch: [10][1470/11272] Time 0.745 (1.013) Data 0.001 (0.133) Loss 2.7991 (2.6273) Prec@1 30.625 (36.387) Prec@5 60.000 (67.032) Epoch: [10][1480/11272] Time 1.290 (1.013) Data 0.426 (0.133) Loss 2.5373 (2.6270) Prec@1 32.500 (36.393) Prec@5 68.750 (67.049) Epoch: [10][1490/11272] Time 0.856 (1.012) Data 0.001 (0.133) Loss 2.7486 (2.6267) Prec@1 34.375 (36.393) Prec@5 66.250 (67.059) Epoch: [10][1500/11272] Time 0.846 (1.012) Data 0.054 (0.133) Loss 2.8157 (2.6268) Prec@1 35.625 (36.393) Prec@5 65.625 (67.063) Epoch: [10][1510/11272] Time 1.126 (1.012) Data 0.342 (0.133) Loss 2.7235 (2.6268) Prec@1 30.625 (36.389) Prec@5 65.000 (67.062) Epoch: [10][1520/11272] Time 0.889 (1.011) Data 0.002 (0.133) Loss 3.0955 (2.6274) Prec@1 25.625 (36.371) Prec@5 58.125 (67.049) Epoch: [10][1530/11272] Time 2.283 (1.013) Data 1.495 (0.135) Loss 2.6729 (2.6277) Prec@1 38.125 (36.368) Prec@5 68.750 (67.034) Epoch: [10][1540/11272] Time 0.745 (1.013) Data 0.002 (0.136) Loss 2.8814 (2.6276) Prec@1 31.250 (36.371) Prec@5 61.250 (67.026) Epoch: [10][1550/11272] Time 0.928 (1.014) Data 0.002 (0.137) Loss 2.5833 (2.6278) Prec@1 33.125 (36.371) Prec@5 67.500 (67.017) Epoch: [10][1560/11272] Time 0.903 (1.014) Data 0.002 (0.137) Loss 2.3844 (2.6274) Prec@1 42.500 (36.381) Prec@5 69.375 (67.013) Epoch: [10][1570/11272] Time 1.029 (1.014) Data 0.274 (0.137) Loss 2.7900 (2.6275) Prec@1 35.625 (36.380) Prec@5 65.625 (67.014) Epoch: [10][1580/11272] Time 0.752 (1.013) Data 0.002 (0.137) Loss 2.5392 (2.6266) Prec@1 34.375 (36.394) Prec@5 65.625 (67.029) Epoch: [10][1590/11272] Time 1.033 (1.014) Data 0.132 (0.138) Loss 2.8705 (2.6268) Prec@1 31.250 (36.378) Prec@5 63.125 (67.022) Epoch: [10][1600/11272] Time 0.808 (1.014) Data 0.001 (0.138) Loss 2.5862 (2.6268) Prec@1 37.500 (36.372) Prec@5 69.375 (67.022) Epoch: [10][1610/11272] Time 1.393 (1.014) Data 0.612 (0.139) Loss 2.9149 (2.6267) Prec@1 32.500 (36.370) Prec@5 63.125 (67.023) Epoch: [10][1620/11272] Time 0.785 (1.013) Data 0.002 (0.139) Loss 2.4390 (2.6267) Prec@1 40.625 (36.369) Prec@5 72.500 (67.030) Epoch: [10][1630/11272] Time 0.848 (1.013) Data 0.001 (0.139) Loss 2.4811 (2.6267) Prec@1 38.750 (36.372) Prec@5 71.875 (67.028) Epoch: [10][1640/11272] Time 0.908 (1.013) Data 0.002 (0.139) Loss 2.6058 (2.6261) Prec@1 44.375 (36.385) Prec@5 65.000 (67.041) Epoch: [10][1650/11272] Time 0.774 (1.013) Data 0.002 (0.139) Loss 2.4105 (2.6261) Prec@1 35.000 (36.385) Prec@5 74.375 (67.046) Epoch: [10][1660/11272] Time 1.669 (1.013) Data 0.749 (0.139) Loss 2.7309 (2.6264) Prec@1 35.000 (36.385) Prec@5 64.375 (67.035) Epoch: [10][1670/11272] Time 0.896 (1.012) Data 0.002 (0.139) Loss 2.6152 (2.6264) Prec@1 32.500 (36.380) Prec@5 68.125 (67.036) Epoch: [10][1680/11272] Time 1.321 (1.013) Data 0.602 (0.140) Loss 2.8658 (2.6264) Prec@1 24.375 (36.367) Prec@5 65.625 (67.046) Epoch: [10][1690/11272] Time 0.742 (1.012) Data 0.001 (0.139) Loss 2.8431 (2.6263) Prec@1 35.625 (36.370) Prec@5 65.000 (67.044) Epoch: [10][1700/11272] Time 2.360 (1.014) Data 1.463 (0.141) Loss 2.6619 (2.6260) Prec@1 37.500 (36.383) Prec@5 69.375 (67.054) Epoch: [10][1710/11272] Time 0.833 (1.013) Data 0.001 (0.141) Loss 2.8592 (2.6263) Prec@1 34.375 (36.385) Prec@5 64.375 (67.045) Epoch: [10][1720/11272] Time 0.846 (1.013) Data 0.086 (0.141) Loss 2.6225 (2.6263) Prec@1 37.500 (36.379) Prec@5 68.125 (67.049) Epoch: [10][1730/11272] Time 0.784 (1.013) Data 0.002 (0.141) Loss 2.5802 (2.6257) Prec@1 35.000 (36.391) Prec@5 64.375 (67.061) Epoch: [10][1740/11272] Time 1.231 (1.012) Data 0.321 (0.141) Loss 2.6700 (2.6258) Prec@1 36.250 (36.406) Prec@5 68.125 (67.068) Epoch: [10][1750/11272] Time 0.863 (1.012) Data 0.001 (0.141) Loss 2.6974 (2.6258) Prec@1 38.125 (36.411) Prec@5 65.000 (67.063) Epoch: [10][1760/11272] Time 1.086 (1.012) Data 0.322 (0.141) Loss 2.8080 (2.6265) Prec@1 33.750 (36.408) Prec@5 61.250 (67.044) Epoch: [10][1770/11272] Time 0.728 (1.012) Data 0.001 (0.141) Loss 2.4417 (2.6265) Prec@1 41.875 (36.413) Prec@5 71.250 (67.041) Epoch: [10][1780/11272] Time 1.021 (1.011) Data 0.141 (0.141) Loss 2.6782 (2.6265) Prec@1 35.625 (36.419) Prec@5 66.875 (67.044) Epoch: [10][1790/11272] Time 0.750 (1.012) Data 0.003 (0.142) Loss 2.3968 (2.6265) Prec@1 40.000 (36.420) Prec@5 72.500 (67.042) Epoch: [10][1800/11272] Time 0.761 (1.012) Data 0.002 (0.143) Loss 2.3300 (2.6266) Prec@1 43.125 (36.423) Prec@5 76.250 (67.042) Epoch: [10][1810/11272] Time 0.905 (1.011) Data 0.045 (0.142) Loss 2.7239 (2.6267) Prec@1 35.625 (36.419) Prec@5 65.625 (67.047) Epoch: [10][1820/11272] Time 0.840 (1.011) Data 0.001 (0.142) Loss 2.5896 (2.6268) Prec@1 35.000 (36.416) Prec@5 67.500 (67.046) Epoch: [10][1830/11272] Time 0.750 (1.011) Data 0.002 (0.143) Loss 2.6266 (2.6271) Prec@1 36.250 (36.420) Prec@5 68.750 (67.041) Epoch: [10][1840/11272] Time 0.759 (1.012) Data 0.001 (0.143) Loss 2.7183 (2.6272) Prec@1 33.125 (36.411) Prec@5 63.750 (67.038) Epoch: [10][1850/11272] Time 0.877 (1.011) Data 0.010 (0.143) Loss 2.5293 (2.6270) Prec@1 38.125 (36.411) Prec@5 66.875 (67.044) Epoch: [10][1860/11272] Time 0.842 (1.012) Data 0.001 (0.144) Loss 2.6964 (2.6272) Prec@1 36.875 (36.408) Prec@5 63.750 (67.041) Epoch: [10][1870/11272] Time 0.750 (1.012) Data 0.002 (0.144) Loss 2.7444 (2.6271) Prec@1 33.125 (36.408) Prec@5 57.500 (67.034) Epoch: [10][1880/11272] Time 0.736 (1.012) Data 0.001 (0.144) Loss 2.5408 (2.6268) Prec@1 36.875 (36.414) Prec@5 67.500 (67.038) Epoch: [10][1890/11272] Time 1.044 (1.011) Data 0.210 (0.144) Loss 2.3583 (2.6264) Prec@1 40.625 (36.420) Prec@5 71.250 (67.040) Epoch: [10][1900/11272] Time 0.862 (1.011) Data 0.001 (0.144) Loss 2.3488 (2.6263) Prec@1 40.625 (36.420) Prec@5 75.625 (67.042) Epoch: [10][1910/11272] Time 0.742 (1.010) Data 0.001 (0.144) Loss 2.7079 (2.6264) Prec@1 33.750 (36.416) Prec@5 63.750 (67.039) Epoch: [10][1920/11272] Time 1.259 (1.011) Data 0.314 (0.145) Loss 2.5099 (2.6267) Prec@1 34.375 (36.412) Prec@5 68.750 (67.031) Epoch: [10][1930/11272] Time 0.868 (1.011) Data 0.002 (0.145) Loss 2.7007 (2.6267) Prec@1 33.750 (36.412) Prec@5 66.250 (67.031) Epoch: [10][1940/11272] Time 1.354 (1.010) Data 0.582 (0.145) Loss 2.8193 (2.6266) Prec@1 35.000 (36.412) Prec@5 61.875 (67.034) Epoch: [10][1950/11272] Time 0.758 (1.010) Data 0.001 (0.145) Loss 2.6318 (2.6265) Prec@1 36.250 (36.415) Prec@5 70.625 (67.042) Epoch: [10][1960/11272] Time 0.873 (1.010) Data 0.002 (0.145) Loss 2.9215 (2.6267) Prec@1 30.625 (36.415) Prec@5 61.250 (67.040) Epoch: [10][1970/11272] Time 0.935 (1.010) Data 0.076 (0.145) Loss 3.1154 (2.6269) Prec@1 28.750 (36.414) Prec@5 58.750 (67.033) Epoch: [10][1980/11272] Time 0.966 (1.009) Data 0.211 (0.145) Loss 2.7571 (2.6273) Prec@1 34.375 (36.408) Prec@5 63.125 (67.033) Epoch: [10][1990/11272] Time 0.745 (1.009) Data 0.001 (0.145) Loss 2.6993 (2.6275) Prec@1 34.375 (36.401) Prec@5 65.625 (67.024) Epoch: [10][2000/11272] Time 1.073 (1.009) Data 0.212 (0.145) Loss 2.7187 (2.6276) Prec@1 38.750 (36.396) Prec@5 66.875 (67.031) Epoch: [10][2010/11272] Time 0.870 (1.008) Data 0.001 (0.145) Loss 2.8923 (2.6278) Prec@1 31.875 (36.392) Prec@5 64.375 (67.026) Epoch: [10][2020/11272] Time 0.737 (1.008) Data 0.001 (0.145) Loss 2.9996 (2.6277) Prec@1 29.375 (36.394) Prec@5 61.875 (67.029) Epoch: [10][2030/11272] Time 1.089 (1.008) Data 0.320 (0.145) Loss 2.5801 (2.6279) Prec@1 38.750 (36.396) Prec@5 67.500 (67.023) Epoch: [10][2040/11272] Time 0.860 (1.008) Data 0.001 (0.145) Loss 2.5189 (2.6280) Prec@1 40.625 (36.396) Prec@5 68.750 (67.024) Epoch: [10][2050/11272] Time 1.486 (1.008) Data 0.722 (0.145) Loss 2.4912 (2.6284) Prec@1 39.375 (36.390) Prec@5 71.875 (67.018) Epoch: [10][2060/11272] Time 1.224 (1.008) Data 0.435 (0.145) Loss 2.4586 (2.6281) Prec@1 38.750 (36.400) Prec@5 68.750 (67.022) Epoch: [10][2070/11272] Time 0.875 (1.008) Data 0.002 (0.145) Loss 2.6845 (2.6280) Prec@1 37.500 (36.409) Prec@5 66.875 (67.022) Epoch: [10][2080/11272] Time 1.047 (1.007) Data 0.147 (0.145) Loss 2.5858 (2.6278) Prec@1 37.500 (36.409) Prec@5 66.250 (67.031) Epoch: [10][2090/11272] Time 0.907 (1.007) Data 0.126 (0.145) Loss 2.6408 (2.6278) Prec@1 36.250 (36.411) Prec@5 65.625 (67.034) Epoch: [10][2100/11272] Time 0.755 (1.007) Data 0.002 (0.145) Loss 2.4895 (2.6276) Prec@1 38.750 (36.422) Prec@5 68.125 (67.037) Epoch: [10][2110/11272] Time 1.664 (1.008) Data 0.770 (0.146) Loss 2.7765 (2.6275) Prec@1 28.750 (36.417) Prec@5 65.000 (67.035) Epoch: [10][2120/11272] Time 0.867 (1.007) Data 0.002 (0.145) Loss 2.5380 (2.6278) Prec@1 38.125 (36.416) Prec@5 64.375 (67.028) Epoch: [10][2130/11272] Time 0.807 (1.007) Data 0.003 (0.145) Loss 2.4203 (2.6277) Prec@1 42.500 (36.422) Prec@5 71.250 (67.031) Epoch: [10][2140/11272] Time 0.780 (1.007) Data 0.002 (0.146) Loss 2.8334 (2.6281) Prec@1 33.750 (36.412) Prec@5 63.125 (67.023) Epoch: [10][2150/11272] Time 0.941 (1.007) Data 0.054 (0.145) Loss 2.4671 (2.6280) Prec@1 35.000 (36.409) Prec@5 73.750 (67.028) Epoch: [10][2160/11272] Time 0.854 (1.007) Data 0.002 (0.146) Loss 2.6315 (2.6280) Prec@1 40.000 (36.413) Prec@5 67.500 (67.030) Epoch: [10][2170/11272] Time 0.901 (1.007) Data 0.121 (0.145) Loss 2.7232 (2.6278) Prec@1 33.750 (36.414) Prec@5 63.750 (67.036) Epoch: [10][2180/11272] Time 0.918 (1.006) Data 0.002 (0.145) Loss 2.9043 (2.6279) Prec@1 35.625 (36.410) Prec@5 61.250 (67.034) Epoch: [10][2190/11272] Time 0.875 (1.006) Data 0.002 (0.145) Loss 2.6390 (2.6279) Prec@1 36.875 (36.404) Prec@5 66.875 (67.032) Epoch: [10][2200/11272] Time 0.798 (1.006) Data 0.002 (0.145) Loss 2.8200 (2.6277) Prec@1 30.000 (36.403) Prec@5 64.375 (67.031) Epoch: [10][2210/11272] Time 0.980 (1.006) Data 0.225 (0.146) Loss 2.4316 (2.6277) Prec@1 37.500 (36.406) Prec@5 71.250 (67.028) Epoch: [10][2220/11272] Time 0.854 (1.006) Data 0.001 (0.146) Loss 2.5470 (2.6275) Prec@1 35.625 (36.406) Prec@5 71.250 (67.034) Epoch: [10][2230/11272] Time 0.862 (1.006) Data 0.002 (0.146) Loss 2.5584 (2.6273) Prec@1 41.250 (36.410) Prec@5 67.500 (67.036) Epoch: [10][2240/11272] Time 0.806 (1.006) Data 0.002 (0.146) Loss 2.4872 (2.6275) Prec@1 36.250 (36.401) Prec@5 71.250 (67.029) Epoch: [10][2250/11272] Time 0.774 (1.006) Data 0.002 (0.146) Loss 2.5702 (2.6277) Prec@1 39.375 (36.392) Prec@5 68.125 (67.019) Epoch: [10][2260/11272] Time 0.926 (1.006) Data 0.001 (0.146) Loss 2.7611 (2.6277) Prec@1 33.750 (36.392) Prec@5 66.875 (67.025) Epoch: [10][2270/11272] Time 0.887 (1.006) Data 0.002 (0.146) Loss 2.6048 (2.6275) Prec@1 35.625 (36.389) Prec@5 68.125 (67.029) Epoch: [10][2280/11272] Time 0.783 (1.006) Data 0.002 (0.146) Loss 2.6740 (2.6275) Prec@1 35.000 (36.386) Prec@5 71.250 (67.032) Epoch: [10][2290/11272] Time 0.767 (1.006) Data 0.002 (0.146) Loss 2.7518 (2.6273) Prec@1 36.250 (36.390) Prec@5 66.875 (67.034) Epoch: [10][2300/11272] Time 0.890 (1.005) Data 0.002 (0.146) Loss 2.7521 (2.6279) Prec@1 35.625 (36.389) Prec@5 66.875 (67.022) Epoch: [10][2310/11272] Time 0.889 (1.005) Data 0.001 (0.146) Loss 2.5756 (2.6279) Prec@1 38.750 (36.386) Prec@5 68.750 (67.019) Epoch: [10][2320/11272] Time 0.745 (1.005) Data 0.001 (0.146) Loss 2.6654 (2.6278) Prec@1 38.750 (36.394) Prec@5 63.125 (67.021) Epoch: [10][2330/11272] Time 0.918 (1.005) Data 0.001 (0.146) Loss 2.3674 (2.6275) Prec@1 40.000 (36.400) Prec@5 71.875 (67.025) Epoch: [10][2340/11272] Time 1.680 (1.005) Data 0.780 (0.146) Loss 2.9511 (2.6277) Prec@1 33.750 (36.398) Prec@5 58.750 (67.018) Epoch: [10][2350/11272] Time 0.773 (1.005) Data 0.002 (0.146) Loss 2.2548 (2.6270) Prec@1 45.000 (36.407) Prec@5 75.000 (67.030) Epoch: [10][2360/11272] Time 0.766 (1.005) Data 0.002 (0.146) Loss 2.3958 (2.6266) Prec@1 39.375 (36.414) Prec@5 73.125 (67.037) Epoch: [10][2370/11272] Time 0.934 (1.005) Data 0.002 (0.146) Loss 2.6420 (2.6267) Prec@1 35.000 (36.419) Prec@5 66.250 (67.031) Epoch: [10][2380/11272] Time 0.823 (1.004) Data 0.001 (0.146) Loss 2.6674 (2.6264) Prec@1 35.000 (36.425) Prec@5 65.625 (67.037) Epoch: [10][2390/11272] Time 0.776 (1.004) Data 0.002 (0.146) Loss 2.5813 (2.6261) Prec@1 40.625 (36.427) Prec@5 66.250 (67.043) Epoch: [10][2400/11272] Time 0.786 (1.005) Data 0.002 (0.146) Loss 2.8188 (2.6260) Prec@1 30.000 (36.424) Prec@5 63.750 (67.045) Epoch: [10][2410/11272] Time 1.347 (1.005) Data 0.438 (0.146) Loss 2.6968 (2.6259) Prec@1 32.500 (36.424) Prec@5 62.500 (67.048) Epoch: [10][2420/11272] Time 0.877 (1.005) Data 0.013 (0.146) Loss 2.6652 (2.6260) Prec@1 34.375 (36.419) Prec@5 68.125 (67.048) Epoch: [10][2430/11272] Time 0.786 (1.005) Data 0.002 (0.146) Loss 2.6369 (2.6261) Prec@1 36.250 (36.415) Prec@5 68.750 (67.050) Epoch: [10][2440/11272] Time 0.767 (1.005) Data 0.001 (0.146) Loss 2.6915 (2.6264) Prec@1 36.250 (36.415) Prec@5 63.125 (67.045) Epoch: [10][2450/11272] Time 1.813 (1.005) Data 0.903 (0.147) Loss 2.5303 (2.6262) Prec@1 34.375 (36.416) Prec@5 70.000 (67.049) Epoch: [10][2460/11272] Time 1.490 (1.005) Data 0.673 (0.147) Loss 2.7188 (2.6262) Prec@1 31.250 (36.416) Prec@5 61.250 (67.047) Epoch: [10][2470/11272] Time 0.823 (1.005) Data 0.025 (0.147) Loss 2.3388 (2.6262) Prec@1 42.500 (36.413) Prec@5 72.500 (67.043) Epoch: [10][2480/11272] Time 0.924 (1.005) Data 0.002 (0.147) Loss 2.8520 (2.6265) Prec@1 31.875 (36.410) Prec@5 61.875 (67.036) Epoch: [10][2490/11272] Time 1.004 (1.006) Data 0.081 (0.147) Loss 2.3414 (2.6267) Prec@1 43.750 (36.405) Prec@5 74.375 (67.034) Epoch: [10][2500/11272] Time 0.784 (1.006) Data 0.001 (0.147) Loss 2.5441 (2.6271) Prec@1 37.500 (36.398) Prec@5 67.500 (67.027) Epoch: [10][2510/11272] Time 0.793 (1.006) Data 0.045 (0.148) Loss 2.6147 (2.6271) Prec@1 36.875 (36.404) Prec@5 67.500 (67.029) Epoch: [10][2520/11272] Time 1.198 (1.006) Data 0.287 (0.148) Loss 2.6252 (2.6275) Prec@1 33.750 (36.391) Prec@5 66.875 (67.017) Epoch: [10][2530/11272] Time 0.872 (1.006) Data 0.002 (0.148) Loss 2.6860 (2.6270) Prec@1 33.125 (36.396) Prec@5 68.125 (67.026) Epoch: [10][2540/11272] Time 1.255 (1.007) Data 0.435 (0.149) Loss 2.6679 (2.6269) Prec@1 36.875 (36.405) Prec@5 64.375 (67.028) Epoch: [10][2550/11272] Time 0.768 (1.006) Data 0.002 (0.149) Loss 2.4631 (2.6270) Prec@1 35.625 (36.410) Prec@5 69.375 (67.030) Epoch: [10][2560/11272] Time 1.170 (1.007) Data 0.207 (0.149) Loss 2.6810 (2.6271) Prec@1 35.625 (36.402) Prec@5 64.375 (67.024) Epoch: [10][2570/11272] Time 0.862 (1.007) Data 0.001 (0.149) Loss 2.5048 (2.6275) Prec@1 37.500 (36.394) Prec@5 69.375 (67.017) Epoch: [10][2580/11272] Time 0.822 (1.007) Data 0.002 (0.149) Loss 2.8270 (2.6276) Prec@1 32.500 (36.394) Prec@5 61.250 (67.014) Epoch: [10][2590/11272] Time 0.887 (1.007) Data 0.002 (0.150) Loss 2.4851 (2.6274) Prec@1 38.750 (36.395) Prec@5 71.250 (67.014) Epoch: [10][2600/11272] Time 0.881 (1.008) Data 0.002 (0.150) Loss 2.6266 (2.6275) Prec@1 40.000 (36.393) Prec@5 65.000 (67.012) Epoch: [10][2610/11272] Time 0.764 (1.007) Data 0.002 (0.150) Loss 2.7546 (2.6276) Prec@1 31.875 (36.391) Prec@5 64.375 (67.006) Epoch: [10][2620/11272] Time 0.747 (1.007) Data 0.002 (0.150) Loss 2.7027 (2.6276) Prec@1 36.875 (36.392) Prec@5 61.875 (67.007) Epoch: [10][2630/11272] Time 0.884 (1.007) Data 0.002 (0.150) Loss 2.6069 (2.6279) Prec@1 38.750 (36.388) Prec@5 68.125 (67.001) Epoch: [10][2640/11272] Time 0.883 (1.007) Data 0.001 (0.150) Loss 2.7126 (2.6279) Prec@1 41.250 (36.394) Prec@5 67.500 (67.004) Epoch: [10][2650/11272] Time 0.781 (1.007) Data 0.002 (0.150) Loss 2.7731 (2.6278) Prec@1 33.750 (36.393) Prec@5 66.250 (67.006) Epoch: [10][2660/11272] Time 0.739 (1.007) Data 0.001 (0.150) Loss 2.5716 (2.6278) Prec@1 35.625 (36.400) Prec@5 70.000 (67.008) Epoch: [10][2670/11272] Time 0.910 (1.007) Data 0.001 (0.150) Loss 2.5661 (2.6275) Prec@1 32.500 (36.404) Prec@5 71.250 (67.018) Epoch: [10][2680/11272] Time 0.868 (1.007) Data 0.001 (0.150) Loss 2.6460 (2.6273) Prec@1 36.875 (36.406) Prec@5 67.500 (67.018) Epoch: [10][2690/11272] Time 0.852 (1.007) Data 0.064 (0.150) Loss 2.6733 (2.6272) Prec@1 35.625 (36.406) Prec@5 65.625 (67.022) Epoch: [10][2700/11272] Time 0.793 (1.007) Data 0.002 (0.150) Loss 2.7457 (2.6271) Prec@1 33.750 (36.407) Prec@5 61.875 (67.018) Epoch: [10][2710/11272] Time 0.853 (1.007) Data 0.001 (0.150) Loss 2.7100 (2.6269) Prec@1 31.875 (36.411) Prec@5 65.625 (67.022) Epoch: [10][2720/11272] Time 0.772 (1.007) Data 0.003 (0.150) Loss 2.7106 (2.6266) Prec@1 31.875 (36.413) Prec@5 65.625 (67.035) Epoch: [10][2730/11272] Time 0.830 (1.006) Data 0.035 (0.150) Loss 2.5956 (2.6266) Prec@1 39.375 (36.413) Prec@5 69.375 (67.032) Epoch: [10][2740/11272] Time 0.902 (1.007) Data 0.002 (0.150) Loss 2.6594 (2.6262) Prec@1 34.375 (36.424) Prec@5 66.250 (67.038) Epoch: [10][2750/11272] Time 1.001 (1.006) Data 0.002 (0.150) Loss 2.4554 (2.6263) Prec@1 45.625 (36.420) Prec@5 67.500 (67.037) Epoch: [10][2760/11272] Time 1.408 (1.007) Data 0.624 (0.151) Loss 2.6787 (2.6264) Prec@1 33.750 (36.416) Prec@5 61.875 (67.031) Epoch: [10][2770/11272] Time 0.750 (1.007) Data 0.002 (0.151) Loss 2.9311 (2.6268) Prec@1 29.375 (36.409) Prec@5 63.750 (67.026) Epoch: [10][2780/11272] Time 0.892 (1.007) Data 0.002 (0.152) Loss 2.7073 (2.6267) Prec@1 38.125 (36.412) Prec@5 64.375 (67.027) Epoch: [10][2790/11272] Time 0.833 (1.007) Data 0.001 (0.151) Loss 2.7923 (2.6266) Prec@1 33.750 (36.413) Prec@5 60.625 (67.027) Epoch: [10][2800/11272] Time 0.746 (1.007) Data 0.002 (0.152) Loss 2.5895 (2.6265) Prec@1 35.625 (36.415) Prec@5 70.000 (67.029) Epoch: [10][2810/11272] Time 0.743 (1.007) Data 0.002 (0.152) Loss 2.5918 (2.6265) Prec@1 34.375 (36.413) Prec@5 65.625 (67.029) Epoch: [10][2820/11272] Time 0.886 (1.007) Data 0.001 (0.152) Loss 2.6104 (2.6266) Prec@1 36.250 (36.413) Prec@5 65.625 (67.028) Epoch: [10][2830/11272] Time 0.872 (1.007) Data 0.001 (0.152) Loss 2.5664 (2.6265) Prec@1 38.750 (36.422) Prec@5 70.625 (67.029) Epoch: [10][2840/11272] Time 0.900 (1.007) Data 0.002 (0.152) Loss 2.5117 (2.6264) Prec@1 35.625 (36.418) Prec@5 68.125 (67.032) Epoch: [10][2850/11272] Time 0.855 (1.007) Data 0.001 (0.152) Loss 2.4815 (2.6262) Prec@1 41.250 (36.422) Prec@5 68.750 (67.036) Epoch: [10][2860/11272] Time 3.008 (1.007) Data 2.098 (0.153) Loss 2.6813 (2.6266) Prec@1 33.125 (36.413) Prec@5 66.875 (67.031) Epoch: [10][2870/11272] Time 0.735 (1.008) Data 0.001 (0.154) Loss 2.7585 (2.6265) Prec@1 33.750 (36.414) Prec@5 67.500 (67.032) Epoch: [10][2880/11272] Time 1.233 (1.009) Data 0.498 (0.155) Loss 2.5010 (2.6264) Prec@1 37.500 (36.412) Prec@5 67.500 (67.032) Epoch: [10][2890/11272] Time 0.803 (1.010) Data 0.001 (0.156) Loss 2.6371 (2.6261) Prec@1 38.750 (36.414) Prec@5 66.875 (67.038) Epoch: [10][2900/11272] Time 2.222 (1.011) Data 1.383 (0.158) Loss 2.6442 (2.6264) Prec@1 34.375 (36.405) Prec@5 68.750 (67.036) Epoch: [10][2910/11272] Time 0.747 (1.012) Data 0.002 (0.158) Loss 2.5849 (2.6264) Prec@1 35.000 (36.409) Prec@5 66.875 (67.041) Epoch: [10][2920/11272] Time 2.846 (1.013) Data 2.061 (0.160) Loss 2.7543 (2.6264) Prec@1 34.375 (36.409) Prec@5 65.625 (67.041) Epoch: [10][2930/11272] Time 0.837 (1.014) Data 0.001 (0.161) Loss 2.7875 (2.6264) Prec@1 39.375 (36.411) Prec@5 60.625 (67.039) Epoch: [10][2940/11272] Time 2.455 (1.015) Data 1.583 (0.162) Loss 2.6415 (2.6265) Prec@1 39.375 (36.406) Prec@5 67.500 (67.042) Epoch: [10][2950/11272] Time 0.782 (1.016) Data 0.002 (0.163) Loss 2.7019 (2.6265) Prec@1 30.625 (36.400) Prec@5 63.750 (67.043) Epoch: [10][2960/11272] Time 2.143 (1.017) Data 1.378 (0.164) Loss 2.4917 (2.6264) Prec@1 34.375 (36.399) Prec@5 71.250 (67.045) Epoch: [10][2970/11272] Time 1.792 (1.018) Data 0.884 (0.165) Loss 2.7214 (2.6263) Prec@1 33.750 (36.397) Prec@5 62.500 (67.044) Epoch: [10][2980/11272] Time 1.640 (1.019) Data 0.818 (0.166) Loss 2.7008 (2.6265) Prec@1 34.375 (36.393) Prec@5 66.875 (67.038) Epoch: [10][2990/11272] Time 1.761 (1.020) Data 0.987 (0.167) Loss 2.6686 (2.6264) Prec@1 35.000 (36.399) Prec@5 65.625 (67.040) Epoch: [10][3000/11272] Time 0.881 (1.021) Data 0.002 (0.168) Loss 2.7286 (2.6263) Prec@1 35.000 (36.398) Prec@5 63.125 (67.040) Epoch: [10][3010/11272] Time 2.689 (1.022) Data 1.782 (0.170) Loss 2.8320 (2.6263) Prec@1 33.125 (36.396) Prec@5 66.250 (67.040) Epoch: [10][3020/11272] Time 0.738 (1.023) Data 0.002 (0.170) Loss 2.4849 (2.6262) Prec@1 43.125 (36.399) Prec@5 70.000 (67.043) Epoch: [10][3030/11272] Time 2.397 (1.024) Data 1.604 (0.171) Loss 2.6256 (2.6263) Prec@1 37.500 (36.399) Prec@5 62.500 (67.041) Epoch: [10][3040/11272] Time 1.461 (1.025) Data 0.530 (0.173) Loss 2.5474 (2.6264) Prec@1 38.750 (36.403) Prec@5 67.500 (67.040) Epoch: [10][3050/11272] Time 2.288 (1.026) Data 1.369 (0.174) Loss 2.7891 (2.6265) Prec@1 28.125 (36.399) Prec@5 65.000 (67.038) Epoch: [10][3060/11272] Time 0.770 (1.026) Data 0.001 (0.174) Loss 2.5934 (2.6267) Prec@1 35.000 (36.394) Prec@5 72.500 (67.037) Epoch: [10][3070/11272] Time 0.748 (1.027) Data 0.002 (0.175) Loss 2.8086 (2.6268) Prec@1 35.000 (36.389) Prec@5 60.625 (67.032) Epoch: [10][3080/11272] Time 0.864 (1.028) Data 0.002 (0.176) Loss 2.9024 (2.6269) Prec@1 34.375 (36.387) Prec@5 61.250 (67.033) Epoch: [10][3090/11272] Time 1.072 (1.029) Data 0.193 (0.177) Loss 2.6237 (2.6269) Prec@1 36.875 (36.393) Prec@5 62.500 (67.031) Epoch: [10][3100/11272] Time 0.797 (1.030) Data 0.001 (0.178) Loss 2.3941 (2.6270) Prec@1 41.250 (36.395) Prec@5 67.500 (67.027) Epoch: [10][3110/11272] Time 1.854 (1.031) Data 0.874 (0.179) Loss 2.9717 (2.6272) Prec@1 31.875 (36.388) Prec@5 60.625 (67.023) Epoch: [10][3120/11272] Time 0.872 (1.032) Data 0.001 (0.180) Loss 3.0813 (2.6272) Prec@1 28.125 (36.390) Prec@5 59.375 (67.027) Epoch: [10][3130/11272] Time 1.531 (1.033) Data 0.769 (0.181) Loss 2.6827 (2.6272) Prec@1 34.375 (36.390) Prec@5 68.125 (67.028) Epoch: [10][3140/11272] Time 0.790 (1.034) Data 0.002 (0.182) Loss 2.4667 (2.6273) Prec@1 41.250 (36.388) Prec@5 70.625 (67.028) Epoch: [10][3150/11272] Time 2.682 (1.035) Data 1.775 (0.183) Loss 2.6659 (2.6274) Prec@1 33.750 (36.385) Prec@5 67.500 (67.026) Epoch: [10][3160/11272] Time 0.859 (1.036) Data 0.001 (0.184) Loss 2.4513 (2.6272) Prec@1 40.000 (36.390) Prec@5 68.125 (67.030) Epoch: [10][3170/11272] Time 2.189 (1.037) Data 1.467 (0.186) Loss 2.6248 (2.6273) Prec@1 33.125 (36.392) Prec@5 71.875 (67.033) Epoch: [10][3180/11272] Time 0.740 (1.037) Data 0.002 (0.186) Loss 2.5964 (2.6271) Prec@1 41.250 (36.403) Prec@5 65.000 (67.036) Epoch: [10][3190/11272] Time 2.800 (1.039) Data 1.913 (0.188) Loss 2.5528 (2.6270) Prec@1 36.250 (36.403) Prec@5 70.625 (67.034) Epoch: [10][3200/11272] Time 0.897 (1.039) Data 0.002 (0.188) Loss 2.9022 (2.6271) Prec@1 27.500 (36.396) Prec@5 60.625 (67.035) Epoch: [10][3210/11272] Time 2.246 (1.041) Data 1.430 (0.190) Loss 2.4860 (2.6269) Prec@1 38.125 (36.396) Prec@5 68.750 (67.035) Epoch: [10][3220/11272] Time 0.728 (1.041) Data 0.001 (0.190) Loss 2.8001 (2.6270) Prec@1 29.375 (36.389) Prec@5 60.625 (67.033) Epoch: [10][3230/11272] Time 2.680 (1.042) Data 1.795 (0.192) Loss 2.5585 (2.6268) Prec@1 38.125 (36.391) Prec@5 65.000 (67.035) Epoch: [10][3240/11272] Time 1.676 (1.043) Data 0.799 (0.193) Loss 2.8717 (2.6270) Prec@1 28.125 (36.388) Prec@5 65.000 (67.030) Epoch: [10][3250/11272] Time 0.752 (1.044) Data 0.002 (0.194) Loss 2.8445 (2.6272) Prec@1 30.625 (36.384) Prec@5 65.625 (67.029) Epoch: [10][3260/11272] Time 1.602 (1.045) Data 0.648 (0.195) Loss 2.8214 (2.6271) Prec@1 33.750 (36.385) Prec@5 62.500 (67.028) Epoch: [10][3270/11272] Time 0.872 (1.046) Data 0.001 (0.196) Loss 2.7022 (2.6271) Prec@1 32.500 (36.388) Prec@5 64.375 (67.024) Epoch: [10][3280/11272] Time 1.162 (1.047) Data 0.401 (0.197) Loss 2.6477 (2.6271) Prec@1 30.625 (36.388) Prec@5 65.625 (67.026) Epoch: [10][3290/11272] Time 0.792 (1.048) Data 0.002 (0.198) Loss 2.5963 (2.6270) Prec@1 36.875 (36.389) Prec@5 70.000 (67.027) Epoch: [10][3300/11272] Time 0.832 (1.049) Data 0.002 (0.199) Loss 2.4564 (2.6269) Prec@1 41.875 (36.392) Prec@5 69.375 (67.031) Epoch: [10][3310/11272] Time 0.867 (1.050) Data 0.001 (0.200) Loss 2.5358 (2.6268) Prec@1 42.500 (36.396) Prec@5 70.000 (67.033) Epoch: [10][3320/11272] Time 0.738 (1.051) Data 0.002 (0.201) Loss 2.6869 (2.6266) Prec@1 35.625 (36.404) Prec@5 66.875 (67.037) Epoch: [10][3330/11272] Time 1.680 (1.051) Data 0.885 (0.202) Loss 2.6014 (2.6269) Prec@1 38.750 (36.400) Prec@5 63.750 (67.027) Epoch: [10][3340/11272] Time 0.907 (1.052) Data 0.001 (0.203) Loss 2.6592 (2.6269) Prec@1 31.875 (36.402) Prec@5 66.875 (67.027) Epoch: [10][3350/11272] Time 2.647 (1.053) Data 1.754 (0.204) Loss 2.7629 (2.6267) Prec@1 29.375 (36.400) Prec@5 65.000 (67.030) Epoch: [10][3360/11272] Time 1.861 (1.054) Data 1.085 (0.204) Loss 2.6159 (2.6266) Prec@1 32.500 (36.401) Prec@5 66.875 (67.032) Epoch: [10][3370/11272] Time 0.750 (1.054) Data 0.002 (0.205) Loss 2.6149 (2.6266) Prec@1 38.750 (36.406) Prec@5 68.750 (67.033) Epoch: [10][3380/11272] Time 2.503 (1.055) Data 1.592 (0.206) Loss 2.6939 (2.6266) Prec@1 40.000 (36.408) Prec@5 68.750 (67.037) Epoch: [10][3390/11272] Time 0.911 (1.056) Data 0.154 (0.206) Loss 2.6594 (2.6268) Prec@1 33.750 (36.401) Prec@5 62.500 (67.036) Epoch: [10][3400/11272] Time 1.314 (1.056) Data 0.534 (0.207) Loss 2.6140 (2.6267) Prec@1 36.250 (36.400) Prec@5 70.625 (67.038) Epoch: [10][3410/11272] Time 0.923 (1.057) Data 0.002 (0.208) Loss 2.9159 (2.6266) Prec@1 31.250 (36.399) Prec@5 59.375 (67.039) Epoch: [10][3420/11272] Time 1.521 (1.058) Data 0.616 (0.209) Loss 2.6820 (2.6266) Prec@1 38.125 (36.397) Prec@5 67.500 (67.036) Epoch: [10][3430/11272] Time 0.747 (1.059) Data 0.001 (0.210) Loss 2.4562 (2.6269) Prec@1 40.000 (36.390) Prec@5 65.625 (67.029) Epoch: [10][3440/11272] Time 1.123 (1.060) Data 0.364 (0.211) Loss 2.6120 (2.6270) Prec@1 40.625 (36.391) Prec@5 62.500 (67.027) Epoch: [10][3450/11272] Time 0.881 (1.060) Data 0.002 (0.211) Loss 2.7252 (2.6268) Prec@1 31.250 (36.392) Prec@5 68.750 (67.032) Epoch: [10][3460/11272] Time 2.344 (1.061) Data 1.454 (0.212) Loss 2.8086 (2.6270) Prec@1 28.125 (36.380) Prec@5 61.250 (67.026) Epoch: [10][3470/11272] Time 0.743 (1.062) Data 0.002 (0.213) Loss 2.6390 (2.6270) Prec@1 33.125 (36.379) Prec@5 65.625 (67.027) Epoch: [10][3480/11272] Time 0.749 (1.062) Data 0.002 (0.213) Loss 2.4672 (2.6268) Prec@1 39.375 (36.381) Prec@5 72.500 (67.034) Epoch: [10][3490/11272] Time 0.869 (1.063) Data 0.002 (0.214) Loss 2.5028 (2.6267) Prec@1 39.375 (36.384) Prec@5 70.625 (67.035) Epoch: [10][3500/11272] Time 2.010 (1.063) Data 1.145 (0.215) Loss 2.6403 (2.6267) Prec@1 35.000 (36.380) Prec@5 66.250 (67.033) Epoch: [10][3510/11272] Time 0.771 (1.064) Data 0.013 (0.216) Loss 2.7119 (2.6268) Prec@1 39.375 (36.378) Prec@5 68.125 (67.032) Epoch: [10][3520/11272] Time 1.056 (1.065) Data 0.076 (0.216) Loss 2.5020 (2.6268) Prec@1 40.000 (36.377) Prec@5 68.750 (67.032) Epoch: [10][3530/11272] Time 0.936 (1.065) Data 0.002 (0.217) Loss 2.4640 (2.6268) Prec@1 38.125 (36.374) Prec@5 72.500 (67.031) Epoch: [10][3540/11272] Time 3.411 (1.066) Data 2.630 (0.218) Loss 2.4818 (2.6268) Prec@1 38.125 (36.374) Prec@5 71.875 (67.033) Epoch: [10][3550/11272] Time 1.272 (1.067) Data 0.510 (0.219) Loss 2.6347 (2.6269) Prec@1 33.750 (36.371) Prec@5 65.625 (67.031) Epoch: [10][3560/11272] Time 2.356 (1.067) Data 1.452 (0.219) Loss 2.6713 (2.6270) Prec@1 38.750 (36.372) Prec@5 65.625 (67.030) Epoch: [10][3570/11272] Time 0.944 (1.068) Data 0.039 (0.220) Loss 2.7121 (2.6271) Prec@1 32.500 (36.369) Prec@5 66.250 (67.031) Epoch: [10][3580/11272] Time 0.741 (1.068) Data 0.001 (0.220) Loss 2.7086 (2.6269) Prec@1 40.625 (36.372) Prec@5 61.250 (67.036) Epoch: [10][3590/11272] Time 0.745 (1.069) Data 0.002 (0.221) Loss 2.4743 (2.6267) Prec@1 40.625 (36.377) Prec@5 69.375 (67.043) Epoch: [10][3600/11272] Time 1.068 (1.070) Data 0.171 (0.222) Loss 2.7357 (2.6266) Prec@1 35.000 (36.377) Prec@5 65.000 (67.044) Epoch: [10][3610/11272] Time 0.885 (1.070) Data 0.001 (0.223) Loss 2.7397 (2.6266) Prec@1 31.875 (36.378) Prec@5 64.375 (67.041) Epoch: [10][3620/11272] Time 1.197 (1.071) Data 0.458 (0.223) Loss 2.8334 (2.6267) Prec@1 34.375 (36.374) Prec@5 61.250 (67.038) Epoch: [10][3630/11272] Time 0.761 (1.071) Data 0.002 (0.224) Loss 2.8878 (2.6270) Prec@1 31.875 (36.367) Prec@5 63.750 (67.032) Epoch: [10][3640/11272] Time 2.569 (1.072) Data 1.673 (0.225) Loss 2.7328 (2.6270) Prec@1 33.750 (36.367) Prec@5 67.500 (67.029) Epoch: [10][3650/11272] Time 0.830 (1.073) Data 0.004 (0.225) Loss 2.7433 (2.6269) Prec@1 35.000 (36.369) Prec@5 68.125 (67.030) Epoch: [10][3660/11272] Time 1.092 (1.073) Data 0.342 (0.226) Loss 2.8488 (2.6271) Prec@1 30.625 (36.365) Prec@5 65.000 (67.027) Epoch: [10][3670/11272] Time 0.865 (1.074) Data 0.001 (0.227) Loss 2.7140 (2.6271) Prec@1 33.125 (36.364) Prec@5 65.625 (67.028) Epoch: [10][3680/11272] Time 1.916 (1.075) Data 1.004 (0.228) Loss 2.5598 (2.6271) Prec@1 33.750 (36.361) Prec@5 77.500 (67.029) Epoch: [10][3690/11272] Time 1.147 (1.075) Data 0.401 (0.228) Loss 2.4302 (2.6270) Prec@1 41.250 (36.364) Prec@5 70.625 (67.031) Epoch: [10][3700/11272] Time 0.737 (1.076) Data 0.002 (0.229) Loss 2.9044 (2.6270) Prec@1 33.125 (36.369) Prec@5 62.500 (67.034) Epoch: [10][3710/11272] Time 0.855 (1.076) Data 0.001 (0.229) Loss 2.8439 (2.6272) Prec@1 30.625 (36.364) Prec@5 63.750 (67.028) Epoch: [10][3720/11272] Time 0.872 (1.076) Data 0.001 (0.230) Loss 2.3889 (2.6274) Prec@1 40.000 (36.366) Prec@5 71.875 (67.024) Epoch: [10][3730/11272] Time 0.734 (1.077) Data 0.002 (0.230) Loss 2.7464 (2.6276) Prec@1 35.625 (36.362) Prec@5 63.750 (67.016) Epoch: [10][3740/11272] Time 3.214 (1.078) Data 2.427 (0.231) Loss 2.4781 (2.6278) Prec@1 43.125 (36.358) Prec@5 68.750 (67.015) Epoch: [10][3750/11272] Time 0.861 (1.078) Data 0.002 (0.232) Loss 2.4603 (2.6277) Prec@1 41.875 (36.357) Prec@5 70.625 (67.017) Epoch: [10][3760/11272] Time 2.599 (1.079) Data 1.720 (0.233) Loss 2.7047 (2.6277) Prec@1 31.875 (36.356) Prec@5 68.125 (67.016) Epoch: [10][3770/11272] Time 0.743 (1.079) Data 0.002 (0.233) Loss 2.7370 (2.6278) Prec@1 36.875 (36.355) Prec@5 65.625 (67.013) Epoch: [10][3780/11272] Time 2.004 (1.080) Data 1.075 (0.234) Loss 2.4270 (2.6276) Prec@1 41.875 (36.357) Prec@5 74.375 (67.018) Epoch: [10][3790/11272] Time 0.873 (1.081) Data 0.001 (0.234) Loss 2.5471 (2.6275) Prec@1 41.250 (36.358) Prec@5 70.625 (67.021) Epoch: [10][3800/11272] Time 0.741 (1.081) Data 0.002 (0.235) Loss 2.7106 (2.6273) Prec@1 36.875 (36.363) Prec@5 61.875 (67.027) Epoch: [10][3810/11272] Time 0.754 (1.082) Data 0.002 (0.235) Loss 2.8451 (2.6272) Prec@1 36.250 (36.365) Prec@5 61.875 (67.030) Epoch: [10][3820/11272] Time 1.302 (1.082) Data 0.426 (0.236) Loss 2.7882 (2.6274) Prec@1 36.875 (36.359) Prec@5 61.875 (67.026) Epoch: [10][3830/11272] Time 0.855 (1.083) Data 0.001 (0.237) Loss 2.9317 (2.6272) Prec@1 29.375 (36.360) Prec@5 65.625 (67.029) Epoch: [10][3840/11272] Time 2.665 (1.083) Data 1.892 (0.237) Loss 2.9259 (2.6273) Prec@1 30.000 (36.355) Prec@5 60.625 (67.026) Epoch: [10][3850/11272] Time 0.830 (1.084) Data 0.002 (0.238) Loss 2.5854 (2.6274) Prec@1 33.125 (36.349) Prec@5 71.250 (67.024) Epoch: [10][3860/11272] Time 0.866 (1.084) Data 0.001 (0.238) Loss 2.7503 (2.6273) Prec@1 37.500 (36.349) Prec@5 65.000 (67.027) Epoch: [10][3870/11272] Time 0.906 (1.084) Data 0.001 (0.238) Loss 2.3045 (2.6272) Prec@1 38.750 (36.345) Prec@5 72.500 (67.030) Epoch: [10][3880/11272] Time 0.736 (1.085) Data 0.002 (0.239) Loss 2.6984 (2.6272) Prec@1 32.500 (36.345) Prec@5 68.125 (67.033) Epoch: [10][3890/11272] Time 0.749 (1.086) Data 0.002 (0.240) Loss 2.8166 (2.6273) Prec@1 29.375 (36.342) Prec@5 66.875 (67.033) Epoch: [10][3900/11272] Time 1.794 (1.086) Data 0.894 (0.240) Loss 2.5029 (2.6273) Prec@1 38.125 (36.343) Prec@5 66.875 (67.033) Epoch: [10][3910/11272] Time 1.878 (1.086) Data 1.127 (0.241) Loss 2.7325 (2.6273) Prec@1 32.500 (36.342) Prec@5 64.375 (67.033) Epoch: [10][3920/11272] Time 1.258 (1.087) Data 0.478 (0.241) Loss 2.7637 (2.6274) Prec@1 43.125 (36.345) Prec@5 63.750 (67.031) Epoch: [10][3930/11272] Time 1.213 (1.087) Data 0.339 (0.242) Loss 2.6035 (2.6273) Prec@1 36.875 (36.350) Prec@5 63.750 (67.034) Epoch: [10][3940/11272] Time 1.506 (1.088) Data 0.614 (0.243) Loss 2.6414 (2.6272) Prec@1 36.250 (36.350) Prec@5 66.875 (67.033) Epoch: [10][3950/11272] Time 0.781 (1.088) Data 0.002 (0.243) Loss 2.5675 (2.6272) Prec@1 37.500 (36.354) Prec@5 65.000 (67.030) Epoch: [10][3960/11272] Time 0.730 (1.089) Data 0.001 (0.244) Loss 2.3591 (2.6270) Prec@1 39.375 (36.362) Prec@5 69.375 (67.033) Epoch: [10][3970/11272] Time 0.901 (1.089) Data 0.002 (0.244) Loss 2.3846 (2.6270) Prec@1 39.375 (36.363) Prec@5 71.250 (67.031) Epoch: [10][3980/11272] Time 0.860 (1.089) Data 0.002 (0.244) Loss 2.5540 (2.6271) Prec@1 38.125 (36.362) Prec@5 65.625 (67.030) Epoch: [10][3990/11272] Time 0.747 (1.090) Data 0.001 (0.245) Loss 2.8136 (2.6270) Prec@1 35.625 (36.367) Prec@5 66.250 (67.032) Epoch: [10][4000/11272] Time 0.734 (1.091) Data 0.002 (0.245) Loss 2.5316 (2.6271) Prec@1 35.000 (36.361) Prec@5 71.250 (67.029) Epoch: [10][4010/11272] Time 0.864 (1.091) Data 0.002 (0.246) Loss 2.6794 (2.6272) Prec@1 33.750 (36.359) Prec@5 64.375 (67.027) Epoch: [10][4020/11272] Time 1.069 (1.092) Data 0.159 (0.247) Loss 2.5446 (2.6273) Prec@1 40.625 (36.360) Prec@5 70.000 (67.026) Epoch: [10][4030/11272] Time 0.755 (1.092) Data 0.003 (0.247) Loss 2.6594 (2.6273) Prec@1 36.250 (36.362) Prec@5 68.750 (67.026) Epoch: [10][4040/11272] Time 0.866 (1.092) Data 0.002 (0.247) Loss 2.6122 (2.6272) Prec@1 38.750 (36.361) Prec@5 68.750 (67.026) Epoch: [10][4050/11272] Time 1.034 (1.092) Data 0.141 (0.247) Loss 2.5392 (2.6273) Prec@1 39.375 (36.362) Prec@5 71.250 (67.029) Epoch: [10][4060/11272] Time 0.779 (1.093) Data 0.002 (0.248) Loss 2.8054 (2.6274) Prec@1 29.375 (36.361) Prec@5 62.500 (67.026) Epoch: [10][4070/11272] Time 0.728 (1.093) Data 0.001 (0.248) Loss 2.6424 (2.6273) Prec@1 36.875 (36.361) Prec@5 67.500 (67.028) Epoch: [10][4080/11272] Time 1.384 (1.093) Data 0.489 (0.249) Loss 2.9440 (2.6274) Prec@1 30.625 (36.359) Prec@5 60.625 (67.026) Epoch: [10][4090/11272] Time 1.210 (1.093) Data 0.343 (0.249) Loss 2.9399 (2.6278) Prec@1 28.125 (36.351) Prec@5 61.875 (67.018) Epoch: [10][4100/11272] Time 0.743 (1.094) Data 0.001 (0.250) Loss 2.5121 (2.6278) Prec@1 33.750 (36.349) Prec@5 71.250 (67.020) Epoch: [10][4110/11272] Time 1.764 (1.094) Data 1.002 (0.250) Loss 2.8716 (2.6279) Prec@1 33.125 (36.349) Prec@5 62.500 (67.020) Epoch: [10][4120/11272] Time 0.875 (1.095) Data 0.001 (0.251) Loss 2.7164 (2.6279) Prec@1 33.750 (36.346) Prec@5 65.000 (67.021) Epoch: [10][4130/11272] Time 0.803 (1.095) Data 0.001 (0.251) Loss 2.3504 (2.6276) Prec@1 43.125 (36.348) Prec@5 68.125 (67.026) Epoch: [10][4140/11272] Time 2.365 (1.096) Data 1.612 (0.252) Loss 2.6822 (2.6277) Prec@1 36.250 (36.348) Prec@5 71.250 (67.024) Epoch: [10][4150/11272] Time 0.751 (1.097) Data 0.002 (0.253) Loss 2.7181 (2.6276) Prec@1 32.500 (36.351) Prec@5 65.000 (67.026) Epoch: [10][4160/11272] Time 3.065 (1.098) Data 2.152 (0.253) Loss 2.2359 (2.6273) Prec@1 43.750 (36.357) Prec@5 75.000 (67.032) Epoch: [10][4170/11272] Time 0.847 (1.098) Data 0.002 (0.254) Loss 2.4630 (2.6273) Prec@1 44.375 (36.363) Prec@5 68.750 (67.032) Epoch: [10][4180/11272] Time 1.805 (1.098) Data 1.056 (0.254) Loss 2.6516 (2.6274) Prec@1 36.875 (36.362) Prec@5 65.625 (67.028) Epoch: [10][4190/11272] Time 1.619 (1.099) Data 0.749 (0.255) Loss 2.6584 (2.6274) Prec@1 36.250 (36.364) Prec@5 65.000 (67.027) Epoch: [10][4200/11272] Time 1.031 (1.099) Data 0.159 (0.255) Loss 2.4367 (2.6274) Prec@1 40.000 (36.366) Prec@5 71.250 (67.030) Epoch: [10][4210/11272] Time 0.732 (1.100) Data 0.002 (0.256) Loss 2.7418 (2.6273) Prec@1 35.000 (36.368) Prec@5 66.875 (67.032) Epoch: [10][4220/11272] Time 0.746 (1.100) Data 0.001 (0.257) Loss 2.6456 (2.6273) Prec@1 34.375 (36.365) Prec@5 65.000 (67.031) Epoch: [10][4230/11272] Time 0.867 (1.101) Data 0.002 (0.257) Loss 2.5248 (2.6272) Prec@1 37.500 (36.370) Prec@5 71.250 (67.031) Epoch: [10][4240/11272] Time 0.862 (1.101) Data 0.002 (0.258) Loss 2.8943 (2.6274) Prec@1 25.000 (36.369) Prec@5 60.000 (67.026) Epoch: [10][4250/11272] Time 2.094 (1.102) Data 1.330 (0.259) Loss 2.8922 (2.6274) Prec@1 31.250 (36.369) Prec@5 62.500 (67.023) Epoch: [10][4260/11272] Time 0.745 (1.102) Data 0.002 (0.259) Loss 2.5740 (2.6274) Prec@1 35.000 (36.365) Prec@5 67.500 (67.025) Epoch: [10][4270/11272] Time 0.843 (1.102) Data 0.001 (0.259) Loss 2.4257 (2.6273) Prec@1 41.250 (36.366) Prec@5 66.875 (67.027) Epoch: [10][4280/11272] Time 1.167 (1.103) Data 0.228 (0.260) Loss 2.7148 (2.6274) Prec@1 36.875 (36.364) Prec@5 59.375 (67.022) Epoch: [10][4290/11272] Time 0.771 (1.103) Data 0.002 (0.260) Loss 2.6521 (2.6275) Prec@1 41.875 (36.365) Prec@5 65.000 (67.019) Epoch: [10][4300/11272] Time 0.929 (1.104) Data 0.015 (0.261) Loss 2.5000 (2.6275) Prec@1 38.125 (36.366) Prec@5 71.250 (67.019) Epoch: [10][4310/11272] Time 1.891 (1.104) Data 0.988 (0.261) Loss 2.7716 (2.6276) Prec@1 31.875 (36.366) Prec@5 69.375 (67.020) Epoch: [10][4320/11272] Time 0.874 (1.105) Data 0.107 (0.262) Loss 2.3613 (2.6273) Prec@1 40.000 (36.370) Prec@5 76.250 (67.026) Epoch: [10][4330/11272] Time 0.786 (1.105) Data 0.002 (0.262) Loss 2.7517 (2.6275) Prec@1 31.250 (36.366) Prec@5 64.375 (67.022) Epoch: [10][4340/11272] Time 0.873 (1.105) Data 0.001 (0.262) Loss 2.5492 (2.6275) Prec@1 38.750 (36.364) Prec@5 67.500 (67.023) Epoch: [10][4350/11272] Time 0.903 (1.105) Data 0.002 (0.263) Loss 2.4501 (2.6274) Prec@1 37.500 (36.363) Prec@5 74.375 (67.026) Epoch: [10][4360/11272] Time 0.776 (1.106) Data 0.003 (0.263) Loss 2.6524 (2.6273) Prec@1 31.875 (36.363) Prec@5 69.375 (67.029) Epoch: [10][4370/11272] Time 0.771 (1.106) Data 0.002 (0.263) Loss 2.6236 (2.6271) Prec@1 36.875 (36.364) Prec@5 68.125 (67.033) Epoch: [10][4380/11272] Time 0.840 (1.106) Data 0.002 (0.263) Loss 2.4407 (2.6271) Prec@1 37.500 (36.363) Prec@5 72.500 (67.034) Epoch: [10][4390/11272] Time 0.893 (1.106) Data 0.002 (0.264) Loss 2.4810 (2.6271) Prec@1 41.250 (36.363) Prec@5 71.250 (67.035) Epoch: [10][4400/11272] Time 4.040 (1.107) Data 3.273 (0.265) Loss 2.8810 (2.6273) Prec@1 33.125 (36.358) Prec@5 64.375 (67.031) Epoch: [10][4410/11272] Time 0.800 (1.107) Data 0.003 (0.265) Loss 2.4862 (2.6275) Prec@1 36.875 (36.355) Prec@5 70.625 (67.025) Epoch: [10][4420/11272] Time 0.865 (1.107) Data 0.002 (0.265) Loss 2.7836 (2.6276) Prec@1 33.750 (36.353) Prec@5 64.375 (67.023) Epoch: [10][4430/11272] Time 0.853 (1.107) Data 0.002 (0.265) Loss 2.5437 (2.6276) Prec@1 43.750 (36.353) Prec@5 65.625 (67.023) Epoch: [10][4440/11272] Time 0.771 (1.108) Data 0.003 (0.266) Loss 2.4890 (2.6277) Prec@1 35.625 (36.349) Prec@5 68.750 (67.020) Epoch: [10][4450/11272] Time 0.854 (1.108) Data 0.001 (0.265) Loss 2.7578 (2.6278) Prec@1 41.250 (36.355) Prec@5 68.125 (67.017) Epoch: [10][4460/11272] Time 0.893 (1.108) Data 0.025 (0.266) Loss 2.7573 (2.6277) Prec@1 34.375 (36.358) Prec@5 68.125 (67.021) Epoch: [10][4470/11272] Time 0.764 (1.109) Data 0.001 (0.266) Loss 2.7512 (2.6278) Prec@1 38.750 (36.357) Prec@5 63.125 (67.017) Epoch: [10][4480/11272] Time 0.745 (1.109) Data 0.001 (0.266) Loss 2.4520 (2.6279) Prec@1 38.125 (36.355) Prec@5 70.000 (67.015) Epoch: [10][4490/11272] Time 0.891 (1.109) Data 0.001 (0.267) Loss 2.4472 (2.6280) Prec@1 42.500 (36.355) Prec@5 69.375 (67.017) Epoch: [10][4500/11272] Time 0.868 (1.109) Data 0.001 (0.267) Loss 2.4836 (2.6279) Prec@1 39.375 (36.356) Prec@5 69.375 (67.020) Epoch: [10][4510/11272] Time 0.753 (1.109) Data 0.002 (0.267) Loss 2.6683 (2.6279) Prec@1 33.125 (36.354) Prec@5 65.000 (67.020) Epoch: [10][4520/11272] Time 0.754 (1.110) Data 0.002 (0.267) Loss 2.5937 (2.6279) Prec@1 38.750 (36.353) Prec@5 70.625 (67.022) Epoch: [10][4530/11272] Time 0.890 (1.110) Data 0.001 (0.268) Loss 2.6819 (2.6280) Prec@1 38.750 (36.348) Prec@5 66.875 (67.019) Epoch: [10][4540/11272] Time 0.872 (1.110) Data 0.002 (0.268) Loss 2.3789 (2.6281) Prec@1 46.250 (36.349) Prec@5 73.750 (67.023) Epoch: [10][4550/11272] Time 0.771 (1.110) Data 0.002 (0.268) Loss 2.2818 (2.6281) Prec@1 46.250 (36.350) Prec@5 75.000 (67.023) Epoch: [10][4560/11272] Time 0.755 (1.110) Data 0.002 (0.268) Loss 2.4113 (2.6280) Prec@1 41.250 (36.351) Prec@5 69.375 (67.022) Epoch: [10][4570/11272] Time 0.887 (1.110) Data 0.002 (0.268) Loss 2.6539 (2.6281) Prec@1 34.375 (36.348) Prec@5 68.125 (67.021) Epoch: [10][4580/11272] Time 1.928 (1.111) Data 1.149 (0.269) Loss 2.9468 (2.6281) Prec@1 33.125 (36.345) Prec@5 61.250 (67.023) Epoch: [10][4590/11272] Time 0.742 (1.111) Data 0.001 (0.269) Loss 2.4507 (2.6278) Prec@1 34.375 (36.348) Prec@5 69.375 (67.026) Epoch: [10][4600/11272] Time 0.912 (1.111) Data 0.002 (0.269) Loss 2.5147 (2.6277) Prec@1 40.625 (36.352) Prec@5 68.750 (67.026) Epoch: [10][4610/11272] Time 2.023 (1.111) Data 1.107 (0.269) Loss 2.7501 (2.6276) Prec@1 31.875 (36.352) Prec@5 63.125 (67.028) Epoch: [10][4620/11272] Time 0.780 (1.111) Data 0.002 (0.269) Loss 3.0793 (2.6276) Prec@1 28.750 (36.355) Prec@5 58.750 (67.032) Epoch: [10][4630/11272] Time 0.734 (1.111) Data 0.002 (0.269) Loss 2.7166 (2.6277) Prec@1 35.000 (36.352) Prec@5 65.000 (67.032) Epoch: [10][4640/11272] Time 1.206 (1.111) Data 0.350 (0.269) Loss 2.9455 (2.6276) Prec@1 31.875 (36.354) Prec@5 63.125 (67.035) Epoch: [10][4650/11272] Time 0.863 (1.111) Data 0.001 (0.270) Loss 2.6296 (2.6277) Prec@1 31.250 (36.354) Prec@5 64.375 (67.032) Epoch: [10][4660/11272] Time 0.746 (1.111) Data 0.002 (0.270) Loss 2.5940 (2.6278) Prec@1 39.375 (36.353) Prec@5 68.750 (67.033) Epoch: [10][4670/11272] Time 0.738 (1.111) Data 0.002 (0.270) Loss 2.4159 (2.6277) Prec@1 36.875 (36.354) Prec@5 76.250 (67.034) Epoch: [10][4680/11272] Time 2.455 (1.112) Data 1.553 (0.270) Loss 2.8712 (2.6277) Prec@1 35.000 (36.355) Prec@5 63.125 (67.033) Epoch: [10][4690/11272] Time 0.957 (1.112) Data 0.083 (0.270) Loss 2.7533 (2.6276) Prec@1 30.625 (36.354) Prec@5 64.375 (67.036) Epoch: [10][4700/11272] Time 0.758 (1.112) Data 0.001 (0.270) Loss 2.7126 (2.6277) Prec@1 33.750 (36.354) Prec@5 65.625 (67.033) Epoch: [10][4710/11272] Time 0.891 (1.112) Data 0.002 (0.270) Loss 2.5414 (2.6277) Prec@1 36.250 (36.354) Prec@5 71.250 (67.035) Epoch: [10][4720/11272] Time 1.300 (1.112) Data 0.371 (0.271) Loss 2.7022 (2.6277) Prec@1 35.000 (36.355) Prec@5 63.750 (67.031) Epoch: [10][4730/11272] Time 0.739 (1.112) Data 0.002 (0.271) Loss 2.7562 (2.6275) Prec@1 31.875 (36.356) Prec@5 61.875 (67.035) Epoch: [10][4740/11272] Time 0.747 (1.113) Data 0.002 (0.271) Loss 2.6089 (2.6276) Prec@1 30.625 (36.353) Prec@5 70.625 (67.036) Epoch: [10][4750/11272] Time 0.851 (1.113) Data 0.002 (0.271) Loss 3.0081 (2.6276) Prec@1 28.125 (36.356) Prec@5 61.875 (67.035) Epoch: [10][4760/11272] Time 1.749 (1.113) Data 0.856 (0.272) Loss 2.5294 (2.6278) Prec@1 34.375 (36.353) Prec@5 67.500 (67.033) Epoch: [10][4770/11272] Time 0.753 (1.113) Data 0.001 (0.272) Loss 2.6401 (2.6279) Prec@1 38.125 (36.351) Prec@5 66.250 (67.034) Epoch: [10][4780/11272] Time 2.260 (1.113) Data 1.521 (0.272) Loss 2.9456 (2.6280) Prec@1 29.375 (36.347) Prec@5 63.750 (67.032) Epoch: [10][4790/11272] Time 0.905 (1.114) Data 0.001 (0.272) Loss 2.4690 (2.6281) Prec@1 40.000 (36.344) Prec@5 70.625 (67.030) Epoch: [10][4800/11272] Time 1.745 (1.114) Data 0.863 (0.272) Loss 2.5473 (2.6280) Prec@1 41.250 (36.347) Prec@5 67.500 (67.035) Epoch: [10][4810/11272] Time 0.748 (1.114) Data 0.002 (0.272) Loss 2.6386 (2.6280) Prec@1 33.125 (36.348) Prec@5 61.250 (67.035) Epoch: [10][4820/11272] Time 2.016 (1.114) Data 1.191 (0.273) Loss 2.6976 (2.6279) Prec@1 37.500 (36.349) Prec@5 67.500 (67.037) Epoch: [10][4830/11272] Time 0.874 (1.114) Data 0.002 (0.273) Loss 2.7119 (2.6279) Prec@1 31.875 (36.347) Prec@5 64.375 (67.038) Epoch: [10][4840/11272] Time 1.086 (1.114) Data 0.321 (0.273) Loss 2.7837 (2.6278) Prec@1 38.125 (36.347) Prec@5 64.375 (67.041) Epoch: [10][4850/11272] Time 0.751 (1.114) Data 0.002 (0.273) Loss 2.7461 (2.6277) Prec@1 37.500 (36.352) Prec@5 63.750 (67.043) Epoch: [10][4860/11272] Time 0.863 (1.114) Data 0.002 (0.273) Loss 2.5350 (2.6277) Prec@1 40.625 (36.350) Prec@5 66.875 (67.043) Epoch: [10][4870/11272] Time 2.307 (1.115) Data 1.410 (0.274) Loss 2.5846 (2.6277) Prec@1 38.750 (36.348) Prec@5 66.250 (67.040) Epoch: [10][4880/11272] Time 0.902 (1.115) Data 0.155 (0.274) Loss 2.8880 (2.6279) Prec@1 35.000 (36.346) Prec@5 60.000 (67.038) Epoch: [10][4890/11272] Time 1.134 (1.115) Data 0.347 (0.274) Loss 2.7140 (2.6279) Prec@1 31.875 (36.347) Prec@5 67.500 (67.038) Epoch: [10][4900/11272] Time 1.447 (1.115) Data 0.551 (0.274) Loss 2.4741 (2.6279) Prec@1 40.000 (36.344) Prec@5 75.000 (67.037) Epoch: [10][4910/11272] Time 1.119 (1.115) Data 0.244 (0.274) Loss 2.8022 (2.6280) Prec@1 35.000 (36.345) Prec@5 64.375 (67.035) Epoch: [10][4920/11272] Time 1.174 (1.115) Data 0.444 (0.274) Loss 2.6847 (2.6282) Prec@1 38.125 (36.343) Prec@5 68.750 (67.033) Epoch: [10][4930/11272] Time 0.764 (1.115) Data 0.001 (0.274) Loss 2.7438 (2.6284) Prec@1 33.750 (36.338) Prec@5 67.500 (67.029) Epoch: [10][4940/11272] Time 1.040 (1.115) Data 0.146 (0.274) Loss 2.5085 (2.6284) Prec@1 38.750 (36.341) Prec@5 68.750 (67.028) Epoch: [10][4950/11272] Time 0.854 (1.115) Data 0.001 (0.275) Loss 2.7879 (2.6285) Prec@1 34.375 (36.342) Prec@5 63.750 (67.027) Epoch: [10][4960/11272] Time 1.066 (1.115) Data 0.316 (0.275) Loss 2.7089 (2.6284) Prec@1 32.500 (36.346) Prec@5 67.500 (67.032) Epoch: [10][4970/11272] Time 0.843 (1.115) Data 0.001 (0.275) Loss 2.7650 (2.6283) Prec@1 33.750 (36.343) Prec@5 69.375 (67.037) Epoch: [10][4980/11272] Time 0.884 (1.115) Data 0.001 (0.275) Loss 2.7878 (2.6284) Prec@1 33.750 (36.341) Prec@5 64.375 (67.034) Epoch: [10][4990/11272] Time 0.737 (1.116) Data 0.001 (0.275) Loss 2.7182 (2.6285) Prec@1 37.500 (36.339) Prec@5 66.250 (67.032) Epoch: [10][5000/11272] Time 1.816 (1.116) Data 1.041 (0.275) Loss 2.6875 (2.6284) Prec@1 35.000 (36.343) Prec@5 68.125 (67.035) Epoch: [10][5010/11272] Time 1.017 (1.116) Data 0.135 (0.275) Loss 2.6118 (2.6285) Prec@1 35.000 (36.339) Prec@5 66.875 (67.031) Epoch: [10][5020/11272] Time 0.933 (1.116) Data 0.001 (0.275) Loss 2.6577 (2.6284) Prec@1 38.750 (36.343) Prec@5 67.500 (67.030) Epoch: [10][5030/11272] Time 1.511 (1.116) Data 0.749 (0.275) Loss 2.1755 (2.6282) Prec@1 40.625 (36.347) Prec@5 77.500 (67.036) Epoch: [10][5040/11272] Time 0.746 (1.116) Data 0.002 (0.275) Loss 2.4527 (2.6282) Prec@1 35.625 (36.349) Prec@5 76.875 (67.039) Epoch: [10][5050/11272] Time 0.888 (1.116) Data 0.002 (0.276) Loss 2.7806 (2.6282) Prec@1 34.375 (36.349) Prec@5 60.000 (67.039) Epoch: [10][5060/11272] Time 0.879 (1.116) Data 0.002 (0.276) Loss 2.7084 (2.6283) Prec@1 33.750 (36.346) Prec@5 70.000 (67.037) Epoch: [10][5070/11272] Time 0.735 (1.116) Data 0.002 (0.276) Loss 2.6973 (2.6284) Prec@1 34.375 (36.345) Prec@5 63.750 (67.034) Epoch: [10][5080/11272] Time 0.770 (1.116) Data 0.002 (0.276) Loss 2.5019 (2.6285) Prec@1 34.375 (36.345) Prec@5 66.875 (67.034) Epoch: [10][5090/11272] Time 1.721 (1.116) Data 0.809 (0.276) Loss 3.0187 (2.6287) Prec@1 31.875 (36.341) Prec@5 57.500 (67.028) Epoch: [10][5100/11272] Time 0.878 (1.116) Data 0.001 (0.276) Loss 2.6617 (2.6286) Prec@1 37.500 (36.343) Prec@5 65.000 (67.029) Epoch: [10][5110/11272] Time 1.293 (1.116) Data 0.511 (0.276) Loss 2.7357 (2.6286) Prec@1 40.000 (36.344) Prec@5 68.125 (67.032) Epoch: [10][5120/11272] Time 1.088 (1.116) Data 0.194 (0.276) Loss 2.8910 (2.6286) Prec@1 27.500 (36.343) Prec@5 55.000 (67.031) Epoch: [10][5130/11272] Time 0.888 (1.116) Data 0.002 (0.276) Loss 2.6223 (2.6286) Prec@1 36.250 (36.342) Prec@5 63.750 (67.032) Epoch: [10][5140/11272] Time 0.784 (1.116) Data 0.002 (0.276) Loss 2.9004 (2.6286) Prec@1 34.375 (36.342) Prec@5 61.250 (67.033) Epoch: [10][5150/11272] Time 0.735 (1.117) Data 0.002 (0.277) Loss 2.6262 (2.6286) Prec@1 36.875 (36.342) Prec@5 64.375 (67.034) Epoch: [10][5160/11272] Time 0.865 (1.116) Data 0.002 (0.277) Loss 2.4002 (2.6286) Prec@1 43.750 (36.341) Prec@5 68.750 (67.033) Epoch: [10][5170/11272] Time 0.889 (1.116) Data 0.001 (0.276) Loss 2.7472 (2.6286) Prec@1 31.875 (36.337) Prec@5 64.375 (67.033) Epoch: [10][5180/11272] Time 0.740 (1.116) Data 0.002 (0.276) Loss 2.3904 (2.6285) Prec@1 36.875 (36.339) Prec@5 70.625 (67.036) Epoch: [10][5190/11272] Time 0.746 (1.116) Data 0.002 (0.276) Loss 2.6518 (2.6285) Prec@1 35.625 (36.337) Prec@5 65.625 (67.034) Epoch: [10][5200/11272] Time 0.874 (1.116) Data 0.002 (0.276) Loss 2.8429 (2.6284) Prec@1 33.125 (36.340) Prec@5 66.250 (67.037) Epoch: [10][5210/11272] Time 0.871 (1.116) Data 0.001 (0.277) Loss 2.6165 (2.6284) Prec@1 38.750 (36.341) Prec@5 67.500 (67.038) Epoch: [10][5220/11272] Time 0.780 (1.116) Data 0.002 (0.277) Loss 2.5333 (2.6284) Prec@1 42.500 (36.339) Prec@5 68.750 (67.037) Epoch: [10][5230/11272] Time 1.784 (1.116) Data 1.028 (0.277) Loss 2.4862 (2.6284) Prec@1 39.375 (36.338) Prec@5 65.000 (67.040) Epoch: [10][5240/11272] Time 0.866 (1.116) Data 0.002 (0.277) Loss 2.5000 (2.6283) Prec@1 38.750 (36.340) Prec@5 70.625 (67.042) Epoch: [10][5250/11272] Time 1.642 (1.116) Data 0.863 (0.277) Loss 2.7553 (2.6283) Prec@1 32.500 (36.339) Prec@5 66.250 (67.040) Epoch: [10][5260/11272] Time 0.792 (1.116) Data 0.028 (0.277) Loss 2.6479 (2.6285) Prec@1 35.625 (36.337) Prec@5 65.625 (67.035) Epoch: [10][5270/11272] Time 0.866 (1.117) Data 0.002 (0.277) Loss 2.6587 (2.6284) Prec@1 34.375 (36.338) Prec@5 71.250 (67.036) Epoch: [10][5280/11272] Time 0.882 (1.116) Data 0.002 (0.277) Loss 2.5468 (2.6284) Prec@1 38.125 (36.340) Prec@5 70.000 (67.034) Epoch: [10][5290/11272] Time 0.800 (1.117) Data 0.002 (0.277) Loss 2.5240 (2.6284) Prec@1 35.000 (36.340) Prec@5 69.375 (67.033) Epoch: [10][5300/11272] Time 0.769 (1.117) Data 0.002 (0.277) Loss 2.3710 (2.6284) Prec@1 38.750 (36.338) Prec@5 72.500 (67.034) Epoch: [10][5310/11272] Time 0.870 (1.117) Data 0.002 (0.277) Loss 2.7830 (2.6285) Prec@1 32.500 (36.336) Prec@5 61.250 (67.030) Epoch: [10][5320/11272] Time 0.893 (1.117) Data 0.002 (0.278) Loss 2.7727 (2.6285) Prec@1 35.000 (36.338) Prec@5 63.125 (67.031) Epoch: [10][5330/11272] Time 0.743 (1.117) Data 0.002 (0.277) Loss 2.9226 (2.6284) Prec@1 28.125 (36.340) Prec@5 62.500 (67.033) Epoch: [10][5340/11272] Time 1.078 (1.117) Data 0.316 (0.278) Loss 2.5270 (2.6283) Prec@1 40.625 (36.341) Prec@5 68.750 (67.037) Epoch: [10][5350/11272] Time 0.867 (1.117) Data 0.002 (0.278) Loss 2.5562 (2.6283) Prec@1 32.500 (36.338) Prec@5 66.875 (67.037) Epoch: [10][5360/11272] Time 2.102 (1.117) Data 1.202 (0.278) Loss 2.3185 (2.6281) Prec@1 40.625 (36.341) Prec@5 73.750 (67.040) Epoch: [10][5370/11272] Time 0.738 (1.117) Data 0.002 (0.278) Loss 2.8285 (2.6282) Prec@1 30.000 (36.340) Prec@5 60.625 (67.037) Epoch: [10][5380/11272] Time 3.469 (1.117) Data 2.542 (0.278) Loss 2.6233 (2.6282) Prec@1 38.125 (36.340) Prec@5 65.625 (67.036) Epoch: [10][5390/11272] Time 0.902 (1.117) Data 0.001 (0.278) Loss 2.7406 (2.6283) Prec@1 34.375 (36.337) Prec@5 61.250 (67.033) Epoch: [10][5400/11272] Time 0.739 (1.117) Data 0.002 (0.278) Loss 2.4131 (2.6283) Prec@1 38.750 (36.340) Prec@5 73.125 (67.036) Epoch: [10][5410/11272] Time 0.732 (1.117) Data 0.002 (0.278) Loss 2.6721 (2.6283) Prec@1 35.000 (36.340) Prec@5 65.625 (67.036) Epoch: [10][5420/11272] Time 0.884 (1.117) Data 0.001 (0.278) Loss 2.4455 (2.6282) Prec@1 36.875 (36.343) Prec@5 66.250 (67.036) Epoch: [10][5430/11272] Time 0.856 (1.117) Data 0.001 (0.278) Loss 2.4425 (2.6281) Prec@1 38.750 (36.342) Prec@5 70.000 (67.037) Epoch: [10][5440/11272] Time 0.749 (1.117) Data 0.002 (0.278) Loss 2.6850 (2.6282) Prec@1 31.875 (36.341) Prec@5 62.500 (67.036) Epoch: [10][5450/11272] Time 0.840 (1.117) Data 0.065 (0.278) Loss 2.6756 (2.6282) Prec@1 35.625 (36.339) Prec@5 68.750 (67.035) Epoch: [10][5460/11272] Time 1.344 (1.117) Data 0.469 (0.278) Loss 2.4858 (2.6282) Prec@1 40.000 (36.341) Prec@5 66.875 (67.034) Epoch: [10][5470/11272] Time 0.844 (1.117) Data 0.001 (0.278) Loss 2.4223 (2.6283) Prec@1 43.750 (36.338) Prec@5 73.125 (67.033) Epoch: [10][5480/11272] Time 0.739 (1.117) Data 0.002 (0.278) Loss 2.4636 (2.6285) Prec@1 41.250 (36.337) Prec@5 68.750 (67.031) Epoch: [10][5490/11272] Time 1.319 (1.117) Data 0.559 (0.279) Loss 2.3935 (2.6285) Prec@1 38.125 (36.338) Prec@5 73.125 (67.029) Epoch: [10][5500/11272] Time 0.895 (1.117) Data 0.002 (0.279) Loss 2.3907 (2.6284) Prec@1 44.375 (36.340) Prec@5 69.375 (67.032) Epoch: [10][5510/11272] Time 1.372 (1.117) Data 0.649 (0.279) Loss 2.5766 (2.6285) Prec@1 36.875 (36.338) Prec@5 69.375 (67.032) Epoch: [10][5520/11272] Time 0.742 (1.117) Data 0.002 (0.278) Loss 2.6154 (2.6284) Prec@1 36.250 (36.340) Prec@5 66.250 (67.033) Epoch: [10][5530/11272] Time 1.402 (1.117) Data 0.533 (0.279) Loss 2.5397 (2.6284) Prec@1 38.125 (36.341) Prec@5 70.625 (67.033) Epoch: [10][5540/11272] Time 0.890 (1.117) Data 0.002 (0.279) Loss 2.7437 (2.6284) Prec@1 32.500 (36.341) Prec@5 66.250 (67.032) Epoch: [10][5550/11272] Time 0.746 (1.117) Data 0.002 (0.278) Loss 2.5158 (2.6285) Prec@1 36.250 (36.340) Prec@5 70.625 (67.032) Epoch: [10][5560/11272] Time 1.036 (1.118) Data 0.293 (0.279) Loss 2.5878 (2.6284) Prec@1 30.000 (36.339) Prec@5 65.625 (67.034) Epoch: [10][5570/11272] Time 0.845 (1.117) Data 0.001 (0.279) Loss 2.2926 (2.6284) Prec@1 44.375 (36.338) Prec@5 75.000 (67.034) Epoch: [10][5580/11272] Time 1.689 (1.117) Data 0.820 (0.279) Loss 2.6789 (2.6285) Prec@1 35.625 (36.338) Prec@5 67.500 (67.032) Epoch: [10][5590/11272] Time 0.899 (1.117) Data 0.137 (0.279) Loss 2.5415 (2.6284) Prec@1 38.750 (36.338) Prec@5 70.625 (67.036) Epoch: [10][5600/11272] Time 0.738 (1.117) Data 0.002 (0.279) Loss 2.2381 (2.6284) Prec@1 43.750 (36.337) Prec@5 75.000 (67.039) Epoch: [10][5610/11272] Time 0.877 (1.117) Data 0.002 (0.279) Loss 2.5648 (2.6285) Prec@1 36.250 (36.336) Prec@5 68.750 (67.038) Epoch: [10][5620/11272] Time 0.869 (1.117) Data 0.002 (0.279) Loss 2.8725 (2.6286) Prec@1 31.250 (36.332) Prec@5 60.000 (67.035) Epoch: [10][5630/11272] Time 0.748 (1.117) Data 0.001 (0.279) Loss 2.6357 (2.6287) Prec@1 42.500 (36.330) Prec@5 65.000 (67.031) Epoch: [10][5640/11272] Time 0.895 (1.117) Data 0.001 (0.279) Loss 2.6527 (2.6287) Prec@1 31.875 (36.331) Prec@5 68.125 (67.031) Epoch: [10][5650/11272] Time 1.828 (1.117) Data 0.884 (0.279) Loss 2.4165 (2.6286) Prec@1 39.375 (36.337) Prec@5 67.500 (67.032) Epoch: [10][5660/11272] Time 0.743 (1.117) Data 0.002 (0.279) Loss 2.6548 (2.6287) Prec@1 38.125 (36.336) Prec@5 68.125 (67.029) Epoch: [10][5670/11272] Time 0.746 (1.117) Data 0.002 (0.279) Loss 2.5929 (2.6288) Prec@1 43.125 (36.336) Prec@5 69.375 (67.028) Epoch: [10][5680/11272] Time 1.022 (1.117) Data 0.139 (0.279) Loss 2.6842 (2.6288) Prec@1 38.125 (36.338) Prec@5 63.750 (67.027) Epoch: [10][5690/11272] Time 0.877 (1.117) Data 0.002 (0.279) Loss 2.5012 (2.6288) Prec@1 38.750 (36.340) Prec@5 66.875 (67.028) Epoch: [10][5700/11272] Time 1.322 (1.117) Data 0.523 (0.279) Loss 2.4679 (2.6288) Prec@1 41.875 (36.339) Prec@5 69.375 (67.026) Epoch: [10][5710/11272] Time 0.744 (1.117) Data 0.001 (0.279) Loss 2.6406 (2.6289) Prec@1 41.250 (36.338) Prec@5 65.625 (67.022) Epoch: [10][5720/11272] Time 1.226 (1.117) Data 0.363 (0.279) Loss 2.5327 (2.6289) Prec@1 35.625 (36.338) Prec@5 73.750 (67.024) Epoch: [10][5730/11272] Time 0.926 (1.117) Data 0.002 (0.279) Loss 2.7836 (2.6291) Prec@1 36.250 (36.335) Prec@5 63.750 (67.019) Epoch: [10][5740/11272] Time 1.694 (1.117) Data 0.922 (0.279) Loss 2.5420 (2.6291) Prec@1 41.875 (36.340) Prec@5 70.625 (67.020) Epoch: [10][5750/11272] Time 0.785 (1.117) Data 0.049 (0.279) Loss 2.5718 (2.6289) Prec@1 35.000 (36.341) Prec@5 68.125 (67.027) Epoch: [10][5760/11272] Time 0.926 (1.117) Data 0.002 (0.279) Loss 2.7559 (2.6288) Prec@1 33.125 (36.343) Prec@5 65.000 (67.027) Epoch: [10][5770/11272] Time 1.727 (1.117) Data 0.965 (0.279) Loss 2.8435 (2.6289) Prec@1 36.875 (36.342) Prec@5 58.750 (67.026) Epoch: [10][5780/11272] Time 1.972 (1.117) Data 1.207 (0.279) Loss 2.8423 (2.6289) Prec@1 34.375 (36.342) Prec@5 63.750 (67.025) Epoch: [10][5790/11272] Time 1.001 (1.117) Data 0.103 (0.279) Loss 2.7341 (2.6289) Prec@1 30.625 (36.341) Prec@5 63.750 (67.024) Epoch: [10][5800/11272] Time 1.710 (1.117) Data 0.772 (0.279) Loss 2.4476 (2.6290) Prec@1 42.500 (36.340) Prec@5 70.000 (67.024) Epoch: [10][5810/11272] Time 1.558 (1.117) Data 0.785 (0.279) Loss 2.7458 (2.6290) Prec@1 36.250 (36.339) Prec@5 66.875 (67.027) Epoch: [10][5820/11272] Time 0.768 (1.117) Data 0.002 (0.279) Loss 2.4748 (2.6290) Prec@1 40.000 (36.341) Prec@5 69.375 (67.028) Epoch: [10][5830/11272] Time 0.863 (1.117) Data 0.002 (0.279) Loss 2.6564 (2.6289) Prec@1 38.750 (36.343) Prec@5 66.250 (67.029) Epoch: [10][5840/11272] Time 0.908 (1.117) Data 0.002 (0.279) Loss 2.5760 (2.6289) Prec@1 37.500 (36.342) Prec@5 70.000 (67.029) Epoch: [10][5850/11272] Time 1.503 (1.117) Data 0.718 (0.279) Loss 2.5137 (2.6288) Prec@1 40.625 (36.347) Prec@5 65.000 (67.033) Epoch: [10][5860/11272] Time 0.740 (1.117) Data 0.002 (0.279) Loss 2.4625 (2.6286) Prec@1 38.125 (36.349) Prec@5 72.500 (67.037) Epoch: [10][5870/11272] Time 1.255 (1.117) Data 0.356 (0.279) Loss 2.6146 (2.6286) Prec@1 36.250 (36.353) Prec@5 67.500 (67.038) Epoch: [10][5880/11272] Time 0.867 (1.117) Data 0.001 (0.279) Loss 2.4452 (2.6286) Prec@1 38.125 (36.353) Prec@5 68.750 (67.037) Epoch: [10][5890/11272] Time 2.263 (1.117) Data 1.499 (0.279) Loss 2.6356 (2.6285) Prec@1 40.000 (36.359) Prec@5 65.625 (67.037) Epoch: [10][5900/11272] Time 0.890 (1.117) Data 0.001 (0.279) Loss 2.4729 (2.6285) Prec@1 43.750 (36.362) Prec@5 73.125 (67.038) Epoch: [10][5910/11272] Time 2.005 (1.117) Data 1.074 (0.279) Loss 2.3336 (2.6284) Prec@1 44.375 (36.363) Prec@5 71.875 (67.039) Epoch: [10][5920/11272] Time 0.773 (1.117) Data 0.001 (0.279) Loss 2.4313 (2.6285) Prec@1 38.750 (36.362) Prec@5 70.625 (67.039) Epoch: [10][5930/11272] Time 0.739 (1.117) Data 0.002 (0.279) Loss 2.7389 (2.6286) Prec@1 31.250 (36.361) Prec@5 65.625 (67.039) Epoch: [10][5940/11272] Time 1.076 (1.117) Data 0.165 (0.279) Loss 2.3632 (2.6285) Prec@1 43.750 (36.363) Prec@5 68.750 (67.039) Epoch: [10][5950/11272] Time 1.944 (1.117) Data 1.040 (0.279) Loss 2.6412 (2.6285) Prec@1 36.875 (36.365) Prec@5 66.875 (67.040) Epoch: [10][5960/11272] Time 1.272 (1.117) Data 0.492 (0.279) Loss 2.6965 (2.6285) Prec@1 39.375 (36.365) Prec@5 65.625 (67.041) Epoch: [10][5970/11272] Time 0.795 (1.117) Data 0.002 (0.279) Loss 2.7935 (2.6286) Prec@1 31.875 (36.362) Prec@5 65.000 (67.038) Epoch: [10][5980/11272] Time 1.710 (1.117) Data 0.833 (0.279) Loss 2.7155 (2.6287) Prec@1 33.750 (36.360) Prec@5 68.125 (67.037) Epoch: [10][5990/11272] Time 0.845 (1.117) Data 0.002 (0.279) Loss 2.6458 (2.6287) Prec@1 38.750 (36.360) Prec@5 72.500 (67.039) Epoch: [10][6000/11272] Time 1.959 (1.117) Data 1.200 (0.279) Loss 2.4910 (2.6286) Prec@1 40.000 (36.361) Prec@5 72.500 (67.041) Epoch: [10][6010/11272] Time 0.747 (1.117) Data 0.002 (0.279) Loss 2.6501 (2.6287) Prec@1 31.875 (36.358) Prec@5 66.250 (67.039) Epoch: [10][6020/11272] Time 1.615 (1.116) Data 0.640 (0.279) Loss 2.5968 (2.6286) Prec@1 40.625 (36.362) Prec@5 68.750 (67.040) Epoch: [10][6030/11272] Time 0.893 (1.116) Data 0.002 (0.279) Loss 2.5436 (2.6285) Prec@1 33.750 (36.363) Prec@5 73.125 (67.042) Epoch: [10][6040/11272] Time 0.762 (1.116) Data 0.002 (0.279) Loss 2.4430 (2.6287) Prec@1 42.500 (36.361) Prec@5 71.250 (67.040) Epoch: [10][6050/11272] Time 1.272 (1.116) Data 0.318 (0.279) Loss 2.6496 (2.6287) Prec@1 39.375 (36.361) Prec@5 66.250 (67.040) Epoch: [10][6060/11272] Time 0.858 (1.116) Data 0.002 (0.279) Loss 2.5629 (2.6288) Prec@1 35.625 (36.361) Prec@5 70.000 (67.037) Epoch: [10][6070/11272] Time 0.970 (1.116) Data 0.201 (0.279) Loss 2.5137 (2.6288) Prec@1 36.875 (36.362) Prec@5 70.000 (67.036) Epoch: [10][6080/11272] Time 0.742 (1.116) Data 0.002 (0.279) Loss 2.6476 (2.6289) Prec@1 36.875 (36.360) Prec@5 67.500 (67.034) Epoch: [10][6090/11272] Time 1.197 (1.116) Data 0.276 (0.279) Loss 2.8045 (2.6291) Prec@1 35.625 (36.357) Prec@5 61.875 (67.030) Epoch: [10][6100/11272] Time 0.846 (1.116) Data 0.002 (0.279) Loss 2.9347 (2.6291) Prec@1 32.500 (36.356) Prec@5 58.750 (67.030) Epoch: [10][6110/11272] Time 0.757 (1.116) Data 0.001 (0.279) Loss 2.6161 (2.6291) Prec@1 38.125 (36.354) Prec@5 70.625 (67.032) Epoch: [10][6120/11272] Time 0.776 (1.116) Data 0.001 (0.279) Loss 2.8284 (2.6293) Prec@1 33.750 (36.354) Prec@5 60.625 (67.026) Epoch: [10][6130/11272] Time 1.987 (1.116) Data 1.106 (0.279) Loss 2.3681 (2.6293) Prec@1 43.125 (36.354) Prec@5 70.625 (67.025) Epoch: [10][6140/11272] Time 0.953 (1.116) Data 0.002 (0.278) Loss 2.7335 (2.6294) Prec@1 31.875 (36.352) Prec@5 63.125 (67.021) Epoch: [10][6150/11272] Time 0.721 (1.115) Data 0.001 (0.278) Loss 2.7189 (2.6293) Prec@1 34.375 (36.356) Prec@5 67.500 (67.024) Epoch: [10][6160/11272] Time 0.740 (1.115) Data 0.002 (0.278) Loss 2.6966 (2.6294) Prec@1 34.375 (36.353) Prec@5 65.000 (67.020) Epoch: [10][6170/11272] Time 0.832 (1.115) Data 0.001 (0.278) Loss 2.5418 (2.6295) Prec@1 38.750 (36.352) Prec@5 71.250 (67.018) Epoch: [10][6180/11272] Time 0.746 (1.115) Data 0.001 (0.278) Loss 2.5784 (2.6295) Prec@1 36.250 (36.352) Prec@5 68.125 (67.020) Epoch: [10][6190/11272] Time 0.746 (1.115) Data 0.002 (0.278) Loss 2.7391 (2.6296) Prec@1 30.000 (36.350) Prec@5 64.375 (67.017) Epoch: [10][6200/11272] Time 0.872 (1.115) Data 0.001 (0.278) Loss 2.4826 (2.6297) Prec@1 36.250 (36.346) Prec@5 70.625 (67.017) Epoch: [10][6210/11272] Time 1.333 (1.115) Data 0.512 (0.278) Loss 2.8410 (2.6296) Prec@1 26.250 (36.345) Prec@5 63.750 (67.019) Epoch: [10][6220/11272] Time 0.737 (1.114) Data 0.002 (0.278) Loss 2.8746 (2.6297) Prec@1 30.625 (36.344) Prec@5 62.500 (67.019) Epoch: [10][6230/11272] Time 0.767 (1.114) Data 0.002 (0.277) Loss 2.9140 (2.6296) Prec@1 23.750 (36.341) Prec@5 62.500 (67.019) Epoch: [10][6240/11272] Time 0.887 (1.114) Data 0.002 (0.277) Loss 2.8489 (2.6296) Prec@1 36.875 (36.342) Prec@5 59.375 (67.020) Epoch: [10][6250/11272] Time 0.869 (1.114) Data 0.002 (0.277) Loss 2.4502 (2.6296) Prec@1 36.875 (36.341) Prec@5 73.125 (67.021) Epoch: [10][6260/11272] Time 1.157 (1.114) Data 0.393 (0.277) Loss 2.7126 (2.6297) Prec@1 31.875 (36.342) Prec@5 63.125 (67.020) Epoch: [10][6270/11272] Time 0.865 (1.114) Data 0.121 (0.277) Loss 2.6015 (2.6297) Prec@1 32.500 (36.341) Prec@5 68.750 (67.020) Epoch: [10][6280/11272] Time 1.043 (1.114) Data 0.188 (0.277) Loss 2.7127 (2.6298) Prec@1 33.125 (36.336) Prec@5 66.875 (67.017) Epoch: [10][6290/11272] Time 1.612 (1.114) Data 0.739 (0.277) Loss 2.9770 (2.6300) Prec@1 28.125 (36.330) Prec@5 61.875 (67.012) Epoch: [10][6300/11272] Time 1.145 (1.113) Data 0.391 (0.277) Loss 2.2715 (2.6302) Prec@1 48.125 (36.330) Prec@5 76.875 (67.011) Epoch: [10][6310/11272] Time 1.990 (1.113) Data 1.112 (0.277) Loss 2.6969 (2.6303) Prec@1 35.625 (36.328) Prec@5 63.750 (67.008) Epoch: [10][6320/11272] Time 1.071 (1.113) Data 0.164 (0.277) Loss 2.7253 (2.6304) Prec@1 30.000 (36.323) Prec@5 63.125 (67.006) Epoch: [10][6330/11272] Time 1.814 (1.113) Data 1.030 (0.277) Loss 2.8971 (2.6305) Prec@1 30.000 (36.320) Prec@5 62.500 (67.006) Epoch: [10][6340/11272] Time 1.264 (1.113) Data 0.536 (0.277) Loss 2.6068 (2.6305) Prec@1 38.750 (36.321) Prec@5 66.875 (67.006) Epoch: [10][6350/11272] Time 0.912 (1.113) Data 0.014 (0.276) Loss 2.5183 (2.6304) Prec@1 40.625 (36.323) Prec@5 70.000 (67.005) Epoch: [10][6360/11272] Time 1.799 (1.113) Data 0.918 (0.276) Loss 2.4008 (2.6304) Prec@1 38.750 (36.322) Prec@5 77.500 (67.007) Epoch: [10][6370/11272] Time 0.770 (1.113) Data 0.002 (0.276) Loss 2.8149 (2.6304) Prec@1 36.250 (36.320) Prec@5 66.875 (67.006) Epoch: [10][6380/11272] Time 0.735 (1.113) Data 0.001 (0.276) Loss 3.0467 (2.6304) Prec@1 35.000 (36.321) Prec@5 60.625 (67.008) Epoch: [10][6390/11272] Time 0.880 (1.113) Data 0.002 (0.276) Loss 2.8423 (2.6305) Prec@1 30.625 (36.320) Prec@5 67.500 (67.008) Epoch: [10][6400/11272] Time 0.877 (1.113) Data 0.002 (0.276) Loss 2.4328 (2.6304) Prec@1 36.250 (36.318) Prec@5 76.250 (67.010) Epoch: [10][6410/11272] Time 0.772 (1.112) Data 0.001 (0.276) Loss 2.5678 (2.6304) Prec@1 33.125 (36.316) Prec@5 70.625 (67.009) Epoch: [10][6420/11272] Time 0.767 (1.112) Data 0.003 (0.276) Loss 2.5603 (2.6304) Prec@1 41.250 (36.315) Prec@5 68.125 (67.009) Epoch: [10][6430/11272] Time 0.890 (1.112) Data 0.002 (0.276) Loss 2.7696 (2.6304) Prec@1 35.000 (36.314) Prec@5 62.500 (67.008) Epoch: [10][6440/11272] Time 0.745 (1.112) Data 0.004 (0.276) Loss 2.6171 (2.6305) Prec@1 36.250 (36.313) Prec@5 64.375 (67.007) Epoch: [10][6450/11272] Time 0.755 (1.112) Data 0.001 (0.276) Loss 2.5263 (2.6303) Prec@1 40.000 (36.316) Prec@5 66.875 (67.010) Epoch: [10][6460/11272] Time 1.163 (1.112) Data 0.281 (0.275) Loss 2.5726 (2.6303) Prec@1 41.875 (36.318) Prec@5 67.500 (67.011) Epoch: [10][6470/11272] Time 0.914 (1.112) Data 0.002 (0.275) Loss 2.3765 (2.6302) Prec@1 44.375 (36.320) Prec@5 73.750 (67.013) Epoch: [10][6480/11272] Time 1.610 (1.112) Data 0.841 (0.275) Loss 2.4720 (2.6303) Prec@1 36.250 (36.318) Prec@5 73.750 (67.011) Epoch: [10][6490/11272] Time 0.746 (1.111) Data 0.002 (0.275) Loss 2.3005 (2.6302) Prec@1 35.625 (36.317) Prec@5 74.375 (67.013) Epoch: [10][6500/11272] Time 0.967 (1.112) Data 0.049 (0.275) Loss 2.6871 (2.6303) Prec@1 34.375 (36.317) Prec@5 67.500 (67.014) Epoch: [10][6510/11272] Time 0.889 (1.111) Data 0.002 (0.275) Loss 2.5563 (2.6301) Prec@1 40.000 (36.319) Prec@5 70.625 (67.018) Epoch: [10][6520/11272] Time 0.795 (1.111) Data 0.002 (0.275) Loss 2.5543 (2.6301) Prec@1 40.000 (36.319) Prec@5 66.250 (67.018) Epoch: [10][6530/11272] Time 0.742 (1.111) Data 0.002 (0.275) Loss 2.5990 (2.6301) Prec@1 30.000 (36.316) Prec@5 67.500 (67.018) Epoch: [10][6540/11272] Time 0.922 (1.111) Data 0.002 (0.275) Loss 2.7405 (2.6303) Prec@1 32.500 (36.313) Prec@5 68.125 (67.015) Epoch: [10][6550/11272] Time 0.849 (1.111) Data 0.002 (0.275) Loss 2.7743 (2.6303) Prec@1 31.250 (36.312) Prec@5 65.000 (67.013) Epoch: [10][6560/11272] Time 0.857 (1.111) Data 0.086 (0.275) Loss 2.5855 (2.6303) Prec@1 38.750 (36.312) Prec@5 70.625 (67.013) Epoch: [10][6570/11272] Time 0.867 (1.111) Data 0.002 (0.274) Loss 2.5807 (2.6302) Prec@1 36.875 (36.315) Prec@5 68.125 (67.015) Epoch: [10][6580/11272] Time 1.836 (1.111) Data 0.950 (0.275) Loss 2.4827 (2.6303) Prec@1 44.375 (36.315) Prec@5 72.500 (67.014) Epoch: [10][6590/11272] Time 0.784 (1.110) Data 0.001 (0.274) Loss 2.5466 (2.6302) Prec@1 43.125 (36.319) Prec@5 68.750 (67.017) Epoch: [10][6600/11272] Time 0.739 (1.110) Data 0.001 (0.274) Loss 2.3571 (2.6302) Prec@1 39.375 (36.318) Prec@5 71.250 (67.017) Epoch: [10][6610/11272] Time 0.949 (1.110) Data 0.002 (0.274) Loss 2.3173 (2.6301) Prec@1 40.625 (36.318) Prec@5 76.875 (67.019) Epoch: [10][6620/11272] Time 1.096 (1.110) Data 0.184 (0.274) Loss 2.4683 (2.6300) Prec@1 38.750 (36.321) Prec@5 68.750 (67.020) Epoch: [10][6630/11272] Time 0.745 (1.110) Data 0.001 (0.274) Loss 2.4691 (2.6301) Prec@1 39.375 (36.320) Prec@5 71.875 (67.018) Epoch: [10][6640/11272] Time 1.607 (1.110) Data 0.842 (0.274) Loss 2.7178 (2.6300) Prec@1 35.625 (36.320) Prec@5 69.375 (67.021) Epoch: [10][6650/11272] Time 0.862 (1.109) Data 0.002 (0.274) Loss 2.5180 (2.6301) Prec@1 39.375 (36.321) Prec@5 70.000 (67.020) Epoch: [10][6660/11272] Time 0.901 (1.109) Data 0.002 (0.273) Loss 2.6111 (2.6299) Prec@1 35.000 (36.322) Prec@5 70.000 (67.025) Epoch: [10][6670/11272] Time 1.471 (1.109) Data 0.709 (0.273) Loss 2.5612 (2.6299) Prec@1 40.625 (36.325) Prec@5 67.500 (67.025) Epoch: [10][6680/11272] Time 0.766 (1.109) Data 0.001 (0.273) Loss 2.4765 (2.6299) Prec@1 40.625 (36.325) Prec@5 71.250 (67.024) Epoch: [10][6690/11272] Time 2.019 (1.109) Data 1.112 (0.273) Loss 2.4571 (2.6298) Prec@1 38.125 (36.327) Prec@5 67.500 (67.024) Epoch: [10][6700/11272] Time 1.469 (1.109) Data 0.705 (0.273) Loss 2.6650 (2.6300) Prec@1 40.000 (36.324) Prec@5 65.000 (67.021) Epoch: [10][6710/11272] Time 0.740 (1.109) Data 0.002 (0.273) Loss 2.4879 (2.6301) Prec@1 32.500 (36.321) Prec@5 65.625 (67.019) Epoch: [10][6720/11272] Time 0.878 (1.109) Data 0.002 (0.273) Loss 2.6199 (2.6301) Prec@1 36.250 (36.320) Prec@5 65.000 (67.020) Epoch: [10][6730/11272] Time 0.887 (1.108) Data 0.002 (0.273) Loss 2.7603 (2.6302) Prec@1 37.500 (36.321) Prec@5 62.500 (67.016) Epoch: [10][6740/11272] Time 1.004 (1.108) Data 0.239 (0.273) Loss 2.6322 (2.6302) Prec@1 37.500 (36.323) Prec@5 67.500 (67.017) Epoch: [10][6750/11272] Time 0.748 (1.108) Data 0.002 (0.272) Loss 2.6924 (2.6302) Prec@1 33.125 (36.322) Prec@5 67.500 (67.017) Epoch: [10][6760/11272] Time 1.334 (1.108) Data 0.431 (0.272) Loss 2.5546 (2.6302) Prec@1 35.625 (36.322) Prec@5 65.000 (67.016) Epoch: [10][6770/11272] Time 0.862 (1.108) Data 0.001 (0.272) Loss 2.5942 (2.6303) Prec@1 33.750 (36.320) Prec@5 70.000 (67.015) Epoch: [10][6780/11272] Time 1.777 (1.108) Data 1.023 (0.272) Loss 2.5548 (2.6302) Prec@1 37.500 (36.322) Prec@5 67.500 (67.018) Epoch: [10][6790/11272] Time 0.736 (1.108) Data 0.002 (0.272) Loss 2.6479 (2.6302) Prec@1 37.500 (36.326) Prec@5 65.000 (67.017) Epoch: [10][6800/11272] Time 1.542 (1.108) Data 0.657 (0.272) Loss 2.7015 (2.6303) Prec@1 31.875 (36.322) Prec@5 66.875 (67.015) Epoch: [10][6810/11272] Time 0.926 (1.108) Data 0.058 (0.272) Loss 2.3740 (2.6302) Prec@1 34.375 (36.323) Prec@5 71.250 (67.016) Epoch: [10][6820/11272] Time 1.210 (1.108) Data 0.400 (0.272) Loss 2.5576 (2.6302) Prec@1 38.125 (36.322) Prec@5 68.750 (67.017) Epoch: [10][6830/11272] Time 0.871 (1.107) Data 0.002 (0.272) Loss 2.4302 (2.6302) Prec@1 40.625 (36.324) Prec@5 70.000 (67.018) Epoch: [10][6840/11272] Time 1.585 (1.107) Data 0.704 (0.272) Loss 2.6128 (2.6302) Prec@1 33.750 (36.322) Prec@5 67.500 (67.018) Epoch: [10][6850/11272] Time 0.771 (1.107) Data 0.001 (0.272) Loss 2.6956 (2.6302) Prec@1 31.875 (36.323) Prec@5 64.375 (67.016) Epoch: [10][6860/11272] Time 0.746 (1.107) Data 0.002 (0.271) Loss 2.6899 (2.6303) Prec@1 38.125 (36.323) Prec@5 62.500 (67.012) Epoch: [10][6870/11272] Time 0.839 (1.107) Data 0.001 (0.271) Loss 2.3703 (2.6304) Prec@1 39.375 (36.321) Prec@5 70.625 (67.009) Epoch: [10][6880/11272] Time 1.224 (1.107) Data 0.419 (0.271) Loss 2.5140 (2.6304) Prec@1 41.875 (36.321) Prec@5 70.625 (67.010) Epoch: [10][6890/11272] Time 0.735 (1.107) Data 0.002 (0.271) Loss 2.5614 (2.6302) Prec@1 31.250 (36.324) Prec@5 66.875 (67.011) Epoch: [10][6900/11272] Time 0.789 (1.106) Data 0.001 (0.271) Loss 2.8763 (2.6304) Prec@1 31.250 (36.320) Prec@5 63.750 (67.009) Epoch: [10][6910/11272] Time 0.851 (1.106) Data 0.002 (0.271) Loss 2.7313 (2.6306) Prec@1 33.125 (36.319) Prec@5 66.250 (67.006) Epoch: [10][6920/11272] Time 0.883 (1.106) Data 0.002 (0.271) Loss 2.8422 (2.6307) Prec@1 30.625 (36.317) Prec@5 64.375 (67.005) Epoch: [10][6930/11272] Time 0.740 (1.106) Data 0.002 (0.271) Loss 2.8270 (2.6307) Prec@1 36.250 (36.316) Prec@5 59.375 (67.003) Epoch: [10][6940/11272] Time 0.917 (1.106) Data 0.137 (0.271) Loss 2.6732 (2.6307) Prec@1 35.625 (36.317) Prec@5 61.250 (67.003) Epoch: [10][6950/11272] Time 0.845 (1.106) Data 0.002 (0.271) Loss 2.5703 (2.6305) Prec@1 40.000 (36.319) Prec@5 67.500 (67.004) Epoch: [10][6960/11272] Time 1.188 (1.106) Data 0.269 (0.271) Loss 2.2209 (2.6304) Prec@1 43.750 (36.322) Prec@5 71.875 (67.008) Epoch: [10][6970/11272] Time 0.768 (1.106) Data 0.026 (0.271) Loss 2.8581 (2.6304) Prec@1 36.875 (36.322) Prec@5 60.625 (67.006) Epoch: [10][6980/11272] Time 0.897 (1.106) Data 0.002 (0.271) Loss 2.8253 (2.6306) Prec@1 35.625 (36.320) Prec@5 61.875 (67.003) Epoch: [10][6990/11272] Time 0.865 (1.106) Data 0.002 (0.270) Loss 2.2742 (2.6306) Prec@1 43.750 (36.319) Prec@5 76.875 (67.004) Epoch: [10][7000/11272] Time 0.847 (1.106) Data 0.002 (0.271) Loss 2.3462 (2.6305) Prec@1 40.625 (36.318) Prec@5 76.250 (67.005) Epoch: [10][7010/11272] Time 0.751 (1.105) Data 0.002 (0.270) Loss 2.6431 (2.6306) Prec@1 36.250 (36.315) Prec@5 66.875 (67.002) Epoch: [10][7020/11272] Time 0.888 (1.105) Data 0.002 (0.270) Loss 2.7238 (2.6306) Prec@1 33.750 (36.316) Prec@5 68.125 (67.003) Epoch: [10][7030/11272] Time 0.866 (1.105) Data 0.001 (0.270) Loss 2.6160 (2.6307) Prec@1 35.625 (36.315) Prec@5 65.000 (67.001) Epoch: [10][7040/11272] Time 0.772 (1.105) Data 0.002 (0.270) Loss 2.9303 (2.6308) Prec@1 31.250 (36.313) Prec@5 60.625 (67.001) Epoch: [10][7050/11272] Time 0.742 (1.105) Data 0.001 (0.270) Loss 2.4626 (2.6308) Prec@1 40.000 (36.312) Prec@5 69.375 (67.001) Epoch: [10][7060/11272] Time 0.917 (1.105) Data 0.011 (0.270) Loss 2.7418 (2.6308) Prec@1 38.125 (36.311) Prec@5 64.375 (66.999) Epoch: [10][7070/11272] Time 0.846 (1.105) Data 0.002 (0.270) Loss 2.5806 (2.6308) Prec@1 38.750 (36.312) Prec@5 70.625 (67.000) Epoch: [10][7080/11272] Time 1.023 (1.105) Data 0.257 (0.270) Loss 2.7028 (2.6307) Prec@1 36.250 (36.313) Prec@5 66.875 (67.000) Epoch: [10][7090/11272] Time 0.789 (1.104) Data 0.002 (0.270) Loss 2.8933 (2.6307) Prec@1 32.500 (36.313) Prec@5 58.750 (67.001) Epoch: [10][7100/11272] Time 0.901 (1.104) Data 0.002 (0.270) Loss 2.6280 (2.6306) Prec@1 44.375 (36.318) Prec@5 65.625 (67.002) Epoch: [10][7110/11272] Time 1.612 (1.104) Data 0.828 (0.270) Loss 2.7204 (2.6306) Prec@1 34.375 (36.318) Prec@5 65.000 (67.001) Epoch: [10][7120/11272] Time 0.783 (1.104) Data 0.025 (0.269) Loss 2.4686 (2.6308) Prec@1 38.750 (36.315) Prec@5 67.500 (67.000) Epoch: [10][7130/11272] Time 1.197 (1.104) Data 0.299 (0.269) Loss 2.8640 (2.6307) Prec@1 36.250 (36.315) Prec@5 62.500 (67.002) Epoch: [10][7140/11272] Time 0.911 (1.104) Data 0.002 (0.269) Loss 2.3606 (2.6307) Prec@1 39.375 (36.316) Prec@5 71.875 (67.003) Epoch: [10][7150/11272] Time 0.957 (1.104) Data 0.197 (0.269) Loss 2.6879 (2.6307) Prec@1 35.000 (36.315) Prec@5 65.625 (67.003) Epoch: [10][7160/11272] Time 0.743 (1.104) Data 0.002 (0.269) Loss 2.4028 (2.6308) Prec@1 40.625 (36.312) Prec@5 72.500 (67.002) Epoch: [10][7170/11272] Time 0.879 (1.104) Data 0.002 (0.269) Loss 2.3677 (2.6307) Prec@1 38.750 (36.314) Prec@5 72.500 (67.005) Epoch: [10][7180/11272] Time 0.873 (1.103) Data 0.001 (0.269) Loss 2.4948 (2.6308) Prec@1 33.750 (36.311) Prec@5 70.625 (67.005) Epoch: [10][7190/11272] Time 0.749 (1.103) Data 0.002 (0.269) Loss 2.6377 (2.6308) Prec@1 40.625 (36.312) Prec@5 66.875 (67.004) Epoch: [10][7200/11272] Time 0.729 (1.103) Data 0.001 (0.269) Loss 2.4627 (2.6307) Prec@1 32.500 (36.312) Prec@5 71.250 (67.008) Epoch: [10][7210/11272] Time 0.875 (1.103) Data 0.002 (0.268) Loss 2.5410 (2.6308) Prec@1 34.375 (36.310) Prec@5 70.625 (67.008) Epoch: [10][7220/11272] Time 0.953 (1.103) Data 0.078 (0.268) Loss 2.6517 (2.6308) Prec@1 36.250 (36.310) Prec@5 69.375 (67.008) Epoch: [10][7230/11272] Time 0.934 (1.103) Data 0.173 (0.268) Loss 2.4252 (2.6308) Prec@1 39.375 (36.310) Prec@5 70.000 (67.007) Epoch: [10][7240/11272] Time 0.880 (1.103) Data 0.002 (0.268) Loss 2.6224 (2.6309) Prec@1 35.000 (36.309) Prec@5 61.250 (67.006) Epoch: [10][7250/11272] Time 0.893 (1.103) Data 0.002 (0.268) Loss 2.5713 (2.6310) Prec@1 38.750 (36.307) Prec@5 65.625 (67.003) Epoch: [10][7260/11272] Time 0.793 (1.102) Data 0.001 (0.268) Loss 2.4991 (2.6310) Prec@1 43.125 (36.306) Prec@5 68.750 (67.002) Epoch: [10][7270/11272] Time 0.799 (1.102) Data 0.002 (0.268) Loss 2.6244 (2.6310) Prec@1 39.375 (36.307) Prec@5 65.000 (67.003) Epoch: [10][7280/11272] Time 0.872 (1.102) Data 0.001 (0.268) Loss 2.6144 (2.6309) Prec@1 33.125 (36.308) Prec@5 70.000 (67.005) Epoch: [10][7290/11272] Time 0.960 (1.102) Data 0.002 (0.267) Loss 2.6384 (2.6309) Prec@1 35.000 (36.308) Prec@5 68.750 (67.006) Epoch: [10][7300/11272] Time 0.753 (1.102) Data 0.002 (0.267) Loss 2.8135 (2.6309) Prec@1 27.500 (36.306) Prec@5 66.250 (67.006) Epoch: [10][7310/11272] Time 0.999 (1.101) Data 0.215 (0.267) Loss 2.5288 (2.6308) Prec@1 38.125 (36.307) Prec@5 66.250 (67.008) Epoch: [10][7320/11272] Time 0.866 (1.102) Data 0.001 (0.267) Loss 2.8050 (2.6308) Prec@1 31.250 (36.306) Prec@5 62.500 (67.009) Epoch: [10][7330/11272] Time 0.901 (1.101) Data 0.002 (0.267) Loss 2.6967 (2.6309) Prec@1 35.625 (36.302) Prec@5 61.875 (67.006) Epoch: [10][7340/11272] Time 2.316 (1.101) Data 1.524 (0.267) Loss 2.6082 (2.6308) Prec@1 36.875 (36.301) Prec@5 66.250 (67.007) Epoch: [10][7350/11272] Time 0.743 (1.101) Data 0.001 (0.267) Loss 2.2803 (2.6309) Prec@1 43.750 (36.302) Prec@5 71.875 (67.007) Epoch: [10][7360/11272] Time 0.886 (1.101) Data 0.001 (0.267) Loss 2.7024 (2.6308) Prec@1 40.625 (36.303) Prec@5 70.000 (67.009) Epoch: [10][7370/11272] Time 0.766 (1.101) Data 0.004 (0.266) Loss 2.5337 (2.6308) Prec@1 38.750 (36.303) Prec@5 67.500 (67.009) Epoch: [10][7380/11272] Time 1.663 (1.101) Data 0.908 (0.266) Loss 2.5413 (2.6308) Prec@1 42.500 (36.303) Prec@5 68.750 (67.010) Epoch: [10][7390/11272] Time 1.516 (1.101) Data 0.616 (0.266) Loss 2.8917 (2.6309) Prec@1 34.375 (36.302) Prec@5 64.375 (67.011) Epoch: [10][7400/11272] Time 1.196 (1.100) Data 0.296 (0.266) Loss 2.5121 (2.6309) Prec@1 41.250 (36.301) Prec@5 66.875 (67.010) Epoch: [10][7410/11272] Time 0.742 (1.100) Data 0.002 (0.266) Loss 2.4943 (2.6309) Prec@1 36.875 (36.300) Prec@5 70.000 (67.010) Epoch: [10][7420/11272] Time 1.259 (1.100) Data 0.447 (0.266) Loss 2.6305 (2.6309) Prec@1 36.250 (36.300) Prec@5 69.375 (67.011) Epoch: [10][7430/11272] Time 0.852 (1.100) Data 0.002 (0.266) Loss 2.8416 (2.6309) Prec@1 33.125 (36.300) Prec@5 59.375 (67.010) Epoch: [10][7440/11272] Time 1.808 (1.100) Data 0.873 (0.266) Loss 2.7207 (2.6310) Prec@1 36.250 (36.299) Prec@5 66.875 (67.009) Epoch: [10][7450/11272] Time 0.771 (1.100) Data 0.002 (0.266) Loss 2.4971 (2.6310) Prec@1 35.000 (36.297) Prec@5 74.375 (67.009) Epoch: [10][7460/11272] Time 1.622 (1.100) Data 0.808 (0.265) Loss 2.6360 (2.6310) Prec@1 38.125 (36.296) Prec@5 68.750 (67.009) Epoch: [10][7470/11272] Time 0.866 (1.099) Data 0.002 (0.265) Loss 2.8615 (2.6311) Prec@1 31.250 (36.296) Prec@5 66.875 (67.009) Epoch: [10][7480/11272] Time 0.865 (1.099) Data 0.002 (0.265) Loss 2.5745 (2.6311) Prec@1 31.875 (36.297) Prec@5 71.875 (67.010) Epoch: [10][7490/11272] Time 0.763 (1.099) Data 0.002 (0.265) Loss 2.6767 (2.6311) Prec@1 34.375 (36.297) Prec@5 66.250 (67.009) Epoch: [10][7500/11272] Time 0.856 (1.099) Data 0.002 (0.265) Loss 2.6323 (2.6311) Prec@1 37.500 (36.299) Prec@5 67.500 (67.008) Epoch: [10][7510/11272] Time 0.890 (1.099) Data 0.001 (0.265) Loss 2.3598 (2.6311) Prec@1 41.875 (36.302) Prec@5 71.250 (67.008) Epoch: [10][7520/11272] Time 0.794 (1.099) Data 0.001 (0.265) Loss 2.6825 (2.6310) Prec@1 36.250 (36.301) Prec@5 66.250 (67.008) Epoch: [10][7530/11272] Time 0.807 (1.098) Data 0.030 (0.264) Loss 2.5720 (2.6310) Prec@1 40.000 (36.303) Prec@5 68.750 (67.010) Epoch: [10][7540/11272] Time 1.923 (1.098) Data 1.013 (0.264) Loss 2.8342 (2.6310) Prec@1 28.125 (36.300) Prec@5 62.500 (67.008) Epoch: [10][7550/11272] Time 1.075 (1.098) Data 0.167 (0.264) Loss 2.6951 (2.6311) Prec@1 37.500 (36.301) Prec@5 65.000 (67.004) Epoch: [10][7560/11272] Time 0.739 (1.098) Data 0.002 (0.264) Loss 2.4974 (2.6312) Prec@1 44.375 (36.299) Prec@5 66.875 (67.004) Epoch: [10][7570/11272] Time 0.773 (1.098) Data 0.002 (0.264) Loss 2.4819 (2.6313) Prec@1 40.000 (36.296) Prec@5 71.250 (67.005) Epoch: [10][7580/11272] Time 0.860 (1.098) Data 0.001 (0.264) Loss 2.8718 (2.6313) Prec@1 31.875 (36.294) Prec@5 58.750 (67.003) Epoch: [10][7590/11272] Time 0.849 (1.098) Data 0.001 (0.264) Loss 2.8025 (2.6313) Prec@1 34.375 (36.296) Prec@5 65.000 (67.004) Epoch: [10][7600/11272] Time 0.742 (1.097) Data 0.001 (0.263) Loss 2.6676 (2.6314) Prec@1 31.875 (36.292) Prec@5 66.875 (67.003) Epoch: [10][7610/11272] Time 0.739 (1.097) Data 0.002 (0.263) Loss 2.6723 (2.6314) Prec@1 35.625 (36.294) Prec@5 63.125 (67.001) Epoch: [10][7620/11272] Time 0.859 (1.097) Data 0.002 (0.263) Loss 2.7136 (2.6313) Prec@1 36.250 (36.294) Prec@5 66.875 (67.002) Epoch: [10][7630/11272] Time 0.738 (1.097) Data 0.003 (0.263) Loss 2.4691 (2.6312) Prec@1 38.125 (36.295) Prec@5 70.625 (67.002) Epoch: [10][7640/11272] Time 0.762 (1.097) Data 0.002 (0.263) Loss 2.6096 (2.6312) Prec@1 36.875 (36.296) Prec@5 65.625 (67.003) Epoch: [10][7650/11272] Time 1.335 (1.096) Data 0.457 (0.263) Loss 2.4359 (2.6313) Prec@1 41.250 (36.295) Prec@5 69.375 (67.002) Epoch: [10][7660/11272] Time 1.025 (1.096) Data 0.172 (0.263) Loss 2.5469 (2.6312) Prec@1 35.625 (36.295) Prec@5 70.000 (67.003) Epoch: [10][7670/11272] Time 1.748 (1.096) Data 0.974 (0.263) Loss 2.6845 (2.6313) Prec@1 34.375 (36.294) Prec@5 64.375 (67.000) Epoch: [10][7680/11272] Time 0.748 (1.096) Data 0.002 (0.262) Loss 2.6954 (2.6313) Prec@1 34.375 (36.295) Prec@5 66.875 (66.999) Epoch: [10][7690/11272] Time 0.858 (1.096) Data 0.001 (0.262) Loss 2.8781 (2.6314) Prec@1 37.500 (36.295) Prec@5 62.500 (66.997) Epoch: [10][7700/11272] Time 0.875 (1.096) Data 0.002 (0.262) Loss 2.7064 (2.6312) Prec@1 38.125 (36.299) Prec@5 65.625 (66.999) Epoch: [10][7710/11272] Time 0.772 (1.095) Data 0.006 (0.262) Loss 2.6962 (2.6313) Prec@1 40.000 (36.298) Prec@5 70.000 (66.999) Epoch: [10][7720/11272] Time 0.957 (1.095) Data 0.181 (0.262) Loss 2.3654 (2.6313) Prec@1 40.000 (36.300) Prec@5 74.375 (67.000) Epoch: [10][7730/11272] Time 0.868 (1.095) Data 0.001 (0.262) Loss 2.7943 (2.6312) Prec@1 36.250 (36.301) Prec@5 60.000 (67.002) Epoch: [10][7740/11272] Time 1.326 (1.095) Data 0.446 (0.262) Loss 2.4153 (2.6311) Prec@1 41.875 (36.304) Prec@5 70.625 (67.005) Epoch: [10][7750/11272] Time 0.735 (1.095) Data 0.002 (0.261) Loss 2.3058 (2.6311) Prec@1 49.375 (36.305) Prec@5 72.500 (67.004) Epoch: [10][7760/11272] Time 0.845 (1.095) Data 0.001 (0.261) Loss 2.4920 (2.6311) Prec@1 37.500 (36.304) Prec@5 72.500 (67.004) Epoch: [10][7770/11272] Time 0.860 (1.094) Data 0.001 (0.261) Loss 2.7319 (2.6311) Prec@1 36.250 (36.303) Prec@5 60.625 (67.002) Epoch: [10][7780/11272] Time 1.086 (1.094) Data 0.331 (0.261) Loss 2.3166 (2.6311) Prec@1 40.000 (36.306) Prec@5 69.375 (67.002) Epoch: [10][7790/11272] Time 0.742 (1.094) Data 0.002 (0.261) Loss 2.6948 (2.6310) Prec@1 38.750 (36.305) Prec@5 69.375 (67.004) Epoch: [10][7800/11272] Time 0.856 (1.094) Data 0.001 (0.261) Loss 2.7868 (2.6311) Prec@1 33.750 (36.303) Prec@5 61.250 (67.002) Epoch: [10][7810/11272] Time 0.835 (1.094) Data 0.001 (0.260) Loss 2.5864 (2.6311) Prec@1 36.250 (36.303) Prec@5 63.750 (67.000) Epoch: [10][7820/11272] Time 0.741 (1.094) Data 0.002 (0.260) Loss 2.7949 (2.6311) Prec@1 31.250 (36.304) Prec@5 68.125 (67.001) Epoch: [10][7830/11272] Time 0.774 (1.093) Data 0.001 (0.260) Loss 2.3189 (2.6311) Prec@1 45.625 (36.305) Prec@5 77.500 (67.004) Epoch: [10][7840/11272] Time 1.791 (1.093) Data 0.910 (0.260) Loss 2.7400 (2.6311) Prec@1 35.625 (36.306) Prec@5 64.375 (67.003) Epoch: [10][7850/11272] Time 0.862 (1.093) Data 0.002 (0.260) Loss 2.7543 (2.6312) Prec@1 32.500 (36.301) Prec@5 65.625 (67.000) Epoch: [10][7860/11272] Time 1.345 (1.093) Data 0.549 (0.260) Loss 2.7593 (2.6312) Prec@1 35.000 (36.300) Prec@5 66.875 (66.999) Epoch: [10][7870/11272] Time 0.741 (1.093) Data 0.002 (0.260) Loss 2.8031 (2.6313) Prec@1 33.125 (36.299) Prec@5 60.000 (66.998) Epoch: [10][7880/11272] Time 1.687 (1.093) Data 0.794 (0.260) Loss 3.0438 (2.6313) Prec@1 31.875 (36.299) Prec@5 57.500 (66.998) Epoch: [10][7890/11272] Time 0.849 (1.092) Data 0.002 (0.259) Loss 2.8026 (2.6314) Prec@1 32.500 (36.298) Prec@5 65.000 (66.998) Epoch: [10][7900/11272] Time 0.742 (1.092) Data 0.001 (0.259) Loss 2.5523 (2.6313) Prec@1 37.500 (36.298) Prec@5 67.500 (67.000) Epoch: [10][7910/11272] Time 0.917 (1.092) Data 0.001 (0.259) Loss 2.3419 (2.6312) Prec@1 40.000 (36.298) Prec@5 75.000 (67.000) Epoch: [10][7920/11272] Time 1.532 (1.092) Data 0.647 (0.259) Loss 2.5540 (2.6313) Prec@1 37.500 (36.296) Prec@5 71.250 (67.000) Epoch: [10][7930/11272] Time 1.036 (1.092) Data 0.239 (0.259) Loss 2.8653 (2.6313) Prec@1 32.500 (36.296) Prec@5 61.250 (67.000) Epoch: [10][7940/11272] Time 0.747 (1.091) Data 0.001 (0.259) Loss 2.5235 (2.6312) Prec@1 34.375 (36.296) Prec@5 69.375 (67.000) Epoch: [10][7950/11272] Time 0.878 (1.091) Data 0.002 (0.259) Loss 2.8041 (2.6312) Prec@1 31.250 (36.297) Prec@5 64.375 (67.001) Epoch: [10][7960/11272] Time 0.840 (1.091) Data 0.001 (0.258) Loss 2.6508 (2.6312) Prec@1 37.500 (36.297) Prec@5 68.750 (67.001) Epoch: [10][7970/11272] Time 0.741 (1.091) Data 0.001 (0.258) Loss 2.9568 (2.6312) Prec@1 29.375 (36.297) Prec@5 54.375 (67.000) Epoch: [10][7980/11272] Time 0.737 (1.091) Data 0.002 (0.258) Loss 2.3641 (2.6311) Prec@1 43.125 (36.298) Prec@5 70.000 (67.001) Epoch: [10][7990/11272] Time 1.825 (1.091) Data 0.954 (0.258) Loss 2.6181 (2.6311) Prec@1 39.375 (36.296) Prec@5 65.000 (67.000) Epoch: [10][8000/11272] Time 0.838 (1.090) Data 0.002 (0.258) Loss 2.8644 (2.6312) Prec@1 32.500 (36.294) Prec@5 60.625 (66.998) Epoch: [10][8010/11272] Time 0.725 (1.090) Data 0.001 (0.258) Loss 2.6149 (2.6312) Prec@1 32.500 (36.293) Prec@5 68.125 (66.997) Epoch: [10][8020/11272] Time 0.734 (1.090) Data 0.001 (0.258) Loss 2.5987 (2.6312) Prec@1 35.000 (36.290) Prec@5 65.000 (66.997) Epoch: [10][8030/11272] Time 0.862 (1.090) Data 0.001 (0.257) Loss 2.5618 (2.6312) Prec@1 38.750 (36.291) Prec@5 70.000 (66.997) Epoch: [10][8040/11272] Time 0.737 (1.090) Data 0.001 (0.257) Loss 2.9369 (2.6312) Prec@1 26.250 (36.290) Prec@5 56.250 (66.995) Epoch: [10][8050/11272] Time 0.745 (1.089) Data 0.002 (0.257) Loss 2.8096 (2.6313) Prec@1 32.500 (36.290) Prec@5 59.375 (66.993) Epoch: [10][8060/11272] Time 0.841 (1.089) Data 0.002 (0.257) Loss 2.6304 (2.6312) Prec@1 38.125 (36.292) Prec@5 66.250 (66.992) Epoch: [10][8070/11272] Time 1.026 (1.089) Data 0.196 (0.257) Loss 2.4696 (2.6312) Prec@1 40.000 (36.292) Prec@5 72.500 (66.994) Epoch: [10][8080/11272] Time 0.739 (1.089) Data 0.001 (0.257) Loss 2.4987 (2.6311) Prec@1 36.250 (36.292) Prec@5 68.750 (66.995) Epoch: [10][8090/11272] Time 1.171 (1.089) Data 0.439 (0.257) Loss 2.7463 (2.6311) Prec@1 33.125 (36.292) Prec@5 64.375 (66.996) Epoch: [10][8100/11272] Time 0.853 (1.089) Data 0.002 (0.256) Loss 2.9859 (2.6312) Prec@1 26.875 (36.290) Prec@5 61.875 (66.993) Epoch: [10][8110/11272] Time 0.857 (1.088) Data 0.002 (0.256) Loss 2.5679 (2.6312) Prec@1 38.750 (36.290) Prec@5 66.875 (66.992) Epoch: [10][8120/11272] Time 0.748 (1.088) Data 0.001 (0.256) Loss 2.7441 (2.6312) Prec@1 35.625 (36.289) Prec@5 67.500 (66.992) Epoch: [10][8130/11272] Time 1.635 (1.088) Data 0.865 (0.256) Loss 2.5004 (2.6312) Prec@1 39.375 (36.290) Prec@5 70.625 (66.994) Epoch: [10][8140/11272] Time 0.859 (1.088) Data 0.002 (0.256) Loss 2.4225 (2.6311) Prec@1 42.500 (36.291) Prec@5 68.750 (66.994) Epoch: [10][8150/11272] Time 2.071 (1.088) Data 1.200 (0.256) Loss 2.3328 (2.6311) Prec@1 41.250 (36.292) Prec@5 74.375 (66.993) Epoch: [10][8160/11272] Time 0.741 (1.088) Data 0.001 (0.256) Loss 2.5009 (2.6312) Prec@1 36.250 (36.291) Prec@5 72.500 (66.992) Epoch: [10][8170/11272] Time 0.821 (1.087) Data 0.001 (0.256) Loss 2.6360 (2.6313) Prec@1 35.000 (36.288) Prec@5 66.875 (66.990) Epoch: [10][8180/11272] Time 0.819 (1.087) Data 0.002 (0.256) Loss 2.4657 (2.6313) Prec@1 35.000 (36.286) Prec@5 70.000 (66.989) Epoch: [10][8190/11272] Time 1.546 (1.087) Data 0.800 (0.255) Loss 2.8944 (2.6314) Prec@1 30.000 (36.287) Prec@5 66.250 (66.990) Epoch: [10][8200/11272] Time 0.754 (1.087) Data 0.002 (0.255) Loss 2.4960 (2.6313) Prec@1 41.250 (36.286) Prec@5 68.750 (66.991) Epoch: [10][8210/11272] Time 1.167 (1.087) Data 0.267 (0.255) Loss 2.9327 (2.6313) Prec@1 30.000 (36.287) Prec@5 63.125 (66.992) Epoch: [10][8220/11272] Time 0.843 (1.087) Data 0.002 (0.255) Loss 2.8186 (2.6314) Prec@1 28.125 (36.284) Prec@5 68.750 (66.989) Epoch: [10][8230/11272] Time 0.720 (1.086) Data 0.001 (0.255) Loss 2.6314 (2.6315) Prec@1 35.000 (36.283) Prec@5 64.375 (66.989) Epoch: [10][8240/11272] Time 0.733 (1.086) Data 0.001 (0.255) Loss 2.4353 (2.6315) Prec@1 36.875 (36.283) Prec@5 68.125 (66.988) Epoch: [10][8250/11272] Time 0.836 (1.086) Data 0.001 (0.255) Loss 2.4581 (2.6315) Prec@1 39.375 (36.280) Prec@5 71.250 (66.988) Epoch: [10][8260/11272] Time 1.550 (1.086) Data 0.682 (0.255) Loss 3.1607 (2.6316) Prec@1 25.000 (36.278) Prec@5 53.750 (66.986) Epoch: [10][8270/11272] Time 0.740 (1.086) Data 0.001 (0.254) Loss 2.1640 (2.6316) Prec@1 49.375 (36.279) Prec@5 71.875 (66.988) Epoch: [10][8280/11272] Time 0.742 (1.086) Data 0.001 (0.254) Loss 2.4301 (2.6315) Prec@1 41.875 (36.280) Prec@5 69.375 (66.988) Epoch: [10][8290/11272] Time 0.855 (1.085) Data 0.001 (0.254) Loss 2.5125 (2.6315) Prec@1 36.875 (36.281) Prec@5 68.750 (66.988) Epoch: [10][8300/11272] Time 0.983 (1.085) Data 0.216 (0.254) Loss 2.8762 (2.6315) Prec@1 31.875 (36.281) Prec@5 60.000 (66.988) Epoch: [10][8310/11272] Time 0.739 (1.085) Data 0.001 (0.254) Loss 2.6580 (2.6315) Prec@1 40.625 (36.282) Prec@5 66.875 (66.989) Epoch: [10][8320/11272] Time 0.835 (1.085) Data 0.001 (0.254) Loss 2.5901 (2.6314) Prec@1 34.375 (36.283) Prec@5 71.250 (66.989) Epoch: [10][8330/11272] Time 0.844 (1.085) Data 0.001 (0.254) Loss 2.7715 (2.6314) Prec@1 36.875 (36.283) Prec@5 60.625 (66.990) Epoch: [10][8340/11272] Time 0.751 (1.085) Data 0.002 (0.253) Loss 2.5116 (2.6315) Prec@1 37.500 (36.281) Prec@5 67.500 (66.989) Epoch: [10][8350/11272] Time 2.205 (1.085) Data 1.403 (0.254) Loss 2.5569 (2.6313) Prec@1 33.125 (36.283) Prec@5 68.125 (66.991) Epoch: [10][8360/11272] Time 0.869 (1.084) Data 0.002 (0.253) Loss 2.6781 (2.6314) Prec@1 33.125 (36.283) Prec@5 63.125 (66.991) Epoch: [10][8370/11272] Time 0.857 (1.084) Data 0.013 (0.253) Loss 2.8353 (2.6314) Prec@1 32.500 (36.284) Prec@5 63.750 (66.992) Epoch: [10][8380/11272] Time 0.749 (1.084) Data 0.001 (0.253) Loss 2.3878 (2.6314) Prec@1 44.375 (36.284) Prec@5 66.875 (66.991) Epoch: [10][8390/11272] Time 1.600 (1.084) Data 0.829 (0.253) Loss 2.4112 (2.6314) Prec@1 36.875 (36.283) Prec@5 72.500 (66.991) Epoch: [10][8400/11272] Time 0.976 (1.084) Data 0.127 (0.253) Loss 2.7918 (2.6314) Prec@1 34.375 (36.283) Prec@5 61.875 (66.991) Epoch: [10][8410/11272] Time 0.838 (1.084) Data 0.002 (0.253) Loss 2.5587 (2.6314) Prec@1 39.375 (36.283) Prec@5 63.750 (66.992) Epoch: [10][8420/11272] Time 1.484 (1.084) Data 0.716 (0.253) Loss 2.4429 (2.6314) Prec@1 44.375 (36.283) Prec@5 72.500 (66.992) Epoch: [10][8430/11272] Time 0.859 (1.083) Data 0.001 (0.252) Loss 2.5357 (2.6313) Prec@1 33.750 (36.284) Prec@5 70.000 (66.993) Epoch: [10][8440/11272] Time 0.825 (1.083) Data 0.001 (0.252) Loss 2.4451 (2.6313) Prec@1 40.625 (36.283) Prec@5 68.750 (66.994) Epoch: [10][8450/11272] Time 0.733 (1.083) Data 0.001 (0.252) Loss 2.4560 (2.6312) Prec@1 43.125 (36.284) Prec@5 70.000 (66.996) Epoch: [10][8460/11272] Time 0.751 (1.083) Data 0.001 (0.252) Loss 2.5413 (2.6312) Prec@1 36.875 (36.284) Prec@5 70.000 (66.996) Epoch: [10][8470/11272] Time 0.860 (1.083) Data 0.001 (0.252) Loss 2.6437 (2.6311) Prec@1 38.750 (36.286) Prec@5 66.875 (66.995) Epoch: [10][8480/11272] Time 1.229 (1.082) Data 0.353 (0.252) Loss 2.5722 (2.6311) Prec@1 36.250 (36.286) Prec@5 71.250 (66.995) Epoch: [10][8490/11272] Time 0.769 (1.082) Data 0.001 (0.251) Loss 2.8911 (2.6311) Prec@1 35.625 (36.286) Prec@5 60.000 (66.995) Epoch: [10][8500/11272] Time 1.260 (1.082) Data 0.494 (0.251) Loss 2.6071 (2.6310) Prec@1 31.250 (36.287) Prec@5 67.500 (66.997) Epoch: [10][8510/11272] Time 0.846 (1.082) Data 0.001 (0.251) Loss 2.8936 (2.6311) Prec@1 29.375 (36.286) Prec@5 63.125 (66.997) Epoch: [10][8520/11272] Time 0.842 (1.082) Data 0.002 (0.251) Loss 2.6847 (2.6310) Prec@1 33.750 (36.287) Prec@5 64.375 (67.000) Epoch: [10][8530/11272] Time 0.763 (1.082) Data 0.002 (0.251) Loss 2.4329 (2.6309) Prec@1 38.125 (36.288) Prec@5 71.250 (67.001) Epoch: [10][8540/11272] Time 1.586 (1.081) Data 0.874 (0.251) Loss 2.5617 (2.6309) Prec@1 34.375 (36.287) Prec@5 63.750 (67.002) Epoch: [10][8550/11272] Time 0.842 (1.081) Data 0.001 (0.251) Loss 2.5447 (2.6309) Prec@1 40.625 (36.285) Prec@5 69.375 (67.001) Epoch: [10][8560/11272] Time 0.739 (1.081) Data 0.022 (0.250) Loss 2.6743 (2.6309) Prec@1 38.750 (36.286) Prec@5 66.250 (67.000) Epoch: [10][8570/11272] Time 0.742 (1.081) Data 0.002 (0.250) Loss 2.6554 (2.6309) Prec@1 33.750 (36.287) Prec@5 67.500 (67.001) Epoch: [10][8580/11272] Time 2.174 (1.081) Data 1.312 (0.250) Loss 2.4608 (2.6308) Prec@1 31.875 (36.288) Prec@5 73.125 (67.003) Epoch: [10][8590/11272] Time 0.821 (1.080) Data 0.001 (0.250) Loss 2.6535 (2.6307) Prec@1 32.500 (36.288) Prec@5 67.500 (67.003) Epoch: [10][8600/11272] Time 0.741 (1.080) Data 0.001 (0.250) Loss 2.6146 (2.6308) Prec@1 36.250 (36.286) Prec@5 65.000 (67.002) Epoch: [10][8610/11272] Time 0.742 (1.080) Data 0.001 (0.250) Loss 2.7083 (2.6308) Prec@1 34.375 (36.285) Prec@5 66.875 (67.001) Epoch: [10][8620/11272] Time 0.894 (1.080) Data 0.052 (0.250) Loss 2.7456 (2.6309) Prec@1 32.500 (36.283) Prec@5 63.750 (67.000) Epoch: [10][8630/11272] Time 0.851 (1.080) Data 0.001 (0.249) Loss 2.5101 (2.6309) Prec@1 40.625 (36.282) Prec@5 67.500 (67.000) Epoch: [10][8640/11272] Time 1.414 (1.080) Data 0.627 (0.249) Loss 2.7613 (2.6309) Prec@1 32.500 (36.282) Prec@5 65.625 (67.000) Epoch: [10][8650/11272] Time 0.740 (1.079) Data 0.002 (0.249) Loss 2.6753 (2.6309) Prec@1 33.750 (36.281) Prec@5 61.875 (67.000) Epoch: [10][8660/11272] Time 0.843 (1.079) Data 0.001 (0.249) Loss 2.3302 (2.6308) Prec@1 41.250 (36.283) Prec@5 71.875 (67.002) Epoch: [10][8670/11272] Time 0.871 (1.079) Data 0.001 (0.249) Loss 2.8018 (2.6309) Prec@1 33.750 (36.282) Prec@5 61.250 (67.001) Epoch: [10][8680/11272] Time 0.747 (1.079) Data 0.001 (0.249) Loss 2.5767 (2.6309) Prec@1 33.125 (36.282) Prec@5 69.375 (66.999) Epoch: [10][8690/11272] Time 0.825 (1.079) Data 0.002 (0.249) Loss 2.9129 (2.6309) Prec@1 29.375 (36.281) Prec@5 58.750 (66.999) Epoch: [10][8700/11272] Time 0.879 (1.079) Data 0.001 (0.248) Loss 2.6765 (2.6310) Prec@1 33.125 (36.279) Prec@5 64.375 (66.998) Epoch: [10][8710/11272] Time 0.752 (1.078) Data 0.001 (0.248) Loss 2.7506 (2.6309) Prec@1 28.750 (36.278) Prec@5 65.625 (67.000) Epoch: [10][8720/11272] Time 0.741 (1.078) Data 0.001 (0.248) Loss 2.3810 (2.6310) Prec@1 41.250 (36.277) Prec@5 72.500 (67.000) Epoch: [10][8730/11272] Time 0.894 (1.078) Data 0.002 (0.248) Loss 2.4486 (2.6310) Prec@1 41.250 (36.277) Prec@5 67.500 (66.998) Epoch: [10][8740/11272] Time 0.845 (1.078) Data 0.001 (0.248) Loss 2.7955 (2.6310) Prec@1 37.500 (36.276) Prec@5 60.625 (66.999) Epoch: [10][8750/11272] Time 2.447 (1.078) Data 1.667 (0.248) Loss 2.4671 (2.6310) Prec@1 35.625 (36.276) Prec@5 72.500 (67.001) Epoch: [10][8760/11272] Time 0.727 (1.078) Data 0.002 (0.248) Loss 2.7271 (2.6310) Prec@1 34.375 (36.275) Prec@5 65.625 (67.002) Epoch: [10][8770/11272] Time 0.823 (1.077) Data 0.001 (0.247) Loss 2.5017 (2.6310) Prec@1 37.500 (36.276) Prec@5 72.500 (67.004) Epoch: [10][8780/11272] Time 0.836 (1.077) Data 0.001 (0.247) Loss 2.6693 (2.6310) Prec@1 33.125 (36.275) Prec@5 68.750 (67.004) Epoch: [10][8790/11272] Time 1.498 (1.077) Data 0.734 (0.247) Loss 2.7386 (2.6310) Prec@1 32.500 (36.274) Prec@5 68.750 (67.004) Epoch: [10][8800/11272] Time 0.784 (1.077) Data 0.002 (0.247) Loss 2.5947 (2.6310) Prec@1 38.125 (36.276) Prec@5 68.125 (67.005) Epoch: [10][8810/11272] Time 0.822 (1.077) Data 0.001 (0.247) Loss 2.7086 (2.6310) Prec@1 37.500 (36.276) Prec@5 65.625 (67.004) Epoch: [10][8820/11272] Time 0.857 (1.076) Data 0.002 (0.247) Loss 2.8590 (2.6311) Prec@1 33.750 (36.276) Prec@5 63.750 (67.003) Epoch: [10][8830/11272] Time 1.188 (1.076) Data 0.422 (0.246) Loss 2.6647 (2.6311) Prec@1 38.750 (36.275) Prec@5 69.375 (67.002) Epoch: [10][8840/11272] Time 0.843 (1.076) Data 0.001 (0.246) Loss 2.8580 (2.6311) Prec@1 34.375 (36.274) Prec@5 63.125 (67.003) Epoch: [10][8850/11272] Time 0.850 (1.076) Data 0.001 (0.246) Loss 2.4916 (2.6310) Prec@1 36.875 (36.275) Prec@5 68.750 (67.002) Epoch: [10][8860/11272] Time 0.742 (1.076) Data 0.001 (0.246) Loss 2.8689 (2.6311) Prec@1 36.875 (36.275) Prec@5 60.625 (67.001) Epoch: [10][8870/11272] Time 0.745 (1.075) Data 0.002 (0.246) Loss 2.6328 (2.6311) Prec@1 33.750 (36.272) Prec@5 70.000 (67.002) Epoch: [10][8880/11272] Time 0.865 (1.075) Data 0.002 (0.246) Loss 2.5879 (2.6312) Prec@1 36.250 (36.271) Prec@5 66.250 (66.999) Epoch: [10][8890/11272] Time 1.285 (1.075) Data 0.389 (0.246) Loss 2.7315 (2.6312) Prec@1 34.375 (36.270) Prec@5 63.125 (67.000) Epoch: [10][8900/11272] Time 0.754 (1.075) Data 0.002 (0.245) Loss 2.7105 (2.6312) Prec@1 36.875 (36.269) Prec@5 63.750 (67.000) Epoch: [10][8910/11272] Time 0.755 (1.075) Data 0.002 (0.245) Loss 2.5921 (2.6313) Prec@1 39.375 (36.268) Prec@5 65.625 (66.998) Epoch: [10][8920/11272] Time 0.857 (1.074) Data 0.001 (0.245) Loss 2.7374 (2.6314) Prec@1 35.000 (36.266) Prec@5 65.000 (66.996) Epoch: [10][8930/11272] Time 0.849 (1.074) Data 0.001 (0.245) Loss 2.3799 (2.6314) Prec@1 43.125 (36.267) Prec@5 70.625 (66.996) Epoch: [10][8940/11272] Time 0.745 (1.074) Data 0.002 (0.245) Loss 2.5681 (2.6314) Prec@1 35.000 (36.266) Prec@5 66.875 (66.995) Epoch: [10][8950/11272] Time 1.726 (1.074) Data 0.950 (0.245) Loss 2.6113 (2.6314) Prec@1 34.375 (36.266) Prec@5 70.000 (66.994) Epoch: [10][8960/11272] Time 0.856 (1.074) Data 0.001 (0.244) Loss 2.5603 (2.6315) Prec@1 35.000 (36.263) Prec@5 69.375 (66.994) Epoch: [10][8970/11272] Time 0.755 (1.074) Data 0.001 (0.244) Loss 2.6039 (2.6314) Prec@1 38.125 (36.265) Prec@5 69.375 (66.994) Epoch: [10][8980/11272] Time 0.770 (1.073) Data 0.001 (0.244) Loss 2.5723 (2.6315) Prec@1 37.500 (36.263) Prec@5 70.625 (66.994) Epoch: [10][8990/11272] Time 0.885 (1.073) Data 0.001 (0.244) Loss 2.5391 (2.6314) Prec@1 41.875 (36.266) Prec@5 68.125 (66.996) Epoch: [10][9000/11272] Time 0.881 (1.073) Data 0.003 (0.244) Loss 2.7929 (2.6314) Prec@1 33.125 (36.264) Prec@5 65.000 (66.995) Epoch: [10][9010/11272] Time 1.301 (1.073) Data 0.548 (0.244) Loss 2.8090 (2.6313) Prec@1 33.750 (36.266) Prec@5 63.750 (66.996) Epoch: [10][9020/11272] Time 0.756 (1.073) Data 0.002 (0.243) Loss 2.6361 (2.6313) Prec@1 38.125 (36.266) Prec@5 66.875 (66.996) Epoch: [10][9030/11272] Time 0.841 (1.072) Data 0.001 (0.243) Loss 2.6446 (2.6314) Prec@1 33.750 (36.265) Prec@5 70.000 (66.996) Epoch: [10][9040/11272] Time 0.845 (1.072) Data 0.002 (0.243) Loss 2.6794 (2.6313) Prec@1 36.875 (36.266) Prec@5 64.375 (66.996) Epoch: [10][9050/11272] Time 0.770 (1.072) Data 0.002 (0.243) Loss 2.5659 (2.6314) Prec@1 38.125 (36.266) Prec@5 65.000 (66.994) Epoch: [10][9060/11272] Time 0.730 (1.072) Data 0.002 (0.243) Loss 2.8574 (2.6314) Prec@1 28.750 (36.266) Prec@5 61.875 (66.992) Epoch: [10][9070/11272] Time 0.846 (1.072) Data 0.001 (0.243) Loss 2.5863 (2.6314) Prec@1 38.125 (36.266) Prec@5 68.125 (66.993) Epoch: [10][9080/11272] Time 0.888 (1.071) Data 0.004 (0.242) Loss 2.6396 (2.6314) Prec@1 36.250 (36.265) Prec@5 65.625 (66.992) Epoch: [10][9090/11272] Time 1.095 (1.071) Data 0.331 (0.242) Loss 2.9003 (2.6314) Prec@1 33.750 (36.265) Prec@5 60.000 (66.991) Epoch: [10][9100/11272] Time 0.814 (1.071) Data 0.001 (0.242) Loss 2.6329 (2.6314) Prec@1 35.625 (36.267) Prec@5 70.000 (66.993) Epoch: [10][9110/11272] Time 2.580 (1.071) Data 1.700 (0.243) Loss 2.6487 (2.6314) Prec@1 36.875 (36.268) Prec@5 65.625 (66.993) Epoch: [10][9120/11272] Time 0.747 (1.071) Data 0.002 (0.242) Loss 2.6343 (2.6313) Prec@1 38.750 (36.268) Prec@5 65.625 (66.994) Epoch: [10][9130/11272] Time 1.738 (1.071) Data 0.968 (0.242) Loss 2.6465 (2.6313) Prec@1 33.750 (36.267) Prec@5 66.875 (66.993) Epoch: [10][9140/11272] Time 0.859 (1.071) Data 0.001 (0.242) Loss 2.8236 (2.6315) Prec@1 38.125 (36.266) Prec@5 61.875 (66.990) Epoch: [10][9150/11272] Time 1.992 (1.071) Data 1.056 (0.242) Loss 2.5603 (2.6315) Prec@1 36.875 (36.266) Prec@5 68.750 (66.989) Epoch: [10][9160/11272] Time 0.748 (1.071) Data 0.001 (0.242) Loss 2.5271 (2.6314) Prec@1 36.875 (36.267) Prec@5 70.000 (66.991) Epoch: [10][9170/11272] Time 0.736 (1.071) Data 0.001 (0.242) Loss 2.4951 (2.6313) Prec@1 33.125 (36.267) Prec@5 71.250 (66.992) Epoch: [10][9180/11272] Time 0.855 (1.070) Data 0.001 (0.242) Loss 2.3975 (2.6312) Prec@1 41.250 (36.267) Prec@5 68.750 (66.994) Epoch: [10][9190/11272] Time 1.622 (1.070) Data 0.759 (0.242) Loss 2.8435 (2.6313) Prec@1 36.875 (36.267) Prec@5 68.125 (66.995) Epoch: [10][9200/11272] Time 0.739 (1.070) Data 0.002 (0.241) Loss 2.6471 (2.6313) Prec@1 30.625 (36.268) Prec@5 68.125 (66.996) Epoch: [10][9210/11272] Time 0.990 (1.070) Data 0.221 (0.241) Loss 2.6985 (2.6313) Prec@1 35.625 (36.267) Prec@5 66.875 (66.995) Epoch: [10][9220/11272] Time 0.845 (1.070) Data 0.002 (0.241) Loss 2.8276 (2.6313) Prec@1 34.375 (36.266) Prec@5 61.875 (66.994) Epoch: [10][9230/11272] Time 0.741 (1.069) Data 0.003 (0.241) Loss 2.6914 (2.6313) Prec@1 32.500 (36.266) Prec@5 68.125 (66.994) Epoch: [10][9240/11272] Time 0.746 (1.069) Data 0.002 (0.241) Loss 2.5942 (2.6313) Prec@1 38.750 (36.267) Prec@5 64.375 (66.994) Epoch: [10][9250/11272] Time 0.966 (1.069) Data 0.033 (0.240) Loss 2.5387 (2.6313) Prec@1 41.250 (36.269) Prec@5 66.875 (66.996) Epoch: [10][9260/11272] Time 0.848 (1.069) Data 0.002 (0.240) Loss 2.6841 (2.6313) Prec@1 35.000 (36.268) Prec@5 73.125 (66.995) Epoch: [10][9270/11272] Time 0.747 (1.069) Data 0.001 (0.240) Loss 2.4495 (2.6313) Prec@1 35.625 (36.270) Prec@5 73.750 (66.997) Epoch: [10][9280/11272] Time 0.736 (1.068) Data 0.001 (0.240) Loss 2.4672 (2.6313) Prec@1 30.000 (36.269) Prec@5 69.375 (66.999) Epoch: [10][9290/11272] Time 0.845 (1.068) Data 0.001 (0.240) Loss 2.5578 (2.6313) Prec@1 37.500 (36.268) Prec@5 70.000 (66.997) Epoch: [10][9300/11272] Time 0.859 (1.068) Data 0.001 (0.240) Loss 2.6733 (2.6314) Prec@1 33.125 (36.268) Prec@5 68.125 (66.998) Epoch: [10][9310/11272] Time 0.776 (1.068) Data 0.001 (0.239) Loss 2.7075 (2.6315) Prec@1 38.125 (36.266) Prec@5 65.000 (66.995) Epoch: [10][9320/11272] Time 0.747 (1.068) Data 0.002 (0.239) Loss 2.7651 (2.6315) Prec@1 40.625 (36.265) Prec@5 64.375 (66.994) Epoch: [10][9330/11272] Time 0.864 (1.067) Data 0.002 (0.239) Loss 2.7805 (2.6316) Prec@1 31.250 (36.265) Prec@5 67.500 (66.993) Epoch: [10][9340/11272] Time 1.049 (1.067) Data 0.222 (0.239) Loss 2.4197 (2.6316) Prec@1 43.125 (36.263) Prec@5 71.875 (66.992) Epoch: [10][9350/11272] Time 0.737 (1.067) Data 0.001 (0.239) Loss 2.6417 (2.6316) Prec@1 34.375 (36.263) Prec@5 62.500 (66.990) Epoch: [10][9360/11272] Time 0.853 (1.067) Data 0.001 (0.238) Loss 2.7072 (2.6316) Prec@1 32.500 (36.264) Prec@5 68.750 (66.991) Epoch: [10][9370/11272] Time 0.941 (1.066) Data 0.038 (0.238) Loss 2.2503 (2.6315) Prec@1 40.625 (36.264) Prec@5 71.875 (66.993) Epoch: [10][9380/11272] Time 0.740 (1.066) Data 0.001 (0.238) Loss 2.6343 (2.6315) Prec@1 34.375 (36.262) Prec@5 68.750 (66.992) Epoch: [10][9390/11272] Time 0.730 (1.066) Data 0.001 (0.238) Loss 2.5640 (2.6315) Prec@1 36.875 (36.261) Prec@5 68.125 (66.992) Epoch: [10][9400/11272] Time 0.827 (1.066) Data 0.001 (0.238) Loss 2.7833 (2.6315) Prec@1 31.250 (36.262) Prec@5 61.250 (66.991) Epoch: [10][9410/11272] Time 0.870 (1.066) Data 0.002 (0.238) Loss 2.4015 (2.6314) Prec@1 42.500 (36.264) Prec@5 72.500 (66.993) Epoch: [10][9420/11272] Time 0.748 (1.065) Data 0.001 (0.237) Loss 2.6780 (2.6315) Prec@1 31.250 (36.264) Prec@5 70.625 (66.993) Epoch: [10][9430/11272] Time 0.762 (1.065) Data 0.001 (0.237) Loss 2.6177 (2.6315) Prec@1 37.500 (36.266) Prec@5 65.625 (66.993) Epoch: [10][9440/11272] Time 0.838 (1.065) Data 0.001 (0.237) Loss 2.6908 (2.6315) Prec@1 40.000 (36.267) Prec@5 64.375 (66.992) Epoch: [10][9450/11272] Time 1.760 (1.065) Data 0.949 (0.237) Loss 2.7103 (2.6315) Prec@1 36.250 (36.266) Prec@5 64.375 (66.991) Epoch: [10][9460/11272] Time 0.741 (1.065) Data 0.001 (0.237) Loss 2.8271 (2.6315) Prec@1 33.125 (36.265) Prec@5 63.125 (66.992) Epoch: [10][9470/11272] Time 1.153 (1.064) Data 0.395 (0.237) Loss 2.6065 (2.6315) Prec@1 38.125 (36.265) Prec@5 68.125 (66.993) Epoch: [10][9480/11272] Time 0.846 (1.064) Data 0.002 (0.236) Loss 2.4124 (2.6315) Prec@1 41.250 (36.265) Prec@5 73.125 (66.993) Epoch: [10][9490/11272] Time 1.674 (1.064) Data 0.896 (0.236) Loss 2.3810 (2.6314) Prec@1 39.375 (36.268) Prec@5 71.875 (66.994) Epoch: [10][9500/11272] Time 0.779 (1.064) Data 0.002 (0.236) Loss 2.6160 (2.6314) Prec@1 31.875 (36.270) Prec@5 66.875 (66.996) Epoch: [10][9510/11272] Time 1.091 (1.064) Data 0.159 (0.236) Loss 2.5117 (2.6314) Prec@1 41.875 (36.268) Prec@5 70.625 (66.996) Epoch: [10][9520/11272] Time 0.849 (1.063) Data 0.002 (0.235) Loss 2.8084 (2.6314) Prec@1 31.875 (36.267) Prec@5 63.125 (66.995) Epoch: [10][9530/11272] Time 0.776 (1.063) Data 0.001 (0.235) Loss 2.5443 (2.6315) Prec@1 38.125 (36.267) Prec@5 68.750 (66.995) Epoch: [10][9540/11272] Time 0.759 (1.063) Data 0.001 (0.235) Loss 2.4207 (2.6315) Prec@1 39.375 (36.266) Prec@5 70.625 (66.997) Epoch: [10][9550/11272] Time 0.853 (1.063) Data 0.001 (0.235) Loss 2.8128 (2.6315) Prec@1 37.500 (36.266) Prec@5 67.500 (66.995) Epoch: [10][9560/11272] Time 0.812 (1.062) Data 0.001 (0.235) Loss 2.7346 (2.6316) Prec@1 38.750 (36.266) Prec@5 65.000 (66.995) Epoch: [10][9570/11272] Time 1.260 (1.062) Data 0.464 (0.235) Loss 2.7310 (2.6316) Prec@1 36.875 (36.267) Prec@5 62.500 (66.992) Epoch: [10][9580/11272] Time 0.743 (1.062) Data 0.002 (0.234) Loss 2.4694 (2.6316) Prec@1 40.625 (36.269) Prec@5 68.750 (66.993) Epoch: [10][9590/11272] Time 0.842 (1.062) Data 0.001 (0.234) Loss 2.5948 (2.6316) Prec@1 33.750 (36.270) Prec@5 66.250 (66.993) Epoch: [10][9600/11272] Time 0.888 (1.061) Data 0.002 (0.234) Loss 2.6055 (2.6316) Prec@1 37.500 (36.272) Prec@5 70.000 (66.992) Epoch: [10][9610/11272] Time 1.086 (1.061) Data 0.315 (0.234) Loss 2.5635 (2.6316) Prec@1 38.125 (36.270) Prec@5 67.500 (66.992) Epoch: [10][9620/11272] Time 0.879 (1.061) Data 0.002 (0.234) Loss 2.4109 (2.6316) Prec@1 42.500 (36.271) Prec@5 70.000 (66.992) Epoch: [10][9630/11272] Time 0.863 (1.061) Data 0.001 (0.233) Loss 2.6746 (2.6316) Prec@1 36.250 (36.270) Prec@5 69.375 (66.991) Epoch: [10][9640/11272] Time 0.737 (1.061) Data 0.001 (0.233) Loss 2.5470 (2.6315) Prec@1 34.375 (36.272) Prec@5 67.500 (66.993) Epoch: [10][9650/11272] Time 0.758 (1.060) Data 0.001 (0.233) Loss 2.7260 (2.6315) Prec@1 34.375 (36.272) Prec@5 63.125 (66.993) Epoch: [10][9660/11272] Time 0.924 (1.060) Data 0.002 (0.233) Loss 2.4274 (2.6315) Prec@1 40.625 (36.274) Prec@5 71.875 (66.994) Epoch: [10][9670/11272] Time 0.862 (1.060) Data 0.001 (0.232) Loss 2.6245 (2.6315) Prec@1 36.250 (36.274) Prec@5 64.375 (66.991) Epoch: [10][9680/11272] Time 0.734 (1.060) Data 0.001 (0.232) Loss 2.6760 (2.6316) Prec@1 40.000 (36.275) Prec@5 63.750 (66.990) Epoch: [10][9690/11272] Time 0.748 (1.060) Data 0.002 (0.232) Loss 2.5488 (2.6315) Prec@1 33.125 (36.275) Prec@5 68.125 (66.990) Epoch: [10][9700/11272] Time 0.868 (1.059) Data 0.001 (0.232) Loss 2.4913 (2.6316) Prec@1 44.375 (36.275) Prec@5 69.375 (66.989) Epoch: [10][9710/11272] Time 0.916 (1.059) Data 0.001 (0.232) Loss 2.6321 (2.6315) Prec@1 35.625 (36.277) Prec@5 65.625 (66.990) Epoch: [10][9720/11272] Time 0.750 (1.059) Data 0.002 (0.232) Loss 2.6161 (2.6315) Prec@1 39.375 (36.276) Prec@5 65.625 (66.990) Epoch: [10][9730/11272] Time 0.748 (1.059) Data 0.002 (0.231) Loss 2.6138 (2.6314) Prec@1 31.250 (36.277) Prec@5 66.250 (66.992) Epoch: [10][9740/11272] Time 0.816 (1.058) Data 0.001 (0.231) Loss 2.8307 (2.6314) Prec@1 33.750 (36.278) Prec@5 60.000 (66.992) Epoch: [10][9750/11272] Time 0.841 (1.058) Data 0.001 (0.231) Loss 2.7232 (2.6314) Prec@1 36.875 (36.276) Prec@5 62.500 (66.992) Epoch: [10][9760/11272] Time 0.743 (1.058) Data 0.001 (0.231) Loss 2.6077 (2.6315) Prec@1 36.875 (36.275) Prec@5 69.375 (66.991) Epoch: [10][9770/11272] Time 0.812 (1.058) Data 0.001 (0.231) Loss 2.4469 (2.6314) Prec@1 43.750 (36.276) Prec@5 70.000 (66.992) Epoch: [10][9780/11272] Time 1.461 (1.058) Data 0.635 (0.230) Loss 2.7174 (2.6314) Prec@1 35.000 (36.275) Prec@5 65.000 (66.992) Epoch: [10][9790/11272] Time 0.736 (1.057) Data 0.002 (0.230) Loss 2.7691 (2.6313) Prec@1 33.750 (36.277) Prec@5 63.750 (66.994) Epoch: [10][9800/11272] Time 0.753 (1.057) Data 0.001 (0.230) Loss 2.9605 (2.6314) Prec@1 30.000 (36.277) Prec@5 59.375 (66.993) Epoch: [10][9810/11272] Time 0.841 (1.057) Data 0.001 (0.230) Loss 2.3796 (2.6313) Prec@1 46.250 (36.277) Prec@5 71.875 (66.993) Epoch: [10][9820/11272] Time 0.903 (1.057) Data 0.002 (0.230) Loss 2.8474 (2.6313) Prec@1 31.875 (36.278) Prec@5 64.375 (66.994) Epoch: [10][9830/11272] Time 0.975 (1.057) Data 0.210 (0.230) Loss 2.5166 (2.6312) Prec@1 40.625 (36.278) Prec@5 66.875 (66.995) Epoch: [10][9840/11272] Time 0.753 (1.056) Data 0.002 (0.229) Loss 2.3736 (2.6312) Prec@1 44.375 (36.279) Prec@5 70.000 (66.996) Epoch: [10][9850/11272] Time 0.871 (1.056) Data 0.001 (0.229) Loss 2.6829 (2.6313) Prec@1 38.750 (36.279) Prec@5 66.250 (66.995) Epoch: [10][9860/11272] Time 0.843 (1.056) Data 0.001 (0.229) Loss 2.6466 (2.6313) Prec@1 29.375 (36.278) Prec@5 63.125 (66.995) Epoch: [10][9870/11272] Time 1.083 (1.056) Data 0.296 (0.229) Loss 2.6651 (2.6313) Prec@1 38.750 (36.277) Prec@5 63.750 (66.994) Epoch: [10][9880/11272] Time 0.749 (1.055) Data 0.001 (0.229) Loss 2.8426 (2.6313) Prec@1 37.500 (36.277) Prec@5 66.875 (66.995) Epoch: [10][9890/11272] Time 0.831 (1.055) Data 0.001 (0.228) Loss 2.5735 (2.6313) Prec@1 35.625 (36.278) Prec@5 66.250 (66.994) Epoch: [10][9900/11272] Time 0.746 (1.055) Data 0.001 (0.228) Loss 2.6920 (2.6313) Prec@1 35.000 (36.278) Prec@5 64.375 (66.993) Epoch: [10][9910/11272] Time 0.740 (1.055) Data 0.002 (0.228) Loss 2.7320 (2.6313) Prec@1 35.625 (36.277) Prec@5 65.625 (66.993) Epoch: [10][9920/11272] Time 0.853 (1.055) Data 0.002 (0.228) Loss 2.5303 (2.6313) Prec@1 36.250 (36.276) Prec@5 66.250 (66.992) Epoch: [10][9930/11272] Time 0.876 (1.054) Data 0.002 (0.228) Loss 2.5327 (2.6313) Prec@1 40.625 (36.276) Prec@5 69.375 (66.992) Epoch: [10][9940/11272] Time 0.752 (1.054) Data 0.002 (0.227) Loss 2.3743 (2.6313) Prec@1 42.500 (36.277) Prec@5 65.625 (66.992) Epoch: [10][9950/11272] Time 0.729 (1.054) Data 0.002 (0.227) Loss 2.4537 (2.6312) Prec@1 32.500 (36.277) Prec@5 70.625 (66.994) Epoch: [10][9960/11272] Time 0.887 (1.054) Data 0.001 (0.227) Loss 2.7909 (2.6313) Prec@1 35.625 (36.274) Prec@5 68.125 (66.992) Epoch: [10][9970/11272] Time 0.872 (1.054) Data 0.002 (0.227) Loss 2.5387 (2.6312) Prec@1 37.500 (36.276) Prec@5 68.750 (66.992) Epoch: [10][9980/11272] Time 0.744 (1.053) Data 0.002 (0.227) Loss 2.5301 (2.6312) Prec@1 35.625 (36.276) Prec@5 70.625 (66.993) Epoch: [10][9990/11272] Time 0.744 (1.053) Data 0.002 (0.226) Loss 2.4441 (2.6312) Prec@1 40.000 (36.275) Prec@5 66.875 (66.991) Epoch: [10][10000/11272] Time 0.887 (1.053) Data 0.002 (0.226) Loss 2.8186 (2.6312) Prec@1 31.875 (36.274) Prec@5 63.750 (66.991) Epoch: [10][10010/11272] Time 0.815 (1.053) Data 0.002 (0.226) Loss 2.7886 (2.6312) Prec@1 28.125 (36.274) Prec@5 63.750 (66.993) Epoch: [10][10020/11272] Time 0.732 (1.053) Data 0.001 (0.226) Loss 2.6180 (2.6311) Prec@1 33.125 (36.274) Prec@5 65.000 (66.992) Epoch: [10][10030/11272] Time 0.887 (1.052) Data 0.001 (0.226) Loss 2.5761 (2.6311) Prec@1 38.125 (36.275) Prec@5 66.875 (66.992) Epoch: [10][10040/11272] Time 0.861 (1.052) Data 0.002 (0.225) Loss 3.0709 (2.6311) Prec@1 24.375 (36.275) Prec@5 60.000 (66.992) Epoch: [10][10050/11272] Time 0.747 (1.052) Data 0.002 (0.225) Loss 2.7335 (2.6311) Prec@1 34.375 (36.276) Prec@5 66.875 (66.994) Epoch: [10][10060/11272] Time 0.724 (1.052) Data 0.001 (0.225) Loss 2.6188 (2.6311) Prec@1 43.750 (36.275) Prec@5 63.750 (66.993) Epoch: [10][10070/11272] Time 0.846 (1.052) Data 0.001 (0.225) Loss 2.5176 (2.6310) Prec@1 40.625 (36.275) Prec@5 67.500 (66.993) Epoch: [10][10080/11272] Time 0.857 (1.051) Data 0.001 (0.225) Loss 2.7713 (2.6311) Prec@1 34.375 (36.273) Prec@5 62.500 (66.991) Epoch: [10][10090/11272] Time 0.753 (1.051) Data 0.002 (0.224) Loss 2.6733 (2.6311) Prec@1 37.500 (36.273) Prec@5 69.375 (66.991) Epoch: [10][10100/11272] Time 0.748 (1.051) Data 0.001 (0.224) Loss 2.6714 (2.6311) Prec@1 35.625 (36.274) Prec@5 63.125 (66.990) Epoch: [10][10110/11272] Time 0.870 (1.051) Data 0.001 (0.224) Loss 2.6095 (2.6311) Prec@1 38.750 (36.274) Prec@5 65.625 (66.990) Epoch: [10][10120/11272] Time 0.854 (1.050) Data 0.002 (0.224) Loss 2.6688 (2.6311) Prec@1 33.125 (36.274) Prec@5 70.000 (66.989) Epoch: [10][10130/11272] Time 0.742 (1.050) Data 0.002 (0.224) Loss 2.5765 (2.6311) Prec@1 38.125 (36.275) Prec@5 68.125 (66.990) Epoch: [10][10140/11272] Time 0.739 (1.050) Data 0.001 (0.223) Loss 2.5491 (2.6310) Prec@1 34.375 (36.276) Prec@5 69.375 (66.991) Epoch: [10][10150/11272] Time 0.895 (1.050) Data 0.001 (0.223) Loss 2.8247 (2.6310) Prec@1 34.375 (36.277) Prec@5 64.375 (66.991) Epoch: [10][10160/11272] Time 0.751 (1.049) Data 0.003 (0.223) Loss 2.6388 (2.6311) Prec@1 38.125 (36.274) Prec@5 67.500 (66.989) Epoch: [10][10170/11272] Time 0.749 (1.049) Data 0.002 (0.223) Loss 2.6107 (2.6312) Prec@1 36.250 (36.273) Prec@5 68.750 (66.989) Epoch: [10][10180/11272] Time 0.844 (1.049) Data 0.002 (0.223) Loss 2.5644 (2.6312) Prec@1 36.875 (36.273) Prec@5 70.625 (66.989) Epoch: [10][10190/11272] Time 0.828 (1.049) Data 0.001 (0.222) Loss 2.6890 (2.6312) Prec@1 35.000 (36.273) Prec@5 61.875 (66.988) Epoch: [10][10200/11272] Time 0.752 (1.049) Data 0.002 (0.222) Loss 2.4846 (2.6313) Prec@1 43.750 (36.272) Prec@5 71.875 (66.987) Epoch: [10][10210/11272] Time 0.750 (1.048) Data 0.001 (0.222) Loss 2.6639 (2.6313) Prec@1 31.250 (36.273) Prec@5 65.000 (66.988) Epoch: [10][10220/11272] Time 0.844 (1.048) Data 0.001 (0.222) Loss 2.6270 (2.6313) Prec@1 40.000 (36.273) Prec@5 68.750 (66.987) Epoch: [10][10230/11272] Time 0.808 (1.048) Data 0.001 (0.222) Loss 2.7775 (2.6313) Prec@1 33.750 (36.274) Prec@5 65.625 (66.987) Epoch: [10][10240/11272] Time 0.718 (1.048) Data 0.001 (0.221) Loss 2.5557 (2.6313) Prec@1 42.500 (36.274) Prec@5 65.000 (66.986) Epoch: [10][10250/11272] Time 1.328 (1.048) Data 0.557 (0.221) Loss 2.7992 (2.6314) Prec@1 37.500 (36.271) Prec@5 63.125 (66.984) Epoch: [10][10260/11272] Time 0.884 (1.047) Data 0.001 (0.221) Loss 2.4489 (2.6313) Prec@1 40.625 (36.273) Prec@5 67.500 (66.985) Epoch: [10][10270/11272] Time 0.878 (1.047) Data 0.001 (0.221) Loss 2.5248 (2.6313) Prec@1 33.750 (36.273) Prec@5 64.375 (66.985) Epoch: [10][10280/11272] Time 0.744 (1.047) Data 0.002 (0.221) Loss 2.4473 (2.6313) Prec@1 35.625 (36.272) Prec@5 71.875 (66.984) Epoch: [10][10290/11272] Time 0.858 (1.047) Data 0.001 (0.221) Loss 2.7098 (2.6313) Prec@1 33.750 (36.271) Prec@5 65.625 (66.984) Epoch: [10][10300/11272] Time 0.842 (1.047) Data 0.001 (0.220) Loss 2.7578 (2.6313) Prec@1 31.875 (36.271) Prec@5 63.750 (66.983) Epoch: [10][10310/11272] Time 0.747 (1.046) Data 0.001 (0.220) Loss 2.4740 (2.6314) Prec@1 37.500 (36.269) Prec@5 63.750 (66.983) Epoch: [10][10320/11272] Time 0.730 (1.046) Data 0.001 (0.220) Loss 2.6084 (2.6313) Prec@1 36.875 (36.270) Prec@5 69.375 (66.983) Epoch: [10][10330/11272] Time 0.887 (1.046) Data 0.001 (0.220) Loss 2.7056 (2.6314) Prec@1 40.000 (36.270) Prec@5 68.125 (66.984) Epoch: [10][10340/11272] Time 0.851 (1.046) Data 0.001 (0.220) Loss 2.4192 (2.6313) Prec@1 39.375 (36.270) Prec@5 76.250 (66.983) Epoch: [10][10350/11272] Time 0.752 (1.045) Data 0.002 (0.219) Loss 2.6330 (2.6314) Prec@1 33.750 (36.269) Prec@5 68.125 (66.983) Epoch: [10][10360/11272] Time 0.735 (1.045) Data 0.001 (0.219) Loss 2.5607 (2.6314) Prec@1 37.500 (36.268) Prec@5 66.875 (66.983) Epoch: [10][10370/11272] Time 0.816 (1.045) Data 0.001 (0.219) Loss 2.4993 (2.6313) Prec@1 41.875 (36.269) Prec@5 68.125 (66.985) Epoch: [10][10380/11272] Time 0.862 (1.045) Data 0.001 (0.219) Loss 2.5394 (2.6313) Prec@1 38.125 (36.270) Prec@5 68.125 (66.985) Epoch: [10][10390/11272] Time 0.771 (1.045) Data 0.001 (0.219) Loss 2.6394 (2.6313) Prec@1 36.250 (36.272) Prec@5 66.250 (66.985) Epoch: [10][10400/11272] Time 0.972 (1.044) Data 0.181 (0.218) Loss 2.7915 (2.6313) Prec@1 35.000 (36.272) Prec@5 65.000 (66.985) Epoch: [10][10410/11272] Time 0.846 (1.044) Data 0.001 (0.218) Loss 2.5097 (2.6312) Prec@1 38.750 (36.274) Prec@5 71.875 (66.986) Epoch: [10][10420/11272] Time 0.743 (1.044) Data 0.003 (0.218) Loss 2.8376 (2.6313) Prec@1 33.750 (36.273) Prec@5 60.625 (66.984) Epoch: [10][10430/11272] Time 0.746 (1.044) Data 0.002 (0.218) Loss 2.7418 (2.6313) Prec@1 31.250 (36.272) Prec@5 66.250 (66.985) Epoch: [10][10440/11272] Time 0.855 (1.044) Data 0.001 (0.218) Loss 2.5803 (2.6313) Prec@1 38.750 (36.271) Prec@5 69.375 (66.985) Epoch: [10][10450/11272] Time 0.863 (1.043) Data 0.001 (0.218) Loss 2.4236 (2.6313) Prec@1 41.875 (36.270) Prec@5 68.750 (66.985) Epoch: [10][10460/11272] Time 0.789 (1.043) Data 0.002 (0.217) Loss 2.8569 (2.6313) Prec@1 35.000 (36.270) Prec@5 60.625 (66.986) Epoch: [10][10470/11272] Time 0.741 (1.043) Data 0.001 (0.217) Loss 2.8419 (2.6313) Prec@1 33.750 (36.269) Prec@5 61.875 (66.984) Epoch: [10][10480/11272] Time 0.874 (1.043) Data 0.001 (0.217) Loss 2.7190 (2.6313) Prec@1 36.250 (36.270) Prec@5 65.000 (66.984) Epoch: [10][10490/11272] Time 0.883 (1.043) Data 0.002 (0.217) Loss 2.5309 (2.6313) Prec@1 37.500 (36.269) Prec@5 70.625 (66.986) Epoch: [10][10500/11272] Time 0.734 (1.042) Data 0.001 (0.216) Loss 2.5079 (2.6313) Prec@1 39.375 (36.269) Prec@5 71.250 (66.987) Epoch: [10][10510/11272] Time 0.754 (1.042) Data 0.002 (0.216) Loss 2.5547 (2.6312) Prec@1 37.500 (36.269) Prec@5 68.750 (66.986) Epoch: [10][10520/11272] Time 0.841 (1.042) Data 0.001 (0.216) Loss 2.7471 (2.6312) Prec@1 33.750 (36.271) Prec@5 62.500 (66.987) Epoch: [10][10530/11272] Time 1.027 (1.042) Data 0.104 (0.216) Loss 2.4893 (2.6312) Prec@1 35.625 (36.271) Prec@5 68.750 (66.987) Epoch: [10][10540/11272] Time 0.750 (1.041) Data 0.001 (0.216) Loss 2.6195 (2.6312) Prec@1 41.250 (36.272) Prec@5 65.625 (66.987) Epoch: [10][10550/11272] Time 0.856 (1.041) Data 0.001 (0.216) Loss 2.6909 (2.6311) Prec@1 36.250 (36.274) Prec@5 65.625 (66.988) Epoch: [10][10560/11272] Time 0.864 (1.041) Data 0.001 (0.215) Loss 2.6187 (2.6312) Prec@1 36.250 (36.273) Prec@5 68.125 (66.987) Epoch: [10][10570/11272] Time 0.738 (1.041) Data 0.001 (0.215) Loss 2.7202 (2.6312) Prec@1 38.125 (36.271) Prec@5 65.000 (66.987) Epoch: [10][10580/11272] Time 0.765 (1.041) Data 0.001 (0.215) Loss 2.8420 (2.6312) Prec@1 31.875 (36.271) Prec@5 60.000 (66.986) Epoch: [10][10590/11272] Time 0.854 (1.040) Data 0.001 (0.215) Loss 2.3854 (2.6311) Prec@1 40.625 (36.272) Prec@5 70.000 (66.988) Epoch: [10][10600/11272] Time 0.855 (1.040) Data 0.001 (0.215) Loss 2.6494 (2.6312) Prec@1 33.125 (36.271) Prec@5 66.875 (66.988) Epoch: [10][10610/11272] Time 0.749 (1.040) Data 0.002 (0.214) Loss 2.6839 (2.6312) Prec@1 35.000 (36.271) Prec@5 65.625 (66.988) Epoch: [10][10620/11272] Time 0.785 (1.040) Data 0.002 (0.214) Loss 2.4691 (2.6311) Prec@1 40.625 (36.273) Prec@5 70.000 (66.989) Epoch: [10][10630/11272] Time 0.908 (1.040) Data 0.002 (0.214) Loss 2.8070 (2.6312) Prec@1 30.000 (36.271) Prec@5 61.250 (66.988) Epoch: [10][10640/11272] Time 0.883 (1.039) Data 0.002 (0.214) Loss 2.6583 (2.6312) Prec@1 40.625 (36.271) Prec@5 63.750 (66.987) Epoch: [10][10650/11272] Time 0.740 (1.039) Data 0.001 (0.214) Loss 2.3031 (2.6312) Prec@1 47.500 (36.273) Prec@5 71.250 (66.988) Epoch: [10][10660/11272] Time 0.738 (1.039) Data 0.002 (0.213) Loss 3.0170 (2.6313) Prec@1 27.500 (36.271) Prec@5 61.875 (66.986) Epoch: [10][10670/11272] Time 0.895 (1.039) Data 0.002 (0.213) Loss 2.6619 (2.6313) Prec@1 34.375 (36.271) Prec@5 68.125 (66.986) Epoch: [10][10680/11272] Time 0.839 (1.039) Data 0.001 (0.213) Loss 2.4948 (2.6313) Prec@1 38.750 (36.272) Prec@5 69.375 (66.986) Epoch: [10][10690/11272] Time 0.753 (1.038) Data 0.002 (0.213) Loss 2.4766 (2.6313) Prec@1 35.625 (36.272) Prec@5 70.625 (66.986) Epoch: [10][10700/11272] Time 0.892 (1.038) Data 0.002 (0.213) Loss 2.8517 (2.6312) Prec@1 31.250 (36.271) Prec@5 62.500 (66.987) Epoch: [10][10710/11272] Time 1.260 (1.038) Data 0.400 (0.213) Loss 2.7746 (2.6312) Prec@1 38.125 (36.274) Prec@5 63.750 (66.990) Epoch: [10][10720/11272] Time 0.731 (1.038) Data 0.001 (0.212) Loss 2.6085 (2.6312) Prec@1 41.875 (36.272) Prec@5 65.000 (66.988) Epoch: [10][10730/11272] Time 0.757 (1.038) Data 0.001 (0.212) Loss 2.8084 (2.6312) Prec@1 35.000 (36.272) Prec@5 68.125 (66.988) Epoch: [10][10740/11272] Time 0.854 (1.037) Data 0.002 (0.212) Loss 2.6082 (2.6312) Prec@1 36.250 (36.273) Prec@5 66.250 (66.988) Epoch: [10][10750/11272] Time 0.861 (1.037) Data 0.001 (0.212) Loss 2.5850 (2.6313) Prec@1 36.250 (36.272) Prec@5 70.625 (66.987) Epoch: [10][10760/11272] Time 0.743 (1.037) Data 0.001 (0.212) Loss 2.7585 (2.6313) Prec@1 36.250 (36.272) Prec@5 68.125 (66.987) Epoch: [10][10770/11272] Time 0.733 (1.037) Data 0.002 (0.211) Loss 2.8315 (2.6313) Prec@1 35.625 (36.273) Prec@5 65.625 (66.988) Epoch: [10][10780/11272] Time 0.848 (1.037) Data 0.002 (0.211) Loss 2.5607 (2.6313) Prec@1 35.000 (36.271) Prec@5 67.500 (66.987) Epoch: [10][10790/11272] Time 0.885 (1.036) Data 0.001 (0.211) Loss 2.6809 (2.6313) Prec@1 29.375 (36.271) Prec@5 70.625 (66.988) Epoch: [10][10800/11272] Time 0.744 (1.036) Data 0.001 (0.211) Loss 2.5172 (2.6313) Prec@1 34.375 (36.272) Prec@5 70.625 (66.988) Epoch: [10][10810/11272] Time 0.796 (1.036) Data 0.002 (0.211) Loss 2.4130 (2.6312) Prec@1 37.500 (36.273) Prec@5 71.875 (66.989) Epoch: [10][10820/11272] Time 0.906 (1.036) Data 0.002 (0.210) Loss 3.1555 (2.6313) Prec@1 25.000 (36.272) Prec@5 55.625 (66.988) Epoch: [10][10830/11272] Time 0.741 (1.036) Data 0.001 (0.210) Loss 2.6034 (2.6313) Prec@1 37.500 (36.271) Prec@5 70.000 (66.989) Epoch: [10][10840/11272] Time 0.740 (1.035) Data 0.001 (0.210) Loss 2.4399 (2.6312) Prec@1 43.750 (36.274) Prec@5 69.375 (66.989) Epoch: [10][10850/11272] Time 0.861 (1.035) Data 0.001 (0.210) Loss 2.6146 (2.6312) Prec@1 37.500 (36.276) Prec@5 63.125 (66.990) Epoch: [10][10860/11272] Time 0.873 (1.035) Data 0.001 (0.210) Loss 2.7017 (2.6312) Prec@1 38.125 (36.276) Prec@5 60.000 (66.989) Epoch: [10][10870/11272] Time 0.739 (1.035) Data 0.002 (0.209) Loss 2.4588 (2.6313) Prec@1 38.125 (36.275) Prec@5 67.500 (66.989) Epoch: [10][10880/11272] Time 0.766 (1.034) Data 0.001 (0.209) Loss 2.6579 (2.6313) Prec@1 41.875 (36.277) Prec@5 66.875 (66.990) Epoch: [10][10890/11272] Time 0.845 (1.034) Data 0.001 (0.209) Loss 2.7643 (2.6313) Prec@1 36.875 (36.276) Prec@5 65.000 (66.989) Epoch: [10][10900/11272] Time 0.833 (1.034) Data 0.002 (0.209) Loss 2.6052 (2.6312) Prec@1 36.250 (36.278) Prec@5 68.125 (66.991) Epoch: [10][10910/11272] Time 0.754 (1.034) Data 0.002 (0.209) Loss 2.6698 (2.6313) Prec@1 39.375 (36.277) Prec@5 68.125 (66.991) Epoch: [10][10920/11272] Time 0.747 (1.034) Data 0.002 (0.209) Loss 2.8516 (2.6313) Prec@1 33.750 (36.277) Prec@5 62.500 (66.990) Epoch: [10][10930/11272] Time 0.897 (1.033) Data 0.002 (0.208) Loss 2.8520 (2.6314) Prec@1 31.875 (36.276) Prec@5 58.750 (66.988) Epoch: [10][10940/11272] Time 0.886 (1.033) Data 0.002 (0.208) Loss 2.6442 (2.6314) Prec@1 36.875 (36.275) Prec@5 66.875 (66.989) Epoch: [10][10950/11272] Time 0.741 (1.033) Data 0.002 (0.208) Loss 2.4560 (2.6314) Prec@1 33.125 (36.274) Prec@5 67.500 (66.989) Epoch: [10][10960/11272] Time 0.816 (1.033) Data 0.001 (0.208) Loss 2.8940 (2.6315) Prec@1 32.500 (36.273) Prec@5 64.375 (66.989) Epoch: [10][10970/11272] Time 0.843 (1.033) Data 0.001 (0.208) Loss 2.5409 (2.6315) Prec@1 36.875 (36.273) Prec@5 66.875 (66.988) Epoch: [10][10980/11272] Time 0.749 (1.032) Data 0.001 (0.207) Loss 2.8495 (2.6315) Prec@1 33.750 (36.273) Prec@5 61.875 (66.987) Epoch: [10][10990/11272] Time 0.787 (1.032) Data 0.002 (0.207) Loss 2.6447 (2.6316) Prec@1 36.250 (36.273) Prec@5 64.375 (66.986) Epoch: [10][11000/11272] Time 0.834 (1.032) Data 0.001 (0.207) Loss 2.4597 (2.6316) Prec@1 39.375 (36.274) Prec@5 70.000 (66.986) Epoch: [10][11010/11272] Time 0.892 (1.032) Data 0.002 (0.207) Loss 2.3241 (2.6316) Prec@1 41.250 (36.274) Prec@5 71.875 (66.984) Epoch: [10][11020/11272] Time 0.744 (1.032) Data 0.001 (0.207) Loss 2.4219 (2.6315) Prec@1 43.125 (36.274) Prec@5 73.125 (66.985) Epoch: [10][11030/11272] Time 0.760 (1.031) Data 0.002 (0.206) Loss 2.6328 (2.6315) Prec@1 32.500 (36.274) Prec@5 66.875 (66.985) Epoch: [10][11040/11272] Time 0.894 (1.031) Data 0.001 (0.206) Loss 2.7134 (2.6316) Prec@1 37.500 (36.273) Prec@5 67.500 (66.985) Epoch: [10][11050/11272] Time 0.849 (1.031) Data 0.002 (0.206) Loss 2.6295 (2.6316) Prec@1 30.000 (36.271) Prec@5 67.500 (66.985) Epoch: [10][11060/11272] Time 0.771 (1.031) Data 0.002 (0.206) Loss 2.3849 (2.6317) Prec@1 37.500 (36.270) Prec@5 71.875 (66.985) Epoch: [10][11070/11272] Time 0.766 (1.031) Data 0.001 (0.206) Loss 2.4326 (2.6316) Prec@1 37.500 (36.270) Prec@5 70.000 (66.987) Epoch: [10][11080/11272] Time 0.852 (1.030) Data 0.001 (0.206) Loss 2.6013 (2.6316) Prec@1 33.750 (36.269) Prec@5 70.000 (66.985) Epoch: [10][11090/11272] Time 0.745 (1.030) Data 0.003 (0.205) Loss 2.4203 (2.6316) Prec@1 39.375 (36.270) Prec@5 68.125 (66.985) Epoch: [10][11100/11272] Time 0.765 (1.030) Data 0.002 (0.205) Loss 2.6709 (2.6316) Prec@1 34.375 (36.271) Prec@5 68.750 (66.985) Epoch: [10][11110/11272] Time 0.871 (1.030) Data 0.001 (0.205) Loss 2.5237 (2.6316) Prec@1 38.125 (36.270) Prec@5 70.000 (66.984) Epoch: [10][11120/11272] Time 0.862 (1.030) Data 0.002 (0.205) Loss 2.5495 (2.6315) Prec@1 40.625 (36.271) Prec@5 69.375 (66.987) Epoch: [10][11130/11272] Time 0.739 (1.029) Data 0.001 (0.205) Loss 2.5262 (2.6314) Prec@1 41.250 (36.273) Prec@5 70.000 (66.989) Epoch: [10][11140/11272] Time 0.744 (1.029) Data 0.001 (0.204) Loss 2.5674 (2.6315) Prec@1 42.500 (36.274) Prec@5 67.500 (66.988) Epoch: [10][11150/11272] Time 0.875 (1.029) Data 0.002 (0.204) Loss 2.5145 (2.6314) Prec@1 41.250 (36.276) Prec@5 68.750 (66.988) Epoch: [10][11160/11272] Time 0.850 (1.029) Data 0.001 (0.204) Loss 2.2828 (2.6315) Prec@1 48.750 (36.276) Prec@5 73.750 (66.986) Epoch: [10][11170/11272] Time 0.746 (1.029) Data 0.002 (0.204) Loss 2.5173 (2.6316) Prec@1 35.625 (36.276) Prec@5 69.375 (66.986) Epoch: [10][11180/11272] Time 0.736 (1.028) Data 0.002 (0.204) Loss 2.9584 (2.6316) Prec@1 32.500 (36.275) Prec@5 58.125 (66.985) Epoch: [10][11190/11272] Time 0.851 (1.028) Data 0.002 (0.204) Loss 2.7399 (2.6316) Prec@1 38.750 (36.276) Prec@5 63.125 (66.984) Epoch: [10][11200/11272] Time 0.835 (1.028) Data 0.002 (0.203) Loss 2.6579 (2.6316) Prec@1 36.250 (36.276) Prec@5 66.250 (66.984) Epoch: [10][11210/11272] Time 0.768 (1.028) Data 0.001 (0.203) Loss 2.9652 (2.6316) Prec@1 33.125 (36.277) Prec@5 60.625 (66.984) Epoch: [10][11220/11272] Time 0.845 (1.028) Data 0.001 (0.203) Loss 2.4991 (2.6315) Prec@1 43.125 (36.278) Prec@5 71.875 (66.985) Epoch: [10][11230/11272] Time 0.849 (1.027) Data 0.001 (0.203) Loss 2.6547 (2.6315) Prec@1 30.625 (36.277) Prec@5 70.625 (66.986) Epoch: [10][11240/11272] Time 0.742 (1.027) Data 0.001 (0.203) Loss 2.8694 (2.6315) Prec@1 34.375 (36.278) Prec@5 63.125 (66.985) Epoch: [10][11250/11272] Time 0.757 (1.027) Data 0.001 (0.202) Loss 2.6463 (2.6316) Prec@1 36.250 (36.276) Prec@5 67.500 (66.985) Epoch: [10][11260/11272] Time 0.830 (1.027) Data 0.001 (0.202) Loss 2.5001 (2.6316) Prec@1 42.500 (36.276) Prec@5 68.750 (66.984) Epoch: [10][11270/11272] Time 0.789 (1.027) Data 0.000 (0.202) Loss 2.7321 (2.6316) Prec@1 31.875 (36.275) Prec@5 66.875 (66.983) Test: [0/229] Time 4.640 (4.640) Loss 1.5707 (1.5707) Prec@1 53.125 (53.125) Prec@5 89.375 (89.375) Test: [10/229] Time 0.314 (0.912) Loss 1.2440 (2.0938) Prec@1 65.000 (47.500) Prec@5 92.500 (78.693) Test: [20/229] Time 0.987 (0.776) Loss 2.5651 (2.2413) Prec@1 38.125 (44.256) Prec@5 71.250 (75.833) Test: [30/229] Time 0.370 (0.739) Loss 2.7844 (2.1634) Prec@1 25.000 (45.202) Prec@5 64.375 (76.532) Test: [40/229] Time 1.389 (0.729) Loss 0.8566 (2.1389) Prec@1 81.875 (45.610) Prec@5 88.750 (76.890) Test: [50/229] Time 0.869 (0.724) Loss 3.2798 (2.1782) Prec@1 15.625 (45.159) Prec@5 54.375 (75.821) Test: [60/229] Time 0.348 (0.716) Loss 3.0893 (2.2018) Prec@1 28.125 (44.314) Prec@5 57.500 (75.297) Test: [70/229] Time 1.017 (0.716) Loss 1.8353 (2.2293) Prec@1 46.250 (43.257) Prec@5 80.625 (74.842) Test: [80/229] Time 0.608 (0.710) Loss 2.7485 (2.2538) Prec@1 18.750 (42.091) Prec@5 71.875 (74.815) Test: [90/229] Time 0.315 (0.706) Loss 2.5551 (2.2770) Prec@1 40.625 (41.593) Prec@5 68.750 (74.430) Test: [100/229] Time 1.068 (0.712) Loss 1.7733 (2.2578) Prec@1 66.250 (42.333) Prec@5 83.750 (74.777) Test: [110/229] Time 0.334 (0.715) Loss 1.6762 (2.2481) Prec@1 58.750 (42.477) Prec@5 81.250 (74.904) Test: [120/229] Time 0.812 (0.704) Loss 3.2936 (2.2580) Prec@1 21.875 (42.087) Prec@5 58.125 (74.814) Test: [130/229] Time 0.356 (0.708) Loss 1.9199 (2.2403) Prec@1 50.000 (42.524) Prec@5 85.000 (75.162) Test: [140/229] Time 0.337 (0.702) Loss 2.8011 (2.2636) Prec@1 30.625 (42.008) Prec@5 66.875 (74.743) Test: [150/229] Time 1.257 (0.705) Loss 1.6005 (2.2929) Prec@1 64.375 (41.465) Prec@5 83.125 (74.354) Test: [160/229] Time 0.347 (0.706) Loss 2.7553 (2.2981) Prec@1 33.125 (41.452) Prec@5 72.500 (74.328) Test: [170/229] Time 0.743 (0.704) Loss 2.1259 (2.3097) Prec@1 45.000 (41.118) Prec@5 78.750 (74.042) Test: [180/229] Time 0.991 (0.705) Loss 3.4249 (2.3239) Prec@1 31.250 (41.112) Prec@5 43.125 (73.640) Test: [190/229] Time 1.124 (0.708) Loss 1.5318 (2.3124) Prec@1 57.500 (41.384) Prec@5 85.625 (73.815) Test: [200/229] Time 0.720 (0.706) Loss 2.3052 (2.3020) Prec@1 38.750 (41.542) Prec@5 70.625 (74.083) Test: [210/229] Time 0.328 (0.711) Loss 1.9196 (2.2916) Prec@1 41.875 (41.792) Prec@5 85.625 (74.328) Test: [220/229] Time 1.357 (0.716) Loss 2.1950 (2.2811) Prec@1 40.625 (42.104) Prec@5 73.125 (74.446) * Prec@1 42.569 Prec@5 74.688 Epoch: [11][0/11272] Time 6.124 (6.124) Data 2.942 (2.942) Loss 2.7883 (2.7883) Prec@1 28.750 (28.750) Prec@5 63.750 (63.750) Epoch: [11][10/11272] Time 0.909 (1.285) Data 0.002 (0.269) Loss 2.8111 (2.6476) Prec@1 31.875 (35.568) Prec@5 68.125 (66.420) Epoch: [11][20/11272] Time 0.734 (1.058) Data 0.002 (0.142) Loss 2.5415 (2.6305) Prec@1 37.500 (35.804) Prec@5 67.500 (66.548) Epoch: [11][30/11272] Time 0.894 (0.985) Data 0.001 (0.096) Loss 2.5944 (2.6147) Prec@1 34.375 (35.948) Prec@5 65.000 (66.774) Epoch: [11][40/11272] Time 0.870 (0.945) Data 0.002 (0.073) Loss 2.6085 (2.6199) Prec@1 38.125 (36.479) Prec@5 61.875 (66.555) Epoch: [11][50/11272] Time 0.754 (0.917) Data 0.002 (0.059) Loss 2.5676 (2.6152) Prec@1 30.000 (36.630) Prec@5 67.500 (66.618) Epoch: [11][60/11272] Time 0.777 (0.901) Data 0.002 (0.050) Loss 2.6455 (2.6258) Prec@1 38.125 (36.455) Prec@5 70.000 (66.414) Epoch: [11][70/11272] Time 0.907 (0.888) Data 0.002 (0.043) Loss 2.5154 (2.6222) Prec@1 43.750 (36.461) Prec@5 68.125 (66.655) Epoch: [11][80/11272] Time 0.911 (0.879) Data 0.002 (0.038) Loss 2.4434 (2.6299) Prec@1 36.875 (36.412) Prec@5 71.875 (66.659) Epoch: [11][90/11272] Time 0.739 (0.871) Data 0.001 (0.034) Loss 2.3205 (2.6270) Prec@1 41.875 (36.429) Prec@5 70.625 (66.841) Epoch: [11][100/11272] Time 0.797 (0.865) Data 0.003 (0.031) Loss 2.6627 (2.6297) Prec@1 32.500 (36.361) Prec@5 67.500 (66.931) Epoch: [11][110/11272] Time 0.865 (0.863) Data 0.002 (0.028) Loss 2.5234 (2.6267) Prec@1 43.125 (36.447) Prec@5 70.000 (66.959) Epoch: [11][120/11272] Time 0.863 (0.858) Data 0.001 (0.026) Loss 2.5829 (2.6239) Prec@1 43.750 (36.643) Prec@5 71.875 (67.107) Epoch: [11][130/11272] Time 0.718 (0.854) Data 0.002 (0.024) Loss 2.6911 (2.6224) Prec@1 38.125 (36.632) Prec@5 63.750 (67.099) Epoch: [11][140/11272] Time 0.744 (0.850) Data 0.002 (0.022) Loss 2.3833 (2.6206) Prec@1 44.375 (36.715) Prec@5 70.625 (67.141) Epoch: [11][150/11272] Time 0.824 (0.848) Data 0.002 (0.021) Loss 2.6146 (2.6187) Prec@1 35.000 (36.817) Prec@5 66.250 (67.173) Epoch: [11][160/11272] Time 0.759 (0.846) Data 0.004 (0.020) Loss 2.7260 (2.6205) Prec@1 37.500 (36.782) Prec@5 61.875 (67.205) Epoch: [11][170/11272] Time 0.744 (0.844) Data 0.001 (0.019) Loss 2.3058 (2.6171) Prec@1 41.250 (36.842) Prec@5 73.125 (67.266) Epoch: [11][180/11272] Time 0.901 (0.844) Data 0.002 (0.018) Loss 2.5259 (2.6174) Prec@1 39.375 (36.761) Prec@5 67.500 (67.224) Epoch: [11][190/11272] Time 0.932 (0.843) Data 0.002 (0.017) Loss 2.4852 (2.6147) Prec@1 34.375 (36.744) Prec@5 72.500 (67.287) Epoch: [11][200/11272] Time 0.757 (0.841) Data 0.002 (0.016) Loss 2.4823 (2.6148) Prec@1 38.750 (36.729) Prec@5 70.625 (67.338) Epoch: [11][210/11272] Time 0.765 (0.840) Data 0.002 (0.016) Loss 2.5931 (2.6111) Prec@1 31.250 (36.736) Prec@5 68.750 (67.399) Epoch: [11][220/11272] Time 0.853 (0.839) Data 0.001 (0.015) Loss 2.6384 (2.6124) Prec@1 36.875 (36.677) Prec@5 63.750 (67.325) Epoch: [11][230/11272] Time 0.941 (0.839) Data 0.002 (0.014) Loss 2.8320 (2.6157) Prec@1 31.250 (36.640) Prec@5 61.250 (67.251) Epoch: [11][240/11272] Time 0.789 (0.838) Data 0.002 (0.014) Loss 2.4711 (2.6150) Prec@1 32.500 (36.574) Prec@5 68.750 (67.220) Epoch: [11][250/11272] Time 0.762 (0.837) Data 0.002 (0.013) Loss 2.7925 (2.6167) Prec@1 33.750 (36.529) Prec@5 62.500 (67.236) Epoch: [11][260/11272] Time 0.903 (0.837) Data 0.002 (0.013) Loss 2.7281 (2.6188) Prec@1 32.500 (36.444) Prec@5 68.125 (67.215) Epoch: [11][270/11272] Time 0.881 (0.836) Data 0.001 (0.012) Loss 2.6559 (2.6191) Prec@1 39.375 (36.407) Prec@5 61.875 (67.226) Epoch: [11][280/11272] Time 0.785 (0.836) Data 0.002 (0.012) Loss 2.7904 (2.6205) Prec@1 29.375 (36.390) Prec@5 65.000 (67.204) Epoch: [11][290/11272] Time 0.885 (0.836) Data 0.001 (0.012) Loss 2.6668 (2.6204) Prec@1 35.625 (36.372) Prec@5 66.250 (67.201) Epoch: [11][300/11272] Time 0.926 (0.835) Data 0.002 (0.011) Loss 2.7581 (2.6243) Prec@1 33.125 (36.312) Prec@5 65.625 (67.126) Epoch: [11][310/11272] Time 0.775 (0.834) Data 0.001 (0.011) Loss 2.8469 (2.6261) Prec@1 35.625 (36.330) Prec@5 64.375 (67.060) Epoch: [11][320/11272] Time 0.752 (0.834) Data 0.001 (0.011) Loss 2.6628 (2.6265) Prec@1 31.250 (36.310) Prec@5 65.625 (67.033) Epoch: [11][330/11272] Time 0.907 (0.834) Data 0.002 (0.011) Loss 2.6632 (2.6237) Prec@1 35.000 (36.367) Prec@5 68.750 (67.105) Epoch: [11][340/11272] Time 0.889 (0.834) Data 0.003 (0.010) Loss 2.6879 (2.6251) Prec@1 39.375 (36.395) Prec@5 64.375 (67.102) Epoch: [11][350/11272] Time 0.747 (0.833) Data 0.001 (0.010) Loss 2.5713 (2.6235) Prec@1 35.000 (36.416) Prec@5 70.000 (67.147) Epoch: [11][360/11272] Time 0.744 (0.833) Data 0.001 (0.010) Loss 2.5262 (2.6231) Prec@1 35.625 (36.453) Prec@5 68.125 (67.176) Epoch: [11][370/11272] Time 0.892 (0.833) Data 0.002 (0.010) Loss 2.7589 (2.6233) Prec@1 36.250 (36.434) Prec@5 61.875 (67.161) Epoch: [11][380/11272] Time 0.825 (0.832) Data 0.002 (0.009) Loss 2.7644 (2.6227) Prec@1 31.875 (36.465) Prec@5 65.625 (67.174) Epoch: [11][390/11272] Time 0.759 (0.832) Data 0.002 (0.009) Loss 2.8073 (2.6224) Prec@1 33.125 (36.463) Prec@5 61.875 (67.156) Epoch: [11][400/11272] Time 0.769 (0.831) Data 0.002 (0.009) Loss 2.6947 (2.6240) Prec@1 38.750 (36.460) Prec@5 66.875 (67.096) Epoch: [11][410/11272] Time 0.948 (0.832) Data 0.001 (0.009) Loss 2.4609 (2.6243) Prec@1 45.000 (36.460) Prec@5 71.250 (67.102) Epoch: [11][420/11272] Time 0.745 (0.831) Data 0.004 (0.009) Loss 2.9131 (2.6248) Prec@1 30.625 (36.453) Prec@5 65.625 (67.083) Epoch: [11][430/11272] Time 0.745 (0.831) Data 0.002 (0.008) Loss 2.3297 (2.6248) Prec@1 40.000 (36.468) Prec@5 72.500 (67.094) Epoch: [11][440/11272] Time 0.873 (0.831) Data 0.002 (0.008) Loss 2.8543 (2.6244) Prec@1 33.125 (36.487) Prec@5 66.250 (67.106) Epoch: [11][450/11272] Time 0.862 (0.831) Data 0.002 (0.008) Loss 2.3154 (2.6241) Prec@1 46.875 (36.490) Prec@5 72.500 (67.102) Epoch: [11][460/11272] Time 0.758 (0.831) Data 0.002 (0.008) Loss 2.6466 (2.6240) Prec@1 35.625 (36.497) Prec@5 66.875 (67.097) Epoch: [11][470/11272] Time 0.757 (0.830) Data 0.001 (0.008) Loss 2.5234 (2.6228) Prec@1 41.250 (36.529) Prec@5 71.250 (67.118) Epoch: [11][480/11272] Time 0.851 (0.830) Data 0.002 (0.008) Loss 2.5207 (2.6229) Prec@1 34.375 (36.511) Prec@5 71.875 (67.100) Epoch: [11][490/11272] Time 0.814 (0.830) Data 0.001 (0.008) Loss 2.7729 (2.6234) Prec@1 32.500 (36.482) Prec@5 66.875 (67.085) Epoch: [11][500/11272] Time 0.746 (0.829) Data 0.001 (0.007) Loss 2.5781 (2.6248) Prec@1 36.875 (36.441) Prec@5 67.500 (67.065) Epoch: [11][510/11272] Time 0.771 (0.830) Data 0.002 (0.007) Loss 2.5931 (2.6242) Prec@1 36.250 (36.433) Prec@5 71.250 (67.078) Epoch: [11][520/11272] Time 0.898 (0.830) Data 0.001 (0.007) Loss 2.4156 (2.6252) Prec@1 41.250 (36.407) Prec@5 69.375 (67.059) Epoch: [11][530/11272] Time 0.884 (0.829) Data 0.002 (0.007) Loss 2.5617 (2.6246) Prec@1 41.875 (36.448) Prec@5 69.375 (67.067) Epoch: [11][540/11272] Time 0.803 (0.829) Data 0.002 (0.007) Loss 2.5873 (2.6235) Prec@1 38.125 (36.480) Prec@5 71.250 (67.070) Epoch: [11][550/11272] Time 0.918 (0.829) Data 0.003 (0.007) Loss 2.6812 (2.6242) Prec@1 33.750 (36.469) Prec@5 66.875 (67.052) Epoch: [11][560/11272] Time 0.844 (0.829) Data 0.001 (0.007) Loss 2.7259 (2.6238) Prec@1 36.250 (36.492) Prec@5 65.625 (67.064) Epoch: [11][570/11272] Time 0.755 (0.829) Data 0.002 (0.007) Loss 2.8512 (2.6234) Prec@1 33.750 (36.519) Prec@5 61.250 (67.064) Epoch: [11][580/11272] Time 0.762 (0.829) Data 0.002 (0.007) Loss 2.9466 (2.6251) Prec@1 31.250 (36.478) Prec@5 58.750 (67.020) Epoch: [11][590/11272] Time 0.883 (0.830) Data 0.002 (0.007) Loss 2.7156 (2.6257) Prec@1 36.875 (36.481) Prec@5 60.625 (67.014) Epoch: [11][600/11272] Time 0.903 (0.830) Data 0.002 (0.007) Loss 2.4260 (2.6240) Prec@1 35.625 (36.512) Prec@5 68.125 (67.049) Epoch: [11][610/11272] Time 0.761 (0.829) Data 0.001 (0.006) Loss 2.5731 (2.6238) Prec@1 35.000 (36.541) Prec@5 68.750 (67.064) Epoch: [11][620/11272] Time 0.764 (0.829) Data 0.001 (0.006) Loss 2.6351 (2.6249) Prec@1 35.625 (36.506) Prec@5 66.875 (67.034) Epoch: [11][630/11272] Time 0.917 (0.829) Data 0.002 (0.006) Loss 2.5480 (2.6257) Prec@1 34.375 (36.500) Prec@5 71.875 (67.027) Epoch: [11][640/11272] Time 0.871 (0.830) Data 0.002 (0.006) Loss 2.6777 (2.6254) Prec@1 30.625 (36.499) Prec@5 68.125 (67.014) Epoch: [11][650/11272] Time 0.746 (0.830) Data 0.002 (0.006) Loss 2.7055 (2.6269) Prec@1 35.000 (36.491) Prec@5 66.250 (66.990) Epoch: [11][660/11272] Time 0.807 (0.830) Data 0.001 (0.006) Loss 2.3944 (2.6261) Prec@1 36.875 (36.506) Prec@5 69.375 (67.013) Epoch: [11][670/11272] Time 0.878 (0.830) Data 0.003 (0.006) Loss 2.5553 (2.6257) Prec@1 34.375 (36.510) Prec@5 67.500 (67.011) Epoch: [11][680/11272] Time 0.873 (0.829) Data 0.002 (0.006) Loss 2.4092 (2.6245) Prec@1 40.625 (36.538) Prec@5 71.875 (67.033) Epoch: [11][690/11272] Time 0.740 (0.829) Data 0.002 (0.006) Loss 2.7836 (2.6250) Prec@1 31.250 (36.536) Prec@5 64.375 (67.024) Epoch: [11][700/11272] Time 0.891 (0.829) Data 0.002 (0.006) Loss 2.5825 (2.6238) Prec@1 34.375 (36.567) Prec@5 71.875 (67.051) Epoch: [11][710/11272] Time 0.921 (0.829) Data 0.002 (0.006) Loss 2.5089 (2.6240) Prec@1 40.000 (36.568) Prec@5 68.125 (67.041) Epoch: [11][720/11272] Time 0.770 (0.829) Data 0.002 (0.006) Loss 2.6451 (2.6235) Prec@1 35.625 (36.558) Prec@5 65.000 (67.043) Epoch: [11][730/11272] Time 0.761 (0.828) Data 0.002 (0.006) Loss 2.9227 (2.6248) Prec@1 28.750 (36.529) Prec@5 60.000 (67.015) Epoch: [11][740/11272] Time 0.907 (0.828) Data 0.001 (0.006) Loss 2.4689 (2.6240) Prec@1 38.125 (36.552) Prec@5 68.750 (67.033) Epoch: [11][750/11272] Time 0.880 (0.828) Data 0.002 (0.006) Loss 2.4378 (2.6238) Prec@1 40.625 (36.554) Prec@5 73.750 (67.053) Epoch: [11][760/11272] Time 0.740 (0.828) Data 0.002 (0.006) Loss 2.4711 (2.6232) Prec@1 46.250 (36.558) Prec@5 68.125 (67.066) Epoch: [11][770/11272] Time 0.713 (0.828) Data 0.001 (0.005) Loss 2.8984 (2.6232) Prec@1 33.750 (36.568) Prec@5 63.125 (67.068) Epoch: [11][780/11272] Time 0.831 (0.828) Data 0.001 (0.005) Loss 2.3400 (2.6227) Prec@1 41.875 (36.579) Prec@5 72.500 (67.063) Epoch: [11][790/11272] Time 0.857 (0.827) Data 0.002 (0.005) Loss 2.4329 (2.6221) Prec@1 43.125 (36.584) Prec@5 65.000 (67.064) Epoch: [11][800/11272] Time 0.743 (0.827) Data 0.002 (0.005) Loss 2.6919 (2.6226) Prec@1 36.250 (36.576) Prec@5 66.875 (67.057) Epoch: [11][810/11272] Time 0.769 (0.827) Data 0.002 (0.005) Loss 2.7320 (2.6228) Prec@1 31.875 (36.552) Prec@5 65.625 (67.050) Epoch: [11][820/11272] Time 0.863 (0.827) Data 0.002 (0.005) Loss 2.4658 (2.6214) Prec@1 43.125 (36.588) Prec@5 71.250 (67.075) Epoch: [11][830/11272] Time 0.782 (0.827) Data 0.002 (0.005) Loss 2.5982 (2.6213) Prec@1 35.625 (36.600) Prec@5 65.000 (67.073) Epoch: [11][840/11272] Time 0.772 (0.827) Data 0.002 (0.005) Loss 2.6492 (2.6221) Prec@1 34.375 (36.583) Prec@5 68.125 (67.065) Epoch: [11][850/11272] Time 0.850 (0.827) Data 0.001 (0.005) Loss 2.4198 (2.6214) Prec@1 42.500 (36.603) Prec@5 69.375 (67.075) Epoch: [11][860/11272] Time 0.865 (0.827) Data 0.002 (0.005) Loss 2.5794 (2.6220) Prec@1 36.250 (36.585) Prec@5 68.750 (67.061) Epoch: [11][870/11272] Time 0.760 (0.827) Data 0.002 (0.005) Loss 2.7557 (2.6224) Prec@1 29.375 (36.568) Prec@5 61.875 (67.057) Epoch: [11][880/11272] Time 0.779 (0.826) Data 0.002 (0.005) Loss 2.7477 (2.6224) Prec@1 38.750 (36.556) Prec@5 63.750 (67.062) Epoch: [11][890/11272] Time 0.875 (0.826) Data 0.002 (0.005) Loss 2.7636 (2.6227) Prec@1 27.500 (36.545) Prec@5 66.250 (67.060) Epoch: [11][900/11272] Time 0.877 (0.826) Data 0.002 (0.005) Loss 2.6257 (2.6230) Prec@1 35.000 (36.543) Prec@5 68.750 (67.066) Epoch: [11][910/11272] Time 0.814 (0.826) Data 0.002 (0.005) Loss 2.7639 (2.6239) Prec@1 37.500 (36.524) Prec@5 65.000 (67.053) Epoch: [11][920/11272] Time 0.751 (0.826) Data 0.002 (0.005) Loss 2.5083 (2.6236) Prec@1 43.125 (36.541) Prec@5 68.750 (67.060) Epoch: [11][930/11272] Time 0.844 (0.826) Data 0.002 (0.005) Loss 2.4911 (2.6242) Prec@1 36.875 (36.508) Prec@5 69.375 (67.061) Epoch: [11][940/11272] Time 0.873 (0.826) Data 0.002 (0.005) Loss 2.4252 (2.6235) Prec@1 38.125 (36.524) Prec@5 71.250 (67.074) Epoch: [11][950/11272] Time 0.806 (0.826) Data 0.001 (0.005) Loss 2.8976 (2.6235) Prec@1 31.875 (36.542) Prec@5 61.250 (67.072) Epoch: [11][960/11272] Time 0.933 (0.826) Data 0.001 (0.005) Loss 2.4341 (2.6244) Prec@1 40.625 (36.519) Prec@5 71.250 (67.057) Epoch: [11][970/11272] Time 0.844 (0.826) Data 0.001 (0.005) Loss 2.7237 (2.6247) Prec@1 31.875 (36.519) Prec@5 65.625 (67.049) Epoch: [11][980/11272] Time 0.821 (0.826) Data 0.001 (0.005) Loss 2.6039 (2.6246) Prec@1 37.500 (36.521) Prec@5 66.250 (67.058) Epoch: [11][990/11272] Time 0.801 (0.826) Data 0.002 (0.005) Loss 2.7003 (2.6254) Prec@1 34.375 (36.509) Prec@5 65.625 (67.040) Epoch: [11][1000/11272] Time 0.849 (0.826) Data 0.002 (0.005) Loss 2.5441 (2.6261) Prec@1 38.125 (36.499) Prec@5 68.750 (67.033) Epoch: [11][1010/11272] Time 0.915 (0.826) Data 0.002 (0.005) Loss 2.8128 (2.6259) Prec@1 36.875 (36.495) Prec@5 61.875 (67.033) Epoch: [11][1020/11272] Time 0.788 (0.825) Data 0.001 (0.005) Loss 2.5858 (2.6262) Prec@1 43.750 (36.510) Prec@5 69.375 (67.026) Epoch: [11][1030/11272] Time 0.745 (0.825) Data 0.002 (0.004) Loss 2.7177 (2.6267) Prec@1 33.125 (36.494) Prec@5 60.625 (67.015) Epoch: [11][1040/11272] Time 0.891 (0.826) Data 0.002 (0.004) Loss 2.7864 (2.6271) Prec@1 31.250 (36.479) Prec@5 62.500 (66.993) Epoch: [11][1050/11272] Time 0.875 (0.826) Data 0.002 (0.004) Loss 2.6561 (2.6266) Prec@1 38.750 (36.487) Prec@5 63.125 (66.998) Epoch: [11][1060/11272] Time 0.737 (0.826) Data 0.002 (0.004) Loss 2.5209 (2.6266) Prec@1 38.125 (36.486) Prec@5 68.750 (67.003) Epoch: [11][1070/11272] Time 0.776 (0.826) Data 0.002 (0.004) Loss 2.4264 (2.6259) Prec@1 41.250 (36.513) Prec@5 70.000 (67.023) Epoch: [11][1080/11272] Time 0.868 (0.826) Data 0.002 (0.004) Loss 2.6317 (2.6259) Prec@1 38.125 (36.503) Prec@5 70.000 (67.031) Epoch: [11][1090/11272] Time 0.769 (0.825) Data 0.003 (0.004) Loss 2.6081 (2.6258) Prec@1 33.125 (36.494) Prec@5 67.500 (67.041) Epoch: [11][1100/11272] Time 0.798 (0.825) Data 0.002 (0.004) Loss 2.5404 (2.6255) Prec@1 32.500 (36.504) Prec@5 67.500 (67.045) Epoch: [11][1110/11272] Time 0.887 (0.826) Data 0.002 (0.004) Loss 2.4783 (2.6249) Prec@1 36.875 (36.514) Prec@5 70.625 (67.060) Epoch: [11][1120/11272] Time 0.879 (0.825) Data 0.002 (0.004) Loss 2.2871 (2.6246) Prec@1 41.250 (36.508) Prec@5 73.750 (67.065) Epoch: [11][1130/11272] Time 0.740 (0.825) Data 0.002 (0.004) Loss 2.7942 (2.6236) Prec@1 30.625 (36.519) Prec@5 66.250 (67.088) Epoch: [11][1140/11272] Time 0.791 (0.825) Data 0.002 (0.004) Loss 2.1944 (2.6236) Prec@1 46.875 (36.517) Prec@5 73.750 (67.082) Epoch: [11][1150/11272] Time 0.881 (0.826) Data 0.002 (0.004) Loss 2.4563 (2.6238) Prec@1 37.500 (36.499) Prec@5 68.125 (67.081) Epoch: [11][1160/11272] Time 0.864 (0.825) Data 0.001 (0.004) Loss 2.7684 (2.6244) Prec@1 35.000 (36.473) Prec@5 68.750 (67.075) Epoch: [11][1170/11272] Time 0.750 (0.825) Data 0.002 (0.004) Loss 2.5821 (2.6248) Prec@1 34.375 (36.457) Prec@5 68.750 (67.062) Epoch: [11][1180/11272] Time 0.784 (0.825) Data 0.003 (0.004) Loss 2.5472 (2.6250) Prec@1 38.125 (36.438) Prec@5 68.125 (67.066) Epoch: [11][1190/11272] Time 0.857 (0.825) Data 0.001 (0.004) Loss 2.5696 (2.6251) Prec@1 44.375 (36.437) Prec@5 68.125 (67.074) Epoch: [11][1200/11272] Time 0.895 (0.825) Data 0.001 (0.004) Loss 2.6161 (2.6255) Prec@1 40.000 (36.433) Prec@5 58.125 (67.058) Epoch: [11][1210/11272] Time 0.746 (0.825) Data 0.002 (0.004) Loss 2.5714 (2.6258) Prec@1 39.375 (36.427) Prec@5 73.750 (67.063) Epoch: [11][1220/11272] Time 0.899 (0.826) Data 0.001 (0.004) Loss 2.4505 (2.6259) Prec@1 41.250 (36.424) Prec@5 71.875 (67.059) Epoch: [11][1230/11272] Time 0.877 (0.826) Data 0.002 (0.004) Loss 2.5326 (2.6262) Prec@1 38.750 (36.430) Prec@5 71.250 (67.056) Epoch: [11][1240/11272] Time 0.751 (0.825) Data 0.001 (0.004) Loss 2.7343 (2.6257) Prec@1 31.250 (36.428) Prec@5 63.125 (67.064) Epoch: [11][1250/11272] Time 0.746 (0.825) Data 0.002 (0.004) Loss 2.6806 (2.6254) Prec@1 37.500 (36.431) Prec@5 65.625 (67.074) Epoch: [11][1260/11272] Time 0.902 (0.825) Data 0.002 (0.004) Loss 2.2974 (2.6250) Prec@1 42.500 (36.437) Prec@5 74.375 (67.090) Epoch: [11][1270/11272] Time 0.837 (0.825) Data 0.002 (0.004) Loss 2.5012 (2.6248) Prec@1 36.250 (36.428) Prec@5 70.000 (67.091) Epoch: [11][1280/11272] Time 0.775 (0.825) Data 0.002 (0.004) Loss 2.6984 (2.6244) Prec@1 38.125 (36.436) Prec@5 66.875 (67.096) Epoch: [11][1290/11272] Time 0.740 (0.825) Data 0.001 (0.004) Loss 2.4776 (2.6244) Prec@1 41.250 (36.433) Prec@5 73.125 (67.106) Epoch: [11][1300/11272] Time 0.934 (0.825) Data 0.001 (0.004) Loss 2.9989 (2.6249) Prec@1 26.875 (36.410) Prec@5 59.375 (67.108) Epoch: [11][1310/11272] Time 0.846 (0.825) Data 0.001 (0.004) Loss 2.5949 (2.6255) Prec@1 35.625 (36.394) Prec@5 68.750 (67.092) Epoch: [11][1320/11272] Time 0.751 (0.825) Data 0.002 (0.004) Loss 2.6780 (2.6248) Prec@1 35.625 (36.413) Prec@5 65.000 (67.106) Epoch: [11][1330/11272] Time 0.817 (0.825) Data 0.002 (0.004) Loss 2.4932 (2.6248) Prec@1 35.625 (36.407) Prec@5 66.250 (67.105) Epoch: [11][1340/11272] Time 0.874 (0.825) Data 0.001 (0.004) Loss 2.7129 (2.6250) Prec@1 31.875 (36.408) Prec@5 70.000 (67.103) Epoch: [11][1350/11272] Time 0.771 (0.825) Data 0.003 (0.004) Loss 2.5449 (2.6250) Prec@1 36.250 (36.410) Prec@5 69.375 (67.107) Epoch: [11][1360/11272] Time 0.802 (0.825) Data 0.002 (0.004) Loss 2.9689 (2.6249) Prec@1 31.875 (36.416) Prec@5 59.375 (67.105) Epoch: [11][1370/11272] Time 0.825 (0.825) Data 0.001 (0.004) Loss 2.5487 (2.6255) Prec@1 41.250 (36.415) Prec@5 68.750 (67.098) Epoch: [11][1380/11272] Time 0.889 (0.825) Data 0.002 (0.004) Loss 2.8059 (2.6257) Prec@1 34.375 (36.421) Prec@5 65.000 (67.094) Epoch: [11][1390/11272] Time 0.780 (0.825) Data 0.002 (0.004) Loss 2.7307 (2.6252) Prec@1 36.875 (36.436) Prec@5 63.125 (67.095) Epoch: [11][1400/11272] Time 0.750 (0.825) Data 0.002 (0.004) Loss 2.7012 (2.6260) Prec@1 33.750 (36.423) Prec@5 68.125 (67.078) Epoch: [11][1410/11272] Time 0.854 (0.825) Data 0.001 (0.004) Loss 2.4101 (2.6256) Prec@1 36.250 (36.429) Prec@5 75.000 (67.090) Epoch: [11][1420/11272] Time 0.881 (0.825) Data 0.002 (0.004) Loss 2.8456 (2.6259) Prec@1 31.875 (36.433) Prec@5 64.375 (67.081) Epoch: [11][1430/11272] Time 0.735 (0.825) Data 0.001 (0.004) Loss 2.6929 (2.6263) Prec@1 39.375 (36.426) Prec@5 66.250 (67.075) Epoch: [11][1440/11272] Time 0.753 (0.825) Data 0.002 (0.004) Loss 2.6231 (2.6264) Prec@1 39.375 (36.428) Prec@5 65.625 (67.069) Epoch: [11][1450/11272] Time 0.951 (0.825) Data 0.002 (0.004) Loss 2.7026 (2.6262) Prec@1 32.500 (36.437) Prec@5 64.375 (67.077) Epoch: [11][1460/11272] Time 0.842 (0.825) Data 0.001 (0.004) Loss 2.6663 (2.6266) Prec@1 39.375 (36.442) Prec@5 66.250 (67.066) Epoch: [11][1470/11272] Time 0.767 (0.825) Data 0.002 (0.004) Loss 2.4677 (2.6264) Prec@1 40.625 (36.453) Prec@5 71.250 (67.071) Epoch: [11][1480/11272] Time 0.890 (0.825) Data 0.002 (0.004) Loss 2.4763 (2.6260) Prec@1 35.625 (36.461) Prec@5 70.625 (67.074) Epoch: [11][1490/11272] Time 0.876 (0.825) Data 0.002 (0.004) Loss 2.5815 (2.6259) Prec@1 36.250 (36.454) Prec@5 66.875 (67.081) Epoch: [11][1500/11272] Time 0.748 (0.825) Data 0.003 (0.004) Loss 2.8819 (2.6263) Prec@1 30.625 (36.449) Prec@5 61.875 (67.068) Epoch: [11][1510/11272] Time 0.747 (0.825) Data 0.002 (0.004) Loss 2.7441 (2.6257) Prec@1 31.250 (36.464) Prec@5 64.375 (67.080) Epoch: [11][1520/11272] Time 0.872 (0.825) Data 0.001 (0.004) Loss 2.7730 (2.6257) Prec@1 31.875 (36.461) Prec@5 65.000 (67.076) Epoch: [11][1530/11272] Time 0.889 (0.825) Data 0.002 (0.004) Loss 2.6933 (2.6258) Prec@1 33.125 (36.452) Prec@5 67.500 (67.071) Epoch: [11][1540/11272] Time 0.827 (0.825) Data 0.001 (0.004) Loss 2.5413 (2.6255) Prec@1 34.375 (36.451) Prec@5 69.375 (67.070) Epoch: [11][1550/11272] Time 0.766 (0.825) Data 0.002 (0.004) Loss 2.4717 (2.6253) Prec@1 37.500 (36.449) Prec@5 70.625 (67.076) Epoch: [11][1560/11272] Time 0.913 (0.825) Data 0.002 (0.004) Loss 2.4943 (2.6246) Prec@1 38.750 (36.460) Prec@5 69.375 (67.091) Epoch: [11][1570/11272] Time 0.867 (0.825) Data 0.002 (0.004) Loss 2.6879 (2.6240) Prec@1 36.875 (36.475) Prec@5 68.125 (67.099) Epoch: [11][1580/11272] Time 0.820 (0.825) Data 0.002 (0.004) Loss 2.7521 (2.6239) Prec@1 32.500 (36.479) Prec@5 65.625 (67.113) Epoch: [11][1590/11272] Time 0.739 (0.825) Data 0.001 (0.003) Loss 2.7182 (2.6236) Prec@1 34.375 (36.477) Prec@5 63.125 (67.118) Epoch: [11][1600/11272] Time 0.920 (0.825) Data 0.005 (0.003) Loss 2.6787 (2.6236) Prec@1 31.875 (36.474) Prec@5 66.250 (67.117) Epoch: [11][1610/11272] Time 0.872 (0.825) Data 0.002 (0.003) Loss 2.7237 (2.6234) Prec@1 34.375 (36.484) Prec@5 62.500 (67.115) Epoch: [11][1620/11272] Time 0.766 (0.825) Data 0.002 (0.003) Loss 2.5385 (2.6235) Prec@1 36.875 (36.476) Prec@5 71.250 (67.118) Epoch: [11][1630/11272] Time 0.870 (0.825) Data 0.001 (0.003) Loss 2.6735 (2.6240) Prec@1 36.875 (36.462) Prec@5 66.875 (67.112) Epoch: [11][1640/11272] Time 0.941 (0.825) Data 0.002 (0.003) Loss 2.6327 (2.6239) Prec@1 39.375 (36.467) Prec@5 69.375 (67.110) Epoch: [11][1650/11272] Time 0.750 (0.825) Data 0.002 (0.003) Loss 2.3285 (2.6237) Prec@1 38.750 (36.467) Prec@5 72.500 (67.123) Epoch: [11][1660/11272] Time 0.772 (0.825) Data 0.001 (0.003) Loss 2.6293 (2.6241) Prec@1 38.750 (36.468) Prec@5 71.250 (67.116) Epoch: [11][1670/11272] Time 0.883 (0.825) Data 0.002 (0.003) Loss 2.6687 (2.6239) Prec@1 35.625 (36.469) Prec@5 64.375 (67.115) Epoch: [11][1680/11272] Time 0.881 (0.825) Data 0.001 (0.003) Loss 2.5189 (2.6235) Prec@1 37.500 (36.475) Prec@5 69.375 (67.126) Epoch: [11][1690/11272] Time 0.740 (0.825) Data 0.002 (0.003) Loss 2.7797 (2.6236) Prec@1 32.500 (36.476) Prec@5 65.000 (67.132) Epoch: [11][1700/11272] Time 0.761 (0.825) Data 0.002 (0.003) Loss 2.6799 (2.6237) Prec@1 34.375 (36.469) Prec@5 67.500 (67.130) Epoch: [11][1710/11272] Time 0.927 (0.825) Data 0.001 (0.003) Loss 2.3431 (2.6238) Prec@1 40.000 (36.458) Prec@5 72.500 (67.129) Epoch: [11][1720/11272] Time 0.874 (0.825) Data 0.002 (0.003) Loss 2.9637 (2.6241) Prec@1 31.250 (36.453) Prec@5 61.250 (67.118) Epoch: [11][1730/11272] Time 0.759 (0.825) Data 0.002 (0.003) Loss 2.6984 (2.6245) Prec@1 35.000 (36.455) Prec@5 66.250 (67.111) Epoch: [11][1740/11272] Time 0.768 (0.825) Data 0.002 (0.003) Loss 2.6981 (2.6243) Prec@1 32.500 (36.459) Prec@5 69.375 (67.122) Epoch: [11][1750/11272] Time 0.859 (0.825) Data 0.001 (0.003) Loss 2.8449 (2.6247) Prec@1 33.125 (36.452) Prec@5 64.375 (67.115) Epoch: [11][1760/11272] Time 0.738 (0.825) Data 0.001 (0.003) Loss 2.2481 (2.6244) Prec@1 43.750 (36.459) Prec@5 75.000 (67.120) Epoch: [11][1770/11272] Time 0.738 (0.825) Data 0.001 (0.003) Loss 2.6927 (2.6241) Prec@1 31.875 (36.457) Prec@5 65.625 (67.126) Epoch: [11][1780/11272] Time 0.915 (0.825) Data 0.002 (0.003) Loss 2.5443 (2.6244) Prec@1 33.750 (36.449) Prec@5 66.875 (67.119) Epoch: [11][1790/11272] Time 0.832 (0.825) Data 0.002 (0.003) Loss 2.7079 (2.6244) Prec@1 33.750 (36.450) Prec@5 68.750 (67.124) Epoch: [11][1800/11272] Time 0.803 (0.825) Data 0.002 (0.003) Loss 2.5496 (2.6238) Prec@1 42.500 (36.459) Prec@5 65.625 (67.132) Epoch: [11][1810/11272] Time 0.765 (0.825) Data 0.002 (0.003) Loss 2.8405 (2.6238) Prec@1 31.250 (36.454) Prec@5 62.500 (67.136) Epoch: [11][1820/11272] Time 0.896 (0.825) Data 0.002 (0.003) Loss 2.6907 (2.6237) Prec@1 36.250 (36.451) Prec@5 67.500 (67.137) Epoch: [11][1830/11272] Time 0.875 (0.825) Data 0.002 (0.003) Loss 2.6545 (2.6240) Prec@1 42.500 (36.450) Prec@5 68.750 (67.131) Epoch: [11][1840/11272] Time 0.748 (0.825) Data 0.001 (0.003) Loss 2.4816 (2.6241) Prec@1 40.000 (36.449) Prec@5 71.250 (67.132) Epoch: [11][1850/11272] Time 0.761 (0.825) Data 0.002 (0.003) Loss 2.5682 (2.6240) Prec@1 33.750 (36.448) Prec@5 70.000 (67.137) Epoch: [11][1860/11272] Time 0.871 (0.825) Data 0.002 (0.003) Loss 2.6332 (2.6239) Prec@1 33.750 (36.448) Prec@5 65.625 (67.144) Epoch: [11][1870/11272] Time 0.922 (0.825) Data 0.002 (0.003) Loss 2.5894 (2.6239) Prec@1 30.625 (36.450) Prec@5 71.875 (67.145) Epoch: [11][1880/11272] Time 0.829 (0.825) Data 0.003 (0.003) Loss 2.5064 (2.6239) Prec@1 38.125 (36.442) Prec@5 75.000 (67.144) Epoch: [11][1890/11272] Time 0.938 (0.825) Data 0.001 (0.003) Loss 2.6788 (2.6241) Prec@1 30.625 (36.436) Prec@5 66.250 (67.139) Epoch: [11][1900/11272] Time 0.880 (0.825) Data 0.002 (0.003) Loss 2.6486 (2.6241) Prec@1 35.625 (36.438) Prec@5 67.500 (67.144) Epoch: [11][1910/11272] Time 0.748 (0.825) Data 0.001 (0.003) Loss 2.7254 (2.6243) Prec@1 35.000 (36.439) Prec@5 65.625 (67.145) Epoch: [11][1920/11272] Time 0.737 (0.825) Data 0.001 (0.003) Loss 2.4753 (2.6240) Prec@1 36.875 (36.444) Prec@5 71.250 (67.147) Epoch: [11][1930/11272] Time 0.984 (0.825) Data 0.002 (0.003) Loss 2.6663 (2.6236) Prec@1 33.125 (36.453) Prec@5 67.500 (67.158) Epoch: [11][1940/11272] Time 0.891 (0.825) Data 0.002 (0.003) Loss 2.4561 (2.6235) Prec@1 40.000 (36.451) Prec@5 70.000 (67.156) Epoch: [11][1950/11272] Time 0.743 (0.825) Data 0.001 (0.003) Loss 2.7207 (2.6237) Prec@1 36.875 (36.446) Prec@5 65.000 (67.155) Epoch: [11][1960/11272] Time 0.745 (0.825) Data 0.002 (0.003) Loss 2.8430 (2.6238) Prec@1 33.125 (36.440) Prec@5 61.250 (67.151) Epoch: [11][1970/11272] Time 0.928 (0.825) Data 0.002 (0.003) Loss 2.6608 (2.6240) Prec@1 38.125 (36.441) Prec@5 65.000 (67.145) Epoch: [11][1980/11272] Time 0.896 (0.825) Data 0.001 (0.003) Loss 2.7634 (2.6243) Prec@1 31.875 (36.439) Prec@5 66.875 (67.139) Epoch: [11][1990/11272] Time 0.749 (0.825) Data 0.002 (0.003) Loss 2.7429 (2.6247) Prec@1 35.000 (36.436) Prec@5 68.750 (67.130) Epoch: [11][2000/11272] Time 0.775 (0.825) Data 0.002 (0.003) Loss 2.4849 (2.6250) Prec@1 36.875 (36.423) Prec@5 70.000 (67.124) Epoch: [11][2010/11272] Time 0.868 (0.825) Data 0.001 (0.003) Loss 2.3475 (2.6248) Prec@1 43.125 (36.431) Prec@5 71.250 (67.126) Epoch: [11][2020/11272] Time 0.824 (0.825) Data 0.004 (0.003) Loss 2.5925 (2.6251) Prec@1 41.250 (36.434) Prec@5 65.625 (67.114) Epoch: [11][2030/11272] Time 0.761 (0.825) Data 0.002 (0.003) Loss 2.8765 (2.6254) Prec@1 34.375 (36.428) Prec@5 60.625 (67.106) Epoch: [11][2040/11272] Time 0.900 (0.825) Data 0.001 (0.003) Loss 2.3288 (2.6250) Prec@1 40.625 (36.441) Prec@5 71.875 (67.112) Epoch: [11][2050/11272] Time 0.895 (0.825) Data 0.002 (0.003) Loss 2.6792 (2.6250) Prec@1 33.750 (36.447) Prec@5 70.000 (67.119) Epoch: [11][2060/11272] Time 0.746 (0.825) Data 0.002 (0.003) Loss 2.4924 (2.6251) Prec@1 40.000 (36.444) Prec@5 69.375 (67.119) Epoch: [11][2070/11272] Time 0.755 (0.825) Data 0.002 (0.003) Loss 2.4069 (2.6247) Prec@1 43.125 (36.446) Prec@5 71.250 (67.129) Epoch: [11][2080/11272] Time 0.896 (0.825) Data 0.002 (0.003) Loss 2.7967 (2.6249) Prec@1 35.625 (36.451) Prec@5 60.000 (67.128) Epoch: [11][2090/11272] Time 0.849 (0.825) Data 0.001 (0.003) Loss 2.5189 (2.6247) Prec@1 33.125 (36.453) Prec@5 71.875 (67.128) Epoch: [11][2100/11272] Time 0.751 (0.825) Data 0.001 (0.003) Loss 2.7152 (2.6244) Prec@1 34.375 (36.456) Prec@5 64.375 (67.137) Epoch: [11][2110/11272] Time 0.774 (0.825) Data 0.002 (0.003) Loss 2.8665 (2.6247) Prec@1 30.625 (36.447) Prec@5 61.250 (67.130) Epoch: [11][2120/11272] Time 0.877 (0.825) Data 0.002 (0.003) Loss 2.6101 (2.6245) Prec@1 40.625 (36.448) Prec@5 68.125 (67.130) Epoch: [11][2130/11272] Time 0.877 (0.825) Data 0.002 (0.003) Loss 2.6740 (2.6246) Prec@1 33.750 (36.449) Prec@5 66.875 (67.132) Epoch: [11][2140/11272] Time 0.748 (0.825) Data 0.002 (0.003) Loss 2.7442 (2.6246) Prec@1 36.250 (36.445) Prec@5 66.875 (67.130) Epoch: [11][2150/11272] Time 0.891 (0.825) Data 0.002 (0.003) Loss 2.6872 (2.6247) Prec@1 37.500 (36.444) Prec@5 68.750 (67.127) Epoch: [11][2160/11272] Time 0.875 (0.825) Data 0.002 (0.003) Loss 2.7801 (2.6245) Prec@1 28.750 (36.445) Prec@5 63.750 (67.131) Epoch: [11][2170/11272] Time 0.751 (0.825) Data 0.001 (0.003) Loss 2.5549 (2.6243) Prec@1 36.250 (36.443) Prec@5 68.125 (67.141) Epoch: [11][2180/11272] Time 0.750 (0.825) Data 0.002 (0.003) Loss 2.6076 (2.6243) Prec@1 32.500 (36.444) Prec@5 73.125 (67.146) Epoch: [11][2190/11272] Time 0.903 (0.825) Data 0.001 (0.003) Loss 2.9290 (2.6243) Prec@1 29.375 (36.441) Prec@5 62.500 (67.143) Epoch: [11][2200/11272] Time 0.889 (0.825) Data 0.002 (0.003) Loss 2.5691 (2.6245) Prec@1 36.875 (36.438) Prec@5 67.500 (67.140) Epoch: [11][2210/11272] Time 0.802 (0.825) Data 0.002 (0.003) Loss 2.8206 (2.6249) Prec@1 26.875 (36.431) Prec@5 63.750 (67.132) Epoch: [11][2220/11272] Time 0.763 (0.825) Data 0.001 (0.003) Loss 2.7306 (2.6249) Prec@1 31.250 (36.439) Prec@5 66.875 (67.134) Epoch: [11][2230/11272] Time 0.889 (0.825) Data 0.002 (0.003) Loss 2.5627 (2.6245) Prec@1 35.000 (36.451) Prec@5 66.250 (67.142) Epoch: [11][2240/11272] Time 0.891 (0.825) Data 0.002 (0.003) Loss 2.7299 (2.6245) Prec@1 32.500 (36.456) Prec@5 66.250 (67.141) Epoch: [11][2250/11272] Time 0.753 (0.825) Data 0.001 (0.003) Loss 2.6173 (2.6242) Prec@1 43.125 (36.460) Prec@5 65.625 (67.141) Epoch: [11][2260/11272] Time 0.744 (0.825) Data 0.002 (0.003) Loss 2.6103 (2.6240) Prec@1 33.125 (36.463) Prec@5 68.750 (67.147) Epoch: [11][2270/11272] Time 0.898 (0.825) Data 0.002 (0.003) Loss 2.3739 (2.6239) Prec@1 38.750 (36.462) Prec@5 69.375 (67.144) Epoch: [11][2280/11272] Time 0.772 (0.825) Data 0.003 (0.003) Loss 2.6050 (2.6239) Prec@1 35.625 (36.465) Prec@5 69.375 (67.141) Epoch: [11][2290/11272] Time 0.749 (0.825) Data 0.001 (0.003) Loss 2.7051 (2.6240) Prec@1 34.375 (36.465) Prec@5 63.750 (67.141) Epoch: [11][2300/11272] Time 0.926 (0.825) Data 0.002 (0.003) Loss 2.6695 (2.6241) Prec@1 36.250 (36.461) Prec@5 66.875 (67.136) Epoch: [11][2310/11272] Time 0.919 (0.825) Data 0.001 (0.003) Loss 2.7920 (2.6241) Prec@1 31.875 (36.463) Prec@5 60.625 (67.133) Epoch: [11][2320/11272] Time 0.841 (0.825) Data 0.001 (0.003) Loss 2.5309 (2.6243) Prec@1 42.500 (36.465) Prec@5 63.750 (67.133) Epoch: [11][2330/11272] Time 0.767 (0.825) Data 0.002 (0.003) Loss 2.7278 (2.6241) Prec@1 36.250 (36.475) Prec@5 66.875 (67.144) Epoch: [11][2340/11272] Time 0.896 (0.825) Data 0.001 (0.003) Loss 2.8098 (2.6242) Prec@1 33.750 (36.475) Prec@5 64.375 (67.137) Epoch: [11][2350/11272] Time 0.988 (0.825) Data 0.002 (0.003) Loss 2.8678 (2.6242) Prec@1 28.750 (36.470) Prec@5 63.750 (67.135) Epoch: [11][2360/11272] Time 0.767 (0.825) Data 0.001 (0.003) Loss 2.8604 (2.6240) Prec@1 33.125 (36.477) Prec@5 63.125 (67.141) Epoch: [11][2370/11272] Time 0.743 (0.825) Data 0.002 (0.003) Loss 2.6123 (2.6245) Prec@1 31.875 (36.465) Prec@5 64.375 (67.130) Epoch: [11][2380/11272] Time 0.879 (0.825) Data 0.002 (0.003) Loss 2.5696 (2.6247) Prec@1 41.250 (36.465) Prec@5 70.625 (67.125) Epoch: [11][2390/11272] Time 0.865 (0.825) Data 0.002 (0.003) Loss 2.4962 (2.6248) Prec@1 38.125 (36.461) Prec@5 70.000 (67.120) Epoch: [11][2400/11272] Time 0.751 (0.825) Data 0.001 (0.003) Loss 2.6754 (2.6247) Prec@1 33.750 (36.464) Prec@5 64.375 (67.122) Epoch: [11][2410/11272] Time 0.929 (0.825) Data 0.002 (0.003) Loss 2.6045 (2.6247) Prec@1 33.125 (36.461) Prec@5 68.750 (67.123) Epoch: [11][2420/11272] Time 0.900 (0.825) Data 0.001 (0.003) Loss 2.4991 (2.6245) Prec@1 40.000 (36.465) Prec@5 66.875 (67.123) Epoch: [11][2430/11272] Time 0.754 (0.825) Data 0.001 (0.003) Loss 2.8684 (2.6243) Prec@1 31.875 (36.460) Prec@5 63.750 (67.127) Epoch: [11][2440/11272] Time 0.738 (0.825) Data 0.002 (0.003) Loss 2.3989 (2.6244) Prec@1 40.000 (36.459) Prec@5 73.750 (67.128) Epoch: [11][2450/11272] Time 0.896 (0.825) Data 0.002 (0.003) Loss 2.4914 (2.6243) Prec@1 44.375 (36.462) Prec@5 68.125 (67.127) Epoch: [11][2460/11272] Time 0.901 (0.825) Data 0.002 (0.003) Loss 2.6324 (2.6242) Prec@1 37.500 (36.464) Prec@5 64.375 (67.127) Epoch: [11][2470/11272] Time 0.743 (0.825) Data 0.002 (0.003) Loss 2.6834 (2.6245) Prec@1 31.875 (36.460) Prec@5 61.875 (67.121) Epoch: [11][2480/11272] Time 0.756 (0.825) Data 0.002 (0.003) Loss 3.0011 (2.6244) Prec@1 30.000 (36.456) Prec@5 61.250 (67.126) Epoch: [11][2490/11272] Time 0.878 (0.825) Data 0.002 (0.003) Loss 2.6500 (2.6246) Prec@1 38.750 (36.452) Prec@5 66.875 (67.120) Epoch: [11][2500/11272] Time 0.853 (0.825) Data 0.002 (0.003) Loss 2.4473 (2.6240) Prec@1 38.750 (36.460) Prec@5 70.000 (67.129) Epoch: [11][2510/11272] Time 0.739 (0.825) Data 0.001 (0.003) Loss 2.8477 (2.6242) Prec@1 31.875 (36.453) Prec@5 63.750 (67.123) Epoch: [11][2520/11272] Time 0.743 (0.825) Data 0.002 (0.003) Loss 2.4018 (2.6244) Prec@1 43.750 (36.445) Prec@5 69.375 (67.122) Epoch: [11][2530/11272] Time 0.888 (0.825) Data 0.002 (0.003) Loss 2.6037 (2.6245) Prec@1 35.625 (36.438) Prec@5 64.375 (67.116) Epoch: [11][2540/11272] Time 0.893 (0.825) Data 0.002 (0.003) Loss 2.9187 (2.6243) Prec@1 31.250 (36.439) Prec@5 63.750 (67.119) Epoch: [11][2550/11272] Time 0.775 (0.825) Data 0.003 (0.003) Loss 2.5662 (2.6243) Prec@1 36.250 (36.438) Prec@5 68.750 (67.119) Epoch: [11][2560/11272] Time 0.895 (0.825) Data 0.002 (0.003) Loss 2.6538 (2.6244) Prec@1 35.000 (36.435) Prec@5 60.625 (67.116) Epoch: [11][2570/11272] Time 0.951 (0.825) Data 0.002 (0.003) Loss 2.4129 (2.6246) Prec@1 41.250 (36.431) Prec@5 70.000 (67.110) Epoch: [11][2580/11272] Time 0.746 (0.825) Data 0.001 (0.003) Loss 2.3032 (2.6246) Prec@1 40.625 (36.426) Prec@5 75.000 (67.114) Epoch: [11][2590/11272] Time 0.796 (0.825) Data 0.001 (0.003) Loss 2.2996 (2.6245) Prec@1 43.750 (36.428) Prec@5 73.125 (67.114) Epoch: [11][2600/11272] Time 0.854 (0.825) Data 0.001 (0.003) Loss 2.6327 (2.6248) Prec@1 38.750 (36.425) Prec@5 66.875 (67.110) Epoch: [11][2610/11272] Time 0.888 (0.825) Data 0.001 (0.003) Loss 2.6391 (2.6245) Prec@1 38.750 (36.428) Prec@5 68.750 (67.118) Epoch: [11][2620/11272] Time 0.808 (0.825) Data 0.002 (0.003) Loss 2.3131 (2.6243) Prec@1 44.375 (36.429) Prec@5 76.250 (67.121) Epoch: [11][2630/11272] Time 0.782 (0.825) Data 0.002 (0.003) Loss 2.6591 (2.6243) Prec@1 38.125 (36.429) Prec@5 67.500 (67.118) Epoch: [11][2640/11272] Time 0.858 (0.825) Data 0.001 (0.003) Loss 2.6634 (2.6245) Prec@1 41.250 (36.428) Prec@5 67.500 (67.116) Epoch: [11][2650/11272] Time 0.852 (0.825) Data 0.002 (0.003) Loss 2.6397 (2.6243) Prec@1 36.250 (36.428) Prec@5 65.625 (67.116) Epoch: [11][2660/11272] Time 0.767 (0.825) Data 0.001 (0.003) Loss 2.8423 (2.6244) Prec@1 36.250 (36.428) Prec@5 64.375 (67.112) Epoch: [11][2670/11272] Time 0.747 (0.825) Data 0.002 (0.003) Loss 2.5968 (2.6245) Prec@1 39.375 (36.424) Prec@5 65.625 (67.111) Epoch: [11][2680/11272] Time 0.881 (0.825) Data 0.001 (0.003) Loss 2.7426 (2.6246) Prec@1 33.125 (36.419) Prec@5 66.250 (67.106) Epoch: [11][2690/11272] Time 0.739 (0.825) Data 0.002 (0.003) Loss 2.7452 (2.6245) Prec@1 33.750 (36.419) Prec@5 66.875 (67.109) Epoch: [11][2700/11272] Time 0.766 (0.825) Data 0.001 (0.003) Loss 2.5243 (2.6249) Prec@1 42.500 (36.412) Prec@5 71.875 (67.102) Epoch: [11][2710/11272] Time 0.872 (0.825) Data 0.001 (0.003) Loss 2.4098 (2.6244) Prec@1 38.125 (36.423) Prec@5 68.125 (67.112) Epoch: [11][2720/11272] Time 0.852 (0.825) Data 0.002 (0.003) Loss 2.5298 (2.6246) Prec@1 38.125 (36.419) Prec@5 67.500 (67.108) Epoch: [11][2730/11272] Time 0.819 (0.825) Data 0.003 (0.003) Loss 2.4746 (2.6246) Prec@1 40.000 (36.421) Prec@5 70.000 (67.107) Epoch: [11][2740/11272] Time 0.747 (0.825) Data 0.001 (0.003) Loss 2.4159 (2.6243) Prec@1 41.875 (36.431) Prec@5 72.500 (67.114) Epoch: [11][2750/11272] Time 0.876 (0.825) Data 0.002 (0.003) Loss 2.7562 (2.6241) Prec@1 31.875 (36.435) Prec@5 68.125 (67.115) Epoch: [11][2760/11272] Time 0.896 (0.825) Data 0.002 (0.003) Loss 2.5928 (2.6246) Prec@1 34.375 (36.427) Prec@5 66.875 (67.109) Epoch: [11][2770/11272] Time 0.781 (0.825) Data 0.002 (0.003) Loss 2.3788 (2.6248) Prec@1 46.250 (36.431) Prec@5 67.500 (67.100) Epoch: [11][2780/11272] Time 0.746 (0.825) Data 0.002 (0.003) Loss 2.5178 (2.6246) Prec@1 36.875 (36.435) Prec@5 72.500 (67.105) Epoch: [11][2790/11272] Time 0.916 (0.825) Data 0.002 (0.003) Loss 2.6892 (2.6247) Prec@1 36.250 (36.432) Prec@5 63.750 (67.105) Epoch: [11][2800/11272] Time 0.822 (0.825) Data 0.002 (0.003) Loss 2.1920 (2.6244) Prec@1 50.625 (36.439) Prec@5 75.000 (67.110) Epoch: [11][2810/11272] Time 0.755 (0.825) Data 0.002 (0.003) Loss 2.6163 (2.6244) Prec@1 30.625 (36.437) Prec@5 65.625 (67.109) Epoch: [11][2820/11272] Time 0.917 (0.825) Data 0.001 (0.003) Loss 2.4043 (2.6242) Prec@1 37.500 (36.443) Prec@5 68.125 (67.107) Epoch: [11][2830/11272] Time 0.953 (0.825) Data 0.002 (0.003) Loss 2.5652 (2.6244) Prec@1 37.500 (36.446) Prec@5 68.750 (67.106) Epoch: [11][2840/11272] Time 0.765 (0.825) Data 0.001 (0.003) Loss 2.8740 (2.6246) Prec@1 32.500 (36.444) Prec@5 61.875 (67.100) Epoch: [11][2850/11272] Time 0.812 (0.825) Data 0.004 (0.003) Loss 3.0218 (2.6249) Prec@1 31.250 (36.436) Prec@5 59.375 (67.092) Epoch: [11][2860/11272] Time 0.889 (0.825) Data 0.001 (0.003) Loss 2.3621 (2.6247) Prec@1 42.500 (36.441) Prec@5 67.500 (67.096) Epoch: [11][2870/11272] Time 0.902 (0.825) Data 0.001 (0.003) Loss 2.4435 (2.6248) Prec@1 37.500 (36.437) Prec@5 69.375 (67.100) Epoch: [11][2880/11272] Time 0.753 (0.825) Data 0.002 (0.003) Loss 2.5470 (2.6248) Prec@1 35.000 (36.437) Prec@5 70.625 (67.100) Epoch: [11][2890/11272] Time 0.807 (0.825) Data 0.001 (0.003) Loss 2.6563 (2.6248) Prec@1 34.375 (36.443) Prec@5 65.000 (67.101) Epoch: [11][2900/11272] Time 0.938 (0.825) Data 0.002 (0.003) Loss 2.7945 (2.6244) Prec@1 32.500 (36.449) Prec@5 64.375 (67.107) Epoch: [11][2910/11272] Time 0.874 (0.825) Data 0.001 (0.003) Loss 2.5607 (2.6243) Prec@1 38.750 (36.451) Prec@5 66.875 (67.108) Epoch: [11][2920/11272] Time 0.754 (0.825) Data 0.002 (0.003) Loss 2.5293 (2.6243) Prec@1 43.125 (36.454) Prec@5 65.000 (67.104) Epoch: [11][2930/11272] Time 0.729 (0.825) Data 0.002 (0.003) Loss 2.4216 (2.6244) Prec@1 35.000 (36.453) Prec@5 70.000 (67.101) Epoch: [11][2940/11272] Time 0.901 (0.825) Data 0.001 (0.003) Loss 2.5915 (2.6242) Prec@1 36.875 (36.455) Prec@5 65.625 (67.106) Epoch: [11][2950/11272] Time 0.745 (0.825) Data 0.004 (0.003) Loss 2.4422 (2.6243) Prec@1 41.250 (36.457) Prec@5 70.625 (67.107) Epoch: [11][2960/11272] Time 0.763 (0.825) Data 0.002 (0.003) Loss 2.7174 (2.6244) Prec@1 35.000 (36.457) Prec@5 63.125 (67.100) Epoch: [11][2970/11272] Time 0.917 (0.825) Data 0.002 (0.003) Loss 2.6974 (2.6245) Prec@1 35.000 (36.452) Prec@5 66.250 (67.096) Epoch: [11][2980/11272] Time 0.861 (0.825) Data 0.002 (0.003) Loss 2.5422 (2.6246) Prec@1 33.125 (36.449) Prec@5 71.875 (67.095) Epoch: [11][2990/11272] Time 0.750 (0.825) Data 0.002 (0.003) Loss 2.1861 (2.6246) Prec@1 44.375 (36.451) Prec@5 75.625 (67.099) Epoch: [11][3000/11272] Time 0.765 (0.825) Data 0.002 (0.003) Loss 2.6303 (2.6246) Prec@1 39.375 (36.450) Prec@5 69.375 (67.098) Epoch: [11][3010/11272] Time 0.897 (0.825) Data 0.002 (0.003) Loss 2.7763 (2.6247) Prec@1 36.250 (36.451) Prec@5 63.750 (67.099) Epoch: [11][3020/11272] Time 0.905 (0.825) Data 0.001 (0.003) Loss 2.6128 (2.6245) Prec@1 38.750 (36.456) Prec@5 67.500 (67.102) Epoch: [11][3030/11272] Time 0.761 (0.825) Data 0.001 (0.003) Loss 2.6509 (2.6246) Prec@1 35.000 (36.451) Prec@5 66.875 (67.099) Epoch: [11][3040/11272] Time 0.732 (0.825) Data 0.001 (0.003) Loss 2.5370 (2.6246) Prec@1 39.375 (36.450) Prec@5 65.625 (67.100) Epoch: [11][3050/11272] Time 0.907 (0.825) Data 0.001 (0.003) Loss 2.4769 (2.6247) Prec@1 36.875 (36.449) Prec@5 70.000 (67.094) Epoch: [11][3060/11272] Time 0.859 (0.825) Data 0.002 (0.003) Loss 2.5750 (2.6246) Prec@1 35.625 (36.450) Prec@5 66.875 (67.095) Epoch: [11][3070/11272] Time 0.770 (0.825) Data 0.001 (0.003) Loss 2.5806 (2.6248) Prec@1 36.875 (36.446) Prec@5 68.125 (67.090) Epoch: [11][3080/11272] Time 0.986 (0.825) Data 0.002 (0.003) Loss 2.7205 (2.6250) Prec@1 32.500 (36.440) Prec@5 62.500 (67.086) Epoch: [11][3090/11272] Time 0.877 (0.825) Data 0.002 (0.003) Loss 2.3845 (2.6252) Prec@1 40.000 (36.436) Prec@5 70.625 (67.083) Epoch: [11][3100/11272] Time 0.776 (0.825) Data 0.002 (0.003) Loss 2.4934 (2.6252) Prec@1 36.250 (36.436) Prec@5 70.000 (67.085) Epoch: [11][3110/11272] Time 0.779 (0.825) Data 0.002 (0.003) Loss 2.5328 (2.6250) Prec@1 40.625 (36.442) Prec@5 69.375 (67.094) Epoch: [11][3120/11272] Time 0.904 (0.825) Data 0.002 (0.003) Loss 2.4958 (2.6251) Prec@1 39.375 (36.441) Prec@5 72.500 (67.092) Epoch: [11][3130/11272] Time 0.836 (0.825) Data 0.002 (0.003) Loss 2.5930 (2.6248) Prec@1 40.625 (36.446) Prec@5 70.000 (67.099) Epoch: [11][3140/11272] Time 0.762 (0.825) Data 0.002 (0.003) Loss 2.6458 (2.6249) Prec@1 37.500 (36.445) Prec@5 66.250 (67.093) Epoch: [11][3150/11272] Time 0.757 (0.825) Data 0.002 (0.003) Loss 2.4247 (2.6246) Prec@1 40.000 (36.454) Prec@5 70.000 (67.096) Epoch: [11][3160/11272] Time 0.935 (0.825) Data 0.002 (0.003) Loss 2.4838 (2.6246) Prec@1 41.875 (36.457) Prec@5 71.875 (67.096) Epoch: [11][3170/11272] Time 0.845 (0.825) Data 0.001 (0.003) Loss 2.5469 (2.6244) Prec@1 36.250 (36.454) Prec@5 68.125 (67.100) Epoch: [11][3180/11272] Time 0.748 (0.825) Data 0.002 (0.003) Loss 2.5357 (2.6244) Prec@1 39.375 (36.453) Prec@5 71.875 (67.102) Epoch: [11][3190/11272] Time 0.750 (0.825) Data 0.002 (0.003) Loss 2.7527 (2.6245) Prec@1 29.375 (36.447) Prec@5 63.750 (67.101) Epoch: [11][3200/11272] Time 0.889 (0.825) Data 0.002 (0.003) Loss 2.6892 (2.6242) Prec@1 30.625 (36.450) Prec@5 65.000 (67.106) Epoch: [11][3210/11272] Time 0.772 (0.825) Data 0.004 (0.003) Loss 2.3371 (2.6242) Prec@1 42.500 (36.455) Prec@5 73.750 (67.104) Epoch: [11][3220/11272] Time 0.808 (0.825) Data 0.001 (0.003) Loss 2.4688 (2.6241) Prec@1 38.125 (36.453) Prec@5 76.250 (67.108) Epoch: [11][3230/11272] Time 0.884 (0.825) Data 0.001 (0.003) Loss 2.3515 (2.6237) Prec@1 39.375 (36.460) Prec@5 77.500 (67.118) Epoch: [11][3240/11272] Time 0.875 (0.825) Data 0.001 (0.003) Loss 2.5342 (2.6238) Prec@1 39.375 (36.461) Prec@5 67.500 (67.114) Epoch: [11][3250/11272] Time 0.734 (0.825) Data 0.002 (0.003) Loss 2.6959 (2.6238) Prec@1 37.500 (36.462) Prec@5 70.625 (67.114) Epoch: [11][3260/11272] Time 0.853 (0.825) Data 0.002 (0.003) Loss 2.5524 (2.6237) Prec@1 33.750 (36.461) Prec@5 66.875 (67.118) Epoch: [11][3270/11272] Time 0.904 (0.825) Data 0.002 (0.003) Loss 2.6528 (2.6238) Prec@1 35.000 (36.461) Prec@5 66.875 (67.112) Epoch: [11][3280/11272] Time 0.870 (0.825) Data 0.002 (0.003) Loss 2.8340 (2.6239) Prec@1 37.500 (36.461) Prec@5 61.250 (67.107) Epoch: [11][3290/11272] Time 0.768 (0.825) Data 0.002 (0.003) Loss 2.7134 (2.6239) Prec@1 42.500 (36.460) Prec@5 65.000 (67.106) Epoch: [11][3300/11272] Time 0.742 (0.825) Data 0.001 (0.003) Loss 2.5969 (2.6237) Prec@1 40.625 (36.467) Prec@5 65.000 (67.110) Epoch: [11][3310/11272] Time 0.856 (0.825) Data 0.001 (0.003) Loss 2.4928 (2.6238) Prec@1 37.500 (36.467) Prec@5 72.500 (67.113) Epoch: [11][3320/11272] Time 0.858 (0.825) Data 0.001 (0.003) Loss 2.7133 (2.6237) Prec@1 37.500 (36.469) Prec@5 65.000 (67.117) Epoch: [11][3330/11272] Time 0.781 (0.825) Data 0.001 (0.003) Loss 2.7168 (2.6238) Prec@1 35.625 (36.467) Prec@5 70.000 (67.124) Epoch: [11][3340/11272] Time 0.881 (0.825) Data 0.002 (0.003) Loss 2.6099 (2.6237) Prec@1 37.500 (36.473) Prec@5 67.500 (67.129) Epoch: [11][3350/11272] Time 0.871 (0.825) Data 0.002 (0.003) Loss 2.5533 (2.6238) Prec@1 39.375 (36.470) Prec@5 67.500 (67.125) Epoch: [11][3360/11272] Time 0.772 (0.825) Data 0.001 (0.003) Loss 2.7890 (2.6237) Prec@1 31.250 (36.476) Prec@5 60.000 (67.126) Epoch: [11][3370/11272] Time 0.755 (0.825) Data 0.001 (0.003) Loss 2.7297 (2.6237) Prec@1 33.750 (36.477) Prec@5 65.000 (67.127) Epoch: [11][3380/11272] Time 0.882 (0.825) Data 0.002 (0.003) Loss 2.5961 (2.6236) Prec@1 41.250 (36.478) Prec@5 70.625 (67.128) Epoch: [11][3390/11272] Time 0.868 (0.825) Data 0.001 (0.003) Loss 2.5502 (2.6235) Prec@1 39.375 (36.475) Prec@5 70.625 (67.129) Epoch: [11][3400/11272] Time 0.750 (0.825) Data 0.002 (0.003) Loss 2.7913 (2.6238) Prec@1 36.250 (36.471) Prec@5 65.625 (67.122) Epoch: [11][3410/11272] Time 0.727 (0.825) Data 0.002 (0.003) Loss 2.5410 (2.6236) Prec@1 43.125 (36.471) Prec@5 69.375 (67.127) Epoch: [11][3420/11272] Time 0.894 (0.825) Data 0.002 (0.003) Loss 2.7381 (2.6234) Prec@1 36.875 (36.476) Prec@5 67.500 (67.130) Epoch: [11][3430/11272] Time 0.853 (0.825) Data 0.002 (0.003) Loss 2.4806 (2.6236) Prec@1 33.750 (36.477) Prec@5 68.125 (67.129) Epoch: [11][3440/11272] Time 0.742 (0.825) Data 0.001 (0.003) Loss 2.6952 (2.6235) Prec@1 35.000 (36.480) Prec@5 66.250 (67.132) Epoch: [11][3450/11272] Time 0.764 (0.825) Data 0.002 (0.003) Loss 2.8702 (2.6235) Prec@1 33.125 (36.481) Prec@5 60.625 (67.131) Epoch: [11][3460/11272] Time 0.905 (0.825) Data 0.002 (0.002) Loss 2.6034 (2.6237) Prec@1 41.250 (36.478) Prec@5 67.500 (67.125) Epoch: [11][3470/11272] Time 0.877 (0.825) Data 0.002 (0.002) Loss 2.6875 (2.6238) Prec@1 35.000 (36.478) Prec@5 68.125 (67.124) Epoch: [11][3480/11272] Time 0.738 (0.825) Data 0.002 (0.002) Loss 2.4563 (2.6237) Prec@1 41.250 (36.483) Prec@5 68.125 (67.128) Epoch: [11][3490/11272] Time 0.894 (0.825) Data 0.001 (0.002) Loss 2.6913 (2.6238) Prec@1 33.750 (36.480) Prec@5 68.125 (67.123) Epoch: [11][3500/11272] Time 0.878 (0.825) Data 0.001 (0.002) Loss 2.4781 (2.6237) Prec@1 39.375 (36.483) Prec@5 70.625 (67.130) Epoch: [11][3510/11272] Time 0.737 (0.825) Data 0.001 (0.002) Loss 2.5973 (2.6237) Prec@1 36.875 (36.484) Prec@5 70.625 (67.132) Epoch: [11][3520/11272] Time 0.783 (0.825) Data 0.002 (0.002) Loss 2.4586 (2.6236) Prec@1 37.500 (36.490) Prec@5 71.875 (67.132) Epoch: [11][3530/11272] Time 0.934 (0.825) Data 0.002 (0.002) Loss 2.6771 (2.6236) Prec@1 35.000 (36.492) Prec@5 65.000 (67.129) Epoch: [11][3540/11272] Time 0.885 (0.825) Data 0.002 (0.002) Loss 2.7041 (2.6236) Prec@1 38.125 (36.492) Prec@5 63.750 (67.127) Epoch: [11][3550/11272] Time 0.775 (0.825) Data 0.001 (0.002) Loss 2.4619 (2.6235) Prec@1 40.000 (36.493) Prec@5 68.750 (67.127) Epoch: [11][3560/11272] Time 0.747 (0.825) Data 0.001 (0.002) Loss 2.6500 (2.6234) Prec@1 30.625 (36.495) Prec@5 65.625 (67.127) Epoch: [11][3570/11272] Time 0.869 (0.825) Data 0.001 (0.002) Loss 2.7858 (2.6234) Prec@1 32.500 (36.495) Prec@5 60.625 (67.126) Epoch: [11][3580/11272] Time 0.923 (0.825) Data 0.002 (0.002) Loss 2.6797 (2.6236) Prec@1 33.125 (36.486) Prec@5 71.875 (67.125) Epoch: [11][3590/11272] Time 0.758 (0.825) Data 0.001 (0.002) Loss 2.5817 (2.6236) Prec@1 38.750 (36.488) Prec@5 68.125 (67.123) Epoch: [11][3600/11272] Time 0.764 (0.825) Data 0.001 (0.002) Loss 2.7377 (2.6235) Prec@1 38.125 (36.491) Prec@5 64.375 (67.125) Epoch: [11][3610/11272] Time 0.893 (0.825) Data 0.002 (0.002) Loss 2.2693 (2.6235) Prec@1 40.000 (36.490) Prec@5 73.750 (67.125) Epoch: [11][3620/11272] Time 0.748 (0.825) Data 0.002 (0.002) Loss 2.5123 (2.6234) Prec@1 40.000 (36.490) Prec@5 66.875 (67.127) Epoch: [11][3630/11272] Time 0.776 (0.825) Data 0.002 (0.002) Loss 2.6057 (2.6232) Prec@1 40.625 (36.494) Prec@5 68.125 (67.129) Epoch: [11][3640/11272] Time 0.923 (0.825) Data 0.002 (0.002) Loss 2.5819 (2.6233) Prec@1 36.250 (36.490) Prec@5 68.125 (67.129) Epoch: [11][3650/11272] Time 0.875 (0.825) Data 0.002 (0.002) Loss 2.8599 (2.6233) Prec@1 34.375 (36.487) Prec@5 60.000 (67.125) Epoch: [11][3660/11272] Time 0.768 (0.825) Data 0.002 (0.002) Loss 2.6164 (2.6235) Prec@1 35.000 (36.486) Prec@5 65.625 (67.122) Epoch: [11][3670/11272] Time 0.777 (0.825) Data 0.002 (0.002) Loss 2.4165 (2.6237) Prec@1 36.875 (36.481) Prec@5 74.375 (67.121) Epoch: [11][3680/11272] Time 0.925 (0.825) Data 0.001 (0.002) Loss 2.6981 (2.6237) Prec@1 35.625 (36.482) Prec@5 65.625 (67.121) Epoch: [11][3690/11272] Time 0.870 (0.825) Data 0.002 (0.002) Loss 2.5234 (2.6237) Prec@1 31.250 (36.475) Prec@5 68.750 (67.121) Epoch: [11][3700/11272] Time 0.782 (0.825) Data 0.002 (0.002) Loss 2.3302 (2.6236) Prec@1 40.625 (36.475) Prec@5 74.375 (67.120) Epoch: [11][3710/11272] Time 0.775 (0.825) Data 0.002 (0.002) Loss 2.6541 (2.6235) Prec@1 35.625 (36.477) Prec@5 66.250 (67.123) Epoch: [11][3720/11272] Time 0.898 (0.825) Data 0.001 (0.002) Loss 2.7441 (2.6234) Prec@1 31.250 (36.480) Prec@5 65.000 (67.122) Epoch: [11][3730/11272] Time 0.880 (0.825) Data 0.002 (0.002) Loss 2.8480 (2.6236) Prec@1 31.250 (36.477) Prec@5 60.625 (67.118) Epoch: [11][3740/11272] Time 0.809 (0.825) Data 0.001 (0.002) Loss 2.8623 (2.6237) Prec@1 34.375 (36.476) Prec@5 59.375 (67.117) Epoch: [11][3750/11272] Time 0.879 (0.825) Data 0.001 (0.002) Loss 2.6804 (2.6236) Prec@1 33.125 (36.482) Prec@5 61.875 (67.119) Epoch: [11][3760/11272] Time 0.908 (0.825) Data 0.001 (0.002) Loss 2.7148 (2.6234) Prec@1 35.625 (36.485) Prec@5 65.625 (67.120) Epoch: [11][3770/11272] Time 0.731 (0.825) Data 0.001 (0.002) Loss 2.6992 (2.6233) Prec@1 35.000 (36.490) Prec@5 66.250 (67.122) Epoch: [11][3780/11272] Time 0.735 (0.825) Data 0.002 (0.002) Loss 2.8282 (2.6235) Prec@1 30.625 (36.487) Prec@5 56.875 (67.121) Epoch: [11][3790/11272] Time 0.911 (0.825) Data 0.002 (0.002) Loss 2.4382 (2.6235) Prec@1 35.000 (36.485) Prec@5 71.875 (67.124) Epoch: [11][3800/11272] Time 0.931 (0.825) Data 0.002 (0.002) Loss 2.3312 (2.6235) Prec@1 44.375 (36.484) Prec@5 76.250 (67.125) Epoch: [11][3810/11272] Time 0.741 (0.825) Data 0.002 (0.002) Loss 2.6118 (2.6235) Prec@1 33.125 (36.486) Prec@5 67.500 (67.126) Epoch: [11][3820/11272] Time 0.771 (0.825) Data 0.002 (0.002) Loss 2.4701 (2.6234) Prec@1 40.625 (36.488) Prec@5 71.250 (67.129) Epoch: [11][3830/11272] Time 0.913 (0.825) Data 0.003 (0.002) Loss 3.0080 (2.6232) Prec@1 27.500 (36.492) Prec@5 60.000 (67.132) Epoch: [11][3840/11272] Time 0.857 (0.825) Data 0.001 (0.002) Loss 2.7232 (2.6233) Prec@1 35.000 (36.491) Prec@5 63.125 (67.130) Epoch: [11][3850/11272] Time 0.749 (0.825) Data 0.002 (0.002) Loss 2.7231 (2.6231) Prec@1 34.375 (36.495) Prec@5 61.875 (67.133) Epoch: [11][3860/11272] Time 0.806 (0.825) Data 0.002 (0.002) Loss 2.6965 (2.6229) Prec@1 32.500 (36.500) Prec@5 64.375 (67.138) Epoch: [11][3870/11272] Time 0.908 (0.825) Data 0.002 (0.002) Loss 2.6568 (2.6231) Prec@1 38.125 (36.500) Prec@5 71.875 (67.134) Epoch: [11][3880/11272] Time 0.748 (0.825) Data 0.005 (0.002) Loss 2.6017 (2.6229) Prec@1 33.125 (36.502) Prec@5 66.875 (67.137) Epoch: [11][3890/11272] Time 0.761 (0.825) Data 0.002 (0.002) Loss 2.6525 (2.6231) Prec@1 33.750 (36.499) Prec@5 66.250 (67.135) Epoch: [11][3900/11272] Time 0.894 (0.825) Data 0.001 (0.002) Loss 2.3825 (2.6227) Prec@1 38.750 (36.507) Prec@5 71.875 (67.141) Epoch: [11][3910/11272] Time 0.949 (0.825) Data 0.001 (0.002) Loss 2.5001 (2.6226) Prec@1 38.125 (36.509) Prec@5 71.250 (67.144) Epoch: [11][3920/11272] Time 0.780 (0.825) Data 0.002 (0.002) Loss 2.7189 (2.6223) Prec@1 35.625 (36.516) Prec@5 66.875 (67.153) Epoch: [11][3930/11272] Time 0.742 (0.825) Data 0.002 (0.002) Loss 2.7766 (2.6224) Prec@1 35.625 (36.512) Prec@5 62.500 (67.151) Epoch: [11][3940/11272] Time 0.872 (0.825) Data 0.001 (0.002) Loss 2.5872 (2.6225) Prec@1 38.750 (36.509) Prec@5 69.375 (67.151) Epoch: [11][3950/11272] Time 0.854 (0.825) Data 0.001 (0.002) Loss 2.6012 (2.6225) Prec@1 35.625 (36.510) Prec@5 65.000 (67.151) Epoch: [11][3960/11272] Time 0.741 (0.825) Data 0.001 (0.002) Loss 2.7830 (2.6227) Prec@1 31.875 (36.509) Prec@5 64.375 (67.149) Epoch: [11][3970/11272] Time 0.745 (0.825) Data 0.002 (0.002) Loss 2.5535 (2.6226) Prec@1 40.625 (36.510) Prec@5 65.625 (67.152) Epoch: [11][3980/11272] Time 0.990 (0.825) Data 0.002 (0.002) Loss 2.5280 (2.6225) Prec@1 40.000 (36.513) Prec@5 66.250 (67.154) Epoch: [11][3990/11272] Time 0.871 (0.825) Data 0.002 (0.002) Loss 2.5910 (2.6226) Prec@1 36.875 (36.509) Prec@5 66.875 (67.151) Epoch: [11][4000/11272] Time 0.819 (0.825) Data 0.002 (0.002) Loss 2.6912 (2.6228) Prec@1 36.250 (36.508) Prec@5 61.875 (67.148) Epoch: [11][4010/11272] Time 0.879 (0.825) Data 0.002 (0.002) Loss 2.6688 (2.6229) Prec@1 38.125 (36.508) Prec@5 68.125 (67.146) Epoch: [11][4020/11272] Time 0.885 (0.825) Data 0.002 (0.002) Loss 2.6118 (2.6228) Prec@1 38.125 (36.509) Prec@5 63.750 (67.148) Epoch: [11][4030/11272] Time 0.752 (0.825) Data 0.002 (0.002) Loss 2.8869 (2.6231) Prec@1 34.375 (36.506) Prec@5 63.125 (67.143) Epoch: [11][4040/11272] Time 0.769 (0.825) Data 0.002 (0.002) Loss 2.7490 (2.6232) Prec@1 30.000 (36.502) Prec@5 66.875 (67.141) Epoch: [11][4050/11272] Time 0.844 (0.825) Data 0.002 (0.002) Loss 2.5821 (2.6232) Prec@1 35.000 (36.503) Prec@5 72.500 (67.140) Epoch: [11][4060/11272] Time 0.917 (0.825) Data 0.002 (0.002) Loss 2.6341 (2.6231) Prec@1 30.000 (36.504) Prec@5 70.000 (67.142) Epoch: [11][4070/11272] Time 0.777 (0.825) Data 0.002 (0.002) Loss 2.6253 (2.6231) Prec@1 40.625 (36.507) Prec@5 65.625 (67.146) Epoch: [11][4080/11272] Time 0.750 (0.825) Data 0.002 (0.002) Loss 2.6413 (2.6232) Prec@1 34.375 (36.503) Prec@5 66.250 (67.143) Epoch: [11][4090/11272] Time 0.887 (0.825) Data 0.001 (0.002) Loss 2.7129 (2.6233) Prec@1 35.625 (36.503) Prec@5 70.000 (67.143) Epoch: [11][4100/11272] Time 0.883 (0.825) Data 0.001 (0.002) Loss 2.6185 (2.6232) Prec@1 37.500 (36.505) Prec@5 66.250 (67.145) Epoch: [11][4110/11272] Time 0.760 (0.825) Data 0.001 (0.002) Loss 2.6117 (2.6232) Prec@1 36.875 (36.502) Prec@5 65.625 (67.146) Epoch: [11][4120/11272] Time 0.760 (0.825) Data 0.001 (0.002) Loss 2.6395 (2.6230) Prec@1 34.375 (36.506) Prec@5 65.625 (67.153) Epoch: [11][4130/11272] Time 0.893 (0.825) Data 0.002 (0.002) Loss 2.5690 (2.6230) Prec@1 36.875 (36.505) Prec@5 70.000 (67.152) Epoch: [11][4140/11272] Time 0.780 (0.825) Data 0.004 (0.002) Loss 2.5145 (2.6230) Prec@1 39.375 (36.502) Prec@5 70.625 (67.153) Epoch: [11][4150/11272] Time 0.775 (0.825) Data 0.002 (0.002) Loss 2.6003 (2.6230) Prec@1 35.000 (36.497) Prec@5 68.125 (67.154) Epoch: [11][4160/11272] Time 0.885 (0.825) Data 0.002 (0.002) Loss 2.4800 (2.6231) Prec@1 41.875 (36.498) Prec@5 70.625 (67.152) Epoch: [11][4170/11272] Time 0.880 (0.825) Data 0.002 (0.002) Loss 2.7102 (2.6233) Prec@1 34.375 (36.495) Prec@5 66.250 (67.147) Epoch: [11][4180/11272] Time 0.804 (0.825) Data 0.001 (0.002) Loss 2.5159 (2.6233) Prec@1 40.000 (36.495) Prec@5 68.125 (67.148) Epoch: [11][4190/11272] Time 0.758 (0.825) Data 0.002 (0.002) Loss 2.8347 (2.6233) Prec@1 31.250 (36.495) Prec@5 68.125 (67.149) Epoch: [11][4200/11272] Time 0.855 (0.825) Data 0.002 (0.002) Loss 2.7235 (2.6233) Prec@1 31.250 (36.497) Prec@5 70.000 (67.147) Epoch: [11][4210/11272] Time 0.888 (0.825) Data 0.002 (0.002) Loss 2.5503 (2.6234) Prec@1 36.875 (36.495) Prec@5 66.250 (67.148) Epoch: [11][4220/11272] Time 0.793 (0.825) Data 0.004 (0.002) Loss 2.5194 (2.6233) Prec@1 33.125 (36.492) Prec@5 70.000 (67.148) Epoch: [11][4230/11272] Time 0.750 (0.825) Data 0.001 (0.002) Loss 2.7538 (2.6234) Prec@1 31.875 (36.490) Prec@5 66.875 (67.144) Epoch: [11][4240/11272] Time 0.904 (0.825) Data 0.003 (0.002) Loss 2.6042 (2.6237) Prec@1 41.250 (36.487) Prec@5 69.375 (67.137) Epoch: [11][4250/11272] Time 0.917 (0.825) Data 0.002 (0.002) Loss 2.5439 (2.6237) Prec@1 36.250 (36.483) Prec@5 68.125 (67.138) Epoch: [11][4260/11272] Time 0.767 (0.825) Data 0.002 (0.002) Loss 2.8170 (2.6238) Prec@1 31.875 (36.482) Prec@5 64.375 (67.138) Epoch: [11][4270/11272] Time 0.885 (0.825) Data 0.002 (0.002) Loss 2.7720 (2.6236) Prec@1 35.000 (36.487) Prec@5 64.375 (67.140) Epoch: [11][4280/11272] Time 0.862 (0.825) Data 0.001 (0.002) Loss 2.6380 (2.6235) Prec@1 36.250 (36.485) Prec@5 68.750 (67.140) Epoch: [11][4290/11272] Time 0.746 (0.825) Data 0.002 (0.002) Loss 2.5716 (2.6235) Prec@1 36.875 (36.482) Prec@5 70.625 (67.140) Epoch: [11][4300/11272] Time 0.749 (0.825) Data 0.002 (0.002) Loss 2.4834 (2.6235) Prec@1 41.250 (36.480) Prec@5 66.250 (67.138) Epoch: [11][4310/11272] Time 0.908 (0.825) Data 0.002 (0.002) Loss 2.6374 (2.6234) Prec@1 38.750 (36.480) Prec@5 66.875 (67.138) Epoch: [11][4320/11272] Time 0.909 (0.825) Data 0.001 (0.002) Loss 2.6495 (2.6236) Prec@1 41.250 (36.480) Prec@5 66.875 (67.131) Epoch: [11][4330/11272] Time 0.734 (0.825) Data 0.001 (0.002) Loss 2.7624 (2.6237) Prec@1 35.625 (36.480) Prec@5 62.500 (67.128) Epoch: [11][4340/11272] Time 0.738 (0.825) Data 0.002 (0.002) Loss 2.4660 (2.6235) Prec@1 33.125 (36.481) Prec@5 69.375 (67.131) Epoch: [11][4350/11272] Time 0.855 (0.825) Data 0.002 (0.002) Loss 2.5184 (2.6235) Prec@1 38.125 (36.482) Prec@5 69.375 (67.130) Epoch: [11][4360/11272] Time 0.880 (0.825) Data 0.002 (0.002) Loss 2.6353 (2.6235) Prec@1 41.250 (36.485) Prec@5 68.125 (67.133) Epoch: [11][4370/11272] Time 0.757 (0.825) Data 0.004 (0.002) Loss 2.6069 (2.6234) Prec@1 35.625 (36.485) Prec@5 66.875 (67.136) Epoch: [11][4380/11272] Time 0.751 (0.825) Data 0.001 (0.002) Loss 2.6877 (2.6233) Prec@1 36.250 (36.483) Prec@5 68.750 (67.137) Epoch: [11][4390/11272] Time 0.824 (0.825) Data 0.001 (0.002) Loss 2.7009 (2.6232) Prec@1 34.375 (36.486) Prec@5 68.750 (67.140) Epoch: [11][4400/11272] Time 0.954 (0.825) Data 0.002 (0.002) Loss 2.5986 (2.6231) Prec@1 38.750 (36.487) Prec@5 70.000 (67.143) Epoch: [11][4410/11272] Time 0.786 (0.825) Data 0.001 (0.002) Loss 2.6579 (2.6233) Prec@1 38.125 (36.486) Prec@5 68.750 (67.141) Epoch: [11][4420/11272] Time 0.893 (0.825) Data 0.002 (0.002) Loss 2.5231 (2.6232) Prec@1 35.625 (36.487) Prec@5 67.500 (67.142) Epoch: [11][4430/11272] Time 0.869 (0.825) Data 0.001 (0.002) Loss 2.6304 (2.6233) Prec@1 34.375 (36.485) Prec@5 67.500 (67.138) Epoch: [11][4440/11272] Time 0.746 (0.825) Data 0.002 (0.002) Loss 2.8951 (2.6235) Prec@1 33.125 (36.481) Prec@5 63.750 (67.134) Epoch: [11][4450/11272] Time 0.747 (0.825) Data 0.001 (0.002) Loss 2.4639 (2.6235) Prec@1 39.375 (36.483) Prec@5 71.875 (67.133) Epoch: [11][4460/11272] Time 0.904 (0.825) Data 0.002 (0.002) Loss 2.5871 (2.6233) Prec@1 34.375 (36.486) Prec@5 67.500 (67.137) Epoch: [11][4470/11272] Time 0.878 (0.825) Data 0.001 (0.002) Loss 2.7209 (2.6232) Prec@1 29.375 (36.487) Prec@5 67.500 (67.141) Epoch: [11][4480/11272] Time 0.746 (0.825) Data 0.001 (0.002) Loss 2.3611 (2.6232) Prec@1 43.125 (36.486) Prec@5 68.750 (67.140) Epoch: [11][4490/11272] Time 0.808 (0.825) Data 0.003 (0.002) Loss 2.9528 (2.6233) Prec@1 35.625 (36.489) Prec@5 61.875 (67.140) Epoch: [11][4500/11272] Time 0.877 (0.825) Data 0.001 (0.002) Loss 2.4861 (2.6232) Prec@1 40.000 (36.488) Prec@5 66.250 (67.141) Epoch: [11][4510/11272] Time 0.858 (0.825) Data 0.001 (0.002) Loss 2.4932 (2.6231) Prec@1 43.125 (36.491) Prec@5 71.875 (67.141) Epoch: [11][4520/11272] Time 0.739 (0.825) Data 0.001 (0.002) Loss 2.4585 (2.6231) Prec@1 34.375 (36.491) Prec@5 67.500 (67.140) Epoch: [11][4530/11272] Time 0.752 (0.825) Data 0.001 (0.002) Loss 2.6836 (2.6231) Prec@1 35.000 (36.491) Prec@5 66.250 (67.140) Epoch: [11][4540/11272] Time 0.917 (0.825) Data 0.002 (0.002) Loss 2.6721 (2.6232) Prec@1 33.750 (36.490) Prec@5 63.750 (67.135) Epoch: [11][4550/11272] Time 0.744 (0.825) Data 0.001 (0.002) Loss 2.7674 (2.6233) Prec@1 39.375 (36.493) Prec@5 61.875 (67.137) Epoch: [11][4560/11272] Time 0.752 (0.825) Data 0.001 (0.002) Loss 2.6405 (2.6235) Prec@1 35.625 (36.491) Prec@5 63.750 (67.134) Epoch: [11][4570/11272] Time 0.903 (0.825) Data 0.002 (0.002) Loss 2.5346 (2.6234) Prec@1 41.875 (36.493) Prec@5 65.625 (67.136) Epoch: [11][4580/11272] Time 0.899 (0.825) Data 0.002 (0.002) Loss 2.9230 (2.6234) Prec@1 34.375 (36.495) Prec@5 60.000 (67.135) Epoch: [11][4590/11272] Time 0.761 (0.825) Data 0.002 (0.002) Loss 2.7732 (2.6236) Prec@1 33.125 (36.493) Prec@5 63.750 (67.132) Epoch: [11][4600/11272] Time 0.765 (0.825) Data 0.002 (0.002) Loss 2.7004 (2.6238) Prec@1 36.250 (36.489) Prec@5 67.500 (67.130) Epoch: [11][4610/11272] Time 0.896 (0.825) Data 0.002 (0.002) Loss 2.6773 (2.6236) Prec@1 36.875 (36.495) Prec@5 70.000 (67.131) Epoch: [11][4620/11272] Time 0.869 (0.825) Data 0.001 (0.002) Loss 2.6581 (2.6236) Prec@1 35.625 (36.496) Prec@5 65.000 (67.129) Epoch: [11][4630/11272] Time 0.744 (0.825) Data 0.002 (0.002) Loss 2.7091 (2.6236) Prec@1 32.500 (36.495) Prec@5 63.750 (67.129) Epoch: [11][4640/11272] Time 0.747 (0.825) Data 0.002 (0.002) Loss 2.6552 (2.6238) Prec@1 33.125 (36.495) Prec@5 67.500 (67.124) Epoch: [11][4650/11272] Time 0.865 (0.825) Data 0.001 (0.002) Loss 2.4943 (2.6240) Prec@1 38.125 (36.489) Prec@5 68.125 (67.121) Epoch: [11][4660/11272] Time 0.861 (0.825) Data 0.002 (0.002) Loss 2.7156 (2.6240) Prec@1 38.125 (36.488) Prec@5 67.500 (67.121) Epoch: [11][4670/11272] Time 0.749 (0.825) Data 0.001 (0.002) Loss 2.6875 (2.6240) Prec@1 31.875 (36.489) Prec@5 63.750 (67.121) Epoch: [11][4680/11272] Time 0.866 (0.825) Data 0.001 (0.002) Loss 2.6988 (2.6240) Prec@1 31.875 (36.489) Prec@5 66.250 (67.121) Epoch: [11][4690/11272] Time 0.872 (0.825) Data 0.001 (0.002) Loss 2.4753 (2.6239) Prec@1 37.500 (36.488) Prec@5 70.625 (67.125) Epoch: [11][4700/11272] Time 0.758 (0.825) Data 0.002 (0.002) Loss 2.3603 (2.6238) Prec@1 42.500 (36.490) Prec@5 66.875 (67.126) Epoch: [11][4710/11272] Time 0.724 (0.825) Data 0.001 (0.002) Loss 2.5631 (2.6237) Prec@1 38.750 (36.493) Prec@5 69.375 (67.127) Epoch: [11][4720/11272] Time 0.876 (0.825) Data 0.001 (0.002) Loss 2.7507 (2.6239) Prec@1 38.125 (36.488) Prec@5 64.375 (67.123) Epoch: [11][4730/11272] Time 0.909 (0.825) Data 0.002 (0.002) Loss 2.6631 (2.6239) Prec@1 36.250 (36.488) Prec@5 66.250 (67.127) Epoch: [11][4740/11272] Time 0.750 (0.825) Data 0.002 (0.002) Loss 2.4881 (2.6241) Prec@1 38.125 (36.487) Prec@5 68.125 (67.122) Epoch: [11][4750/11272] Time 0.743 (0.825) Data 0.002 (0.002) Loss 2.8256 (2.6242) Prec@1 33.750 (36.485) Prec@5 68.125 (67.120) Epoch: [11][4760/11272] Time 0.880 (0.825) Data 0.001 (0.002) Loss 2.5193 (2.6243) Prec@1 33.750 (36.484) Prec@5 70.000 (67.117) Epoch: [11][4770/11272] Time 0.874 (0.825) Data 0.001 (0.002) Loss 2.6005 (2.6244) Prec@1 41.250 (36.483) Prec@5 70.000 (67.116) Epoch: [11][4780/11272] Time 0.766 (0.825) Data 0.002 (0.002) Loss 2.6999 (2.6245) Prec@1 35.625 (36.482) Prec@5 66.250 (67.115) Epoch: [11][4790/11272] Time 0.750 (0.825) Data 0.002 (0.002) Loss 2.8104 (2.6244) Prec@1 30.000 (36.485) Prec@5 60.000 (67.118) Epoch: [11][4800/11272] Time 0.863 (0.825) Data 0.002 (0.002) Loss 2.7353 (2.6243) Prec@1 33.125 (36.487) Prec@5 66.250 (67.121) Epoch: [11][4810/11272] Time 0.742 (0.825) Data 0.002 (0.002) Loss 2.7136 (2.6244) Prec@1 38.125 (36.483) Prec@5 63.125 (67.118) Epoch: [11][4820/11272] Time 0.785 (0.825) Data 0.001 (0.002) Loss 2.6457 (2.6245) Prec@1 33.125 (36.481) Prec@5 72.500 (67.118) Epoch: [11][4830/11272] Time 0.860 (0.825) Data 0.002 (0.002) Loss 2.5081 (2.6246) Prec@1 35.000 (36.480) Prec@5 68.750 (67.114) Epoch: [11][4840/11272] Time 0.882 (0.825) Data 0.001 (0.002) Loss 2.5988 (2.6247) Prec@1 40.625 (36.481) Prec@5 67.500 (67.113) Epoch: [11][4850/11272] Time 0.758 (0.825) Data 0.002 (0.002) Loss 2.8214 (2.6246) Prec@1 30.000 (36.480) Prec@5 63.750 (67.117) Epoch: [11][4860/11272] Time 0.748 (0.825) Data 0.002 (0.002) Loss 2.5284 (2.6247) Prec@1 29.375 (36.478) Prec@5 70.625 (67.115) Epoch: [11][4870/11272] Time 0.898 (0.825) Data 0.002 (0.002) Loss 2.6588 (2.6249) Prec@1 30.625 (36.473) Prec@5 68.750 (67.114) Epoch: [11][4880/11272] Time 0.879 (0.825) Data 0.001 (0.002) Loss 2.6039 (2.6248) Prec@1 40.625 (36.474) Prec@5 65.000 (67.115) Epoch: [11][4890/11272] Time 0.797 (0.825) Data 0.001 (0.002) Loss 2.9441 (2.6249) Prec@1 31.250 (36.473) Prec@5 59.375 (67.113) Epoch: [11][4900/11272] Time 0.751 (0.825) Data 0.002 (0.002) Loss 2.9467 (2.6251) Prec@1 31.875 (36.468) Prec@5 61.875 (67.110) Epoch: [11][4910/11272] Time 0.916 (0.825) Data 0.001 (0.002) Loss 2.4801 (2.6250) Prec@1 43.125 (36.468) Prec@5 68.750 (67.110) Epoch: [11][4920/11272] Time 0.855 (0.825) Data 0.002 (0.002) Loss 2.6298 (2.6249) Prec@1 32.500 (36.469) Prec@5 69.375 (67.111) Epoch: [11][4930/11272] Time 0.758 (0.825) Data 0.001 (0.002) Loss 2.3213 (2.6248) Prec@1 41.875 (36.472) Prec@5 71.250 (67.110) Epoch: [11][4940/11272] Time 0.869 (0.825) Data 0.001 (0.002) Loss 2.7984 (2.6249) Prec@1 33.750 (36.469) Prec@5 63.125 (67.108) Epoch: [11][4950/11272] Time 0.882 (0.825) Data 0.002 (0.002) Loss 2.3963 (2.6249) Prec@1 41.250 (36.471) Prec@5 73.750 (67.109) Epoch: [11][4960/11272] Time 0.789 (0.825) Data 0.001 (0.002) Loss 2.5972 (2.6249) Prec@1 38.750 (36.471) Prec@5 70.000 (67.112) Epoch: [11][4970/11272] Time 0.759 (0.825) Data 0.001 (0.002) Loss 2.7548 (2.6249) Prec@1 31.875 (36.473) Prec@5 63.750 (67.110) Epoch: [11][4980/11272] Time 0.866 (0.825) Data 0.001 (0.002) Loss 2.5081 (2.6248) Prec@1 41.875 (36.473) Prec@5 68.750 (67.108) Epoch: [11][4990/11272] Time 0.931 (0.825) Data 0.002 (0.002) Loss 2.7792 (2.6248) Prec@1 31.875 (36.474) Prec@5 63.750 (67.106) Epoch: [11][5000/11272] Time 0.745 (0.825) Data 0.001 (0.002) Loss 2.8443 (2.6250) Prec@1 35.625 (36.473) Prec@5 60.625 (67.102) Epoch: [11][5010/11272] Time 0.736 (0.825) Data 0.001 (0.002) Loss 2.6094 (2.6250) Prec@1 31.875 (36.470) Prec@5 66.250 (67.100) Epoch: [11][5020/11272] Time 0.853 (0.825) Data 0.001 (0.002) Loss 2.5742 (2.6250) Prec@1 36.250 (36.472) Prec@5 68.750 (67.098) Epoch: [11][5030/11272] Time 0.883 (0.825) Data 0.001 (0.002) Loss 2.5905 (2.6251) Prec@1 35.625 (36.471) Prec@5 68.750 (67.097) Epoch: [11][5040/11272] Time 0.777 (0.825) Data 0.002 (0.002) Loss 2.6631 (2.6251) Prec@1 32.500 (36.472) Prec@5 65.000 (67.097) Epoch: [11][5050/11272] Time 0.767 (0.825) Data 0.002 (0.002) Loss 2.5776 (2.6251) Prec@1 40.625 (36.471) Prec@5 71.250 (67.098) Epoch: [11][5060/11272] Time 0.966 (0.825) Data 0.002 (0.002) Loss 2.5686 (2.6250) Prec@1 36.875 (36.472) Prec@5 67.500 (67.099) Epoch: [11][5070/11272] Time 0.793 (0.825) Data 0.005 (0.002) Loss 2.7008 (2.6251) Prec@1 38.750 (36.472) Prec@5 65.000 (67.099) Epoch: [11][5080/11272] Time 0.780 (0.825) Data 0.002 (0.002) Loss 2.7904 (2.6252) Prec@1 34.375 (36.471) Prec@5 61.250 (67.096) Epoch: [11][5090/11272] Time 0.873 (0.825) Data 0.002 (0.002) Loss 2.3649 (2.6252) Prec@1 35.000 (36.471) Prec@5 75.000 (67.100) Epoch: [11][5100/11272] Time 0.898 (0.825) Data 0.002 (0.002) Loss 2.6686 (2.6251) Prec@1 31.875 (36.471) Prec@5 65.000 (67.099) Epoch: [11][5110/11272] Time 0.781 (0.825) Data 0.002 (0.002) Loss 2.5047 (2.6251) Prec@1 36.875 (36.471) Prec@5 70.625 (67.100) Epoch: [11][5120/11272] Time 0.747 (0.825) Data 0.001 (0.002) Loss 2.5412 (2.6252) Prec@1 33.750 (36.470) Prec@5 67.500 (67.099) Epoch: [11][5130/11272] Time 0.858 (0.825) Data 0.001 (0.002) Loss 2.5961 (2.6251) Prec@1 35.625 (36.471) Prec@5 70.000 (67.100) Epoch: [11][5140/11272] Time 0.884 (0.825) Data 0.001 (0.002) Loss 2.9234 (2.6251) Prec@1 26.875 (36.470) Prec@5 61.250 (67.104) Epoch: [11][5150/11272] Time 0.783 (0.825) Data 0.002 (0.002) Loss 2.6220 (2.6250) Prec@1 36.875 (36.471) Prec@5 68.750 (67.108) Epoch: [11][5160/11272] Time 0.765 (0.825) Data 0.001 (0.002) Loss 2.6275 (2.6250) Prec@1 33.750 (36.473) Prec@5 71.875 (67.109) Epoch: [11][5170/11272] Time 0.878 (0.825) Data 0.002 (0.002) Loss 2.6753 (2.6250) Prec@1 33.125 (36.473) Prec@5 65.625 (67.106) Epoch: [11][5180/11272] Time 0.850 (0.825) Data 0.001 (0.002) Loss 2.4798 (2.6251) Prec@1 41.875 (36.475) Prec@5 70.000 (67.105) Epoch: [11][5190/11272] Time 0.764 (0.825) Data 0.002 (0.002) Loss 2.6130 (2.6251) Prec@1 38.125 (36.471) Prec@5 68.125 (67.104) Epoch: [11][5200/11272] Time 0.868 (0.825) Data 0.001 (0.002) Loss 2.6560 (2.6252) Prec@1 35.625 (36.469) Prec@5 65.625 (67.101) Epoch: [11][5210/11272] Time 0.860 (0.825) Data 0.002 (0.002) Loss 2.9771 (2.6254) Prec@1 30.000 (36.466) Prec@5 60.625 (67.100) Epoch: [11][5220/11272] Time 0.776 (0.825) Data 0.002 (0.002) Loss 2.8441 (2.6255) Prec@1 36.250 (36.469) Prec@5 66.875 (67.098) Epoch: [11][5230/11272] Time 0.776 (0.825) Data 0.006 (0.002) Loss 2.7170 (2.6255) Prec@1 33.125 (36.470) Prec@5 66.250 (67.097) Epoch: [11][5240/11272] Time 0.876 (0.825) Data 0.001 (0.002) Loss 2.8391 (2.6255) Prec@1 36.250 (36.468) Prec@5 63.125 (67.098) Epoch: [11][5250/11272] Time 0.918 (0.825) Data 0.002 (0.002) Loss 2.7159 (2.6258) Prec@1 31.250 (36.461) Prec@5 62.500 (67.092) Epoch: [11][5260/11272] Time 0.735 (0.825) Data 0.002 (0.002) Loss 2.7868 (2.6258) Prec@1 37.500 (36.462) Prec@5 67.500 (67.092) Epoch: [11][5270/11272] Time 0.759 (0.825) Data 0.002 (0.002) Loss 2.7140 (2.6259) Prec@1 36.250 (36.462) Prec@5 66.250 (67.091) Epoch: [11][5280/11272] Time 0.932 (0.825) Data 0.001 (0.002) Loss 2.4725 (2.6258) Prec@1 38.125 (36.461) Prec@5 73.750 (67.092) Epoch: [11][5290/11272] Time 0.858 (0.825) Data 0.002 (0.002) Loss 2.6634 (2.6258) Prec@1 35.000 (36.458) Prec@5 66.875 (67.093) Epoch: [11][5300/11272] Time 0.802 (0.825) Data 0.002 (0.002) Loss 2.7459 (2.6258) Prec@1 38.125 (36.461) Prec@5 63.750 (67.092) Epoch: [11][5310/11272] Time 0.737 (0.825) Data 0.002 (0.002) Loss 2.7509 (2.6259) Prec@1 32.500 (36.458) Prec@5 66.250 (67.092) Epoch: [11][5320/11272] Time 0.873 (0.825) Data 0.002 (0.002) Loss 2.3954 (2.6258) Prec@1 43.125 (36.461) Prec@5 71.875 (67.094) Epoch: [11][5330/11272] Time 0.903 (0.825) Data 0.002 (0.002) Loss 2.9400 (2.6258) Prec@1 33.125 (36.462) Prec@5 64.375 (67.098) Epoch: [11][5340/11272] Time 0.753 (0.825) Data 0.002 (0.002) Loss 2.8641 (2.6258) Prec@1 32.500 (36.461) Prec@5 60.625 (67.097) Epoch: [11][5350/11272] Time 0.904 (0.825) Data 0.002 (0.002) Loss 2.7116 (2.6259) Prec@1 36.875 (36.460) Prec@5 63.750 (67.096) Epoch: [11][5360/11272] Time 0.850 (0.825) Data 0.001 (0.002) Loss 2.9134 (2.6258) Prec@1 30.000 (36.461) Prec@5 61.875 (67.098) Epoch: [11][5370/11272] Time 0.743 (0.825) Data 0.002 (0.002) Loss 2.5177 (2.6259) Prec@1 37.500 (36.458) Prec@5 71.250 (67.096) Epoch: [11][5380/11272] Time 0.801 (0.825) Data 0.001 (0.002) Loss 2.6392 (2.6260) Prec@1 35.625 (36.457) Prec@5 63.125 (67.092) Epoch: [11][5390/11272] Time 0.886 (0.825) Data 0.001 (0.002) Loss 2.6815 (2.6261) Prec@1 37.500 (36.458) Prec@5 61.875 (67.091) Epoch: [11][5400/11272] Time 0.866 (0.825) Data 0.001 (0.002) Loss 2.6536 (2.6261) Prec@1 36.875 (36.460) Prec@5 69.375 (67.091) Epoch: [11][5410/11272] Time 0.763 (0.825) Data 0.002 (0.002) Loss 2.5624 (2.6260) Prec@1 36.250 (36.460) Prec@5 66.250 (67.092) Epoch: [11][5420/11272] Time 0.737 (0.825) Data 0.002 (0.002) Loss 2.7033 (2.6261) Prec@1 36.250 (36.460) Prec@5 66.875 (67.091) Epoch: [11][5430/11272] Time 0.863 (0.825) Data 0.001 (0.002) Loss 2.5047 (2.6260) Prec@1 36.875 (36.460) Prec@5 73.125 (67.091) Epoch: [11][5440/11272] Time 0.903 (0.825) Data 0.001 (0.002) Loss 2.4528 (2.6259) Prec@1 39.375 (36.460) Prec@5 67.500 (67.095) Epoch: [11][5450/11272] Time 0.748 (0.825) Data 0.002 (0.002) Loss 2.7402 (2.6259) Prec@1 27.500 (36.460) Prec@5 61.875 (67.098) Epoch: [11][5460/11272] Time 0.775 (0.825) Data 0.001 (0.002) Loss 2.3950 (2.6259) Prec@1 38.750 (36.459) Prec@5 70.625 (67.098) Epoch: [11][5470/11272] Time 0.896 (0.825) Data 0.002 (0.002) Loss 2.5230 (2.6259) Prec@1 38.750 (36.460) Prec@5 69.375 (67.098) Epoch: [11][5480/11272] Time 0.766 (0.825) Data 0.001 (0.002) Loss 2.7538 (2.6261) Prec@1 32.500 (36.458) Prec@5 65.000 (67.095) Epoch: [11][5490/11272] Time 0.788 (0.825) Data 0.002 (0.002) Loss 2.7075 (2.6262) Prec@1 37.500 (36.456) Prec@5 68.125 (67.093) Epoch: [11][5500/11272] Time 0.864 (0.825) Data 0.002 (0.002) Loss 2.5725 (2.6262) Prec@1 39.375 (36.454) Prec@5 70.000 (67.092) Epoch: [11][5510/11272] Time 0.901 (0.825) Data 0.002 (0.002) Loss 2.6339 (2.6263) Prec@1 35.000 (36.454) Prec@5 68.125 (67.091) Epoch: [11][5520/11272] Time 0.742 (0.825) Data 0.002 (0.002) Loss 2.7905 (2.6264) Prec@1 34.375 (36.450) Prec@5 63.125 (67.085) Epoch: [11][5530/11272] Time 0.736 (0.825) Data 0.002 (0.002) Loss 2.8416 (2.6265) Prec@1 34.375 (36.449) Prec@5 63.750 (67.082) Epoch: [11][5540/11272] Time 0.865 (0.825) Data 0.002 (0.002) Loss 3.0410 (2.6264) Prec@1 25.625 (36.450) Prec@5 59.375 (67.085) Epoch: [11][5550/11272] Time 0.850 (0.825) Data 0.002 (0.002) Loss 2.8442 (2.6264) Prec@1 35.625 (36.453) Prec@5 65.625 (67.086) Epoch: [11][5560/11272] Time 0.765 (0.825) Data 0.001 (0.002) Loss 2.7818 (2.6264) Prec@1 35.625 (36.454) Prec@5 63.750 (67.086) Epoch: [11][5570/11272] Time 0.759 (0.825) Data 0.002 (0.002) Loss 2.3976 (2.6264) Prec@1 41.875 (36.450) Prec@5 73.750 (67.086) Epoch: [11][5580/11272] Time 0.862 (0.825) Data 0.001 (0.002) Loss 2.4417 (2.6263) Prec@1 40.000 (36.450) Prec@5 72.500 (67.090) Epoch: [11][5590/11272] Time 0.858 (0.825) Data 0.001 (0.002) Loss 2.5943 (2.6263) Prec@1 33.125 (36.450) Prec@5 66.250 (67.092) Epoch: [11][5600/11272] Time 0.755 (0.825) Data 0.002 (0.002) Loss 2.7529 (2.6263) Prec@1 31.875 (36.448) Prec@5 69.375 (67.091) Epoch: [11][5610/11272] Time 0.944 (0.825) Data 0.002 (0.002) Loss 2.7572 (2.6263) Prec@1 35.625 (36.448) Prec@5 62.500 (67.090) Epoch: [11][5620/11272] Time 0.887 (0.825) Data 0.002 (0.002) Loss 2.5313 (2.6263) Prec@1 38.125 (36.450) Prec@5 67.500 (67.093) Epoch: [11][5630/11272] Time 0.748 (0.825) Data 0.002 (0.002) Loss 2.4610 (2.6261) Prec@1 36.875 (36.450) Prec@5 68.750 (67.096) Epoch: [11][5640/11272] Time 0.754 (0.825) Data 0.001 (0.002) Loss 3.1278 (2.6261) Prec@1 25.625 (36.448) Prec@5 60.000 (67.094) Epoch: [11][5650/11272] Time 0.927 (0.825) Data 0.001 (0.002) Loss 2.6469 (2.6262) Prec@1 35.625 (36.446) Prec@5 69.375 (67.093) Epoch: [11][5660/11272] Time 0.868 (0.825) Data 0.001 (0.002) Loss 2.5538 (2.6262) Prec@1 36.250 (36.444) Prec@5 68.750 (67.093) Epoch: [11][5670/11272] Time 0.751 (0.825) Data 0.002 (0.002) Loss 2.3636 (2.6262) Prec@1 45.000 (36.444) Prec@5 72.500 (67.094) Epoch: [11][5680/11272] Time 0.780 (0.825) Data 0.002 (0.002) Loss 2.4683 (2.6261) Prec@1 38.125 (36.445) Prec@5 70.000 (67.097) Epoch: [11][5690/11272] Time 0.898 (0.825) Data 0.002 (0.002) Loss 2.4742 (2.6261) Prec@1 40.625 (36.447) Prec@5 68.125 (67.096) Epoch: [11][5700/11272] Time 0.950 (0.825) Data 0.002 (0.002) Loss 2.6616 (2.6261) Prec@1 35.000 (36.447) Prec@5 61.250 (67.095) Epoch: [11][5710/11272] Time 0.747 (0.825) Data 0.001 (0.002) Loss 2.5835 (2.6260) Prec@1 39.375 (36.450) Prec@5 65.625 (67.096) Epoch: [11][5720/11272] Time 0.747 (0.825) Data 0.001 (0.002) Loss 2.7958 (2.6260) Prec@1 38.750 (36.450) Prec@5 60.625 (67.095) Epoch: [11][5730/11272] Time 0.851 (0.825) Data 0.002 (0.002) Loss 2.5629 (2.6261) Prec@1 35.000 (36.449) Prec@5 66.875 (67.096) Epoch: [11][5740/11272] Time 0.763 (0.825) Data 0.003 (0.002) Loss 2.3535 (2.6260) Prec@1 38.125 (36.449) Prec@5 70.625 (67.095) Epoch: [11][5750/11272] Time 0.740 (0.825) Data 0.002 (0.002) Loss 2.3570 (2.6261) Prec@1 46.250 (36.450) Prec@5 71.250 (67.094) Epoch: [11][5760/11272] Time 0.927 (0.825) Data 0.002 (0.002) Loss 2.8186 (2.6262) Prec@1 31.875 (36.445) Prec@5 65.625 (67.091) Epoch: [11][5770/11272] Time 0.862 (0.825) Data 0.002 (0.002) Loss 2.6190 (2.6263) Prec@1 39.375 (36.444) Prec@5 67.500 (67.089) Epoch: [11][5780/11272] Time 0.759 (0.825) Data 0.002 (0.002) Loss 2.5979 (2.6262) Prec@1 36.250 (36.447) Prec@5 64.375 (67.090) Epoch: [11][5790/11272] Time 0.804 (0.825) Data 0.002 (0.002) Loss 2.6911 (2.6263) Prec@1 30.625 (36.446) Prec@5 63.750 (67.090) Epoch: [11][5800/11272] Time 0.985 (0.825) Data 0.002 (0.002) Loss 2.6807 (2.6263) Prec@1 36.875 (36.447) Prec@5 66.875 (67.089) Epoch: [11][5810/11272] Time 0.858 (0.825) Data 0.002 (0.002) Loss 2.5994 (2.6263) Prec@1 33.125 (36.446) Prec@5 69.375 (67.091) Epoch: [11][5820/11272] Time 0.735 (0.825) Data 0.001 (0.002) Loss 2.7885 (2.6263) Prec@1 35.625 (36.448) Prec@5 63.750 (67.092) Epoch: [11][5830/11272] Time 0.740 (0.825) Data 0.001 (0.002) Loss 2.7046 (2.6263) Prec@1 32.500 (36.445) Prec@5 70.625 (67.092) Epoch: [11][5840/11272] Time 0.834 (0.825) Data 0.001 (0.002) Loss 2.7682 (2.6263) Prec@1 33.750 (36.446) Prec@5 68.125 (67.093) Epoch: [11][5850/11272] Time 0.881 (0.825) Data 0.002 (0.002) Loss 2.5371 (2.6262) Prec@1 39.375 (36.449) Prec@5 68.750 (67.093) Epoch: [11][5860/11272] Time 0.791 (0.825) Data 0.002 (0.002) Loss 2.4637 (2.6261) Prec@1 39.375 (36.451) Prec@5 69.375 (67.094) Epoch: [11][5870/11272] Time 0.897 (0.825) Data 0.002 (0.002) Loss 2.5822 (2.6261) Prec@1 35.000 (36.451) Prec@5 66.250 (67.094) Epoch: [11][5880/11272] Time 0.881 (0.825) Data 0.001 (0.002) Loss 2.4728 (2.6261) Prec@1 39.375 (36.451) Prec@5 67.500 (67.095) Epoch: [11][5890/11272] Time 0.776 (0.825) Data 0.002 (0.002) Loss 2.7701 (2.6262) Prec@1 31.875 (36.448) Prec@5 65.000 (67.093) Epoch: [11][5900/11272] Time 0.739 (0.825) Data 0.001 (0.002) Loss 2.4280 (2.6262) Prec@1 38.125 (36.449) Prec@5 75.625 (67.096) Epoch: [11][5910/11272] Time 0.901 (0.825) Data 0.002 (0.002) Loss 2.6200 (2.6262) Prec@1 30.625 (36.446) Prec@5 68.125 (67.096) Epoch: [11][5920/11272] Time 0.900 (0.825) Data 0.002 (0.002) Loss 2.7570 (2.6263) Prec@1 35.000 (36.442) Prec@5 68.125 (67.094) Epoch: [11][5930/11272] Time 0.747 (0.825) Data 0.001 (0.002) Loss 2.7960 (2.6264) Prec@1 32.500 (36.441) Prec@5 64.375 (67.092) Epoch: [11][5940/11272] Time 0.742 (0.825) Data 0.001 (0.002) Loss 2.7617 (2.6266) Prec@1 34.375 (36.438) Prec@5 63.125 (67.089) Epoch: [11][5950/11272] Time 0.879 (0.825) Data 0.001 (0.002) Loss 2.1369 (2.6264) Prec@1 46.875 (36.440) Prec@5 77.500 (67.094) Epoch: [11][5960/11272] Time 0.888 (0.825) Data 0.002 (0.002) Loss 2.6491 (2.6264) Prec@1 32.500 (36.439) Prec@5 68.125 (67.093) Epoch: [11][5970/11272] Time 0.753 (0.825) Data 0.002 (0.002) Loss 2.3509 (2.6264) Prec@1 43.750 (36.439) Prec@5 71.250 (67.093) Epoch: [11][5980/11272] Time 0.749 (0.825) Data 0.001 (0.002) Loss 2.4705 (2.6264) Prec@1 38.750 (36.438) Prec@5 73.125 (67.092) Epoch: [11][5990/11272] Time 0.911 (0.825) Data 0.002 (0.002) Loss 2.7439 (2.6265) Prec@1 33.750 (36.433) Prec@5 63.750 (67.092) Epoch: [11][6000/11272] Time 0.753 (0.825) Data 0.004 (0.002) Loss 2.7729 (2.6266) Prec@1 33.125 (36.429) Prec@5 63.125 (67.091) Epoch: [11][6010/11272] Time 0.759 (0.825) Data 0.002 (0.002) Loss 2.5223 (2.6266) Prec@1 36.250 (36.429) Prec@5 71.250 (67.092) Epoch: [11][6020/11272] Time 0.869 (0.825) Data 0.001 (0.002) Loss 2.4481 (2.6267) Prec@1 42.500 (36.427) Prec@5 70.000 (67.091) Epoch: [11][6030/11272] Time 0.855 (0.825) Data 0.002 (0.002) Loss 2.5676 (2.6267) Prec@1 35.000 (36.426) Prec@5 68.125 (67.091) Epoch: [11][6040/11272] Time 0.746 (0.825) Data 0.001 (0.002) Loss 2.7439 (2.6268) Prec@1 35.000 (36.425) Prec@5 65.000 (67.089) Epoch: [11][6050/11272] Time 0.795 (0.825) Data 0.002 (0.002) Loss 2.6217 (2.6268) Prec@1 32.500 (36.425) Prec@5 66.875 (67.087) Epoch: [11][6060/11272] Time 0.917 (0.825) Data 0.002 (0.002) Loss 2.4455 (2.6267) Prec@1 38.750 (36.428) Prec@5 68.750 (67.090) Epoch: [11][6070/11272] Time 0.828 (0.825) Data 0.001 (0.002) Loss 2.5334 (2.6266) Prec@1 40.000 (36.430) Prec@5 68.125 (67.091) Epoch: [11][6080/11272] Time 0.747 (0.825) Data 0.002 (0.002) Loss 2.4948 (2.6266) Prec@1 40.000 (36.432) Prec@5 72.500 (67.090) Epoch: [11][6090/11272] Time 0.758 (0.825) Data 0.002 (0.002) Loss 2.3141 (2.6265) Prec@1 44.375 (36.434) Prec@5 74.375 (67.095) Epoch: [11][6100/11272] Time 0.856 (0.825) Data 0.001 (0.002) Loss 2.4426 (2.6265) Prec@1 43.125 (36.436) Prec@5 63.125 (67.095) Epoch: [11][6110/11272] Time 0.880 (0.825) Data 0.001 (0.002) Loss 2.7231 (2.6265) Prec@1 38.125 (36.435) Prec@5 68.750 (67.096) Epoch: [11][6120/11272] Time 0.816 (0.825) Data 0.002 (0.002) Loss 2.7549 (2.6265) Prec@1 32.500 (36.436) Prec@5 63.125 (67.096) Epoch: [11][6130/11272] Time 0.945 (0.825) Data 0.002 (0.002) Loss 2.6877 (2.6265) Prec@1 36.875 (36.437) Prec@5 64.375 (67.096) Epoch: [11][6140/11272] Time 0.876 (0.825) Data 0.001 (0.002) Loss 2.7622 (2.6263) Prec@1 36.250 (36.440) Prec@5 67.500 (67.100) Epoch: [11][6150/11272] Time 0.813 (0.825) Data 0.002 (0.002) Loss 2.8936 (2.6264) Prec@1 33.750 (36.441) Prec@5 65.000 (67.099) Epoch: [11][6160/11272] Time 0.800 (0.825) Data 0.004 (0.002) Loss 2.5629 (2.6264) Prec@1 38.125 (36.439) Prec@5 71.250 (67.098) Epoch: [11][6170/11272] Time 0.855 (0.825) Data 0.001 (0.002) Loss 2.7344 (2.6265) Prec@1 35.625 (36.438) Prec@5 68.125 (67.097) Epoch: [11][6180/11272] Time 0.906 (0.825) Data 0.002 (0.002) Loss 2.5277 (2.6265) Prec@1 40.000 (36.438) Prec@5 68.750 (67.098) Epoch: [11][6190/11272] Time 0.745 (0.825) Data 0.002 (0.002) Loss 2.5350 (2.6265) Prec@1 35.625 (36.437) Prec@5 66.875 (67.097) Epoch: [11][6200/11272] Time 0.733 (0.825) Data 0.001 (0.002) Loss 2.8283 (2.6264) Prec@1 32.500 (36.437) Prec@5 68.125 (67.100) Epoch: [11][6210/11272] Time 0.944 (0.825) Data 0.001 (0.002) Loss 2.6514 (2.6264) Prec@1 36.250 (36.436) Prec@5 63.125 (67.102) Epoch: [11][6220/11272] Time 0.858 (0.825) Data 0.001 (0.002) Loss 2.7540 (2.6265) Prec@1 33.750 (36.436) Prec@5 65.000 (67.097) Epoch: [11][6230/11272] Time 0.755 (0.825) Data 0.002 (0.002) Loss 2.6772 (2.6265) Prec@1 37.500 (36.434) Prec@5 68.125 (67.098) Epoch: [11][6240/11272] Time 0.762 (0.825) Data 0.001 (0.002) Loss 2.4788 (2.6264) Prec@1 43.750 (36.436) Prec@5 65.625 (67.098) Epoch: [11][6250/11272] Time 0.897 (0.825) Data 0.002 (0.002) Loss 2.4317 (2.6263) Prec@1 40.625 (36.440) Prec@5 68.125 (67.100) Epoch: [11][6260/11272] Time 0.877 (0.825) Data 0.002 (0.002) Loss 2.5306 (2.6263) Prec@1 35.000 (36.439) Prec@5 70.000 (67.102) Epoch: [11][6270/11272] Time 0.746 (0.825) Data 0.002 (0.002) Loss 2.3413 (2.6263) Prec@1 39.375 (36.438) Prec@5 76.875 (67.102) Epoch: [11][6280/11272] Time 0.932 (0.825) Data 0.001 (0.002) Loss 2.6161 (2.6262) Prec@1 37.500 (36.443) Prec@5 67.500 (67.105) Epoch: [11][6290/11272] Time 0.877 (0.825) Data 0.001 (0.002) Loss 2.6024 (2.6263) Prec@1 38.750 (36.442) Prec@5 69.375 (67.105) Epoch: [11][6300/11272] Time 0.799 (0.825) Data 0.002 (0.002) Loss 2.6077 (2.6264) Prec@1 36.250 (36.441) Prec@5 67.500 (67.103) Epoch: [11][6310/11272] Time 0.731 (0.825) Data 0.001 (0.002) Loss 2.4538 (2.6265) Prec@1 46.875 (36.437) Prec@5 69.375 (67.100) Epoch: [11][6320/11272] Time 0.928 (0.825) Data 0.002 (0.002) Loss 2.3156 (2.6266) Prec@1 42.500 (36.436) Prec@5 75.000 (67.099) Epoch: [11][6330/11272] Time 0.860 (0.825) Data 0.001 (0.002) Loss 2.6073 (2.6265) Prec@1 35.625 (36.439) Prec@5 70.625 (67.100) Epoch: [11][6340/11272] Time 0.741 (0.825) Data 0.002 (0.002) Loss 2.5149 (2.6265) Prec@1 37.500 (36.440) Prec@5 68.125 (67.101) Epoch: [11][6350/11272] Time 0.773 (0.825) Data 0.002 (0.002) Loss 2.7478 (2.6264) Prec@1 35.625 (36.444) Prec@5 61.875 (67.104) Epoch: [11][6360/11272] Time 0.909 (0.825) Data 0.001 (0.002) Loss 2.7996 (2.6264) Prec@1 30.625 (36.442) Prec@5 67.500 (67.104) Epoch: [11][6370/11272] Time 0.887 (0.825) Data 0.001 (0.002) Loss 2.4598 (2.6264) Prec@1 41.250 (36.441) Prec@5 72.500 (67.105) Epoch: [11][6380/11272] Time 0.736 (0.825) Data 0.001 (0.002) Loss 2.4075 (2.6263) Prec@1 39.375 (36.441) Prec@5 68.750 (67.106) Epoch: [11][6390/11272] Time 0.750 (0.825) Data 0.002 (0.002) Loss 2.9643 (2.6265) Prec@1 31.875 (36.439) Prec@5 58.125 (67.103) Epoch: [11][6400/11272] Time 0.864 (0.825) Data 0.002 (0.002) Loss 2.7441 (2.6264) Prec@1 35.000 (36.443) Prec@5 60.625 (67.102) Epoch: [11][6410/11272] Time 0.745 (0.825) Data 0.002 (0.002) Loss 2.3629 (2.6264) Prec@1 44.375 (36.445) Prec@5 67.500 (67.102) Epoch: [11][6420/11272] Time 0.805 (0.825) Data 0.002 (0.002) Loss 2.5517 (2.6264) Prec@1 36.250 (36.445) Prec@5 70.625 (67.104) Epoch: [11][6430/11272] Time 0.874 (0.825) Data 0.002 (0.002) Loss 2.5692 (2.6262) Prec@1 32.500 (36.446) Prec@5 66.250 (67.107) Epoch: [11][6440/11272] Time 0.886 (0.825) Data 0.002 (0.002) Loss 2.5524 (2.6263) Prec@1 35.625 (36.442) Prec@5 68.750 (67.106) Epoch: [11][6450/11272] Time 0.716 (0.825) Data 0.001 (0.002) Loss 2.7368 (2.6264) Prec@1 35.000 (36.440) Prec@5 68.125 (67.106) Epoch: [11][6460/11272] Time 0.745 (0.825) Data 0.002 (0.002) Loss 2.7779 (2.6264) Prec@1 34.375 (36.440) Prec@5 63.750 (67.107) Epoch: [11][6470/11272] Time 0.860 (0.825) Data 0.002 (0.002) Loss 2.6411 (2.6263) Prec@1 33.750 (36.441) Prec@5 71.875 (67.108) Epoch: [11][6480/11272] Time 0.914 (0.825) Data 0.002 (0.002) Loss 2.6365 (2.6261) Prec@1 38.125 (36.444) Prec@5 68.125 (67.112) Epoch: [11][6490/11272] Time 0.816 (0.825) Data 0.001 (0.002) Loss 2.7043 (2.6261) Prec@1 32.500 (36.445) Prec@5 65.000 (67.112) Epoch: [11][6500/11272] Time 0.783 (0.825) Data 0.002 (0.002) Loss 2.3629 (2.6261) Prec@1 39.375 (36.445) Prec@5 72.500 (67.113) Epoch: [11][6510/11272] Time 0.899 (0.825) Data 0.002 (0.002) Loss 2.7001 (2.6261) Prec@1 35.625 (36.444) Prec@5 64.375 (67.111) Epoch: [11][6520/11272] Time 0.847 (0.825) Data 0.001 (0.002) Loss 2.5318 (2.6260) Prec@1 37.500 (36.443) Prec@5 70.625 (67.113) Epoch: [11][6530/11272] Time 0.728 (0.825) Data 0.001 (0.002) Loss 2.8556 (2.6261) Prec@1 30.625 (36.441) Prec@5 63.125 (67.112) Epoch: [11][6540/11272] Time 0.876 (0.825) Data 0.001 (0.002) Loss 2.6503 (2.6262) Prec@1 31.875 (36.439) Prec@5 68.125 (67.111) Epoch: [11][6550/11272] Time 0.914 (0.825) Data 0.002 (0.002) Loss 2.4734 (2.6261) Prec@1 40.000 (36.439) Prec@5 66.250 (67.112) Epoch: [11][6560/11272] Time 0.782 (0.825) Data 0.003 (0.002) Loss 2.3616 (2.6260) Prec@1 43.750 (36.438) Prec@5 70.625 (67.114) Epoch: [11][6570/11272] Time 0.715 (0.825) Data 0.002 (0.002) Loss 2.7988 (2.6261) Prec@1 33.750 (36.438) Prec@5 63.750 (67.112) Epoch: [11][6580/11272] Time 0.906 (0.825) Data 0.002 (0.002) Loss 2.7380 (2.6261) Prec@1 30.000 (36.437) Prec@5 64.375 (67.112) Epoch: [11][6590/11272] Time 0.881 (0.825) Data 0.001 (0.002) Loss 2.4665 (2.6260) Prec@1 33.750 (36.436) Prec@5 68.750 (67.113) Epoch: [11][6600/11272] Time 0.752 (0.824) Data 0.001 (0.002) Loss 2.2256 (2.6260) Prec@1 43.750 (36.437) Prec@5 73.125 (67.115) Epoch: [11][6610/11272] Time 0.740 (0.824) Data 0.002 (0.002) Loss 2.6683 (2.6260) Prec@1 39.375 (36.438) Prec@5 65.000 (67.114) Epoch: [11][6620/11272] Time 0.876 (0.824) Data 0.001 (0.002) Loss 2.7818 (2.6260) Prec@1 34.375 (36.438) Prec@5 64.375 (67.113) Epoch: [11][6630/11272] Time 0.886 (0.824) Data 0.001 (0.002) Loss 2.4761 (2.6258) Prec@1 36.875 (36.439) Prec@5 70.000 (67.116) Epoch: [11][6640/11272] Time 0.777 (0.824) Data 0.002 (0.002) Loss 2.7605 (2.6259) Prec@1 31.875 (36.437) Prec@5 66.250 (67.116) Epoch: [11][6650/11272] Time 0.750 (0.824) Data 0.001 (0.002) Loss 2.6482 (2.6259) Prec@1 40.000 (36.436) Prec@5 61.875 (67.115) Epoch: [11][6660/11272] Time 0.887 (0.824) Data 0.001 (0.002) Loss 2.7019 (2.6259) Prec@1 33.125 (36.433) Prec@5 65.625 (67.114) Epoch: [11][6670/11272] Time 0.752 (0.824) Data 0.004 (0.002) Loss 2.4890 (2.6259) Prec@1 36.875 (36.434) Prec@5 68.750 (67.114) Epoch: [11][6680/11272] Time 0.746 (0.824) Data 0.001 (0.002) Loss 2.6782 (2.6259) Prec@1 36.875 (36.433) Prec@5 63.750 (67.114) Epoch: [11][6690/11272] Time 0.866 (0.824) Data 0.002 (0.002) Loss 2.8008 (2.6261) Prec@1 34.375 (36.431) Prec@5 63.125 (67.109) Epoch: [11][6700/11272] Time 0.877 (0.824) Data 0.002 (0.002) Loss 2.6907 (2.6261) Prec@1 40.625 (36.432) Prec@5 65.625 (67.107) Epoch: [11][6710/11272] Time 0.771 (0.824) Data 0.002 (0.002) Loss 2.7924 (2.6260) Prec@1 35.625 (36.432) Prec@5 61.875 (67.106) Epoch: [11][6720/11272] Time 0.774 (0.824) Data 0.002 (0.002) Loss 2.7472 (2.6261) Prec@1 35.625 (36.432) Prec@5 70.625 (67.106) Epoch: [11][6730/11272] Time 0.943 (0.825) Data 0.002 (0.002) Loss 2.7459 (2.6261) Prec@1 35.000 (36.431) Prec@5 65.625 (67.105) Epoch: [11][6740/11272] Time 0.887 (0.825) Data 0.001 (0.002) Loss 2.4713 (2.6262) Prec@1 38.750 (36.432) Prec@5 70.000 (67.105) Epoch: [11][6750/11272] Time 0.793 (0.825) Data 0.001 (0.002) Loss 2.6895 (2.6261) Prec@1 36.875 (36.434) Prec@5 66.875 (67.106) Epoch: [11][6760/11272] Time 0.771 (0.825) Data 0.001 (0.002) Loss 2.5041 (2.6260) Prec@1 41.250 (36.433) Prec@5 66.250 (67.106) Epoch: [11][6770/11272] Time 0.895 (0.825) Data 0.002 (0.002) Loss 2.7291 (2.6259) Prec@1 36.875 (36.433) Prec@5 60.625 (67.109) Epoch: [11][6780/11272] Time 0.863 (0.825) Data 0.002 (0.002) Loss 2.8194 (2.6260) Prec@1 30.625 (36.432) Prec@5 63.125 (67.106) Epoch: [11][6790/11272] Time 0.764 (0.825) Data 0.002 (0.002) Loss 2.3192 (2.6259) Prec@1 46.875 (36.436) Prec@5 75.000 (67.108) Epoch: [11][6800/11272] Time 0.911 (0.825) Data 0.001 (0.002) Loss 2.7374 (2.6260) Prec@1 31.875 (36.433) Prec@5 68.125 (67.107) Epoch: [11][6810/11272] Time 0.880 (0.825) Data 0.001 (0.002) Loss 2.4739 (2.6260) Prec@1 38.750 (36.434) Prec@5 67.500 (67.108) Epoch: [11][6820/11272] Time 0.806 (0.825) Data 0.002 (0.002) Loss 2.6468 (2.6261) Prec@1 38.750 (36.432) Prec@5 64.375 (67.105) Epoch: [11][6830/11272] Time 0.758 (0.825) Data 0.002 (0.002) Loss 2.6576 (2.6261) Prec@1 34.375 (36.432) Prec@5 66.250 (67.104) Epoch: [11][6840/11272] Time 0.895 (0.825) Data 0.002 (0.002) Loss 2.7050 (2.6260) Prec@1 31.875 (36.432) Prec@5 62.500 (67.103) Epoch: [11][6850/11272] Time 0.907 (0.825) Data 0.002 (0.002) Loss 2.9188 (2.6261) Prec@1 33.125 (36.432) Prec@5 59.375 (67.103) Epoch: [11][6860/11272] Time 0.749 (0.825) Data 0.002 (0.002) Loss 2.6724 (2.6261) Prec@1 36.250 (36.433) Prec@5 67.500 (67.104) Epoch: [11][6870/11272] Time 0.765 (0.825) Data 0.002 (0.002) Loss 2.4585 (2.6261) Prec@1 42.500 (36.433) Prec@5 67.500 (67.103) Epoch: [11][6880/11272] Time 0.889 (0.825) Data 0.001 (0.002) Loss 2.6544 (2.6260) Prec@1 33.125 (36.435) Prec@5 65.625 (67.104) Epoch: [11][6890/11272] Time 0.902 (0.825) Data 0.001 (0.002) Loss 2.6427 (2.6259) Prec@1 38.125 (36.435) Prec@5 68.125 (67.105) Epoch: [11][6900/11272] Time 0.742 (0.825) Data 0.001 (0.002) Loss 2.7896 (2.6261) Prec@1 38.125 (36.433) Prec@5 65.000 (67.101) Epoch: [11][6910/11272] Time 0.777 (0.825) Data 0.002 (0.002) Loss 2.8025 (2.6261) Prec@1 32.500 (36.433) Prec@5 60.625 (67.099) Epoch: [11][6920/11272] Time 0.874 (0.825) Data 0.002 (0.002) Loss 2.7601 (2.6262) Prec@1 33.750 (36.434) Prec@5 61.250 (67.098) Epoch: [11][6930/11272] Time 0.756 (0.825) Data 0.003 (0.002) Loss 2.3808 (2.6261) Prec@1 39.375 (36.432) Prec@5 72.500 (67.099) Epoch: [11][6940/11272] Time 0.756 (0.825) Data 0.001 (0.002) Loss 2.9241 (2.6263) Prec@1 30.000 (36.431) Prec@5 60.625 (67.096) Epoch: [11][6950/11272] Time 0.930 (0.825) Data 0.003 (0.002) Loss 2.6005 (2.6262) Prec@1 36.250 (36.433) Prec@5 65.625 (67.096) Epoch: [11][6960/11272] Time 0.868 (0.825) Data 0.002 (0.002) Loss 2.5375 (2.6263) Prec@1 38.125 (36.432) Prec@5 68.750 (67.095) Epoch: [11][6970/11272] Time 0.756 (0.825) Data 0.002 (0.002) Loss 2.6908 (2.6262) Prec@1 36.875 (36.434) Prec@5 69.375 (67.097) Epoch: [11][6980/11272] Time 0.809 (0.825) Data 0.002 (0.002) Loss 2.5476 (2.6261) Prec@1 40.000 (36.433) Prec@5 68.125 (67.098) Epoch: [11][6990/11272] Time 0.890 (0.825) Data 0.001 (0.002) Loss 2.4706 (2.6261) Prec@1 40.000 (36.433) Prec@5 65.625 (67.096) Epoch: [11][7000/11272] Time 0.889 (0.825) Data 0.002 (0.002) Loss 2.3910 (2.6261) Prec@1 43.125 (36.434) Prec@5 70.625 (67.095) Epoch: [11][7010/11272] Time 0.807 (0.825) Data 0.002 (0.002) Loss 2.7465 (2.6262) Prec@1 35.000 (36.429) Prec@5 66.875 (67.096) Epoch: [11][7020/11272] Time 0.755 (0.825) Data 0.001 (0.002) Loss 2.9521 (2.6262) Prec@1 26.250 (36.429) Prec@5 59.375 (67.097) Epoch: [11][7030/11272] Time 0.886 (0.825) Data 0.001 (0.002) Loss 2.6833 (2.6262) Prec@1 31.250 (36.430) Prec@5 68.750 (67.095) Epoch: [11][7040/11272] Time 0.876 (0.825) Data 0.001 (0.002) Loss 2.7154 (2.6262) Prec@1 31.875 (36.429) Prec@5 72.500 (67.095) Epoch: [11][7050/11272] Time 0.753 (0.825) Data 0.003 (0.002) Loss 2.6542 (2.6263) Prec@1 36.250 (36.429) Prec@5 68.750 (67.094) Epoch: [11][7060/11272] Time 0.893 (0.825) Data 0.001 (0.002) Loss 2.7675 (2.6262) Prec@1 34.375 (36.431) Prec@5 65.625 (67.097) Epoch: [11][7070/11272] Time 0.886 (0.825) Data 0.002 (0.002) Loss 2.5286 (2.6261) Prec@1 35.000 (36.431) Prec@5 70.000 (67.097) Epoch: [11][7080/11272] Time 0.771 (0.825) Data 0.001 (0.002) Loss 2.6397 (2.6261) Prec@1 32.500 (36.430) Prec@5 66.250 (67.095) Epoch: [11][7090/11272] Time 0.759 (0.825) Data 0.002 (0.002) Loss 2.6007 (2.6261) Prec@1 37.500 (36.430) Prec@5 65.625 (67.097) Epoch: [11][7100/11272] Time 0.883 (0.825) Data 0.001 (0.002) Loss 2.5323 (2.6261) Prec@1 36.875 (36.427) Prec@5 66.250 (67.095) Epoch: [11][7110/11272] Time 0.912 (0.825) Data 0.002 (0.002) Loss 2.5657 (2.6260) Prec@1 36.875 (36.430) Prec@5 70.000 (67.098) Epoch: [11][7120/11272] Time 0.757 (0.825) Data 0.001 (0.002) Loss 2.6608 (2.6261) Prec@1 35.625 (36.430) Prec@5 67.500 (67.097) Epoch: [11][7130/11272] Time 0.756 (0.825) Data 0.002 (0.002) Loss 2.5431 (2.6260) Prec@1 40.000 (36.433) Prec@5 64.375 (67.097) Epoch: [11][7140/11272] Time 0.875 (0.825) Data 0.002 (0.002) Loss 2.2695 (2.6259) Prec@1 38.750 (36.434) Prec@5 73.750 (67.096) Epoch: [11][7150/11272] Time 0.899 (0.825) Data 0.002 (0.002) Loss 2.5630 (2.6259) Prec@1 36.875 (36.433) Prec@5 68.750 (67.094) Epoch: [11][7160/11272] Time 0.812 (0.825) Data 0.002 (0.002) Loss 2.8000 (2.6258) Prec@1 31.250 (36.434) Prec@5 63.750 (67.097) Epoch: [11][7170/11272] Time 0.722 (0.825) Data 0.001 (0.002) Loss 2.6421 (2.6257) Prec@1 36.250 (36.434) Prec@5 69.375 (67.097) Epoch: [11][7180/11272] Time 0.877 (0.825) Data 0.001 (0.002) Loss 2.7335 (2.6257) Prec@1 36.250 (36.438) Prec@5 63.125 (67.099) Epoch: [11][7190/11272] Time 0.951 (0.825) Data 0.002 (0.002) Loss 2.5951 (2.6257) Prec@1 39.375 (36.436) Prec@5 68.125 (67.101) Epoch: [11][7200/11272] Time 0.762 (0.825) Data 0.002 (0.002) Loss 2.7198 (2.6257) Prec@1 36.250 (36.437) Prec@5 64.375 (67.100) Epoch: [11][7210/11272] Time 0.878 (0.825) Data 0.002 (0.002) Loss 2.7442 (2.6257) Prec@1 36.875 (36.435) Prec@5 63.750 (67.100) Epoch: [11][7220/11272] Time 0.869 (0.825) Data 0.001 (0.002) Loss 2.6231 (2.6257) Prec@1 38.750 (36.436) Prec@5 64.375 (67.099) Epoch: [11][7230/11272] Time 0.739 (0.825) Data 0.002 (0.002) Loss 2.5806 (2.6256) Prec@1 38.125 (36.435) Prec@5 68.125 (67.101) Epoch: [11][7240/11272] Time 0.793 (0.825) Data 0.002 (0.002) Loss 2.6621 (2.6258) Prec@1 33.125 (36.430) Prec@5 63.125 (67.099) Epoch: [11][7250/11272] Time 0.909 (0.825) Data 0.001 (0.002) Loss 2.7419 (2.6258) Prec@1 29.375 (36.429) Prec@5 63.125 (67.101) Epoch: [11][7260/11272] Time 0.925 (0.825) Data 0.002 (0.002) Loss 2.6313 (2.6257) Prec@1 33.750 (36.430) Prec@5 67.500 (67.101) Epoch: [11][7270/11272] Time 0.750 (0.825) Data 0.001 (0.002) Loss 2.5419 (2.6256) Prec@1 36.875 (36.430) Prec@5 70.625 (67.104) Epoch: [11][7280/11272] Time 0.831 (0.825) Data 0.002 (0.002) Loss 2.7806 (2.6257) Prec@1 36.875 (36.431) Prec@5 63.750 (67.102) Epoch: [11][7290/11272] Time 0.961 (0.825) Data 0.002 (0.002) Loss 2.3950 (2.6256) Prec@1 38.750 (36.434) Prec@5 73.750 (67.104) Epoch: [11][7300/11272] Time 0.866 (0.825) Data 0.001 (0.002) Loss 2.2960 (2.6256) Prec@1 43.750 (36.433) Prec@5 71.875 (67.105) Epoch: [11][7310/11272] Time 0.752 (0.825) Data 0.002 (0.002) Loss 2.5595 (2.6257) Prec@1 38.125 (36.435) Prec@5 66.875 (67.104) Epoch: [11][7320/11272] Time 0.744 (0.825) Data 0.001 (0.002) Loss 2.7247 (2.6258) Prec@1 33.750 (36.434) Prec@5 59.375 (67.099) Epoch: [11][7330/11272] Time 0.882 (0.825) Data 0.001 (0.002) Loss 2.5509 (2.6258) Prec@1 36.875 (36.433) Prec@5 68.125 (67.098) Epoch: [11][7340/11272] Time 0.742 (0.825) Data 0.002 (0.002) Loss 2.8241 (2.6260) Prec@1 33.750 (36.429) Prec@5 63.750 (67.096) Epoch: [11][7350/11272] Time 0.735 (0.825) Data 0.002 (0.002) Loss 2.6767 (2.6260) Prec@1 32.500 (36.427) Prec@5 66.875 (67.096) Epoch: [11][7360/11272] Time 0.907 (0.825) Data 0.001 (0.002) Loss 2.4552 (2.6259) Prec@1 41.250 (36.430) Prec@5 68.750 (67.099) Epoch: [11][7370/11272] Time 0.897 (0.825) Data 0.002 (0.002) Loss 2.6299 (2.6259) Prec@1 36.875 (36.430) Prec@5 66.875 (67.100) Epoch: [11][7380/11272] Time 0.781 (0.825) Data 0.002 (0.002) Loss 2.5944 (2.6258) Prec@1 33.750 (36.433) Prec@5 65.625 (67.102) Epoch: [11][7390/11272] Time 0.830 (0.825) Data 0.002 (0.002) Loss 2.8118 (2.6258) Prec@1 35.000 (36.434) Prec@5 65.000 (67.103) Epoch: [11][7400/11272] Time 0.857 (0.825) Data 0.002 (0.002) Loss 2.4445 (2.6257) Prec@1 40.625 (36.434) Prec@5 70.000 (67.105) Epoch: [11][7410/11272] Time 0.953 (0.825) Data 0.002 (0.002) Loss 2.6066 (2.6255) Prec@1 35.000 (36.438) Prec@5 70.000 (67.108) Epoch: [11][7420/11272] Time 0.779 (0.825) Data 0.002 (0.002) Loss 2.6212 (2.6255) Prec@1 33.750 (36.436) Prec@5 67.500 (67.108) Epoch: [11][7430/11272] Time 0.789 (0.825) Data 0.002 (0.002) Loss 2.9247 (2.6255) Prec@1 31.250 (36.435) Prec@5 60.000 (67.106) Epoch: [11][7440/11272] Time 0.890 (0.825) Data 0.002 (0.002) Loss 2.8459 (2.6257) Prec@1 30.000 (36.432) Prec@5 65.000 (67.105) Epoch: [11][7450/11272] Time 0.883 (0.825) Data 0.002 (0.002) Loss 2.6307 (2.6256) Prec@1 34.375 (36.434) Prec@5 65.625 (67.107) Epoch: [11][7460/11272] Time 0.755 (0.825) Data 0.001 (0.002) Loss 2.3442 (2.6256) Prec@1 43.125 (36.434) Prec@5 71.250 (67.105) Epoch: [11][7470/11272] Time 0.922 (0.825) Data 0.002 (0.002) Loss 2.7397 (2.6256) Prec@1 36.875 (36.436) Prec@5 68.125 (67.105) Epoch: [11][7480/11272] Time 0.854 (0.825) Data 0.002 (0.002) Loss 2.4653 (2.6256) Prec@1 40.000 (36.435) Prec@5 70.000 (67.105) Epoch: [11][7490/11272] Time 0.745 (0.825) Data 0.001 (0.002) Loss 2.8039 (2.6255) Prec@1 32.500 (36.437) Prec@5 62.500 (67.107) Epoch: [11][7500/11272] Time 0.789 (0.825) Data 0.002 (0.002) Loss 2.4574 (2.6255) Prec@1 40.625 (36.437) Prec@5 69.375 (67.105) Epoch: [11][7510/11272] Time 0.905 (0.825) Data 0.001 (0.002) Loss 2.5652 (2.6255) Prec@1 33.750 (36.435) Prec@5 68.750 (67.104) Epoch: [11][7520/11272] Time 0.886 (0.825) Data 0.001 (0.002) Loss 2.4169 (2.6254) Prec@1 37.500 (36.435) Prec@5 71.875 (67.106) Epoch: [11][7530/11272] Time 0.777 (0.825) Data 0.002 (0.002) Loss 2.4505 (2.6255) Prec@1 39.375 (36.432) Prec@5 73.750 (67.104) Epoch: [11][7540/11272] Time 0.779 (0.825) Data 0.001 (0.002) Loss 2.8304 (2.6256) Prec@1 31.250 (36.430) Prec@5 61.250 (67.101) Epoch: [11][7550/11272] Time 0.829 (0.825) Data 0.001 (0.002) Loss 2.5796 (2.6255) Prec@1 33.125 (36.430) Prec@5 71.875 (67.101) Epoch: [11][7560/11272] Time 0.873 (0.825) Data 0.001 (0.002) Loss 2.5658 (2.6255) Prec@1 35.000 (36.429) Prec@5 69.375 (67.103) Epoch: [11][7570/11272] Time 0.756 (0.825) Data 0.002 (0.002) Loss 2.5461 (2.6254) Prec@1 39.375 (36.431) Prec@5 66.250 (67.105) Epoch: [11][7580/11272] Time 0.746 (0.825) Data 0.001 (0.002) Loss 2.6419 (2.6254) Prec@1 38.125 (36.431) Prec@5 69.375 (67.105) Epoch: [11][7590/11272] Time 0.890 (0.825) Data 0.002 (0.002) Loss 2.4254 (2.6255) Prec@1 36.875 (36.431) Prec@5 72.500 (67.105) Epoch: [11][7600/11272] Time 0.768 (0.825) Data 0.005 (0.002) Loss 2.7578 (2.6255) Prec@1 34.375 (36.430) Prec@5 65.000 (67.105) Epoch: [11][7610/11272] Time 0.796 (0.825) Data 0.003 (0.002) Loss 3.0299 (2.6255) Prec@1 26.875 (36.428) Prec@5 58.750 (67.106) Epoch: [11][7620/11272] Time 0.862 (0.825) Data 0.001 (0.002) Loss 2.7884 (2.6255) Prec@1 36.250 (36.429) Prec@5 66.875 (67.106) Epoch: [11][7630/11272] Time 0.974 (0.825) Data 0.002 (0.002) Loss 2.6214 (2.6254) Prec@1 38.125 (36.432) Prec@5 68.125 (67.108) Epoch: [11][7640/11272] Time 0.757 (0.825) Data 0.002 (0.002) Loss 2.6093 (2.6254) Prec@1 40.000 (36.432) Prec@5 70.625 (67.106) Epoch: [11][7650/11272] Time 0.748 (0.825) Data 0.002 (0.002) Loss 2.5028 (2.6255) Prec@1 31.875 (36.430) Prec@5 67.500 (67.106) Epoch: [11][7660/11272] Time 0.880 (0.825) Data 0.002 (0.002) Loss 2.6009 (2.6254) Prec@1 41.250 (36.431) Prec@5 70.625 (67.106) Epoch: [11][7670/11272] Time 0.877 (0.825) Data 0.002 (0.002) Loss 2.6998 (2.6254) Prec@1 35.625 (36.429) Prec@5 60.625 (67.107) Epoch: [11][7680/11272] Time 0.767 (0.825) Data 0.002 (0.002) Loss 2.5569 (2.6254) Prec@1 43.750 (36.432) Prec@5 64.375 (67.107) Epoch: [11][7690/11272] Time 0.794 (0.825) Data 0.002 (0.002) Loss 2.5525 (2.6254) Prec@1 34.375 (36.431) Prec@5 66.875 (67.106) Epoch: [11][7700/11272] Time 0.897 (0.825) Data 0.001 (0.002) Loss 2.7105 (2.6254) Prec@1 40.000 (36.432) Prec@5 65.625 (67.105) Epoch: [11][7710/11272] Time 0.869 (0.825) Data 0.002 (0.002) Loss 2.6922 (2.6254) Prec@1 31.875 (36.431) Prec@5 65.000 (67.105) Epoch: [11][7720/11272] Time 0.745 (0.825) Data 0.002 (0.002) Loss 2.6235 (2.6254) Prec@1 35.625 (36.431) Prec@5 70.625 (67.107) Epoch: [11][7730/11272] Time 0.892 (0.825) Data 0.002 (0.002) Loss 2.4303 (2.6254) Prec@1 42.500 (36.431) Prec@5 70.625 (67.108) Epoch: [11][7740/11272] Time 0.882 (0.825) Data 0.001 (0.002) Loss 2.7775 (2.6254) Prec@1 35.000 (36.433) Prec@5 67.500 (67.108) Epoch: [11][7750/11272] Time 0.747 (0.825) Data 0.002 (0.002) Loss 2.5107 (2.6254) Prec@1 39.375 (36.434) Prec@5 70.625 (67.107) Epoch: [11][7760/11272] Time 0.747 (0.825) Data 0.001 (0.002) Loss 2.8315 (2.6254) Prec@1 33.125 (36.432) Prec@5 68.125 (67.107) Epoch: [11][7770/11272] Time 0.915 (0.825) Data 0.002 (0.002) Loss 2.5012 (2.6254) Prec@1 46.875 (36.433) Prec@5 71.250 (67.107) Epoch: [11][7780/11272] Time 0.851 (0.825) Data 0.001 (0.002) Loss 2.7041 (2.6254) Prec@1 34.375 (36.432) Prec@5 66.875 (67.106) Epoch: [11][7790/11272] Time 0.855 (0.825) Data 0.002 (0.002) Loss 2.6054 (2.6254) Prec@1 41.250 (36.432) Prec@5 66.875 (67.106) Epoch: [11][7800/11272] Time 0.751 (0.825) Data 0.002 (0.002) Loss 2.7317 (2.6254) Prec@1 32.500 (36.430) Prec@5 66.875 (67.107) Epoch: [11][7810/11272] Time 0.905 (0.825) Data 0.002 (0.002) Loss 2.7178 (2.6255) Prec@1 30.000 (36.428) Prec@5 65.625 (67.106) Epoch: [11][7820/11272] Time 0.890 (0.825) Data 0.001 (0.002) Loss 2.5083 (2.6255) Prec@1 40.000 (36.428) Prec@5 68.750 (67.105) Epoch: [11][7830/11272] Time 0.758 (0.825) Data 0.002 (0.002) Loss 2.7471 (2.6254) Prec@1 38.750 (36.430) Prec@5 66.250 (67.106) Epoch: [11][7840/11272] Time 0.748 (0.825) Data 0.002 (0.002) Loss 2.3967 (2.6254) Prec@1 41.250 (36.428) Prec@5 75.625 (67.107) Epoch: [11][7850/11272] Time 0.876 (0.825) Data 0.002 (0.002) Loss 2.7795 (2.6254) Prec@1 35.625 (36.427) Prec@5 66.875 (67.108) Epoch: [11][7860/11272] Time 0.741 (0.825) Data 0.003 (0.002) Loss 2.6559 (2.6254) Prec@1 36.875 (36.429) Prec@5 66.250 (67.109) Epoch: [11][7870/11272] Time 0.749 (0.825) Data 0.002 (0.002) Loss 2.9897 (2.6255) Prec@1 30.625 (36.427) Prec@5 56.250 (67.107) Epoch: [11][7880/11272] Time 0.913 (0.825) Data 0.002 (0.002) Loss 2.7448 (2.6255) Prec@1 32.500 (36.427) Prec@5 62.500 (67.105) Epoch: [11][7890/11272] Time 0.902 (0.825) Data 0.002 (0.002) Loss 2.3639 (2.6254) Prec@1 36.250 (36.427) Prec@5 75.000 (67.106) Epoch: [11][7900/11272] Time 0.764 (0.825) Data 0.001 (0.002) Loss 2.6347 (2.6254) Prec@1 35.000 (36.426) Prec@5 65.000 (67.106) Epoch: [11][7910/11272] Time 0.747 (0.825) Data 0.001 (0.002) Loss 2.7965 (2.6254) Prec@1 36.875 (36.428) Prec@5 64.375 (67.105) Epoch: [11][7920/11272] Time 0.866 (0.825) Data 0.001 (0.002) Loss 2.5897 (2.6253) Prec@1 40.000 (36.429) Prec@5 65.625 (67.104) Epoch: [11][7930/11272] Time 0.924 (0.825) Data 0.002 (0.002) Loss 2.6021 (2.6253) Prec@1 31.875 (36.430) Prec@5 65.000 (67.105) Epoch: [11][7940/11272] Time 0.743 (0.825) Data 0.002 (0.002) Loss 2.6681 (2.6253) Prec@1 35.000 (36.433) Prec@5 70.625 (67.106) Epoch: [11][7950/11272] Time 0.779 (0.825) Data 0.002 (0.002) Loss 2.6869 (2.6252) Prec@1 38.125 (36.434) Prec@5 68.125 (67.109) Epoch: [11][7960/11272] Time 0.910 (0.825) Data 0.002 (0.002) Loss 2.6044 (2.6252) Prec@1 38.750 (36.434) Prec@5 66.875 (67.109) Epoch: [11][7970/11272] Time 0.921 (0.825) Data 0.002 (0.002) Loss 2.3875 (2.6251) Prec@1 43.750 (36.437) Prec@5 70.000 (67.111) Epoch: [11][7980/11272] Time 0.820 (0.825) Data 0.001 (0.002) Loss 2.3833 (2.6250) Prec@1 42.500 (36.440) Prec@5 73.125 (67.112) Epoch: [11][7990/11272] Time 0.898 (0.825) Data 0.001 (0.002) Loss 2.9286 (2.6250) Prec@1 32.500 (36.440) Prec@5 63.125 (67.113) Epoch: [11][8000/11272] Time 0.920 (0.825) Data 0.003 (0.002) Loss 2.3480 (2.6250) Prec@1 41.875 (36.442) Prec@5 73.750 (67.115) Epoch: [11][8010/11272] Time 0.748 (0.825) Data 0.002 (0.002) Loss 2.7734 (2.6250) Prec@1 33.750 (36.440) Prec@5 61.875 (67.113) Epoch: [11][8020/11272] Time 0.784 (0.825) Data 0.002 (0.002) Loss 2.6957 (2.6250) Prec@1 30.625 (36.440) Prec@5 67.500 (67.113) Epoch: [11][8030/11272] Time 0.927 (0.825) Data 0.001 (0.002) Loss 2.5721 (2.6250) Prec@1 38.125 (36.440) Prec@5 70.625 (67.115) Epoch: [11][8040/11272] Time 0.880 (0.825) Data 0.002 (0.002) Loss 2.5575 (2.6251) Prec@1 35.000 (36.437) Prec@5 68.125 (67.114) Epoch: [11][8050/11272] Time 0.755 (0.825) Data 0.002 (0.002) Loss 2.5464 (2.6251) Prec@1 39.375 (36.435) Prec@5 66.250 (67.113) Epoch: [11][8060/11272] Time 0.764 (0.825) Data 0.001 (0.002) Loss 2.6060 (2.6251) Prec@1 38.750 (36.433) Prec@5 65.625 (67.113) Epoch: [11][8070/11272] Time 0.881 (0.825) Data 0.001 (0.002) Loss 2.6213 (2.6251) Prec@1 37.500 (36.434) Prec@5 68.750 (67.113) Epoch: [11][8080/11272] Time 0.957 (0.825) Data 0.002 (0.002) Loss 2.4798 (2.6252) Prec@1 37.500 (36.431) Prec@5 68.125 (67.111) Epoch: [11][8090/11272] Time 0.742 (0.825) Data 0.002 (0.002) Loss 2.4336 (2.6252) Prec@1 40.625 (36.431) Prec@5 70.000 (67.112) Epoch: [11][8100/11272] Time 0.746 (0.825) Data 0.001 (0.002) Loss 2.3983 (2.6252) Prec@1 35.000 (36.430) Prec@5 71.250 (67.111) Epoch: [11][8110/11272] Time 0.891 (0.825) Data 0.002 (0.002) Loss 2.5636 (2.6252) Prec@1 35.625 (36.432) Prec@5 70.000 (67.109) Epoch: [11][8120/11272] Time 0.885 (0.825) Data 0.002 (0.002) Loss 2.3689 (2.6251) Prec@1 40.000 (36.433) Prec@5 77.500 (67.111) Epoch: [11][8130/11272] Time 0.717 (0.825) Data 0.001 (0.002) Loss 2.8797 (2.6252) Prec@1 33.750 (36.434) Prec@5 66.250 (67.109) Epoch: [11][8140/11272] Time 0.895 (0.825) Data 0.002 (0.002) Loss 2.4996 (2.6251) Prec@1 36.875 (36.435) Prec@5 72.500 (67.111) Epoch: [11][8150/11272] Time 0.953 (0.825) Data 0.002 (0.002) Loss 2.7506 (2.6252) Prec@1 30.625 (36.434) Prec@5 66.875 (67.111) Epoch: [11][8160/11272] Time 0.743 (0.825) Data 0.001 (0.002) Loss 2.3793 (2.6252) Prec@1 44.375 (36.436) Prec@5 74.375 (67.113) Epoch: [11][8170/11272] Time 0.745 (0.825) Data 0.001 (0.002) Loss 2.3964 (2.6251) Prec@1 39.375 (36.439) Prec@5 73.125 (67.115) Epoch: [11][8180/11272] Time 0.915 (0.825) Data 0.002 (0.002) Loss 2.7115 (2.6251) Prec@1 35.625 (36.438) Prec@5 68.125 (67.115) Epoch: [11][8190/11272] Time 0.917 (0.825) Data 0.002 (0.002) Loss 2.6502 (2.6252) Prec@1 32.500 (36.437) Prec@5 69.375 (67.115) Epoch: [11][8200/11272] Time 0.827 (0.825) Data 0.002 (0.002) Loss 2.6909 (2.6252) Prec@1 33.750 (36.436) Prec@5 67.500 (67.115) Epoch: [11][8210/11272] Time 0.744 (0.825) Data 0.001 (0.002) Loss 2.6830 (2.6252) Prec@1 37.500 (36.436) Prec@5 65.625 (67.113) Epoch: [11][8220/11272] Time 0.874 (0.825) Data 0.002 (0.002) Loss 2.7789 (2.6253) Prec@1 38.750 (36.435) Prec@5 63.750 (67.111) Epoch: [11][8230/11272] Time 0.873 (0.825) Data 0.002 (0.002) Loss 2.4079 (2.6254) Prec@1 42.500 (36.435) Prec@5 70.625 (67.108) Epoch: [11][8240/11272] Time 0.753 (0.825) Data 0.002 (0.002) Loss 3.1149 (2.6256) Prec@1 31.250 (36.431) Prec@5 55.000 (67.104) Epoch: [11][8250/11272] Time 0.745 (0.825) Data 0.002 (0.002) Loss 2.6330 (2.6255) Prec@1 36.875 (36.431) Prec@5 64.375 (67.105) Epoch: [11][8260/11272] Time 0.879 (0.825) Data 0.001 (0.002) Loss 2.5119 (2.6255) Prec@1 41.250 (36.431) Prec@5 68.125 (67.106) Epoch: [11][8270/11272] Time 0.756 (0.825) Data 0.001 (0.002) Loss 2.4876 (2.6255) Prec@1 36.875 (36.433) Prec@5 70.625 (67.107) Epoch: [11][8280/11272] Time 0.780 (0.825) Data 0.002 (0.002) Loss 2.6988 (2.6255) Prec@1 35.625 (36.432) Prec@5 70.000 (67.108) Epoch: [11][8290/11272] Time 0.862 (0.825) Data 0.001 (0.002) Loss 2.5097 (2.6256) Prec@1 32.500 (36.430) Prec@5 71.250 (67.107) Epoch: [11][8300/11272] Time 0.924 (0.825) Data 0.002 (0.002) Loss 2.6717 (2.6255) Prec@1 34.375 (36.430) Prec@5 63.750 (67.109) Epoch: [11][8310/11272] Time 0.724 (0.825) Data 0.002 (0.002) Loss 2.8504 (2.6254) Prec@1 28.125 (36.430) Prec@5 65.000 (67.109) Epoch: [11][8320/11272] Time 0.760 (0.825) Data 0.002 (0.002) Loss 2.9923 (2.6255) Prec@1 33.125 (36.430) Prec@5 62.500 (67.108) Epoch: [11][8330/11272] Time 0.882 (0.825) Data 0.002 (0.002) Loss 2.7371 (2.6254) Prec@1 31.875 (36.431) Prec@5 62.500 (67.107) Epoch: [11][8340/11272] Time 0.913 (0.825) Data 0.002 (0.002) Loss 2.3124 (2.6254) Prec@1 45.000 (36.433) Prec@5 74.375 (67.109) Epoch: [11][8350/11272] Time 0.779 (0.825) Data 0.002 (0.002) Loss 2.7224 (2.6254) Prec@1 36.875 (36.431) Prec@5 65.000 (67.109) Epoch: [11][8360/11272] Time 0.742 (0.825) Data 0.002 (0.002) Loss 2.4250 (2.6254) Prec@1 40.625 (36.432) Prec@5 68.750 (67.109) Epoch: [11][8370/11272] Time 0.963 (0.825) Data 0.002 (0.002) Loss 2.5484 (2.6254) Prec@1 41.250 (36.433) Prec@5 65.000 (67.108) Epoch: [11][8380/11272] Time 0.914 (0.825) Data 0.002 (0.002) Loss 2.1272 (2.6253) Prec@1 50.625 (36.435) Prec@5 77.500 (67.110) Epoch: [11][8390/11272] Time 0.751 (0.825) Data 0.002 (0.002) Loss 2.5683 (2.6253) Prec@1 40.625 (36.436) Prec@5 64.375 (67.110) Epoch: [11][8400/11272] Time 0.918 (0.825) Data 0.002 (0.002) Loss 2.4312 (2.6252) Prec@1 38.125 (36.434) Prec@5 71.875 (67.111) Epoch: [11][8410/11272] Time 0.892 (0.825) Data 0.001 (0.002) Loss 3.0168 (2.6253) Prec@1 30.000 (36.435) Prec@5 63.125 (67.111) Epoch: [11][8420/11272] Time 0.751 (0.825) Data 0.001 (0.002) Loss 2.7120 (2.6252) Prec@1 37.500 (36.435) Prec@5 66.250 (67.111) Epoch: [11][8430/11272] Time 0.732 (0.825) Data 0.001 (0.002) Loss 2.6864 (2.6252) Prec@1 31.875 (36.435) Prec@5 63.125 (67.110) Epoch: [11][8440/11272] Time 0.891 (0.825) Data 0.001 (0.002) Loss 2.6702 (2.6252) Prec@1 35.000 (36.437) Prec@5 67.500 (67.109) Epoch: [11][8450/11272] Time 0.920 (0.825) Data 0.003 (0.002) Loss 2.2672 (2.6252) Prec@1 46.250 (36.439) Prec@5 75.000 (67.111) Epoch: [11][8460/11272] Time 0.789 (0.825) Data 0.001 (0.002) Loss 2.5861 (2.6252) Prec@1 38.750 (36.439) Prec@5 65.000 (67.110) Epoch: [11][8470/11272] Time 0.751 (0.825) Data 0.002 (0.002) Loss 2.4221 (2.6252) Prec@1 40.000 (36.441) Prec@5 71.875 (67.110) Epoch: [11][8480/11272] Time 0.891 (0.825) Data 0.002 (0.002) Loss 2.5964 (2.6252) Prec@1 39.375 (36.440) Prec@5 65.625 (67.110) Epoch: [11][8490/11272] Time 0.924 (0.825) Data 0.002 (0.002) Loss 2.7150 (2.6253) Prec@1 33.750 (36.440) Prec@5 65.000 (67.108) Epoch: [11][8500/11272] Time 0.748 (0.825) Data 0.002 (0.002) Loss 2.6243 (2.6254) Prec@1 40.000 (36.436) Prec@5 66.875 (67.106) Epoch: [11][8510/11272] Time 0.792 (0.825) Data 0.002 (0.002) Loss 2.5681 (2.6255) Prec@1 41.875 (36.436) Prec@5 68.125 (67.106) Epoch: [11][8520/11272] Time 0.895 (0.825) Data 0.002 (0.002) Loss 2.6588 (2.6255) Prec@1 30.625 (36.433) Prec@5 61.875 (67.105) Epoch: [11][8530/11272] Time 0.773 (0.825) Data 0.004 (0.002) Loss 2.4003 (2.6256) Prec@1 42.500 (36.433) Prec@5 73.750 (67.106) Epoch: [11][8540/11272] Time 0.748 (0.825) Data 0.001 (0.002) Loss 2.7228 (2.6256) Prec@1 35.625 (36.434) Prec@5 65.000 (67.103) Epoch: [11][8550/11272] Time 0.843 (0.825) Data 0.002 (0.002) Loss 2.9516 (2.6256) Prec@1 33.125 (36.434) Prec@5 65.625 (67.103) Epoch: [11][8560/11272] Time 0.873 (0.825) Data 0.002 (0.002) Loss 2.4644 (2.6257) Prec@1 40.625 (36.434) Prec@5 70.625 (67.101) Epoch: [11][8570/11272] Time 0.736 (0.825) Data 0.002 (0.002) Loss 2.4904 (2.6257) Prec@1 42.500 (36.436) Prec@5 67.500 (67.103) Epoch: [11][8580/11272] Time 0.745 (0.825) Data 0.002 (0.002) Loss 2.7519 (2.6258) Prec@1 31.250 (36.434) Prec@5 67.500 (67.103) Epoch: [11][8590/11272] Time 0.887 (0.825) Data 0.002 (0.002) Loss 2.7949 (2.6258) Prec@1 33.750 (36.433) Prec@5 66.875 (67.103) Epoch: [11][8600/11272] Time 0.870 (0.825) Data 0.002 (0.002) Loss 2.7404 (2.6258) Prec@1 39.375 (36.432) Prec@5 68.750 (67.103) Epoch: [11][8610/11272] Time 0.823 (0.825) Data 0.002 (0.002) Loss 2.6018 (2.6258) Prec@1 41.250 (36.432) Prec@5 70.000 (67.102) Epoch: [11][8620/11272] Time 0.773 (0.825) Data 0.002 (0.002) Loss 2.5827 (2.6257) Prec@1 36.250 (36.435) Prec@5 72.500 (67.106) Epoch: [11][8630/11272] Time 0.910 (0.825) Data 0.002 (0.002) Loss 2.7741 (2.6258) Prec@1 34.375 (36.435) Prec@5 62.500 (67.104) Epoch: [11][8640/11272] Time 0.955 (0.826) Data 0.001 (0.002) Loss 2.4799 (2.6258) Prec@1 36.250 (36.435) Prec@5 68.125 (67.104) Epoch: [11][8650/11272] Time 0.752 (0.826) Data 0.002 (0.002) Loss 2.6616 (2.6259) Prec@1 40.625 (36.434) Prec@5 63.750 (67.103) Epoch: [11][8660/11272] Time 0.916 (0.826) Data 0.002 (0.002) Loss 2.7837 (2.6258) Prec@1 33.125 (36.434) Prec@5 63.750 (67.104) Epoch: [11][8670/11272] Time 0.901 (0.826) Data 0.002 (0.002) Loss 2.5404 (2.6258) Prec@1 41.250 (36.434) Prec@5 67.500 (67.105) Epoch: [11][8680/11272] Time 0.773 (0.826) Data 0.001 (0.002) Loss 2.4949 (2.6257) Prec@1 38.750 (36.436) Prec@5 70.000 (67.106) Epoch: [11][8690/11272] Time 0.741 (0.826) Data 0.001 (0.002) Loss 2.7700 (2.6257) Prec@1 37.500 (36.438) Prec@5 64.375 (67.107) Epoch: [11][8700/11272] Time 0.883 (0.826) Data 0.002 (0.002) Loss 2.4511 (2.6257) Prec@1 43.125 (36.437) Prec@5 72.500 (67.106) Epoch: [11][8710/11272] Time 0.881 (0.826) Data 0.002 (0.002) Loss 2.7318 (2.6258) Prec@1 36.250 (36.435) Prec@5 65.000 (67.106) Epoch: [11][8720/11272] Time 0.745 (0.826) Data 0.002 (0.002) Loss 2.5688 (2.6258) Prec@1 42.500 (36.436) Prec@5 65.625 (67.104) Epoch: [11][8730/11272] Time 0.776 (0.826) Data 0.002 (0.002) Loss 2.4992 (2.6258) Prec@1 40.625 (36.437) Prec@5 69.375 (67.105) Epoch: [11][8740/11272] Time 0.917 (0.826) Data 0.003 (0.002) Loss 2.6324 (2.6259) Prec@1 33.750 (36.438) Prec@5 67.500 (67.102) Epoch: [11][8750/11272] Time 0.926 (0.826) Data 0.002 (0.002) Loss 2.6878 (2.6259) Prec@1 36.250 (36.438) Prec@5 68.750 (67.103) Epoch: [11][8760/11272] Time 0.745 (0.826) Data 0.002 (0.002) Loss 2.7554 (2.6259) Prec@1 32.500 (36.438) Prec@5 61.875 (67.103) Epoch: [11][8770/11272] Time 0.752 (0.826) Data 0.002 (0.002) Loss 2.5702 (2.6258) Prec@1 37.500 (36.440) Prec@5 66.875 (67.102) Epoch: [11][8780/11272] Time 0.885 (0.826) Data 0.002 (0.002) Loss 2.6108 (2.6258) Prec@1 37.500 (36.440) Prec@5 65.625 (67.101) Epoch: [11][8790/11272] Time 0.761 (0.826) Data 0.004 (0.002) Loss 2.6398 (2.6259) Prec@1 40.625 (36.439) Prec@5 71.875 (67.100) Epoch: [11][8800/11272] Time 0.748 (0.826) Data 0.002 (0.002) Loss 2.5636 (2.6260) Prec@1 39.375 (36.438) Prec@5 68.750 (67.098) Epoch: [11][8810/11272] Time 0.900 (0.826) Data 0.002 (0.002) Loss 2.7174 (2.6259) Prec@1 38.750 (36.440) Prec@5 63.125 (67.099) Epoch: [11][8820/11272] Time 0.884 (0.826) Data 0.001 (0.002) Loss 2.7775 (2.6260) Prec@1 31.250 (36.439) Prec@5 65.000 (67.099) Epoch: [11][8830/11272] Time 0.796 (0.826) Data 0.002 (0.002) Loss 2.6321 (2.6260) Prec@1 40.625 (36.442) Prec@5 66.875 (67.098) Epoch: [11][8840/11272] Time 0.781 (0.826) Data 0.001 (0.002) Loss 2.6400 (2.6259) Prec@1 35.625 (36.443) Prec@5 66.875 (67.098) Epoch: [11][8850/11272] Time 0.927 (0.826) Data 0.002 (0.002) Loss 2.6631 (2.6258) Prec@1 35.000 (36.445) Prec@5 66.875 (67.100) Epoch: [11][8860/11272] Time 0.940 (0.826) Data 0.002 (0.002) Loss 2.7662 (2.6258) Prec@1 34.375 (36.447) Prec@5 67.500 (67.100) Epoch: [11][8870/11272] Time 0.785 (0.826) Data 0.002 (0.002) Loss 2.5459 (2.6258) Prec@1 37.500 (36.447) Prec@5 64.375 (67.102) Epoch: [11][8880/11272] Time 0.775 (0.826) Data 0.002 (0.002) Loss 2.7098 (2.6259) Prec@1 36.250 (36.446) Prec@5 60.625 (67.100) Epoch: [11][8890/11272] Time 0.865 (0.826) Data 0.002 (0.002) Loss 2.5145 (2.6258) Prec@1 38.125 (36.445) Prec@5 68.125 (67.100) Epoch: [11][8900/11272] Time 0.875 (0.826) Data 0.002 (0.002) Loss 2.8071 (2.6258) Prec@1 36.875 (36.446) Prec@5 65.625 (67.100) Epoch: [11][8910/11272] Time 0.833 (0.826) Data 0.002 (0.002) Loss 2.5918 (2.6259) Prec@1 38.125 (36.445) Prec@5 70.000 (67.100) Epoch: [11][8920/11272] Time 0.954 (0.826) Data 0.002 (0.002) Loss 2.6466 (2.6259) Prec@1 39.375 (36.445) Prec@5 65.625 (67.099) Epoch: [11][8930/11272] Time 0.891 (0.826) Data 0.002 (0.002) Loss 2.7475 (2.6259) Prec@1 34.375 (36.444) Prec@5 70.000 (67.098) Epoch: [11][8940/11272] Time 0.794 (0.826) Data 0.002 (0.002) Loss 2.8243 (2.6259) Prec@1 28.125 (36.443) Prec@5 64.375 (67.100) Epoch: [11][8950/11272] Time 0.747 (0.826) Data 0.002 (0.002) Loss 2.4152 (2.6259) Prec@1 44.375 (36.443) Prec@5 73.750 (67.101) Epoch: [11][8960/11272] Time 0.917 (0.826) Data 0.002 (0.002) Loss 2.4618 (2.6258) Prec@1 36.875 (36.445) Prec@5 70.625 (67.101) Epoch: [11][8970/11272] Time 0.857 (0.826) Data 0.002 (0.002) Loss 2.4704 (2.6258) Prec@1 36.250 (36.446) Prec@5 70.000 (67.103) Epoch: [11][8980/11272] Time 0.791 (0.826) Data 0.002 (0.002) Loss 2.8767 (2.6258) Prec@1 31.250 (36.446) Prec@5 62.500 (67.104) Epoch: [11][8990/11272] Time 0.787 (0.826) Data 0.002 (0.002) Loss 2.7048 (2.6259) Prec@1 34.375 (36.446) Prec@5 63.125 (67.103) Epoch: [11][9000/11272] Time 0.914 (0.826) Data 0.002 (0.002) Loss 2.7141 (2.6259) Prec@1 37.500 (36.445) Prec@5 66.875 (67.103) Epoch: [11][9010/11272] Time 0.936 (0.826) Data 0.002 (0.002) Loss 2.9468 (2.6260) Prec@1 30.625 (36.443) Prec@5 62.500 (67.101) Epoch: [11][9020/11272] Time 0.734 (0.826) Data 0.002 (0.002) Loss 2.8569 (2.6261) Prec@1 33.750 (36.441) Prec@5 64.375 (67.100) Epoch: [11][9030/11272] Time 0.737 (0.826) Data 0.002 (0.002) Loss 2.6335 (2.6261) Prec@1 33.750 (36.442) Prec@5 67.500 (67.101) Epoch: [11][9040/11272] Time 0.861 (0.826) Data 0.002 (0.002) Loss 2.6920 (2.6261) Prec@1 37.500 (36.442) Prec@5 68.125 (67.102) Epoch: [11][9050/11272] Time 0.915 (0.826) Data 0.002 (0.002) Loss 2.3954 (2.6261) Prec@1 39.375 (36.443) Prec@5 71.250 (67.103) Epoch: [11][9060/11272] Time 0.743 (0.826) Data 0.002 (0.002) Loss 2.6458 (2.6261) Prec@1 33.750 (36.442) Prec@5 70.000 (67.104) Epoch: [11][9070/11272] Time 0.899 (0.826) Data 0.002 (0.002) Loss 2.5244 (2.6261) Prec@1 40.000 (36.442) Prec@5 72.500 (67.104) Epoch: [11][9080/11272] Time 0.871 (0.826) Data 0.001 (0.002) Loss 2.4777 (2.6260) Prec@1 38.125 (36.442) Prec@5 71.250 (67.106) Epoch: [11][9090/11272] Time 0.746 (0.826) Data 0.001 (0.002) Loss 2.5518 (2.6261) Prec@1 42.500 (36.440) Prec@5 68.125 (67.106) Epoch: [11][9100/11272] Time 0.777 (0.826) Data 0.002 (0.002) Loss 2.8153 (2.6262) Prec@1 33.750 (36.439) Prec@5 65.625 (67.105) Epoch: [11][9110/11272] Time 0.969 (0.826) Data 0.002 (0.002) Loss 2.9962 (2.6263) Prec@1 31.250 (36.439) Prec@5 59.375 (67.102) Epoch: [11][9120/11272] Time 0.871 (0.826) Data 0.001 (0.002) Loss 2.8300 (2.6263) Prec@1 36.250 (36.438) Prec@5 62.500 (67.101) Epoch: [11][9130/11272] Time 0.746 (0.826) Data 0.001 (0.002) Loss 2.7336 (2.6264) Prec@1 30.625 (36.436) Prec@5 61.875 (67.099) Epoch: [11][9140/11272] Time 0.753 (0.826) Data 0.001 (0.002) Loss 2.5855 (2.6264) Prec@1 34.375 (36.435) Prec@5 72.500 (67.099) Epoch: [11][9150/11272] Time 0.821 (0.826) Data 0.001 (0.002) Loss 2.8128 (2.6264) Prec@1 30.000 (36.435) Prec@5 63.125 (67.099) Epoch: [11][9160/11272] Time 0.873 (0.826) Data 0.002 (0.002) Loss 2.5258 (2.6264) Prec@1 40.000 (36.437) Prec@5 69.375 (67.098) Epoch: [11][9170/11272] Time 0.752 (0.826) Data 0.002 (0.002) Loss 2.5851 (2.6263) Prec@1 39.375 (36.438) Prec@5 70.000 (67.100) Epoch: [11][9180/11272] Time 0.786 (0.826) Data 0.002 (0.002) Loss 2.8055 (2.6263) Prec@1 35.000 (36.439) Prec@5 61.250 (67.101) Epoch: [11][9190/11272] Time 0.903 (0.826) Data 0.002 (0.002) Loss 2.6599 (2.6262) Prec@1 30.000 (36.438) Prec@5 65.625 (67.101) Epoch: [11][9200/11272] Time 0.771 (0.826) Data 0.001 (0.002) Loss 2.9675 (2.6263) Prec@1 32.500 (36.437) Prec@5 61.250 (67.102) Epoch: [11][9210/11272] Time 0.809 (0.826) Data 0.002 (0.002) Loss 2.8859 (2.6264) Prec@1 31.250 (36.435) Prec@5 55.625 (67.098) Epoch: [11][9220/11272] Time 0.918 (0.826) Data 0.001 (0.002) Loss 2.3491 (2.6264) Prec@1 37.500 (36.434) Prec@5 72.500 (67.095) Epoch: [11][9230/11272] Time 0.838 (0.826) Data 0.002 (0.002) Loss 2.8093 (2.6265) Prec@1 34.375 (36.432) Prec@5 66.250 (67.095) Epoch: [11][9240/11272] Time 0.796 (0.826) Data 0.002 (0.002) Loss 2.8758 (2.6265) Prec@1 33.125 (36.432) Prec@5 61.875 (67.093) Epoch: [11][9250/11272] Time 0.729 (0.826) Data 0.001 (0.002) Loss 2.7168 (2.6266) Prec@1 32.500 (36.431) Prec@5 65.000 (67.094) Epoch: [11][9260/11272] Time 0.906 (0.826) Data 0.002 (0.002) Loss 2.6711 (2.6266) Prec@1 40.000 (36.431) Prec@5 65.000 (67.093) Epoch: [11][9270/11272] Time 0.838 (0.826) Data 0.001 (0.002) Loss 2.4671 (2.6266) Prec@1 32.500 (36.429) Prec@5 72.500 (67.091) Epoch: [11][9280/11272] Time 0.776 (0.826) Data 0.002 (0.002) Loss 2.5149 (2.6266) Prec@1 38.750 (36.429) Prec@5 62.500 (67.090) Epoch: [11][9290/11272] Time 0.743 (0.826) Data 0.002 (0.002) Loss 2.7955 (2.6267) Prec@1 36.250 (36.428) Prec@5 68.125 (67.091) Epoch: [11][9300/11272] Time 0.875 (0.826) Data 0.002 (0.002) Loss 2.8790 (2.6267) Prec@1 35.625 (36.429) Prec@5 63.125 (67.091) Epoch: [11][9310/11272] Time 0.854 (0.826) Data 0.002 (0.002) Loss 2.3443 (2.6266) Prec@1 42.500 (36.430) Prec@5 68.750 (67.091) Epoch: [11][9320/11272] Time 0.740 (0.826) Data 0.002 (0.002) Loss 2.8364 (2.6265) Prec@1 33.125 (36.431) Prec@5 61.875 (67.093) Epoch: [11][9330/11272] Time 0.941 (0.826) Data 0.002 (0.002) Loss 2.2834 (2.6265) Prec@1 36.875 (36.432) Prec@5 76.875 (67.094) Epoch: [11][9340/11272] Time 0.845 (0.826) Data 0.001 (0.002) Loss 2.5910 (2.6265) Prec@1 41.875 (36.432) Prec@5 66.875 (67.095) Epoch: [11][9350/11272] Time 0.743 (0.826) Data 0.002 (0.002) Loss 2.8171 (2.6265) Prec@1 35.625 (36.432) Prec@5 63.125 (67.096) Epoch: [11][9360/11272] Time 0.767 (0.826) Data 0.001 (0.002) Loss 2.7190 (2.6265) Prec@1 32.500 (36.430) Prec@5 64.375 (67.096) Epoch: [11][9370/11272] Time 0.892 (0.826) Data 0.002 (0.002) Loss 2.4467 (2.6265) Prec@1 41.250 (36.430) Prec@5 69.375 (67.096) Epoch: [11][9380/11272] Time 0.899 (0.826) Data 0.002 (0.002) Loss 2.7211 (2.6265) Prec@1 35.000 (36.430) Prec@5 62.500 (67.095) Epoch: [11][9390/11272] Time 0.741 (0.826) Data 0.002 (0.002) Loss 2.6341 (2.6266) Prec@1 37.500 (36.430) Prec@5 65.625 (67.094) Epoch: [11][9400/11272] Time 0.751 (0.826) Data 0.002 (0.002) Loss 2.5890 (2.6266) Prec@1 34.375 (36.430) Prec@5 65.625 (67.093) Epoch: [11][9410/11272] Time 0.900 (0.826) Data 0.002 (0.002) Loss 2.7466 (2.6265) Prec@1 33.750 (36.431) Prec@5 64.375 (67.094) Epoch: [11][9420/11272] Time 0.922 (0.826) Data 0.002 (0.002) Loss 2.6490 (2.6265) Prec@1 33.750 (36.429) Prec@5 67.500 (67.095) Epoch: [11][9430/11272] Time 0.771 (0.826) Data 0.001 (0.002) Loss 2.5841 (2.6266) Prec@1 36.250 (36.429) Prec@5 65.625 (67.094) Epoch: [11][9440/11272] Time 0.788 (0.826) Data 0.001 (0.002) Loss 2.7677 (2.6266) Prec@1 37.500 (36.431) Prec@5 68.125 (67.096) Epoch: [11][9450/11272] Time 0.910 (0.826) Data 0.002 (0.002) Loss 2.4986 (2.6266) Prec@1 36.250 (36.431) Prec@5 67.500 (67.095) Epoch: [11][9460/11272] Time 0.786 (0.826) Data 0.003 (0.002) Loss 2.2705 (2.6266) Prec@1 40.625 (36.432) Prec@5 74.375 (67.097) Epoch: [11][9470/11272] Time 0.745 (0.826) Data 0.001 (0.002) Loss 2.9222 (2.6267) Prec@1 34.375 (36.431) Prec@5 61.875 (67.095) Epoch: [11][9480/11272] Time 0.891 (0.826) Data 0.001 (0.002) Loss 2.5819 (2.6267) Prec@1 35.000 (36.433) Prec@5 71.250 (67.095) Epoch: [11][9490/11272] Time 0.860 (0.826) Data 0.001 (0.002) Loss 2.5157 (2.6268) Prec@1 39.375 (36.431) Prec@5 69.375 (67.093) Epoch: [11][9500/11272] Time 0.764 (0.826) Data 0.002 (0.002) Loss 2.3252 (2.6268) Prec@1 43.750 (36.430) Prec@5 71.250 (67.093) Epoch: [11][9510/11272] Time 0.813 (0.826) Data 0.002 (0.002) Loss 2.7479 (2.6269) Prec@1 31.875 (36.429) Prec@5 63.125 (67.092) Epoch: [11][9520/11272] Time 0.880 (0.826) Data 0.001 (0.002) Loss 2.6450 (2.6269) Prec@1 34.375 (36.429) Prec@5 68.125 (67.091) Epoch: [11][9530/11272] Time 0.908 (0.826) Data 0.001 (0.002) Loss 2.7030 (2.6270) Prec@1 36.250 (36.428) Prec@5 65.000 (67.090) Epoch: [11][9540/11272] Time 0.774 (0.826) Data 0.002 (0.002) Loss 2.7064 (2.6270) Prec@1 33.750 (36.426) Prec@5 67.500 (67.090) Epoch: [11][9550/11272] Time 0.734 (0.826) Data 0.001 (0.002) Loss 2.7210 (2.6269) Prec@1 34.375 (36.428) Prec@5 62.500 (67.091) Epoch: [11][9560/11272] Time 0.956 (0.826) Data 0.002 (0.002) Loss 2.7497 (2.6270) Prec@1 32.500 (36.426) Prec@5 61.250 (67.089) Epoch: [11][9570/11272] Time 0.908 (0.826) Data 0.002 (0.002) Loss 2.5382 (2.6270) Prec@1 40.000 (36.425) Prec@5 67.500 (67.089) Epoch: [11][9580/11272] Time 0.781 (0.826) Data 0.002 (0.002) Loss 2.5358 (2.6268) Prec@1 35.625 (36.427) Prec@5 69.375 (67.091) Epoch: [11][9590/11272] Time 0.902 (0.826) Data 0.002 (0.002) Loss 2.2665 (2.6269) Prec@1 42.500 (36.425) Prec@5 73.125 (67.090) Epoch: [11][9600/11272] Time 0.910 (0.826) Data 0.003 (0.002) Loss 2.5815 (2.6269) Prec@1 33.750 (36.423) Prec@5 68.750 (67.089) Epoch: [11][9610/11272] Time 0.762 (0.826) Data 0.001 (0.002) Loss 3.0033 (2.6270) Prec@1 31.250 (36.422) Prec@5 60.000 (67.088) Epoch: [11][9620/11272] Time 0.772 (0.826) Data 0.001 (0.002) Loss 2.7067 (2.6269) Prec@1 33.750 (36.423) Prec@5 67.500 (67.088) Epoch: [11][9630/11272] Time 0.969 (0.826) Data 0.002 (0.002) Loss 2.8111 (2.6270) Prec@1 30.000 (36.420) Prec@5 64.375 (67.088) Epoch: [11][9640/11272] Time 0.905 (0.826) Data 0.002 (0.002) Loss 2.4594 (2.6271) Prec@1 35.000 (36.420) Prec@5 72.500 (67.088) Epoch: [11][9650/11272] Time 0.796 (0.826) Data 0.002 (0.002) Loss 2.3810 (2.6271) Prec@1 40.625 (36.419) Prec@5 72.500 (67.088) Epoch: [11][9660/11272] Time 0.742 (0.826) Data 0.002 (0.002) Loss 2.4070 (2.6271) Prec@1 43.125 (36.421) Prec@5 71.250 (67.088) Epoch: [11][9670/11272] Time 0.896 (0.826) Data 0.001 (0.002) Loss 2.7708 (2.6270) Prec@1 31.250 (36.423) Prec@5 66.250 (67.091) Epoch: [11][9680/11272] Time 0.854 (0.826) Data 0.001 (0.002) Loss 2.8883 (2.6270) Prec@1 33.750 (36.423) Prec@5 60.625 (67.090) Epoch: [11][9690/11272] Time 0.735 (0.826) Data 0.002 (0.002) Loss 2.6834 (2.6270) Prec@1 35.000 (36.423) Prec@5 66.875 (67.091) Epoch: [11][9700/11272] Time 0.713 (0.826) Data 0.001 (0.002) Loss 2.6574 (2.6270) Prec@1 39.375 (36.422) Prec@5 66.875 (67.091) Epoch: [11][9710/11272] Time 0.944 (0.826) Data 0.002 (0.002) Loss 2.5489 (2.6270) Prec@1 39.375 (36.422) Prec@5 66.250 (67.091) Epoch: [11][9720/11272] Time 0.769 (0.826) Data 0.005 (0.002) Loss 2.8361 (2.6270) Prec@1 33.750 (36.423) Prec@5 63.750 (67.092) Epoch: [11][9730/11272] Time 0.775 (0.826) Data 0.002 (0.002) Loss 2.5928 (2.6270) Prec@1 31.250 (36.422) Prec@5 65.625 (67.091) Epoch: [11][9740/11272] Time 0.872 (0.826) Data 0.002 (0.002) Loss 2.4749 (2.6270) Prec@1 39.375 (36.421) Prec@5 68.750 (67.091) Epoch: [11][9750/11272] Time 0.915 (0.826) Data 0.002 (0.002) Loss 2.6165 (2.6271) Prec@1 34.375 (36.420) Prec@5 70.000 (67.089) Epoch: [11][9760/11272] Time 0.745 (0.826) Data 0.001 (0.002) Loss 2.5346 (2.6271) Prec@1 40.000 (36.422) Prec@5 70.625 (67.090) Epoch: [11][9770/11272] Time 0.771 (0.826) Data 0.002 (0.002) Loss 3.0394 (2.6271) Prec@1 31.250 (36.422) Prec@5 54.375 (67.089) Epoch: [11][9780/11272] Time 0.900 (0.826) Data 0.001 (0.002) Loss 2.4972 (2.6271) Prec@1 38.125 (36.421) Prec@5 69.375 (67.090) Epoch: [11][9790/11272] Time 0.879 (0.826) Data 0.002 (0.002) Loss 2.5346 (2.6272) Prec@1 33.750 (36.421) Prec@5 68.750 (67.088) Epoch: [11][9800/11272] Time 0.810 (0.826) Data 0.002 (0.002) Loss 2.6519 (2.6272) Prec@1 32.500 (36.421) Prec@5 66.250 (67.088) Epoch: [11][9810/11272] Time 0.755 (0.826) Data 0.002 (0.002) Loss 3.1286 (2.6273) Prec@1 31.875 (36.420) Prec@5 61.250 (67.087) Epoch: [11][9820/11272] Time 0.866 (0.826) Data 0.001 (0.002) Loss 2.6224 (2.6273) Prec@1 33.750 (36.421) Prec@5 67.500 (67.087) Epoch: [11][9830/11272] Time 0.880 (0.826) Data 0.002 (0.002) Loss 2.5833 (2.6274) Prec@1 40.000 (36.420) Prec@5 67.500 (67.085) Epoch: [11][9840/11272] Time 0.778 (0.826) Data 0.002 (0.002) Loss 2.9413 (2.6274) Prec@1 25.000 (36.418) Prec@5 61.250 (67.084) Epoch: [11][9850/11272] Time 0.895 (0.826) Data 0.002 (0.002) Loss 2.8936 (2.6274) Prec@1 33.125 (36.418) Prec@5 62.500 (67.085) Epoch: [11][9860/11272] Time 0.845 (0.826) Data 0.001 (0.002) Loss 2.6747 (2.6274) Prec@1 33.750 (36.418) Prec@5 68.750 (67.084) Epoch: [11][9870/11272] Time 0.760 (0.826) Data 0.002 (0.002) Loss 2.7566 (2.6274) Prec@1 33.750 (36.418) Prec@5 68.125 (67.084) Epoch: [11][9880/11272] Time 0.731 (0.826) Data 0.001 (0.002) Loss 2.7206 (2.6275) Prec@1 29.375 (36.417) Prec@5 65.000 (67.082) Epoch: [11][9890/11272] Time 0.879 (0.826) Data 0.001 (0.002) Loss 2.6106 (2.6275) Prec@1 38.125 (36.417) Prec@5 64.375 (67.081) Epoch: [11][9900/11272] Time 0.894 (0.826) Data 0.002 (0.002) Loss 2.6452 (2.6274) Prec@1 35.625 (36.420) Prec@5 66.250 (67.081) Epoch: [11][9910/11272] Time 0.779 (0.826) Data 0.002 (0.002) Loss 2.8518 (2.6275) Prec@1 34.375 (36.419) Prec@5 60.000 (67.080) Epoch: [11][9920/11272] Time 0.746 (0.826) Data 0.002 (0.002) Loss 2.6084 (2.6275) Prec@1 38.750 (36.418) Prec@5 69.375 (67.081) Epoch: [11][9930/11272] Time 0.954 (0.826) Data 0.002 (0.002) Loss 2.6930 (2.6275) Prec@1 36.875 (36.418) Prec@5 66.250 (67.081) Epoch: [11][9940/11272] Time 0.917 (0.826) Data 0.002 (0.002) Loss 2.8065 (2.6275) Prec@1 29.375 (36.419) Prec@5 65.625 (67.082) Epoch: [11][9950/11272] Time 0.778 (0.826) Data 0.002 (0.002) Loss 2.7620 (2.6274) Prec@1 36.875 (36.419) Prec@5 62.500 (67.082) Epoch: [11][9960/11272] Time 0.809 (0.826) Data 0.003 (0.002) Loss 2.7205 (2.6274) Prec@1 32.500 (36.419) Prec@5 68.750 (67.083) Epoch: [11][9970/11272] Time 0.856 (0.826) Data 0.002 (0.002) Loss 2.8448 (2.6274) Prec@1 28.125 (36.420) Prec@5 64.375 (67.083) Epoch: [11][9980/11272] Time 0.904 (0.826) Data 0.002 (0.002) Loss 2.5581 (2.6275) Prec@1 40.625 (36.419) Prec@5 69.375 (67.082) Epoch: [11][9990/11272] Time 0.758 (0.826) Data 0.002 (0.002) Loss 2.4879 (2.6276) Prec@1 40.625 (36.418) Prec@5 70.000 (67.081) Epoch: [11][10000/11272] Time 0.973 (0.826) Data 0.002 (0.002) Loss 2.6174 (2.6276) Prec@1 37.500 (36.419) Prec@5 70.625 (67.082) Epoch: [11][10010/11272] Time 0.954 (0.826) Data 0.002 (0.002) Loss 2.4329 (2.6276) Prec@1 38.125 (36.417) Prec@5 71.875 (67.082) Epoch: [11][10020/11272] Time 0.766 (0.826) Data 0.002 (0.002) Loss 2.7327 (2.6277) Prec@1 31.875 (36.418) Prec@5 58.750 (67.081) Epoch: [11][10030/11272] Time 0.746 (0.826) Data 0.002 (0.002) Loss 2.5514 (2.6276) Prec@1 36.875 (36.417) Prec@5 70.625 (67.083) Epoch: [11][10040/11272] Time 0.870 (0.826) Data 0.002 (0.002) Loss 2.5933 (2.6276) Prec@1 31.250 (36.418) Prec@5 69.375 (67.083) Epoch: [11][10050/11272] Time 0.873 (0.826) Data 0.001 (0.002) Loss 2.7551 (2.6276) Prec@1 34.375 (36.418) Prec@5 66.250 (67.083) Epoch: [11][10060/11272] Time 0.777 (0.826) Data 0.002 (0.002) Loss 2.6195 (2.6275) Prec@1 38.750 (36.418) Prec@5 65.000 (67.083) Epoch: [11][10070/11272] Time 0.777 (0.826) Data 0.002 (0.002) Loss 2.5077 (2.6275) Prec@1 41.250 (36.419) Prec@5 68.750 (67.083) Epoch: [11][10080/11272] Time 0.926 (0.826) Data 0.002 (0.002) Loss 2.6001 (2.6276) Prec@1 34.375 (36.416) Prec@5 66.875 (67.080) Epoch: [11][10090/11272] Time 0.967 (0.826) Data 0.002 (0.002) Loss 2.7067 (2.6276) Prec@1 36.250 (36.416) Prec@5 65.625 (67.081) Epoch: [11][10100/11272] Time 0.743 (0.826) Data 0.002 (0.002) Loss 2.7884 (2.6277) Prec@1 33.750 (36.414) Prec@5 63.750 (67.079) Epoch: [11][10110/11272] Time 0.807 (0.826) Data 0.002 (0.002) Loss 2.5054 (2.6277) Prec@1 40.625 (36.413) Prec@5 73.125 (67.078) Epoch: [11][10120/11272] Time 0.939 (0.826) Data 0.003 (0.002) Loss 2.7751 (2.6276) Prec@1 38.125 (36.413) Prec@5 66.250 (67.080) Epoch: [11][10130/11272] Time 0.791 (0.826) Data 0.002 (0.002) Loss 2.8085 (2.6277) Prec@1 29.375 (36.415) Prec@5 64.375 (67.081) Epoch: [11][10140/11272] Time 0.758 (0.826) Data 0.002 (0.002) Loss 2.2646 (2.6276) Prec@1 43.125 (36.415) Prec@5 70.625 (67.081) Epoch: [11][10150/11272] Time 0.873 (0.826) Data 0.001 (0.002) Loss 2.7280 (2.6276) Prec@1 34.375 (36.415) Prec@5 62.500 (67.082) Epoch: [11][10160/11272] Time 0.899 (0.826) Data 0.002 (0.002) Loss 2.7813 (2.6275) Prec@1 34.375 (36.417) Prec@5 65.625 (67.084) Epoch: [11][10170/11272] Time 0.770 (0.826) Data 0.002 (0.002) Loss 2.5921 (2.6274) Prec@1 36.875 (36.416) Prec@5 66.875 (67.084) Epoch: [11][10180/11272] Time 0.757 (0.826) Data 0.001 (0.002) Loss 2.7811 (2.6274) Prec@1 29.375 (36.416) Prec@5 64.375 (67.083) Epoch: [11][10190/11272] Time 0.951 (0.826) Data 0.002 (0.002) Loss 2.6995 (2.6274) Prec@1 33.750 (36.415) Prec@5 67.500 (67.084) Epoch: [11][10200/11272] Time 0.930 (0.826) Data 0.002 (0.002) Loss 2.7612 (2.6275) Prec@1 31.875 (36.413) Prec@5 63.125 (67.083) Epoch: [11][10210/11272] Time 0.759 (0.826) Data 0.001 (0.002) Loss 2.6127 (2.6275) Prec@1 38.125 (36.413) Prec@5 69.375 (67.082) Epoch: [11][10220/11272] Time 0.761 (0.826) Data 0.002 (0.002) Loss 2.5344 (2.6275) Prec@1 35.000 (36.414) Prec@5 73.125 (67.084) Epoch: [11][10230/11272] Time 0.937 (0.826) Data 0.003 (0.002) Loss 2.5423 (2.6274) Prec@1 39.375 (36.415) Prec@5 73.750 (67.084) Epoch: [11][10240/11272] Time 0.878 (0.826) Data 0.001 (0.002) Loss 2.6571 (2.6274) Prec@1 38.125 (36.417) Prec@5 67.500 (67.086) Epoch: [11][10250/11272] Time 0.776 (0.826) Data 0.002 (0.002) Loss 2.7834 (2.6274) Prec@1 35.000 (36.416) Prec@5 62.500 (67.085) Epoch: [11][10260/11272] Time 0.842 (0.826) Data 0.002 (0.002) Loss 2.4636 (2.6274) Prec@1 38.125 (36.415) Prec@5 72.500 (67.086) Epoch: [11][10270/11272] Time 0.836 (0.826) Data 0.002 (0.002) Loss 2.5928 (2.6273) Prec@1 36.875 (36.416) Prec@5 69.375 (67.087) Epoch: [11][10280/11272] Time 0.802 (0.826) Data 0.002 (0.002) Loss 2.7956 (2.6273) Prec@1 31.875 (36.418) Prec@5 65.000 (67.089) Epoch: [11][10290/11272] Time 0.788 (0.826) Data 0.002 (0.002) Loss 2.6675 (2.6273) Prec@1 36.875 (36.418) Prec@5 62.500 (67.090) Epoch: [11][10300/11272] Time 0.883 (0.826) Data 0.001 (0.002) Loss 2.7191 (2.6273) Prec@1 35.625 (36.417) Prec@5 63.750 (67.089) Epoch: [11][10310/11272] Time 0.836 (0.826) Data 0.002 (0.002) Loss 2.8054 (2.6273) Prec@1 30.000 (36.419) Prec@5 61.250 (67.088) Epoch: [11][10320/11272] Time 0.760 (0.826) Data 0.002 (0.002) Loss 2.5413 (2.6273) Prec@1 37.500 (36.417) Prec@5 70.000 (67.088) Epoch: [11][10330/11272] Time 0.770 (0.826) Data 0.002 (0.002) Loss 2.6351 (2.6273) Prec@1 33.750 (36.417) Prec@5 68.125 (67.086) Epoch: [11][10340/11272] Time 0.864 (0.826) Data 0.002 (0.002) Loss 2.5498 (2.6274) Prec@1 35.625 (36.416) Prec@5 68.125 (67.086) Epoch: [11][10350/11272] Time 0.871 (0.826) Data 0.001 (0.002) Loss 2.5601 (2.6274) Prec@1 34.375 (36.416) Prec@5 67.500 (67.085) Epoch: [11][10360/11272] Time 0.741 (0.826) Data 0.002 (0.002) Loss 2.6070 (2.6274) Prec@1 35.000 (36.416) Prec@5 69.375 (67.085) Epoch: [11][10370/11272] Time 0.734 (0.826) Data 0.002 (0.002) Loss 2.6428 (2.6275) Prec@1 38.750 (36.415) Prec@5 67.500 (67.085) Epoch: [11][10380/11272] Time 0.853 (0.826) Data 0.001 (0.002) Loss 2.5315 (2.6275) Prec@1 37.500 (36.415) Prec@5 66.875 (67.085) Epoch: [11][10390/11272] Time 0.758 (0.826) Data 0.004 (0.002) Loss 2.7445 (2.6275) Prec@1 37.500 (36.415) Prec@5 65.000 (67.085) Epoch: [11][10400/11272] Time 0.744 (0.826) Data 0.001 (0.002) Loss 2.5418 (2.6275) Prec@1 36.250 (36.415) Prec@5 69.375 (67.084) Epoch: [11][10410/11272] Time 0.926 (0.826) Data 0.002 (0.002) Loss 2.6967 (2.6275) Prec@1 36.875 (36.415) Prec@5 66.250 (67.085) Epoch: [11][10420/11272] Time 0.904 (0.826) Data 0.002 (0.002) Loss 2.6082 (2.6275) Prec@1 33.125 (36.415) Prec@5 67.500 (67.086) Epoch: [11][10430/11272] Time 0.731 (0.826) Data 0.002 (0.002) Loss 2.7287 (2.6275) Prec@1 33.125 (36.413) Prec@5 64.375 (67.085) Epoch: [11][10440/11272] Time 0.754 (0.826) Data 0.002 (0.002) Loss 2.6608 (2.6275) Prec@1 35.000 (36.413) Prec@5 63.750 (67.087) Epoch: [11][10450/11272] Time 0.946 (0.826) Data 0.002 (0.002) Loss 2.8863 (2.6275) Prec@1 33.125 (36.415) Prec@5 62.500 (67.087) Epoch: [11][10460/11272] Time 0.964 (0.826) Data 0.002 (0.002) Loss 2.5953 (2.6274) Prec@1 36.250 (36.416) Prec@5 66.250 (67.090) Epoch: [11][10470/11272] Time 0.747 (0.826) Data 0.002 (0.002) Loss 2.5872 (2.6274) Prec@1 40.000 (36.417) Prec@5 68.125 (67.090) Epoch: [11][10480/11272] Time 0.822 (0.826) Data 0.002 (0.002) Loss 2.6801 (2.6275) Prec@1 34.375 (36.416) Prec@5 66.250 (67.089) Epoch: [11][10490/11272] Time 0.904 (0.826) Data 0.001 (0.002) Loss 2.4323 (2.6274) Prec@1 36.875 (36.416) Prec@5 71.875 (67.091) Epoch: [11][10500/11272] Time 0.872 (0.826) Data 0.002 (0.002) Loss 2.4492 (2.6273) Prec@1 39.375 (36.419) Prec@5 66.250 (67.092) Epoch: [11][10510/11272] Time 0.758 (0.826) Data 0.002 (0.002) Loss 2.7067 (2.6273) Prec@1 36.875 (36.420) Prec@5 63.125 (67.092) Epoch: [11][10520/11272] Time 0.877 (0.826) Data 0.002 (0.002) Loss 2.5578 (2.6272) Prec@1 39.375 (36.422) Prec@5 66.875 (67.093) Epoch: [11][10530/11272] Time 0.919 (0.826) Data 0.001 (0.002) Loss 2.4705 (2.6272) Prec@1 37.500 (36.422) Prec@5 69.375 (67.095) Epoch: [11][10540/11272] Time 0.744 (0.826) Data 0.002 (0.002) Loss 2.7913 (2.6272) Prec@1 30.625 (36.421) Prec@5 61.250 (67.092) Epoch: [11][10550/11272] Time 0.731 (0.826) Data 0.001 (0.002) Loss 2.7813 (2.6273) Prec@1 32.500 (36.420) Prec@5 63.750 (67.091) Epoch: [11][10560/11272] Time 0.877 (0.826) Data 0.001 (0.002) Loss 2.6859 (2.6273) Prec@1 32.500 (36.420) Prec@5 65.000 (67.091) Epoch: [11][10570/11272] Time 0.879 (0.826) Data 0.002 (0.002) Loss 2.7234 (2.6273) Prec@1 34.375 (36.420) Prec@5 66.250 (67.091) Epoch: [11][10580/11272] Time 0.738 (0.826) Data 0.001 (0.002) Loss 2.5320 (2.6274) Prec@1 38.125 (36.421) Prec@5 70.625 (67.090) Epoch: [11][10590/11272] Time 0.767 (0.826) Data 0.002 (0.002) Loss 2.6748 (2.6274) Prec@1 36.250 (36.419) Prec@5 66.875 (67.089) Epoch: [11][10600/11272] Time 0.905 (0.826) Data 0.002 (0.002) Loss 2.7244 (2.6275) Prec@1 31.250 (36.419) Prec@5 68.125 (67.087) Epoch: [11][10610/11272] Time 0.808 (0.826) Data 0.001 (0.002) Loss 2.6354 (2.6275) Prec@1 40.000 (36.418) Prec@5 65.000 (67.087) Epoch: [11][10620/11272] Time 0.725 (0.826) Data 0.001 (0.002) Loss 2.3464 (2.6275) Prec@1 38.125 (36.418) Prec@5 76.250 (67.088) Epoch: [11][10630/11272] Time 0.813 (0.826) Data 0.002 (0.002) Loss 2.6175 (2.6275) Prec@1 35.625 (36.418) Prec@5 66.875 (67.088) Epoch: [11][10640/11272] Time 0.910 (0.826) Data 0.001 (0.002) Loss 2.6032 (2.6275) Prec@1 39.375 (36.418) Prec@5 66.875 (67.087) Epoch: [11][10650/11272] Time 0.754 (0.826) Data 0.004 (0.002) Loss 3.0005 (2.6275) Prec@1 28.125 (36.417) Prec@5 57.500 (67.086) Epoch: [11][10660/11272] Time 0.769 (0.826) Data 0.002 (0.002) Loss 2.5520 (2.6275) Prec@1 38.750 (36.417) Prec@5 68.125 (67.087) Epoch: [11][10670/11272] Time 0.900 (0.826) Data 0.002 (0.002) Loss 2.6454 (2.6275) Prec@1 37.500 (36.417) Prec@5 65.625 (67.087) Epoch: [11][10680/11272] Time 0.903 (0.826) Data 0.002 (0.002) Loss 2.3510 (2.6274) Prec@1 40.000 (36.419) Prec@5 75.000 (67.090) Epoch: [11][10690/11272] Time 0.774 (0.826) Data 0.001 (0.002) Loss 2.6784 (2.6274) Prec@1 31.250 (36.420) Prec@5 66.875 (67.089) Epoch: [11][10700/11272] Time 0.782 (0.826) Data 0.002 (0.002) Loss 2.6571 (2.6274) Prec@1 37.500 (36.420) Prec@5 69.375 (67.088) Epoch: [11][10710/11272] Time 0.922 (0.826) Data 0.003 (0.002) Loss 2.6127 (2.6273) Prec@1 36.250 (36.422) Prec@5 68.125 (67.089) Epoch: [11][10720/11272] Time 0.888 (0.826) Data 0.002 (0.002) Loss 2.8119 (2.6273) Prec@1 33.125 (36.422) Prec@5 61.250 (67.089) Epoch: [11][10730/11272] Time 0.737 (0.826) Data 0.001 (0.002) Loss 2.5412 (2.6273) Prec@1 38.125 (36.422) Prec@5 66.875 (67.089) Epoch: [11][10740/11272] Time 0.792 (0.826) Data 0.002 (0.002) Loss 2.4614 (2.6274) Prec@1 38.750 (36.420) Prec@5 71.875 (67.087) Epoch: [11][10750/11272] Time 0.917 (0.826) Data 0.002 (0.002) Loss 2.5329 (2.6274) Prec@1 35.000 (36.420) Prec@5 72.500 (67.086) Epoch: [11][10760/11272] Time 0.867 (0.826) Data 0.001 (0.002) Loss 2.8881 (2.6274) Prec@1 36.250 (36.420) Prec@5 56.875 (67.085) Epoch: [11][10770/11272] Time 0.783 (0.826) Data 0.002 (0.002) Loss 2.7537 (2.6275) Prec@1 33.125 (36.418) Prec@5 61.875 (67.084) Epoch: [11][10780/11272] Time 0.895 (0.826) Data 0.002 (0.002) Loss 2.8072 (2.6275) Prec@1 36.875 (36.420) Prec@5 65.000 (67.083) Epoch: [11][10790/11272] Time 0.925 (0.826) Data 0.001 (0.002) Loss 2.5774 (2.6275) Prec@1 33.125 (36.419) Prec@5 67.500 (67.084) Epoch: [11][10800/11272] Time 0.790 (0.826) Data 0.001 (0.002) Loss 2.9666 (2.6275) Prec@1 30.000 (36.419) Prec@5 58.125 (67.085) Epoch: [11][10810/11272] Time 0.801 (0.826) Data 0.002 (0.002) Loss 2.6503 (2.6275) Prec@1 30.000 (36.419) Prec@5 66.250 (67.084) Epoch: [11][10820/11272] Time 0.883 (0.826) Data 0.002 (0.002) Loss 2.5614 (2.6275) Prec@1 39.375 (36.419) Prec@5 68.125 (67.083) Epoch: [11][10830/11272] Time 0.932 (0.826) Data 0.002 (0.002) Loss 2.6978 (2.6276) Prec@1 35.625 (36.417) Prec@5 63.750 (67.083) Epoch: [11][10840/11272] Time 0.776 (0.826) Data 0.001 (0.002) Loss 2.7185 (2.6276) Prec@1 33.750 (36.416) Prec@5 62.500 (67.082) Epoch: [11][10850/11272] Time 0.801 (0.826) Data 0.002 (0.002) Loss 2.7115 (2.6275) Prec@1 34.375 (36.417) Prec@5 64.375 (67.084) Epoch: [11][10860/11272] Time 0.862 (0.826) Data 0.001 (0.002) Loss 2.8367 (2.6275) Prec@1 35.625 (36.418) Prec@5 61.250 (67.084) Epoch: [11][10870/11272] Time 0.854 (0.826) Data 0.002 (0.002) Loss 2.7841 (2.6275) Prec@1 39.375 (36.418) Prec@5 65.000 (67.084) Epoch: [11][10880/11272] Time 0.766 (0.826) Data 0.002 (0.002) Loss 2.5158 (2.6276) Prec@1 40.625 (36.417) Prec@5 68.750 (67.083) Epoch: [11][10890/11272] Time 0.743 (0.826) Data 0.002 (0.002) Loss 2.6936 (2.6276) Prec@1 33.125 (36.416) Prec@5 66.250 (67.082) Epoch: [11][10900/11272] Time 0.894 (0.826) Data 0.002 (0.002) Loss 2.9372 (2.6277) Prec@1 30.000 (36.414) Prec@5 62.500 (67.081) Epoch: [11][10910/11272] Time 0.870 (0.826) Data 0.001 (0.002) Loss 2.5241 (2.6277) Prec@1 41.250 (36.413) Prec@5 68.750 (67.080) Epoch: [11][10920/11272] Time 0.742 (0.826) Data 0.002 (0.002) Loss 3.1177 (2.6278) Prec@1 30.000 (36.412) Prec@5 58.125 (67.078) Epoch: [11][10930/11272] Time 0.944 (0.826) Data 0.002 (0.002) Loss 2.5952 (2.6278) Prec@1 37.500 (36.412) Prec@5 65.000 (67.076) Epoch: [11][10940/11272] Time 0.846 (0.826) Data 0.001 (0.002) Loss 2.7785 (2.6279) Prec@1 32.500 (36.412) Prec@5 65.000 (67.075) Epoch: [11][10950/11272] Time 0.743 (0.826) Data 0.001 (0.002) Loss 2.6750 (2.6279) Prec@1 33.125 (36.410) Prec@5 64.375 (67.073) Epoch: [11][10960/11272] Time 0.747 (0.826) Data 0.002 (0.002) Loss 2.4029 (2.6280) Prec@1 39.375 (36.411) Prec@5 71.250 (67.073) Epoch: [11][10970/11272] Time 0.927 (0.826) Data 0.001 (0.002) Loss 2.6060 (2.6280) Prec@1 36.875 (36.409) Prec@5 67.500 (67.072) Epoch: [11][10980/11272] Time 0.875 (0.826) Data 0.001 (0.002) Loss 2.6869 (2.6280) Prec@1 30.625 (36.410) Prec@5 65.625 (67.073) Epoch: [11][10990/11272] Time 0.750 (0.826) Data 0.002 (0.002) Loss 2.5599 (2.6279) Prec@1 32.500 (36.411) Prec@5 67.500 (67.075) Epoch: [11][11000/11272] Time 0.769 (0.826) Data 0.002 (0.002) Loss 2.8314 (2.6280) Prec@1 34.375 (36.409) Prec@5 63.750 (67.073) Epoch: [11][11010/11272] Time 0.977 (0.826) Data 0.002 (0.002) Loss 2.6388 (2.6280) Prec@1 40.000 (36.409) Prec@5 68.125 (67.071) Epoch: [11][11020/11272] Time 0.910 (0.826) Data 0.002 (0.002) Loss 2.6159 (2.6280) Prec@1 33.125 (36.409) Prec@5 68.750 (67.071) Epoch: [11][11030/11272] Time 0.798 (0.826) Data 0.002 (0.002) Loss 2.6793 (2.6280) Prec@1 38.125 (36.411) Prec@5 65.000 (67.071) Epoch: [11][11040/11272] Time 0.748 (0.826) Data 0.002 (0.002) Loss 2.4671 (2.6279) Prec@1 37.500 (36.412) Prec@5 71.250 (67.073) Epoch: [11][11050/11272] Time 0.908 (0.826) Data 0.002 (0.002) Loss 2.5123 (2.6279) Prec@1 38.750 (36.413) Prec@5 68.125 (67.072) Epoch: [11][11060/11272] Time 0.739 (0.826) Data 0.002 (0.002) Loss 2.4866 (2.6279) Prec@1 31.875 (36.412) Prec@5 71.250 (67.073) Epoch: [11][11070/11272] Time 0.751 (0.826) Data 0.002 (0.002) Loss 2.7737 (2.6280) Prec@1 34.375 (36.411) Prec@5 66.875 (67.072) Epoch: [11][11080/11272] Time 0.883 (0.826) Data 0.002 (0.002) Loss 2.6414 (2.6280) Prec@1 30.000 (36.409) Prec@5 66.250 (67.072) Epoch: [11][11090/11272] Time 0.908 (0.826) Data 0.002 (0.002) Loss 2.6190 (2.6280) Prec@1 33.125 (36.410) Prec@5 66.875 (67.071) Epoch: [11][11100/11272] Time 0.782 (0.826) Data 0.002 (0.002) Loss 2.3673 (2.6280) Prec@1 41.250 (36.411) Prec@5 69.375 (67.071) Epoch: [11][11110/11272] Time 0.775 (0.826) Data 0.002 (0.002) Loss 2.5204 (2.6279) Prec@1 35.000 (36.412) Prec@5 66.250 (67.071) Epoch: [11][11120/11272] Time 0.878 (0.826) Data 0.002 (0.002) Loss 2.7743 (2.6280) Prec@1 31.875 (36.413) Prec@5 63.125 (67.070) Epoch: [11][11130/11272] Time 0.902 (0.826) Data 0.002 (0.002) Loss 2.6707 (2.6279) Prec@1 33.125 (36.414) Prec@5 66.250 (67.069) Epoch: [11][11140/11272] Time 0.746 (0.826) Data 0.002 (0.002) Loss 2.6087 (2.6279) Prec@1 32.500 (36.413) Prec@5 66.875 (67.069) Epoch: [11][11150/11272] Time 0.780 (0.826) Data 0.002 (0.002) Loss 2.6561 (2.6279) Prec@1 38.750 (36.415) Prec@5 65.000 (67.068) Epoch: [11][11160/11272] Time 0.880 (0.826) Data 0.003 (0.002) Loss 2.3903 (2.6279) Prec@1 40.625 (36.414) Prec@5 72.500 (67.068) Epoch: [11][11170/11272] Time 0.899 (0.826) Data 0.002 (0.002) Loss 2.7810 (2.6279) Prec@1 31.250 (36.415) Prec@5 63.125 (67.068) Epoch: [11][11180/11272] Time 0.757 (0.826) Data 0.001 (0.002) Loss 2.6646 (2.6279) Prec@1 42.500 (36.415) Prec@5 68.750 (67.068) Epoch: [11][11190/11272] Time 0.914 (0.826) Data 0.002 (0.002) Loss 2.9650 (2.6279) Prec@1 31.250 (36.415) Prec@5 59.375 (67.068) Epoch: [11][11200/11272] Time 0.887 (0.826) Data 0.002 (0.002) Loss 2.7862 (2.6280) Prec@1 33.750 (36.414) Prec@5 63.750 (67.068) Epoch: [11][11210/11272] Time 0.751 (0.826) Data 0.002 (0.002) Loss 2.7462 (2.6280) Prec@1 30.625 (36.413) Prec@5 64.375 (67.068) Epoch: [11][11220/11272] Time 0.754 (0.826) Data 0.002 (0.002) Loss 2.7032 (2.6280) Prec@1 33.750 (36.412) Prec@5 61.250 (67.066) Epoch: [11][11230/11272] Time 0.945 (0.826) Data 0.002 (0.002) Loss 2.4782 (2.6280) Prec@1 35.000 (36.411) Prec@5 67.500 (67.065) Epoch: [11][11240/11272] Time 0.861 (0.826) Data 0.001 (0.002) Loss 2.5532 (2.6280) Prec@1 38.125 (36.413) Prec@5 68.125 (67.065) Epoch: [11][11250/11272] Time 0.723 (0.826) Data 0.001 (0.002) Loss 2.7821 (2.6280) Prec@1 31.250 (36.413) Prec@5 60.000 (67.065) Epoch: [11][11260/11272] Time 0.747 (0.826) Data 0.002 (0.002) Loss 2.4012 (2.6280) Prec@1 36.875 (36.412) Prec@5 73.750 (67.065) Epoch: [11][11270/11272] Time 0.814 (0.826) Data 0.000 (0.002) Loss 2.4170 (2.6279) Prec@1 40.625 (36.412) Prec@5 73.125 (67.066) Test: [0/229] Time 3.689 (3.689) Loss 1.7681 (1.7681) Prec@1 43.125 (43.125) Prec@5 92.500 (92.500) Test: [10/229] Time 0.321 (0.823) Loss 1.4025 (2.3301) Prec@1 58.125 (41.023) Prec@5 93.750 (75.682) Test: [20/229] Time 1.363 (0.708) Loss 2.7554 (2.2256) Prec@1 38.750 (43.601) Prec@5 68.750 (76.786) Test: [30/229] Time 0.375 (0.671) Loss 2.6936 (2.1304) Prec@1 21.875 (45.081) Prec@5 71.250 (78.185) Test: [40/229] Time 1.250 (0.668) Loss 1.0849 (2.1448) Prec@1 77.500 (45.137) Prec@5 85.625 (77.500) Test: [50/229] Time 0.742 (0.650) Loss 2.8932 (2.2312) Prec@1 25.000 (43.542) Prec@5 63.750 (75.588) Test: [60/229] Time 0.438 (0.638) Loss 2.5647 (2.2271) Prec@1 28.750 (43.381) Prec@5 70.000 (75.482) Test: [70/229] Time 0.432 (0.637) Loss 2.1572 (2.2530) Prec@1 48.125 (43.072) Prec@5 75.000 (74.982) Test: [80/229] Time 0.751 (0.633) Loss 2.3802 (2.2699) Prec@1 40.000 (42.662) Prec@5 76.875 (75.000) Test: [90/229] Time 0.634 (0.633) Loss 2.0431 (2.2924) Prec@1 55.000 (42.040) Prec@5 76.875 (74.794) Test: [100/229] Time 1.225 (0.633) Loss 2.2684 (2.2675) Prec@1 46.875 (42.902) Prec@5 76.875 (75.272) Test: [110/229] Time 0.718 (0.627) Loss 2.0484 (2.2491) Prec@1 41.250 (43.226) Prec@5 81.875 (75.524) Test: [120/229] Time 0.514 (0.628) Loss 3.6313 (2.3016) Prec@1 11.875 (41.813) Prec@5 51.875 (74.876) Test: [130/229] Time 0.331 (0.628) Loss 2.4171 (2.2878) Prec@1 41.875 (42.109) Prec@5 72.500 (74.995) Test: [140/229] Time 0.606 (0.620) Loss 2.3980 (2.3122) Prec@1 39.375 (41.582) Prec@5 71.875 (74.433) Test: [150/229] Time 0.347 (0.619) Loss 1.8509 (2.3338) Prec@1 58.750 (41.188) Prec@5 81.875 (74.164) Test: [160/229] Time 0.464 (0.623) Loss 2.5414 (2.3385) Prec@1 36.875 (41.153) Prec@5 77.500 (74.154) Test: [170/229] Time 0.402 (0.620) Loss 3.4865 (2.3674) Prec@1 16.250 (40.435) Prec@5 56.250 (73.637) Test: [180/229] Time 0.828 (0.621) Loss 2.3866 (2.3665) Prec@1 36.875 (40.546) Prec@5 71.250 (73.519) Test: [190/229] Time 0.408 (0.619) Loss 2.2177 (2.3496) Prec@1 38.125 (40.893) Prec@5 80.625 (73.760) Test: [200/229] Time 0.498 (0.623) Loss 2.8957 (2.3378) Prec@1 33.125 (41.113) Prec@5 57.500 (74.036) Test: [210/229] Time 0.431 (0.621) Loss 1.5419 (2.3212) Prec@1 59.375 (41.517) Prec@5 86.875 (74.280) Test: [220/229] Time 0.325 (0.624) Loss 2.2367 (2.3032) Prec@1 46.250 (42.056) Prec@5 75.625 (74.553) * Prec@1 42.484 Prec@5 74.779 Epoch: [12][0/11272] Time 3.490 (3.490) Data 2.087 (2.087) Loss 2.5928 (2.5928) Prec@1 36.875 (36.875) Prec@5 66.250 (66.250) Epoch: [12][10/11272] Time 0.950 (1.216) Data 0.002 (0.191) Loss 2.5978 (2.6249) Prec@1 35.625 (36.818) Prec@5 62.500 (66.705) Epoch: [12][20/11272] Time 0.771 (1.033) Data 0.002 (0.101) Loss 2.4094 (2.5931) Prec@1 40.625 (37.530) Prec@5 68.750 (66.786) Epoch: [12][30/11272] Time 0.737 (0.968) Data 0.001 (0.069) Loss 2.7593 (2.6024) Prec@1 31.875 (37.399) Prec@5 63.750 (66.673) Epoch: [12][40/11272] Time 0.858 (0.936) Data 0.002 (0.053) Loss 2.8080 (2.6257) Prec@1 32.500 (36.784) Prec@5 65.000 (66.509) Epoch: [12][50/11272] Time 0.899 (0.919) Data 0.002 (0.043) Loss 2.5525 (2.6354) Prec@1 36.875 (36.520) Prec@5 68.125 (66.409) Epoch: [12][60/11272] Time 0.748 (0.904) Data 0.002 (0.036) Loss 2.6270 (2.6331) Prec@1 39.375 (36.598) Prec@5 63.125 (66.557) Epoch: [12][70/11272] Time 0.875 (0.897) Data 0.002 (0.031) Loss 2.8237 (2.6344) Prec@1 26.875 (36.426) Prec@5 65.000 (66.611) Epoch: [12][80/11272] Time 0.897 (0.891) Data 0.002 (0.028) Loss 2.5913 (2.6288) Prec@1 43.125 (36.466) Prec@5 68.125 (66.852) Epoch: [12][90/11272] Time 0.899 (0.885) Data 0.002 (0.025) Loss 2.5797 (2.6193) Prec@1 38.750 (36.690) Prec@5 71.250 (67.026) Epoch: [12][100/11272] Time 0.754 (0.880) Data 0.002 (0.022) Loss 2.5331 (2.6186) Prec@1 41.250 (36.726) Prec@5 66.875 (66.974) Epoch: [12][110/11272] Time 0.766 (0.877) Data 0.002 (0.021) Loss 2.4410 (2.6122) Prec@1 41.250 (36.762) Prec@5 72.500 (67.134) Epoch: [12][120/11272] Time 0.899 (0.874) Data 0.002 (0.019) Loss 2.8635 (2.6152) Prec@1 33.750 (36.699) Prec@5 65.000 (67.056) Epoch: [12][130/11272] Time 0.809 (0.871) Data 0.002 (0.018) Loss 2.5468 (2.6236) Prec@1 36.250 (36.503) Prec@5 70.000 (66.923) Epoch: [12][140/11272] Time 0.760 (0.870) Data 0.002 (0.017) Loss 2.3883 (2.6235) Prec@1 41.250 (36.396) Prec@5 73.750 (66.933) Epoch: [12][150/11272] Time 0.911 (0.868) Data 0.002 (0.016) Loss 2.7145 (2.6285) Prec@1 32.500 (36.258) Prec@5 66.875 (66.867) Epoch: [12][160/11272] Time 0.897 (0.866) Data 0.002 (0.015) Loss 2.6414 (2.6257) Prec@1 37.500 (36.378) Prec@5 66.250 (66.922) Epoch: [12][170/11272] Time 0.746 (0.864) Data 0.002 (0.014) Loss 2.4005 (2.6264) Prec@1 40.000 (36.360) Prec@5 71.250 (66.923) Epoch: [12][180/11272] Time 0.804 (0.862) Data 0.003 (0.013) Loss 2.5353 (2.6232) Prec@1 38.125 (36.506) Prec@5 70.625 (67.044) Epoch: [12][190/11272] Time 0.924 (0.861) Data 0.003 (0.013) Loss 2.5879 (2.6208) Prec@1 35.625 (36.584) Prec@5 72.500 (67.019) Epoch: [12][200/11272] Time 0.933 (0.860) Data 0.002 (0.012) Loss 2.4752 (2.6224) Prec@1 38.125 (36.514) Prec@5 70.625 (66.996) Epoch: [12][210/11272] Time 0.790 (0.859) Data 0.002 (0.012) Loss 2.7375 (2.6224) Prec@1 35.625 (36.493) Prec@5 67.500 (66.916) Epoch: [12][220/11272] Time 0.740 (0.858) Data 0.001 (0.011) Loss 2.3946 (2.6223) Prec@1 40.625 (36.473) Prec@5 71.875 (66.906) Epoch: [12][230/11272] Time 0.920 (0.857) Data 0.002 (0.011) Loss 2.5391 (2.6202) Prec@1 37.500 (36.553) Prec@5 75.000 (67.008) Epoch: [12][240/11272] Time 0.974 (0.857) Data 0.002 (0.010) Loss 2.7704 (2.6230) Prec@1 31.875 (36.457) Prec@5 60.000 (66.981) Epoch: [12][250/11272] Time 0.771 (0.856) Data 0.002 (0.010) Loss 2.4958 (2.6222) Prec@1 37.500 (36.399) Prec@5 71.250 (66.995) Epoch: [12][260/11272] Time 0.925 (0.857) Data 0.002 (0.010) Loss 2.6225 (2.6201) Prec@1 33.750 (36.434) Prec@5 68.125 (67.026) Epoch: [12][270/11272] Time 0.959 (0.857) Data 0.002 (0.010) Loss 2.7007 (2.6168) Prec@1 30.000 (36.451) Prec@5 66.875 (67.089) Epoch: [12][280/11272] Time 0.753 (0.856) Data 0.001 (0.009) Loss 2.4511 (2.6137) Prec@1 41.250 (36.546) Prec@5 68.125 (67.140) Epoch: [12][290/11272] Time 0.800 (0.855) Data 0.002 (0.009) Loss 2.5207 (2.6118) Prec@1 37.500 (36.600) Prec@5 68.750 (67.174) Epoch: [12][300/11272] Time 0.901 (0.855) Data 0.002 (0.009) Loss 2.7275 (2.6142) Prec@1 35.000 (36.547) Prec@5 67.500 (67.143) Epoch: [12][310/11272] Time 0.897 (0.855) Data 0.002 (0.009) Loss 2.7178 (2.6152) Prec@1 34.375 (36.566) Prec@5 68.750 (67.126) Epoch: [12][320/11272] Time 0.859 (0.855) Data 0.002 (0.008) Loss 2.5950 (2.6171) Prec@1 37.500 (36.511) Prec@5 65.000 (67.079) Epoch: [12][330/11272] Time 0.758 (0.854) Data 0.002 (0.008) Loss 2.8397 (2.6180) Prec@1 25.625 (36.450) Prec@5 61.875 (67.056) Epoch: [12][340/11272] Time 0.880 (0.854) Data 0.002 (0.008) Loss 2.6012 (2.6172) Prec@1 38.125 (36.433) Prec@5 66.875 (67.111) Epoch: [12][350/11272] Time 0.929 (0.854) Data 0.002 (0.008) Loss 2.4203 (2.6169) Prec@1 36.875 (36.425) Prec@5 70.625 (67.123) Epoch: [12][360/11272] Time 0.774 (0.853) Data 0.002 (0.008) Loss 2.4819 (2.6167) Prec@1 38.750 (36.442) Prec@5 73.125 (67.155) Epoch: [12][370/11272] Time 0.775 (0.853) Data 0.002 (0.007) Loss 2.6388 (2.6153) Prec@1 36.250 (36.469) Prec@5 65.625 (67.168) Epoch: [12][380/11272] Time 0.899 (0.852) Data 0.002 (0.007) Loss 2.6599 (2.6163) Prec@1 36.875 (36.491) Prec@5 68.750 (67.149) Epoch: [12][390/11272] Time 0.751 (0.851) Data 0.004 (0.007) Loss 2.5065 (2.6147) Prec@1 41.250 (36.554) Prec@5 65.000 (67.161) Epoch: [12][400/11272] Time 0.808 (0.851) Data 0.002 (0.007) Loss 2.5785 (2.6152) Prec@1 38.750 (36.546) Prec@5 68.125 (67.143) Epoch: [12][410/11272] Time 0.906 (0.851) Data 0.002 (0.007) Loss 2.8238 (2.6160) Prec@1 29.375 (36.530) Prec@5 65.625 (67.124) Epoch: [12][420/11272] Time 0.925 (0.851) Data 0.002 (0.007) Loss 2.4916 (2.6172) Prec@1 33.750 (36.482) Prec@5 71.250 (67.105) Epoch: [12][430/11272] Time 0.785 (0.851) Data 0.002 (0.007) Loss 2.6852 (2.6186) Prec@1 40.625 (36.466) Prec@5 66.250 (67.091) Epoch: [12][440/11272] Time 0.743 (0.851) Data 0.002 (0.007) Loss 2.4892 (2.6172) Prec@1 36.250 (36.507) Prec@5 71.250 (67.115) Epoch: [12][450/11272] Time 0.970 (0.851) Data 0.002 (0.006) Loss 2.2599 (2.6155) Prec@1 43.750 (36.558) Prec@5 73.125 (67.162) Epoch: [12][460/11272] Time 0.954 (0.851) Data 0.001 (0.006) Loss 2.6001 (2.6159) Prec@1 33.750 (36.544) Prec@5 70.625 (67.171) Epoch: [12][470/11272] Time 0.770 (0.850) Data 0.002 (0.006) Loss 2.6911 (2.6148) Prec@1 36.875 (36.562) Prec@5 62.500 (67.171) Epoch: [12][480/11272] Time 0.745 (0.850) Data 0.001 (0.006) Loss 2.6336 (2.6161) Prec@1 35.000 (36.567) Prec@5 66.875 (67.179) Epoch: [12][490/11272] Time 0.915 (0.850) Data 0.002 (0.006) Loss 2.9227 (2.6180) Prec@1 29.375 (36.548) Prec@5 60.000 (67.135) Epoch: [12][500/11272] Time 0.896 (0.849) Data 0.002 (0.006) Loss 2.6150 (2.6178) Prec@1 40.625 (36.576) Prec@5 66.250 (67.152) Epoch: [12][510/11272] Time 0.819 (0.849) Data 0.002 (0.006) Loss 2.4451 (2.6165) Prec@1 40.625 (36.628) Prec@5 70.625 (67.183) Epoch: [12][520/11272] Time 0.900 (0.850) Data 0.002 (0.006) Loss 2.7246 (2.6164) Prec@1 31.875 (36.619) Prec@5 60.000 (67.181) Epoch: [12][530/11272] Time 0.917 (0.850) Data 0.002 (0.006) Loss 2.7918 (2.6171) Prec@1 31.250 (36.591) Prec@5 63.125 (67.175) Epoch: [12][540/11272] Time 0.746 (0.850) Data 0.001 (0.006) Loss 2.5999 (2.6181) Prec@1 36.875 (36.562) Prec@5 73.125 (67.156) Epoch: [12][550/11272] Time 0.757 (0.849) Data 0.002 (0.006) Loss 2.8012 (2.6176) Prec@1 35.000 (36.603) Prec@5 61.250 (67.180) Epoch: [12][560/11272] Time 0.924 (0.849) Data 0.002 (0.006) Loss 2.8072 (2.6160) Prec@1 29.375 (36.618) Prec@5 63.125 (67.184) Epoch: [12][570/11272] Time 0.875 (0.848) Data 0.002 (0.005) Loss 2.7108 (2.6151) Prec@1 37.500 (36.652) Prec@5 63.125 (67.181) Epoch: [12][580/11272] Time 0.842 (0.848) Data 0.002 (0.005) Loss 2.7249 (2.6140) Prec@1 38.750 (36.670) Prec@5 67.500 (67.202) Epoch: [12][590/11272] Time 0.742 (0.848) Data 0.002 (0.005) Loss 2.5408 (2.6148) Prec@1 41.250 (36.629) Prec@5 65.625 (67.198) Epoch: [12][600/11272] Time 0.912 (0.848) Data 0.002 (0.005) Loss 2.7305 (2.6163) Prec@1 31.875 (36.598) Prec@5 69.375 (67.163) Epoch: [12][610/11272] Time 0.906 (0.848) Data 0.002 (0.005) Loss 2.5666 (2.6163) Prec@1 39.375 (36.595) Prec@5 66.875 (67.169) Epoch: [12][620/11272] Time 0.750 (0.848) Data 0.002 (0.005) Loss 2.7946 (2.6164) Prec@1 35.000 (36.590) Prec@5 61.875 (67.142) Epoch: [12][630/11272] Time 0.773 (0.848) Data 0.002 (0.005) Loss 2.5111 (2.6155) Prec@1 37.500 (36.589) Prec@5 67.500 (67.124) Epoch: [12][640/11272] Time 0.941 (0.848) Data 0.002 (0.005) Loss 2.5565 (2.6146) Prec@1 41.250 (36.591) Prec@5 69.375 (67.158) Epoch: [12][650/11272] Time 0.789 (0.848) Data 0.004 (0.005) Loss 2.5342 (2.6144) Prec@1 40.000 (36.594) Prec@5 70.625 (67.175) Epoch: [12][660/11272] Time 0.783 (0.848) Data 0.002 (0.005) Loss 2.6899 (2.6140) Prec@1 33.125 (36.595) Prec@5 67.500 (67.181) Epoch: [12][670/11272] Time 0.913 (0.847) Data 0.002 (0.005) Loss 2.8327 (2.6154) Prec@1 32.500 (36.578) Prec@5 63.750 (67.148) Epoch: [12][680/11272] Time 0.889 (0.847) Data 0.002 (0.005) Loss 2.5361 (2.6143) Prec@1 38.750 (36.608) Prec@5 69.375 (67.188) Epoch: [12][690/11272] Time 0.812 (0.847) Data 0.002 (0.005) Loss 2.5834 (2.6145) Prec@1 38.125 (36.619) Prec@5 65.000 (67.176) Epoch: [12][700/11272] Time 0.760 (0.847) Data 0.002 (0.005) Loss 2.5000 (2.6145) Prec@1 41.250 (36.633) Prec@5 67.500 (67.189) Epoch: [12][710/11272] Time 0.866 (0.847) Data 0.002 (0.005) Loss 2.5858 (2.6133) Prec@1 37.500 (36.634) Prec@5 65.000 (67.221) Epoch: [12][720/11272] Time 0.948 (0.847) Data 0.002 (0.005) Loss 2.5205 (2.6136) Prec@1 35.625 (36.635) Prec@5 68.750 (67.229) Epoch: [12][730/11272] Time 0.752 (0.847) Data 0.002 (0.005) Loss 2.8659 (2.6132) Prec@1 26.250 (36.615) Prec@5 65.000 (67.245) Epoch: [12][740/11272] Time 0.801 (0.847) Data 0.005 (0.005) Loss 2.5394 (2.6130) Prec@1 35.625 (36.614) Prec@5 73.125 (67.246) Epoch: [12][750/11272] Time 0.899 (0.847) Data 0.002 (0.005) Loss 2.6048 (2.6136) Prec@1 36.250 (36.620) Prec@5 68.750 (67.245) Epoch: [12][760/11272] Time 0.886 (0.847) Data 0.002 (0.005) Loss 2.5832 (2.6146) Prec@1 39.375 (36.623) Prec@5 69.375 (67.220) Epoch: [12][770/11272] Time 0.810 (0.847) Data 0.002 (0.005) Loss 2.7448 (2.6150) Prec@1 34.375 (36.619) Prec@5 63.750 (67.203) Epoch: [12][780/11272] Time 0.903 (0.847) Data 0.002 (0.005) Loss 2.6007 (2.6153) Prec@1 36.250 (36.639) Prec@5 67.500 (67.194) Epoch: [12][790/11272] Time 0.937 (0.847) Data 0.002 (0.004) Loss 2.5167 (2.6159) Prec@1 36.250 (36.628) Prec@5 68.125 (67.182) Epoch: [12][800/11272] Time 0.760 (0.847) Data 0.001 (0.004) Loss 2.5594 (2.6155) Prec@1 39.375 (36.643) Prec@5 70.000 (67.197) Epoch: [12][810/11272] Time 0.787 (0.847) Data 0.001 (0.004) Loss 2.3162 (2.6159) Prec@1 41.875 (36.635) Prec@5 78.750 (67.207) Epoch: [12][820/11272] Time 0.891 (0.847) Data 0.002 (0.004) Loss 2.6314 (2.6171) Prec@1 40.000 (36.624) Prec@5 61.875 (67.173) Epoch: [12][830/11272] Time 0.894 (0.847) Data 0.002 (0.004) Loss 2.4537 (2.6169) Prec@1 41.875 (36.628) Prec@5 72.500 (67.180) Epoch: [12][840/11272] Time 0.840 (0.846) Data 0.002 (0.004) Loss 2.7750 (2.6165) Prec@1 41.875 (36.637) Prec@5 64.375 (67.180) Epoch: [12][850/11272] Time 0.764 (0.846) Data 0.002 (0.004) Loss 2.5498 (2.6167) Prec@1 36.875 (36.627) Prec@5 69.375 (67.176) Epoch: [12][860/11272] Time 0.897 (0.846) Data 0.002 (0.004) Loss 2.6267 (2.6162) Prec@1 39.375 (36.631) Prec@5 67.500 (67.189) Epoch: [12][870/11272] Time 0.927 (0.846) Data 0.002 (0.004) Loss 2.5202 (2.6165) Prec@1 31.875 (36.631) Prec@5 66.875 (67.168) Epoch: [12][880/11272] Time 0.759 (0.846) Data 0.001 (0.004) Loss 2.7837 (2.6166) Prec@1 30.625 (36.631) Prec@5 66.250 (67.173) Epoch: [12][890/11272] Time 0.777 (0.846) Data 0.002 (0.004) Loss 2.6294 (2.6166) Prec@1 38.750 (36.629) Prec@5 63.750 (67.177) Epoch: [12][900/11272] Time 0.909 (0.846) Data 0.003 (0.004) Loss 2.5228 (2.6163) Prec@1 35.625 (36.630) Prec@5 69.375 (67.180) Epoch: [12][910/11272] Time 0.883 (0.846) Data 0.002 (0.004) Loss 2.5223 (2.6163) Prec@1 36.875 (36.634) Prec@5 76.250 (67.191) Epoch: [12][920/11272] Time 0.763 (0.846) Data 0.001 (0.004) Loss 2.6200 (2.6165) Prec@1 40.000 (36.635) Prec@5 68.750 (67.180) Epoch: [12][930/11272] Time 0.871 (0.846) Data 0.002 (0.004) Loss 2.7690 (2.6166) Prec@1 35.625 (36.643) Prec@5 63.750 (67.182) Epoch: [12][940/11272] Time 0.898 (0.846) Data 0.002 (0.004) Loss 2.7466 (2.6165) Prec@1 36.875 (36.648) Prec@5 63.750 (67.185) Epoch: [12][950/11272] Time 0.751 (0.845) Data 0.002 (0.004) Loss 2.4611 (2.6173) Prec@1 37.500 (36.625) Prec@5 70.000 (67.168) Epoch: [12][960/11272] Time 0.749 (0.845) Data 0.002 (0.004) Loss 2.7969 (2.6181) Prec@1 34.375 (36.629) Prec@5 61.875 (67.153) Epoch: [12][970/11272] Time 0.873 (0.845) Data 0.002 (0.004) Loss 2.7505 (2.6177) Prec@1 35.000 (36.630) Prec@5 61.875 (67.158) Epoch: [12][980/11272] Time 0.930 (0.845) Data 0.002 (0.004) Loss 2.5504 (2.6186) Prec@1 31.875 (36.622) Prec@5 71.250 (67.148) Epoch: [12][990/11272] Time 0.810 (0.845) Data 0.002 (0.004) Loss 2.4050 (2.6183) Prec@1 37.500 (36.623) Prec@5 72.500 (67.151) Epoch: [12][1000/11272] Time 0.808 (0.845) Data 0.002 (0.004) Loss 2.6166 (2.6180) Prec@1 33.750 (36.607) Prec@5 68.750 (67.170) Epoch: [12][1010/11272] Time 0.894 (0.845) Data 0.002 (0.004) Loss 2.7372 (2.6175) Prec@1 30.625 (36.609) Prec@5 68.750 (67.181) Epoch: [12][1020/11272] Time 0.948 (0.845) Data 0.002 (0.004) Loss 2.3185 (2.6163) Prec@1 42.500 (36.614) Prec@5 77.500 (67.206) Epoch: [12][1030/11272] Time 0.748 (0.845) Data 0.001 (0.004) Loss 2.4220 (2.6160) Prec@1 38.125 (36.617) Prec@5 70.625 (67.211) Epoch: [12][1040/11272] Time 0.796 (0.845) Data 0.002 (0.004) Loss 2.6402 (2.6166) Prec@1 36.250 (36.617) Prec@5 68.125 (67.202) Epoch: [12][1050/11272] Time 0.882 (0.845) Data 0.001 (0.004) Loss 2.7028 (2.6171) Prec@1 39.375 (36.612) Prec@5 65.625 (67.193) Epoch: [12][1060/11272] Time 0.797 (0.845) Data 0.002 (0.004) Loss 2.6125 (2.6175) Prec@1 38.125 (36.608) Prec@5 66.875 (67.188) Epoch: [12][1070/11272] Time 0.793 (0.845) Data 0.002 (0.004) Loss 2.6877 (2.6171) Prec@1 36.875 (36.606) Prec@5 69.375 (67.211) Epoch: [12][1080/11272] Time 0.984 (0.845) Data 0.002 (0.004) Loss 2.8078 (2.6174) Prec@1 35.000 (36.611) Prec@5 60.000 (67.198) Epoch: [12][1090/11272] Time 0.852 (0.845) Data 0.002 (0.004) Loss 2.6994 (2.6178) Prec@1 39.375 (36.607) Prec@5 64.375 (67.188) Epoch: [12][1100/11272] Time 0.773 (0.845) Data 0.002 (0.004) Loss 2.7481 (2.6183) Prec@1 36.875 (36.600) Prec@5 63.750 (67.182) Epoch: [12][1110/11272] Time 0.774 (0.845) Data 0.002 (0.004) Loss 2.8061 (2.6185) Prec@1 36.875 (36.599) Prec@5 64.375 (67.178) Epoch: [12][1120/11272] Time 0.948 (0.845) Data 0.002 (0.004) Loss 2.8279 (2.6188) Prec@1 32.500 (36.601) Prec@5 62.500 (67.172) Epoch: [12][1130/11272] Time 0.891 (0.845) Data 0.002 (0.004) Loss 2.6405 (2.6191) Prec@1 38.125 (36.587) Prec@5 59.375 (67.165) Epoch: [12][1140/11272] Time 0.772 (0.845) Data 0.001 (0.004) Loss 2.5203 (2.6185) Prec@1 39.375 (36.596) Prec@5 70.625 (67.183) Epoch: [12][1150/11272] Time 0.764 (0.845) Data 0.002 (0.004) Loss 2.3802 (2.6189) Prec@1 38.750 (36.594) Prec@5 71.875 (67.184) Epoch: [12][1160/11272] Time 0.992 (0.845) Data 0.002 (0.004) Loss 2.4760 (2.6189) Prec@1 37.500 (36.591) Prec@5 71.250 (67.174) Epoch: [12][1170/11272] Time 0.907 (0.845) Data 0.003 (0.004) Loss 2.6221 (2.6191) Prec@1 38.125 (36.581) Prec@5 68.125 (67.161) Epoch: [12][1180/11272] Time 0.779 (0.845) Data 0.002 (0.004) Loss 2.7797 (2.6193) Prec@1 35.000 (36.570) Prec@5 65.625 (67.172) Epoch: [12][1190/11272] Time 0.941 (0.845) Data 0.002 (0.004) Loss 2.4819 (2.6193) Prec@1 40.625 (36.577) Prec@5 72.500 (67.177) Epoch: [12][1200/11272] Time 0.932 (0.845) Data 0.002 (0.004) Loss 2.8019 (2.6194) Prec@1 33.750 (36.561) Prec@5 66.250 (67.179) Epoch: [12][1210/11272] Time 0.717 (0.845) Data 0.002 (0.004) Loss 2.7635 (2.6194) Prec@1 36.875 (36.554) Prec@5 61.250 (67.175) Epoch: [12][1220/11272] Time 0.763 (0.845) Data 0.002 (0.004) Loss 2.8162 (2.6192) Prec@1 35.625 (36.564) Prec@5 61.875 (67.172) Epoch: [12][1230/11272] Time 0.884 (0.845) Data 0.002 (0.004) Loss 2.6855 (2.6195) Prec@1 33.125 (36.561) Prec@5 65.625 (67.166) Epoch: [12][1240/11272] Time 0.903 (0.845) Data 0.002 (0.004) Loss 2.7585 (2.6195) Prec@1 35.000 (36.554) Prec@5 69.375 (67.166) Epoch: [12][1250/11272] Time 0.811 (0.845) Data 0.002 (0.004) Loss 2.3341 (2.6197) Prec@1 42.500 (36.551) Prec@5 73.125 (67.173) Epoch: [12][1260/11272] Time 0.743 (0.845) Data 0.002 (0.004) Loss 2.7031 (2.6191) Prec@1 33.750 (36.560) Prec@5 66.250 (67.194) Epoch: [12][1270/11272] Time 0.928 (0.845) Data 0.002 (0.003) Loss 2.9121 (2.6195) Prec@1 33.750 (36.554) Prec@5 60.625 (67.189) Epoch: [12][1280/11272] Time 0.900 (0.845) Data 0.002 (0.003) Loss 2.5567 (2.6198) Prec@1 36.875 (36.543) Prec@5 66.250 (67.184) Epoch: [12][1290/11272] Time 0.787 (0.845) Data 0.002 (0.003) Loss 2.6559 (2.6200) Prec@1 37.500 (36.540) Prec@5 65.625 (67.177) Epoch: [12][1300/11272] Time 0.768 (0.845) Data 0.001 (0.003) Loss 2.6412 (2.6203) Prec@1 32.500 (36.531) Prec@5 65.000 (67.163) Epoch: [12][1310/11272] Time 0.913 (0.845) Data 0.002 (0.003) Loss 2.5687 (2.6205) Prec@1 39.375 (36.526) Prec@5 67.500 (67.158) Epoch: [12][1320/11272] Time 0.740 (0.844) Data 0.003 (0.003) Loss 2.6242 (2.6205) Prec@1 36.250 (36.522) Prec@5 72.500 (67.173) Epoch: [12][1330/11272] Time 0.776 (0.844) Data 0.002 (0.003) Loss 2.5582 (2.6207) Prec@1 32.500 (36.514) Prec@5 68.125 (67.162) Epoch: [12][1340/11272] Time 0.895 (0.845) Data 0.002 (0.003) Loss 2.4999 (2.6206) Prec@1 37.500 (36.520) Prec@5 70.625 (67.163) Epoch: [12][1350/11272] Time 0.909 (0.845) Data 0.002 (0.003) Loss 2.5043 (2.6207) Prec@1 38.125 (36.528) Prec@5 65.625 (67.160) Epoch: [12][1360/11272] Time 0.780 (0.844) Data 0.002 (0.003) Loss 2.3810 (2.6207) Prec@1 37.500 (36.526) Prec@5 74.375 (67.158) Epoch: [12][1370/11272] Time 0.813 (0.844) Data 0.002 (0.003) Loss 2.6329 (2.6212) Prec@1 31.250 (36.518) Prec@5 70.000 (67.149) Epoch: [12][1380/11272] Time 0.907 (0.845) Data 0.002 (0.003) Loss 2.6489 (2.6212) Prec@1 31.250 (36.517) Prec@5 64.375 (67.141) Epoch: [12][1390/11272] Time 0.899 (0.845) Data 0.002 (0.003) Loss 2.7138 (2.6212) Prec@1 37.500 (36.520) Prec@5 68.750 (67.147) Epoch: [12][1400/11272] Time 0.798 (0.844) Data 0.002 (0.003) Loss 2.4344 (2.6211) Prec@1 37.500 (36.522) Prec@5 71.875 (67.154) Epoch: [12][1410/11272] Time 0.761 (0.844) Data 0.002 (0.003) Loss 2.5509 (2.6209) Prec@1 36.875 (36.532) Prec@5 73.125 (67.157) Epoch: [12][1420/11272] Time 0.866 (0.844) Data 0.001 (0.003) Loss 2.7918 (2.6211) Prec@1 31.875 (36.536) Prec@5 63.750 (67.164) Epoch: [12][1430/11272] Time 0.897 (0.844) Data 0.002 (0.003) Loss 2.5083 (2.6214) Prec@1 35.625 (36.522) Prec@5 70.625 (67.160) Epoch: [12][1440/11272] Time 0.755 (0.844) Data 0.002 (0.003) Loss 2.4107 (2.6216) Prec@1 41.250 (36.519) Prec@5 73.125 (67.160) Epoch: [12][1450/11272] Time 0.886 (0.845) Data 0.002 (0.003) Loss 2.5206 (2.6218) Prec@1 37.500 (36.505) Prec@5 66.250 (67.146) Epoch: [12][1460/11272] Time 0.894 (0.845) Data 0.002 (0.003) Loss 2.5380 (2.6216) Prec@1 38.750 (36.516) Prec@5 70.000 (67.151) Epoch: [12][1470/11272] Time 0.768 (0.845) Data 0.001 (0.003) Loss 2.3362 (2.6221) Prec@1 40.625 (36.504) Prec@5 71.250 (67.138) Epoch: [12][1480/11272] Time 0.783 (0.845) Data 0.002 (0.003) Loss 2.6331 (2.6222) Prec@1 35.625 (36.506) Prec@5 68.750 (67.138) Epoch: [12][1490/11272] Time 0.912 (0.845) Data 0.002 (0.003) Loss 2.4243 (2.6225) Prec@1 44.375 (36.508) Prec@5 70.625 (67.126) Epoch: [12][1500/11272] Time 0.904 (0.845) Data 0.002 (0.003) Loss 2.7439 (2.6226) Prec@1 36.250 (36.513) Prec@5 63.750 (67.114) Epoch: [12][1510/11272] Time 0.795 (0.844) Data 0.002 (0.003) Loss 2.5990 (2.6227) Prec@1 36.250 (36.512) Prec@5 70.000 (67.111) Epoch: [12][1520/11272] Time 0.795 (0.844) Data 0.002 (0.003) Loss 2.5330 (2.6229) Prec@1 40.000 (36.516) Prec@5 70.625 (67.108) Epoch: [12][1530/11272] Time 0.839 (0.845) Data 0.002 (0.003) Loss 2.4410 (2.6224) Prec@1 37.500 (36.527) Prec@5 68.750 (67.112) Epoch: [12][1540/11272] Time 0.868 (0.845) Data 0.002 (0.003) Loss 2.4597 (2.6216) Prec@1 40.625 (36.536) Prec@5 71.250 (67.129) Epoch: [12][1550/11272] Time 0.784 (0.845) Data 0.004 (0.003) Loss 2.6942 (2.6219) Prec@1 36.250 (36.528) Prec@5 66.250 (67.126) Epoch: [12][1560/11272] Time 0.795 (0.845) Data 0.002 (0.003) Loss 2.5897 (2.6212) Prec@1 41.875 (36.551) Prec@5 70.625 (67.132) Epoch: [12][1570/11272] Time 0.887 (0.845) Data 0.002 (0.003) Loss 2.6948 (2.6213) Prec@1 33.750 (36.543) Prec@5 65.625 (67.133) Epoch: [12][1580/11272] Time 0.748 (0.844) Data 0.004 (0.003) Loss 2.8707 (2.6217) Prec@1 31.875 (36.536) Prec@5 60.000 (67.122) Epoch: [12][1590/11272] Time 0.794 (0.844) Data 0.002 (0.003) Loss 2.7274 (2.6223) Prec@1 33.125 (36.528) Prec@5 65.625 (67.114) Epoch: [12][1600/11272] Time 0.880 (0.844) Data 0.002 (0.003) Loss 2.5524 (2.6220) Prec@1 39.375 (36.535) Prec@5 65.000 (67.115) Epoch: [12][1610/11272] Time 0.988 (0.844) Data 0.002 (0.003) Loss 2.5076 (2.6216) Prec@1 38.750 (36.551) Prec@5 70.625 (67.126) Epoch: [12][1620/11272] Time 0.769 (0.844) Data 0.002 (0.003) Loss 2.4608 (2.6216) Prec@1 42.500 (36.557) Prec@5 70.625 (67.135) Epoch: [12][1630/11272] Time 0.792 (0.844) Data 0.002 (0.003) Loss 2.5325 (2.6214) Prec@1 37.500 (36.560) Prec@5 70.000 (67.138) Epoch: [12][1640/11272] Time 0.916 (0.844) Data 0.002 (0.003) Loss 2.5277 (2.6212) Prec@1 35.625 (36.565) Prec@5 66.875 (67.142) Epoch: [12][1650/11272] Time 0.936 (0.844) Data 0.002 (0.003) Loss 2.5925 (2.6205) Prec@1 34.375 (36.569) Prec@5 70.000 (67.161) Epoch: [12][1660/11272] Time 0.765 (0.844) Data 0.002 (0.003) Loss 2.8544 (2.6204) Prec@1 34.375 (36.573) Prec@5 61.875 (67.162) Epoch: [12][1670/11272] Time 0.739 (0.844) Data 0.001 (0.003) Loss 2.3773 (2.6209) Prec@1 42.500 (36.574) Prec@5 72.500 (67.155) Epoch: [12][1680/11272] Time 0.893 (0.844) Data 0.002 (0.003) Loss 2.9474 (2.6210) Prec@1 31.250 (36.568) Prec@5 61.875 (67.149) Epoch: [12][1690/11272] Time 0.912 (0.844) Data 0.002 (0.003) Loss 2.6126 (2.6212) Prec@1 38.750 (36.570) Prec@5 66.875 (67.154) Epoch: [12][1700/11272] Time 0.773 (0.844) Data 0.002 (0.003) Loss 2.4811 (2.6209) Prec@1 41.250 (36.572) Prec@5 70.625 (67.169) Epoch: [12][1710/11272] Time 0.861 (0.844) Data 0.002 (0.003) Loss 2.4501 (2.6213) Prec@1 38.750 (36.559) Prec@5 74.375 (67.166) Epoch: [12][1720/11272] Time 0.926 (0.845) Data 0.002 (0.003) Loss 2.5635 (2.6211) Prec@1 34.375 (36.560) Prec@5 70.000 (67.167) Epoch: [12][1730/11272] Time 0.752 (0.845) Data 0.001 (0.003) Loss 2.4496 (2.6207) Prec@1 41.875 (36.564) Prec@5 69.375 (67.173) Epoch: [12][1740/11272] Time 0.806 (0.845) Data 0.002 (0.003) Loss 2.6187 (2.6204) Prec@1 33.750 (36.561) Prec@5 63.750 (67.186) Epoch: [12][1750/11272] Time 0.853 (0.845) Data 0.002 (0.003) Loss 2.3990 (2.6203) Prec@1 40.000 (36.560) Prec@5 73.125 (67.183) Epoch: [12][1760/11272] Time 0.873 (0.844) Data 0.002 (0.003) Loss 2.6572 (2.6204) Prec@1 37.500 (36.564) Prec@5 62.500 (67.178) Epoch: [12][1770/11272] Time 0.832 (0.844) Data 0.002 (0.003) Loss 2.6641 (2.6202) Prec@1 31.875 (36.567) Prec@5 66.250 (67.177) Epoch: [12][1780/11272] Time 0.762 (0.844) Data 0.002 (0.003) Loss 2.9100 (2.6199) Prec@1 35.000 (36.576) Prec@5 60.000 (67.188) Epoch: [12][1790/11272] Time 0.942 (0.844) Data 0.002 (0.003) Loss 2.4837 (2.6199) Prec@1 37.500 (36.564) Prec@5 73.125 (67.187) Epoch: [12][1800/11272] Time 0.893 (0.844) Data 0.002 (0.003) Loss 2.5589 (2.6197) Prec@1 40.625 (36.572) Prec@5 66.250 (67.191) Epoch: [12][1810/11272] Time 0.740 (0.844) Data 0.001 (0.003) Loss 2.6266 (2.6200) Prec@1 39.375 (36.570) Prec@5 65.000 (67.183) Epoch: [12][1820/11272] Time 0.824 (0.844) Data 0.002 (0.003) Loss 2.6502 (2.6196) Prec@1 35.625 (36.569) Prec@5 68.125 (67.195) Epoch: [12][1830/11272] Time 0.875 (0.844) Data 0.002 (0.003) Loss 2.3532 (2.6194) Prec@1 44.375 (36.562) Prec@5 73.125 (67.195) Epoch: [12][1840/11272] Time 0.932 (0.844) Data 0.002 (0.003) Loss 2.4315 (2.6187) Prec@1 45.000 (36.581) Prec@5 68.125 (67.208) Epoch: [12][1850/11272] Time 0.803 (0.844) Data 0.002 (0.003) Loss 2.5262 (2.6185) Prec@1 33.750 (36.583) Prec@5 70.000 (67.211) Epoch: [12][1860/11272] Time 0.891 (0.844) Data 0.002 (0.003) Loss 2.4208 (2.6181) Prec@1 39.375 (36.595) Prec@5 68.125 (67.220) Epoch: [12][1870/11272] Time 0.931 (0.844) Data 0.002 (0.003) Loss 2.4883 (2.6177) Prec@1 30.625 (36.601) Prec@5 67.500 (67.230) Epoch: [12][1880/11272] Time 0.749 (0.844) Data 0.002 (0.003) Loss 2.8538 (2.6179) Prec@1 31.250 (36.591) Prec@5 60.625 (67.228) Epoch: [12][1890/11272] Time 0.779 (0.844) Data 0.002 (0.003) Loss 2.7456 (2.6179) Prec@1 33.750 (36.583) Prec@5 63.125 (67.228) Epoch: [12][1900/11272] Time 0.855 (0.844) Data 0.002 (0.003) Loss 2.3501 (2.6178) Prec@1 40.000 (36.584) Prec@5 73.750 (67.233) Epoch: [12][1910/11272] Time 0.931 (0.844) Data 0.002 (0.003) Loss 2.8045 (2.6180) Prec@1 30.000 (36.577) Prec@5 63.750 (67.235) Epoch: [12][1920/11272] Time 0.792 (0.844) Data 0.002 (0.003) Loss 2.5580 (2.6181) Prec@1 40.625 (36.571) Prec@5 68.125 (67.234) Epoch: [12][1930/11272] Time 0.817 (0.844) Data 0.002 (0.003) Loss 2.6083 (2.6181) Prec@1 33.750 (36.566) Prec@5 66.250 (67.239) Epoch: [12][1940/11272] Time 0.953 (0.844) Data 0.002 (0.003) Loss 2.6539 (2.6184) Prec@1 35.625 (36.562) Prec@5 68.750 (67.239) Epoch: [12][1950/11272] Time 0.984 (0.844) Data 0.001 (0.003) Loss 2.5611 (2.6184) Prec@1 40.625 (36.566) Prec@5 69.375 (67.241) Epoch: [12][1960/11272] Time 0.778 (0.844) Data 0.002 (0.003) Loss 2.7269 (2.6184) Prec@1 31.875 (36.561) Prec@5 68.750 (67.245) Epoch: [12][1970/11272] Time 0.828 (0.844) Data 0.002 (0.003) Loss 2.7267 (2.6184) Prec@1 36.250 (36.560) Prec@5 65.625 (67.238) Epoch: [12][1980/11272] Time 0.955 (0.844) Data 0.002 (0.003) Loss 2.5549 (2.6178) Prec@1 36.250 (36.566) Prec@5 70.000 (67.250) Epoch: [12][1990/11272] Time 0.779 (0.844) Data 0.002 (0.003) Loss 2.5455 (2.6181) Prec@1 37.500 (36.565) Prec@5 67.500 (67.249) Epoch: [12][2000/11272] Time 0.766 (0.844) Data 0.001 (0.003) Loss 2.5998 (2.6180) Prec@1 33.750 (36.567) Prec@5 68.750 (67.256) Epoch: [12][2010/11272] Time 0.893 (0.844) Data 0.002 (0.003) Loss 2.6014 (2.6175) Prec@1 35.625 (36.577) Prec@5 71.875 (67.268) Epoch: [12][2020/11272] Time 0.930 (0.844) Data 0.002 (0.003) Loss 2.4163 (2.6173) Prec@1 38.125 (36.573) Prec@5 69.375 (67.274) Epoch: [12][2030/11272] Time 0.786 (0.844) Data 0.002 (0.003) Loss 2.6765 (2.6174) Prec@1 41.250 (36.578) Prec@5 63.750 (67.276) Epoch: [12][2040/11272] Time 0.801 (0.844) Data 0.002 (0.003) Loss 2.7284 (2.6174) Prec@1 28.125 (36.574) Prec@5 61.875 (67.272) Epoch: [12][2050/11272] Time 0.887 (0.844) Data 0.002 (0.003) Loss 2.6582 (2.6174) Prec@1 35.000 (36.577) Prec@5 62.500 (67.268) Epoch: [12][2060/11272] Time 0.905 (0.844) Data 0.002 (0.003) Loss 2.8010 (2.6173) Prec@1 30.625 (36.583) Prec@5 65.625 (67.278) Epoch: [12][2070/11272] Time 0.729 (0.844) Data 0.001 (0.003) Loss 2.6860 (2.6173) Prec@1 35.625 (36.584) Prec@5 65.000 (67.280) Epoch: [12][2080/11272] Time 0.748 (0.844) Data 0.002 (0.003) Loss 2.5425 (2.6172) Prec@1 40.625 (36.580) Prec@5 65.625 (67.286) Epoch: [12][2090/11272] Time 0.882 (0.844) Data 0.001 (0.003) Loss 2.9067 (2.6171) Prec@1 25.625 (36.579) Prec@5 62.500 (67.290) Epoch: [12][2100/11272] Time 0.924 (0.844) Data 0.002 (0.003) Loss 2.6530 (2.6171) Prec@1 41.875 (36.588) Prec@5 65.000 (67.292) Epoch: [12][2110/11272] Time 0.751 (0.844) Data 0.002 (0.003) Loss 2.7724 (2.6171) Prec@1 37.500 (36.585) Prec@5 62.500 (67.294) Epoch: [12][2120/11272] Time 0.896 (0.844) Data 0.002 (0.003) Loss 2.9361 (2.6171) Prec@1 31.875 (36.582) Prec@5 60.000 (67.296) Epoch: [12][2130/11272] Time 0.943 (0.844) Data 0.002 (0.003) Loss 2.8752 (2.6172) Prec@1 33.750 (36.579) Prec@5 64.375 (67.293) Epoch: [12][2140/11272] Time 0.763 (0.844) Data 0.001 (0.003) Loss 2.9052 (2.6171) Prec@1 31.250 (36.582) Prec@5 61.875 (67.295) Epoch: [12][2150/11272] Time 0.785 (0.844) Data 0.002 (0.003) Loss 2.4524 (2.6169) Prec@1 41.875 (36.585) Prec@5 70.625 (67.298) Epoch: [12][2160/11272] Time 0.888 (0.844) Data 0.002 (0.003) Loss 2.3750 (2.6168) Prec@1 42.500 (36.590) Prec@5 73.125 (67.297) Epoch: [12][2170/11272] Time 0.886 (0.844) Data 0.002 (0.003) Loss 2.5047 (2.6166) Prec@1 38.750 (36.595) Prec@5 69.375 (67.307) Epoch: [12][2180/11272] Time 0.770 (0.844) Data 0.002 (0.003) Loss 2.6741 (2.6165) Prec@1 35.000 (36.598) Prec@5 67.500 (67.309) Epoch: [12][2190/11272] Time 0.797 (0.844) Data 0.002 (0.003) Loss 2.5268 (2.6164) Prec@1 38.750 (36.601) Prec@5 69.375 (67.312) Epoch: [12][2200/11272] Time 0.893 (0.844) Data 0.002 (0.003) Loss 2.6853 (2.6165) Prec@1 33.125 (36.601) Prec@5 65.000 (67.317) Epoch: [12][2210/11272] Time 0.857 (0.844) Data 0.002 (0.003) Loss 2.7417 (2.6168) Prec@1 35.625 (36.593) Prec@5 64.375 (67.312) Epoch: [12][2220/11272] Time 0.769 (0.844) Data 0.002 (0.003) Loss 2.5669 (2.6171) Prec@1 35.625 (36.588) Prec@5 68.125 (67.306) Epoch: [12][2230/11272] Time 0.782 (0.844) Data 0.002 (0.003) Loss 2.6365 (2.6171) Prec@1 43.125 (36.591) Prec@5 66.250 (67.309) Epoch: [12][2240/11272] Time 0.934 (0.844) Data 0.002 (0.003) Loss 2.4291 (2.6173) Prec@1 39.375 (36.588) Prec@5 70.625 (67.303) Epoch: [12][2250/11272] Time 0.775 (0.844) Data 0.003 (0.003) Loss 2.6923 (2.6172) Prec@1 28.750 (36.589) Prec@5 66.250 (67.301) Epoch: [12][2260/11272] Time 0.747 (0.844) Data 0.002 (0.003) Loss 2.5917 (2.6173) Prec@1 37.500 (36.588) Prec@5 66.250 (67.297) Epoch: [12][2270/11272] Time 0.871 (0.844) Data 0.002 (0.003) Loss 2.9858 (2.6177) Prec@1 33.750 (36.584) Prec@5 57.500 (67.293) Epoch: [12][2280/11272] Time 0.903 (0.844) Data 0.002 (0.003) Loss 2.5769 (2.6175) Prec@1 41.250 (36.586) Prec@5 67.500 (67.294) Epoch: [12][2290/11272] Time 0.809 (0.844) Data 0.003 (0.003) Loss 2.5480 (2.6177) Prec@1 32.500 (36.587) Prec@5 70.000 (67.297) Epoch: [12][2300/11272] Time 0.806 (0.844) Data 0.002 (0.003) Loss 2.7342 (2.6177) Prec@1 30.625 (36.587) Prec@5 68.125 (67.301) Epoch: [12][2310/11272] Time 0.920 (0.844) Data 0.002 (0.003) Loss 2.8128 (2.6179) Prec@1 33.750 (36.582) Prec@5 63.125 (67.303) Epoch: [12][2320/11272] Time 0.905 (0.844) Data 0.002 (0.003) Loss 2.6535 (2.6178) Prec@1 35.625 (36.584) Prec@5 68.750 (67.307) Epoch: [12][2330/11272] Time 0.801 (0.844) Data 0.003 (0.003) Loss 2.3089 (2.6177) Prec@1 40.000 (36.586) Prec@5 73.750 (67.309) Epoch: [12][2340/11272] Time 0.743 (0.844) Data 0.002 (0.003) Loss 2.4855 (2.6177) Prec@1 32.500 (36.583) Prec@5 72.500 (67.309) Epoch: [12][2350/11272] Time 0.886 (0.844) Data 0.002 (0.003) Loss 2.6025 (2.6177) Prec@1 37.500 (36.578) Prec@5 68.125 (67.312) Epoch: [12][2360/11272] Time 0.863 (0.844) Data 0.002 (0.003) Loss 2.6044 (2.6178) Prec@1 36.250 (36.580) Prec@5 63.125 (67.309) Epoch: [12][2370/11272] Time 0.767 (0.844) Data 0.002 (0.003) Loss 2.6045 (2.6179) Prec@1 36.250 (36.578) Prec@5 66.875 (67.307) Epoch: [12][2380/11272] Time 0.907 (0.844) Data 0.002 (0.003) Loss 2.6686 (2.6180) Prec@1 38.125 (36.572) Prec@5 70.000 (67.308) Epoch: [12][2390/11272] Time 0.931 (0.844) Data 0.002 (0.003) Loss 2.6849 (2.6181) Prec@1 34.375 (36.568) Prec@5 64.375 (67.304) Epoch: [12][2400/11272] Time 0.771 (0.844) Data 0.002 (0.003) Loss 2.5122 (2.6177) Prec@1 32.500 (36.574) Prec@5 71.250 (67.310) Epoch: [12][2410/11272] Time 0.768 (0.844) Data 0.002 (0.003) Loss 2.4658 (2.6175) Prec@1 39.375 (36.579) Prec@5 68.125 (67.316) Epoch: [12][2420/11272] Time 0.896 (0.844) Data 0.002 (0.003) Loss 2.5005 (2.6171) Prec@1 37.500 (36.580) Prec@5 70.000 (67.322) Epoch: [12][2430/11272] Time 0.897 (0.844) Data 0.002 (0.003) Loss 2.6917 (2.6171) Prec@1 38.125 (36.581) Prec@5 63.750 (67.321) Epoch: [12][2440/11272] Time 0.763 (0.844) Data 0.002 (0.003) Loss 2.8307 (2.6172) Prec@1 36.875 (36.577) Prec@5 64.375 (67.320) Epoch: [12][2450/11272] Time 0.755 (0.844) Data 0.002 (0.003) Loss 2.3357 (2.6175) Prec@1 42.500 (36.576) Prec@5 73.750 (67.310) Epoch: [12][2460/11272] Time 0.891 (0.844) Data 0.002 (0.003) Loss 2.3473 (2.6175) Prec@1 36.875 (36.576) Prec@5 71.875 (67.312) Epoch: [12][2470/11272] Time 0.874 (0.844) Data 0.002 (0.003) Loss 2.5137 (2.6174) Prec@1 38.750 (36.574) Prec@5 70.000 (67.313) Epoch: [12][2480/11272] Time 0.816 (0.844) Data 0.002 (0.003) Loss 2.6906 (2.6172) Prec@1 31.875 (36.577) Prec@5 63.125 (67.315) Epoch: [12][2490/11272] Time 0.748 (0.844) Data 0.002 (0.003) Loss 2.3353 (2.6171) Prec@1 42.500 (36.586) Prec@5 71.250 (67.317) Epoch: [12][2500/11272] Time 0.879 (0.844) Data 0.002 (0.003) Loss 2.4217 (2.6171) Prec@1 39.375 (36.585) Prec@5 72.500 (67.316) Epoch: [12][2510/11272] Time 0.754 (0.844) Data 0.004 (0.003) Loss 2.7666 (2.6170) Prec@1 28.750 (36.589) Prec@5 61.875 (67.318) Epoch: [12][2520/11272] Time 0.808 (0.844) Data 0.002 (0.003) Loss 2.7267 (2.6171) Prec@1 35.000 (36.580) Prec@5 66.250 (67.312) Epoch: [12][2530/11272] Time 0.925 (0.844) Data 0.002 (0.003) Loss 2.4276 (2.6170) Prec@1 43.125 (36.588) Prec@5 67.500 (67.317) Epoch: [12][2540/11272] Time 0.932 (0.844) Data 0.002 (0.003) Loss 2.6366 (2.6169) Prec@1 35.625 (36.587) Prec@5 63.750 (67.323) Epoch: [12][2550/11272] Time 0.828 (0.844) Data 0.002 (0.003) Loss 2.5031 (2.6169) Prec@1 42.500 (36.589) Prec@5 66.250 (67.320) Epoch: [12][2560/11272] Time 0.768 (0.844) Data 0.002 (0.003) Loss 2.5774 (2.6169) Prec@1 40.625 (36.587) Prec@5 66.250 (67.316) Epoch: [12][2570/11272] Time 0.933 (0.844) Data 0.002 (0.003) Loss 2.3297 (2.6166) Prec@1 34.375 (36.584) Prec@5 69.375 (67.318) Epoch: [12][2580/11272] Time 0.875 (0.844) Data 0.002 (0.003) Loss 2.6722 (2.6165) Prec@1 32.500 (36.584) Prec@5 70.000 (67.321) Epoch: [12][2590/11272] Time 0.808 (0.844) Data 0.002 (0.003) Loss 2.7286 (2.6162) Prec@1 36.875 (36.591) Prec@5 62.500 (67.323) Epoch: [12][2600/11272] Time 0.808 (0.844) Data 0.002 (0.003) Loss 2.5068 (2.6162) Prec@1 41.250 (36.597) Prec@5 70.000 (67.324) Epoch: [12][2610/11272] Time 0.873 (0.844) Data 0.002 (0.003) Loss 2.4287 (2.6163) Prec@1 40.625 (36.596) Prec@5 70.625 (67.320) Epoch: [12][2620/11272] Time 0.870 (0.844) Data 0.001 (0.003) Loss 2.6684 (2.6165) Prec@1 36.875 (36.592) Prec@5 67.500 (67.317) Epoch: [12][2630/11272] Time 0.761 (0.844) Data 0.002 (0.003) Loss 2.8398 (2.6164) Prec@1 35.000 (36.596) Prec@5 60.000 (67.314) Epoch: [12][2640/11272] Time 0.917 (0.844) Data 0.002 (0.003) Loss 2.4136 (2.6165) Prec@1 40.625 (36.596) Prec@5 73.750 (67.309) Epoch: [12][2650/11272] Time 0.928 (0.844) Data 0.003 (0.003) Loss 2.8524 (2.6169) Prec@1 33.750 (36.586) Prec@5 61.875 (67.301) Epoch: [12][2660/11272] Time 0.748 (0.844) Data 0.002 (0.003) Loss 2.5153 (2.6168) Prec@1 36.875 (36.593) Prec@5 70.625 (67.298) Epoch: [12][2670/11272] Time 0.784 (0.844) Data 0.002 (0.003) Loss 2.6531 (2.6164) Prec@1 33.125 (36.598) Prec@5 66.250 (67.300) Epoch: [12][2680/11272] Time 0.852 (0.844) Data 0.001 (0.003) Loss 2.5418 (2.6160) Prec@1 39.375 (36.609) Prec@5 70.000 (67.306) Epoch: [12][2690/11272] Time 0.905 (0.844) Data 0.002 (0.003) Loss 2.8567 (2.6161) Prec@1 29.375 (36.610) Prec@5 57.500 (67.297) Epoch: [12][2700/11272] Time 0.757 (0.844) Data 0.002 (0.003) Loss 2.7364 (2.6159) Prec@1 31.875 (36.614) Prec@5 65.000 (67.306) Epoch: [12][2710/11272] Time 0.788 (0.844) Data 0.002 (0.003) Loss 2.4244 (2.6157) Prec@1 34.375 (36.618) Prec@5 71.250 (67.309) Epoch: [12][2720/11272] Time 0.910 (0.844) Data 0.001 (0.003) Loss 2.7706 (2.6157) Prec@1 31.250 (36.617) Prec@5 63.125 (67.308) Epoch: [12][2730/11272] Time 0.914 (0.844) Data 0.002 (0.003) Loss 2.4458 (2.6161) Prec@1 37.500 (36.609) Prec@5 69.375 (67.300) Epoch: [12][2740/11272] Time 0.750 (0.844) Data 0.002 (0.003) Loss 2.5665 (2.6164) Prec@1 34.375 (36.603) Prec@5 70.000 (67.293) Epoch: [12][2750/11272] Time 0.820 (0.844) Data 0.002 (0.003) Loss 2.5586 (2.6164) Prec@1 40.625 (36.602) Prec@5 67.500 (67.292) Epoch: [12][2760/11272] Time 0.925 (0.844) Data 0.002 (0.003) Loss 2.7705 (2.6163) Prec@1 35.000 (36.604) Prec@5 61.250 (67.296) Epoch: [12][2770/11272] Time 0.877 (0.844) Data 0.001 (0.003) Loss 2.5927 (2.6164) Prec@1 35.625 (36.602) Prec@5 68.125 (67.297) Epoch: [12][2780/11272] Time 0.764 (0.844) Data 0.001 (0.003) Loss 2.4408 (2.6161) Prec@1 39.375 (36.612) Prec@5 68.750 (67.303) Epoch: [12][2790/11272] Time 0.870 (0.844) Data 0.002 (0.003) Loss 2.6675 (2.6162) Prec@1 32.500 (36.613) Prec@5 67.500 (67.301) Epoch: [12][2800/11272] Time 0.905 (0.844) Data 0.002 (0.003) Loss 2.6508 (2.6166) Prec@1 30.000 (36.604) Prec@5 69.375 (67.294) Epoch: [12][2810/11272] Time 0.810 (0.844) Data 0.002 (0.003) Loss 2.4431 (2.6166) Prec@1 39.375 (36.602) Prec@5 70.000 (67.292) Epoch: [12][2820/11272] Time 0.765 (0.844) Data 0.002 (0.003) Loss 2.6934 (2.6167) Prec@1 35.000 (36.599) Prec@5 66.250 (67.288) Epoch: [12][2830/11272] Time 0.981 (0.844) Data 0.002 (0.003) Loss 2.5138 (2.6165) Prec@1 38.125 (36.607) Prec@5 67.500 (67.292) Epoch: [12][2840/11272] Time 0.910 (0.844) Data 0.002 (0.003) Loss 2.5999 (2.6166) Prec@1 39.375 (36.610) Prec@5 68.750 (67.293) Epoch: [12][2850/11272] Time 0.751 (0.844) Data 0.002 (0.003) Loss 2.3442 (2.6167) Prec@1 42.500 (36.613) Prec@5 75.000 (67.296) Epoch: [12][2860/11272] Time 0.771 (0.844) Data 0.002 (0.003) Loss 2.4235 (2.6167) Prec@1 42.500 (36.615) Prec@5 70.000 (67.295) Epoch: [12][2870/11272] Time 0.861 (0.844) Data 0.002 (0.003) Loss 2.7107 (2.6167) Prec@1 35.000 (36.617) Prec@5 69.375 (67.298) Epoch: [12][2880/11272] Time 0.967 (0.844) Data 0.002 (0.003) Loss 2.4877 (2.6170) Prec@1 39.375 (36.614) Prec@5 69.375 (67.293) Epoch: [12][2890/11272] Time 0.821 (0.844) Data 0.002 (0.003) Loss 2.5521 (2.6169) Prec@1 38.125 (36.609) Prec@5 61.875 (67.288) Epoch: [12][2900/11272] Time 0.792 (0.844) Data 0.002 (0.003) Loss 2.6503 (2.6171) Prec@1 39.375 (36.607) Prec@5 67.500 (67.283) Epoch: [12][2910/11272] Time 0.899 (0.844) Data 0.001 (0.003) Loss 2.8180 (2.6175) Prec@1 31.875 (36.601) Prec@5 60.000 (67.275) Epoch: [12][2920/11272] Time 0.773 (0.844) Data 0.002 (0.003) Loss 2.4934 (2.6175) Prec@1 40.625 (36.600) Prec@5 66.250 (67.270) Epoch: [12][2930/11272] Time 0.797 (0.844) Data 0.002 (0.003) Loss 2.4079 (2.6178) Prec@1 41.250 (36.597) Prec@5 68.750 (67.262) Epoch: [12][2940/11272] Time 0.868 (0.844) Data 0.002 (0.003) Loss 2.7452 (2.6179) Prec@1 35.000 (36.597) Prec@5 66.875 (67.259) Epoch: [12][2950/11272] Time 0.909 (0.844) Data 0.002 (0.003) Loss 2.7626 (2.6180) Prec@1 35.625 (36.596) Prec@5 59.375 (67.254) Epoch: [12][2960/11272] Time 0.769 (0.844) Data 0.002 (0.003) Loss 2.3207 (2.6182) Prec@1 40.000 (36.600) Prec@5 73.750 (67.249) Epoch: [12][2970/11272] Time 0.761 (0.844) Data 0.002 (0.003) Loss 2.4827 (2.6185) Prec@1 39.375 (36.601) Prec@5 64.375 (67.241) Epoch: [12][2980/11272] Time 0.859 (0.843) Data 0.002 (0.003) Loss 2.9445 (2.6187) Prec@1 29.375 (36.595) Prec@5 61.250 (67.238) Epoch: [12][2990/11272] Time 0.906 (0.843) Data 0.002 (0.003) Loss 2.7061 (2.6188) Prec@1 35.000 (36.589) Prec@5 65.625 (67.232) Epoch: [12][3000/11272] Time 0.761 (0.843) Data 0.002 (0.003) Loss 2.5026 (2.6190) Prec@1 35.000 (36.584) Prec@5 69.375 (67.228) Epoch: [12][3010/11272] Time 0.821 (0.843) Data 0.002 (0.003) Loss 2.5785 (2.6193) Prec@1 38.125 (36.577) Prec@5 66.875 (67.224) Epoch: [12][3020/11272] Time 0.947 (0.843) Data 0.002 (0.003) Loss 2.8642 (2.6194) Prec@1 33.750 (36.573) Prec@5 62.500 (67.219) Epoch: [12][3030/11272] Time 0.922 (0.844) Data 0.002 (0.003) Loss 2.3636 (2.6195) Prec@1 41.250 (36.571) Prec@5 73.125 (67.216) Epoch: [12][3040/11272] Time 0.774 (0.844) Data 0.002 (0.003) Loss 2.6079 (2.6195) Prec@1 35.625 (36.570) Prec@5 70.000 (67.220) Epoch: [12][3050/11272] Time 0.901 (0.844) Data 0.001 (0.003) Loss 2.7384 (2.6196) Prec@1 39.375 (36.572) Prec@5 67.500 (67.218) Epoch: [12][3060/11272] Time 0.909 (0.844) Data 0.004 (0.003) Loss 2.5933 (2.6196) Prec@1 36.875 (36.577) Prec@5 67.500 (67.215) Epoch: [12][3070/11272] Time 0.800 (0.844) Data 0.002 (0.003) Loss 2.6051 (2.6198) Prec@1 38.750 (36.571) Prec@5 65.000 (67.213) Epoch: [12][3080/11272] Time 0.754 (0.844) Data 0.002 (0.003) Loss 2.8900 (2.6198) Prec@1 34.375 (36.568) Prec@5 61.875 (67.215) Epoch: [12][3090/11272] Time 0.923 (0.843) Data 0.002 (0.003) Loss 2.5601 (2.6200) Prec@1 40.000 (36.572) Prec@5 68.750 (67.211) Epoch: [12][3100/11272] Time 0.926 (0.843) Data 0.002 (0.003) Loss 2.7518 (2.6199) Prec@1 34.375 (36.570) Prec@5 66.250 (67.216) Epoch: [12][3110/11272] Time 0.768 (0.843) Data 0.002 (0.003) Loss 2.6166 (2.6201) Prec@1 33.750 (36.565) Prec@5 66.875 (67.209) Epoch: [12][3120/11272] Time 0.756 (0.844) Data 0.002 (0.003) Loss 2.4940 (2.6198) Prec@1 38.750 (36.571) Prec@5 69.375 (67.214) Epoch: [12][3130/11272] Time 0.956 (0.844) Data 0.002 (0.003) Loss 2.8804 (2.6199) Prec@1 36.250 (36.576) Prec@5 64.375 (67.209) Epoch: [12][3140/11272] Time 0.918 (0.844) Data 0.002 (0.003) Loss 2.9632 (2.6199) Prec@1 26.875 (36.575) Prec@5 58.750 (67.206) Epoch: [12][3150/11272] Time 0.786 (0.844) Data 0.002 (0.003) Loss 2.5834 (2.6196) Prec@1 38.125 (36.580) Prec@5 65.000 (67.212) Epoch: [12][3160/11272] Time 0.811 (0.844) Data 0.001 (0.003) Loss 2.4047 (2.6195) Prec@1 39.375 (36.579) Prec@5 73.750 (67.211) Epoch: [12][3170/11272] Time 0.928 (0.844) Data 0.002 (0.003) Loss 2.6238 (2.6194) Prec@1 37.500 (36.580) Prec@5 62.500 (67.209) Epoch: [12][3180/11272] Time 0.745 (0.843) Data 0.003 (0.003) Loss 2.7467 (2.6195) Prec@1 31.875 (36.584) Prec@5 63.750 (67.206) Epoch: [12][3190/11272] Time 0.820 (0.843) Data 0.002 (0.003) Loss 2.2498 (2.6194) Prec@1 46.875 (36.586) Prec@5 72.500 (67.207) Epoch: [12][3200/11272] Time 0.829 (0.843) Data 0.001 (0.003) Loss 2.5135 (2.6195) Prec@1 41.250 (36.582) Prec@5 70.000 (67.207) Epoch: [12][3210/11272] Time 0.905 (0.843) Data 0.002 (0.003) Loss 2.5375 (2.6196) Prec@1 40.000 (36.584) Prec@5 66.250 (67.203) Epoch: [12][3220/11272] Time 0.780 (0.843) Data 0.002 (0.003) Loss 2.4281 (2.6194) Prec@1 35.625 (36.589) Prec@5 71.875 (67.208) Epoch: [12][3230/11272] Time 0.760 (0.843) Data 0.002 (0.002) Loss 2.6461 (2.6193) Prec@1 32.500 (36.586) Prec@5 70.625 (67.210) Epoch: [12][3240/11272] Time 0.870 (0.843) Data 0.002 (0.002) Loss 2.4731 (2.6195) Prec@1 38.125 (36.588) Prec@5 68.125 (67.206) Epoch: [12][3250/11272] Time 0.915 (0.843) Data 0.002 (0.002) Loss 2.7717 (2.6195) Prec@1 32.500 (36.584) Prec@5 67.500 (67.211) Epoch: [12][3260/11272] Time 0.757 (0.843) Data 0.002 (0.002) Loss 2.7890 (2.6193) Prec@1 32.500 (36.586) Prec@5 63.125 (67.214) Epoch: [12][3270/11272] Time 0.778 (0.843) Data 0.002 (0.002) Loss 2.8777 (2.6193) Prec@1 31.250 (36.580) Prec@5 59.375 (67.214) Epoch: [12][3280/11272] Time 0.933 (0.843) Data 0.002 (0.002) Loss 2.5265 (2.6191) Prec@1 35.625 (36.577) Prec@5 73.750 (67.214) Epoch: [12][3290/11272] Time 0.852 (0.843) Data 0.002 (0.002) Loss 2.7147 (2.6193) Prec@1 35.625 (36.571) Prec@5 64.375 (67.208) Epoch: [12][3300/11272] Time 0.741 (0.843) Data 0.002 (0.002) Loss 2.7596 (2.6192) Prec@1 35.625 (36.574) Prec@5 63.125 (67.212) Epoch: [12][3310/11272] Time 0.881 (0.843) Data 0.002 (0.002) Loss 2.8549 (2.6195) Prec@1 33.125 (36.565) Prec@5 64.375 (67.205) Epoch: [12][3320/11272] Time 0.908 (0.843) Data 0.002 (0.002) Loss 2.6418 (2.6196) Prec@1 39.375 (36.566) Prec@5 64.375 (67.204) Epoch: [12][3330/11272] Time 0.743 (0.843) Data 0.002 (0.002) Loss 2.4752 (2.6194) Prec@1 42.500 (36.567) Prec@5 66.875 (67.203) Epoch: [12][3340/11272] Time 0.756 (0.843) Data 0.001 (0.002) Loss 2.7934 (2.6193) Prec@1 33.750 (36.567) Prec@5 68.750 (67.206) Epoch: [12][3350/11272] Time 0.877 (0.843) Data 0.002 (0.002) Loss 2.4618 (2.6194) Prec@1 33.750 (36.563) Prec@5 75.000 (67.208) Epoch: [12][3360/11272] Time 0.918 (0.843) Data 0.002 (0.002) Loss 2.6482 (2.6194) Prec@1 33.750 (36.560) Prec@5 69.375 (67.206) Epoch: [12][3370/11272] Time 0.755 (0.843) Data 0.002 (0.002) Loss 2.6297 (2.6194) Prec@1 37.500 (36.564) Prec@5 66.250 (67.206) Epoch: [12][3380/11272] Time 0.768 (0.843) Data 0.002 (0.002) Loss 2.8185 (2.6194) Prec@1 31.250 (36.560) Prec@5 62.500 (67.205) Epoch: [12][3390/11272] Time 0.897 (0.843) Data 0.002 (0.002) Loss 2.4592 (2.6195) Prec@1 38.125 (36.555) Prec@5 68.750 (67.200) Epoch: [12][3400/11272] Time 0.834 (0.843) Data 0.002 (0.002) Loss 2.6191 (2.6194) Prec@1 33.125 (36.553) Prec@5 72.500 (67.207) Epoch: [12][3410/11272] Time 0.765 (0.843) Data 0.002 (0.002) Loss 2.6188 (2.6193) Prec@1 36.250 (36.551) Prec@5 66.875 (67.210) Epoch: [12][3420/11272] Time 0.764 (0.843) Data 0.002 (0.002) Loss 2.5898 (2.6194) Prec@1 35.000 (36.546) Prec@5 70.000 (67.211) Epoch: [12][3430/11272] Time 0.861 (0.843) Data 0.002 (0.002) Loss 2.4286 (2.6192) Prec@1 40.000 (36.550) Prec@5 68.750 (67.215) Epoch: [12][3440/11272] Time 0.785 (0.842) Data 0.005 (0.002) Loss 2.5681 (2.6192) Prec@1 39.375 (36.553) Prec@5 71.250 (67.215) Epoch: [12][3450/11272] Time 0.773 (0.842) Data 0.002 (0.002) Loss 2.5705 (2.6192) Prec@1 37.500 (36.552) Prec@5 68.750 (67.215) Epoch: [12][3460/11272] Time 0.859 (0.842) Data 0.002 (0.002) Loss 2.6566 (2.6193) Prec@1 34.375 (36.547) Prec@5 66.875 (67.214) Epoch: [12][3470/11272] Time 0.890 (0.842) Data 0.002 (0.002) Loss 2.6922 (2.6192) Prec@1 40.000 (36.551) Prec@5 65.000 (67.218) Epoch: [12][3480/11272] Time 0.744 (0.842) Data 0.002 (0.002) Loss 2.7311 (2.6192) Prec@1 31.250 (36.551) Prec@5 67.500 (67.220) Epoch: [12][3490/11272] Time 0.751 (0.842) Data 0.002 (0.002) Loss 2.5127 (2.6190) Prec@1 36.250 (36.555) Prec@5 71.250 (67.224) Epoch: [12][3500/11272] Time 0.924 (0.842) Data 0.002 (0.002) Loss 2.5176 (2.6189) Prec@1 35.625 (36.554) Prec@5 70.625 (67.227) Epoch: [12][3510/11272] Time 0.852 (0.842) Data 0.001 (0.002) Loss 2.6000 (2.6188) Prec@1 34.375 (36.556) Prec@5 67.500 (67.223) Epoch: [12][3520/11272] Time 0.794 (0.842) Data 0.002 (0.002) Loss 2.6454 (2.6188) Prec@1 36.875 (36.558) Prec@5 65.000 (67.223) Epoch: [12][3530/11272] Time 0.822 (0.842) Data 0.003 (0.002) Loss 2.6687 (2.6190) Prec@1 31.250 (36.553) Prec@5 67.500 (67.221) Epoch: [12][3540/11272] Time 0.932 (0.842) Data 0.003 (0.002) Loss 2.5925 (2.6188) Prec@1 38.750 (36.555) Prec@5 66.250 (67.221) Epoch: [12][3550/11272] Time 0.910 (0.842) Data 0.002 (0.002) Loss 2.9324 (2.6189) Prec@1 30.000 (36.557) Prec@5 58.125 (67.218) Epoch: [12][3560/11272] Time 0.753 (0.842) Data 0.002 (0.002) Loss 2.6990 (2.6189) Prec@1 36.875 (36.561) Prec@5 64.375 (67.217) Epoch: [12][3570/11272] Time 0.895 (0.842) Data 0.002 (0.002) Loss 2.5021 (2.6187) Prec@1 35.000 (36.562) Prec@5 68.750 (67.220) Epoch: [12][3580/11272] Time 0.878 (0.842) Data 0.002 (0.002) Loss 2.6303 (2.6187) Prec@1 35.000 (36.565) Prec@5 68.125 (67.220) Epoch: [12][3590/11272] Time 0.785 (0.842) Data 0.002 (0.002) Loss 2.6468 (2.6186) Prec@1 37.500 (36.565) Prec@5 66.875 (67.220) Epoch: [12][3600/11272] Time 0.748 (0.842) Data 0.002 (0.002) Loss 2.4178 (2.6186) Prec@1 40.000 (36.561) Prec@5 68.750 (67.222) Epoch: [12][3610/11272] Time 0.897 (0.842) Data 0.001 (0.002) Loss 2.4140 (2.6183) Prec@1 45.625 (36.568) Prec@5 68.750 (67.226) Epoch: [12][3620/11272] Time 0.883 (0.842) Data 0.002 (0.002) Loss 2.5744 (2.6184) Prec@1 36.875 (36.561) Prec@5 66.250 (67.225) Epoch: [12][3630/11272] Time 0.765 (0.842) Data 0.002 (0.002) Loss 2.5701 (2.6181) Prec@1 38.125 (36.568) Prec@5 68.125 (67.228) Epoch: [12][3640/11272] Time 0.834 (0.842) Data 0.002 (0.002) Loss 2.7989 (2.6182) Prec@1 30.000 (36.564) Prec@5 64.375 (67.223) Epoch: [12][3650/11272] Time 0.898 (0.842) Data 0.002 (0.002) Loss 2.8320 (2.6183) Prec@1 35.000 (36.562) Prec@5 64.375 (67.224) Epoch: [12][3660/11272] Time 0.851 (0.842) Data 0.002 (0.002) Loss 2.5021 (2.6183) Prec@1 39.375 (36.565) Prec@5 70.000 (67.223) Epoch: [12][3670/11272] Time 0.760 (0.842) Data 0.002 (0.002) Loss 2.5914 (2.6184) Prec@1 40.625 (36.566) Prec@5 65.000 (67.223) Epoch: [12][3680/11272] Time 0.763 (0.842) Data 0.002 (0.002) Loss 2.8528 (2.6187) Prec@1 35.000 (36.559) Prec@5 63.125 (67.216) Epoch: [12][3690/11272] Time 0.875 (0.842) Data 0.002 (0.002) Loss 2.2805 (2.6185) Prec@1 44.375 (36.564) Prec@5 73.750 (67.222) Epoch: [12][3700/11272] Time 0.848 (0.842) Data 0.002 (0.002) Loss 2.7513 (2.6186) Prec@1 37.500 (36.564) Prec@5 63.125 (67.220) Epoch: [12][3710/11272] Time 0.810 (0.842) Data 0.003 (0.002) Loss 2.9067 (2.6190) Prec@1 34.375 (36.559) Prec@5 58.750 (67.213) Epoch: [12][3720/11272] Time 0.968 (0.842) Data 0.002 (0.002) Loss 2.5055 (2.6190) Prec@1 36.875 (36.556) Prec@5 65.625 (67.212) Epoch: [12][3730/11272] Time 0.850 (0.842) Data 0.002 (0.002) Loss 2.5829 (2.6191) Prec@1 36.875 (36.554) Prec@5 66.875 (67.209) Epoch: [12][3740/11272] Time 0.783 (0.842) Data 0.002 (0.002) Loss 2.9203 (2.6192) Prec@1 28.750 (36.552) Prec@5 60.625 (67.206) Epoch: [12][3750/11272] Time 0.760 (0.842) Data 0.002 (0.002) Loss 2.5795 (2.6193) Prec@1 38.750 (36.551) Prec@5 66.250 (67.204) Epoch: [12][3760/11272] Time 0.926 (0.842) Data 0.002 (0.002) Loss 2.7219 (2.6194) Prec@1 36.875 (36.547) Prec@5 63.125 (67.201) Epoch: [12][3770/11272] Time 0.894 (0.842) Data 0.002 (0.002) Loss 2.7291 (2.6195) Prec@1 37.500 (36.546) Prec@5 66.250 (67.198) Epoch: [12][3780/11272] Time 0.796 (0.842) Data 0.002 (0.002) Loss 2.5526 (2.6195) Prec@1 41.875 (36.546) Prec@5 66.250 (67.195) Epoch: [12][3790/11272] Time 0.744 (0.842) Data 0.001 (0.002) Loss 2.6230 (2.6196) Prec@1 32.500 (36.544) Prec@5 65.000 (67.195) Epoch: [12][3800/11272] Time 0.898 (0.842) Data 0.002 (0.002) Loss 2.7391 (2.6197) Prec@1 38.125 (36.541) Prec@5 63.125 (67.191) Epoch: [12][3810/11272] Time 0.879 (0.842) Data 0.002 (0.002) Loss 2.6535 (2.6198) Prec@1 38.125 (36.541) Prec@5 66.875 (67.188) Epoch: [12][3820/11272] Time 0.778 (0.842) Data 0.001 (0.002) Loss 2.4672 (2.6199) Prec@1 43.125 (36.543) Prec@5 70.625 (67.186) Epoch: [12][3830/11272] Time 0.723 (0.842) Data 0.001 (0.002) Loss 2.6944 (2.6201) Prec@1 33.125 (36.543) Prec@5 66.250 (67.183) Epoch: [12][3840/11272] Time 0.895 (0.842) Data 0.002 (0.002) Loss 2.6091 (2.6203) Prec@1 33.125 (36.538) Prec@5 68.750 (67.181) Epoch: [12][3850/11272] Time 0.754 (0.842) Data 0.001 (0.002) Loss 2.7748 (2.6204) Prec@1 32.500 (36.538) Prec@5 65.625 (67.179) Epoch: [12][3860/11272] Time 0.784 (0.842) Data 0.002 (0.002) Loss 2.6611 (2.6205) Prec@1 36.250 (36.535) Prec@5 67.500 (67.184) Epoch: [12][3870/11272] Time 0.882 (0.842) Data 0.002 (0.002) Loss 2.7510 (2.6205) Prec@1 31.875 (36.535) Prec@5 65.625 (67.184) Epoch: [12][3880/11272] Time 0.866 (0.842) Data 0.003 (0.002) Loss 2.6100 (2.6205) Prec@1 32.500 (36.533) Prec@5 65.000 (67.186) Epoch: [12][3890/11272] Time 0.769 (0.842) Data 0.002 (0.002) Loss 2.4754 (2.6208) Prec@1 38.125 (36.531) Prec@5 69.375 (67.185) Epoch: [12][3900/11272] Time 0.747 (0.842) Data 0.002 (0.002) Loss 2.5840 (2.6206) Prec@1 41.250 (36.533) Prec@5 68.125 (67.185) Epoch: [12][3910/11272] Time 0.876 (0.842) Data 0.004 (0.002) Loss 2.4120 (2.6205) Prec@1 38.125 (36.537) Prec@5 70.625 (67.189) Epoch: [12][3920/11272] Time 0.882 (0.842) Data 0.001 (0.002) Loss 2.4657 (2.6205) Prec@1 38.125 (36.538) Prec@5 65.000 (67.189) Epoch: [12][3930/11272] Time 0.764 (0.842) Data 0.002 (0.002) Loss 2.3736 (2.6204) Prec@1 34.375 (36.536) Prec@5 71.875 (67.188) Epoch: [12][3940/11272] Time 0.738 (0.842) Data 0.002 (0.002) Loss 2.6235 (2.6206) Prec@1 37.500 (36.536) Prec@5 64.375 (67.184) Epoch: [12][3950/11272] Time 0.941 (0.842) Data 0.002 (0.002) Loss 2.4991 (2.6203) Prec@1 40.625 (36.540) Prec@5 73.125 (67.189) Epoch: [12][3960/11272] Time 0.816 (0.842) Data 0.002 (0.002) Loss 2.6557 (2.6203) Prec@1 34.375 (36.543) Prec@5 65.000 (67.192) Epoch: [12][3970/11272] Time 0.819 (0.842) Data 0.003 (0.002) Loss 2.6941 (2.6203) Prec@1 36.875 (36.541) Prec@5 65.000 (67.192) Epoch: [12][3980/11272] Time 0.887 (0.842) Data 0.001 (0.002) Loss 2.9535 (2.6206) Prec@1 28.750 (36.539) Prec@5 58.125 (67.185) Epoch: [12][3990/11272] Time 0.902 (0.842) Data 0.001 (0.002) Loss 2.7835 (2.6206) Prec@1 35.625 (36.539) Prec@5 63.750 (67.182) Epoch: [12][4000/11272] Time 0.755 (0.842) Data 0.002 (0.002) Loss 2.7123 (2.6205) Prec@1 38.125 (36.540) Prec@5 69.375 (67.185) Epoch: [12][4010/11272] Time 0.733 (0.842) Data 0.002 (0.002) Loss 2.4558 (2.6205) Prec@1 40.625 (36.542) Prec@5 68.750 (67.185) Epoch: [12][4020/11272] Time 0.889 (0.842) Data 0.002 (0.002) Loss 2.2986 (2.6204) Prec@1 41.250 (36.543) Prec@5 71.875 (67.188) Epoch: [12][4030/11272] Time 0.972 (0.842) Data 0.001 (0.002) Loss 2.4778 (2.6204) Prec@1 35.000 (36.542) Prec@5 71.875 (67.187) Epoch: [12][4040/11272] Time 0.835 (0.842) Data 0.002 (0.002) Loss 2.6177 (2.6204) Prec@1 33.750 (36.541) Prec@5 62.500 (67.189) Epoch: [12][4050/11272] Time 0.766 (0.842) Data 0.002 (0.002) Loss 2.4151 (2.6205) Prec@1 38.750 (36.541) Prec@5 71.875 (67.183) Epoch: [12][4060/11272] Time 0.892 (0.842) Data 0.002 (0.002) Loss 2.4860 (2.6205) Prec@1 37.500 (36.537) Prec@5 72.500 (67.184) Epoch: [12][4070/11272] Time 0.892 (0.842) Data 0.002 (0.002) Loss 2.8331 (2.6206) Prec@1 28.750 (36.532) Prec@5 63.750 (67.182) Epoch: [12][4080/11272] Time 0.738 (0.841) Data 0.002 (0.002) Loss 2.7847 (2.6206) Prec@1 38.750 (36.533) Prec@5 63.125 (67.180) Epoch: [12][4090/11272] Time 0.826 (0.841) Data 0.002 (0.002) Loss 2.4218 (2.6205) Prec@1 41.875 (36.532) Prec@5 69.375 (67.180) Epoch: [12][4100/11272] Time 0.935 (0.841) Data 0.001 (0.002) Loss 2.4916 (2.6204) Prec@1 34.375 (36.534) Prec@5 65.625 (67.183) Epoch: [12][4110/11272] Time 0.764 (0.841) Data 0.004 (0.002) Loss 2.7765 (2.6205) Prec@1 35.625 (36.534) Prec@5 61.875 (67.182) Epoch: [12][4120/11272] Time 0.799 (0.841) Data 0.002 (0.002) Loss 2.7373 (2.6204) Prec@1 35.000 (36.533) Prec@5 62.500 (67.181) Epoch: [12][4130/11272] Time 0.891 (0.841) Data 0.002 (0.002) Loss 2.6896 (2.6205) Prec@1 36.250 (36.529) Prec@5 64.375 (67.181) Epoch: [12][4140/11272] Time 0.851 (0.841) Data 0.002 (0.002) Loss 2.5625 (2.6203) Prec@1 38.125 (36.533) Prec@5 68.125 (67.185) Epoch: [12][4150/11272] Time 0.762 (0.841) Data 0.002 (0.002) Loss 2.7262 (2.6204) Prec@1 37.500 (36.531) Prec@5 65.625 (67.184) Epoch: [12][4160/11272] Time 0.747 (0.841) Data 0.002 (0.002) Loss 2.5868 (2.6204) Prec@1 35.000 (36.534) Prec@5 67.500 (67.184) Epoch: [12][4170/11272] Time 0.891 (0.841) Data 0.002 (0.002) Loss 2.5106 (2.6204) Prec@1 36.875 (36.534) Prec@5 69.375 (67.183) Epoch: [12][4180/11272] Time 0.851 (0.841) Data 0.001 (0.002) Loss 2.5629 (2.6203) Prec@1 39.375 (36.537) Prec@5 68.125 (67.182) Epoch: [12][4190/11272] Time 0.769 (0.841) Data 0.002 (0.002) Loss 2.4757 (2.6203) Prec@1 38.125 (36.537) Prec@5 72.500 (67.183) Epoch: [12][4200/11272] Time 0.828 (0.841) Data 0.002 (0.002) Loss 2.6016 (2.6204) Prec@1 41.250 (36.537) Prec@5 67.500 (67.181) Epoch: [12][4210/11272] Time 0.883 (0.841) Data 0.002 (0.002) Loss 2.4625 (2.6205) Prec@1 41.250 (36.536) Prec@5 70.000 (67.181) Epoch: [12][4220/11272] Time 0.920 (0.841) Data 0.002 (0.002) Loss 2.7989 (2.6206) Prec@1 36.875 (36.535) Prec@5 66.250 (67.178) Epoch: [12][4230/11272] Time 0.756 (0.841) Data 0.002 (0.002) Loss 2.5605 (2.6205) Prec@1 35.000 (36.535) Prec@5 71.875 (67.181) Epoch: [12][4240/11272] Time 0.929 (0.841) Data 0.002 (0.002) Loss 2.6162 (2.6205) Prec@1 40.625 (36.530) Prec@5 70.625 (67.181) Epoch: [12][4250/11272] Time 0.889 (0.841) Data 0.002 (0.002) Loss 2.6066 (2.6207) Prec@1 35.000 (36.529) Prec@5 65.625 (67.179) Epoch: [12][4260/11272] Time 0.790 (0.841) Data 0.002 (0.002) Loss 2.4738 (2.6207) Prec@1 43.125 (36.531) Prec@5 71.250 (67.177) Epoch: [12][4270/11272] Time 0.750 (0.841) Data 0.002 (0.002) Loss 2.5172 (2.6208) Prec@1 43.750 (36.532) Prec@5 66.875 (67.176) Epoch: [12][4280/11272] Time 0.931 (0.841) Data 0.002 (0.002) Loss 2.4829 (2.6208) Prec@1 40.000 (36.530) Prec@5 70.625 (67.176) Epoch: [12][4290/11272] Time 0.915 (0.841) Data 0.002 (0.002) Loss 2.7094 (2.6209) Prec@1 36.875 (36.527) Prec@5 65.625 (67.175) Epoch: [12][4300/11272] Time 0.764 (0.841) Data 0.002 (0.002) Loss 2.6929 (2.6209) Prec@1 40.000 (36.525) Prec@5 64.375 (67.174) Epoch: [12][4310/11272] Time 0.750 (0.841) Data 0.002 (0.002) Loss 2.3990 (2.6208) Prec@1 39.375 (36.525) Prec@5 70.625 (67.180) Epoch: [12][4320/11272] Time 0.964 (0.841) Data 0.002 (0.002) Loss 2.6152 (2.6207) Prec@1 37.500 (36.525) Prec@5 68.125 (67.183) Epoch: [12][4330/11272] Time 0.893 (0.841) Data 0.002 (0.002) Loss 2.5658 (2.6208) Prec@1 40.625 (36.522) Prec@5 69.375 (67.182) Epoch: [12][4340/11272] Time 0.770 (0.841) Data 0.002 (0.002) Loss 2.5521 (2.6206) Prec@1 36.875 (36.525) Prec@5 69.375 (67.184) Epoch: [12][4350/11272] Time 0.769 (0.841) Data 0.002 (0.002) Loss 2.5690 (2.6205) Prec@1 36.875 (36.523) Prec@5 68.750 (67.186) Epoch: [12][4360/11272] Time 0.901 (0.841) Data 0.002 (0.002) Loss 2.7524 (2.6205) Prec@1 33.750 (36.521) Prec@5 66.875 (67.189) Epoch: [12][4370/11272] Time 0.757 (0.841) Data 0.004 (0.002) Loss 2.6605 (2.6204) Prec@1 39.375 (36.522) Prec@5 63.750 (67.188) Epoch: [12][4380/11272] Time 0.765 (0.841) Data 0.002 (0.002) Loss 2.5925 (2.6206) Prec@1 39.375 (36.523) Prec@5 66.250 (67.183) Epoch: [12][4390/11272] Time 0.862 (0.841) Data 0.001 (0.002) Loss 2.6898 (2.6207) Prec@1 36.250 (36.523) Prec@5 67.500 (67.181) Epoch: [12][4400/11272] Time 0.915 (0.841) Data 0.003 (0.002) Loss 2.3743 (2.6205) Prec@1 38.750 (36.528) Prec@5 75.000 (67.186) Epoch: [12][4410/11272] Time 0.762 (0.841) Data 0.002 (0.002) Loss 2.3236 (2.6205) Prec@1 43.125 (36.528) Prec@5 74.375 (67.187) Epoch: [12][4420/11272] Time 0.769 (0.841) Data 0.002 (0.002) Loss 2.4800 (2.6205) Prec@1 36.875 (36.526) Prec@5 73.750 (67.190) Epoch: [12][4430/11272] Time 0.904 (0.841) Data 0.002 (0.002) Loss 2.5225 (2.6204) Prec@1 38.125 (36.527) Prec@5 66.875 (67.190) Epoch: [12][4440/11272] Time 0.894 (0.841) Data 0.002 (0.002) Loss 2.9651 (2.6204) Prec@1 29.375 (36.529) Prec@5 60.000 (67.192) Epoch: [12][4450/11272] Time 0.839 (0.841) Data 0.002 (0.002) Loss 2.7199 (2.6205) Prec@1 35.000 (36.525) Prec@5 65.625 (67.188) Epoch: [12][4460/11272] Time 0.784 (0.841) Data 0.001 (0.002) Loss 2.6999 (2.6205) Prec@1 34.375 (36.527) Prec@5 63.750 (67.188) Epoch: [12][4470/11272] Time 0.873 (0.841) Data 0.002 (0.002) Loss 2.4644 (2.6204) Prec@1 33.125 (36.531) Prec@5 73.125 (67.191) Epoch: [12][4480/11272] Time 0.896 (0.841) Data 0.002 (0.002) Loss 2.6477 (2.6205) Prec@1 33.125 (36.530) Prec@5 63.750 (67.190) Epoch: [12][4490/11272] Time 0.752 (0.841) Data 0.002 (0.002) Loss 2.7852 (2.6205) Prec@1 31.250 (36.527) Prec@5 66.875 (67.190) Epoch: [12][4500/11272] Time 0.924 (0.841) Data 0.002 (0.002) Loss 2.4968 (2.6207) Prec@1 38.125 (36.523) Prec@5 68.750 (67.185) Epoch: [12][4510/11272] Time 0.898 (0.841) Data 0.002 (0.002) Loss 2.5629 (2.6207) Prec@1 31.875 (36.522) Prec@5 69.375 (67.186) Epoch: [12][4520/11272] Time 0.801 (0.841) Data 0.002 (0.002) Loss 2.5531 (2.6206) Prec@1 31.875 (36.519) Prec@5 69.375 (67.192) Epoch: [12][4530/11272] Time 0.756 (0.841) Data 0.001 (0.002) Loss 2.7886 (2.6206) Prec@1 37.500 (36.519) Prec@5 61.875 (67.190) Epoch: [12][4540/11272] Time 0.887 (0.841) Data 0.002 (0.002) Loss 2.4605 (2.6207) Prec@1 43.125 (36.520) Prec@5 70.625 (67.189) Epoch: [12][4550/11272] Time 0.845 (0.841) Data 0.002 (0.002) Loss 2.4179 (2.6205) Prec@1 39.375 (36.525) Prec@5 70.625 (67.192) Epoch: [12][4560/11272] Time 0.768 (0.841) Data 0.002 (0.002) Loss 2.7815 (2.6206) Prec@1 33.750 (36.523) Prec@5 60.625 (67.188) Epoch: [12][4570/11272] Time 0.780 (0.841) Data 0.001 (0.002) Loss 2.7122 (2.6207) Prec@1 35.625 (36.524) Prec@5 63.125 (67.184) Epoch: [12][4580/11272] Time 0.932 (0.841) Data 0.002 (0.002) Loss 2.8074 (2.6207) Prec@1 30.000 (36.523) Prec@5 61.250 (67.183) Epoch: [12][4590/11272] Time 0.858 (0.841) Data 0.002 (0.002) Loss 2.5144 (2.6207) Prec@1 35.000 (36.525) Prec@5 68.750 (67.182) Epoch: [12][4600/11272] Time 0.765 (0.840) Data 0.002 (0.002) Loss 2.4849 (2.6207) Prec@1 36.875 (36.528) Prec@5 70.625 (67.184) Epoch: [12][4610/11272] Time 0.745 (0.840) Data 0.002 (0.002) Loss 2.5033 (2.6206) Prec@1 32.500 (36.531) Prec@5 71.250 (67.186) Epoch: [12][4620/11272] Time 0.881 (0.840) Data 0.002 (0.002) Loss 2.4011 (2.6204) Prec@1 41.250 (36.536) Prec@5 68.750 (67.189) Epoch: [12][4630/11272] Time 0.852 (0.840) Data 0.002 (0.002) Loss 2.4989 (2.6204) Prec@1 38.125 (36.533) Prec@5 67.500 (67.188) Epoch: [12][4640/11272] Time 0.826 (0.840) Data 0.002 (0.002) Loss 2.7042 (2.6204) Prec@1 37.500 (36.536) Prec@5 65.625 (67.189) Epoch: [12][4650/11272] Time 0.898 (0.840) Data 0.001 (0.002) Loss 2.7470 (2.6205) Prec@1 35.000 (36.531) Prec@5 63.750 (67.189) Epoch: [12][4660/11272] Time 0.886 (0.840) Data 0.002 (0.002) Loss 2.6772 (2.6205) Prec@1 36.875 (36.535) Prec@5 62.500 (67.189) Epoch: [12][4670/11272] Time 0.781 (0.840) Data 0.002 (0.002) Loss 2.5949 (2.6205) Prec@1 33.750 (36.534) Prec@5 68.750 (67.187) Epoch: [12][4680/11272] Time 0.759 (0.840) Data 0.002 (0.002) Loss 2.7350 (2.6207) Prec@1 31.250 (36.530) Prec@5 70.000 (67.185) Epoch: [12][4690/11272] Time 0.933 (0.840) Data 0.002 (0.002) Loss 2.6581 (2.6207) Prec@1 41.250 (36.531) Prec@5 65.625 (67.182) Epoch: [12][4700/11272] Time 0.891 (0.840) Data 0.002 (0.002) Loss 2.4576 (2.6206) Prec@1 41.250 (36.535) Prec@5 67.500 (67.181) Epoch: [12][4710/11272] Time 0.771 (0.840) Data 0.002 (0.002) Loss 2.5860 (2.6204) Prec@1 37.500 (36.537) Prec@5 67.500 (67.182) Epoch: [12][4720/11272] Time 0.772 (0.840) Data 0.002 (0.002) Loss 2.5064 (2.6205) Prec@1 38.750 (36.535) Prec@5 71.250 (67.180) Epoch: [12][4730/11272] Time 0.858 (0.840) Data 0.001 (0.002) Loss 2.6189 (2.6205) Prec@1 37.500 (36.535) Prec@5 66.250 (67.183) Epoch: [12][4740/11272] Time 0.970 (0.840) Data 0.002 (0.002) Loss 2.5437 (2.6205) Prec@1 36.250 (36.532) Prec@5 68.750 (67.184) Epoch: [12][4750/11272] Time 0.764 (0.840) Data 0.002 (0.002) Loss 2.6392 (2.6204) Prec@1 38.125 (36.537) Prec@5 68.750 (67.187) Epoch: [12][4760/11272] Time 0.769 (0.840) Data 0.002 (0.002) Loss 2.6981 (2.6205) Prec@1 37.500 (36.533) Prec@5 65.625 (67.183) Epoch: [12][4770/11272] Time 0.827 (0.840) Data 0.001 (0.002) Loss 2.3475 (2.6205) Prec@1 41.875 (36.535) Prec@5 73.125 (67.185) Epoch: [12][4780/11272] Time 0.775 (0.840) Data 0.002 (0.002) Loss 2.5215 (2.6204) Prec@1 39.375 (36.534) Prec@5 68.750 (67.185) Epoch: [12][4790/11272] Time 0.758 (0.840) Data 0.002 (0.002) Loss 2.8092 (2.6205) Prec@1 35.000 (36.532) Prec@5 61.250 (67.186) Epoch: [12][4800/11272] Time 0.890 (0.840) Data 0.002 (0.002) Loss 2.7875 (2.6204) Prec@1 36.250 (36.532) Prec@5 65.000 (67.187) Epoch: [12][4810/11272] Time 0.923 (0.840) Data 0.002 (0.002) Loss 2.6034 (2.6206) Prec@1 39.375 (36.532) Prec@5 63.750 (67.184) Epoch: [12][4820/11272] Time 0.759 (0.840) Data 0.002 (0.002) Loss 2.6279 (2.6205) Prec@1 38.125 (36.535) Prec@5 67.500 (67.186) Epoch: [12][4830/11272] Time 0.770 (0.840) Data 0.002 (0.002) Loss 2.7550 (2.6203) Prec@1 32.500 (36.538) Prec@5 59.375 (67.185) Epoch: [12][4840/11272] Time 0.911 (0.840) Data 0.002 (0.002) Loss 2.5666 (2.6203) Prec@1 35.625 (36.536) Prec@5 65.625 (67.185) Epoch: [12][4850/11272] Time 0.862 (0.840) Data 0.002 (0.002) Loss 2.6296 (2.6203) Prec@1 40.625 (36.536) Prec@5 63.750 (67.185) Epoch: [12][4860/11272] Time 0.752 (0.840) Data 0.003 (0.002) Loss 2.8148 (2.6202) Prec@1 31.875 (36.537) Prec@5 60.625 (67.186) Epoch: [12][4870/11272] Time 0.752 (0.840) Data 0.002 (0.002) Loss 2.5381 (2.6202) Prec@1 33.750 (36.533) Prec@5 73.125 (67.186) Epoch: [12][4880/11272] Time 0.844 (0.840) Data 0.001 (0.002) Loss 2.6665 (2.6202) Prec@1 36.250 (36.532) Prec@5 62.500 (67.187) Epoch: [12][4890/11272] Time 0.843 (0.840) Data 0.002 (0.002) Loss 2.7454 (2.6201) Prec@1 34.375 (36.532) Prec@5 61.875 (67.191) Epoch: [12][4900/11272] Time 0.775 (0.840) Data 0.002 (0.002) Loss 2.4346 (2.6201) Prec@1 39.375 (36.532) Prec@5 70.000 (67.190) Epoch: [12][4910/11272] Time 0.853 (0.840) Data 0.001 (0.002) Loss 2.5687 (2.6200) Prec@1 42.500 (36.531) Prec@5 69.375 (67.191) Epoch: [12][4920/11272] Time 0.875 (0.840) Data 0.002 (0.002) Loss 2.7413 (2.6199) Prec@1 32.500 (36.531) Prec@5 61.875 (67.190) Epoch: [12][4930/11272] Time 0.780 (0.840) Data 0.002 (0.002) Loss 2.6609 (2.6198) Prec@1 35.625 (36.533) Prec@5 69.375 (67.194) Epoch: [12][4940/11272] Time 0.741 (0.840) Data 0.001 (0.002) Loss 2.3252 (2.6197) Prec@1 41.250 (36.533) Prec@5 71.250 (67.196) Epoch: [12][4950/11272] Time 0.942 (0.840) Data 0.002 (0.002) Loss 2.5603 (2.6195) Prec@1 35.625 (36.535) Prec@5 68.125 (67.199) Epoch: [12][4960/11272] Time 0.820 (0.840) Data 0.001 (0.002) Loss 2.6871 (2.6196) Prec@1 38.125 (36.537) Prec@5 63.750 (67.197) Epoch: [12][4970/11272] Time 0.772 (0.840) Data 0.002 (0.002) Loss 2.5546 (2.6196) Prec@1 40.625 (36.538) Prec@5 68.125 (67.197) Epoch: [12][4980/11272] Time 0.744 (0.840) Data 0.001 (0.002) Loss 2.9246 (2.6196) Prec@1 31.875 (36.539) Prec@5 66.875 (67.197) Epoch: [12][4990/11272] Time 0.871 (0.840) Data 0.001 (0.002) Loss 2.6162 (2.6195) Prec@1 37.500 (36.542) Prec@5 65.000 (67.199) Epoch: [12][5000/11272] Time 0.875 (0.840) Data 0.002 (0.002) Loss 2.6777 (2.6195) Prec@1 35.000 (36.546) Prec@5 64.375 (67.200) Epoch: [12][5010/11272] Time 0.742 (0.840) Data 0.002 (0.002) Loss 2.7456 (2.6195) Prec@1 32.500 (36.544) Prec@5 65.625 (67.201) Epoch: [12][5020/11272] Time 0.792 (0.840) Data 0.002 (0.002) Loss 2.4400 (2.6194) Prec@1 40.000 (36.546) Prec@5 73.750 (67.206) Epoch: [12][5030/11272] Time 0.909 (0.840) Data 0.001 (0.002) Loss 2.8152 (2.6193) Prec@1 36.250 (36.549) Prec@5 62.500 (67.207) Epoch: [12][5040/11272] Time 0.785 (0.840) Data 0.003 (0.002) Loss 2.9178 (2.6194) Prec@1 30.625 (36.550) Prec@5 62.500 (67.204) Epoch: [12][5050/11272] Time 0.745 (0.839) Data 0.001 (0.002) Loss 2.5109 (2.6195) Prec@1 41.875 (36.548) Prec@5 71.875 (67.202) Epoch: [12][5060/11272] Time 0.908 (0.839) Data 0.001 (0.002) Loss 2.5388 (2.6195) Prec@1 36.250 (36.548) Prec@5 68.750 (67.200) Epoch: [12][5070/11272] Time 0.871 (0.839) Data 0.002 (0.002) Loss 2.7199 (2.6194) Prec@1 37.500 (36.550) Prec@5 69.375 (67.201) Epoch: [12][5080/11272] Time 0.755 (0.839) Data 0.001 (0.002) Loss 2.5940 (2.6194) Prec@1 38.125 (36.550) Prec@5 66.250 (67.202) Epoch: [12][5090/11272] Time 0.749 (0.839) Data 0.002 (0.002) Loss 2.5815 (2.6193) Prec@1 38.750 (36.552) Prec@5 69.375 (67.205) Epoch: [12][5100/11272] Time 0.869 (0.839) Data 0.001 (0.002) Loss 2.8254 (2.6194) Prec@1 37.500 (36.551) Prec@5 63.125 (67.203) Epoch: [12][5110/11272] Time 0.815 (0.839) Data 0.001 (0.002) Loss 2.6011 (2.6194) Prec@1 36.250 (36.549) Prec@5 68.750 (67.202) Epoch: [12][5120/11272] Time 0.753 (0.839) Data 0.002 (0.002) Loss 2.4267 (2.6195) Prec@1 42.500 (36.550) Prec@5 68.125 (67.202) Epoch: [12][5130/11272] Time 0.751 (0.839) Data 0.002 (0.002) Loss 2.7431 (2.6194) Prec@1 31.250 (36.550) Prec@5 68.750 (67.203) Epoch: [12][5140/11272] Time 0.895 (0.839) Data 0.002 (0.002) Loss 2.6849 (2.6194) Prec@1 33.750 (36.546) Prec@5 66.250 (67.201) Epoch: [12][5150/11272] Time 0.856 (0.839) Data 0.001 (0.002) Loss 2.5998 (2.6194) Prec@1 39.375 (36.546) Prec@5 70.000 (67.201) Epoch: [12][5160/11272] Time 0.743 (0.839) Data 0.002 (0.002) Loss 2.1703 (2.6193) Prec@1 43.750 (36.550) Prec@5 74.375 (67.204) Epoch: [12][5170/11272] Time 0.886 (0.839) Data 0.001 (0.002) Loss 2.6548 (2.6193) Prec@1 36.250 (36.549) Prec@5 64.375 (67.203) Epoch: [12][5180/11272] Time 0.858 (0.839) Data 0.001 (0.002) Loss 2.7375 (2.6194) Prec@1 36.250 (36.545) Prec@5 66.875 (67.205) Epoch: [12][5190/11272] Time 0.776 (0.839) Data 0.002 (0.002) Loss 2.5428 (2.6194) Prec@1 39.375 (36.545) Prec@5 68.750 (67.206) Epoch: [12][5200/11272] Time 0.776 (0.839) Data 0.002 (0.002) Loss 2.8593 (2.6194) Prec@1 31.875 (36.543) Prec@5 63.750 (67.209) Epoch: [12][5210/11272] Time 0.887 (0.839) Data 0.001 (0.002) Loss 2.6676 (2.6194) Prec@1 30.000 (36.541) Prec@5 69.375 (67.208) Epoch: [12][5220/11272] Time 0.908 (0.839) Data 0.002 (0.002) Loss 2.3901 (2.6194) Prec@1 43.125 (36.543) Prec@5 72.500 (67.205) Epoch: [12][5230/11272] Time 0.752 (0.839) Data 0.002 (0.002) Loss 2.7709 (2.6196) Prec@1 31.875 (36.541) Prec@5 66.250 (67.202) Epoch: [12][5240/11272] Time 0.740 (0.839) Data 0.002 (0.002) Loss 2.6136 (2.6196) Prec@1 38.125 (36.542) Prec@5 65.625 (67.202) Epoch: [12][5250/11272] Time 0.929 (0.839) Data 0.001 (0.002) Loss 2.4800 (2.6196) Prec@1 38.750 (36.541) Prec@5 65.625 (67.200) Epoch: [12][5260/11272] Time 0.902 (0.839) Data 0.002 (0.002) Loss 2.7184 (2.6196) Prec@1 30.625 (36.540) Prec@5 70.625 (67.199) Epoch: [12][5270/11272] Time 0.815 (0.839) Data 0.001 (0.002) Loss 2.6973 (2.6196) Prec@1 31.875 (36.541) Prec@5 65.625 (67.197) Epoch: [12][5280/11272] Time 0.735 (0.839) Data 0.002 (0.002) Loss 2.4708 (2.6196) Prec@1 42.500 (36.541) Prec@5 71.250 (67.199) Epoch: [12][5290/11272] Time 0.879 (0.839) Data 0.002 (0.002) Loss 2.2322 (2.6194) Prec@1 41.250 (36.545) Prec@5 75.625 (67.201) Epoch: [12][5300/11272] Time 0.808 (0.839) Data 0.003 (0.002) Loss 2.5234 (2.6194) Prec@1 41.250 (36.547) Prec@5 71.875 (67.205) Epoch: [12][5310/11272] Time 0.801 (0.839) Data 0.002 (0.002) Loss 2.6435 (2.6194) Prec@1 35.625 (36.544) Prec@5 68.125 (67.203) Epoch: [12][5320/11272] Time 0.864 (0.839) Data 0.002 (0.002) Loss 2.6573 (2.6194) Prec@1 42.500 (36.545) Prec@5 65.625 (67.202) Epoch: [12][5330/11272] Time 0.857 (0.839) Data 0.002 (0.002) Loss 2.4781 (2.6196) Prec@1 40.000 (36.543) Prec@5 68.750 (67.199) Epoch: [12][5340/11272] Time 0.775 (0.839) Data 0.001 (0.002) Loss 2.5394 (2.6195) Prec@1 42.500 (36.545) Prec@5 70.625 (67.197) Epoch: [12][5350/11272] Time 0.752 (0.839) Data 0.002 (0.002) Loss 2.4703 (2.6196) Prec@1 40.625 (36.543) Prec@5 71.875 (67.195) Epoch: [12][5360/11272] Time 0.957 (0.839) Data 0.002 (0.002) Loss 2.4961 (2.6196) Prec@1 40.000 (36.545) Prec@5 68.750 (67.197) Epoch: [12][5370/11272] Time 0.890 (0.839) Data 0.002 (0.002) Loss 2.5225 (2.6197) Prec@1 38.125 (36.540) Prec@5 69.375 (67.196) Epoch: [12][5380/11272] Time 0.740 (0.839) Data 0.002 (0.002) Loss 2.8206 (2.6198) Prec@1 31.875 (36.539) Prec@5 60.625 (67.194) Epoch: [12][5390/11272] Time 0.806 (0.839) Data 0.002 (0.002) Loss 2.7333 (2.6199) Prec@1 35.625 (36.537) Prec@5 66.250 (67.192) Epoch: [12][5400/11272] Time 0.883 (0.839) Data 0.002 (0.002) Loss 2.5732 (2.6199) Prec@1 36.250 (36.540) Prec@5 66.875 (67.191) Epoch: [12][5410/11272] Time 0.890 (0.839) Data 0.002 (0.002) Loss 2.4543 (2.6199) Prec@1 42.500 (36.542) Prec@5 73.125 (67.192) Epoch: [12][5420/11272] Time 0.751 (0.839) Data 0.002 (0.002) Loss 2.5112 (2.6198) Prec@1 38.125 (36.547) Prec@5 69.375 (67.193) Epoch: [12][5430/11272] Time 0.881 (0.839) Data 0.002 (0.002) Loss 2.3976 (2.6199) Prec@1 38.750 (36.546) Prec@5 70.000 (67.193) Epoch: [12][5440/11272] Time 0.895 (0.839) Data 0.002 (0.002) Loss 2.6209 (2.6200) Prec@1 36.250 (36.545) Prec@5 66.875 (67.189) Epoch: [12][5450/11272] Time 0.727 (0.839) Data 0.001 (0.002) Loss 2.6730 (2.6199) Prec@1 35.000 (36.549) Prec@5 67.500 (67.191) Epoch: [12][5460/11272] Time 0.744 (0.839) Data 0.002 (0.002) Loss 2.8698 (2.6198) Prec@1 30.625 (36.550) Prec@5 59.375 (67.191) Epoch: [12][5470/11272] Time 0.899 (0.839) Data 0.002 (0.002) Loss 2.8131 (2.6198) Prec@1 26.875 (36.546) Prec@5 62.500 (67.191) Epoch: [12][5480/11272] Time 0.859 (0.839) Data 0.001 (0.002) Loss 2.4375 (2.6200) Prec@1 40.000 (36.544) Prec@5 67.500 (67.189) Epoch: [12][5490/11272] Time 0.763 (0.839) Data 0.002 (0.002) Loss 2.9073 (2.6201) Prec@1 35.000 (36.539) Prec@5 63.750 (67.186) Epoch: [12][5500/11272] Time 0.803 (0.839) Data 0.002 (0.002) Loss 2.4961 (2.6201) Prec@1 39.375 (36.540) Prec@5 69.375 (67.186) Epoch: [12][5510/11272] Time 0.906 (0.839) Data 0.002 (0.002) Loss 2.3775 (2.6201) Prec@1 43.750 (36.544) Prec@5 73.750 (67.189) Epoch: [12][5520/11272] Time 0.852 (0.838) Data 0.001 (0.002) Loss 2.7198 (2.6199) Prec@1 35.000 (36.548) Prec@5 64.375 (67.190) Epoch: [12][5530/11272] Time 0.750 (0.838) Data 0.002 (0.002) Loss 2.7364 (2.6200) Prec@1 35.000 (36.550) Prec@5 65.000 (67.190) Epoch: [12][5540/11272] Time 0.781 (0.838) Data 0.002 (0.002) Loss 2.6234 (2.6200) Prec@1 33.750 (36.548) Prec@5 63.750 (67.186) Epoch: [12][5550/11272] Time 0.836 (0.838) Data 0.001 (0.002) Loss 2.5486 (2.6200) Prec@1 34.375 (36.548) Prec@5 71.250 (67.187) Epoch: [12][5560/11272] Time 0.848 (0.838) Data 0.002 (0.002) Loss 2.7912 (2.6200) Prec@1 35.625 (36.548) Prec@5 63.750 (67.189) Epoch: [12][5570/11272] Time 0.825 (0.838) Data 0.002 (0.002) Loss 2.4924 (2.6201) Prec@1 38.125 (36.547) Prec@5 75.625 (67.190) Epoch: [12][5580/11272] Time 0.908 (0.838) Data 0.001 (0.002) Loss 2.5211 (2.6202) Prec@1 36.875 (36.547) Prec@5 71.250 (67.188) Epoch: [12][5590/11272] Time 0.898 (0.838) Data 0.001 (0.002) Loss 2.6932 (2.6203) Prec@1 35.000 (36.542) Prec@5 69.375 (67.186) Epoch: [12][5600/11272] Time 0.750 (0.838) Data 0.001 (0.002) Loss 2.3756 (2.6203) Prec@1 43.750 (36.542) Prec@5 73.125 (67.185) Epoch: [12][5610/11272] Time 0.771 (0.838) Data 0.002 (0.002) Loss 2.8654 (2.6203) Prec@1 33.125 (36.541) Prec@5 63.125 (67.185) Epoch: [12][5620/11272] Time 0.953 (0.838) Data 0.002 (0.002) Loss 2.4793 (2.6202) Prec@1 38.750 (36.543) Prec@5 69.375 (67.187) Epoch: [12][5630/11272] Time 0.872 (0.838) Data 0.001 (0.002) Loss 2.6993 (2.6202) Prec@1 36.250 (36.542) Prec@5 63.750 (67.185) Epoch: [12][5640/11272] Time 0.780 (0.838) Data 0.001 (0.002) Loss 2.6733 (2.6204) Prec@1 43.125 (36.544) Prec@5 71.250 (67.185) Epoch: [12][5650/11272] Time 0.798 (0.838) Data 0.002 (0.002) Loss 2.8550 (2.6204) Prec@1 35.000 (36.546) Prec@5 63.125 (67.185) Epoch: [12][5660/11272] Time 0.862 (0.838) Data 0.001 (0.002) Loss 2.4848 (2.6205) Prec@1 40.625 (36.545) Prec@5 68.750 (67.181) Epoch: [12][5670/11272] Time 0.894 (0.838) Data 0.002 (0.002) Loss 2.7725 (2.6205) Prec@1 29.375 (36.543) Prec@5 66.250 (67.178) Epoch: [12][5680/11272] Time 0.746 (0.838) Data 0.001 (0.002) Loss 2.9214 (2.6206) Prec@1 30.625 (36.542) Prec@5 60.000 (67.178) Epoch: [12][5690/11272] Time 0.778 (0.838) Data 0.002 (0.002) Loss 2.6252 (2.6205) Prec@1 33.125 (36.541) Prec@5 68.125 (67.179) Epoch: [12][5700/11272] Time 0.926 (0.838) Data 0.002 (0.002) Loss 2.5876 (2.6205) Prec@1 33.125 (36.541) Prec@5 69.375 (67.181) Epoch: [12][5710/11272] Time 0.776 (0.838) Data 0.002 (0.002) Loss 2.6176 (2.6206) Prec@1 38.125 (36.542) Prec@5 66.250 (67.180) Epoch: [12][5720/11272] Time 0.790 (0.838) Data 0.001 (0.002) Loss 2.6393 (2.6207) Prec@1 36.875 (36.541) Prec@5 65.000 (67.179) Epoch: [12][5730/11272] Time 0.918 (0.838) Data 0.002 (0.002) Loss 2.6631 (2.6208) Prec@1 36.875 (36.541) Prec@5 65.000 (67.178) Epoch: [12][5740/11272] Time 0.898 (0.838) Data 0.002 (0.002) Loss 2.6152 (2.6208) Prec@1 35.000 (36.538) Prec@5 66.250 (67.179) Epoch: [12][5750/11272] Time 0.755 (0.838) Data 0.002 (0.002) Loss 2.6997 (2.6208) Prec@1 38.750 (36.539) Prec@5 63.750 (67.180) Epoch: [12][5760/11272] Time 0.744 (0.838) Data 0.002 (0.002) Loss 2.5130 (2.6207) Prec@1 41.875 (36.540) Prec@5 70.000 (67.181) Epoch: [12][5770/11272] Time 0.880 (0.838) Data 0.001 (0.002) Loss 2.5660 (2.6207) Prec@1 33.750 (36.539) Prec@5 71.250 (67.186) Epoch: [12][5780/11272] Time 0.885 (0.838) Data 0.002 (0.002) Loss 2.6906 (2.6207) Prec@1 31.875 (36.536) Prec@5 65.625 (67.183) Epoch: [12][5790/11272] Time 0.774 (0.838) Data 0.001 (0.002) Loss 2.7717 (2.6208) Prec@1 32.500 (36.536) Prec@5 66.875 (67.184) Epoch: [12][5800/11272] Time 0.798 (0.838) Data 0.002 (0.002) Loss 2.6519 (2.6208) Prec@1 35.000 (36.537) Prec@5 64.375 (67.182) Epoch: [12][5810/11272] Time 0.890 (0.838) Data 0.002 (0.002) Loss 2.4722 (2.6207) Prec@1 39.375 (36.537) Prec@5 70.625 (67.182) Epoch: [12][5820/11272] Time 0.893 (0.838) Data 0.002 (0.002) Loss 2.6847 (2.6208) Prec@1 35.000 (36.538) Prec@5 62.500 (67.178) Epoch: [12][5830/11272] Time 0.810 (0.838) Data 0.003 (0.002) Loss 2.4939 (2.6208) Prec@1 40.625 (36.538) Prec@5 70.000 (67.177) Epoch: [12][5840/11272] Time 0.888 (0.838) Data 0.001 (0.002) Loss 2.8653 (2.6208) Prec@1 35.000 (36.537) Prec@5 63.750 (67.179) Epoch: [12][5850/11272] Time 0.956 (0.838) Data 0.001 (0.002) Loss 2.6162 (2.6209) Prec@1 36.875 (36.533) Prec@5 67.500 (67.176) Epoch: [12][5860/11272] Time 0.793 (0.838) Data 0.002 (0.002) Loss 3.1449 (2.6211) Prec@1 31.875 (36.532) Prec@5 59.375 (67.174) Epoch: [12][5870/11272] Time 0.772 (0.838) Data 0.002 (0.002) Loss 2.8575 (2.6211) Prec@1 31.875 (36.529) Prec@5 64.375 (67.172) Epoch: [12][5880/11272] Time 0.893 (0.838) Data 0.002 (0.002) Loss 2.6784 (2.6210) Prec@1 38.125 (36.531) Prec@5 64.375 (67.175) Epoch: [12][5890/11272] Time 0.885 (0.838) Data 0.002 (0.002) Loss 2.7832 (2.6212) Prec@1 33.750 (36.525) Prec@5 63.750 (67.172) Epoch: [12][5900/11272] Time 0.793 (0.838) Data 0.001 (0.002) Loss 2.6997 (2.6212) Prec@1 37.500 (36.526) Prec@5 71.250 (67.171) Epoch: [12][5910/11272] Time 0.765 (0.838) Data 0.002 (0.002) Loss 2.3850 (2.6211) Prec@1 45.625 (36.528) Prec@5 72.500 (67.173) Epoch: [12][5920/11272] Time 0.853 (0.838) Data 0.001 (0.002) Loss 2.5340 (2.6211) Prec@1 37.500 (36.526) Prec@5 69.375 (67.174) Epoch: [12][5930/11272] Time 0.845 (0.838) Data 0.001 (0.002) Loss 2.6732 (2.6211) Prec@1 36.875 (36.526) Prec@5 63.750 (67.175) Epoch: [12][5940/11272] Time 0.761 (0.838) Data 0.002 (0.002) Loss 2.5362 (2.6210) Prec@1 37.500 (36.526) Prec@5 71.250 (67.176) Epoch: [12][5950/11272] Time 0.783 (0.838) Data 0.002 (0.002) Loss 2.5748 (2.6211) Prec@1 36.250 (36.526) Prec@5 66.875 (67.176) Epoch: [12][5960/11272] Time 0.883 (0.838) Data 0.002 (0.002) Loss 2.4335 (2.6213) Prec@1 45.000 (36.525) Prec@5 70.000 (67.173) Epoch: [12][5970/11272] Time 0.750 (0.838) Data 0.004 (0.002) Loss 2.8929 (2.6213) Prec@1 31.875 (36.523) Prec@5 68.125 (67.174) Epoch: [12][5980/11272] Time 0.753 (0.838) Data 0.002 (0.002) Loss 2.7651 (2.6213) Prec@1 34.375 (36.523) Prec@5 61.875 (67.172) Epoch: [12][5990/11272] Time 0.913 (0.838) Data 0.002 (0.002) Loss 2.4471 (2.6213) Prec@1 44.375 (36.525) Prec@5 71.250 (67.171) Epoch: [12][6000/11272] Time 0.891 (0.838) Data 0.002 (0.002) Loss 2.4154 (2.6212) Prec@1 41.250 (36.525) Prec@5 75.000 (67.173) Epoch: [12][6010/11272] Time 0.768 (0.838) Data 0.002 (0.002) Loss 2.6821 (2.6212) Prec@1 36.250 (36.526) Prec@5 66.250 (67.174) Epoch: [12][6020/11272] Time 0.736 (0.838) Data 0.001 (0.002) Loss 2.7951 (2.6212) Prec@1 38.125 (36.526) Prec@5 64.375 (67.175) Epoch: [12][6030/11272] Time 0.904 (0.838) Data 0.001 (0.002) Loss 2.4030 (2.6211) Prec@1 40.000 (36.528) Prec@5 75.000 (67.175) Epoch: [12][6040/11272] Time 0.898 (0.838) Data 0.002 (0.002) Loss 2.6011 (2.6211) Prec@1 32.500 (36.528) Prec@5 65.625 (67.176) Epoch: [12][6050/11272] Time 0.805 (0.838) Data 0.002 (0.002) Loss 2.7293 (2.6212) Prec@1 32.500 (36.526) Prec@5 70.000 (67.174) Epoch: [12][6060/11272] Time 0.753 (0.838) Data 0.002 (0.002) Loss 2.3311 (2.6212) Prec@1 48.750 (36.529) Prec@5 73.125 (67.175) Epoch: [12][6070/11272] Time 0.912 (0.838) Data 0.002 (0.002) Loss 2.5570 (2.6211) Prec@1 39.375 (36.527) Prec@5 68.125 (67.174) Epoch: [12][6080/11272] Time 0.912 (0.838) Data 0.002 (0.002) Loss 2.6402 (2.6211) Prec@1 38.125 (36.526) Prec@5 68.750 (67.174) Epoch: [12][6090/11272] Time 0.821 (0.838) Data 0.002 (0.002) Loss 2.6386 (2.6211) Prec@1 33.750 (36.527) Prec@5 63.125 (67.173) Epoch: [12][6100/11272] Time 0.882 (0.838) Data 0.001 (0.002) Loss 2.6278 (2.6212) Prec@1 36.250 (36.525) Prec@5 66.250 (67.171) Epoch: [12][6110/11272] Time 0.855 (0.838) Data 0.001 (0.002) Loss 2.6760 (2.6212) Prec@1 39.375 (36.525) Prec@5 65.625 (67.171) Epoch: [12][6120/11272] Time 0.748 (0.838) Data 0.002 (0.002) Loss 2.5866 (2.6213) Prec@1 36.875 (36.524) Prec@5 65.625 (67.169) Epoch: [12][6130/11272] Time 0.766 (0.838) Data 0.001 (0.002) Loss 2.7700 (2.6212) Prec@1 36.875 (36.526) Prec@5 63.125 (67.170) Epoch: [12][6140/11272] Time 0.962 (0.838) Data 0.003 (0.002) Loss 2.4317 (2.6211) Prec@1 37.500 (36.529) Prec@5 73.750 (67.172) Epoch: [12][6150/11272] Time 0.879 (0.838) Data 0.001 (0.002) Loss 2.5720 (2.6211) Prec@1 40.000 (36.527) Prec@5 66.875 (67.172) Epoch: [12][6160/11272] Time 0.812 (0.838) Data 0.002 (0.002) Loss 2.6141 (2.6212) Prec@1 38.750 (36.524) Prec@5 66.875 (67.171) Epoch: [12][6170/11272] Time 0.763 (0.838) Data 0.002 (0.002) Loss 2.6097 (2.6213) Prec@1 36.250 (36.523) Prec@5 64.375 (67.170) Epoch: [12][6180/11272] Time 0.853 (0.838) Data 0.001 (0.002) Loss 2.6651 (2.6212) Prec@1 38.750 (36.526) Prec@5 65.000 (67.170) Epoch: [12][6190/11272] Time 0.872 (0.838) Data 0.001 (0.002) Loss 2.6898 (2.6210) Prec@1 35.625 (36.530) Prec@5 63.750 (67.173) Epoch: [12][6200/11272] Time 0.813 (0.838) Data 0.002 (0.002) Loss 2.4865 (2.6209) Prec@1 40.000 (36.533) Prec@5 66.250 (67.177) Epoch: [12][6210/11272] Time 0.812 (0.837) Data 0.001 (0.002) Loss 2.5217 (2.6209) Prec@1 36.875 (36.531) Prec@5 69.375 (67.177) Epoch: [12][6220/11272] Time 0.875 (0.837) Data 0.002 (0.002) Loss 2.8163 (2.6210) Prec@1 28.125 (36.526) Prec@5 68.125 (67.176) Epoch: [12][6230/11272] Time 0.742 (0.837) Data 0.003 (0.002) Loss 2.3773 (2.6210) Prec@1 38.125 (36.526) Prec@5 70.000 (67.176) Epoch: [12][6240/11272] Time 0.805 (0.837) Data 0.002 (0.002) Loss 2.4279 (2.6210) Prec@1 41.875 (36.528) Prec@5 69.375 (67.174) Epoch: [12][6250/11272] Time 0.887 (0.837) Data 0.001 (0.002) Loss 2.7155 (2.6211) Prec@1 35.000 (36.529) Prec@5 63.750 (67.171) Epoch: [12][6260/11272] Time 0.838 (0.837) Data 0.002 (0.002) Loss 2.7245 (2.6210) Prec@1 33.750 (36.530) Prec@5 66.875 (67.174) Epoch: [12][6270/11272] Time 0.741 (0.837) Data 0.002 (0.002) Loss 2.6426 (2.6210) Prec@1 34.375 (36.527) Prec@5 66.250 (67.175) Epoch: [12][6280/11272] Time 0.770 (0.837) Data 0.002 (0.002) Loss 2.7371 (2.6210) Prec@1 34.375 (36.527) Prec@5 62.500 (67.176) Epoch: [12][6290/11272] Time 0.920 (0.837) Data 0.002 (0.002) Loss 2.9268 (2.6212) Prec@1 30.625 (36.525) Prec@5 61.875 (67.173) Epoch: [12][6300/11272] Time 0.882 (0.837) Data 0.002 (0.002) Loss 2.8928 (2.6212) Prec@1 31.250 (36.525) Prec@5 64.375 (67.172) Epoch: [12][6310/11272] Time 0.786 (0.837) Data 0.001 (0.002) Loss 2.6144 (2.6211) Prec@1 34.375 (36.526) Prec@5 61.875 (67.173) Epoch: [12][6320/11272] Time 0.795 (0.837) Data 0.002 (0.002) Loss 2.5199 (2.6211) Prec@1 29.375 (36.524) Prec@5 69.375 (67.174) Epoch: [12][6330/11272] Time 0.916 (0.837) Data 0.001 (0.002) Loss 2.3657 (2.6212) Prec@1 39.375 (36.523) Prec@5 71.875 (67.169) Epoch: [12][6340/11272] Time 0.918 (0.837) Data 0.002 (0.002) Loss 2.5522 (2.6213) Prec@1 35.000 (36.521) Prec@5 66.250 (67.169) Epoch: [12][6350/11272] Time 0.749 (0.837) Data 0.002 (0.002) Loss 2.6830 (2.6213) Prec@1 35.000 (36.521) Prec@5 65.000 (67.169) Epoch: [12][6360/11272] Time 0.830 (0.837) Data 0.001 (0.002) Loss 2.6979 (2.6214) Prec@1 32.500 (36.518) Prec@5 63.125 (67.168) Epoch: [12][6370/11272] Time 0.864 (0.837) Data 0.002 (0.002) Loss 2.4029 (2.6215) Prec@1 45.000 (36.520) Prec@5 76.250 (67.170) Epoch: [12][6380/11272] Time 0.779 (0.837) Data 0.002 (0.002) Loss 2.6337 (2.6214) Prec@1 37.500 (36.522) Prec@5 69.375 (67.169) Epoch: [12][6390/11272] Time 0.746 (0.837) Data 0.002 (0.002) Loss 2.6220 (2.6213) Prec@1 33.750 (36.523) Prec@5 66.875 (67.169) Epoch: [12][6400/11272] Time 0.945 (0.837) Data 0.002 (0.002) Loss 2.5551 (2.6213) Prec@1 33.750 (36.522) Prec@5 69.375 (67.168) Epoch: [12][6410/11272] Time 0.903 (0.837) Data 0.002 (0.002) Loss 2.6919 (2.6214) Prec@1 35.625 (36.519) Prec@5 61.875 (67.166) Epoch: [12][6420/11272] Time 0.738 (0.837) Data 0.001 (0.002) Loss 2.5433 (2.6214) Prec@1 30.000 (36.520) Prec@5 70.625 (67.166) Epoch: [12][6430/11272] Time 0.784 (0.837) Data 0.002 (0.002) Loss 2.8594 (2.6214) Prec@1 35.000 (36.519) Prec@5 62.500 (67.167) Epoch: [12][6440/11272] Time 0.845 (0.837) Data 0.002 (0.002) Loss 2.9620 (2.6214) Prec@1 32.500 (36.519) Prec@5 63.125 (67.167) Epoch: [12][6450/11272] Time 0.900 (0.837) Data 0.002 (0.002) Loss 2.7995 (2.6214) Prec@1 34.375 (36.521) Prec@5 63.750 (67.169) Epoch: [12][6460/11272] Time 0.795 (0.837) Data 0.002 (0.002) Loss 2.7415 (2.6214) Prec@1 40.000 (36.520) Prec@5 63.125 (67.168) Epoch: [12][6470/11272] Time 0.801 (0.837) Data 0.001 (0.002) Loss 2.2567 (2.6214) Prec@1 49.375 (36.522) Prec@5 78.125 (67.168) Epoch: [12][6480/11272] Time 0.862 (0.837) Data 0.002 (0.002) Loss 2.7766 (2.6215) Prec@1 30.625 (36.521) Prec@5 61.250 (67.167) Epoch: [12][6490/11272] Time 0.938 (0.837) Data 0.002 (0.002) Loss 2.5265 (2.6214) Prec@1 41.250 (36.523) Prec@5 68.125 (67.168) Epoch: [12][6500/11272] Time 0.779 (0.837) Data 0.002 (0.002) Loss 2.7312 (2.6214) Prec@1 36.875 (36.521) Prec@5 62.500 (67.167) Epoch: [12][6510/11272] Time 0.910 (0.837) Data 0.002 (0.002) Loss 2.6831 (2.6215) Prec@1 36.875 (36.521) Prec@5 66.875 (67.165) Epoch: [12][6520/11272] Time 0.844 (0.837) Data 0.001 (0.002) Loss 2.5577 (2.6216) Prec@1 37.500 (36.520) Prec@5 68.125 (67.163) Epoch: [12][6530/11272] Time 0.774 (0.837) Data 0.002 (0.002) Loss 2.5130 (2.6216) Prec@1 37.500 (36.523) Prec@5 70.000 (67.165) Epoch: [12][6540/11272] Time 0.775 (0.837) Data 0.001 (0.002) Loss 2.5517 (2.6216) Prec@1 38.750 (36.525) Prec@5 67.500 (67.166) Epoch: [12][6550/11272] Time 0.900 (0.837) Data 0.002 (0.002) Loss 2.8502 (2.6215) Prec@1 33.750 (36.527) Prec@5 63.125 (67.167) Epoch: [12][6560/11272] Time 0.884 (0.837) Data 0.002 (0.002) Loss 2.5819 (2.6216) Prec@1 38.750 (36.524) Prec@5 68.750 (67.167) Epoch: [12][6570/11272] Time 0.797 (0.837) Data 0.002 (0.002) Loss 2.5575 (2.6216) Prec@1 35.000 (36.526) Prec@5 63.750 (67.168) Epoch: [12][6580/11272] Time 0.747 (0.837) Data 0.001 (0.002) Loss 2.3828 (2.6216) Prec@1 40.000 (36.525) Prec@5 72.500 (67.168) Epoch: [12][6590/11272] Time 0.878 (0.837) Data 0.001 (0.002) Loss 2.7308 (2.6216) Prec@1 31.250 (36.523) Prec@5 65.625 (67.165) Epoch: [12][6600/11272] Time 0.878 (0.837) Data 0.002 (0.002) Loss 2.5795 (2.6216) Prec@1 41.250 (36.522) Prec@5 70.000 (67.165) Epoch: [12][6610/11272] Time 0.754 (0.837) Data 0.002 (0.002) Loss 2.4612 (2.6217) Prec@1 34.375 (36.520) Prec@5 76.250 (67.164) Epoch: [12][6620/11272] Time 0.746 (0.837) Data 0.002 (0.002) Loss 2.5609 (2.6217) Prec@1 37.500 (36.518) Prec@5 66.250 (67.165) Epoch: [12][6630/11272] Time 0.896 (0.837) Data 0.002 (0.002) Loss 2.7066 (2.6218) Prec@1 36.875 (36.516) Prec@5 65.625 (67.162) Epoch: [12][6640/11272] Time 0.808 (0.837) Data 0.002 (0.002) Loss 2.6123 (2.6219) Prec@1 38.125 (36.517) Prec@5 73.125 (67.160) Epoch: [12][6650/11272] Time 0.738 (0.837) Data 0.002 (0.002) Loss 2.5638 (2.6220) Prec@1 36.250 (36.514) Prec@5 71.250 (67.160) Epoch: [12][6660/11272] Time 0.892 (0.837) Data 0.002 (0.002) Loss 2.4403 (2.6220) Prec@1 38.750 (36.510) Prec@5 68.125 (67.159) Epoch: [12][6670/11272] Time 0.929 (0.837) Data 0.002 (0.002) Loss 2.6697 (2.6220) Prec@1 32.500 (36.508) Prec@5 63.750 (67.161) Epoch: [12][6680/11272] Time 0.803 (0.837) Data 0.002 (0.002) Loss 2.8350 (2.6220) Prec@1 37.500 (36.508) Prec@5 66.250 (67.161) Epoch: [12][6690/11272] Time 0.762 (0.837) Data 0.002 (0.002) Loss 2.5412 (2.6221) Prec@1 35.625 (36.506) Prec@5 63.750 (67.158) Epoch: [12][6700/11272] Time 0.857 (0.837) Data 0.001 (0.002) Loss 2.3995 (2.6222) Prec@1 41.875 (36.506) Prec@5 75.000 (67.156) Epoch: [12][6710/11272] Time 0.829 (0.837) Data 0.001 (0.002) Loss 2.6755 (2.6221) Prec@1 29.375 (36.503) Prec@5 70.000 (67.158) Epoch: [12][6720/11272] Time 0.805 (0.837) Data 0.002 (0.002) Loss 2.4937 (2.6220) Prec@1 40.625 (36.509) Prec@5 62.500 (67.161) Epoch: [12][6730/11272] Time 0.746 (0.837) Data 0.001 (0.002) Loss 2.7749 (2.6219) Prec@1 36.875 (36.510) Prec@5 66.250 (67.162) Epoch: [12][6740/11272] Time 0.881 (0.837) Data 0.001 (0.002) Loss 2.5797 (2.6219) Prec@1 41.250 (36.507) Prec@5 65.625 (67.161) Epoch: [12][6750/11272] Time 0.916 (0.837) Data 0.001 (0.002) Loss 2.4629 (2.6219) Prec@1 40.625 (36.508) Prec@5 67.500 (67.161) Epoch: [12][6760/11272] Time 0.776 (0.837) Data 0.001 (0.002) Loss 2.5777 (2.6219) Prec@1 37.500 (36.506) Prec@5 65.000 (67.161) Epoch: [12][6770/11272] Time 0.986 (0.837) Data 0.002 (0.002) Loss 2.7433 (2.6220) Prec@1 33.750 (36.506) Prec@5 59.375 (67.160) Epoch: [12][6780/11272] Time 0.895 (0.837) Data 0.002 (0.002) Loss 2.6099 (2.6220) Prec@1 35.625 (36.504) Prec@5 63.750 (67.161) Epoch: [12][6790/11272] Time 0.752 (0.837) Data 0.001 (0.002) Loss 2.4917 (2.6220) Prec@1 36.875 (36.505) Prec@5 69.375 (67.160) Epoch: [12][6800/11272] Time 0.792 (0.837) Data 0.002 (0.002) Loss 2.5250 (2.6219) Prec@1 40.000 (36.506) Prec@5 71.250 (67.162) Epoch: [12][6810/11272] Time 0.903 (0.837) Data 0.001 (0.002) Loss 2.8507 (2.6218) Prec@1 28.750 (36.508) Prec@5 63.750 (67.163) Epoch: [12][6820/11272] Time 0.864 (0.837) Data 0.002 (0.002) Loss 2.6528 (2.6218) Prec@1 35.625 (36.509) Prec@5 68.750 (67.163) Epoch: [12][6830/11272] Time 0.746 (0.837) Data 0.001 (0.002) Loss 2.8309 (2.6218) Prec@1 31.875 (36.507) Prec@5 59.375 (67.162) Epoch: [12][6840/11272] Time 0.741 (0.837) Data 0.002 (0.002) Loss 2.2467 (2.6217) Prec@1 44.375 (36.509) Prec@5 75.625 (67.163) Epoch: [12][6850/11272] Time 0.895 (0.837) Data 0.002 (0.002) Loss 2.6911 (2.6218) Prec@1 35.000 (36.508) Prec@5 63.750 (67.161) Epoch: [12][6860/11272] Time 0.881 (0.837) Data 0.002 (0.002) Loss 2.5187 (2.6217) Prec@1 38.750 (36.508) Prec@5 68.125 (67.161) Epoch: [12][6870/11272] Time 0.779 (0.837) Data 0.002 (0.002) Loss 2.4724 (2.6218) Prec@1 38.750 (36.505) Prec@5 70.000 (67.162) Epoch: [12][6880/11272] Time 0.760 (0.837) Data 0.001 (0.002) Loss 2.4782 (2.6218) Prec@1 41.250 (36.505) Prec@5 69.375 (67.162) Epoch: [12][6890/11272] Time 0.845 (0.837) Data 0.001 (0.002) Loss 2.5562 (2.6218) Prec@1 38.125 (36.505) Prec@5 68.125 (67.163) Epoch: [12][6900/11272] Time 0.754 (0.837) Data 0.003 (0.002) Loss 2.6129 (2.6218) Prec@1 33.125 (36.502) Prec@5 70.000 (67.164) Epoch: [12][6910/11272] Time 0.777 (0.837) Data 0.002 (0.002) Loss 2.7915 (2.6218) Prec@1 30.625 (36.504) Prec@5 65.625 (67.166) Epoch: [12][6920/11272] Time 0.884 (0.837) Data 0.001 (0.002) Loss 2.3273 (2.6217) Prec@1 43.125 (36.504) Prec@5 68.750 (67.167) Epoch: [12][6930/11272] Time 0.865 (0.837) Data 0.002 (0.002) Loss 2.4227 (2.6217) Prec@1 36.250 (36.504) Prec@5 70.000 (67.168) Epoch: [12][6940/11272] Time 0.778 (0.837) Data 0.002 (0.002) Loss 2.5817 (2.6217) Prec@1 34.375 (36.503) Prec@5 66.250 (67.167) Epoch: [12][6950/11272] Time 0.733 (0.837) Data 0.001 (0.002) Loss 2.4630 (2.6217) Prec@1 43.750 (36.503) Prec@5 71.250 (67.167) Epoch: [12][6960/11272] Time 0.824 (0.837) Data 0.001 (0.002) Loss 2.6780 (2.6218) Prec@1 33.125 (36.501) Prec@5 70.625 (67.166) Epoch: [12][6970/11272] Time 0.934 (0.837) Data 0.002 (0.002) Loss 2.6400 (2.6218) Prec@1 36.875 (36.501) Prec@5 65.000 (67.167) Epoch: [12][6980/11272] Time 0.766 (0.837) Data 0.004 (0.002) Loss 2.4989 (2.6217) Prec@1 35.625 (36.500) Prec@5 71.875 (67.169) Epoch: [12][6990/11272] Time 0.767 (0.837) Data 0.002 (0.002) Loss 2.7069 (2.6217) Prec@1 36.250 (36.499) Prec@5 65.625 (67.170) Epoch: [12][7000/11272] Time 0.928 (0.837) Data 0.002 (0.002) Loss 2.5099 (2.6217) Prec@1 40.000 (36.499) Prec@5 68.125 (67.170) Epoch: [12][7010/11272] Time 0.866 (0.837) Data 0.002 (0.002) Loss 2.5852 (2.6217) Prec@1 36.875 (36.501) Prec@5 68.125 (67.171) Epoch: [12][7020/11272] Time 0.760 (0.837) Data 0.001 (0.002) Loss 2.7830 (2.6218) Prec@1 34.375 (36.499) Prec@5 66.250 (67.169) Epoch: [12][7030/11272] Time 0.879 (0.837) Data 0.001 (0.002) Loss 2.4614 (2.6219) Prec@1 40.000 (36.497) Prec@5 66.250 (67.167) Epoch: [12][7040/11272] Time 0.910 (0.837) Data 0.004 (0.002) Loss 2.3744 (2.6219) Prec@1 40.000 (36.498) Prec@5 71.875 (67.168) Epoch: [12][7050/11272] Time 0.732 (0.837) Data 0.001 (0.002) Loss 2.6919 (2.6218) Prec@1 33.125 (36.496) Prec@5 69.375 (67.169) Epoch: [12][7060/11272] Time 0.794 (0.837) Data 0.002 (0.002) Loss 2.6533 (2.6219) Prec@1 36.250 (36.495) Prec@5 71.250 (67.170) Epoch: [12][7070/11272] Time 0.889 (0.837) Data 0.002 (0.002) Loss 2.7049 (2.6218) Prec@1 35.625 (36.495) Prec@5 68.125 (67.171) Epoch: [12][7080/11272] Time 0.874 (0.837) Data 0.002 (0.002) Loss 2.7023 (2.6217) Prec@1 36.250 (36.496) Prec@5 66.250 (67.173) Epoch: [12][7090/11272] Time 0.764 (0.837) Data 0.002 (0.002) Loss 2.4670 (2.6218) Prec@1 40.625 (36.495) Prec@5 72.500 (67.170) Epoch: [12][7100/11272] Time 0.748 (0.837) Data 0.001 (0.002) Loss 2.6644 (2.6218) Prec@1 33.750 (36.495) Prec@5 63.750 (67.169) Epoch: [12][7110/11272] Time 0.906 (0.837) Data 0.002 (0.002) Loss 2.8274 (2.6217) Prec@1 33.125 (36.497) Prec@5 63.125 (67.173) Epoch: [12][7120/11272] Time 0.915 (0.837) Data 0.001 (0.002) Loss 2.4312 (2.6217) Prec@1 39.375 (36.498) Prec@5 72.500 (67.175) Epoch: [12][7130/11272] Time 0.753 (0.837) Data 0.002 (0.002) Loss 2.4735 (2.6217) Prec@1 36.875 (36.497) Prec@5 68.750 (67.175) Epoch: [12][7140/11272] Time 0.830 (0.837) Data 0.002 (0.002) Loss 2.5690 (2.6216) Prec@1 38.750 (36.499) Prec@5 68.125 (67.177) Epoch: [12][7150/11272] Time 0.928 (0.837) Data 0.002 (0.002) Loss 2.7090 (2.6216) Prec@1 38.750 (36.499) Prec@5 64.375 (67.174) Epoch: [12][7160/11272] Time 0.741 (0.837) Data 0.003 (0.002) Loss 2.8143 (2.6216) Prec@1 36.250 (36.501) Prec@5 64.375 (67.177) Epoch: [12][7170/11272] Time 0.738 (0.837) Data 0.001 (0.002) Loss 2.6592 (2.6215) Prec@1 35.625 (36.501) Prec@5 68.750 (67.178) Epoch: [12][7180/11272] Time 0.902 (0.837) Data 0.002 (0.002) Loss 2.5469 (2.6215) Prec@1 35.000 (36.501) Prec@5 67.500 (67.179) Epoch: [12][7190/11272] Time 0.858 (0.837) Data 0.002 (0.002) Loss 2.4873 (2.6214) Prec@1 38.125 (36.501) Prec@5 70.000 (67.181) Epoch: [12][7200/11272] Time 0.775 (0.837) Data 0.002 (0.002) Loss 2.7111 (2.6214) Prec@1 30.625 (36.500) Prec@5 68.750 (67.182) Epoch: [12][7210/11272] Time 0.784 (0.837) Data 0.002 (0.002) Loss 2.4614 (2.6213) Prec@1 41.250 (36.503) Prec@5 68.125 (67.185) Epoch: [12][7220/11272] Time 0.887 (0.837) Data 0.003 (0.002) Loss 2.8950 (2.6213) Prec@1 27.500 (36.503) Prec@5 65.000 (67.186) Epoch: [12][7230/11272] Time 0.905 (0.837) Data 0.002 (0.002) Loss 2.7885 (2.6213) Prec@1 36.875 (36.503) Prec@5 64.375 (67.184) Epoch: [12][7240/11272] Time 0.808 (0.837) Data 0.002 (0.002) Loss 2.7033 (2.6213) Prec@1 35.625 (36.504) Prec@5 68.125 (67.185) Epoch: [12][7250/11272] Time 0.749 (0.837) Data 0.002 (0.002) Loss 2.4186 (2.6213) Prec@1 43.125 (36.505) Prec@5 68.125 (67.184) Epoch: [12][7260/11272] Time 0.870 (0.837) Data 0.002 (0.002) Loss 2.5114 (2.6213) Prec@1 38.750 (36.504) Prec@5 71.250 (67.184) Epoch: [12][7270/11272] Time 0.873 (0.837) Data 0.002 (0.002) Loss 2.7946 (2.6213) Prec@1 36.875 (36.505) Prec@5 60.625 (67.181) Epoch: [12][7280/11272] Time 0.746 (0.837) Data 0.001 (0.002) Loss 2.8105 (2.6214) Prec@1 37.500 (36.504) Prec@5 61.875 (67.178) Epoch: [12][7290/11272] Time 0.909 (0.837) Data 0.001 (0.002) Loss 2.3076 (2.6213) Prec@1 42.500 (36.506) Prec@5 72.500 (67.181) Epoch: [12][7300/11272] Time 0.923 (0.837) Data 0.002 (0.002) Loss 2.5106 (2.6212) Prec@1 36.875 (36.508) Prec@5 68.750 (67.184) Epoch: [12][7310/11272] Time 0.755 (0.836) Data 0.002 (0.002) Loss 2.7749 (2.6213) Prec@1 34.375 (36.508) Prec@5 63.750 (67.180) Epoch: [12][7320/11272] Time 0.793 (0.837) Data 0.002 (0.002) Loss 2.5946 (2.6212) Prec@1 42.500 (36.511) Prec@5 70.000 (67.183) Epoch: [12][7330/11272] Time 0.912 (0.837) Data 0.002 (0.002) Loss 2.8108 (2.6212) Prec@1 36.250 (36.512) Prec@5 65.000 (67.182) Epoch: [12][7340/11272] Time 0.887 (0.837) Data 0.001 (0.002) Loss 2.4582 (2.6213) Prec@1 38.750 (36.511) Prec@5 71.250 (67.181) Epoch: [12][7350/11272] Time 0.764 (0.837) Data 0.001 (0.002) Loss 2.5886 (2.6212) Prec@1 36.875 (36.512) Prec@5 67.500 (67.182) Epoch: [12][7360/11272] Time 0.767 (0.837) Data 0.002 (0.002) Loss 2.6948 (2.6213) Prec@1 32.500 (36.512) Prec@5 66.250 (67.180) Epoch: [12][7370/11272] Time 0.905 (0.837) Data 0.001 (0.002) Loss 2.6064 (2.6213) Prec@1 36.875 (36.511) Prec@5 66.250 (67.180) Epoch: [12][7380/11272] Time 0.901 (0.837) Data 0.002 (0.002) Loss 2.6619 (2.6213) Prec@1 37.500 (36.512) Prec@5 65.000 (67.181) Epoch: [12][7390/11272] Time 0.742 (0.837) Data 0.001 (0.002) Loss 2.6590 (2.6213) Prec@1 35.000 (36.511) Prec@5 67.500 (67.180) Epoch: [12][7400/11272] Time 0.808 (0.837) Data 0.001 (0.002) Loss 2.4148 (2.6213) Prec@1 36.875 (36.508) Prec@5 73.125 (67.177) Epoch: [12][7410/11272] Time 0.865 (0.837) Data 0.001 (0.002) Loss 2.6844 (2.6213) Prec@1 36.250 (36.508) Prec@5 62.500 (67.175) Epoch: [12][7420/11272] Time 0.900 (0.837) Data 0.002 (0.002) Loss 2.7356 (2.6213) Prec@1 37.500 (36.507) Prec@5 63.125 (67.176) Epoch: [12][7430/11272] Time 0.754 (0.837) Data 0.002 (0.002) Loss 2.6561 (2.6213) Prec@1 28.750 (36.507) Prec@5 65.625 (67.176) Epoch: [12][7440/11272] Time 0.890 (0.837) Data 0.002 (0.002) Loss 2.7189 (2.6214) Prec@1 30.625 (36.506) Prec@5 65.000 (67.176) Epoch: [12][7450/11272] Time 0.892 (0.837) Data 0.001 (0.002) Loss 2.6112 (2.6213) Prec@1 37.500 (36.507) Prec@5 67.500 (67.178) Epoch: [12][7460/11272] Time 0.807 (0.837) Data 0.002 (0.002) Loss 2.6820 (2.6213) Prec@1 32.500 (36.506) Prec@5 68.125 (67.177) Epoch: [12][7470/11272] Time 0.803 (0.836) Data 0.001 (0.002) Loss 2.5064 (2.6212) Prec@1 37.500 (36.507) Prec@5 68.125 (67.179) Epoch: [12][7480/11272] Time 0.910 (0.837) Data 0.002 (0.002) Loss 2.7191 (2.6212) Prec@1 38.125 (36.509) Prec@5 70.625 (67.179) Epoch: [12][7490/11272] Time 0.888 (0.836) Data 0.002 (0.002) Loss 2.3781 (2.6213) Prec@1 42.500 (36.510) Prec@5 70.625 (67.176) Epoch: [12][7500/11272] Time 0.788 (0.836) Data 0.002 (0.002) Loss 2.5427 (2.6213) Prec@1 40.625 (36.509) Prec@5 74.375 (67.175) Epoch: [12][7510/11272] Time 0.769 (0.836) Data 0.002 (0.002) Loss 2.4944 (2.6212) Prec@1 40.000 (36.510) Prec@5 69.375 (67.176) Epoch: [12][7520/11272] Time 0.873 (0.836) Data 0.001 (0.002) Loss 2.5519 (2.6213) Prec@1 40.000 (36.508) Prec@5 66.875 (67.176) Epoch: [12][7530/11272] Time 0.843 (0.836) Data 0.001 (0.002) Loss 2.4381 (2.6213) Prec@1 43.125 (36.508) Prec@5 72.500 (67.176) Epoch: [12][7540/11272] Time 0.737 (0.836) Data 0.002 (0.002) Loss 2.6854 (2.6213) Prec@1 38.125 (36.508) Prec@5 66.875 (67.176) Epoch: [12][7550/11272] Time 0.754 (0.836) Data 0.001 (0.002) Loss 2.7891 (2.6213) Prec@1 33.750 (36.508) Prec@5 63.125 (67.178) Epoch: [12][7560/11272] Time 0.906 (0.836) Data 0.001 (0.002) Loss 2.4555 (2.6213) Prec@1 43.750 (36.508) Prec@5 69.375 (67.180) Epoch: [12][7570/11272] Time 0.759 (0.836) Data 0.002 (0.002) Loss 2.3814 (2.6213) Prec@1 37.500 (36.507) Prec@5 66.875 (67.177) Epoch: [12][7580/11272] Time 0.770 (0.836) Data 0.002 (0.002) Loss 2.8901 (2.6214) Prec@1 29.375 (36.506) Prec@5 63.750 (67.176) Epoch: [12][7590/11272] Time 0.889 (0.836) Data 0.001 (0.002) Loss 2.5643 (2.6215) Prec@1 36.250 (36.506) Prec@5 65.625 (67.174) Epoch: [12][7600/11272] Time 0.877 (0.836) Data 0.002 (0.002) Loss 2.6291 (2.6215) Prec@1 37.500 (36.505) Prec@5 68.750 (67.175) Epoch: [12][7610/11272] Time 0.779 (0.836) Data 0.005 (0.002) Loss 2.7459 (2.6215) Prec@1 35.000 (36.506) Prec@5 66.250 (67.175) Epoch: [12][7620/11272] Time 0.756 (0.836) Data 0.002 (0.002) Loss 2.6864 (2.6216) Prec@1 31.875 (36.503) Prec@5 64.375 (67.174) Epoch: [12][7630/11272] Time 0.892 (0.836) Data 0.001 (0.002) Loss 2.4751 (2.6216) Prec@1 36.875 (36.503) Prec@5 70.625 (67.176) Epoch: [12][7640/11272] Time 0.900 (0.836) Data 0.002 (0.002) Loss 2.6473 (2.6218) Prec@1 36.875 (36.500) Prec@5 66.875 (67.172) Epoch: [12][7650/11272] Time 0.784 (0.836) Data 0.002 (0.002) Loss 2.3572 (2.6217) Prec@1 40.625 (36.499) Prec@5 73.125 (67.173) Epoch: [12][7660/11272] Time 0.761 (0.836) Data 0.004 (0.002) Loss 2.7253 (2.6218) Prec@1 31.875 (36.497) Prec@5 67.500 (67.173) Epoch: [12][7670/11272] Time 0.921 (0.836) Data 0.002 (0.002) Loss 2.6841 (2.6218) Prec@1 35.000 (36.499) Prec@5 68.125 (67.171) Epoch: [12][7680/11272] Time 0.885 (0.836) Data 0.001 (0.002) Loss 2.4734 (2.6218) Prec@1 40.625 (36.501) Prec@5 72.500 (67.172) Epoch: [12][7690/11272] Time 0.741 (0.836) Data 0.002 (0.002) Loss 2.5533 (2.6218) Prec@1 36.875 (36.501) Prec@5 64.375 (67.170) Epoch: [12][7700/11272] Time 0.925 (0.836) Data 0.002 (0.002) Loss 2.6458 (2.6218) Prec@1 36.875 (36.501) Prec@5 66.875 (67.171) Epoch: [12][7710/11272] Time 0.970 (0.836) Data 0.002 (0.002) Loss 2.6866 (2.6218) Prec@1 31.875 (36.499) Prec@5 68.750 (67.171) Epoch: [12][7720/11272] Time 0.749 (0.836) Data 0.001 (0.002) Loss 2.4841 (2.6218) Prec@1 39.375 (36.498) Prec@5 75.000 (67.173) Epoch: [12][7730/11272] Time 0.745 (0.836) Data 0.002 (0.002) Loss 2.4938 (2.6216) Prec@1 43.125 (36.502) Prec@5 66.875 (67.175) Epoch: [12][7740/11272] Time 0.892 (0.836) Data 0.002 (0.002) Loss 2.6792 (2.6216) Prec@1 40.000 (36.502) Prec@5 65.000 (67.174) Epoch: [12][7750/11272] Time 0.898 (0.836) Data 0.002 (0.002) Loss 2.5977 (2.6216) Prec@1 31.875 (36.503) Prec@5 69.375 (67.175) Epoch: [12][7760/11272] Time 0.784 (0.836) Data 0.001 (0.002) Loss 2.8331 (2.6215) Prec@1 32.500 (36.505) Prec@5 63.125 (67.177) Epoch: [12][7770/11272] Time 0.768 (0.836) Data 0.002 (0.002) Loss 2.5671 (2.6215) Prec@1 36.875 (36.504) Prec@5 66.875 (67.177) Epoch: [12][7780/11272] Time 0.846 (0.836) Data 0.002 (0.002) Loss 2.5766 (2.6215) Prec@1 38.125 (36.503) Prec@5 68.125 (67.177) Epoch: [12][7790/11272] Time 0.869 (0.836) Data 0.001 (0.002) Loss 2.7643 (2.6216) Prec@1 35.000 (36.501) Prec@5 65.000 (67.174) Epoch: [12][7800/11272] Time 0.803 (0.836) Data 0.002 (0.002) Loss 2.7113 (2.6216) Prec@1 29.375 (36.502) Prec@5 69.375 (67.174) Epoch: [12][7810/11272] Time 0.761 (0.836) Data 0.001 (0.002) Loss 2.4589 (2.6216) Prec@1 41.250 (36.502) Prec@5 70.625 (67.174) Epoch: [12][7820/11272] Time 0.878 (0.836) Data 0.002 (0.002) Loss 2.6605 (2.6216) Prec@1 35.000 (36.503) Prec@5 64.375 (67.173) Epoch: [12][7830/11272] Time 0.787 (0.836) Data 0.003 (0.002) Loss 2.5937 (2.6215) Prec@1 38.750 (36.504) Prec@5 70.000 (67.175) Epoch: [12][7840/11272] Time 0.788 (0.836) Data 0.002 (0.002) Loss 2.6805 (2.6215) Prec@1 34.375 (36.504) Prec@5 67.500 (67.175) Epoch: [12][7850/11272] Time 0.898 (0.836) Data 0.002 (0.002) Loss 2.4464 (2.6214) Prec@1 38.125 (36.505) Prec@5 71.250 (67.178) Epoch: [12][7860/11272] Time 0.906 (0.836) Data 0.002 (0.002) Loss 2.8863 (2.6214) Prec@1 31.875 (36.505) Prec@5 60.000 (67.176) Epoch: [12][7870/11272] Time 0.754 (0.836) Data 0.001 (0.002) Loss 2.6034 (2.6215) Prec@1 30.000 (36.503) Prec@5 68.750 (67.174) Epoch: [12][7880/11272] Time 0.808 (0.836) Data 0.002 (0.002) Loss 2.5942 (2.6215) Prec@1 37.500 (36.503) Prec@5 68.750 (67.174) Epoch: [12][7890/11272] Time 0.939 (0.836) Data 0.002 (0.002) Loss 2.2931 (2.6214) Prec@1 41.875 (36.505) Prec@5 73.125 (67.177) Epoch: [12][7900/11272] Time 0.874 (0.836) Data 0.001 (0.002) Loss 2.4673 (2.6214) Prec@1 38.750 (36.506) Prec@5 70.625 (67.179) Epoch: [12][7910/11272] Time 0.766 (0.836) Data 0.002 (0.002) Loss 2.6024 (2.6213) Prec@1 35.000 (36.507) Prec@5 65.625 (67.181) Epoch: [12][7920/11272] Time 0.786 (0.836) Data 0.002 (0.002) Loss 2.3854 (2.6213) Prec@1 37.500 (36.506) Prec@5 75.000 (67.182) Epoch: [12][7930/11272] Time 0.829 (0.836) Data 0.001 (0.002) Loss 2.3884 (2.6212) Prec@1 40.625 (36.509) Prec@5 70.625 (67.184) Epoch: [12][7940/11272] Time 0.857 (0.836) Data 0.001 (0.002) Loss 2.5775 (2.6213) Prec@1 34.375 (36.508) Prec@5 68.750 (67.182) Epoch: [12][7950/11272] Time 0.784 (0.836) Data 0.002 (0.002) Loss 2.7621 (2.6213) Prec@1 36.250 (36.506) Prec@5 66.875 (67.183) Epoch: [12][7960/11272] Time 0.885 (0.836) Data 0.001 (0.002) Loss 2.3562 (2.6212) Prec@1 43.750 (36.508) Prec@5 73.125 (67.184) Epoch: [12][7970/11272] Time 0.850 (0.836) Data 0.001 (0.002) Loss 2.6413 (2.6213) Prec@1 41.250 (36.508) Prec@5 68.125 (67.184) Epoch: [12][7980/11272] Time 0.750 (0.836) Data 0.001 (0.002) Loss 2.6344 (2.6213) Prec@1 33.125 (36.505) Prec@5 70.000 (67.182) Epoch: [12][7990/11272] Time 0.752 (0.836) Data 0.001 (0.002) Loss 2.5334 (2.6214) Prec@1 36.250 (36.503) Prec@5 65.000 (67.180) Epoch: [12][8000/11272] Time 0.871 (0.836) Data 0.002 (0.002) Loss 2.6022 (2.6213) Prec@1 36.875 (36.503) Prec@5 70.000 (67.182) Epoch: [12][8010/11272] Time 0.875 (0.836) Data 0.001 (0.002) Loss 2.8959 (2.6213) Prec@1 30.625 (36.501) Prec@5 61.875 (67.180) Epoch: [12][8020/11272] Time 0.733 (0.836) Data 0.001 (0.002) Loss 2.6688 (2.6213) Prec@1 37.500 (36.502) Prec@5 63.125 (67.181) Epoch: [12][8030/11272] Time 0.731 (0.836) Data 0.001 (0.002) Loss 2.8978 (2.6213) Prec@1 33.125 (36.502) Prec@5 61.250 (67.182) Epoch: [12][8040/11272] Time 0.883 (0.836) Data 0.001 (0.002) Loss 2.4485 (2.6212) Prec@1 38.750 (36.503) Prec@5 68.125 (67.184) Epoch: [12][8050/11272] Time 0.935 (0.836) Data 0.002 (0.002) Loss 2.7168 (2.6212) Prec@1 37.500 (36.503) Prec@5 66.250 (67.183) Epoch: [12][8060/11272] Time 0.763 (0.836) Data 0.002 (0.002) Loss 2.8643 (2.6212) Prec@1 35.000 (36.504) Prec@5 65.000 (67.183) Epoch: [12][8070/11272] Time 0.809 (0.836) Data 0.002 (0.002) Loss 2.5190 (2.6212) Prec@1 40.625 (36.505) Prec@5 64.375 (67.182) Epoch: [12][8080/11272] Time 0.821 (0.836) Data 0.001 (0.002) Loss 2.6225 (2.6214) Prec@1 35.000 (36.503) Prec@5 68.125 (67.179) Epoch: [12][8090/11272] Time 0.773 (0.836) Data 0.004 (0.002) Loss 2.4626 (2.6214) Prec@1 38.750 (36.504) Prec@5 73.750 (67.177) Epoch: [12][8100/11272] Time 0.749 (0.836) Data 0.002 (0.002) Loss 2.7409 (2.6213) Prec@1 31.875 (36.504) Prec@5 59.375 (67.176) Epoch: [12][8110/11272] Time 0.914 (0.836) Data 0.002 (0.002) Loss 2.6381 (2.6213) Prec@1 40.625 (36.505) Prec@5 65.000 (67.177) Epoch: [12][8120/11272] Time 0.843 (0.836) Data 0.001 (0.002) Loss 2.7031 (2.6213) Prec@1 33.125 (36.505) Prec@5 66.875 (67.177) Epoch: [12][8130/11272] Time 0.768 (0.836) Data 0.002 (0.002) Loss 2.7169 (2.6214) Prec@1 33.750 (36.504) Prec@5 65.625 (67.177) Epoch: [12][8140/11272] Time 0.769 (0.836) Data 0.002 (0.002) Loss 2.5378 (2.6214) Prec@1 41.875 (36.503) Prec@5 68.750 (67.177) Epoch: [12][8150/11272] Time 0.889 (0.836) Data 0.001 (0.002) Loss 2.6876 (2.6214) Prec@1 36.875 (36.503) Prec@5 66.250 (67.177) Epoch: [12][8160/11272] Time 0.855 (0.836) Data 0.002 (0.002) Loss 2.6303 (2.6214) Prec@1 38.125 (36.502) Prec@5 69.375 (67.176) Epoch: [12][8170/11272] Time 0.766 (0.836) Data 0.002 (0.002) Loss 2.5648 (2.6214) Prec@1 40.000 (36.503) Prec@5 69.375 (67.177) Epoch: [12][8180/11272] Time 0.783 (0.836) Data 0.002 (0.002) Loss 2.6491 (2.6214) Prec@1 36.875 (36.505) Prec@5 65.625 (67.177) Epoch: [12][8190/11272] Time 0.888 (0.836) Data 0.001 (0.002) Loss 2.8348 (2.6213) Prec@1 28.750 (36.506) Prec@5 65.000 (67.178) Epoch: [12][8200/11272] Time 0.908 (0.836) Data 0.002 (0.002) Loss 2.5464 (2.6213) Prec@1 38.125 (36.504) Prec@5 67.500 (67.177) Epoch: [12][8210/11272] Time 0.764 (0.836) Data 0.002 (0.002) Loss 2.5960 (2.6214) Prec@1 39.375 (36.502) Prec@5 66.250 (67.175) Epoch: [12][8220/11272] Time 0.946 (0.836) Data 0.002 (0.002) Loss 2.9159 (2.6214) Prec@1 35.625 (36.504) Prec@5 61.875 (67.175) Epoch: [12][8230/11272] Time 0.878 (0.836) Data 0.001 (0.002) Loss 2.8153 (2.6214) Prec@1 32.500 (36.502) Prec@5 65.000 (67.174) Epoch: [12][8240/11272] Time 0.770 (0.836) Data 0.002 (0.002) Loss 2.6092 (2.6214) Prec@1 31.875 (36.501) Prec@5 66.875 (67.174) Epoch: [12][8250/11272] Time 0.761 (0.836) Data 0.001 (0.002) Loss 2.5570 (2.6214) Prec@1 37.500 (36.500) Prec@5 68.750 (67.175) Epoch: [12][8260/11272] Time 0.863 (0.836) Data 0.001 (0.002) Loss 2.6747 (2.6214) Prec@1 41.250 (36.502) Prec@5 62.500 (67.176) Epoch: [12][8270/11272] Time 0.897 (0.836) Data 0.002 (0.002) Loss 2.7795 (2.6214) Prec@1 31.250 (36.502) Prec@5 62.500 (67.173) Epoch: [12][8280/11272] Time 0.807 (0.836) Data 0.002 (0.002) Loss 2.5499 (2.6214) Prec@1 35.625 (36.501) Prec@5 68.125 (67.172) Epoch: [12][8290/11272] Time 0.901 (0.836) Data 0.003 (0.002) Loss 2.8452 (2.6215) Prec@1 31.875 (36.499) Prec@5 65.000 (67.172) Epoch: [12][8300/11272] Time 0.885 (0.836) Data 0.001 (0.002) Loss 2.5131 (2.6213) Prec@1 38.125 (36.502) Prec@5 70.625 (67.175) Epoch: [12][8310/11272] Time 0.862 (0.836) Data 0.002 (0.002) Loss 2.6938 (2.6213) Prec@1 31.250 (36.502) Prec@5 66.250 (67.177) Epoch: [12][8320/11272] Time 0.793 (0.836) Data 0.002 (0.002) Loss 2.4802 (2.6212) Prec@1 40.000 (36.504) Prec@5 68.125 (67.179) Epoch: [12][8330/11272] Time 0.742 (0.836) Data 0.002 (0.002) Loss 2.5110 (2.6212) Prec@1 43.125 (36.504) Prec@5 70.000 (67.179) Epoch: [12][8340/11272] Time 0.883 (0.836) Data 0.002 (0.002) Loss 2.8264 (2.6213) Prec@1 33.125 (36.503) Prec@5 62.500 (67.176) Epoch: [12][8350/11272] Time 0.999 (0.836) Data 0.002 (0.002) Loss 2.4887 (2.6213) Prec@1 38.750 (36.502) Prec@5 73.125 (67.177) Epoch: [12][8360/11272] Time 0.839 (0.836) Data 0.002 (0.002) Loss 2.6031 (2.6213) Prec@1 36.875 (36.500) Prec@5 70.625 (67.176) Epoch: [12][8370/11272] Time 0.918 (0.836) Data 0.002 (0.002) Loss 2.4746 (2.6213) Prec@1 38.125 (36.499) Prec@5 68.125 (67.176) Epoch: [12][8380/11272] Time 0.851 (0.836) Data 0.001 (0.002) Loss 2.5259 (2.6213) Prec@1 34.375 (36.499) Prec@5 70.625 (67.176) Epoch: [12][8390/11272] Time 0.759 (0.836) Data 0.001 (0.002) Loss 2.8100 (2.6213) Prec@1 35.625 (36.500) Prec@5 59.375 (67.177) Epoch: [12][8400/11272] Time 0.775 (0.836) Data 0.002 (0.002) Loss 2.5835 (2.6212) Prec@1 34.375 (36.500) Prec@5 69.375 (67.179) Epoch: [12][8410/11272] Time 0.907 (0.836) Data 0.001 (0.002) Loss 2.9180 (2.6213) Prec@1 31.250 (36.498) Prec@5 65.625 (67.178) Epoch: [12][8420/11272] Time 0.869 (0.836) Data 0.001 (0.002) Loss 2.3611 (2.6213) Prec@1 38.750 (36.498) Prec@5 71.250 (67.177) Epoch: [12][8430/11272] Time 0.762 (0.836) Data 0.002 (0.002) Loss 2.5792 (2.6213) Prec@1 41.875 (36.498) Prec@5 68.125 (67.178) Epoch: [12][8440/11272] Time 0.772 (0.836) Data 0.002 (0.002) Loss 2.1009 (2.6212) Prec@1 46.250 (36.501) Prec@5 73.750 (67.181) Epoch: [12][8450/11272] Time 0.882 (0.836) Data 0.002 (0.002) Loss 2.6351 (2.6211) Prec@1 35.000 (36.503) Prec@5 68.125 (67.184) Epoch: [12][8460/11272] Time 0.945 (0.836) Data 0.002 (0.002) Loss 2.7331 (2.6211) Prec@1 36.250 (36.503) Prec@5 64.375 (67.184) Epoch: [12][8470/11272] Time 0.762 (0.836) Data 0.002 (0.002) Loss 2.6992 (2.6211) Prec@1 40.000 (36.504) Prec@5 67.500 (67.184) Epoch: [12][8480/11272] Time 0.868 (0.836) Data 0.002 (0.002) Loss 3.0028 (2.6211) Prec@1 30.625 (36.504) Prec@5 60.625 (67.184) Epoch: [12][8490/11272] Time 0.850 (0.836) Data 0.001 (0.002) Loss 2.4707 (2.6211) Prec@1 41.875 (36.504) Prec@5 70.000 (67.183) Epoch: [12][8500/11272] Time 0.740 (0.836) Data 0.002 (0.002) Loss 2.5713 (2.6211) Prec@1 36.875 (36.505) Prec@5 70.625 (67.184) Epoch: [12][8510/11272] Time 0.783 (0.836) Data 0.002 (0.002) Loss 2.5647 (2.6211) Prec@1 43.125 (36.505) Prec@5 71.250 (67.182) Epoch: [12][8520/11272] Time 0.866 (0.836) Data 0.001 (0.002) Loss 2.7470 (2.6212) Prec@1 31.250 (36.504) Prec@5 66.250 (67.182) Epoch: [12][8530/11272] Time 0.883 (0.836) Data 0.002 (0.002) Loss 2.6735 (2.6212) Prec@1 38.750 (36.506) Prec@5 63.750 (67.181) Epoch: [12][8540/11272] Time 0.750 (0.836) Data 0.002 (0.002) Loss 2.8208 (2.6211) Prec@1 35.625 (36.507) Prec@5 64.375 (67.182) Epoch: [12][8550/11272] Time 0.766 (0.836) Data 0.002 (0.002) Loss 2.3927 (2.6211) Prec@1 40.000 (36.508) Prec@5 71.875 (67.184) Epoch: [12][8560/11272] Time 0.858 (0.836) Data 0.001 (0.002) Loss 2.6173 (2.6211) Prec@1 36.250 (36.509) Prec@5 67.500 (67.183) Epoch: [12][8570/11272] Time 0.845 (0.836) Data 0.001 (0.002) Loss 2.5832 (2.6210) Prec@1 39.375 (36.511) Prec@5 68.125 (67.184) Epoch: [12][8580/11272] Time 0.787 (0.836) Data 0.002 (0.002) Loss 2.8351 (2.6211) Prec@1 33.125 (36.510) Prec@5 65.000 (67.184) Epoch: [12][8590/11272] Time 0.743 (0.836) Data 0.002 (0.002) Loss 2.8635 (2.6211) Prec@1 33.125 (36.511) Prec@5 63.750 (67.185) Epoch: [12][8600/11272] Time 0.883 (0.836) Data 0.002 (0.002) Loss 2.6842 (2.6211) Prec@1 40.000 (36.511) Prec@5 67.500 (67.184) Epoch: [12][8610/11272] Time 0.830 (0.836) Data 0.002 (0.002) Loss 2.5953 (2.6211) Prec@1 36.875 (36.511) Prec@5 67.500 (67.183) Epoch: [12][8620/11272] Time 0.763 (0.836) Data 0.001 (0.002) Loss 2.2131 (2.6210) Prec@1 42.500 (36.512) Prec@5 75.000 (67.185) Epoch: [12][8630/11272] Time 0.880 (0.836) Data 0.002 (0.002) Loss 2.7971 (2.6211) Prec@1 31.875 (36.511) Prec@5 62.500 (67.183) Epoch: [12][8640/11272] Time 0.876 (0.836) Data 0.002 (0.002) Loss 2.8411 (2.6212) Prec@1 33.125 (36.508) Prec@5 60.000 (67.180) Epoch: [12][8650/11272] Time 0.793 (0.836) Data 0.001 (0.002) Loss 2.5475 (2.6212) Prec@1 38.750 (36.509) Prec@5 66.250 (67.179) Epoch: [12][8660/11272] Time 0.766 (0.836) Data 0.002 (0.002) Loss 2.3946 (2.6211) Prec@1 45.000 (36.509) Prec@5 75.625 (67.181) Epoch: [12][8670/11272] Time 0.836 (0.836) Data 0.002 (0.002) Loss 2.4012 (2.6211) Prec@1 41.875 (36.511) Prec@5 66.875 (67.181) Epoch: [12][8680/11272] Time 0.868 (0.836) Data 0.002 (0.002) Loss 2.6892 (2.6211) Prec@1 31.250 (36.511) Prec@5 66.250 (67.183) Epoch: [12][8690/11272] Time 0.817 (0.836) Data 0.002 (0.002) Loss 2.7589 (2.6211) Prec@1 36.875 (36.513) Prec@5 63.125 (67.182) Epoch: [12][8700/11272] Time 0.757 (0.836) Data 0.001 (0.002) Loss 2.9975 (2.6211) Prec@1 33.125 (36.512) Prec@5 57.500 (67.180) Epoch: [12][8710/11272] Time 0.930 (0.836) Data 0.002 (0.002) Loss 2.6489 (2.6212) Prec@1 30.625 (36.511) Prec@5 70.625 (67.180) Epoch: [12][8720/11272] Time 0.891 (0.836) Data 0.001 (0.002) Loss 2.7282 (2.6211) Prec@1 36.875 (36.513) Prec@5 63.750 (67.180) Epoch: [12][8730/11272] Time 0.784 (0.836) Data 0.002 (0.002) Loss 2.5828 (2.6211) Prec@1 35.625 (36.514) Prec@5 67.500 (67.181) Epoch: [12][8740/11272] Time 0.750 (0.836) Data 0.002 (0.002) Loss 2.7922 (2.6211) Prec@1 37.500 (36.515) Prec@5 67.500 (67.183) Epoch: [12][8750/11272] Time 0.860 (0.836) Data 0.001 (0.002) Loss 2.4321 (2.6210) Prec@1 40.625 (36.517) Prec@5 68.125 (67.185) Epoch: [12][8760/11272] Time 0.748 (0.835) Data 0.004 (0.002) Loss 2.5457 (2.6209) Prec@1 36.875 (36.519) Prec@5 68.125 (67.187) Epoch: [12][8770/11272] Time 0.785 (0.836) Data 0.002 (0.002) Loss 2.8466 (2.6209) Prec@1 35.000 (36.519) Prec@5 63.750 (67.187) Epoch: [12][8780/11272] Time 0.900 (0.836) Data 0.002 (0.002) Loss 2.9505 (2.6209) Prec@1 32.500 (36.521) Prec@5 65.000 (67.186) Epoch: [12][8790/11272] Time 0.908 (0.835) Data 0.002 (0.002) Loss 2.7761 (2.6209) Prec@1 32.500 (36.522) Prec@5 66.250 (67.187) Epoch: [12][8800/11272] Time 0.752 (0.835) Data 0.001 (0.002) Loss 2.8559 (2.6209) Prec@1 31.250 (36.521) Prec@5 61.250 (67.186) Epoch: [12][8810/11272] Time 0.740 (0.835) Data 0.001 (0.002) Loss 2.6521 (2.6209) Prec@1 34.375 (36.520) Prec@5 68.125 (67.187) Epoch: [12][8820/11272] Time 0.880 (0.836) Data 0.001 (0.002) Loss 2.5998 (2.6208) Prec@1 42.500 (36.521) Prec@5 65.625 (67.189) Epoch: [12][8830/11272] Time 0.866 (0.835) Data 0.001 (0.002) Loss 2.7639 (2.6209) Prec@1 33.125 (36.520) Prec@5 63.750 (67.187) Epoch: [12][8840/11272] Time 0.750 (0.835) Data 0.002 (0.002) Loss 2.6801 (2.6209) Prec@1 38.125 (36.519) Prec@5 67.500 (67.187) Epoch: [12][8850/11272] Time 0.750 (0.835) Data 0.002 (0.002) Loss 2.8176 (2.6209) Prec@1 28.750 (36.519) Prec@5 68.750 (67.189) Epoch: [12][8860/11272] Time 0.892 (0.835) Data 0.001 (0.002) Loss 2.6588 (2.6210) Prec@1 40.625 (36.518) Prec@5 66.875 (67.189) Epoch: [12][8870/11272] Time 0.870 (0.835) Data 0.003 (0.002) Loss 2.2199 (2.6209) Prec@1 43.750 (36.519) Prec@5 75.625 (67.189) Epoch: [12][8880/11272] Time 0.755 (0.835) Data 0.002 (0.002) Loss 2.7618 (2.6210) Prec@1 32.500 (36.517) Prec@5 66.250 (67.187) Epoch: [12][8890/11272] Time 0.869 (0.835) Data 0.001 (0.002) Loss 2.7437 (2.6209) Prec@1 33.750 (36.518) Prec@5 66.875 (67.188) Epoch: [12][8900/11272] Time 0.893 (0.835) Data 0.001 (0.002) Loss 2.4338 (2.6210) Prec@1 39.375 (36.518) Prec@5 72.500 (67.188) Epoch: [12][8910/11272] Time 0.748 (0.835) Data 0.001 (0.002) Loss 2.5008 (2.6209) Prec@1 35.000 (36.519) Prec@5 70.000 (67.189) Epoch: [12][8920/11272] Time 0.769 (0.835) Data 0.002 (0.002) Loss 2.8507 (2.6210) Prec@1 30.000 (36.517) Prec@5 62.500 (67.188) Epoch: [12][8930/11272] Time 0.893 (0.835) Data 0.002 (0.002) Loss 2.7619 (2.6210) Prec@1 33.750 (36.515) Prec@5 69.375 (67.187) Epoch: [12][8940/11272] Time 0.833 (0.835) Data 0.001 (0.002) Loss 2.8378 (2.6211) Prec@1 30.000 (36.514) Prec@5 65.625 (67.187) Epoch: [12][8950/11272] Time 0.765 (0.835) Data 0.002 (0.002) Loss 2.8210 (2.6211) Prec@1 33.125 (36.514) Prec@5 60.000 (67.184) Epoch: [12][8960/11272] Time 0.739 (0.835) Data 0.001 (0.002) Loss 2.7326 (2.6211) Prec@1 33.750 (36.513) Prec@5 65.000 (67.185) Epoch: [12][8970/11272] Time 0.850 (0.835) Data 0.001 (0.002) Loss 2.6442 (2.6211) Prec@1 33.750 (36.514) Prec@5 63.750 (67.185) Epoch: [12][8980/11272] Time 0.903 (0.835) Data 0.002 (0.002) Loss 2.3385 (2.6210) Prec@1 44.375 (36.515) Prec@5 72.500 (67.186) Epoch: [12][8990/11272] Time 0.775 (0.835) Data 0.002 (0.002) Loss 2.3196 (2.6210) Prec@1 41.875 (36.514) Prec@5 72.500 (67.185) Epoch: [12][9000/11272] Time 0.753 (0.835) Data 0.002 (0.002) Loss 2.6481 (2.6210) Prec@1 30.625 (36.514) Prec@5 67.500 (67.186) Epoch: [12][9010/11272] Time 0.921 (0.835) Data 0.002 (0.002) Loss 2.5390 (2.6211) Prec@1 41.875 (36.512) Prec@5 63.125 (67.184) Epoch: [12][9020/11272] Time 0.776 (0.835) Data 0.004 (0.002) Loss 2.4373 (2.6210) Prec@1 38.750 (36.513) Prec@5 69.375 (67.185) Epoch: [12][9030/11272] Time 0.746 (0.835) Data 0.001 (0.002) Loss 2.3106 (2.6211) Prec@1 39.375 (36.511) Prec@5 74.375 (67.184) Epoch: [12][9040/11272] Time 0.929 (0.835) Data 0.002 (0.002) Loss 2.8862 (2.6211) Prec@1 28.125 (36.510) Prec@5 61.875 (67.183) Epoch: [12][9050/11272] Time 0.936 (0.835) Data 0.002 (0.002) Loss 2.5871 (2.6211) Prec@1 37.500 (36.509) Prec@5 66.250 (67.183) Epoch: [12][9060/11272] Time 0.785 (0.835) Data 0.002 (0.002) Loss 2.6400 (2.6210) Prec@1 40.000 (36.512) Prec@5 70.625 (67.184) Epoch: [12][9070/11272] Time 0.749 (0.835) Data 0.002 (0.002) Loss 2.3660 (2.6209) Prec@1 37.500 (36.513) Prec@5 76.250 (67.185) Epoch: [12][9080/11272] Time 0.845 (0.835) Data 0.001 (0.002) Loss 2.7230 (2.6210) Prec@1 33.125 (36.510) Prec@5 65.000 (67.182) Epoch: [12][9090/11272] Time 0.887 (0.835) Data 0.002 (0.002) Loss 2.4336 (2.6210) Prec@1 40.625 (36.510) Prec@5 70.000 (67.182) Epoch: [12][9100/11272] Time 0.783 (0.835) Data 0.002 (0.002) Loss 2.7456 (2.6210) Prec@1 34.375 (36.511) Prec@5 65.625 (67.181) Epoch: [12][9110/11272] Time 0.754 (0.835) Data 0.002 (0.002) Loss 2.8468 (2.6212) Prec@1 33.125 (36.509) Prec@5 61.875 (67.179) Epoch: [12][9120/11272] Time 0.875 (0.835) Data 0.001 (0.002) Loss 2.6812 (2.6212) Prec@1 38.125 (36.510) Prec@5 65.625 (67.179) Epoch: [12][9130/11272] Time 0.910 (0.835) Data 0.002 (0.002) Loss 2.5212 (2.6211) Prec@1 36.250 (36.511) Prec@5 71.250 (67.180) Epoch: [12][9140/11272] Time 0.789 (0.835) Data 0.002 (0.002) Loss 2.5544 (2.6211) Prec@1 38.750 (36.511) Prec@5 67.500 (67.180) Epoch: [12][9150/11272] Time 0.896 (0.835) Data 0.002 (0.002) Loss 2.7497 (2.6212) Prec@1 34.375 (36.510) Prec@5 69.375 (67.178) Epoch: [12][9160/11272] Time 0.845 (0.835) Data 0.001 (0.002) Loss 2.3637 (2.6212) Prec@1 43.125 (36.513) Prec@5 70.000 (67.178) Epoch: [12][9170/11272] Time 0.774 (0.835) Data 0.002 (0.002) Loss 2.5949 (2.6211) Prec@1 36.250 (36.514) Prec@5 68.125 (67.179) Epoch: [12][9180/11272] Time 0.762 (0.835) Data 0.001 (0.002) Loss 2.4909 (2.6210) Prec@1 31.250 (36.515) Prec@5 68.750 (67.181) Epoch: [12][9190/11272] Time 0.931 (0.835) Data 0.001 (0.002) Loss 2.7938 (2.6211) Prec@1 30.625 (36.512) Prec@5 62.500 (67.179) Epoch: [12][9200/11272] Time 0.869 (0.835) Data 0.001 (0.002) Loss 2.4905 (2.6210) Prec@1 38.750 (36.511) Prec@5 71.250 (67.179) Epoch: [12][9210/11272] Time 0.765 (0.835) Data 0.001 (0.002) Loss 2.7437 (2.6211) Prec@1 38.750 (36.511) Prec@5 66.875 (67.178) Epoch: [12][9220/11272] Time 0.738 (0.835) Data 0.001 (0.002) Loss 2.6060 (2.6210) Prec@1 33.125 (36.510) Prec@5 63.750 (67.178) Epoch: [12][9230/11272] Time 0.907 (0.835) Data 0.001 (0.002) Loss 2.6267 (2.6210) Prec@1 36.250 (36.510) Prec@5 63.750 (67.178) Epoch: [12][9240/11272] Time 0.896 (0.835) Data 0.002 (0.002) Loss 2.7286 (2.6210) Prec@1 35.625 (36.511) Prec@5 68.125 (67.179) Epoch: [12][9250/11272] Time 0.719 (0.835) Data 0.001 (0.002) Loss 2.6495 (2.6211) Prec@1 36.250 (36.510) Prec@5 70.000 (67.179) Epoch: [12][9260/11272] Time 0.779 (0.835) Data 0.002 (0.002) Loss 2.5756 (2.6212) Prec@1 36.250 (36.508) Prec@5 67.500 (67.178) Epoch: [12][9270/11272] Time 0.935 (0.835) Data 0.001 (0.002) Loss 2.7896 (2.6212) Prec@1 31.875 (36.505) Prec@5 64.375 (67.176) Epoch: [12][9280/11272] Time 0.874 (0.835) Data 0.002 (0.002) Loss 2.4939 (2.6211) Prec@1 41.250 (36.508) Prec@5 73.750 (67.178) Epoch: [12][9290/11272] Time 0.763 (0.835) Data 0.001 (0.002) Loss 2.7062 (2.6211) Prec@1 36.875 (36.508) Prec@5 67.500 (67.179) Epoch: [12][9300/11272] Time 0.887 (0.835) Data 0.002 (0.002) Loss 2.4521 (2.6211) Prec@1 40.000 (36.507) Prec@5 71.250 (67.180) Epoch: [12][9310/11272] Time 0.870 (0.835) Data 0.001 (0.002) Loss 2.6460 (2.6212) Prec@1 40.625 (36.506) Prec@5 66.875 (67.178) Epoch: [12][9320/11272] Time 0.784 (0.835) Data 0.002 (0.002) Loss 2.6032 (2.6211) Prec@1 36.250 (36.506) Prec@5 69.375 (67.179) Epoch: [12][9330/11272] Time 0.740 (0.835) Data 0.002 (0.002) Loss 2.5812 (2.6212) Prec@1 36.875 (36.505) Prec@5 66.875 (67.178) Epoch: [12][9340/11272] Time 0.898 (0.835) Data 0.001 (0.002) Loss 2.6367 (2.6212) Prec@1 36.875 (36.504) Prec@5 63.750 (67.175) Epoch: [12][9350/11272] Time 0.924 (0.835) Data 0.002 (0.002) Loss 2.5987 (2.6212) Prec@1 36.875 (36.504) Prec@5 67.500 (67.176) Epoch: [12][9360/11272] Time 0.751 (0.835) Data 0.001 (0.002) Loss 2.5895 (2.6211) Prec@1 36.250 (36.505) Prec@5 68.750 (67.178) Epoch: [12][9370/11272] Time 0.733 (0.835) Data 0.001 (0.002) Loss 2.7511 (2.6211) Prec@1 35.625 (36.505) Prec@5 65.000 (67.176) Epoch: [12][9380/11272] Time 0.916 (0.835) Data 0.002 (0.002) Loss 2.6350 (2.6211) Prec@1 35.625 (36.505) Prec@5 63.750 (67.175) Epoch: [12][9390/11272] Time 0.876 (0.835) Data 0.001 (0.002) Loss 2.6519 (2.6211) Prec@1 35.000 (36.504) Prec@5 68.750 (67.178) Epoch: [12][9400/11272] Time 0.744 (0.835) Data 0.001 (0.002) Loss 2.7927 (2.6211) Prec@1 38.750 (36.506) Prec@5 66.250 (67.180) Epoch: [12][9410/11272] Time 0.761 (0.835) Data 0.001 (0.002) Loss 2.6395 (2.6211) Prec@1 39.375 (36.505) Prec@5 65.000 (67.179) Epoch: [12][9420/11272] Time 0.892 (0.835) Data 0.002 (0.002) Loss 2.7865 (2.6212) Prec@1 32.500 (36.504) Prec@5 62.500 (67.178) Epoch: [12][9430/11272] Time 0.741 (0.835) Data 0.001 (0.002) Loss 2.5191 (2.6212) Prec@1 36.875 (36.504) Prec@5 69.375 (67.178) Epoch: [12][9440/11272] Time 0.747 (0.835) Data 0.001 (0.002) Loss 2.7350 (2.6212) Prec@1 36.250 (36.504) Prec@5 63.750 (67.178) Epoch: [12][9450/11272] Time 0.892 (0.835) Data 0.001 (0.002) Loss 2.4635 (2.6212) Prec@1 40.000 (36.506) Prec@5 71.875 (67.178) Epoch: [12][9460/11272] Time 0.916 (0.835) Data 0.002 (0.002) Loss 2.7561 (2.6211) Prec@1 34.375 (36.508) Prec@5 68.750 (67.177) Epoch: [12][9470/11272] Time 0.766 (0.835) Data 0.002 (0.002) Loss 2.6019 (2.6212) Prec@1 33.125 (36.507) Prec@5 68.125 (67.176) Epoch: [12][9480/11272] Time 0.740 (0.835) Data 0.002 (0.002) Loss 2.5975 (2.6213) Prec@1 39.375 (36.506) Prec@5 65.000 (67.176) Epoch: [12][9490/11272] Time 0.903 (0.835) Data 0.001 (0.002) Loss 2.8499 (2.6213) Prec@1 30.000 (36.506) Prec@5 65.000 (67.175) Epoch: [12][9500/11272] Time 0.845 (0.835) Data 0.001 (0.002) Loss 2.6074 (2.6214) Prec@1 37.500 (36.504) Prec@5 66.875 (67.173) Epoch: [12][9510/11272] Time 0.751 (0.835) Data 0.002 (0.002) Loss 2.4599 (2.6214) Prec@1 37.500 (36.504) Prec@5 68.750 (67.171) Epoch: [12][9520/11272] Time 0.781 (0.835) Data 0.002 (0.002) Loss 2.7516 (2.6215) Prec@1 33.750 (36.501) Prec@5 65.000 (67.169) Epoch: [12][9530/11272] Time 0.849 (0.835) Data 0.001 (0.002) Loss 2.3419 (2.6215) Prec@1 39.375 (36.501) Prec@5 71.250 (67.169) Epoch: [12][9540/11272] Time 0.917 (0.835) Data 0.001 (0.002) Loss 2.4080 (2.6215) Prec@1 41.250 (36.502) Prec@5 72.500 (67.171) Epoch: [12][9550/11272] Time 0.759 (0.835) Data 0.001 (0.002) Loss 2.4441 (2.6215) Prec@1 43.125 (36.503) Prec@5 68.750 (67.171) Epoch: [12][9560/11272] Time 0.875 (0.835) Data 0.001 (0.002) Loss 2.4888 (2.6215) Prec@1 39.375 (36.504) Prec@5 68.750 (67.170) Epoch: [12][9570/11272] Time 0.911 (0.835) Data 0.002 (0.002) Loss 2.6659 (2.6215) Prec@1 40.625 (36.504) Prec@5 67.500 (67.170) Epoch: [12][9580/11272] Time 0.771 (0.835) Data 0.001 (0.002) Loss 2.4631 (2.6215) Prec@1 39.375 (36.505) Prec@5 71.250 (67.170) Epoch: [12][9590/11272] Time 0.751 (0.835) Data 0.001 (0.002) Loss 2.9977 (2.6215) Prec@1 35.625 (36.506) Prec@5 61.250 (67.169) Epoch: [12][9600/11272] Time 0.863 (0.835) Data 0.002 (0.002) Loss 2.8982 (2.6216) Prec@1 29.375 (36.505) Prec@5 58.125 (67.166) Epoch: [12][9610/11272] Time 0.896 (0.835) Data 0.002 (0.002) Loss 2.9614 (2.6217) Prec@1 26.250 (36.500) Prec@5 62.500 (67.164) Epoch: [12][9620/11272] Time 0.757 (0.835) Data 0.002 (0.002) Loss 2.9935 (2.6217) Prec@1 28.750 (36.501) Prec@5 63.125 (67.164) Epoch: [12][9630/11272] Time 0.749 (0.835) Data 0.001 (0.002) Loss 2.7499 (2.6217) Prec@1 31.875 (36.501) Prec@5 64.375 (67.164) Epoch: [12][9640/11272] Time 0.893 (0.835) Data 0.002 (0.002) Loss 2.7403 (2.6217) Prec@1 34.375 (36.501) Prec@5 65.625 (67.163) Epoch: [12][9650/11272] Time 0.900 (0.835) Data 0.003 (0.002) Loss 2.2401 (2.6217) Prec@1 41.875 (36.503) Prec@5 77.500 (67.165) Epoch: [12][9660/11272] Time 0.819 (0.835) Data 0.002 (0.002) Loss 2.8351 (2.6216) Prec@1 33.750 (36.504) Prec@5 70.000 (67.167) Epoch: [12][9670/11272] Time 0.739 (0.835) Data 0.001 (0.002) Loss 2.5817 (2.6216) Prec@1 36.250 (36.503) Prec@5 67.500 (67.167) Epoch: [12][9680/11272] Time 0.858 (0.835) Data 0.001 (0.002) Loss 2.5749 (2.6216) Prec@1 37.500 (36.505) Prec@5 66.250 (67.169) Epoch: [12][9690/11272] Time 0.817 (0.835) Data 0.004 (0.002) Loss 2.5861 (2.6216) Prec@1 35.000 (36.505) Prec@5 66.250 (67.169) Epoch: [12][9700/11272] Time 0.770 (0.835) Data 0.001 (0.002) Loss 2.7165 (2.6216) Prec@1 37.500 (36.505) Prec@5 63.125 (67.169) Epoch: [12][9710/11272] Time 0.863 (0.835) Data 0.001 (0.002) Loss 2.4615 (2.6215) Prec@1 39.375 (36.506) Prec@5 71.250 (67.170) Epoch: [12][9720/11272] Time 0.922 (0.835) Data 0.002 (0.002) Loss 2.8512 (2.6216) Prec@1 28.125 (36.504) Prec@5 59.375 (67.168) Epoch: [12][9730/11272] Time 0.749 (0.835) Data 0.002 (0.002) Loss 2.4136 (2.6215) Prec@1 40.000 (36.507) Prec@5 71.250 (67.169) Epoch: [12][9740/11272] Time 0.749 (0.835) Data 0.002 (0.002) Loss 2.4747 (2.6215) Prec@1 41.250 (36.506) Prec@5 73.750 (67.168) Epoch: [12][9750/11272] Time 0.885 (0.835) Data 0.002 (0.002) Loss 2.7179 (2.6215) Prec@1 31.250 (36.507) Prec@5 66.875 (67.168) Epoch: [12][9760/11272] Time 0.903 (0.835) Data 0.001 (0.002) Loss 2.5170 (2.6215) Prec@1 36.875 (36.507) Prec@5 68.125 (67.168) Epoch: [12][9770/11272] Time 0.753 (0.835) Data 0.002 (0.002) Loss 2.8292 (2.6215) Prec@1 35.000 (36.506) Prec@5 63.750 (67.168) Epoch: [12][9780/11272] Time 0.754 (0.835) Data 0.002 (0.002) Loss 2.5607 (2.6215) Prec@1 34.375 (36.508) Prec@5 68.125 (67.170) Epoch: [12][9790/11272] Time 0.895 (0.835) Data 0.001 (0.002) Loss 2.6165 (2.6215) Prec@1 35.000 (36.508) Prec@5 71.875 (67.170) Epoch: [12][9800/11272] Time 0.875 (0.835) Data 0.002 (0.002) Loss 2.4856 (2.6215) Prec@1 41.250 (36.508) Prec@5 70.000 (67.170) Epoch: [12][9810/11272] Time 0.750 (0.835) Data 0.001 (0.002) Loss 2.7440 (2.6215) Prec@1 37.500 (36.506) Prec@5 65.000 (67.169) Epoch: [12][9820/11272] Time 0.858 (0.835) Data 0.002 (0.002) Loss 2.6782 (2.6215) Prec@1 37.500 (36.507) Prec@5 62.500 (67.168) Epoch: [12][9830/11272] Time 0.907 (0.835) Data 0.002 (0.002) Loss 2.7276 (2.6216) Prec@1 32.500 (36.506) Prec@5 64.375 (67.166) Epoch: [12][9840/11272] Time 0.745 (0.835) Data 0.001 (0.002) Loss 2.6148 (2.6216) Prec@1 31.875 (36.506) Prec@5 70.000 (67.166) Epoch: [12][9850/11272] Time 0.775 (0.835) Data 0.001 (0.002) Loss 2.6462 (2.6216) Prec@1 33.125 (36.504) Prec@5 65.000 (67.165) Epoch: [12][9860/11272] Time 0.899 (0.835) Data 0.001 (0.002) Loss 2.6416 (2.6216) Prec@1 37.500 (36.504) Prec@5 67.500 (67.165) Epoch: [12][9870/11272] Time 0.881 (0.835) Data 0.001 (0.002) Loss 2.8192 (2.6217) Prec@1 33.750 (36.501) Prec@5 65.625 (67.165) Epoch: [12][9880/11272] Time 0.750 (0.835) Data 0.002 (0.002) Loss 2.5667 (2.6217) Prec@1 38.125 (36.502) Prec@5 66.875 (67.165) Epoch: [12][9890/11272] Time 0.783 (0.835) Data 0.002 (0.002) Loss 2.4743 (2.6216) Prec@1 37.500 (36.503) Prec@5 72.500 (67.166) Epoch: [12][9900/11272] Time 0.856 (0.835) Data 0.002 (0.002) Loss 2.5160 (2.6216) Prec@1 36.250 (36.503) Prec@5 72.500 (67.166) Epoch: [12][9910/11272] Time 0.877 (0.835) Data 0.002 (0.002) Loss 2.4482 (2.6216) Prec@1 40.625 (36.504) Prec@5 72.500 (67.167) Epoch: [12][9920/11272] Time 0.778 (0.835) Data 0.001 (0.002) Loss 2.8212 (2.6216) Prec@1 30.625 (36.502) Prec@5 61.875 (67.167) Epoch: [12][9930/11272] Time 0.755 (0.835) Data 0.002 (0.002) Loss 2.7377 (2.6217) Prec@1 37.500 (36.501) Prec@5 65.625 (67.166) Epoch: [12][9940/11272] Time 0.866 (0.835) Data 0.002 (0.002) Loss 2.6082 (2.6216) Prec@1 36.250 (36.502) Prec@5 71.875 (67.167) Epoch: [12][9950/11272] Time 0.753 (0.835) Data 0.004 (0.002) Loss 2.5178 (2.6215) Prec@1 39.375 (36.500) Prec@5 70.625 (67.167) Epoch: [12][9960/11272] Time 0.742 (0.835) Data 0.001 (0.002) Loss 2.8735 (2.6215) Prec@1 33.750 (36.501) Prec@5 63.125 (67.167) Epoch: [12][9970/11272] Time 0.921 (0.835) Data 0.002 (0.002) Loss 2.7993 (2.6214) Prec@1 33.750 (36.501) Prec@5 70.625 (67.169) Epoch: [12][9980/11272] Time 0.892 (0.835) Data 0.002 (0.002) Loss 2.6901 (2.6214) Prec@1 37.500 (36.503) Prec@5 66.250 (67.170) Epoch: [12][9990/11272] Time 0.793 (0.835) Data 0.002 (0.002) Loss 2.6385 (2.6214) Prec@1 33.750 (36.502) Prec@5 69.375 (67.170) Epoch: [12][10000/11272] Time 0.738 (0.835) Data 0.002 (0.002) Loss 2.5637 (2.6214) Prec@1 34.375 (36.504) Prec@5 71.875 (67.171) Epoch: [12][10010/11272] Time 0.937 (0.835) Data 0.002 (0.002) Loss 2.4092 (2.6213) Prec@1 36.250 (36.505) Prec@5 69.375 (67.173) Epoch: [12][10020/11272] Time 0.881 (0.835) Data 0.002 (0.002) Loss 2.5850 (2.6212) Prec@1 36.250 (36.507) Prec@5 68.125 (67.174) Epoch: [12][10030/11272] Time 0.855 (0.835) Data 0.002 (0.002) Loss 2.5662 (2.6213) Prec@1 33.125 (36.505) Prec@5 67.500 (67.174) Epoch: [12][10040/11272] Time 0.742 (0.835) Data 0.002 (0.002) Loss 2.5873 (2.6214) Prec@1 43.750 (36.503) Prec@5 62.500 (67.170) Epoch: [12][10050/11272] Time 0.909 (0.835) Data 0.001 (0.002) Loss 2.6605 (2.6214) Prec@1 39.375 (36.505) Prec@5 61.250 (67.169) Epoch: [12][10060/11272] Time 0.879 (0.835) Data 0.002 (0.002) Loss 2.8446 (2.6213) Prec@1 32.500 (36.506) Prec@5 62.500 (67.172) Epoch: [12][10070/11272] Time 0.741 (0.835) Data 0.002 (0.002) Loss 2.5844 (2.6213) Prec@1 35.625 (36.507) Prec@5 64.375 (67.172) Epoch: [12][10080/11272] Time 0.871 (0.835) Data 0.002 (0.002) Loss 2.6185 (2.6212) Prec@1 34.375 (36.508) Prec@5 69.375 (67.172) Epoch: [12][10090/11272] Time 0.885 (0.835) Data 0.001 (0.002) Loss 2.5709 (2.6212) Prec@1 41.250 (36.508) Prec@5 71.875 (67.173) Epoch: [12][10100/11272] Time 0.753 (0.835) Data 0.001 (0.002) Loss 2.4572 (2.6212) Prec@1 41.250 (36.509) Prec@5 68.750 (67.172) Epoch: [12][10110/11272] Time 0.815 (0.835) Data 0.002 (0.002) Loss 2.9144 (2.6212) Prec@1 32.500 (36.511) Prec@5 62.500 (67.173) Epoch: [12][10120/11272] Time 0.867 (0.835) Data 0.001 (0.002) Loss 2.3996 (2.6212) Prec@1 38.750 (36.511) Prec@5 69.375 (67.172) Epoch: [12][10130/11272] Time 0.867 (0.835) Data 0.001 (0.002) Loss 2.6922 (2.6212) Prec@1 37.500 (36.510) Prec@5 65.000 (67.172) Epoch: [12][10140/11272] Time 0.750 (0.835) Data 0.001 (0.002) Loss 2.7018 (2.6211) Prec@1 31.250 (36.511) Prec@5 69.375 (67.173) Epoch: [12][10150/11272] Time 0.727 (0.835) Data 0.001 (0.002) Loss 2.7140 (2.6212) Prec@1 36.250 (36.510) Prec@5 63.125 (67.170) Epoch: [12][10160/11272] Time 0.915 (0.835) Data 0.001 (0.002) Loss 2.6734 (2.6212) Prec@1 33.125 (36.509) Prec@5 58.125 (67.169) Epoch: [12][10170/11272] Time 0.865 (0.835) Data 0.001 (0.002) Loss 2.7147 (2.6211) Prec@1 33.750 (36.510) Prec@5 70.625 (67.170) Epoch: [12][10180/11272] Time 0.755 (0.835) Data 0.001 (0.002) Loss 2.3962 (2.6210) Prec@1 43.750 (36.512) Prec@5 70.000 (67.171) Epoch: [12][10190/11272] Time 0.752 (0.835) Data 0.002 (0.002) Loss 2.7662 (2.6210) Prec@1 34.375 (36.512) Prec@5 68.125 (67.171) Epoch: [12][10200/11272] Time 0.904 (0.835) Data 0.002 (0.002) Loss 2.8389 (2.6210) Prec@1 32.500 (36.513) Prec@5 65.625 (67.171) Epoch: [12][10210/11272] Time 0.877 (0.835) Data 0.002 (0.002) Loss 2.6300 (2.6211) Prec@1 38.125 (36.512) Prec@5 70.000 (67.170) Epoch: [12][10220/11272] Time 0.750 (0.835) Data 0.002 (0.002) Loss 2.3045 (2.6211) Prec@1 43.750 (36.513) Prec@5 70.625 (67.170) Epoch: [12][10230/11272] Time 0.936 (0.835) Data 0.002 (0.002) Loss 2.4126 (2.6211) Prec@1 43.750 (36.513) Prec@5 70.000 (67.169) Epoch: [12][10240/11272] Time 0.895 (0.835) Data 0.002 (0.002) Loss 2.5737 (2.6212) Prec@1 36.250 (36.511) Prec@5 68.125 (67.168) Epoch: [12][10250/11272] Time 0.747 (0.835) Data 0.001 (0.002) Loss 2.7283 (2.6211) Prec@1 35.625 (36.512) Prec@5 66.875 (67.170) Epoch: [12][10260/11272] Time 0.781 (0.835) Data 0.002 (0.002) Loss 2.7310 (2.6211) Prec@1 30.625 (36.511) Prec@5 62.500 (67.170) Epoch: [12][10270/11272] Time 0.883 (0.835) Data 0.002 (0.002) Loss 2.3934 (2.6212) Prec@1 36.250 (36.511) Prec@5 73.750 (67.171) Epoch: [12][10280/11272] Time 0.857 (0.834) Data 0.001 (0.002) Loss 2.7783 (2.6211) Prec@1 30.625 (36.511) Prec@5 63.125 (67.171) Epoch: [12][10290/11272] Time 0.777 (0.834) Data 0.002 (0.002) Loss 2.5542 (2.6211) Prec@1 41.875 (36.512) Prec@5 68.750 (67.171) Epoch: [12][10300/11272] Time 0.781 (0.834) Data 0.002 (0.002) Loss 2.6324 (2.6211) Prec@1 33.750 (36.511) Prec@5 65.000 (67.172) Epoch: [12][10310/11272] Time 0.821 (0.834) Data 0.001 (0.002) Loss 2.3523 (2.6211) Prec@1 40.000 (36.512) Prec@5 71.875 (67.172) Epoch: [12][10320/11272] Time 0.932 (0.834) Data 0.002 (0.002) Loss 2.5355 (2.6211) Prec@1 36.250 (36.512) Prec@5 66.875 (67.173) Epoch: [12][10330/11272] Time 0.753 (0.834) Data 0.002 (0.002) Loss 2.6153 (2.6211) Prec@1 36.250 (36.511) Prec@5 68.750 (67.174) Epoch: [12][10340/11272] Time 0.750 (0.834) Data 0.002 (0.002) Loss 2.6707 (2.6211) Prec@1 38.750 (36.511) Prec@5 64.375 (67.175) Epoch: [12][10350/11272] Time 0.932 (0.834) Data 0.002 (0.002) Loss 2.6310 (2.6211) Prec@1 36.875 (36.513) Prec@5 66.875 (67.175) Epoch: [12][10360/11272] Time 0.774 (0.834) Data 0.002 (0.002) Loss 2.5693 (2.6210) Prec@1 38.125 (36.513) Prec@5 66.875 (67.176) Epoch: [12][10370/11272] Time 0.750 (0.834) Data 0.001 (0.002) Loss 2.6031 (2.6211) Prec@1 39.375 (36.512) Prec@5 66.875 (67.175) Epoch: [12][10380/11272] Time 0.889 (0.834) Data 0.002 (0.002) Loss 2.6066 (2.6211) Prec@1 37.500 (36.512) Prec@5 64.375 (67.175) Epoch: [12][10390/11272] Time 0.840 (0.834) Data 0.002 (0.002) Loss 2.5582 (2.6211) Prec@1 38.125 (36.511) Prec@5 70.000 (67.174) Epoch: [12][10400/11272] Time 0.757 (0.834) Data 0.001 (0.002) Loss 2.8060 (2.6212) Prec@1 31.875 (36.510) Prec@5 60.625 (67.173) Epoch: [12][10410/11272] Time 0.746 (0.834) Data 0.002 (0.002) Loss 2.5331 (2.6212) Prec@1 35.000 (36.511) Prec@5 70.625 (67.174) Epoch: [12][10420/11272] Time 0.892 (0.834) Data 0.002 (0.002) Loss 2.5467 (2.6211) Prec@1 38.750 (36.513) Prec@5 65.625 (67.174) Epoch: [12][10430/11272] Time 0.857 (0.834) Data 0.001 (0.002) Loss 2.6439 (2.6212) Prec@1 35.000 (36.512) Prec@5 65.000 (67.174) Epoch: [12][10440/11272] Time 0.802 (0.834) Data 0.001 (0.002) Loss 2.5317 (2.6211) Prec@1 42.500 (36.513) Prec@5 69.375 (67.175) Epoch: [12][10450/11272] Time 0.758 (0.834) Data 0.002 (0.002) Loss 2.5898 (2.6212) Prec@1 40.000 (36.513) Prec@5 66.250 (67.174) Epoch: [12][10460/11272] Time 0.943 (0.834) Data 0.001 (0.002) Loss 2.3640 (2.6211) Prec@1 43.125 (36.513) Prec@5 71.250 (67.174) Epoch: [12][10470/11272] Time 0.881 (0.834) Data 0.002 (0.002) Loss 2.6546 (2.6212) Prec@1 35.625 (36.512) Prec@5 68.125 (67.172) Epoch: [12][10480/11272] Time 0.785 (0.834) Data 0.001 (0.002) Loss 2.3615 (2.6212) Prec@1 42.500 (36.511) Prec@5 73.125 (67.173) Epoch: [12][10490/11272] Time 0.897 (0.834) Data 0.001 (0.002) Loss 2.5001 (2.6213) Prec@1 39.375 (36.510) Prec@5 68.750 (67.171) Epoch: [12][10500/11272] Time 0.921 (0.834) Data 0.002 (0.002) Loss 2.6861 (2.6212) Prec@1 34.375 (36.511) Prec@5 65.625 (67.171) Epoch: [12][10510/11272] Time 0.765 (0.834) Data 0.002 (0.002) Loss 2.4120 (2.6213) Prec@1 39.375 (36.509) Prec@5 70.625 (67.170) Epoch: [12][10520/11272] Time 0.790 (0.834) Data 0.002 (0.002) Loss 2.6400 (2.6213) Prec@1 36.250 (36.509) Prec@5 63.125 (67.170) Epoch: [12][10530/11272] Time 0.896 (0.834) Data 0.002 (0.002) Loss 2.6763 (2.6213) Prec@1 40.000 (36.509) Prec@5 67.500 (67.170) Epoch: [12][10540/11272] Time 0.872 (0.834) Data 0.002 (0.002) Loss 2.7548 (2.6214) Prec@1 35.625 (36.507) Prec@5 64.375 (67.169) Epoch: [12][10550/11272] Time 0.769 (0.834) Data 0.002 (0.002) Loss 2.5778 (2.6214) Prec@1 36.250 (36.506) Prec@5 66.250 (67.167) Epoch: [12][10560/11272] Time 0.834 (0.834) Data 0.002 (0.002) Loss 2.4700 (2.6213) Prec@1 41.250 (36.508) Prec@5 66.875 (67.168) Epoch: [12][10570/11272] Time 0.878 (0.834) Data 0.001 (0.002) Loss 2.6738 (2.6214) Prec@1 36.250 (36.508) Prec@5 62.500 (67.167) Epoch: [12][10580/11272] Time 0.864 (0.834) Data 0.001 (0.002) Loss 2.5310 (2.6213) Prec@1 37.500 (36.509) Prec@5 73.125 (67.169) Epoch: [12][10590/11272] Time 0.775 (0.834) Data 0.002 (0.002) Loss 2.6622 (2.6213) Prec@1 36.875 (36.509) Prec@5 66.250 (67.170) Epoch: [12][10600/11272] Time 0.760 (0.834) Data 0.001 (0.002) Loss 2.6044 (2.6213) Prec@1 38.125 (36.507) Prec@5 65.000 (67.170) Epoch: [12][10610/11272] Time 0.908 (0.834) Data 0.001 (0.002) Loss 2.5114 (2.6213) Prec@1 38.125 (36.509) Prec@5 73.750 (67.171) Epoch: [12][10620/11272] Time 0.747 (0.834) Data 0.003 (0.002) Loss 2.4232 (2.6213) Prec@1 37.500 (36.508) Prec@5 71.875 (67.171) Epoch: [12][10630/11272] Time 0.715 (0.834) Data 0.002 (0.002) Loss 2.6573 (2.6213) Prec@1 35.000 (36.508) Prec@5 68.125 (67.171) Epoch: [12][10640/11272] Time 0.921 (0.834) Data 0.002 (0.002) Loss 2.7279 (2.6214) Prec@1 32.500 (36.508) Prec@5 62.500 (67.168) Epoch: [12][10650/11272] Time 0.842 (0.834) Data 0.001 (0.002) Loss 2.6205 (2.6213) Prec@1 31.250 (36.508) Prec@5 66.250 (67.169) Epoch: [12][10660/11272] Time 0.730 (0.834) Data 0.002 (0.002) Loss 2.5551 (2.6214) Prec@1 37.500 (36.506) Prec@5 68.750 (67.167) Epoch: [12][10670/11272] Time 0.763 (0.834) Data 0.002 (0.002) Loss 2.4977 (2.6214) Prec@1 36.875 (36.505) Prec@5 70.000 (67.167) Epoch: [12][10680/11272] Time 0.887 (0.834) Data 0.002 (0.002) Loss 2.4484 (2.6214) Prec@1 45.000 (36.505) Prec@5 66.875 (67.167) Epoch: [12][10690/11272] Time 0.853 (0.834) Data 0.001 (0.002) Loss 2.6310 (2.6214) Prec@1 36.250 (36.505) Prec@5 71.250 (67.168) Epoch: [12][10700/11272] Time 0.751 (0.834) Data 0.002 (0.002) Loss 2.8360 (2.6214) Prec@1 26.875 (36.501) Prec@5 63.750 (67.168) Epoch: [12][10710/11272] Time 0.755 (0.834) Data 0.002 (0.002) Loss 2.5101 (2.6213) Prec@1 37.500 (36.502) Prec@5 72.500 (67.169) Epoch: [12][10720/11272] Time 0.884 (0.834) Data 0.002 (0.002) Loss 2.5589 (2.6213) Prec@1 43.125 (36.504) Prec@5 70.000 (67.170) Epoch: [12][10730/11272] Time 0.827 (0.834) Data 0.001 (0.002) Loss 2.4964 (2.6213) Prec@1 41.250 (36.504) Prec@5 68.125 (67.170) Epoch: [12][10740/11272] Time 0.759 (0.834) Data 0.002 (0.002) Loss 2.6440 (2.6213) Prec@1 34.375 (36.503) Prec@5 68.750 (67.170) Epoch: [12][10750/11272] Time 0.880 (0.834) Data 0.001 (0.002) Loss 2.5255 (2.6213) Prec@1 34.375 (36.503) Prec@5 69.375 (67.170) Epoch: [12][10760/11272] Time 0.869 (0.834) Data 0.001 (0.002) Loss 2.5117 (2.6212) Prec@1 37.500 (36.505) Prec@5 70.625 (67.171) Epoch: [12][10770/11272] Time 0.746 (0.834) Data 0.001 (0.002) Loss 2.6281 (2.6212) Prec@1 38.125 (36.505) Prec@5 65.000 (67.171) Epoch: [12][10780/11272] Time 0.726 (0.834) Data 0.002 (0.002) Loss 2.7501 (2.6213) Prec@1 31.250 (36.502) Prec@5 68.125 (67.169) Epoch: [12][10790/11272] Time 0.851 (0.834) Data 0.002 (0.002) Loss 2.7090 (2.6213) Prec@1 35.625 (36.500) Prec@5 66.875 (67.169) Epoch: [12][10800/11272] Time 0.876 (0.834) Data 0.002 (0.002) Loss 2.8786 (2.6213) Prec@1 31.875 (36.501) Prec@5 61.250 (67.171) Epoch: [12][10810/11272] Time 0.739 (0.834) Data 0.001 (0.002) Loss 2.6821 (2.6212) Prec@1 31.250 (36.501) Prec@5 64.375 (67.170) Epoch: [12][10820/11272] Time 0.750 (0.834) Data 0.002 (0.002) Loss 2.6993 (2.6213) Prec@1 36.250 (36.500) Prec@5 65.625 (67.171) Epoch: [12][10830/11272] Time 0.881 (0.834) Data 0.002 (0.002) Loss 2.7683 (2.6213) Prec@1 33.750 (36.497) Prec@5 62.500 (67.170) Epoch: [12][10840/11272] Time 0.834 (0.834) Data 0.001 (0.002) Loss 2.6401 (2.6214) Prec@1 36.250 (36.497) Prec@5 68.750 (67.169) Epoch: [12][10850/11272] Time 0.768 (0.834) Data 0.002 (0.002) Loss 2.7397 (2.6214) Prec@1 36.250 (36.497) Prec@5 62.500 (67.168) Epoch: [12][10860/11272] Time 0.747 (0.834) Data 0.002 (0.002) Loss 2.4135 (2.6214) Prec@1 37.500 (36.497) Prec@5 67.500 (67.167) Epoch: [12][10870/11272] Time 0.876 (0.834) Data 0.002 (0.002) Loss 2.6354 (2.6214) Prec@1 33.750 (36.497) Prec@5 68.125 (67.169) Epoch: [12][10880/11272] Time 0.741 (0.834) Data 0.003 (0.002) Loss 2.5062 (2.6213) Prec@1 36.250 (36.499) Prec@5 66.250 (67.169) Epoch: [12][10890/11272] Time 0.765 (0.834) Data 0.002 (0.002) Loss 2.7232 (2.6214) Prec@1 31.875 (36.499) Prec@5 66.250 (67.168) Epoch: [12][10900/11272] Time 0.927 (0.834) Data 0.003 (0.002) Loss 2.5519 (2.6213) Prec@1 33.750 (36.499) Prec@5 73.125 (67.168) Epoch: [12][10910/11272] Time 0.883 (0.834) Data 0.001 (0.002) Loss 2.8907 (2.6214) Prec@1 30.625 (36.500) Prec@5 58.750 (67.167) Epoch: [12][10920/11272] Time 0.751 (0.834) Data 0.001 (0.002) Loss 2.5584 (2.6213) Prec@1 40.000 (36.501) Prec@5 70.000 (67.170) Epoch: [12][10930/11272] Time 0.751 (0.834) Data 0.002 (0.002) Loss 2.7163 (2.6212) Prec@1 33.125 (36.504) Prec@5 61.250 (67.170) Epoch: [12][10940/11272] Time 0.926 (0.834) Data 0.002 (0.002) Loss 2.5886 (2.6212) Prec@1 37.500 (36.504) Prec@5 67.500 (67.171) Epoch: [12][10950/11272] Time 0.849 (0.834) Data 0.002 (0.002) Loss 2.6957 (2.6212) Prec@1 30.000 (36.502) Prec@5 64.375 (67.170) Epoch: [12][10960/11272] Time 0.790 (0.834) Data 0.001 (0.002) Loss 2.8370 (2.6212) Prec@1 31.875 (36.503) Prec@5 66.875 (67.171) Epoch: [12][10970/11272] Time 0.750 (0.834) Data 0.001 (0.002) Loss 2.6533 (2.6212) Prec@1 36.875 (36.502) Prec@5 67.500 (67.172) Epoch: [12][10980/11272] Time 0.860 (0.834) Data 0.002 (0.002) Loss 2.7792 (2.6212) Prec@1 35.625 (36.501) Prec@5 67.500 (67.171) Epoch: [12][10990/11272] Time 0.886 (0.834) Data 0.001 (0.002) Loss 2.6199 (2.6212) Prec@1 40.000 (36.502) Prec@5 63.750 (67.170) Epoch: [12][11000/11272] Time 0.781 (0.834) Data 0.002 (0.002) Loss 2.5157 (2.6212) Prec@1 31.250 (36.501) Prec@5 66.875 (67.171) Epoch: [12][11010/11272] Time 0.915 (0.834) Data 0.001 (0.002) Loss 2.5554 (2.6212) Prec@1 36.875 (36.500) Prec@5 67.500 (67.172) Epoch: [12][11020/11272] Time 0.899 (0.834) Data 0.001 (0.002) Loss 2.7933 (2.6212) Prec@1 31.875 (36.501) Prec@5 65.000 (67.174) Epoch: [12][11030/11272] Time 0.798 (0.834) Data 0.002 (0.002) Loss 2.5224 (2.6211) Prec@1 35.625 (36.502) Prec@5 71.250 (67.175) Epoch: [12][11040/11272] Time 0.759 (0.834) Data 0.002 (0.002) Loss 2.4718 (2.6211) Prec@1 41.875 (36.502) Prec@5 67.500 (67.175) Epoch: [12][11050/11272] Time 0.904 (0.834) Data 0.001 (0.002) Loss 2.6430 (2.6211) Prec@1 36.875 (36.501) Prec@5 66.875 (67.176) Epoch: [12][11060/11272] Time 0.922 (0.834) Data 0.002 (0.002) Loss 2.5884 (2.6211) Prec@1 35.625 (36.501) Prec@5 63.750 (67.176) Epoch: [12][11070/11272] Time 0.745 (0.834) Data 0.001 (0.002) Loss 2.6735 (2.6211) Prec@1 33.750 (36.500) Prec@5 68.750 (67.176) Epoch: [12][11080/11272] Time 0.796 (0.834) Data 0.001 (0.002) Loss 2.5573 (2.6211) Prec@1 35.625 (36.499) Prec@5 70.625 (67.176) Epoch: [12][11090/11272] Time 0.943 (0.834) Data 0.003 (0.002) Loss 2.7684 (2.6212) Prec@1 35.625 (36.498) Prec@5 66.875 (67.175) Epoch: [12][11100/11272] Time 1.005 (0.834) Data 0.002 (0.002) Loss 2.7200 (2.6212) Prec@1 33.750 (36.497) Prec@5 61.875 (67.175) Epoch: [12][11110/11272] Time 0.743 (0.834) Data 0.002 (0.002) Loss 2.6342 (2.6213) Prec@1 37.500 (36.495) Prec@5 65.000 (67.174) Epoch: [12][11120/11272] Time 0.737 (0.834) Data 0.001 (0.002) Loss 2.6004 (2.6213) Prec@1 33.125 (36.494) Prec@5 68.125 (67.175) Epoch: [12][11130/11272] Time 0.897 (0.834) Data 0.002 (0.002) Loss 2.6051 (2.6213) Prec@1 38.750 (36.494) Prec@5 66.875 (67.175) Epoch: [12][11140/11272] Time 0.921 (0.834) Data 0.002 (0.002) Loss 2.5105 (2.6213) Prec@1 38.750 (36.494) Prec@5 70.000 (67.174) Epoch: [12][11150/11272] Time 0.750 (0.834) Data 0.002 (0.002) Loss 2.3946 (2.6212) Prec@1 40.000 (36.496) Prec@5 70.000 (67.177) Epoch: [12][11160/11272] Time 0.928 (0.834) Data 0.002 (0.002) Loss 2.8695 (2.6212) Prec@1 31.875 (36.496) Prec@5 60.000 (67.175) Epoch: [12][11170/11272] Time 0.897 (0.834) Data 0.001 (0.002) Loss 2.6594 (2.6212) Prec@1 33.125 (36.495) Prec@5 68.125 (67.175) Epoch: [12][11180/11272] Time 0.760 (0.834) Data 0.002 (0.002) Loss 2.8451 (2.6213) Prec@1 30.625 (36.493) Prec@5 65.625 (67.174) Epoch: [12][11190/11272] Time 0.758 (0.834) Data 0.002 (0.002) Loss 2.5272 (2.6212) Prec@1 40.000 (36.494) Prec@5 65.000 (67.174) Epoch: [12][11200/11272] Time 0.897 (0.834) Data 0.002 (0.002) Loss 2.8443 (2.6213) Prec@1 35.000 (36.493) Prec@5 65.625 (67.174) Epoch: [12][11210/11272] Time 0.837 (0.834) Data 0.002 (0.002) Loss 2.6520 (2.6213) Prec@1 39.375 (36.491) Prec@5 71.250 (67.174) Epoch: [12][11220/11272] Time 0.758 (0.834) Data 0.001 (0.002) Loss 2.7667 (2.6213) Prec@1 34.375 (36.491) Prec@5 62.500 (67.175) Epoch: [12][11230/11272] Time 0.793 (0.834) Data 0.002 (0.002) Loss 2.8437 (2.6213) Prec@1 34.375 (36.492) Prec@5 65.000 (67.175) Epoch: [12][11240/11272] Time 0.920 (0.834) Data 0.001 (0.002) Loss 2.5600 (2.6213) Prec@1 40.625 (36.492) Prec@5 70.000 (67.176) Epoch: [12][11250/11272] Time 0.871 (0.834) Data 0.001 (0.002) Loss 2.8327 (2.6213) Prec@1 36.250 (36.491) Prec@5 61.250 (67.176) Epoch: [12][11260/11272] Time 0.739 (0.834) Data 0.001 (0.002) Loss 2.4762 (2.6214) Prec@1 37.500 (36.490) Prec@5 68.750 (67.174) Epoch: [12][11270/11272] Time 0.746 (0.833) Data 0.000 (0.002) Loss 2.6369 (2.6214) Prec@1 35.625 (36.491) Prec@5 62.500 (67.174) Test: [0/229] Time 1.485 (1.485) Loss 1.3647 (1.3647) Prec@1 57.500 (57.500) Prec@5 91.250 (91.250) Test: [10/229] Time 0.376 (0.503) Loss 1.3954 (2.2652) Prec@1 61.250 (43.295) Prec@5 92.500 (75.227) Test: [20/229] Time 0.453 (0.457) Loss 2.8408 (2.2619) Prec@1 29.375 (42.768) Prec@5 63.750 (75.149) Test: [30/229] Time 0.383 (0.437) Loss 2.5484 (2.2298) Prec@1 32.500 (43.589) Prec@5 68.125 (75.202) Test: [40/229] Time 0.493 (0.427) Loss 1.2051 (2.2697) Prec@1 71.250 (43.171) Prec@5 86.250 (73.979) Test: [50/229] Time 0.367 (0.418) Loss 2.2785 (2.2742) Prec@1 33.125 (43.113) Prec@5 79.375 (73.578) Test: [60/229] Time 0.335 (0.415) Loss 2.6169 (2.2880) Prec@1 27.500 (42.623) Prec@5 70.625 (73.371) Test: [70/229] Time 0.439 (0.416) Loss 2.3836 (2.3039) Prec@1 41.250 (42.192) Prec@5 68.750 (73.257) Test: [80/229] Time 0.351 (0.413) Loss 3.2555 (2.3395) Prec@1 16.875 (41.142) Prec@5 55.000 (72.986) Test: [90/229] Time 0.398 (0.412) Loss 1.9777 (2.3295) Prec@1 55.000 (41.257) Prec@5 77.500 (73.242) Test: [100/229] Time 0.347 (0.410) Loss 2.9095 (2.3305) Prec@1 33.125 (41.621) Prec@5 67.500 (73.175) Test: [110/229] Time 0.445 (0.411) Loss 2.7434 (2.3157) Prec@1 25.000 (41.836) Prec@5 68.750 (73.463) Test: [120/229] Time 0.355 (0.410) Loss 3.4661 (2.3353) Prec@1 22.500 (41.219) Prec@5 54.375 (73.337) Test: [130/229] Time 0.407 (0.409) Loss 1.7069 (2.3128) Prec@1 53.750 (41.675) Prec@5 83.125 (73.783) Test: [140/229] Time 0.400 (0.408) Loss 2.5789 (2.3381) Prec@1 35.625 (41.073) Prec@5 66.875 (73.351) Test: [150/229] Time 0.343 (0.407) Loss 1.5732 (2.3532) Prec@1 65.000 (40.695) Prec@5 86.250 (73.191) Test: [160/229] Time 0.485 (0.407) Loss 2.8165 (2.3540) Prec@1 32.500 (40.741) Prec@5 70.000 (73.261) Test: [170/229] Time 0.407 (0.407) Loss 2.3673 (2.3746) Prec@1 38.750 (40.260) Prec@5 74.375 (72.873) Test: [180/229] Time 0.462 (0.407) Loss 3.1923 (2.3844) Prec@1 19.375 (40.249) Prec@5 53.125 (72.635) Test: [190/229] Time 0.419 (0.407) Loss 1.8192 (2.3635) Prec@1 48.750 (40.779) Prec@5 86.875 (73.001) Test: [200/229] Time 0.360 (0.406) Loss 2.0462 (2.3485) Prec@1 45.625 (40.911) Prec@5 79.375 (73.439) Test: [210/229] Time 0.444 (0.405) Loss 2.0859 (2.3432) Prec@1 34.375 (40.977) Prec@5 82.500 (73.557) Test: [220/229] Time 0.401 (0.404) Loss 2.3585 (2.3276) Prec@1 35.625 (41.372) Prec@5 74.375 (73.840) * Prec@1 41.873 Prec@5 74.061 Epoch: [13][0/11272] Time 3.289 (3.289) Data 2.341 (2.341) Loss 3.0770 (3.0770) Prec@1 28.125 (28.125) Prec@5 61.250 (61.250) Epoch: [13][10/11272] Time 0.869 (1.068) Data 0.002 (0.214) Loss 2.6397 (2.6688) Prec@1 37.500 (35.227) Prec@5 67.500 (67.443) Epoch: [13][20/11272] Time 0.877 (0.955) Data 0.001 (0.113) Loss 2.7384 (2.6205) Prec@1 32.500 (36.726) Prec@5 66.875 (67.798) Epoch: [13][30/11272] Time 0.788 (0.917) Data 0.002 (0.077) Loss 2.6242 (2.5971) Prec@1 33.750 (37.016) Prec@5 67.500 (67.944) Epoch: [13][40/11272] Time 0.769 (0.896) Data 0.002 (0.059) Loss 2.3419 (2.5961) Prec@1 43.750 (37.256) Prec@5 75.625 (68.247) Epoch: [13][50/11272] Time 0.870 (0.879) Data 0.001 (0.048) Loss 2.5945 (2.5826) Prec@1 38.750 (37.500) Prec@5 66.875 (68.493) Epoch: [13][60/11272] Time 0.879 (0.871) Data 0.003 (0.040) Loss 2.6620 (2.5817) Prec@1 37.500 (37.459) Prec@5 66.250 (68.504) Epoch: [13][70/11272] Time 0.770 (0.867) Data 0.002 (0.035) Loss 2.2985 (2.5719) Prec@1 43.125 (37.518) Prec@5 73.750 (68.627) Epoch: [13][80/11272] Time 0.989 (0.866) Data 0.002 (0.031) Loss 2.5767 (2.5849) Prec@1 33.750 (37.299) Prec@5 66.875 (68.287) Epoch: [13][90/11272] Time 0.872 (0.862) Data 0.002 (0.027) Loss 2.3032 (2.5825) Prec@1 41.875 (37.328) Prec@5 76.875 (68.448) Epoch: [13][100/11272] Time 0.750 (0.859) Data 0.001 (0.025) Loss 2.4429 (2.5812) Prec@1 39.375 (37.376) Prec@5 73.125 (68.472) Epoch: [13][110/11272] Time 0.795 (0.859) Data 0.002 (0.023) Loss 2.7345 (2.5924) Prec@1 35.000 (37.370) Prec@5 66.875 (68.305) Epoch: [13][120/11272] Time 0.903 (0.858) Data 0.002 (0.021) Loss 2.2035 (2.5841) Prec@1 42.500 (37.490) Prec@5 73.750 (68.502) Epoch: [13][130/11272] Time 0.889 (0.856) Data 0.002 (0.020) Loss 2.7070 (2.5885) Prec@1 34.375 (37.271) Prec@5 68.750 (68.402) Epoch: [13][140/11272] Time 0.807 (0.854) Data 0.002 (0.018) Loss 2.4703 (2.5849) Prec@1 43.125 (37.283) Prec@5 70.000 (68.515) Epoch: [13][150/11272] Time 0.778 (0.854) Data 0.002 (0.017) Loss 2.5791 (2.5893) Prec@1 36.875 (37.231) Prec@5 64.375 (68.365) Epoch: [13][160/11272] Time 0.858 (0.854) Data 0.002 (0.016) Loss 2.7075 (2.5921) Prec@1 36.875 (37.209) Prec@5 61.250 (68.241) Epoch: [13][170/11272] Time 0.928 (0.852) Data 0.001 (0.015) Loss 2.6192 (2.5948) Prec@1 37.500 (37.142) Prec@5 66.875 (68.198) Epoch: [13][180/11272] Time 0.784 (0.852) Data 0.002 (0.015) Loss 2.7219 (2.5960) Prec@1 35.000 (37.120) Prec@5 63.750 (68.142) Epoch: [13][190/11272] Time 0.784 (0.852) Data 0.002 (0.014) Loss 2.6091 (2.5946) Prec@1 32.500 (37.084) Prec@5 65.625 (68.161) Epoch: [13][200/11272] Time 0.879 (0.852) Data 0.002 (0.013) Loss 2.6748 (2.5981) Prec@1 34.375 (36.925) Prec@5 71.250 (68.106) Epoch: [13][210/11272] Time 0.806 (0.851) Data 0.003 (0.013) Loss 2.3971 (2.5994) Prec@1 39.375 (36.982) Prec@5 71.875 (68.024) Epoch: [13][220/11272] Time 0.800 (0.850) Data 0.001 (0.012) Loss 2.4489 (2.6004) Prec@1 42.500 (36.965) Prec@5 68.750 (67.938) Epoch: [13][230/11272] Time 0.844 (0.850) Data 0.001 (0.012) Loss 2.5081 (2.6020) Prec@1 38.125 (36.867) Prec@5 70.000 (67.892) Epoch: [13][240/11272] Time 0.918 (0.849) Data 0.001 (0.011) Loss 2.7668 (2.6054) Prec@1 34.375 (36.766) Prec@5 63.125 (67.762) Epoch: [13][250/11272] Time 0.758 (0.848) Data 0.001 (0.011) Loss 2.7271 (2.6055) Prec@1 33.125 (36.793) Prec@5 62.500 (67.712) Epoch: [13][260/11272] Time 0.775 (0.848) Data 0.002 (0.011) Loss 2.6590 (2.6024) Prec@1 40.000 (36.904) Prec@5 63.750 (67.708) Epoch: [13][270/11272] Time 0.945 (0.848) Data 0.002 (0.010) Loss 2.6159 (2.6027) Prec@1 32.500 (36.910) Prec@5 72.500 (67.698) Epoch: [13][280/11272] Time 0.882 (0.848) Data 0.002 (0.010) Loss 2.6587 (2.6032) Prec@1 37.500 (36.879) Prec@5 66.875 (67.707) Epoch: [13][290/11272] Time 0.786 (0.847) Data 0.001 (0.010) Loss 2.5051 (2.6028) Prec@1 38.125 (36.836) Prec@5 71.250 (67.670) Epoch: [13][300/11272] Time 0.770 (0.846) Data 0.002 (0.009) Loss 2.6004 (2.6004) Prec@1 41.875 (36.966) Prec@5 64.375 (67.672) Epoch: [13][310/11272] Time 0.888 (0.846) Data 0.002 (0.009) Loss 2.3947 (2.5998) Prec@1 36.250 (36.923) Prec@5 68.125 (67.655) Epoch: [13][320/11272] Time 0.926 (0.846) Data 0.002 (0.009) Loss 2.7178 (2.6009) Prec@1 36.250 (36.898) Prec@5 62.500 (67.611) Epoch: [13][330/11272] Time 0.760 (0.845) Data 0.002 (0.009) Loss 2.8687 (2.6012) Prec@1 30.625 (36.890) Prec@5 62.500 (67.604) Epoch: [13][340/11272] Time 0.911 (0.845) Data 0.001 (0.009) Loss 2.7041 (2.6019) Prec@1 37.500 (36.880) Prec@5 64.375 (67.566) Epoch: [13][350/11272] Time 0.934 (0.845) Data 0.002 (0.008) Loss 2.6773 (2.6009) Prec@1 35.000 (36.895) Prec@5 68.125 (67.593) Epoch: [13][360/11272] Time 0.747 (0.845) Data 0.002 (0.008) Loss 2.4386 (2.6014) Prec@1 40.000 (36.877) Prec@5 68.125 (67.554) Epoch: [13][370/11272] Time 0.773 (0.844) Data 0.002 (0.008) Loss 2.4735 (2.6006) Prec@1 44.375 (36.905) Prec@5 69.375 (67.566) Epoch: [13][380/11272] Time 0.868 (0.844) Data 0.002 (0.008) Loss 2.6345 (2.5980) Prec@1 35.000 (36.918) Prec@5 66.875 (67.608) Epoch: [13][390/11272] Time 0.903 (0.844) Data 0.001 (0.008) Loss 2.5225 (2.5967) Prec@1 36.250 (36.912) Prec@5 63.750 (67.650) Epoch: [13][400/11272] Time 0.777 (0.843) Data 0.002 (0.008) Loss 2.6367 (2.5966) Prec@1 35.000 (36.867) Prec@5 65.000 (67.623) Epoch: [13][410/11272] Time 0.791 (0.843) Data 0.001 (0.007) Loss 2.6367 (2.5956) Prec@1 33.750 (36.840) Prec@5 67.500 (67.667) Epoch: [13][420/11272] Time 0.873 (0.843) Data 0.001 (0.007) Loss 2.7423 (2.5972) Prec@1 32.500 (36.850) Prec@5 66.250 (67.629) Epoch: [13][430/11272] Time 0.860 (0.843) Data 0.001 (0.007) Loss 2.5751 (2.5971) Prec@1 37.500 (36.872) Prec@5 67.500 (67.617) Epoch: [13][440/11272] Time 0.742 (0.843) Data 0.001 (0.007) Loss 2.4936 (2.5978) Prec@1 43.750 (36.868) Prec@5 71.875 (67.618) Epoch: [13][450/11272] Time 0.768 (0.842) Data 0.002 (0.007) Loss 2.5689 (2.5977) Prec@1 41.250 (36.875) Prec@5 68.125 (67.605) Epoch: [13][460/11272] Time 0.911 (0.842) Data 0.001 (0.007) Loss 2.2382 (2.5973) Prec@1 43.125 (36.866) Prec@5 76.875 (67.625) Epoch: [13][470/11272] Time 0.912 (0.842) Data 0.002 (0.007) Loss 2.5005 (2.5955) Prec@1 40.625 (36.891) Prec@5 72.500 (67.673) Epoch: [13][480/11272] Time 0.818 (0.842) Data 0.001 (0.007) Loss 2.6868 (2.5962) Prec@1 36.875 (36.885) Prec@5 68.125 (67.678) Epoch: [13][490/11272] Time 0.918 (0.842) Data 0.001 (0.006) Loss 2.8334 (2.5956) Prec@1 35.000 (36.880) Prec@5 68.750 (67.718) Epoch: [13][500/11272] Time 0.855 (0.842) Data 0.001 (0.006) Loss 2.5289 (2.5979) Prec@1 41.875 (36.860) Prec@5 71.250 (67.691) Epoch: [13][510/11272] Time 0.787 (0.841) Data 0.002 (0.006) Loss 2.6069 (2.5968) Prec@1 45.000 (36.876) Prec@5 67.500 (67.698) Epoch: [13][520/11272] Time 0.796 (0.842) Data 0.002 (0.006) Loss 2.6583 (2.5971) Prec@1 40.625 (36.909) Prec@5 64.375 (67.679) Epoch: [13][530/11272] Time 0.881 (0.842) Data 0.001 (0.006) Loss 2.7305 (2.5968) Prec@1 34.375 (36.894) Prec@5 70.000 (67.682) Epoch: [13][540/11272] Time 0.882 (0.842) Data 0.002 (0.006) Loss 2.4764 (2.5974) Prec@1 38.125 (36.860) Prec@5 74.375 (67.677) Epoch: [13][550/11272] Time 0.806 (0.842) Data 0.002 (0.006) Loss 2.7268 (2.5987) Prec@1 33.125 (36.810) Prec@5 65.625 (67.651) Epoch: [13][560/11272] Time 0.750 (0.842) Data 0.002 (0.006) Loss 2.6233 (2.5990) Prec@1 39.375 (36.822) Prec@5 68.125 (67.659) Epoch: [13][570/11272] Time 0.869 (0.842) Data 0.001 (0.006) Loss 2.4506 (2.5996) Prec@1 39.375 (36.806) Prec@5 71.875 (67.632) Epoch: [13][580/11272] Time 0.893 (0.842) Data 0.002 (0.006) Loss 2.3265 (2.5986) Prec@1 41.250 (36.827) Prec@5 72.500 (67.660) Epoch: [13][590/11272] Time 0.817 (0.842) Data 0.002 (0.006) Loss 2.5987 (2.5986) Prec@1 35.000 (36.819) Prec@5 65.000 (67.645) Epoch: [13][600/11272] Time 0.776 (0.842) Data 0.001 (0.006) Loss 2.7136 (2.5997) Prec@1 31.875 (36.792) Prec@5 69.375 (67.629) Epoch: [13][610/11272] Time 0.885 (0.842) Data 0.002 (0.006) Loss 2.8232 (2.6007) Prec@1 29.375 (36.756) Prec@5 62.500 (67.607) Epoch: [13][620/11272] Time 0.736 (0.842) Data 0.001 (0.005) Loss 2.7926 (2.6013) Prec@1 35.625 (36.739) Prec@5 61.250 (67.604) Epoch: [13][630/11272] Time 0.746 (0.842) Data 0.002 (0.005) Loss 2.9159 (2.6025) Prec@1 28.125 (36.710) Prec@5 65.625 (67.582) Epoch: [13][640/11272] Time 0.858 (0.842) Data 0.002 (0.005) Loss 2.5489 (2.6019) Prec@1 35.625 (36.708) Prec@5 67.500 (67.591) Epoch: [13][650/11272] Time 0.861 (0.842) Data 0.002 (0.005) Loss 2.4922 (2.6018) Prec@1 40.625 (36.721) Prec@5 67.500 (67.596) Epoch: [13][660/11272] Time 0.793 (0.842) Data 0.002 (0.005) Loss 2.7203 (2.6034) Prec@1 35.000 (36.709) Prec@5 62.500 (67.549) Epoch: [13][670/11272] Time 0.779 (0.842) Data 0.002 (0.005) Loss 2.9008 (2.6047) Prec@1 34.375 (36.707) Prec@5 60.625 (67.531) Epoch: [13][680/11272] Time 0.888 (0.841) Data 0.002 (0.005) Loss 2.7163 (2.6036) Prec@1 35.000 (36.737) Prec@5 63.750 (67.546) Epoch: [13][690/11272] Time 0.884 (0.842) Data 0.002 (0.005) Loss 2.3530 (2.6037) Prec@1 44.375 (36.738) Prec@5 73.750 (67.549) Epoch: [13][700/11272] Time 0.787 (0.841) Data 0.001 (0.005) Loss 2.4370 (2.6046) Prec@1 39.375 (36.713) Prec@5 70.625 (67.519) Epoch: [13][710/11272] Time 0.797 (0.841) Data 0.001 (0.005) Loss 2.5172 (2.6048) Prec@1 41.250 (36.704) Prec@5 68.125 (67.504) Epoch: [13][720/11272] Time 0.865 (0.841) Data 0.002 (0.005) Loss 2.5554 (2.6048) Prec@1 36.875 (36.709) Prec@5 64.375 (67.492) Epoch: [13][730/11272] Time 0.852 (0.841) Data 0.001 (0.005) Loss 2.7179 (2.6051) Prec@1 35.000 (36.741) Prec@5 61.875 (67.491) Epoch: [13][740/11272] Time 0.817 (0.841) Data 0.001 (0.005) Loss 2.7131 (2.6052) Prec@1 35.000 (36.743) Prec@5 61.250 (67.474) Epoch: [13][750/11272] Time 0.915 (0.842) Data 0.002 (0.005) Loss 2.5992 (2.6040) Prec@1 33.750 (36.769) Prec@5 68.750 (67.497) Epoch: [13][760/11272] Time 0.875 (0.841) Data 0.002 (0.005) Loss 2.4274 (2.6048) Prec@1 37.500 (36.739) Prec@5 71.250 (67.493) Epoch: [13][770/11272] Time 0.777 (0.841) Data 0.002 (0.005) Loss 2.4643 (2.6057) Prec@1 38.750 (36.737) Prec@5 71.875 (67.474) Epoch: [13][780/11272] Time 0.770 (0.841) Data 0.001 (0.005) Loss 2.7443 (2.6055) Prec@1 39.375 (36.738) Prec@5 64.375 (67.494) Epoch: [13][790/11272] Time 0.935 (0.841) Data 0.002 (0.005) Loss 2.8593 (2.6066) Prec@1 31.250 (36.706) Prec@5 61.875 (67.479) Epoch: [13][800/11272] Time 0.910 (0.841) Data 0.001 (0.005) Loss 2.6246 (2.6062) Prec@1 35.625 (36.715) Prec@5 66.875 (67.498) Epoch: [13][810/11272] Time 0.788 (0.841) Data 0.002 (0.005) Loss 2.5441 (2.6045) Prec@1 35.000 (36.752) Prec@5 72.500 (67.520) Epoch: [13][820/11272] Time 0.783 (0.841) Data 0.002 (0.005) Loss 2.5529 (2.6057) Prec@1 38.750 (36.759) Prec@5 68.750 (67.490) Epoch: [13][830/11272] Time 0.928 (0.842) Data 0.001 (0.005) Loss 2.6339 (2.6060) Prec@1 36.250 (36.757) Prec@5 68.125 (67.485) Epoch: [13][840/11272] Time 0.895 (0.842) Data 0.004 (0.004) Loss 2.8482 (2.6067) Prec@1 31.250 (36.741) Prec@5 64.375 (67.492) Epoch: [13][850/11272] Time 0.788 (0.842) Data 0.002 (0.004) Loss 2.6165 (2.6068) Prec@1 33.750 (36.754) Prec@5 70.000 (67.493) Epoch: [13][860/11272] Time 0.750 (0.842) Data 0.002 (0.004) Loss 2.5281 (2.6079) Prec@1 36.250 (36.730) Prec@5 70.000 (67.472) Epoch: [13][870/11272] Time 0.906 (0.842) Data 0.002 (0.004) Loss 2.6937 (2.6077) Prec@1 36.875 (36.742) Prec@5 70.000 (67.473) Epoch: [13][880/11272] Time 0.752 (0.842) Data 0.003 (0.004) Loss 2.4714 (2.6087) Prec@1 41.250 (36.732) Prec@5 72.500 (67.458) Epoch: [13][890/11272] Time 0.745 (0.842) Data 0.001 (0.004) Loss 2.5555 (2.6083) Prec@1 35.000 (36.732) Prec@5 67.500 (67.462) Epoch: [13][900/11272] Time 0.883 (0.842) Data 0.002 (0.004) Loss 2.7672 (2.6088) Prec@1 36.250 (36.733) Prec@5 65.000 (67.442) Epoch: [13][910/11272] Time 0.909 (0.842) Data 0.003 (0.004) Loss 2.5151 (2.6081) Prec@1 40.625 (36.752) Prec@5 69.375 (67.450) Epoch: [13][920/11272] Time 0.766 (0.842) Data 0.002 (0.004) Loss 2.6895 (2.6074) Prec@1 35.625 (36.778) Prec@5 64.375 (67.459) Epoch: [13][930/11272] Time 0.730 (0.842) Data 0.001 (0.004) Loss 2.7422 (2.6072) Prec@1 30.000 (36.777) Prec@5 65.625 (67.468) Epoch: [13][940/11272] Time 0.959 (0.842) Data 0.002 (0.004) Loss 2.6336 (2.6072) Prec@1 35.000 (36.775) Prec@5 66.875 (67.471) Epoch: [13][950/11272] Time 0.975 (0.842) Data 0.002 (0.004) Loss 2.8077 (2.6077) Prec@1 31.250 (36.765) Prec@5 63.125 (67.469) Epoch: [13][960/11272] Time 0.775 (0.842) Data 0.002 (0.004) Loss 2.3443 (2.6081) Prec@1 42.500 (36.760) Prec@5 72.500 (67.476) Epoch: [13][970/11272] Time 0.750 (0.842) Data 0.002 (0.004) Loss 2.7955 (2.6078) Prec@1 35.625 (36.773) Prec@5 61.875 (67.469) Epoch: [13][980/11272] Time 0.929 (0.842) Data 0.002 (0.004) Loss 2.5360 (2.6083) Prec@1 35.625 (36.765) Prec@5 70.625 (67.464) Epoch: [13][990/11272] Time 0.880 (0.842) Data 0.002 (0.004) Loss 2.4398 (2.6080) Prec@1 35.625 (36.771) Prec@5 70.625 (67.470) Epoch: [13][1000/11272] Time 0.801 (0.842) Data 0.001 (0.004) Loss 2.6329 (2.6071) Prec@1 35.000 (36.779) Prec@5 69.375 (67.488) Epoch: [13][1010/11272] Time 0.937 (0.842) Data 0.004 (0.004) Loss 2.5866 (2.6071) Prec@1 39.375 (36.774) Prec@5 68.750 (67.495) Epoch: [13][1020/11272] Time 0.871 (0.842) Data 0.002 (0.004) Loss 2.3358 (2.6071) Prec@1 40.625 (36.782) Prec@5 70.000 (67.498) Epoch: [13][1030/11272] Time 0.743 (0.842) Data 0.002 (0.004) Loss 2.5501 (2.6062) Prec@1 40.000 (36.793) Prec@5 68.750 (67.518) Epoch: [13][1040/11272] Time 0.795 (0.842) Data 0.002 (0.004) Loss 2.6379 (2.6058) Prec@1 38.125 (36.810) Prec@5 61.875 (67.526) Epoch: [13][1050/11272] Time 0.848 (0.842) Data 0.001 (0.004) Loss 2.3933 (2.6058) Prec@1 40.000 (36.808) Prec@5 74.375 (67.524) Epoch: [13][1060/11272] Time 0.885 (0.841) Data 0.002 (0.004) Loss 2.6606 (2.6055) Prec@1 36.875 (36.813) Prec@5 69.375 (67.525) Epoch: [13][1070/11272] Time 0.790 (0.841) Data 0.001 (0.004) Loss 2.7587 (2.6058) Prec@1 35.000 (36.808) Prec@5 67.500 (67.517) Epoch: [13][1080/11272] Time 0.756 (0.841) Data 0.002 (0.004) Loss 2.3239 (2.6060) Prec@1 45.625 (36.800) Prec@5 75.000 (67.503) Epoch: [13][1090/11272] Time 0.913 (0.841) Data 0.001 (0.004) Loss 2.7534 (2.6058) Prec@1 35.000 (36.804) Prec@5 62.500 (67.500) Epoch: [13][1100/11272] Time 0.882 (0.841) Data 0.002 (0.004) Loss 2.6388 (2.6050) Prec@1 31.875 (36.807) Prec@5 68.125 (67.508) Epoch: [13][1110/11272] Time 0.781 (0.841) Data 0.002 (0.004) Loss 2.5783 (2.6044) Prec@1 40.625 (36.828) Prec@5 65.000 (67.518) Epoch: [13][1120/11272] Time 0.765 (0.841) Data 0.001 (0.004) Loss 2.5346 (2.6044) Prec@1 41.250 (36.824) Prec@5 66.875 (67.521) Epoch: [13][1130/11272] Time 0.983 (0.841) Data 0.001 (0.004) Loss 2.5567 (2.6048) Prec@1 37.500 (36.816) Prec@5 70.000 (67.506) Epoch: [13][1140/11272] Time 0.741 (0.841) Data 0.003 (0.004) Loss 2.6241 (2.6048) Prec@1 36.250 (36.812) Prec@5 65.625 (67.502) Epoch: [13][1150/11272] Time 0.801 (0.841) Data 0.002 (0.004) Loss 2.2995 (2.6045) Prec@1 43.125 (36.827) Prec@5 71.250 (67.500) Epoch: [13][1160/11272] Time 0.911 (0.841) Data 0.002 (0.004) Loss 2.8338 (2.6047) Prec@1 32.500 (36.822) Prec@5 63.125 (67.505) Epoch: [13][1170/11272] Time 0.874 (0.841) Data 0.002 (0.004) Loss 3.0919 (2.6057) Prec@1 30.000 (36.796) Prec@5 59.375 (67.496) Epoch: [13][1180/11272] Time 0.801 (0.841) Data 0.001 (0.004) Loss 2.5873 (2.6056) Prec@1 38.125 (36.809) Prec@5 66.250 (67.502) Epoch: [13][1190/11272] Time 0.815 (0.841) Data 0.002 (0.004) Loss 2.5218 (2.6062) Prec@1 41.250 (36.807) Prec@5 71.250 (67.493) Epoch: [13][1200/11272] Time 1.013 (0.841) Data 0.002 (0.004) Loss 2.5643 (2.6057) Prec@1 38.750 (36.823) Prec@5 67.500 (67.497) Epoch: [13][1210/11272] Time 1.038 (0.841) Data 0.002 (0.004) Loss 2.3812 (2.6055) Prec@1 41.875 (36.831) Prec@5 68.750 (67.495) Epoch: [13][1220/11272] Time 0.780 (0.841) Data 0.002 (0.004) Loss 2.6688 (2.6054) Prec@1 36.875 (36.837) Prec@5 66.250 (67.496) Epoch: [13][1230/11272] Time 0.804 (0.841) Data 0.001 (0.004) Loss 2.9087 (2.6053) Prec@1 35.625 (36.845) Prec@5 61.250 (67.501) Epoch: [13][1240/11272] Time 0.887 (0.841) Data 0.002 (0.004) Loss 2.5927 (2.6058) Prec@1 35.000 (36.822) Prec@5 73.125 (67.498) Epoch: [13][1250/11272] Time 0.868 (0.841) Data 0.001 (0.004) Loss 2.7258 (2.6059) Prec@1 33.125 (36.817) Prec@5 65.625 (67.499) Epoch: [13][1260/11272] Time 0.767 (0.840) Data 0.001 (0.004) Loss 2.4734 (2.6069) Prec@1 42.500 (36.800) Prec@5 70.000 (67.479) Epoch: [13][1270/11272] Time 0.898 (0.840) Data 0.002 (0.004) Loss 2.5446 (2.6066) Prec@1 40.000 (36.810) Prec@5 67.500 (67.488) Epoch: [13][1280/11272] Time 0.885 (0.840) Data 0.002 (0.004) Loss 2.8245 (2.6074) Prec@1 32.500 (36.791) Prec@5 66.250 (67.472) Epoch: [13][1290/11272] Time 0.750 (0.840) Data 0.001 (0.004) Loss 2.4922 (2.6076) Prec@1 36.250 (36.785) Prec@5 69.375 (67.469) Epoch: [13][1300/11272] Time 0.745 (0.840) Data 0.001 (0.003) Loss 2.7072 (2.6081) Prec@1 34.375 (36.778) Prec@5 65.625 (67.464) Epoch: [13][1310/11272] Time 0.892 (0.840) Data 0.002 (0.003) Loss 2.5909 (2.6082) Prec@1 38.125 (36.786) Prec@5 66.875 (67.456) Epoch: [13][1320/11272] Time 0.856 (0.840) Data 0.001 (0.003) Loss 2.9789 (2.6088) Prec@1 30.000 (36.779) Prec@5 61.250 (67.449) Epoch: [13][1330/11272] Time 0.775 (0.840) Data 0.002 (0.003) Loss 2.8199 (2.6096) Prec@1 35.000 (36.770) Prec@5 63.125 (67.431) Epoch: [13][1340/11272] Time 0.822 (0.840) Data 0.001 (0.003) Loss 2.6871 (2.6094) Prec@1 35.000 (36.780) Prec@5 63.125 (67.429) Epoch: [13][1350/11272] Time 0.914 (0.840) Data 0.001 (0.003) Loss 2.6105 (2.6090) Prec@1 35.625 (36.782) Prec@5 66.875 (67.438) Epoch: [13][1360/11272] Time 0.862 (0.840) Data 0.002 (0.003) Loss 2.6031 (2.6089) Prec@1 37.500 (36.790) Prec@5 67.500 (67.440) Epoch: [13][1370/11272] Time 0.780 (0.840) Data 0.001 (0.003) Loss 2.7189 (2.6090) Prec@1 31.875 (36.786) Prec@5 70.625 (67.446) Epoch: [13][1380/11272] Time 0.797 (0.840) Data 0.002 (0.003) Loss 2.4740 (2.6093) Prec@1 35.000 (36.772) Prec@5 73.125 (67.436) Epoch: [13][1390/11272] Time 0.908 (0.840) Data 0.002 (0.003) Loss 2.7544 (2.6093) Prec@1 33.750 (36.775) Prec@5 65.000 (67.436) Epoch: [13][1400/11272] Time 0.923 (0.840) Data 0.002 (0.003) Loss 2.2802 (2.6091) Prec@1 45.000 (36.782) Prec@5 71.250 (67.447) Epoch: [13][1410/11272] Time 0.781 (0.840) Data 0.002 (0.003) Loss 2.5201 (2.6084) Prec@1 37.500 (36.804) Prec@5 66.250 (67.455) Epoch: [13][1420/11272] Time 0.913 (0.840) Data 0.002 (0.003) Loss 2.5422 (2.6088) Prec@1 36.875 (36.798) Prec@5 68.750 (67.452) Epoch: [13][1430/11272] Time 0.920 (0.840) Data 0.002 (0.003) Loss 2.4995 (2.6092) Prec@1 39.375 (36.797) Prec@5 68.750 (67.441) Epoch: [13][1440/11272] Time 0.824 (0.840) Data 0.002 (0.003) Loss 2.5162 (2.6095) Prec@1 38.750 (36.786) Prec@5 70.000 (67.435) Epoch: [13][1450/11272] Time 0.755 (0.840) Data 0.002 (0.003) Loss 2.8326 (2.6094) Prec@1 29.375 (36.778) Prec@5 62.500 (67.443) Epoch: [13][1460/11272] Time 0.844 (0.840) Data 0.002 (0.003) Loss 2.4325 (2.6091) Prec@1 39.375 (36.782) Prec@5 68.750 (67.448) Epoch: [13][1470/11272] Time 0.895 (0.840) Data 0.001 (0.003) Loss 2.4591 (2.6091) Prec@1 37.500 (36.786) Prec@5 66.250 (67.451) Epoch: [13][1480/11272] Time 0.787 (0.840) Data 0.002 (0.003) Loss 2.6931 (2.6091) Prec@1 31.875 (36.788) Prec@5 68.750 (67.447) Epoch: [13][1490/11272] Time 0.797 (0.840) Data 0.001 (0.003) Loss 2.5317 (2.6091) Prec@1 37.500 (36.786) Prec@5 70.000 (67.445) Epoch: [13][1500/11272] Time 0.882 (0.840) Data 0.002 (0.003) Loss 2.8351 (2.6091) Prec@1 34.375 (36.784) Prec@5 60.625 (67.446) Epoch: [13][1510/11272] Time 0.904 (0.840) Data 0.001 (0.003) Loss 2.7084 (2.6088) Prec@1 36.875 (36.795) Prec@5 65.625 (67.464) Epoch: [13][1520/11272] Time 0.737 (0.840) Data 0.002 (0.003) Loss 2.5948 (2.6091) Prec@1 38.750 (36.797) Prec@5 68.750 (67.457) Epoch: [13][1530/11272] Time 0.751 (0.840) Data 0.001 (0.003) Loss 2.8864 (2.6095) Prec@1 26.875 (36.788) Prec@5 65.000 (67.442) Epoch: [13][1540/11272] Time 0.886 (0.840) Data 0.002 (0.003) Loss 2.4575 (2.6095) Prec@1 39.375 (36.791) Prec@5 71.250 (67.436) Epoch: [13][1550/11272] Time 0.784 (0.840) Data 0.001 (0.003) Loss 2.4078 (2.6091) Prec@1 45.000 (36.797) Prec@5 73.750 (67.446) Epoch: [13][1560/11272] Time 0.738 (0.840) Data 0.002 (0.003) Loss 2.2797 (2.6089) Prec@1 43.750 (36.815) Prec@5 71.875 (67.449) Epoch: [13][1570/11272] Time 0.891 (0.840) Data 0.002 (0.003) Loss 2.7378 (2.6090) Prec@1 36.250 (36.817) Prec@5 67.500 (67.452) Epoch: [13][1580/11272] Time 0.904 (0.840) Data 0.002 (0.003) Loss 2.6720 (2.6091) Prec@1 35.000 (36.818) Prec@5 68.125 (67.458) Epoch: [13][1590/11272] Time 0.738 (0.840) Data 0.002 (0.003) Loss 2.7547 (2.6090) Prec@1 31.250 (36.821) Prec@5 65.000 (67.460) Epoch: [13][1600/11272] Time 0.800 (0.839) Data 0.002 (0.003) Loss 2.4516 (2.6087) Prec@1 41.875 (36.823) Prec@5 68.750 (67.465) Epoch: [13][1610/11272] Time 0.949 (0.840) Data 0.004 (0.003) Loss 2.5097 (2.6083) Prec@1 35.625 (36.835) Prec@5 70.000 (67.477) Epoch: [13][1620/11272] Time 0.910 (0.839) Data 0.002 (0.003) Loss 2.6287 (2.6085) Prec@1 39.375 (36.831) Prec@5 66.250 (67.468) Epoch: [13][1630/11272] Time 0.787 (0.839) Data 0.001 (0.003) Loss 2.4521 (2.6086) Prec@1 41.250 (36.830) Prec@5 73.125 (67.469) Epoch: [13][1640/11272] Time 0.725 (0.839) Data 0.002 (0.003) Loss 2.6731 (2.6088) Prec@1 36.250 (36.825) Prec@5 71.875 (67.468) Epoch: [13][1650/11272] Time 0.899 (0.839) Data 0.002 (0.003) Loss 2.7940 (2.6094) Prec@1 35.625 (36.815) Prec@5 60.625 (67.453) Epoch: [13][1660/11272] Time 0.932 (0.839) Data 0.002 (0.003) Loss 2.6983 (2.6094) Prec@1 32.500 (36.816) Prec@5 63.125 (67.450) Epoch: [13][1670/11272] Time 0.755 (0.839) Data 0.001 (0.003) Loss 2.4677 (2.6097) Prec@1 41.875 (36.812) Prec@5 71.250 (67.444) Epoch: [13][1680/11272] Time 0.903 (0.839) Data 0.002 (0.003) Loss 2.6827 (2.6100) Prec@1 33.750 (36.812) Prec@5 65.625 (67.442) Epoch: [13][1690/11272] Time 0.878 (0.839) Data 0.001 (0.003) Loss 2.5992 (2.6103) Prec@1 38.125 (36.799) Prec@5 68.750 (67.433) Epoch: [13][1700/11272] Time 0.773 (0.839) Data 0.002 (0.003) Loss 2.4385 (2.6101) Prec@1 43.750 (36.796) Prec@5 70.625 (67.430) Epoch: [13][1710/11272] Time 0.787 (0.839) Data 0.001 (0.003) Loss 2.9327 (2.6102) Prec@1 30.000 (36.800) Prec@5 60.000 (67.432) Epoch: [13][1720/11272] Time 0.869 (0.839) Data 0.001 (0.003) Loss 2.7369 (2.6105) Prec@1 35.000 (36.792) Prec@5 65.000 (67.423) Epoch: [13][1730/11272] Time 0.942 (0.839) Data 0.002 (0.003) Loss 2.3600 (2.6105) Prec@1 41.250 (36.790) Prec@5 69.375 (67.423) Epoch: [13][1740/11272] Time 0.749 (0.839) Data 0.002 (0.003) Loss 2.5541 (2.6098) Prec@1 36.250 (36.805) Prec@5 68.125 (67.443) Epoch: [13][1750/11272] Time 0.796 (0.839) Data 0.002 (0.003) Loss 2.6667 (2.6103) Prec@1 39.375 (36.796) Prec@5 65.625 (67.434) Epoch: [13][1760/11272] Time 0.967 (0.839) Data 0.001 (0.003) Loss 2.7463 (2.6106) Prec@1 31.875 (36.784) Prec@5 65.000 (67.428) Epoch: [13][1770/11272] Time 0.863 (0.839) Data 0.002 (0.003) Loss 2.5077 (2.6106) Prec@1 36.875 (36.792) Prec@5 68.125 (67.437) Epoch: [13][1780/11272] Time 0.796 (0.839) Data 0.002 (0.003) Loss 2.9348 (2.6104) Prec@1 29.375 (36.797) Prec@5 64.375 (67.440) Epoch: [13][1790/11272] Time 0.751 (0.839) Data 0.001 (0.003) Loss 2.8842 (2.6104) Prec@1 28.750 (36.793) Prec@5 60.625 (67.434) Epoch: [13][1800/11272] Time 0.885 (0.839) Data 0.002 (0.003) Loss 2.5155 (2.6101) Prec@1 38.750 (36.790) Prec@5 68.750 (67.435) Epoch: [13][1810/11272] Time 0.751 (0.839) Data 0.003 (0.003) Loss 2.8128 (2.6102) Prec@1 33.750 (36.791) Prec@5 63.750 (67.433) Epoch: [13][1820/11272] Time 0.779 (0.839) Data 0.002 (0.003) Loss 2.4936 (2.6099) Prec@1 40.000 (36.796) Prec@5 71.250 (67.450) Epoch: [13][1830/11272] Time 0.872 (0.839) Data 0.002 (0.003) Loss 2.5170 (2.6097) Prec@1 38.125 (36.798) Prec@5 68.750 (67.446) Epoch: [13][1840/11272] Time 0.905 (0.839) Data 0.002 (0.003) Loss 2.4916 (2.6101) Prec@1 35.625 (36.788) Prec@5 71.250 (67.434) Epoch: [13][1850/11272] Time 0.816 (0.839) Data 0.002 (0.003) Loss 2.7421 (2.6108) Prec@1 30.625 (36.772) Prec@5 70.625 (67.426) Epoch: [13][1860/11272] Time 0.753 (0.839) Data 0.001 (0.003) Loss 2.5113 (2.6106) Prec@1 35.625 (36.776) Prec@5 68.125 (67.431) Epoch: [13][1870/11272] Time 0.915 (0.839) Data 0.002 (0.003) Loss 2.3741 (2.6101) Prec@1 41.250 (36.783) Prec@5 70.625 (67.442) Epoch: [13][1880/11272] Time 0.853 (0.839) Data 0.002 (0.003) Loss 2.5485 (2.6099) Prec@1 36.250 (36.791) Prec@5 66.250 (67.448) Epoch: [13][1890/11272] Time 0.744 (0.839) Data 0.002 (0.003) Loss 2.7722 (2.6099) Prec@1 33.750 (36.790) Prec@5 65.000 (67.453) Epoch: [13][1900/11272] Time 0.739 (0.839) Data 0.001 (0.003) Loss 2.6955 (2.6102) Prec@1 38.750 (36.788) Prec@5 68.125 (67.450) Epoch: [13][1910/11272] Time 0.889 (0.839) Data 0.002 (0.003) Loss 2.5231 (2.6103) Prec@1 36.250 (36.780) Prec@5 66.250 (67.442) Epoch: [13][1920/11272] Time 0.893 (0.839) Data 0.002 (0.003) Loss 2.6828 (2.6104) Prec@1 36.250 (36.776) Prec@5 66.875 (67.439) Epoch: [13][1930/11272] Time 0.806 (0.839) Data 0.002 (0.003) Loss 2.1958 (2.6106) Prec@1 41.250 (36.772) Prec@5 75.625 (67.442) Epoch: [13][1940/11272] Time 0.909 (0.839) Data 0.001 (0.003) Loss 2.5144 (2.6107) Prec@1 36.250 (36.767) Prec@5 70.000 (67.441) Epoch: [13][1950/11272] Time 0.850 (0.839) Data 0.002 (0.003) Loss 2.7646 (2.6109) Prec@1 30.625 (36.762) Prec@5 60.625 (67.430) Epoch: [13][1960/11272] Time 0.778 (0.839) Data 0.002 (0.003) Loss 2.5936 (2.6112) Prec@1 35.000 (36.750) Prec@5 69.375 (67.433) Epoch: [13][1970/11272] Time 0.741 (0.839) Data 0.001 (0.003) Loss 2.6740 (2.6110) Prec@1 35.000 (36.752) Prec@5 63.750 (67.432) Epoch: [13][1980/11272] Time 0.874 (0.839) Data 0.002 (0.003) Loss 2.4618 (2.6110) Prec@1 38.125 (36.759) Prec@5 70.625 (67.427) Epoch: [13][1990/11272] Time 0.890 (0.839) Data 0.002 (0.003) Loss 2.6220 (2.6111) Prec@1 38.750 (36.754) Prec@5 67.500 (67.426) Epoch: [13][2000/11272] Time 0.786 (0.838) Data 0.001 (0.003) Loss 2.6909 (2.6115) Prec@1 35.000 (36.746) Prec@5 67.500 (67.424) Epoch: [13][2010/11272] Time 0.735 (0.838) Data 0.001 (0.003) Loss 2.6139 (2.6117) Prec@1 39.375 (36.739) Prec@5 69.375 (67.427) Epoch: [13][2020/11272] Time 0.909 (0.838) Data 0.002 (0.003) Loss 2.6923 (2.6118) Prec@1 35.000 (36.737) Prec@5 66.250 (67.426) Epoch: [13][2030/11272] Time 0.887 (0.838) Data 0.002 (0.003) Loss 2.9335 (2.6120) Prec@1 33.750 (36.740) Prec@5 61.875 (67.412) Epoch: [13][2040/11272] Time 0.814 (0.838) Data 0.001 (0.003) Loss 2.4744 (2.6123) Prec@1 36.250 (36.734) Prec@5 69.375 (67.408) Epoch: [13][2050/11272] Time 0.762 (0.838) Data 0.002 (0.003) Loss 2.7889 (2.6126) Prec@1 32.500 (36.729) Prec@5 65.625 (67.403) Epoch: [13][2060/11272] Time 0.894 (0.838) Data 0.001 (0.003) Loss 2.7587 (2.6123) Prec@1 31.875 (36.729) Prec@5 63.750 (67.410) Epoch: [13][2070/11272] Time 0.745 (0.838) Data 0.003 (0.003) Loss 2.7265 (2.6127) Prec@1 36.875 (36.727) Prec@5 63.125 (67.405) Epoch: [13][2080/11272] Time 0.744 (0.838) Data 0.001 (0.003) Loss 2.6028 (2.6127) Prec@1 40.625 (36.726) Prec@5 66.875 (67.405) Epoch: [13][2090/11272] Time 0.861 (0.838) Data 0.001 (0.003) Loss 2.7689 (2.6128) Prec@1 34.375 (36.725) Prec@5 61.875 (67.397) Epoch: [13][2100/11272] Time 0.871 (0.838) Data 0.002 (0.003) Loss 2.7365 (2.6129) Prec@1 38.125 (36.723) Prec@5 64.375 (67.391) Epoch: [13][2110/11272] Time 0.784 (0.838) Data 0.003 (0.003) Loss 2.6325 (2.6128) Prec@1 37.500 (36.722) Prec@5 66.875 (67.391) Epoch: [13][2120/11272] Time 0.771 (0.838) Data 0.002 (0.003) Loss 2.3782 (2.6127) Prec@1 43.750 (36.727) Prec@5 73.125 (67.390) Epoch: [13][2130/11272] Time 0.891 (0.838) Data 0.002 (0.003) Loss 2.5424 (2.6128) Prec@1 33.750 (36.720) Prec@5 70.000 (67.391) Epoch: [13][2140/11272] Time 0.919 (0.838) Data 0.002 (0.003) Loss 2.5239 (2.6128) Prec@1 36.250 (36.718) Prec@5 73.125 (67.394) Epoch: [13][2150/11272] Time 0.757 (0.838) Data 0.002 (0.003) Loss 2.4865 (2.6130) Prec@1 35.000 (36.708) Prec@5 70.000 (67.387) Epoch: [13][2160/11272] Time 0.784 (0.838) Data 0.002 (0.003) Loss 2.7516 (2.6131) Prec@1 30.625 (36.703) Prec@5 68.125 (67.387) Epoch: [13][2170/11272] Time 0.885 (0.838) Data 0.001 (0.003) Loss 2.7280 (2.6131) Prec@1 35.625 (36.707) Prec@5 66.250 (67.388) Epoch: [13][2180/11272] Time 0.845 (0.838) Data 0.001 (0.003) Loss 2.6970 (2.6132) Prec@1 37.500 (36.707) Prec@5 68.125 (67.385) Epoch: [13][2190/11272] Time 0.843 (0.838) Data 0.002 (0.003) Loss 2.5925 (2.6132) Prec@1 35.625 (36.710) Prec@5 69.375 (67.378) Epoch: [13][2200/11272] Time 0.895 (0.838) Data 0.002 (0.003) Loss 2.4754 (2.6130) Prec@1 36.875 (36.714) Prec@5 69.375 (67.378) Epoch: [13][2210/11272] Time 0.821 (0.838) Data 0.001 (0.003) Loss 2.4943 (2.6129) Prec@1 33.125 (36.711) Prec@5 71.250 (67.381) Epoch: [13][2220/11272] Time 0.755 (0.838) Data 0.001 (0.003) Loss 2.5308 (2.6128) Prec@1 38.750 (36.716) Prec@5 66.250 (67.380) Epoch: [13][2230/11272] Time 0.742 (0.838) Data 0.002 (0.003) Loss 2.6565 (2.6126) Prec@1 35.625 (36.716) Prec@5 63.125 (67.381) Epoch: [13][2240/11272] Time 0.917 (0.838) Data 0.002 (0.003) Loss 2.7730 (2.6125) Prec@1 33.125 (36.715) Prec@5 68.750 (67.381) Epoch: [13][2250/11272] Time 0.862 (0.838) Data 0.002 (0.003) Loss 2.5426 (2.6122) Prec@1 40.000 (36.716) Prec@5 66.875 (67.385) Epoch: [13][2260/11272] Time 0.794 (0.838) Data 0.002 (0.003) Loss 3.0546 (2.6122) Prec@1 25.000 (36.714) Prec@5 57.500 (67.381) Epoch: [13][2270/11272] Time 0.782 (0.838) Data 0.002 (0.003) Loss 2.5156 (2.6123) Prec@1 38.125 (36.711) Prec@5 66.250 (67.376) Epoch: [13][2280/11272] Time 0.875 (0.838) Data 0.001 (0.003) Loss 2.3383 (2.6122) Prec@1 35.625 (36.712) Prec@5 72.500 (67.379) Epoch: [13][2290/11272] Time 0.891 (0.838) Data 0.002 (0.003) Loss 2.4062 (2.6120) Prec@1 36.875 (36.709) Prec@5 68.750 (67.378) Epoch: [13][2300/11272] Time 0.784 (0.838) Data 0.002 (0.003) Loss 2.6945 (2.6123) Prec@1 32.500 (36.698) Prec@5 63.125 (67.370) Epoch: [13][2310/11272] Time 0.773 (0.838) Data 0.002 (0.003) Loss 2.3100 (2.6121) Prec@1 41.875 (36.700) Prec@5 75.625 (67.369) Epoch: [13][2320/11272] Time 0.891 (0.838) Data 0.002 (0.003) Loss 2.5309 (2.6119) Prec@1 36.250 (36.706) Prec@5 68.750 (67.374) Epoch: [13][2330/11272] Time 0.845 (0.837) Data 0.001 (0.003) Loss 2.4614 (2.6120) Prec@1 39.375 (36.703) Prec@5 71.250 (67.377) Epoch: [13][2340/11272] Time 0.784 (0.837) Data 0.002 (0.003) Loss 2.3651 (2.6119) Prec@1 36.875 (36.703) Prec@5 78.125 (67.382) Epoch: [13][2350/11272] Time 0.910 (0.837) Data 0.002 (0.003) Loss 2.7981 (2.6118) Prec@1 31.250 (36.702) Prec@5 65.625 (67.384) Epoch: [13][2360/11272] Time 0.914 (0.837) Data 0.001 (0.003) Loss 2.3834 (2.6119) Prec@1 36.875 (36.704) Prec@5 71.875 (67.384) Epoch: [13][2370/11272] Time 0.807 (0.837) Data 0.002 (0.003) Loss 2.6395 (2.6119) Prec@1 35.625 (36.706) Prec@5 70.000 (67.391) Epoch: [13][2380/11272] Time 0.783 (0.837) Data 0.002 (0.003) Loss 2.3494 (2.6117) Prec@1 43.750 (36.711) Prec@5 75.625 (67.397) Epoch: [13][2390/11272] Time 0.883 (0.837) Data 0.001 (0.003) Loss 2.5636 (2.6117) Prec@1 40.625 (36.709) Prec@5 65.000 (67.396) Epoch: [13][2400/11272] Time 0.879 (0.837) Data 0.001 (0.003) Loss 2.7766 (2.6119) Prec@1 31.875 (36.705) Prec@5 64.375 (67.394) Epoch: [13][2410/11272] Time 0.762 (0.837) Data 0.002 (0.003) Loss 2.6176 (2.6119) Prec@1 39.375 (36.707) Prec@5 65.625 (67.394) Epoch: [13][2420/11272] Time 0.799 (0.837) Data 0.001 (0.003) Loss 2.6252 (2.6122) Prec@1 34.375 (36.696) Prec@5 66.250 (67.392) Epoch: [13][2430/11272] Time 0.894 (0.837) Data 0.002 (0.003) Loss 2.6952 (2.6121) Prec@1 36.250 (36.700) Prec@5 65.625 (67.391) Epoch: [13][2440/11272] Time 0.872 (0.837) Data 0.001 (0.003) Loss 2.6891 (2.6123) Prec@1 35.000 (36.696) Prec@5 62.500 (67.382) Epoch: [13][2450/11272] Time 0.744 (0.837) Data 0.001 (0.003) Loss 2.6005 (2.6123) Prec@1 35.625 (36.697) Prec@5 68.125 (67.376) Epoch: [13][2460/11272] Time 0.769 (0.837) Data 0.002 (0.003) Loss 2.5944 (2.6122) Prec@1 33.125 (36.694) Prec@5 65.625 (67.381) Epoch: [13][2470/11272] Time 0.876 (0.837) Data 0.002 (0.003) Loss 2.5492 (2.6121) Prec@1 43.125 (36.701) Prec@5 70.000 (67.384) Epoch: [13][2480/11272] Time 0.749 (0.837) Data 0.001 (0.003) Loss 2.6214 (2.6120) Prec@1 38.750 (36.701) Prec@5 68.125 (67.388) Epoch: [13][2490/11272] Time 0.790 (0.837) Data 0.001 (0.003) Loss 2.7659 (2.6121) Prec@1 35.000 (36.699) Prec@5 61.875 (67.389) Epoch: [13][2500/11272] Time 0.926 (0.837) Data 0.002 (0.003) Loss 2.6968 (2.6122) Prec@1 37.500 (36.697) Prec@5 65.000 (67.385) Epoch: [13][2510/11272] Time 0.870 (0.837) Data 0.002 (0.003) Loss 2.5442 (2.6125) Prec@1 41.250 (36.695) Prec@5 68.750 (67.380) Epoch: [13][2520/11272] Time 0.783 (0.837) Data 0.002 (0.003) Loss 2.5336 (2.6126) Prec@1 40.000 (36.692) Prec@5 68.750 (67.379) Epoch: [13][2530/11272] Time 0.754 (0.837) Data 0.002 (0.003) Loss 2.4408 (2.6124) Prec@1 40.625 (36.694) Prec@5 68.750 (67.381) Epoch: [13][2540/11272] Time 0.859 (0.837) Data 0.002 (0.003) Loss 2.8382 (2.6125) Prec@1 31.875 (36.696) Prec@5 62.500 (67.375) Epoch: [13][2550/11272] Time 0.872 (0.837) Data 0.001 (0.003) Loss 2.7429 (2.6125) Prec@1 40.625 (36.696) Prec@5 65.625 (67.375) Epoch: [13][2560/11272] Time 0.756 (0.837) Data 0.002 (0.003) Loss 2.6261 (2.6125) Prec@1 39.375 (36.696) Prec@5 66.250 (67.376) Epoch: [13][2570/11272] Time 0.744 (0.837) Data 0.002 (0.003) Loss 2.5680 (2.6126) Prec@1 41.250 (36.698) Prec@5 66.875 (67.376) Epoch: [13][2580/11272] Time 0.860 (0.837) Data 0.002 (0.003) Loss 2.6246 (2.6124) Prec@1 33.750 (36.703) Prec@5 64.375 (67.380) Epoch: [13][2590/11272] Time 0.926 (0.837) Data 0.002 (0.003) Loss 2.6269 (2.6125) Prec@1 36.250 (36.703) Prec@5 65.000 (67.375) Epoch: [13][2600/11272] Time 0.735 (0.837) Data 0.002 (0.003) Loss 2.7097 (2.6125) Prec@1 31.250 (36.696) Prec@5 63.750 (67.377) Epoch: [13][2610/11272] Time 0.879 (0.837) Data 0.001 (0.003) Loss 2.7407 (2.6126) Prec@1 36.875 (36.693) Prec@5 63.750 (67.376) Epoch: [13][2620/11272] Time 0.907 (0.836) Data 0.002 (0.003) Loss 2.7365 (2.6127) Prec@1 31.875 (36.694) Prec@5 61.250 (67.372) Epoch: [13][2630/11272] Time 0.761 (0.836) Data 0.001 (0.003) Loss 2.6536 (2.6128) Prec@1 42.500 (36.691) Prec@5 73.125 (67.371) Epoch: [13][2640/11272] Time 0.750 (0.836) Data 0.002 (0.003) Loss 2.7516 (2.6128) Prec@1 37.500 (36.688) Prec@5 68.125 (67.371) Epoch: [13][2650/11272] Time 0.897 (0.836) Data 0.002 (0.003) Loss 2.4697 (2.6128) Prec@1 36.250 (36.688) Prec@5 71.875 (67.375) Epoch: [13][2660/11272] Time 0.852 (0.836) Data 0.001 (0.003) Loss 2.6177 (2.6128) Prec@1 31.875 (36.681) Prec@5 66.875 (67.374) Epoch: [13][2670/11272] Time 0.775 (0.836) Data 0.002 (0.003) Loss 2.7568 (2.6129) Prec@1 32.500 (36.676) Prec@5 65.000 (67.375) Epoch: [13][2680/11272] Time 0.751 (0.836) Data 0.001 (0.003) Loss 2.6709 (2.6130) Prec@1 37.500 (36.677) Prec@5 66.875 (67.371) Epoch: [13][2690/11272] Time 0.953 (0.836) Data 0.001 (0.003) Loss 2.3876 (2.6131) Prec@1 41.250 (36.681) Prec@5 73.750 (67.371) Epoch: [13][2700/11272] Time 0.929 (0.836) Data 0.001 (0.003) Loss 2.8147 (2.6133) Prec@1 32.500 (36.674) Prec@5 66.875 (67.369) Epoch: [13][2710/11272] Time 0.758 (0.836) Data 0.002 (0.003) Loss 2.4984 (2.6134) Prec@1 38.125 (36.670) Prec@5 69.375 (67.363) Epoch: [13][2720/11272] Time 0.760 (0.836) Data 0.002 (0.003) Loss 2.4592 (2.6133) Prec@1 40.625 (36.669) Prec@5 71.250 (67.365) Epoch: [13][2730/11272] Time 0.890 (0.836) Data 0.002 (0.003) Loss 2.6619 (2.6133) Prec@1 37.500 (36.672) Prec@5 68.750 (67.367) Epoch: [13][2740/11272] Time 0.751 (0.836) Data 0.003 (0.003) Loss 2.6188 (2.6133) Prec@1 35.000 (36.675) Prec@5 66.875 (67.365) Epoch: [13][2750/11272] Time 0.765 (0.836) Data 0.002 (0.003) Loss 2.6014 (2.6135) Prec@1 36.250 (36.668) Prec@5 67.500 (67.361) Epoch: [13][2760/11272] Time 0.865 (0.836) Data 0.001 (0.003) Loss 2.7338 (2.6137) Prec@1 35.625 (36.662) Prec@5 64.375 (67.359) Epoch: [13][2770/11272] Time 0.868 (0.836) Data 0.001 (0.003) Loss 2.6209 (2.6136) Prec@1 41.250 (36.663) Prec@5 65.000 (67.358) Epoch: [13][2780/11272] Time 0.743 (0.836) Data 0.002 (0.003) Loss 2.6629 (2.6139) Prec@1 37.500 (36.659) Prec@5 67.500 (67.351) Epoch: [13][2790/11272] Time 0.798 (0.836) Data 0.002 (0.003) Loss 2.6035 (2.6139) Prec@1 32.500 (36.654) Prec@5 66.875 (67.349) Epoch: [13][2800/11272] Time 0.951 (0.836) Data 0.002 (0.003) Loss 2.7423 (2.6142) Prec@1 35.625 (36.649) Prec@5 66.250 (67.347) Epoch: [13][2810/11272] Time 0.855 (0.836) Data 0.001 (0.003) Loss 2.4234 (2.6142) Prec@1 39.375 (36.649) Prec@5 70.625 (67.344) Epoch: [13][2820/11272] Time 0.759 (0.836) Data 0.001 (0.003) Loss 2.5818 (2.6143) Prec@1 36.250 (36.645) Prec@5 67.500 (67.344) Epoch: [13][2830/11272] Time 0.834 (0.836) Data 0.002 (0.003) Loss 2.8491 (2.6141) Prec@1 33.125 (36.650) Prec@5 61.875 (67.352) Epoch: [13][2840/11272] Time 0.886 (0.836) Data 0.003 (0.003) Loss 2.5625 (2.6141) Prec@1 40.625 (36.651) Prec@5 65.625 (67.350) Epoch: [13][2850/11272] Time 0.931 (0.836) Data 0.002 (0.003) Loss 2.7591 (2.6139) Prec@1 32.500 (36.655) Prec@5 63.750 (67.349) Epoch: [13][2860/11272] Time 0.786 (0.836) Data 0.004 (0.003) Loss 2.4173 (2.6141) Prec@1 38.125 (36.654) Prec@5 73.750 (67.346) Epoch: [13][2870/11272] Time 0.914 (0.836) Data 0.002 (0.003) Loss 2.9097 (2.6142) Prec@1 30.625 (36.650) Prec@5 61.250 (67.342) Epoch: [13][2880/11272] Time 0.871 (0.836) Data 0.001 (0.003) Loss 2.4612 (2.6140) Prec@1 35.625 (36.650) Prec@5 71.875 (67.346) Epoch: [13][2890/11272] Time 0.834 (0.836) Data 0.003 (0.003) Loss 2.5073 (2.6136) Prec@1 41.875 (36.658) Prec@5 70.625 (67.351) Epoch: [13][2900/11272] Time 0.733 (0.836) Data 0.001 (0.003) Loss 2.7170 (2.6136) Prec@1 38.125 (36.655) Prec@5 61.875 (67.346) Epoch: [13][2910/11272] Time 0.879 (0.836) Data 0.001 (0.002) Loss 2.3976 (2.6136) Prec@1 41.875 (36.658) Prec@5 76.250 (67.346) Epoch: [13][2920/11272] Time 0.941 (0.836) Data 0.002 (0.002) Loss 2.9591 (2.6137) Prec@1 30.000 (36.658) Prec@5 65.000 (67.346) Epoch: [13][2930/11272] Time 0.708 (0.836) Data 0.001 (0.002) Loss 2.7525 (2.6139) Prec@1 41.875 (36.660) Prec@5 61.875 (67.342) Epoch: [13][2940/11272] Time 0.766 (0.836) Data 0.001 (0.002) Loss 2.4932 (2.6139) Prec@1 37.500 (36.662) Prec@5 75.000 (67.339) Epoch: [13][2950/11272] Time 0.899 (0.836) Data 0.001 (0.002) Loss 2.7909 (2.6140) Prec@1 31.875 (36.658) Prec@5 61.875 (67.334) Epoch: [13][2960/11272] Time 0.949 (0.836) Data 0.002 (0.002) Loss 2.5667 (2.6139) Prec@1 36.250 (36.657) Prec@5 66.250 (67.336) Epoch: [13][2970/11272] Time 0.797 (0.836) Data 0.001 (0.002) Loss 2.3836 (2.6138) Prec@1 38.750 (36.655) Prec@5 73.125 (67.340) Epoch: [13][2980/11272] Time 0.830 (0.836) Data 0.001 (0.002) Loss 2.5495 (2.6136) Prec@1 40.000 (36.657) Prec@5 69.375 (67.346) Epoch: [13][2990/11272] Time 0.884 (0.836) Data 0.002 (0.002) Loss 2.6777 (2.6136) Prec@1 38.750 (36.656) Prec@5 66.875 (67.347) Epoch: [13][3000/11272] Time 0.750 (0.836) Data 0.003 (0.002) Loss 2.5828 (2.6132) Prec@1 36.875 (36.664) Prec@5 67.500 (67.353) Epoch: [13][3010/11272] Time 0.763 (0.836) Data 0.002 (0.002) Loss 2.8601 (2.6134) Prec@1 33.750 (36.659) Prec@5 62.500 (67.351) Epoch: [13][3020/11272] Time 0.898 (0.836) Data 0.002 (0.002) Loss 2.5645 (2.6135) Prec@1 36.250 (36.659) Prec@5 66.250 (67.352) Epoch: [13][3030/11272] Time 0.928 (0.836) Data 0.002 (0.002) Loss 2.9949 (2.6140) Prec@1 28.125 (36.647) Prec@5 57.500 (67.343) Epoch: [13][3040/11272] Time 0.741 (0.836) Data 0.001 (0.002) Loss 2.7928 (2.6142) Prec@1 41.250 (36.645) Prec@5 63.125 (67.340) Epoch: [13][3050/11272] Time 0.754 (0.836) Data 0.002 (0.002) Loss 2.9003 (2.6142) Prec@1 35.625 (36.652) Prec@5 60.625 (67.337) Epoch: [13][3060/11272] Time 0.861 (0.836) Data 0.002 (0.002) Loss 2.6664 (2.6142) Prec@1 32.500 (36.649) Prec@5 65.000 (67.338) Epoch: [13][3070/11272] Time 0.850 (0.836) Data 0.002 (0.002) Loss 2.1953 (2.6141) Prec@1 45.625 (36.650) Prec@5 71.875 (67.345) Epoch: [13][3080/11272] Time 0.742 (0.836) Data 0.002 (0.002) Loss 2.5702 (2.6141) Prec@1 36.875 (36.651) Prec@5 68.750 (67.340) Epoch: [13][3090/11272] Time 0.783 (0.836) Data 0.002 (0.002) Loss 2.6531 (2.6141) Prec@1 38.125 (36.650) Prec@5 61.875 (67.339) Epoch: [13][3100/11272] Time 0.811 (0.835) Data 0.001 (0.002) Loss 2.5394 (2.6139) Prec@1 41.250 (36.652) Prec@5 67.500 (67.340) Epoch: [13][3110/11272] Time 0.919 (0.835) Data 0.001 (0.002) Loss 2.7270 (2.6138) Prec@1 30.625 (36.655) Prec@5 66.250 (67.344) Epoch: [13][3120/11272] Time 0.745 (0.835) Data 0.002 (0.002) Loss 2.2958 (2.6139) Prec@1 43.125 (36.657) Prec@5 76.250 (67.341) Epoch: [13][3130/11272] Time 0.901 (0.835) Data 0.002 (0.002) Loss 2.7062 (2.6140) Prec@1 40.000 (36.656) Prec@5 66.250 (67.335) Epoch: [13][3140/11272] Time 0.908 (0.835) Data 0.001 (0.002) Loss 2.3076 (2.6137) Prec@1 43.125 (36.665) Prec@5 73.125 (67.340) Epoch: [13][3150/11272] Time 0.769 (0.835) Data 0.002 (0.002) Loss 2.8194 (2.6138) Prec@1 33.750 (36.665) Prec@5 65.625 (67.342) Epoch: [13][3160/11272] Time 0.809 (0.835) Data 0.002 (0.002) Loss 2.6922 (2.6139) Prec@1 37.500 (36.664) Prec@5 64.375 (67.335) Epoch: [13][3170/11272] Time 0.850 (0.835) Data 0.001 (0.002) Loss 2.6396 (2.6139) Prec@1 33.125 (36.664) Prec@5 68.750 (67.339) Epoch: [13][3180/11272] Time 0.845 (0.835) Data 0.001 (0.002) Loss 2.5834 (2.6139) Prec@1 34.375 (36.660) Prec@5 69.375 (67.339) Epoch: [13][3190/11272] Time 0.750 (0.835) Data 0.002 (0.002) Loss 2.8269 (2.6140) Prec@1 31.875 (36.656) Prec@5 61.250 (67.334) Epoch: [13][3200/11272] Time 0.780 (0.835) Data 0.002 (0.002) Loss 2.6580 (2.6144) Prec@1 31.875 (36.650) Prec@5 65.625 (67.328) Epoch: [13][3210/11272] Time 0.938 (0.835) Data 0.002 (0.002) Loss 2.6718 (2.6145) Prec@1 38.750 (36.648) Prec@5 63.750 (67.327) Epoch: [13][3220/11272] Time 0.855 (0.835) Data 0.002 (0.002) Loss 2.5169 (2.6146) Prec@1 38.750 (36.648) Prec@5 71.250 (67.328) Epoch: [13][3230/11272] Time 0.818 (0.835) Data 0.002 (0.002) Loss 2.8496 (2.6146) Prec@1 30.625 (36.646) Prec@5 63.125 (67.330) Epoch: [13][3240/11272] Time 0.754 (0.835) Data 0.002 (0.002) Loss 2.7098 (2.6148) Prec@1 35.625 (36.638) Prec@5 66.875 (67.326) Epoch: [13][3250/11272] Time 0.893 (0.835) Data 0.001 (0.002) Loss 2.5955 (2.6150) Prec@1 33.750 (36.634) Prec@5 69.375 (67.326) Epoch: [13][3260/11272] Time 0.827 (0.835) Data 0.001 (0.002) Loss 2.4241 (2.6151) Prec@1 41.250 (36.633) Prec@5 73.750 (67.321) Epoch: [13][3270/11272] Time 0.801 (0.835) Data 0.001 (0.002) Loss 2.9097 (2.6150) Prec@1 27.500 (36.633) Prec@5 61.250 (67.321) Epoch: [13][3280/11272] Time 0.924 (0.835) Data 0.002 (0.002) Loss 2.8256 (2.6152) Prec@1 33.125 (36.630) Prec@5 61.875 (67.314) Epoch: [13][3290/11272] Time 0.905 (0.835) Data 0.001 (0.002) Loss 2.7449 (2.6152) Prec@1 32.500 (36.627) Prec@5 66.250 (67.315) Epoch: [13][3300/11272] Time 0.738 (0.835) Data 0.002 (0.002) Loss 2.6951 (2.6150) Prec@1 36.250 (36.633) Prec@5 70.625 (67.325) Epoch: [13][3310/11272] Time 0.788 (0.835) Data 0.002 (0.002) Loss 2.8084 (2.6151) Prec@1 33.750 (36.633) Prec@5 65.000 (67.322) Epoch: [13][3320/11272] Time 0.882 (0.835) Data 0.001 (0.002) Loss 2.6835 (2.6150) Prec@1 35.000 (36.629) Prec@5 64.375 (67.321) Epoch: [13][3330/11272] Time 0.913 (0.835) Data 0.001 (0.002) Loss 2.6389 (2.6151) Prec@1 36.875 (36.627) Prec@5 63.125 (67.318) Epoch: [13][3340/11272] Time 0.761 (0.835) Data 0.001 (0.002) Loss 2.6835 (2.6150) Prec@1 33.125 (36.627) Prec@5 66.250 (67.320) Epoch: [13][3350/11272] Time 0.765 (0.835) Data 0.002 (0.002) Loss 2.5048 (2.6149) Prec@1 35.000 (36.630) Prec@5 69.375 (67.325) Epoch: [13][3360/11272] Time 0.865 (0.835) Data 0.001 (0.002) Loss 2.5511 (2.6148) Prec@1 40.000 (36.637) Prec@5 70.000 (67.325) Epoch: [13][3370/11272] Time 0.840 (0.835) Data 0.002 (0.002) Loss 2.7885 (2.6149) Prec@1 32.500 (36.633) Prec@5 66.250 (67.324) Epoch: [13][3380/11272] Time 0.740 (0.835) Data 0.001 (0.002) Loss 2.3667 (2.6148) Prec@1 40.625 (36.637) Prec@5 74.375 (67.329) Epoch: [13][3390/11272] Time 0.759 (0.835) Data 0.002 (0.002) Loss 2.6880 (2.6150) Prec@1 35.625 (36.636) Prec@5 66.250 (67.326) Epoch: [13][3400/11272] Time 0.871 (0.835) Data 0.001 (0.002) Loss 2.6257 (2.6150) Prec@1 35.000 (36.634) Prec@5 65.625 (67.323) Epoch: [13][3410/11272] Time 0.746 (0.835) Data 0.002 (0.002) Loss 2.7596 (2.6148) Prec@1 37.500 (36.638) Prec@5 64.375 (67.326) Epoch: [13][3420/11272] Time 0.769 (0.835) Data 0.002 (0.002) Loss 2.8605 (2.6150) Prec@1 32.500 (36.640) Prec@5 64.375 (67.325) Epoch: [13][3430/11272] Time 0.856 (0.835) Data 0.002 (0.002) Loss 2.6359 (2.6150) Prec@1 31.875 (36.637) Prec@5 65.000 (67.322) Epoch: [13][3440/11272] Time 0.888 (0.835) Data 0.002 (0.002) Loss 2.5841 (2.6148) Prec@1 43.750 (36.647) Prec@5 66.875 (67.328) Epoch: [13][3450/11272] Time 0.801 (0.835) Data 0.002 (0.002) Loss 2.4898 (2.6151) Prec@1 38.125 (36.640) Prec@5 70.000 (67.323) Epoch: [13][3460/11272] Time 0.771 (0.835) Data 0.002 (0.002) Loss 2.6933 (2.6152) Prec@1 31.875 (36.639) Prec@5 66.875 (67.321) Epoch: [13][3470/11272] Time 0.902 (0.834) Data 0.002 (0.002) Loss 2.3085 (2.6151) Prec@1 43.750 (36.646) Prec@5 73.750 (67.322) Epoch: [13][3480/11272] Time 0.886 (0.834) Data 0.002 (0.002) Loss 2.6964 (2.6152) Prec@1 33.750 (36.639) Prec@5 67.500 (67.319) Epoch: [13][3490/11272] Time 0.773 (0.834) Data 0.004 (0.002) Loss 2.9701 (2.6154) Prec@1 35.000 (36.639) Prec@5 62.500 (67.314) Epoch: [13][3500/11272] Time 0.741 (0.834) Data 0.002 (0.002) Loss 2.5593 (2.6156) Prec@1 38.125 (36.636) Prec@5 67.500 (67.310) Epoch: [13][3510/11272] Time 0.869 (0.834) Data 0.002 (0.002) Loss 2.4001 (2.6154) Prec@1 38.750 (36.639) Prec@5 70.625 (67.317) Epoch: [13][3520/11272] Time 0.851 (0.834) Data 0.001 (0.002) Loss 2.6700 (2.6154) Prec@1 35.625 (36.644) Prec@5 68.125 (67.321) Epoch: [13][3530/11272] Time 0.749 (0.834) Data 0.001 (0.002) Loss 2.4084 (2.6153) Prec@1 33.750 (36.646) Prec@5 74.375 (67.321) Epoch: [13][3540/11272] Time 0.877 (0.834) Data 0.002 (0.002) Loss 2.4950 (2.6155) Prec@1 38.125 (36.645) Prec@5 69.375 (67.315) Epoch: [13][3550/11272] Time 0.939 (0.834) Data 0.002 (0.002) Loss 2.6868 (2.6155) Prec@1 36.250 (36.644) Prec@5 69.375 (67.315) Epoch: [13][3560/11272] Time 0.787 (0.834) Data 0.003 (0.002) Loss 2.7433 (2.6156) Prec@1 28.750 (36.643) Prec@5 65.000 (67.313) Epoch: [13][3570/11272] Time 0.786 (0.834) Data 0.002 (0.002) Loss 2.5214 (2.6157) Prec@1 39.375 (36.641) Prec@5 66.875 (67.311) Epoch: [13][3580/11272] Time 0.877 (0.834) Data 0.001 (0.002) Loss 3.1667 (2.6159) Prec@1 31.875 (36.638) Prec@5 56.875 (67.306) Epoch: [13][3590/11272] Time 0.887 (0.834) Data 0.002 (0.002) Loss 2.6188 (2.6161) Prec@1 37.500 (36.639) Prec@5 68.750 (67.305) Epoch: [13][3600/11272] Time 0.707 (0.834) Data 0.001 (0.002) Loss 2.4930 (2.6160) Prec@1 40.625 (36.641) Prec@5 70.000 (67.308) Epoch: [13][3610/11272] Time 0.826 (0.834) Data 0.002 (0.002) Loss 2.5389 (2.6160) Prec@1 36.875 (36.639) Prec@5 67.500 (67.310) Epoch: [13][3620/11272] Time 0.853 (0.834) Data 0.001 (0.002) Loss 2.3804 (2.6158) Prec@1 42.500 (36.641) Prec@5 71.875 (67.315) Epoch: [13][3630/11272] Time 0.885 (0.834) Data 0.001 (0.002) Loss 2.4361 (2.6157) Prec@1 35.000 (36.644) Prec@5 73.750 (67.318) Epoch: [13][3640/11272] Time 0.823 (0.834) Data 0.001 (0.002) Loss 2.5283 (2.6158) Prec@1 37.500 (36.641) Prec@5 69.375 (67.314) Epoch: [13][3650/11272] Time 0.786 (0.834) Data 0.002 (0.002) Loss 2.6458 (2.6158) Prec@1 33.125 (36.639) Prec@5 67.500 (67.313) Epoch: [13][3660/11272] Time 0.931 (0.834) Data 0.002 (0.002) Loss 2.6046 (2.6159) Prec@1 35.000 (36.636) Prec@5 66.250 (67.311) Epoch: [13][3670/11272] Time 0.802 (0.834) Data 0.004 (0.002) Loss 2.5840 (2.6159) Prec@1 33.750 (36.635) Prec@5 68.125 (67.308) Epoch: [13][3680/11272] Time 0.783 (0.834) Data 0.002 (0.002) Loss 2.8808 (2.6161) Prec@1 36.875 (36.635) Prec@5 61.250 (67.305) Epoch: [13][3690/11272] Time 0.960 (0.834) Data 0.001 (0.002) Loss 2.9726 (2.6161) Prec@1 29.375 (36.633) Prec@5 63.125 (67.305) Epoch: [13][3700/11272] Time 0.920 (0.834) Data 0.002 (0.002) Loss 2.5192 (2.6160) Prec@1 35.000 (36.636) Prec@5 73.750 (67.308) Epoch: [13][3710/11272] Time 0.756 (0.834) Data 0.002 (0.002) Loss 2.6588 (2.6160) Prec@1 38.750 (36.640) Prec@5 63.750 (67.306) Epoch: [13][3720/11272] Time 0.736 (0.834) Data 0.002 (0.002) Loss 2.4204 (2.6158) Prec@1 37.500 (36.637) Prec@5 71.250 (67.310) Epoch: [13][3730/11272] Time 0.851 (0.834) Data 0.001 (0.002) Loss 2.7558 (2.6159) Prec@1 33.125 (36.636) Prec@5 63.750 (67.306) Epoch: [13][3740/11272] Time 0.920 (0.834) Data 0.002 (0.002) Loss 2.4844 (2.6157) Prec@1 37.500 (36.642) Prec@5 73.125 (67.305) Epoch: [13][3750/11272] Time 0.745 (0.834) Data 0.002 (0.002) Loss 2.5538 (2.6157) Prec@1 37.500 (36.640) Prec@5 67.500 (67.305) Epoch: [13][3760/11272] Time 0.741 (0.834) Data 0.002 (0.002) Loss 2.5295 (2.6158) Prec@1 33.750 (36.638) Prec@5 70.625 (67.307) Epoch: [13][3770/11272] Time 0.860 (0.834) Data 0.001 (0.002) Loss 2.8560 (2.6157) Prec@1 30.625 (36.637) Prec@5 60.000 (67.307) Epoch: [13][3780/11272] Time 0.904 (0.834) Data 0.002 (0.002) Loss 2.7363 (2.6160) Prec@1 35.000 (36.634) Prec@5 65.625 (67.303) Epoch: [13][3790/11272] Time 0.766 (0.834) Data 0.001 (0.002) Loss 2.6224 (2.6159) Prec@1 38.125 (36.640) Prec@5 66.875 (67.307) Epoch: [13][3800/11272] Time 0.849 (0.834) Data 0.001 (0.002) Loss 2.6275 (2.6160) Prec@1 35.000 (36.637) Prec@5 66.250 (67.306) Epoch: [13][3810/11272] Time 0.917 (0.834) Data 0.002 (0.002) Loss 2.6697 (2.6158) Prec@1 34.375 (36.641) Prec@5 65.625 (67.309) Epoch: [13][3820/11272] Time 0.763 (0.834) Data 0.001 (0.002) Loss 2.3493 (2.6159) Prec@1 41.250 (36.642) Prec@5 75.000 (67.308) Epoch: [13][3830/11272] Time 0.782 (0.834) Data 0.002 (0.002) Loss 2.4801 (2.6159) Prec@1 40.000 (36.645) Prec@5 68.125 (67.304) Epoch: [13][3840/11272] Time 0.869 (0.834) Data 0.001 (0.002) Loss 2.4578 (2.6159) Prec@1 41.250 (36.649) Prec@5 70.000 (67.304) Epoch: [13][3850/11272] Time 0.866 (0.834) Data 0.001 (0.002) Loss 2.6229 (2.6162) Prec@1 41.250 (36.647) Prec@5 60.625 (67.297) Epoch: [13][3860/11272] Time 0.770 (0.834) Data 0.002 (0.002) Loss 2.6128 (2.6163) Prec@1 35.000 (36.643) Prec@5 66.250 (67.294) Epoch: [13][3870/11272] Time 0.805 (0.834) Data 0.001 (0.002) Loss 2.6347 (2.6164) Prec@1 38.125 (36.641) Prec@5 68.750 (67.291) Epoch: [13][3880/11272] Time 0.872 (0.834) Data 0.002 (0.002) Loss 2.7065 (2.6165) Prec@1 35.625 (36.642) Prec@5 68.125 (67.290) Epoch: [13][3890/11272] Time 0.906 (0.834) Data 0.001 (0.002) Loss 2.5186 (2.6163) Prec@1 34.375 (36.645) Prec@5 70.625 (67.293) Epoch: [13][3900/11272] Time 0.805 (0.834) Data 0.002 (0.002) Loss 2.5316 (2.6163) Prec@1 37.500 (36.643) Prec@5 66.250 (67.295) Epoch: [13][3910/11272] Time 0.741 (0.834) Data 0.001 (0.002) Loss 2.4515 (2.6162) Prec@1 34.375 (36.642) Prec@5 72.500 (67.298) Epoch: [13][3920/11272] Time 0.882 (0.834) Data 0.002 (0.002) Loss 2.6776 (2.6162) Prec@1 32.500 (36.644) Prec@5 66.875 (67.299) Epoch: [13][3930/11272] Time 0.751 (0.834) Data 0.004 (0.002) Loss 2.7888 (2.6162) Prec@1 33.125 (36.646) Prec@5 58.750 (67.299) Epoch: [13][3940/11272] Time 0.795 (0.834) Data 0.002 (0.002) Loss 2.6761 (2.6161) Prec@1 37.500 (36.647) Prec@5 64.375 (67.299) Epoch: [13][3950/11272] Time 0.878 (0.834) Data 0.002 (0.002) Loss 2.5391 (2.6162) Prec@1 40.000 (36.645) Prec@5 68.750 (67.298) Epoch: [13][3960/11272] Time 0.949 (0.834) Data 0.002 (0.002) Loss 2.5722 (2.6162) Prec@1 38.125 (36.649) Prec@5 68.750 (67.296) Epoch: [13][3970/11272] Time 0.765 (0.834) Data 0.001 (0.002) Loss 2.7720 (2.6162) Prec@1 33.750 (36.648) Prec@5 63.750 (67.296) Epoch: [13][3980/11272] Time 0.767 (0.834) Data 0.001 (0.002) Loss 2.4457 (2.6161) Prec@1 40.000 (36.648) Prec@5 66.875 (67.300) Epoch: [13][3990/11272] Time 0.906 (0.834) Data 0.002 (0.002) Loss 2.8453 (2.6160) Prec@1 33.125 (36.649) Prec@5 65.000 (67.303) Epoch: [13][4000/11272] Time 0.909 (0.834) Data 0.002 (0.002) Loss 2.7340 (2.6161) Prec@1 36.250 (36.647) Prec@5 63.750 (67.304) Epoch: [13][4010/11272] Time 0.784 (0.834) Data 0.002 (0.002) Loss 2.5452 (2.6162) Prec@1 38.750 (36.645) Prec@5 70.000 (67.301) Epoch: [13][4020/11272] Time 0.818 (0.834) Data 0.002 (0.002) Loss 2.6347 (2.6164) Prec@1 41.250 (36.643) Prec@5 63.750 (67.298) Epoch: [13][4030/11272] Time 0.840 (0.834) Data 0.001 (0.002) Loss 2.6148 (2.6166) Prec@1 36.875 (36.638) Prec@5 68.750 (67.295) Epoch: [13][4040/11272] Time 0.841 (0.834) Data 0.001 (0.002) Loss 2.5581 (2.6167) Prec@1 38.125 (36.635) Prec@5 70.000 (67.291) Epoch: [13][4050/11272] Time 0.750 (0.834) Data 0.002 (0.002) Loss 2.6684 (2.6168) Prec@1 40.625 (36.638) Prec@5 65.625 (67.291) Epoch: [13][4060/11272] Time 0.879 (0.834) Data 0.002 (0.002) Loss 2.7442 (2.6167) Prec@1 34.375 (36.637) Prec@5 64.375 (67.295) Epoch: [13][4070/11272] Time 0.879 (0.834) Data 0.002 (0.002) Loss 2.5688 (2.6166) Prec@1 34.375 (36.634) Prec@5 67.500 (67.294) Epoch: [13][4080/11272] Time 0.777 (0.834) Data 0.001 (0.002) Loss 2.5502 (2.6167) Prec@1 38.750 (36.631) Prec@5 67.500 (67.293) Epoch: [13][4090/11272] Time 0.748 (0.834) Data 0.001 (0.002) Loss 2.8030 (2.6167) Prec@1 30.000 (36.629) Prec@5 66.875 (67.295) Epoch: [13][4100/11272] Time 0.861 (0.834) Data 0.002 (0.002) Loss 2.4103 (2.6165) Prec@1 41.250 (36.629) Prec@5 73.125 (67.298) Epoch: [13][4110/11272] Time 0.953 (0.834) Data 0.002 (0.002) Loss 2.7628 (2.6165) Prec@1 34.375 (36.630) Prec@5 64.375 (67.298) Epoch: [13][4120/11272] Time 0.774 (0.834) Data 0.002 (0.002) Loss 2.5263 (2.6164) Prec@1 35.625 (36.630) Prec@5 73.750 (67.301) Epoch: [13][4130/11272] Time 0.805 (0.834) Data 0.002 (0.002) Loss 2.5844 (2.6164) Prec@1 41.250 (36.630) Prec@5 70.625 (67.301) Epoch: [13][4140/11272] Time 0.866 (0.834) Data 0.002 (0.002) Loss 2.7061 (2.6164) Prec@1 37.500 (36.630) Prec@5 68.125 (67.304) Epoch: [13][4150/11272] Time 0.896 (0.834) Data 0.002 (0.002) Loss 2.6520 (2.6163) Prec@1 38.750 (36.632) Prec@5 65.000 (67.307) Epoch: [13][4160/11272] Time 0.772 (0.834) Data 0.002 (0.002) Loss 2.6051 (2.6163) Prec@1 36.250 (36.635) Prec@5 66.875 (67.303) Epoch: [13][4170/11272] Time 0.741 (0.834) Data 0.002 (0.002) Loss 2.7135 (2.6164) Prec@1 38.125 (36.635) Prec@5 67.500 (67.301) Epoch: [13][4180/11272] Time 0.913 (0.834) Data 0.002 (0.002) Loss 2.4705 (2.6165) Prec@1 40.625 (36.634) Prec@5 71.250 (67.299) Epoch: [13][4190/11272] Time 0.853 (0.834) Data 0.001 (0.002) Loss 2.7068 (2.6166) Prec@1 40.000 (36.637) Prec@5 66.875 (67.297) Epoch: [13][4200/11272] Time 0.818 (0.834) Data 0.002 (0.002) Loss 2.8338 (2.6166) Prec@1 35.000 (36.636) Prec@5 66.875 (67.297) Epoch: [13][4210/11272] Time 0.866 (0.834) Data 0.001 (0.002) Loss 2.4879 (2.6166) Prec@1 35.000 (36.637) Prec@5 70.625 (67.298) Epoch: [13][4220/11272] Time 0.881 (0.834) Data 0.001 (0.002) Loss 2.3926 (2.6166) Prec@1 40.625 (36.637) Prec@5 69.375 (67.297) Epoch: [13][4230/11272] Time 0.746 (0.834) Data 0.001 (0.002) Loss 2.7379 (2.6165) Prec@1 36.875 (36.640) Prec@5 66.875 (67.302) Epoch: [13][4240/11272] Time 0.773 (0.834) Data 0.002 (0.002) Loss 2.7675 (2.6164) Prec@1 33.750 (36.642) Prec@5 61.250 (67.303) Epoch: [13][4250/11272] Time 0.897 (0.834) Data 0.002 (0.002) Loss 2.5267 (2.6166) Prec@1 38.125 (36.636) Prec@5 65.000 (67.299) Epoch: [13][4260/11272] Time 0.851 (0.834) Data 0.002 (0.002) Loss 2.6206 (2.6166) Prec@1 38.125 (36.639) Prec@5 68.125 (67.300) Epoch: [13][4270/11272] Time 0.757 (0.834) Data 0.001 (0.002) Loss 2.7015 (2.6167) Prec@1 32.500 (36.637) Prec@5 64.375 (67.301) Epoch: [13][4280/11272] Time 0.808 (0.834) Data 0.002 (0.002) Loss 2.5300 (2.6165) Prec@1 36.250 (36.638) Prec@5 66.875 (67.304) Epoch: [13][4290/11272] Time 0.872 (0.834) Data 0.001 (0.002) Loss 2.7850 (2.6167) Prec@1 33.750 (36.636) Prec@5 64.375 (67.300) Epoch: [13][4300/11272] Time 0.903 (0.834) Data 0.002 (0.002) Loss 2.7266 (2.6165) Prec@1 35.625 (36.641) Prec@5 64.375 (67.303) Epoch: [13][4310/11272] Time 0.719 (0.834) Data 0.001 (0.002) Loss 2.4297 (2.6164) Prec@1 38.750 (36.645) Prec@5 65.625 (67.301) Epoch: [13][4320/11272] Time 0.775 (0.834) Data 0.002 (0.002) Loss 2.4458 (2.6165) Prec@1 36.250 (36.640) Prec@5 67.500 (67.296) Epoch: [13][4330/11272] Time 0.921 (0.834) Data 0.002 (0.002) Loss 2.7187 (2.6165) Prec@1 36.875 (36.639) Prec@5 65.625 (67.299) Epoch: [13][4340/11272] Time 0.753 (0.834) Data 0.001 (0.002) Loss 2.7561 (2.6166) Prec@1 34.375 (36.639) Prec@5 65.000 (67.298) Epoch: [13][4350/11272] Time 0.796 (0.834) Data 0.002 (0.002) Loss 2.7689 (2.6166) Prec@1 36.875 (36.641) Prec@5 68.125 (67.301) Epoch: [13][4360/11272] Time 0.824 (0.834) Data 0.001 (0.002) Loss 2.4527 (2.6166) Prec@1 36.875 (36.642) Prec@5 71.250 (67.304) Epoch: [13][4370/11272] Time 0.867 (0.834) Data 0.002 (0.002) Loss 2.6018 (2.6166) Prec@1 36.875 (36.639) Prec@5 66.875 (67.300) Epoch: [13][4380/11272] Time 0.774 (0.834) Data 0.003 (0.002) Loss 2.5679 (2.6167) Prec@1 36.250 (36.639) Prec@5 68.125 (67.297) Epoch: [13][4390/11272] Time 0.846 (0.834) Data 0.001 (0.002) Loss 2.7145 (2.6166) Prec@1 33.125 (36.641) Prec@5 65.625 (67.300) Epoch: [13][4400/11272] Time 0.898 (0.834) Data 0.001 (0.002) Loss 2.4338 (2.6164) Prec@1 40.625 (36.643) Prec@5 71.250 (67.302) Epoch: [13][4410/11272] Time 0.928 (0.834) Data 0.001 (0.002) Loss 2.7210 (2.6164) Prec@1 31.875 (36.641) Prec@5 65.625 (67.304) Epoch: [13][4420/11272] Time 0.774 (0.834) Data 0.002 (0.002) Loss 2.6139 (2.6165) Prec@1 37.500 (36.640) Prec@5 64.375 (67.297) Epoch: [13][4430/11272] Time 0.762 (0.834) Data 0.002 (0.002) Loss 2.9029 (2.6168) Prec@1 36.250 (36.635) Prec@5 61.875 (67.292) Epoch: [13][4440/11272] Time 0.933 (0.834) Data 0.002 (0.002) Loss 2.5135 (2.6167) Prec@1 36.875 (36.635) Prec@5 68.750 (67.294) Epoch: [13][4450/11272] Time 0.946 (0.834) Data 0.002 (0.002) Loss 2.5606 (2.6167) Prec@1 34.375 (36.629) Prec@5 66.250 (67.291) Epoch: [13][4460/11272] Time 0.770 (0.834) Data 0.002 (0.002) Loss 2.7502 (2.6168) Prec@1 31.875 (36.629) Prec@5 66.250 (67.289) Epoch: [13][4470/11272] Time 0.865 (0.834) Data 0.002 (0.002) Loss 2.5102 (2.6167) Prec@1 40.000 (36.634) Prec@5 66.250 (67.288) Epoch: [13][4480/11272] Time 0.933 (0.834) Data 0.001 (0.002) Loss 2.5243 (2.6167) Prec@1 33.125 (36.629) Prec@5 65.625 (67.286) Epoch: [13][4490/11272] Time 0.790 (0.834) Data 0.001 (0.002) Loss 2.2981 (2.6166) Prec@1 41.250 (36.630) Prec@5 75.000 (67.288) Epoch: [13][4500/11272] Time 0.804 (0.834) Data 0.002 (0.002) Loss 2.5791 (2.6165) Prec@1 42.500 (36.635) Prec@5 65.000 (67.290) Epoch: [13][4510/11272] Time 0.923 (0.834) Data 0.002 (0.002) Loss 2.7437 (2.6165) Prec@1 34.375 (36.636) Prec@5 64.375 (67.290) Epoch: [13][4520/11272] Time 0.897 (0.834) Data 0.002 (0.002) Loss 2.6281 (2.6164) Prec@1 39.375 (36.638) Prec@5 66.875 (67.293) Epoch: [13][4530/11272] Time 0.760 (0.834) Data 0.001 (0.002) Loss 2.7431 (2.6165) Prec@1 34.375 (36.638) Prec@5 65.625 (67.293) Epoch: [13][4540/11272] Time 0.756 (0.834) Data 0.002 (0.002) Loss 2.4183 (2.6163) Prec@1 40.625 (36.642) Prec@5 68.750 (67.294) Epoch: [13][4550/11272] Time 0.901 (0.834) Data 0.001 (0.002) Loss 2.7723 (2.6162) Prec@1 34.375 (36.644) Prec@5 63.125 (67.294) Epoch: [13][4560/11272] Time 0.927 (0.834) Data 0.004 (0.002) Loss 2.4748 (2.6162) Prec@1 42.500 (36.638) Prec@5 68.125 (67.293) Epoch: [13][4570/11272] Time 0.755 (0.834) Data 0.002 (0.002) Loss 2.4470 (2.6163) Prec@1 38.125 (36.639) Prec@5 69.375 (67.293) Epoch: [13][4580/11272] Time 0.743 (0.834) Data 0.001 (0.002) Loss 2.4767 (2.6162) Prec@1 38.125 (36.638) Prec@5 71.250 (67.294) Epoch: [13][4590/11272] Time 0.906 (0.834) Data 0.002 (0.002) Loss 2.3085 (2.6162) Prec@1 43.125 (36.639) Prec@5 75.000 (67.294) Epoch: [13][4600/11272] Time 0.757 (0.834) Data 0.004 (0.002) Loss 2.7382 (2.6163) Prec@1 35.625 (36.638) Prec@5 64.375 (67.292) Epoch: [13][4610/11272] Time 0.829 (0.834) Data 0.002 (0.002) Loss 2.8117 (2.6163) Prec@1 33.750 (36.637) Prec@5 64.375 (67.293) Epoch: [13][4620/11272] Time 0.932 (0.834) Data 0.001 (0.002) Loss 2.7104 (2.6161) Prec@1 36.875 (36.643) Prec@5 65.000 (67.297) Epoch: [13][4630/11272] Time 0.853 (0.834) Data 0.001 (0.002) Loss 2.6730 (2.6161) Prec@1 40.000 (36.644) Prec@5 68.750 (67.296) Epoch: [13][4640/11272] Time 0.742 (0.834) Data 0.001 (0.002) Loss 2.2679 (2.6160) Prec@1 43.750 (36.645) Prec@5 73.750 (67.300) Epoch: [13][4650/11272] Time 0.762 (0.834) Data 0.002 (0.002) Loss 2.4181 (2.6158) Prec@1 39.375 (36.645) Prec@5 73.125 (67.301) Epoch: [13][4660/11272] Time 0.883 (0.834) Data 0.001 (0.002) Loss 2.7262 (2.6159) Prec@1 33.125 (36.644) Prec@5 66.250 (67.295) Epoch: [13][4670/11272] Time 0.979 (0.834) Data 0.002 (0.002) Loss 2.4327 (2.6159) Prec@1 41.875 (36.640) Prec@5 70.625 (67.296) Epoch: [13][4680/11272] Time 0.792 (0.834) Data 0.002 (0.002) Loss 2.7394 (2.6160) Prec@1 33.750 (36.640) Prec@5 69.375 (67.295) Epoch: [13][4690/11272] Time 0.760 (0.834) Data 0.001 (0.002) Loss 2.6717 (2.6160) Prec@1 36.250 (36.641) Prec@5 66.875 (67.296) Epoch: [13][4700/11272] Time 0.963 (0.834) Data 0.002 (0.002) Loss 2.6935 (2.6160) Prec@1 41.250 (36.642) Prec@5 66.250 (67.297) Epoch: [13][4710/11272] Time 0.889 (0.834) Data 0.002 (0.002) Loss 2.6691 (2.6160) Prec@1 35.625 (36.644) Prec@5 69.375 (67.298) Epoch: [13][4720/11272] Time 0.768 (0.834) Data 0.002 (0.002) Loss 2.7641 (2.6161) Prec@1 33.125 (36.643) Prec@5 65.000 (67.295) Epoch: [13][4730/11272] Time 0.878 (0.834) Data 0.002 (0.002) Loss 2.6374 (2.6160) Prec@1 35.625 (36.643) Prec@5 63.750 (67.294) Epoch: [13][4740/11272] Time 0.896 (0.834) Data 0.002 (0.002) Loss 2.5147 (2.6161) Prec@1 38.750 (36.644) Prec@5 68.125 (67.292) Epoch: [13][4750/11272] Time 0.771 (0.834) Data 0.002 (0.002) Loss 2.3202 (2.6160) Prec@1 43.750 (36.644) Prec@5 72.500 (67.293) Epoch: [13][4760/11272] Time 0.756 (0.834) Data 0.001 (0.002) Loss 2.7020 (2.6160) Prec@1 37.500 (36.643) Prec@5 67.500 (67.294) Epoch: [13][4770/11272] Time 0.899 (0.834) Data 0.001 (0.002) Loss 2.9949 (2.6161) Prec@1 30.625 (36.640) Prec@5 62.500 (67.293) Epoch: [13][4780/11272] Time 0.989 (0.834) Data 0.002 (0.002) Loss 2.8324 (2.6161) Prec@1 35.000 (36.638) Prec@5 60.625 (67.292) Epoch: [13][4790/11272] Time 0.781 (0.834) Data 0.002 (0.002) Loss 2.7091 (2.6161) Prec@1 40.625 (36.642) Prec@5 64.375 (67.291) Epoch: [13][4800/11272] Time 0.777 (0.834) Data 0.002 (0.002) Loss 2.6857 (2.6162) Prec@1 36.250 (36.643) Prec@5 69.375 (67.290) Epoch: [13][4810/11272] Time 0.885 (0.834) Data 0.002 (0.002) Loss 2.3916 (2.6162) Prec@1 41.250 (36.644) Prec@5 67.500 (67.286) Epoch: [13][4820/11272] Time 0.900 (0.834) Data 0.002 (0.002) Loss 2.5519 (2.6162) Prec@1 37.500 (36.645) Prec@5 72.500 (67.285) Epoch: [13][4830/11272] Time 0.766 (0.834) Data 0.001 (0.002) Loss 2.7720 (2.6162) Prec@1 35.625 (36.646) Prec@5 66.250 (67.286) Epoch: [13][4840/11272] Time 0.736 (0.834) Data 0.001 (0.002) Loss 2.6361 (2.6162) Prec@1 34.375 (36.648) Prec@5 65.000 (67.285) Epoch: [13][4850/11272] Time 0.909 (0.834) Data 0.002 (0.002) Loss 2.5578 (2.6163) Prec@1 35.000 (36.647) Prec@5 70.000 (67.284) Epoch: [13][4860/11272] Time 0.744 (0.834) Data 0.004 (0.002) Loss 2.4974 (2.6163) Prec@1 36.875 (36.644) Prec@5 70.000 (67.284) Epoch: [13][4870/11272] Time 0.760 (0.834) Data 0.002 (0.002) Loss 2.5056 (2.6163) Prec@1 35.000 (36.642) Prec@5 73.125 (67.286) Epoch: [13][4880/11272] Time 0.850 (0.834) Data 0.002 (0.002) Loss 2.4597 (2.6164) Prec@1 40.625 (36.639) Prec@5 71.875 (67.285) Epoch: [13][4890/11272] Time 0.877 (0.834) Data 0.002 (0.002) Loss 2.8742 (2.6162) Prec@1 37.500 (36.646) Prec@5 60.000 (67.289) Epoch: [13][4900/11272] Time 0.730 (0.834) Data 0.002 (0.002) Loss 2.4323 (2.6162) Prec@1 43.125 (36.647) Prec@5 73.750 (67.291) Epoch: [13][4910/11272] Time 0.750 (0.834) Data 0.002 (0.002) Loss 2.6038 (2.6160) Prec@1 36.875 (36.652) Prec@5 67.500 (67.293) Epoch: [13][4920/11272] Time 0.984 (0.834) Data 0.002 (0.002) Loss 2.7594 (2.6161) Prec@1 34.375 (36.650) Prec@5 65.625 (67.292) Epoch: [13][4930/11272] Time 0.937 (0.834) Data 0.002 (0.002) Loss 2.6090 (2.6161) Prec@1 43.125 (36.652) Prec@5 66.875 (67.292) Epoch: [13][4940/11272] Time 0.769 (0.834) Data 0.002 (0.002) Loss 2.6590 (2.6160) Prec@1 35.625 (36.652) Prec@5 65.000 (67.294) Epoch: [13][4950/11272] Time 0.810 (0.834) Data 0.001 (0.002) Loss 2.7445 (2.6162) Prec@1 35.625 (36.649) Prec@5 64.375 (67.289) Epoch: [13][4960/11272] Time 0.908 (0.834) Data 0.002 (0.002) Loss 2.7216 (2.6161) Prec@1 33.750 (36.651) Prec@5 66.250 (67.289) Epoch: [13][4970/11272] Time 0.958 (0.834) Data 0.002 (0.002) Loss 2.9590 (2.6163) Prec@1 34.375 (36.647) Prec@5 60.000 (67.287) Epoch: [13][4980/11272] Time 0.744 (0.834) Data 0.001 (0.002) Loss 2.4322 (2.6161) Prec@1 39.375 (36.651) Prec@5 72.500 (67.290) Epoch: [13][4990/11272] Time 0.892 (0.834) Data 0.001 (0.002) Loss 2.7265 (2.6161) Prec@1 32.500 (36.649) Prec@5 70.625 (67.294) Epoch: [13][5000/11272] Time 0.884 (0.834) Data 0.002 (0.002) Loss 2.6382 (2.6161) Prec@1 40.000 (36.652) Prec@5 70.625 (67.294) Epoch: [13][5010/11272] Time 0.743 (0.834) Data 0.002 (0.002) Loss 2.5050 (2.6160) Prec@1 42.500 (36.655) Prec@5 66.875 (67.297) Epoch: [13][5020/11272] Time 0.809 (0.834) Data 0.002 (0.002) Loss 2.8061 (2.6160) Prec@1 35.000 (36.655) Prec@5 63.750 (67.294) Epoch: [13][5030/11272] Time 0.833 (0.834) Data 0.001 (0.002) Loss 2.4285 (2.6160) Prec@1 41.875 (36.656) Prec@5 73.125 (67.295) Epoch: [13][5040/11272] Time 0.869 (0.834) Data 0.002 (0.002) Loss 2.5537 (2.6160) Prec@1 38.125 (36.655) Prec@5 65.625 (67.292) Epoch: [13][5050/11272] Time 0.742 (0.834) Data 0.001 (0.002) Loss 2.3735 (2.6161) Prec@1 38.125 (36.653) Prec@5 72.500 (67.291) Epoch: [13][5060/11272] Time 0.765 (0.834) Data 0.002 (0.002) Loss 2.9804 (2.6162) Prec@1 31.875 (36.650) Prec@5 59.375 (67.289) Epoch: [13][5070/11272] Time 0.948 (0.834) Data 0.002 (0.002) Loss 2.6597 (2.6161) Prec@1 37.500 (36.651) Prec@5 68.750 (67.290) Epoch: [13][5080/11272] Time 0.933 (0.834) Data 0.002 (0.002) Loss 2.4396 (2.6160) Prec@1 41.875 (36.653) Prec@5 69.375 (67.291) Epoch: [13][5090/11272] Time 0.747 (0.834) Data 0.002 (0.002) Loss 2.6782 (2.6162) Prec@1 33.750 (36.647) Prec@5 64.375 (67.286) Epoch: [13][5100/11272] Time 0.770 (0.834) Data 0.002 (0.002) Loss 2.4631 (2.6162) Prec@1 41.875 (36.651) Prec@5 72.500 (67.286) Epoch: [13][5110/11272] Time 0.915 (0.834) Data 0.002 (0.002) Loss 2.4344 (2.6161) Prec@1 38.750 (36.651) Prec@5 71.250 (67.290) Epoch: [13][5120/11272] Time 0.900 (0.834) Data 0.002 (0.002) Loss 2.8053 (2.6161) Prec@1 33.750 (36.654) Prec@5 63.750 (67.291) Epoch: [13][5130/11272] Time 0.761 (0.834) Data 0.002 (0.002) Loss 2.6928 (2.6160) Prec@1 31.250 (36.654) Prec@5 67.500 (67.293) Epoch: [13][5140/11272] Time 0.935 (0.834) Data 0.002 (0.002) Loss 2.5953 (2.6161) Prec@1 35.625 (36.655) Prec@5 65.000 (67.292) Epoch: [13][5150/11272] Time 0.897 (0.834) Data 0.002 (0.002) Loss 2.5065 (2.6159) Prec@1 35.000 (36.658) Prec@5 67.500 (67.296) Epoch: [13][5160/11272] Time 0.771 (0.834) Data 0.001 (0.002) Loss 2.5350 (2.6158) Prec@1 36.250 (36.658) Prec@5 71.250 (67.298) Epoch: [13][5170/11272] Time 0.793 (0.834) Data 0.002 (0.002) Loss 2.3993 (2.6157) Prec@1 43.125 (36.658) Prec@5 71.250 (67.302) Epoch: [13][5180/11272] Time 0.919 (0.834) Data 0.002 (0.002) Loss 2.5571 (2.6157) Prec@1 38.125 (36.660) Prec@5 70.000 (67.304) Epoch: [13][5190/11272] Time 0.912 (0.834) Data 0.002 (0.002) Loss 2.7036 (2.6159) Prec@1 33.750 (36.658) Prec@5 65.625 (67.301) Epoch: [13][5200/11272] Time 0.778 (0.834) Data 0.002 (0.002) Loss 2.5783 (2.6159) Prec@1 31.250 (36.657) Prec@5 70.625 (67.302) Epoch: [13][5210/11272] Time 0.743 (0.834) Data 0.001 (0.002) Loss 2.4916 (2.6160) Prec@1 37.500 (36.657) Prec@5 70.625 (67.301) Epoch: [13][5220/11272] Time 0.870 (0.834) Data 0.001 (0.002) Loss 2.8237 (2.6160) Prec@1 36.875 (36.656) Prec@5 63.750 (67.302) Epoch: [13][5230/11272] Time 0.867 (0.834) Data 0.002 (0.002) Loss 2.5232 (2.6160) Prec@1 38.750 (36.656) Prec@5 72.500 (67.303) Epoch: [13][5240/11272] Time 0.748 (0.834) Data 0.002 (0.002) Loss 2.9611 (2.6161) Prec@1 24.375 (36.653) Prec@5 64.375 (67.301) Epoch: [13][5250/11272] Time 0.777 (0.834) Data 0.002 (0.002) Loss 2.7754 (2.6163) Prec@1 34.375 (36.648) Prec@5 63.125 (67.296) Epoch: [13][5260/11272] Time 0.906 (0.834) Data 0.002 (0.002) Loss 2.6904 (2.6163) Prec@1 36.875 (36.652) Prec@5 66.250 (67.296) Epoch: [13][5270/11272] Time 0.816 (0.834) Data 0.002 (0.002) Loss 2.6543 (2.6162) Prec@1 33.750 (36.652) Prec@5 64.375 (67.299) Epoch: [13][5280/11272] Time 0.773 (0.834) Data 0.001 (0.002) Loss 2.4005 (2.6161) Prec@1 38.750 (36.655) Prec@5 70.625 (67.303) Epoch: [13][5290/11272] Time 0.883 (0.834) Data 0.002 (0.002) Loss 2.7222 (2.6161) Prec@1 43.750 (36.655) Prec@5 65.625 (67.302) Epoch: [13][5300/11272] Time 0.884 (0.834) Data 0.001 (0.002) Loss 2.5055 (2.6160) Prec@1 35.625 (36.655) Prec@5 69.375 (67.302) Epoch: [13][5310/11272] Time 0.821 (0.834) Data 0.002 (0.002) Loss 2.5568 (2.6159) Prec@1 36.875 (36.654) Prec@5 68.125 (67.304) Epoch: [13][5320/11272] Time 0.795 (0.834) Data 0.002 (0.002) Loss 2.5714 (2.6159) Prec@1 36.250 (36.653) Prec@5 63.750 (67.307) Epoch: [13][5330/11272] Time 0.911 (0.834) Data 0.002 (0.002) Loss 2.7630 (2.6159) Prec@1 33.125 (36.653) Prec@5 61.250 (67.305) Epoch: [13][5340/11272] Time 0.914 (0.834) Data 0.001 (0.002) Loss 2.3001 (2.6159) Prec@1 40.000 (36.653) Prec@5 75.625 (67.306) Epoch: [13][5350/11272] Time 0.724 (0.834) Data 0.001 (0.002) Loss 2.9183 (2.6160) Prec@1 34.375 (36.651) Prec@5 58.125 (67.303) Epoch: [13][5360/11272] Time 0.751 (0.834) Data 0.001 (0.002) Loss 3.0057 (2.6159) Prec@1 28.750 (36.653) Prec@5 62.500 (67.304) Epoch: [13][5370/11272] Time 0.891 (0.834) Data 0.002 (0.002) Loss 2.3498 (2.6159) Prec@1 41.875 (36.655) Prec@5 73.125 (67.302) Epoch: [13][5380/11272] Time 0.890 (0.834) Data 0.002 (0.002) Loss 2.5290 (2.6159) Prec@1 33.125 (36.652) Prec@5 70.000 (67.303) Epoch: [13][5390/11272] Time 0.757 (0.834) Data 0.002 (0.002) Loss 2.4865 (2.6160) Prec@1 42.500 (36.651) Prec@5 70.625 (67.299) Epoch: [13][5400/11272] Time 0.960 (0.834) Data 0.002 (0.002) Loss 2.7995 (2.6162) Prec@1 34.375 (36.648) Prec@5 65.000 (67.294) Epoch: [13][5410/11272] Time 0.851 (0.834) Data 0.001 (0.002) Loss 2.5350 (2.6162) Prec@1 35.000 (36.647) Prec@5 70.000 (67.296) Epoch: [13][5420/11272] Time 0.775 (0.834) Data 0.001 (0.002) Loss 2.6349 (2.6163) Prec@1 35.625 (36.647) Prec@5 67.500 (67.293) Epoch: [13][5430/11272] Time 0.809 (0.834) Data 0.002 (0.002) Loss 2.6675 (2.6163) Prec@1 36.875 (36.644) Prec@5 63.750 (67.291) Epoch: [13][5440/11272] Time 0.877 (0.834) Data 0.001 (0.002) Loss 2.6044 (2.6162) Prec@1 40.000 (36.649) Prec@5 66.875 (67.294) Epoch: [13][5450/11272] Time 0.884 (0.834) Data 0.002 (0.002) Loss 2.4754 (2.6163) Prec@1 40.625 (36.648) Prec@5 70.000 (67.292) Epoch: [13][5460/11272] Time 0.781 (0.834) Data 0.002 (0.002) Loss 2.7018 (2.6164) Prec@1 36.250 (36.643) Prec@5 65.625 (67.289) Epoch: [13][5470/11272] Time 0.739 (0.834) Data 0.001 (0.002) Loss 2.7468 (2.6163) Prec@1 34.375 (36.641) Prec@5 66.250 (67.291) Epoch: [13][5480/11272] Time 0.892 (0.834) Data 0.002 (0.002) Loss 2.8910 (2.6165) Prec@1 30.625 (36.640) Prec@5 62.500 (67.292) Epoch: [13][5490/11272] Time 0.885 (0.834) Data 0.002 (0.002) Loss 2.5744 (2.6166) Prec@1 36.250 (36.638) Prec@5 67.500 (67.292) Epoch: [13][5500/11272] Time 0.785 (0.834) Data 0.002 (0.002) Loss 2.8094 (2.6165) Prec@1 33.750 (36.641) Prec@5 63.750 (67.293) Epoch: [13][5510/11272] Time 0.762 (0.834) Data 0.001 (0.002) Loss 2.8668 (2.6165) Prec@1 31.875 (36.640) Prec@5 59.375 (67.294) Epoch: [13][5520/11272] Time 0.923 (0.834) Data 0.001 (0.002) Loss 2.7879 (2.6165) Prec@1 36.250 (36.642) Prec@5 63.750 (67.292) Epoch: [13][5530/11272] Time 0.740 (0.834) Data 0.004 (0.002) Loss 2.6663 (2.6166) Prec@1 30.000 (36.642) Prec@5 68.750 (67.291) Epoch: [13][5540/11272] Time 0.770 (0.834) Data 0.002 (0.002) Loss 2.7153 (2.6166) Prec@1 41.250 (36.646) Prec@5 64.375 (67.290) Epoch: [13][5550/11272] Time 0.866 (0.834) Data 0.002 (0.002) Loss 2.5483 (2.6166) Prec@1 43.750 (36.648) Prec@5 71.250 (67.292) Epoch: [13][5560/11272] Time 0.888 (0.834) Data 0.002 (0.002) Loss 2.7711 (2.6165) Prec@1 35.000 (36.649) Prec@5 64.375 (67.292) Epoch: [13][5570/11272] Time 0.761 (0.834) Data 0.002 (0.002) Loss 2.8472 (2.6167) Prec@1 29.375 (36.645) Prec@5 60.625 (67.290) Epoch: [13][5580/11272] Time 0.753 (0.834) Data 0.002 (0.002) Loss 2.8920 (2.6168) Prec@1 34.375 (36.644) Prec@5 60.625 (67.287) Epoch: [13][5590/11272] Time 0.864 (0.834) Data 0.002 (0.002) Loss 2.3118 (2.6168) Prec@1 43.125 (36.646) Prec@5 72.500 (67.288) Epoch: [13][5600/11272] Time 0.874 (0.834) Data 0.002 (0.002) Loss 2.5940 (2.6167) Prec@1 45.000 (36.649) Prec@5 69.375 (67.289) Epoch: [13][5610/11272] Time 0.775 (0.834) Data 0.002 (0.002) Loss 2.5307 (2.6167) Prec@1 40.000 (36.648) Prec@5 71.250 (67.288) Epoch: [13][5620/11272] Time 0.784 (0.834) Data 0.002 (0.002) Loss 2.6191 (2.6167) Prec@1 35.000 (36.649) Prec@5 69.375 (67.287) Epoch: [13][5630/11272] Time 0.904 (0.834) Data 0.002 (0.002) Loss 2.9077 (2.6170) Prec@1 35.625 (36.643) Prec@5 61.250 (67.283) Epoch: [13][5640/11272] Time 0.871 (0.834) Data 0.002 (0.002) Loss 2.7209 (2.6170) Prec@1 30.000 (36.643) Prec@5 66.250 (67.283) Epoch: [13][5650/11272] Time 0.780 (0.834) Data 0.001 (0.002) Loss 2.8001 (2.6169) Prec@1 31.250 (36.644) Prec@5 63.750 (67.282) Epoch: [13][5660/11272] Time 0.954 (0.834) Data 0.002 (0.002) Loss 2.6721 (2.6170) Prec@1 31.250 (36.646) Prec@5 68.125 (67.281) Epoch: [13][5670/11272] Time 0.909 (0.834) Data 0.001 (0.002) Loss 2.5959 (2.6167) Prec@1 36.250 (36.651) Prec@5 70.625 (67.285) Epoch: [13][5680/11272] Time 0.780 (0.834) Data 0.002 (0.002) Loss 2.5589 (2.6168) Prec@1 35.000 (36.652) Prec@5 68.750 (67.284) Epoch: [13][5690/11272] Time 0.742 (0.834) Data 0.002 (0.002) Loss 2.6559 (2.6167) Prec@1 38.125 (36.652) Prec@5 65.625 (67.286) Epoch: [13][5700/11272] Time 0.886 (0.834) Data 0.001 (0.002) Loss 2.6428 (2.6167) Prec@1 33.750 (36.652) Prec@5 63.125 (67.286) Epoch: [13][5710/11272] Time 0.882 (0.834) Data 0.002 (0.002) Loss 2.8281 (2.6167) Prec@1 33.750 (36.653) Prec@5 62.500 (67.285) Epoch: [13][5720/11272] Time 0.778 (0.834) Data 0.002 (0.002) Loss 2.4741 (2.6168) Prec@1 38.125 (36.652) Prec@5 68.750 (67.285) Epoch: [13][5730/11272] Time 0.756 (0.834) Data 0.002 (0.002) Loss 2.7025 (2.6166) Prec@1 31.250 (36.655) Prec@5 63.750 (67.288) Epoch: [13][5740/11272] Time 0.893 (0.835) Data 0.002 (0.002) Loss 2.8137 (2.6167) Prec@1 29.375 (36.655) Prec@5 66.875 (67.289) Epoch: [13][5750/11272] Time 0.901 (0.835) Data 0.002 (0.002) Loss 2.7059 (2.6168) Prec@1 31.250 (36.653) Prec@5 63.750 (67.286) Epoch: [13][5760/11272] Time 0.748 (0.835) Data 0.002 (0.002) Loss 2.4400 (2.6168) Prec@1 41.875 (36.653) Prec@5 70.625 (67.288) Epoch: [13][5770/11272] Time 0.799 (0.835) Data 0.002 (0.002) Loss 2.3728 (2.6168) Prec@1 41.250 (36.654) Prec@5 74.375 (67.290) Epoch: [13][5780/11272] Time 0.864 (0.835) Data 0.002 (0.002) Loss 2.7721 (2.6168) Prec@1 36.250 (36.654) Prec@5 66.250 (67.294) Epoch: [13][5790/11272] Time 0.791 (0.835) Data 0.004 (0.002) Loss 2.5635 (2.6167) Prec@1 43.750 (36.655) Prec@5 70.625 (67.294) Epoch: [13][5800/11272] Time 0.756 (0.835) Data 0.002 (0.002) Loss 2.7141 (2.6166) Prec@1 35.625 (36.655) Prec@5 66.875 (67.299) Epoch: [13][5810/11272] Time 0.893 (0.835) Data 0.002 (0.002) Loss 2.7971 (2.6165) Prec@1 31.250 (36.656) Prec@5 66.875 (67.300) Epoch: [13][5820/11272] Time 0.857 (0.835) Data 0.002 (0.002) Loss 2.8200 (2.6166) Prec@1 32.500 (36.653) Prec@5 64.375 (67.298) Epoch: [13][5830/11272] Time 0.767 (0.835) Data 0.002 (0.002) Loss 2.6067 (2.6167) Prec@1 36.250 (36.652) Prec@5 69.375 (67.297) Epoch: [13][5840/11272] Time 0.733 (0.835) Data 0.001 (0.002) Loss 2.7685 (2.6169) Prec@1 31.875 (36.649) Prec@5 64.375 (67.293) Epoch: [13][5850/11272] Time 0.955 (0.835) Data 0.002 (0.002) Loss 2.6357 (2.6169) Prec@1 38.125 (36.648) Prec@5 62.500 (67.294) Epoch: [13][5860/11272] Time 0.885 (0.835) Data 0.002 (0.002) Loss 2.5504 (2.6169) Prec@1 38.750 (36.648) Prec@5 70.000 (67.295) Epoch: [13][5870/11272] Time 0.750 (0.835) Data 0.002 (0.002) Loss 2.6661 (2.6167) Prec@1 35.625 (36.650) Prec@5 63.125 (67.295) Epoch: [13][5880/11272] Time 0.783 (0.835) Data 0.002 (0.002) Loss 2.8161 (2.6168) Prec@1 32.500 (36.650) Prec@5 63.750 (67.294) Epoch: [13][5890/11272] Time 0.885 (0.835) Data 0.002 (0.002) Loss 2.4922 (2.6168) Prec@1 39.375 (36.647) Prec@5 68.750 (67.294) Epoch: [13][5900/11272] Time 0.861 (0.835) Data 0.001 (0.002) Loss 2.6315 (2.6168) Prec@1 33.750 (36.648) Prec@5 64.375 (67.294) Epoch: [13][5910/11272] Time 0.790 (0.835) Data 0.002 (0.002) Loss 2.7599 (2.6166) Prec@1 35.000 (36.651) Prec@5 60.000 (67.298) Epoch: [13][5920/11272] Time 0.829 (0.835) Data 0.001 (0.002) Loss 2.5576 (2.6165) Prec@1 36.875 (36.650) Prec@5 70.000 (67.302) Epoch: [13][5930/11272] Time 0.900 (0.835) Data 0.002 (0.002) Loss 2.4338 (2.6164) Prec@1 37.500 (36.651) Prec@5 68.750 (67.303) Epoch: [13][5940/11272] Time 0.784 (0.835) Data 0.002 (0.002) Loss 2.7645 (2.6164) Prec@1 38.125 (36.653) Prec@5 63.125 (67.304) Epoch: [13][5950/11272] Time 0.763 (0.835) Data 0.001 (0.002) Loss 2.4092 (2.6165) Prec@1 40.000 (36.649) Prec@5 72.500 (67.300) Epoch: [13][5960/11272] Time 0.927 (0.835) Data 0.001 (0.002) Loss 2.5544 (2.6166) Prec@1 40.000 (36.649) Prec@5 70.000 (67.301) Epoch: [13][5970/11272] Time 0.927 (0.835) Data 0.002 (0.002) Loss 2.5719 (2.6166) Prec@1 33.750 (36.649) Prec@5 70.625 (67.303) Epoch: [13][5980/11272] Time 0.748 (0.835) Data 0.002 (0.002) Loss 2.7629 (2.6166) Prec@1 26.250 (36.648) Prec@5 65.625 (67.300) Epoch: [13][5990/11272] Time 0.800 (0.835) Data 0.002 (0.002) Loss 2.5442 (2.6166) Prec@1 38.125 (36.650) Prec@5 66.875 (67.302) Epoch: [13][6000/11272] Time 0.925 (0.835) Data 0.001 (0.002) Loss 2.6430 (2.6166) Prec@1 39.375 (36.650) Prec@5 68.750 (67.300) Epoch: [13][6010/11272] Time 0.886 (0.835) Data 0.002 (0.002) Loss 2.4144 (2.6166) Prec@1 40.000 (36.649) Prec@5 70.000 (67.300) Epoch: [13][6020/11272] Time 0.738 (0.835) Data 0.002 (0.002) Loss 2.8687 (2.6167) Prec@1 33.125 (36.645) Prec@5 64.375 (67.298) Epoch: [13][6030/11272] Time 0.729 (0.835) Data 0.002 (0.002) Loss 2.3100 (2.6167) Prec@1 40.000 (36.644) Prec@5 74.375 (67.299) Epoch: [13][6040/11272] Time 0.916 (0.835) Data 0.002 (0.002) Loss 2.7225 (2.6167) Prec@1 36.250 (36.647) Prec@5 63.750 (67.298) Epoch: [13][6050/11272] Time 0.944 (0.835) Data 0.002 (0.002) Loss 2.8493 (2.6166) Prec@1 35.000 (36.647) Prec@5 63.750 (67.298) Epoch: [13][6060/11272] Time 0.815 (0.835) Data 0.002 (0.002) Loss 2.7148 (2.6166) Prec@1 33.125 (36.647) Prec@5 63.750 (67.298) Epoch: [13][6070/11272] Time 0.883 (0.835) Data 0.001 (0.002) Loss 2.9070 (2.6167) Prec@1 29.375 (36.645) Prec@5 65.625 (67.296) Epoch: [13][6080/11272] Time 0.884 (0.835) Data 0.002 (0.002) Loss 2.7360 (2.6167) Prec@1 35.000 (36.645) Prec@5 63.125 (67.293) Epoch: [13][6090/11272] Time 0.745 (0.835) Data 0.002 (0.002) Loss 2.5591 (2.6167) Prec@1 36.875 (36.646) Prec@5 65.625 (67.293) Epoch: [13][6100/11272] Time 0.794 (0.835) Data 0.002 (0.002) Loss 2.6135 (2.6168) Prec@1 34.375 (36.645) Prec@5 65.000 (67.293) Epoch: [13][6110/11272] Time 0.897 (0.835) Data 0.001 (0.002) Loss 2.6578 (2.6169) Prec@1 36.875 (36.642) Prec@5 66.875 (67.290) Epoch: [13][6120/11272] Time 0.890 (0.835) Data 0.002 (0.002) Loss 2.8076 (2.6169) Prec@1 30.625 (36.643) Prec@5 63.125 (67.290) Epoch: [13][6130/11272] Time 0.745 (0.835) Data 0.003 (0.002) Loss 2.6268 (2.6168) Prec@1 38.125 (36.645) Prec@5 65.000 (67.291) Epoch: [13][6140/11272] Time 0.794 (0.835) Data 0.002 (0.002) Loss 2.7611 (2.6169) Prec@1 31.250 (36.643) Prec@5 60.625 (67.289) Epoch: [13][6150/11272] Time 0.868 (0.835) Data 0.001 (0.002) Loss 2.6928 (2.6168) Prec@1 36.875 (36.643) Prec@5 65.000 (67.291) Epoch: [13][6160/11272] Time 0.940 (0.835) Data 0.002 (0.002) Loss 2.4600 (2.6168) Prec@1 36.250 (36.645) Prec@5 73.125 (67.290) Epoch: [13][6170/11272] Time 0.784 (0.835) Data 0.002 (0.002) Loss 2.4895 (2.6168) Prec@1 41.875 (36.643) Prec@5 71.875 (67.289) Epoch: [13][6180/11272] Time 0.746 (0.835) Data 0.002 (0.002) Loss 2.4068 (2.6168) Prec@1 43.750 (36.642) Prec@5 68.125 (67.289) Epoch: [13][6190/11272] Time 0.860 (0.835) Data 0.002 (0.002) Loss 2.5494 (2.6168) Prec@1 35.625 (36.643) Prec@5 69.375 (67.290) Epoch: [13][6200/11272] Time 0.740 (0.835) Data 0.002 (0.002) Loss 2.7270 (2.6169) Prec@1 36.250 (36.642) Prec@5 70.625 (67.290) Epoch: [13][6210/11272] Time 0.744 (0.835) Data 0.002 (0.002) Loss 2.6283 (2.6169) Prec@1 40.000 (36.643) Prec@5 66.250 (67.290) Epoch: [13][6220/11272] Time 0.931 (0.835) Data 0.002 (0.002) Loss 2.6749 (2.6169) Prec@1 35.625 (36.644) Prec@5 66.875 (67.290) Epoch: [13][6230/11272] Time 0.936 (0.835) Data 0.003 (0.002) Loss 2.6085 (2.6169) Prec@1 38.125 (36.645) Prec@5 67.500 (67.288) Epoch: [13][6240/11272] Time 0.744 (0.835) Data 0.002 (0.002) Loss 2.4476 (2.6169) Prec@1 39.375 (36.644) Prec@5 71.250 (67.289) Epoch: [13][6250/11272] Time 0.783 (0.835) Data 0.002 (0.002) Loss 2.5687 (2.6168) Prec@1 36.250 (36.646) Prec@5 65.000 (67.292) Epoch: [13][6260/11272] Time 0.910 (0.835) Data 0.002 (0.002) Loss 2.3916 (2.6168) Prec@1 45.000 (36.646) Prec@5 68.750 (67.291) Epoch: [13][6270/11272] Time 0.875 (0.835) Data 0.002 (0.002) Loss 2.5445 (2.6168) Prec@1 35.625 (36.645) Prec@5 70.000 (67.293) Epoch: [13][6280/11272] Time 0.748 (0.835) Data 0.002 (0.002) Loss 2.6102 (2.6169) Prec@1 35.625 (36.646) Prec@5 66.875 (67.292) Epoch: [13][6290/11272] Time 0.797 (0.835) Data 0.002 (0.002) Loss 2.5672 (2.6168) Prec@1 41.875 (36.648) Prec@5 69.375 (67.293) Epoch: [13][6300/11272] Time 0.961 (0.835) Data 0.002 (0.002) Loss 2.6864 (2.6170) Prec@1 37.500 (36.647) Prec@5 70.625 (67.291) Epoch: [13][6310/11272] Time 0.904 (0.835) Data 0.002 (0.002) Loss 2.6537 (2.6170) Prec@1 36.250 (36.644) Prec@5 64.375 (67.292) Epoch: [13][6320/11272] Time 0.716 (0.835) Data 0.002 (0.002) Loss 2.3296 (2.6169) Prec@1 41.250 (36.644) Prec@5 74.375 (67.292) Epoch: [13][6330/11272] Time 0.912 (0.835) Data 0.001 (0.002) Loss 2.4882 (2.6170) Prec@1 36.875 (36.641) Prec@5 68.750 (67.291) Epoch: [13][6340/11272] Time 0.878 (0.835) Data 0.002 (0.002) Loss 2.2539 (2.6170) Prec@1 35.000 (36.641) Prec@5 75.000 (67.292) Epoch: [13][6350/11272] Time 0.809 (0.835) Data 0.001 (0.002) Loss 2.8009 (2.6170) Prec@1 32.500 (36.640) Prec@5 60.000 (67.290) Epoch: [13][6360/11272] Time 0.787 (0.835) Data 0.002 (0.002) Loss 2.7329 (2.6170) Prec@1 36.875 (36.642) Prec@5 63.750 (67.289) Epoch: [13][6370/11272] Time 0.910 (0.835) Data 0.002 (0.002) Loss 2.7627 (2.6169) Prec@1 36.875 (36.646) Prec@5 64.375 (67.293) Epoch: [13][6380/11272] Time 0.876 (0.835) Data 0.001 (0.002) Loss 2.5274 (2.6168) Prec@1 39.375 (36.646) Prec@5 70.000 (67.295) Epoch: [13][6390/11272] Time 0.779 (0.835) Data 0.002 (0.002) Loss 2.4206 (2.6168) Prec@1 38.750 (36.645) Prec@5 70.625 (67.294) Epoch: [13][6400/11272] Time 0.775 (0.835) Data 0.001 (0.002) Loss 2.3971 (2.6166) Prec@1 41.250 (36.648) Prec@5 70.625 (67.296) Epoch: [13][6410/11272] Time 0.933 (0.835) Data 0.002 (0.002) Loss 2.4591 (2.6166) Prec@1 40.625 (36.645) Prec@5 70.000 (67.296) Epoch: [13][6420/11272] Time 0.893 (0.835) Data 0.001 (0.002) Loss 2.3591 (2.6165) Prec@1 40.000 (36.647) Prec@5 75.625 (67.298) Epoch: [13][6430/11272] Time 0.791 (0.835) Data 0.001 (0.002) Loss 2.5746 (2.6165) Prec@1 38.125 (36.648) Prec@5 63.125 (67.299) Epoch: [13][6440/11272] Time 0.792 (0.835) Data 0.002 (0.002) Loss 2.5799 (2.6165) Prec@1 36.250 (36.647) Prec@5 67.500 (67.300) Epoch: [13][6450/11272] Time 0.883 (0.835) Data 0.002 (0.002) Loss 2.5235 (2.6164) Prec@1 41.250 (36.649) Prec@5 70.000 (67.300) Epoch: [13][6460/11272] Time 0.743 (0.835) Data 0.004 (0.002) Loss 2.6207 (2.6164) Prec@1 32.500 (36.648) Prec@5 63.750 (67.300) Epoch: [13][6470/11272] Time 0.775 (0.835) Data 0.002 (0.002) Loss 2.6686 (2.6166) Prec@1 32.500 (36.644) Prec@5 68.750 (67.297) Epoch: [13][6480/11272] Time 0.955 (0.835) Data 0.002 (0.002) Loss 2.5336 (2.6166) Prec@1 35.000 (36.644) Prec@5 68.750 (67.295) Epoch: [13][6490/11272] Time 0.920 (0.835) Data 0.003 (0.002) Loss 2.5243 (2.6166) Prec@1 35.625 (36.643) Prec@5 68.125 (67.295) Epoch: [13][6500/11272] Time 0.832 (0.835) Data 0.001 (0.002) Loss 3.0214 (2.6168) Prec@1 24.375 (36.640) Prec@5 57.500 (67.290) Epoch: [13][6510/11272] Time 0.750 (0.835) Data 0.001 (0.002) Loss 2.6819 (2.6168) Prec@1 35.625 (36.639) Prec@5 65.000 (67.289) Epoch: [13][6520/11272] Time 0.891 (0.835) Data 0.001 (0.002) Loss 2.5677 (2.6169) Prec@1 35.000 (36.637) Prec@5 66.875 (67.290) Epoch: [13][6530/11272] Time 0.874 (0.835) Data 0.002 (0.002) Loss 2.5866 (2.6170) Prec@1 37.500 (36.635) Prec@5 66.875 (67.289) Epoch: [13][6540/11272] Time 0.757 (0.835) Data 0.001 (0.002) Loss 2.5043 (2.6169) Prec@1 38.750 (36.639) Prec@5 75.000 (67.291) Epoch: [13][6550/11272] Time 0.736 (0.835) Data 0.001 (0.002) Loss 2.5368 (2.6168) Prec@1 36.250 (36.638) Prec@5 67.500 (67.291) Epoch: [13][6560/11272] Time 0.894 (0.835) Data 0.002 (0.002) Loss 2.5495 (2.6168) Prec@1 40.000 (36.637) Prec@5 67.500 (67.291) Epoch: [13][6570/11272] Time 0.909 (0.835) Data 0.001 (0.002) Loss 2.5834 (2.6169) Prec@1 35.625 (36.636) Prec@5 66.875 (67.288) Epoch: [13][6580/11272] Time 0.756 (0.835) Data 0.002 (0.002) Loss 2.7878 (2.6168) Prec@1 31.250 (36.638) Prec@5 66.250 (67.289) Epoch: [13][6590/11272] Time 0.923 (0.835) Data 0.001 (0.002) Loss 3.1300 (2.6169) Prec@1 30.625 (36.639) Prec@5 57.500 (67.287) Epoch: [13][6600/11272] Time 0.889 (0.835) Data 0.001 (0.002) Loss 2.9229 (2.6169) Prec@1 28.750 (36.636) Prec@5 61.875 (67.286) Epoch: [13][6610/11272] Time 0.755 (0.835) Data 0.001 (0.002) Loss 2.6502 (2.6170) Prec@1 38.125 (36.633) Prec@5 62.500 (67.284) Epoch: [13][6620/11272] Time 0.768 (0.835) Data 0.001 (0.002) Loss 2.3202 (2.6169) Prec@1 40.625 (36.635) Prec@5 77.500 (67.286) Epoch: [13][6630/11272] Time 0.986 (0.835) Data 0.001 (0.002) Loss 2.5433 (2.6170) Prec@1 33.125 (36.632) Prec@5 68.750 (67.286) Epoch: [13][6640/11272] Time 0.896 (0.835) Data 0.001 (0.002) Loss 2.6349 (2.6168) Prec@1 34.375 (36.635) Prec@5 67.500 (67.287) Epoch: [13][6650/11272] Time 0.745 (0.835) Data 0.002 (0.002) Loss 2.7018 (2.6168) Prec@1 38.125 (36.635) Prec@5 66.250 (67.288) Epoch: [13][6660/11272] Time 0.802 (0.835) Data 0.002 (0.002) Loss 2.8369 (2.6168) Prec@1 26.875 (36.632) Prec@5 62.500 (67.288) Epoch: [13][6670/11272] Time 0.882 (0.835) Data 0.002 (0.002) Loss 2.5770 (2.6167) Prec@1 39.375 (36.633) Prec@5 66.250 (67.289) Epoch: [13][6680/11272] Time 0.850 (0.835) Data 0.002 (0.002) Loss 2.4756 (2.6168) Prec@1 38.125 (36.632) Prec@5 67.500 (67.286) Epoch: [13][6690/11272] Time 0.752 (0.835) Data 0.002 (0.002) Loss 2.7791 (2.6168) Prec@1 34.375 (36.632) Prec@5 65.625 (67.288) Epoch: [13][6700/11272] Time 0.787 (0.835) Data 0.003 (0.002) Loss 2.5231 (2.6167) Prec@1 41.875 (36.632) Prec@5 68.125 (67.287) Epoch: [13][6710/11272] Time 0.919 (0.835) Data 0.002 (0.002) Loss 2.4046 (2.6169) Prec@1 40.625 (36.630) Prec@5 66.250 (67.284) Epoch: [13][6720/11272] Time 0.811 (0.835) Data 0.004 (0.002) Loss 2.5388 (2.6169) Prec@1 36.875 (36.631) Prec@5 68.750 (67.282) Epoch: [13][6730/11272] Time 0.770 (0.835) Data 0.001 (0.002) Loss 2.7838 (2.6171) Prec@1 35.625 (36.628) Prec@5 62.500 (67.278) Epoch: [13][6740/11272] Time 0.907 (0.835) Data 0.002 (0.002) Loss 2.6739 (2.6170) Prec@1 33.750 (36.628) Prec@5 67.500 (67.278) Epoch: [13][6750/11272] Time 0.909 (0.835) Data 0.002 (0.002) Loss 2.6395 (2.6171) Prec@1 37.500 (36.626) Prec@5 65.625 (67.277) Epoch: [13][6760/11272] Time 0.743 (0.835) Data 0.002 (0.002) Loss 2.6333 (2.6170) Prec@1 33.750 (36.625) Prec@5 69.375 (67.278) Epoch: [13][6770/11272] Time 0.758 (0.835) Data 0.002 (0.002) Loss 2.5207 (2.6170) Prec@1 36.875 (36.627) Prec@5 65.000 (67.279) Epoch: [13][6780/11272] Time 0.898 (0.835) Data 0.002 (0.002) Loss 2.7032 (2.6170) Prec@1 31.875 (36.625) Prec@5 65.625 (67.279) Epoch: [13][6790/11272] Time 0.941 (0.835) Data 0.001 (0.002) Loss 2.5829 (2.6169) Prec@1 36.875 (36.628) Prec@5 71.875 (67.283) Epoch: [13][6800/11272] Time 0.760 (0.835) Data 0.002 (0.002) Loss 2.7682 (2.6170) Prec@1 29.375 (36.627) Prec@5 65.000 (67.282) Epoch: [13][6810/11272] Time 0.782 (0.835) Data 0.002 (0.002) Loss 2.6850 (2.6169) Prec@1 35.625 (36.627) Prec@5 65.000 (67.283) Epoch: [13][6820/11272] Time 0.873 (0.835) Data 0.002 (0.002) Loss 2.8888 (2.6169) Prec@1 32.500 (36.625) Prec@5 63.750 (67.283) Epoch: [13][6830/11272] Time 0.853 (0.835) Data 0.002 (0.002) Loss 2.6862 (2.6169) Prec@1 34.375 (36.623) Prec@5 63.750 (67.283) Epoch: [13][6840/11272] Time 0.752 (0.835) Data 0.002 (0.002) Loss 2.5537 (2.6168) Prec@1 35.000 (36.625) Prec@5 68.750 (67.284) Epoch: [13][6850/11272] Time 0.911 (0.835) Data 0.002 (0.002) Loss 2.6625 (2.6168) Prec@1 40.625 (36.626) Prec@5 66.250 (67.285) Epoch: [13][6860/11272] Time 0.942 (0.835) Data 0.002 (0.002) Loss 2.7218 (2.6168) Prec@1 36.250 (36.628) Prec@5 64.375 (67.285) Epoch: [13][6870/11272] Time 0.756 (0.835) Data 0.001 (0.002) Loss 2.5386 (2.6168) Prec@1 35.625 (36.627) Prec@5 71.250 (67.287) Epoch: [13][6880/11272] Time 0.753 (0.835) Data 0.002 (0.002) Loss 2.4185 (2.6167) Prec@1 36.875 (36.629) Prec@5 71.250 (67.288) Epoch: [13][6890/11272] Time 0.941 (0.835) Data 0.002 (0.002) Loss 2.5964 (2.6168) Prec@1 36.250 (36.626) Prec@5 70.625 (67.286) Epoch: [13][6900/11272] Time 0.937 (0.835) Data 0.001 (0.002) Loss 2.7104 (2.6169) Prec@1 31.250 (36.624) Prec@5 68.750 (67.286) Epoch: [13][6910/11272] Time 0.713 (0.835) Data 0.001 (0.002) Loss 2.7119 (2.6169) Prec@1 33.750 (36.622) Prec@5 61.875 (67.286) Epoch: [13][6920/11272] Time 0.758 (0.835) Data 0.002 (0.002) Loss 2.9351 (2.6170) Prec@1 33.750 (36.621) Prec@5 64.375 (67.285) Epoch: [13][6930/11272] Time 0.910 (0.835) Data 0.001 (0.002) Loss 2.4995 (2.6171) Prec@1 38.750 (36.617) Prec@5 66.250 (67.280) Epoch: [13][6940/11272] Time 0.914 (0.835) Data 0.002 (0.002) Loss 2.8431 (2.6172) Prec@1 31.875 (36.616) Prec@5 65.000 (67.280) Epoch: [13][6950/11272] Time 0.790 (0.835) Data 0.002 (0.002) Loss 2.6560 (2.6172) Prec@1 37.500 (36.618) Prec@5 68.125 (67.280) Epoch: [13][6960/11272] Time 0.828 (0.835) Data 0.002 (0.002) Loss 2.3993 (2.6172) Prec@1 40.625 (36.618) Prec@5 75.000 (67.280) Epoch: [13][6970/11272] Time 0.880 (0.835) Data 0.002 (0.002) Loss 2.7894 (2.6172) Prec@1 34.375 (36.618) Prec@5 63.750 (67.279) Epoch: [13][6980/11272] Time 0.900 (0.835) Data 0.002 (0.002) Loss 2.5375 (2.6172) Prec@1 36.250 (36.618) Prec@5 65.625 (67.278) Epoch: [13][6990/11272] Time 0.748 (0.835) Data 0.002 (0.002) Loss 2.5142 (2.6171) Prec@1 35.000 (36.619) Prec@5 68.125 (67.281) Epoch: [13][7000/11272] Time 0.913 (0.835) Data 0.001 (0.002) Loss 2.8028 (2.6172) Prec@1 35.625 (36.618) Prec@5 60.625 (67.280) Epoch: [13][7010/11272] Time 0.846 (0.835) Data 0.001 (0.002) Loss 2.9152 (2.6172) Prec@1 27.500 (36.615) Prec@5 61.250 (67.277) Epoch: [13][7020/11272] Time 0.743 (0.835) Data 0.002 (0.002) Loss 2.6195 (2.6171) Prec@1 36.250 (36.618) Prec@5 68.750 (67.279) Epoch: [13][7030/11272] Time 0.748 (0.835) Data 0.001 (0.002) Loss 2.4857 (2.6170) Prec@1 40.000 (36.621) Prec@5 68.750 (67.280) Epoch: [13][7040/11272] Time 0.910 (0.835) Data 0.001 (0.002) Loss 2.5605 (2.6170) Prec@1 38.750 (36.619) Prec@5 73.125 (67.279) Epoch: [13][7050/11272] Time 0.853 (0.835) Data 0.001 (0.002) Loss 2.4770 (2.6171) Prec@1 40.000 (36.618) Prec@5 71.250 (67.277) Epoch: [13][7060/11272] Time 0.739 (0.835) Data 0.001 (0.002) Loss 2.4329 (2.6171) Prec@1 41.875 (36.621) Prec@5 70.625 (67.279) Epoch: [13][7070/11272] Time 0.768 (0.835) Data 0.002 (0.002) Loss 2.7644 (2.6170) Prec@1 36.250 (36.622) Prec@5 65.625 (67.281) Epoch: [13][7080/11272] Time 0.907 (0.835) Data 0.002 (0.002) Loss 2.8669 (2.6171) Prec@1 33.125 (36.621) Prec@5 60.625 (67.279) Epoch: [13][7090/11272] Time 0.850 (0.835) Data 0.001 (0.002) Loss 2.5592 (2.6171) Prec@1 38.750 (36.622) Prec@5 64.375 (67.281) Epoch: [13][7100/11272] Time 0.791 (0.835) Data 0.002 (0.002) Loss 2.4215 (2.6172) Prec@1 43.750 (36.621) Prec@5 70.000 (67.278) Epoch: [13][7110/11272] Time 0.766 (0.835) Data 0.002 (0.002) Loss 2.5366 (2.6172) Prec@1 35.625 (36.620) Prec@5 68.125 (67.277) Epoch: [13][7120/11272] Time 0.844 (0.835) Data 0.001 (0.002) Loss 2.8541 (2.6173) Prec@1 38.125 (36.620) Prec@5 63.750 (67.275) Epoch: [13][7130/11272] Time 0.811 (0.835) Data 0.001 (0.002) Loss 2.4083 (2.6173) Prec@1 38.125 (36.619) Prec@5 70.625 (67.274) Epoch: [13][7140/11272] Time 0.783 (0.835) Data 0.002 (0.002) Loss 2.5965 (2.6173) Prec@1 35.625 (36.620) Prec@5 71.875 (67.275) Epoch: [13][7150/11272] Time 0.903 (0.835) Data 0.001 (0.002) Loss 2.6036 (2.6173) Prec@1 36.875 (36.618) Prec@5 66.875 (67.276) Epoch: [13][7160/11272] Time 0.949 (0.835) Data 0.002 (0.002) Loss 2.9201 (2.6174) Prec@1 31.250 (36.618) Prec@5 63.125 (67.274) Epoch: [13][7170/11272] Time 0.852 (0.835) Data 0.002 (0.002) Loss 2.3580 (2.6173) Prec@1 39.375 (36.619) Prec@5 77.500 (67.275) Epoch: [13][7180/11272] Time 0.845 (0.835) Data 0.002 (0.002) Loss 2.5862 (2.6172) Prec@1 40.000 (36.620) Prec@5 66.250 (67.275) Epoch: [13][7190/11272] Time 0.886 (0.835) Data 0.002 (0.002) Loss 2.6090 (2.6174) Prec@1 37.500 (36.617) Prec@5 70.000 (67.273) Epoch: [13][7200/11272] Time 0.877 (0.835) Data 0.002 (0.002) Loss 2.5927 (2.6173) Prec@1 37.500 (36.621) Prec@5 67.500 (67.275) Epoch: [13][7210/11272] Time 0.777 (0.835) Data 0.006 (0.002) Loss 2.5525 (2.6173) Prec@1 35.625 (36.620) Prec@5 69.375 (67.278) Epoch: [13][7220/11272] Time 0.792 (0.835) Data 0.002 (0.002) Loss 2.4707 (2.6174) Prec@1 39.375 (36.619) Prec@5 70.000 (67.276) Epoch: [13][7230/11272] Time 0.873 (0.835) Data 0.002 (0.002) Loss 2.7107 (2.6174) Prec@1 35.000 (36.617) Prec@5 59.375 (67.273) Epoch: [13][7240/11272] Time 0.880 (0.835) Data 0.001 (0.002) Loss 2.6220 (2.6175) Prec@1 37.500 (36.618) Prec@5 63.125 (67.273) Epoch: [13][7250/11272] Time 0.745 (0.835) Data 0.002 (0.002) Loss 2.6056 (2.6174) Prec@1 33.750 (36.621) Prec@5 67.500 (67.275) Epoch: [13][7260/11272] Time 0.916 (0.835) Data 0.002 (0.002) Loss 2.6907 (2.6174) Prec@1 36.250 (36.622) Prec@5 61.875 (67.273) Epoch: [13][7270/11272] Time 0.826 (0.835) Data 0.001 (0.002) Loss 2.5877 (2.6173) Prec@1 41.875 (36.623) Prec@5 66.250 (67.275) Epoch: [13][7280/11272] Time 0.746 (0.835) Data 0.002 (0.002) Loss 2.5796 (2.6172) Prec@1 35.000 (36.623) Prec@5 68.125 (67.275) Epoch: [13][7290/11272] Time 0.767 (0.835) Data 0.002 (0.002) Loss 2.6563 (2.6173) Prec@1 38.125 (36.624) Prec@5 68.125 (67.275) Epoch: [13][7300/11272] Time 0.881 (0.835) Data 0.001 (0.002) Loss 2.6858 (2.6173) Prec@1 37.500 (36.624) Prec@5 67.500 (67.274) Epoch: [13][7310/11272] Time 0.909 (0.835) Data 0.001 (0.002) Loss 2.5874 (2.6174) Prec@1 35.000 (36.622) Prec@5 65.000 (67.269) Epoch: [13][7320/11272] Time 0.742 (0.835) Data 0.001 (0.002) Loss 2.5391 (2.6174) Prec@1 37.500 (36.621) Prec@5 68.125 (67.268) Epoch: [13][7330/11272] Time 0.800 (0.835) Data 0.002 (0.002) Loss 2.8336 (2.6176) Prec@1 29.375 (36.618) Prec@5 66.250 (67.268) Epoch: [13][7340/11272] Time 0.854 (0.835) Data 0.001 (0.002) Loss 2.5356 (2.6176) Prec@1 34.375 (36.617) Prec@5 72.500 (67.269) Epoch: [13][7350/11272] Time 0.945 (0.835) Data 0.001 (0.002) Loss 2.6725 (2.6176) Prec@1 30.625 (36.617) Prec@5 66.250 (67.270) Epoch: [13][7360/11272] Time 0.808 (0.835) Data 0.002 (0.002) Loss 2.5301 (2.6176) Prec@1 37.500 (36.617) Prec@5 70.000 (67.269) Epoch: [13][7370/11272] Time 0.785 (0.835) Data 0.002 (0.002) Loss 2.6232 (2.6176) Prec@1 37.500 (36.619) Prec@5 66.250 (67.269) Epoch: [13][7380/11272] Time 0.876 (0.835) Data 0.002 (0.002) Loss 2.3053 (2.6176) Prec@1 45.000 (36.619) Prec@5 71.875 (67.270) Epoch: [13][7390/11272] Time 0.757 (0.835) Data 0.004 (0.002) Loss 2.6828 (2.6176) Prec@1 34.375 (36.618) Prec@5 66.875 (67.271) Epoch: [13][7400/11272] Time 0.774 (0.835) Data 0.001 (0.002) Loss 2.6658 (2.6175) Prec@1 35.625 (36.621) Prec@5 63.125 (67.273) Epoch: [13][7410/11272] Time 0.974 (0.835) Data 0.003 (0.002) Loss 2.8804 (2.6176) Prec@1 30.625 (36.618) Prec@5 56.250 (67.270) Epoch: [13][7420/11272] Time 0.884 (0.835) Data 0.002 (0.002) Loss 2.4490 (2.6177) Prec@1 41.250 (36.617) Prec@5 71.250 (67.268) Epoch: [13][7430/11272] Time 0.765 (0.835) Data 0.002 (0.002) Loss 2.6153 (2.6177) Prec@1 39.375 (36.617) Prec@5 63.750 (67.269) Epoch: [13][7440/11272] Time 0.763 (0.835) Data 0.002 (0.002) Loss 2.5986 (2.6176) Prec@1 38.750 (36.618) Prec@5 64.375 (67.269) Epoch: [13][7450/11272] Time 0.859 (0.835) Data 0.002 (0.002) Loss 2.7748 (2.6176) Prec@1 33.750 (36.616) Prec@5 65.625 (67.270) Epoch: [13][7460/11272] Time 0.878 (0.835) Data 0.001 (0.002) Loss 2.8218 (2.6176) Prec@1 31.250 (36.616) Prec@5 63.750 (67.271) Epoch: [13][7470/11272] Time 0.723 (0.835) Data 0.001 (0.002) Loss 2.4597 (2.6176) Prec@1 41.250 (36.616) Prec@5 68.125 (67.271) Epoch: [13][7480/11272] Time 0.757 (0.835) Data 0.002 (0.002) Loss 2.6796 (2.6176) Prec@1 34.375 (36.615) Prec@5 63.125 (67.270) Epoch: [13][7490/11272] Time 0.932 (0.835) Data 0.001 (0.002) Loss 2.6993 (2.6176) Prec@1 31.875 (36.617) Prec@5 65.625 (67.270) Epoch: [13][7500/11272] Time 0.880 (0.834) Data 0.001 (0.002) Loss 2.5072 (2.6176) Prec@1 35.000 (36.616) Prec@5 71.875 (67.271) Epoch: [13][7510/11272] Time 0.802 (0.834) Data 0.001 (0.002) Loss 2.8144 (2.6176) Prec@1 30.000 (36.615) Prec@5 70.000 (67.272) Epoch: [13][7520/11272] Time 0.898 (0.835) Data 0.001 (0.002) Loss 2.8549 (2.6176) Prec@1 31.875 (36.616) Prec@5 63.750 (67.272) Epoch: [13][7530/11272] Time 0.929 (0.835) Data 0.002 (0.002) Loss 2.5394 (2.6177) Prec@1 36.250 (36.615) Prec@5 70.000 (67.270) Epoch: [13][7540/11272] Time 0.747 (0.834) Data 0.002 (0.002) Loss 2.6999 (2.6177) Prec@1 35.625 (36.615) Prec@5 67.500 (67.270) Epoch: [13][7550/11272] Time 0.793 (0.834) Data 0.001 (0.002) Loss 2.4792 (2.6177) Prec@1 40.000 (36.616) Prec@5 71.875 (67.269) Epoch: [13][7560/11272] Time 0.926 (0.834) Data 0.002 (0.002) Loss 2.6580 (2.6176) Prec@1 37.500 (36.616) Prec@5 66.250 (67.270) Epoch: [13][7570/11272] Time 0.906 (0.834) Data 0.002 (0.002) Loss 2.4559 (2.6175) Prec@1 35.625 (36.619) Prec@5 73.750 (67.273) Epoch: [13][7580/11272] Time 0.809 (0.834) Data 0.001 (0.002) Loss 2.8839 (2.6175) Prec@1 33.125 (36.617) Prec@5 64.375 (67.274) Epoch: [13][7590/11272] Time 0.752 (0.834) Data 0.001 (0.002) Loss 2.5200 (2.6175) Prec@1 36.250 (36.617) Prec@5 70.625 (67.274) Epoch: [13][7600/11272] Time 0.877 (0.835) Data 0.001 (0.003) Loss 2.6185 (2.6174) Prec@1 35.000 (36.616) Prec@5 68.125 (67.275) Epoch: [13][7610/11272] Time 0.852 (0.835) Data 0.001 (0.003) Loss 2.5457 (2.6174) Prec@1 36.875 (36.617) Prec@5 73.125 (67.272) Epoch: [13][7620/11272] Time 0.737 (0.835) Data 0.001 (0.003) Loss 2.6442 (2.6174) Prec@1 38.750 (36.616) Prec@5 66.250 (67.274) Epoch: [13][7630/11272] Time 0.767 (0.835) Data 0.002 (0.003) Loss 2.4917 (2.6174) Prec@1 35.000 (36.615) Prec@5 65.625 (67.272) Epoch: [13][7640/11272] Time 0.885 (0.835) Data 0.002 (0.003) Loss 2.6554 (2.6173) Prec@1 37.500 (36.616) Prec@5 65.625 (67.274) Epoch: [13][7650/11272] Time 0.795 (0.835) Data 0.004 (0.003) Loss 2.5377 (2.6173) Prec@1 39.375 (36.617) Prec@5 69.375 (67.274) Epoch: [13][7660/11272] Time 0.788 (0.835) Data 0.002 (0.003) Loss 2.3798 (2.6173) Prec@1 43.750 (36.620) Prec@5 71.250 (67.273) Epoch: [13][7670/11272] Time 0.904 (0.835) Data 0.001 (0.003) Loss 2.6577 (2.6173) Prec@1 35.000 (36.620) Prec@5 66.250 (67.274) Epoch: [13][7680/11272] Time 0.924 (0.835) Data 0.003 (0.003) Loss 2.4280 (2.6172) Prec@1 44.375 (36.625) Prec@5 68.125 (67.277) Epoch: [13][7690/11272] Time 0.766 (0.835) Data 0.001 (0.003) Loss 2.4197 (2.6172) Prec@1 39.375 (36.625) Prec@5 70.625 (67.278) Epoch: [13][7700/11272] Time 0.754 (0.835) Data 0.002 (0.003) Loss 2.3016 (2.6172) Prec@1 44.375 (36.624) Prec@5 70.000 (67.278) Epoch: [13][7710/11272] Time 0.887 (0.835) Data 0.002 (0.003) Loss 2.5211 (2.6172) Prec@1 40.000 (36.625) Prec@5 71.875 (67.277) Epoch: [13][7720/11272] Time 0.855 (0.835) Data 0.001 (0.003) Loss 2.6184 (2.6171) Prec@1 38.750 (36.628) Prec@5 70.000 (67.279) Epoch: [13][7730/11272] Time 0.746 (0.835) Data 0.001 (0.003) Loss 2.8062 (2.6172) Prec@1 29.375 (36.627) Prec@5 66.250 (67.279) Epoch: [13][7740/11272] Time 0.746 (0.835) Data 0.002 (0.003) Loss 2.5021 (2.6172) Prec@1 37.500 (36.626) Prec@5 69.375 (67.279) Epoch: [13][7750/11272] Time 0.855 (0.835) Data 0.002 (0.003) Loss 2.7318 (2.6173) Prec@1 31.250 (36.624) Prec@5 69.375 (67.277) Epoch: [13][7760/11272] Time 0.804 (0.835) Data 0.001 (0.003) Loss 2.4183 (2.6173) Prec@1 41.875 (36.622) Prec@5 68.750 (67.277) Epoch: [13][7770/11272] Time 0.767 (0.835) Data 0.002 (0.003) Loss 2.9206 (2.6173) Prec@1 33.750 (36.621) Prec@5 61.250 (67.276) Epoch: [13][7780/11272] Time 0.922 (0.835) Data 0.001 (0.003) Loss 2.4678 (2.6172) Prec@1 35.000 (36.624) Prec@5 71.875 (67.277) Epoch: [13][7790/11272] Time 0.865 (0.835) Data 0.001 (0.003) Loss 2.7426 (2.6172) Prec@1 36.875 (36.623) Prec@5 64.375 (67.277) Epoch: [13][7800/11272] Time 0.750 (0.835) Data 0.002 (0.003) Loss 2.5324 (2.6173) Prec@1 36.250 (36.623) Prec@5 68.750 (67.274) Epoch: [13][7810/11272] Time 0.765 (0.835) Data 0.002 (0.003) Loss 2.7479 (2.6175) Prec@1 36.875 (36.619) Prec@5 64.375 (67.272) Epoch: [13][7820/11272] Time 0.897 (0.835) Data 0.001 (0.003) Loss 2.4308 (2.6175) Prec@1 40.000 (36.618) Prec@5 70.625 (67.269) Epoch: [13][7830/11272] Time 0.849 (0.835) Data 0.001 (0.003) Loss 2.6720 (2.6175) Prec@1 37.500 (36.616) Prec@5 65.625 (67.269) Epoch: [13][7840/11272] Time 0.735 (0.835) Data 0.001 (0.003) Loss 2.5188 (2.6176) Prec@1 43.125 (36.616) Prec@5 68.750 (67.266) Epoch: [13][7850/11272] Time 0.746 (0.835) Data 0.001 (0.003) Loss 2.5326 (2.6175) Prec@1 34.375 (36.618) Prec@5 70.625 (67.269) Epoch: [13][7860/11272] Time 0.916 (0.835) Data 0.001 (0.003) Loss 2.5470 (2.6174) Prec@1 42.500 (36.617) Prec@5 70.000 (67.270) Epoch: [13][7870/11272] Time 0.879 (0.835) Data 0.001 (0.003) Loss 2.5511 (2.6175) Prec@1 38.125 (36.616) Prec@5 67.500 (67.268) Epoch: [13][7880/11272] Time 0.759 (0.835) Data 0.001 (0.003) Loss 2.6245 (2.6175) Prec@1 36.250 (36.616) Prec@5 68.125 (67.268) Epoch: [13][7890/11272] Time 0.737 (0.835) Data 0.001 (0.003) Loss 2.5700 (2.6175) Prec@1 36.250 (36.616) Prec@5 66.250 (67.267) Epoch: [13][7900/11272] Time 0.863 (0.835) Data 0.002 (0.003) Loss 2.6469 (2.6174) Prec@1 33.125 (36.618) Prec@5 66.875 (67.266) Epoch: [13][7910/11272] Time 0.877 (0.835) Data 0.002 (0.003) Loss 2.5035 (2.6175) Prec@1 36.250 (36.618) Prec@5 67.500 (67.265) Epoch: [13][7920/11272] Time 0.806 (0.835) Data 0.002 (0.003) Loss 2.9666 (2.6174) Prec@1 33.125 (36.619) Prec@5 60.000 (67.266) Epoch: [13][7930/11272] Time 0.954 (0.835) Data 0.002 (0.003) Loss 2.6476 (2.6174) Prec@1 36.875 (36.618) Prec@5 65.000 (67.267) Epoch: [13][7940/11272] Time 0.914 (0.835) Data 0.002 (0.003) Loss 2.6521 (2.6175) Prec@1 30.625 (36.617) Prec@5 71.875 (67.268) Epoch: [13][7950/11272] Time 0.738 (0.835) Data 0.002 (0.003) Loss 2.6162 (2.6175) Prec@1 36.250 (36.618) Prec@5 68.125 (67.267) Epoch: [13][7960/11272] Time 0.754 (0.835) Data 0.001 (0.003) Loss 2.6813 (2.6175) Prec@1 36.250 (36.618) Prec@5 66.875 (67.267) Epoch: [13][7970/11272] Time 0.839 (0.835) Data 0.001 (0.003) Loss 2.7288 (2.6175) Prec@1 36.875 (36.620) Prec@5 63.125 (67.267) Epoch: [13][7980/11272] Time 0.921 (0.835) Data 0.001 (0.003) Loss 2.7920 (2.6175) Prec@1 41.250 (36.620) Prec@5 66.875 (67.264) Epoch: [13][7990/11272] Time 0.756 (0.835) Data 0.002 (0.003) Loss 2.6948 (2.6175) Prec@1 35.000 (36.619) Prec@5 66.250 (67.265) Epoch: [13][8000/11272] Time 0.737 (0.835) Data 0.001 (0.003) Loss 2.8495 (2.6176) Prec@1 35.625 (36.618) Prec@5 65.000 (67.264) Epoch: [13][8010/11272] Time 0.862 (0.835) Data 0.001 (0.003) Loss 2.5509 (2.6175) Prec@1 36.250 (36.617) Prec@5 67.500 (67.266) Epoch: [13][8020/11272] Time 0.908 (0.835) Data 0.001 (0.003) Loss 2.7629 (2.6175) Prec@1 32.500 (36.618) Prec@5 66.250 (67.267) Epoch: [13][8030/11272] Time 0.785 (0.835) Data 0.002 (0.003) Loss 2.6499 (2.6176) Prec@1 36.250 (36.618) Prec@5 65.625 (67.268) Epoch: [13][8040/11272] Time 0.780 (0.835) Data 0.002 (0.003) Loss 2.7464 (2.6176) Prec@1 31.250 (36.618) Prec@5 60.625 (67.267) Epoch: [13][8050/11272] Time 0.946 (0.835) Data 0.002 (0.003) Loss 2.5063 (2.6176) Prec@1 39.375 (36.618) Prec@5 72.500 (67.267) Epoch: [13][8060/11272] Time 0.841 (0.835) Data 0.002 (0.003) Loss 2.5009 (2.6177) Prec@1 35.625 (36.618) Prec@5 70.625 (67.267) Epoch: [13][8070/11272] Time 0.746 (0.835) Data 0.001 (0.003) Loss 2.8883 (2.6177) Prec@1 36.250 (36.618) Prec@5 66.250 (67.266) Epoch: [13][8080/11272] Time 0.881 (0.835) Data 0.002 (0.003) Loss 2.6964 (2.6178) Prec@1 38.125 (36.617) Prec@5 65.625 (67.265) Epoch: [13][8090/11272] Time 0.871 (0.835) Data 0.002 (0.003) Loss 2.4044 (2.6178) Prec@1 38.750 (36.615) Prec@5 71.250 (67.263) Epoch: [13][8100/11272] Time 0.766 (0.835) Data 0.002 (0.003) Loss 2.7128 (2.6178) Prec@1 35.000 (36.616) Prec@5 68.125 (67.263) Epoch: [13][8110/11272] Time 0.761 (0.835) Data 0.002 (0.003) Loss 2.6572 (2.6178) Prec@1 38.125 (36.616) Prec@5 65.625 (67.262) Epoch: [13][8120/11272] Time 0.894 (0.835) Data 0.002 (0.003) Loss 2.4261 (2.6177) Prec@1 36.250 (36.619) Prec@5 69.375 (67.263) Epoch: [13][8130/11272] Time 0.854 (0.835) Data 0.002 (0.003) Loss 2.5600 (2.6177) Prec@1 38.125 (36.619) Prec@5 70.000 (67.261) Epoch: [13][8140/11272] Time 0.784 (0.835) Data 0.001 (0.003) Loss 2.8458 (2.6178) Prec@1 37.500 (36.618) Prec@5 63.125 (67.259) Epoch: [13][8150/11272] Time 0.788 (0.835) Data 0.002 (0.003) Loss 2.6405 (2.6178) Prec@1 35.625 (36.617) Prec@5 65.625 (67.260) Epoch: [13][8160/11272] Time 0.873 (0.835) Data 0.002 (0.003) Loss 2.7653 (2.6178) Prec@1 27.500 (36.615) Prec@5 66.875 (67.260) Epoch: [13][8170/11272] Time 0.853 (0.835) Data 0.001 (0.003) Loss 2.5616 (2.6178) Prec@1 39.375 (36.614) Prec@5 68.125 (67.259) Epoch: [13][8180/11272] Time 0.755 (0.835) Data 0.002 (0.003) Loss 2.5830 (2.6179) Prec@1 38.125 (36.614) Prec@5 70.000 (67.259) Epoch: [13][8190/11272] Time 0.840 (0.835) Data 0.002 (0.003) Loss 2.5740 (2.6179) Prec@1 39.375 (36.613) Prec@5 70.000 (67.256) Epoch: [13][8200/11272] Time 0.915 (0.835) Data 0.001 (0.003) Loss 2.6140 (2.6181) Prec@1 36.250 (36.610) Prec@5 63.750 (67.252) Epoch: [13][8210/11272] Time 0.753 (0.835) Data 0.002 (0.003) Loss 2.5711 (2.6181) Prec@1 39.375 (36.610) Prec@5 65.000 (67.251) Epoch: [13][8220/11272] Time 0.749 (0.835) Data 0.002 (0.003) Loss 2.8243 (2.6181) Prec@1 29.375 (36.609) Prec@5 61.875 (67.250) Epoch: [13][8230/11272] Time 0.924 (0.835) Data 0.001 (0.003) Loss 2.7559 (2.6181) Prec@1 34.375 (36.608) Prec@5 62.500 (67.250) Epoch: [13][8240/11272] Time 0.839 (0.835) Data 0.001 (0.003) Loss 2.7649 (2.6181) Prec@1 31.250 (36.607) Prec@5 64.375 (67.250) Epoch: [13][8250/11272] Time 0.762 (0.835) Data 0.001 (0.003) Loss 2.5861 (2.6181) Prec@1 33.125 (36.608) Prec@5 69.375 (67.250) Epoch: [13][8260/11272] Time 0.743 (0.835) Data 0.001 (0.003) Loss 2.6869 (2.6182) Prec@1 32.500 (36.606) Prec@5 70.000 (67.250) Epoch: [13][8270/11272] Time 0.877 (0.835) Data 0.002 (0.003) Loss 2.4647 (2.6182) Prec@1 40.625 (36.607) Prec@5 68.125 (67.248) Epoch: [13][8280/11272] Time 0.916 (0.835) Data 0.002 (0.003) Loss 2.8518 (2.6183) Prec@1 31.875 (36.605) Prec@5 61.250 (67.245) Epoch: [13][8290/11272] Time 0.744 (0.835) Data 0.001 (0.003) Loss 2.5932 (2.6183) Prec@1 34.375 (36.603) Prec@5 68.750 (67.246) Epoch: [13][8300/11272] Time 0.777 (0.835) Data 0.001 (0.003) Loss 2.5120 (2.6183) Prec@1 35.000 (36.601) Prec@5 65.000 (67.246) Epoch: [13][8310/11272] Time 0.877 (0.834) Data 0.001 (0.003) Loss 2.2787 (2.6183) Prec@1 43.750 (36.602) Prec@5 74.375 (67.246) Epoch: [13][8320/11272] Time 0.754 (0.834) Data 0.003 (0.003) Loss 2.6421 (2.6183) Prec@1 37.500 (36.600) Prec@5 66.875 (67.245) Epoch: [13][8330/11272] Time 0.750 (0.834) Data 0.001 (0.003) Loss 2.4068 (2.6181) Prec@1 36.250 (36.602) Prec@5 71.875 (67.248) Epoch: [13][8340/11272] Time 0.860 (0.834) Data 0.002 (0.003) Loss 2.5509 (2.6182) Prec@1 33.125 (36.600) Prec@5 66.875 (67.247) Epoch: [13][8350/11272] Time 0.880 (0.834) Data 0.001 (0.003) Loss 2.3551 (2.6183) Prec@1 45.000 (36.600) Prec@5 73.750 (67.246) Epoch: [13][8360/11272] Time 0.817 (0.834) Data 0.002 (0.003) Loss 2.7738 (2.6184) Prec@1 35.000 (36.598) Prec@5 64.375 (67.244) Epoch: [13][8370/11272] Time 0.766 (0.834) Data 0.001 (0.003) Loss 2.5509 (2.6184) Prec@1 39.375 (36.598) Prec@5 66.875 (67.243) Epoch: [13][8380/11272] Time 0.853 (0.834) Data 0.001 (0.003) Loss 2.6327 (2.6183) Prec@1 35.000 (36.598) Prec@5 70.000 (67.247) Epoch: [13][8390/11272] Time 0.870 (0.834) Data 0.002 (0.003) Loss 2.9048 (2.6182) Prec@1 38.750 (36.601) Prec@5 58.750 (67.248) Epoch: [13][8400/11272] Time 0.744 (0.834) Data 0.002 (0.003) Loss 2.3874 (2.6182) Prec@1 40.000 (36.600) Prec@5 75.000 (67.249) Epoch: [13][8410/11272] Time 0.775 (0.834) Data 0.003 (0.003) Loss 2.9171 (2.6183) Prec@1 30.625 (36.600) Prec@5 65.625 (67.246) Epoch: [13][8420/11272] Time 0.933 (0.834) Data 0.002 (0.003) Loss 2.7429 (2.6182) Prec@1 32.500 (36.600) Prec@5 66.875 (67.248) Epoch: [13][8430/11272] Time 0.882 (0.834) Data 0.002 (0.003) Loss 2.2646 (2.6182) Prec@1 41.875 (36.603) Prec@5 72.500 (67.249) Epoch: [13][8440/11272] Time 0.745 (0.834) Data 0.002 (0.003) Loss 2.5767 (2.6182) Prec@1 36.875 (36.602) Prec@5 68.125 (67.248) Epoch: [13][8450/11272] Time 0.853 (0.834) Data 0.001 (0.003) Loss 2.4629 (2.6183) Prec@1 41.250 (36.603) Prec@5 66.875 (67.247) Epoch: [13][8460/11272] Time 0.917 (0.834) Data 0.002 (0.003) Loss 2.6729 (2.6182) Prec@1 38.125 (36.604) Prec@5 67.500 (67.246) Epoch: [13][8470/11272] Time 0.745 (0.834) Data 0.002 (0.003) Loss 2.7099 (2.6182) Prec@1 38.125 (36.605) Prec@5 69.375 (67.247) Epoch: [13][8480/11272] Time 0.742 (0.834) Data 0.001 (0.003) Loss 2.5953 (2.6183) Prec@1 33.750 (36.602) Prec@5 66.250 (67.245) Epoch: [13][8490/11272] Time 0.866 (0.834) Data 0.001 (0.003) Loss 2.4729 (2.6182) Prec@1 44.375 (36.605) Prec@5 72.500 (67.248) Epoch: [13][8500/11272] Time 0.908 (0.834) Data 0.002 (0.003) Loss 2.5090 (2.6182) Prec@1 38.750 (36.606) Prec@5 66.875 (67.249) Epoch: [13][8510/11272] Time 0.751 (0.834) Data 0.001 (0.003) Loss 2.3023 (2.6181) Prec@1 40.625 (36.607) Prec@5 70.000 (67.251) Epoch: [13][8520/11272] Time 0.789 (0.834) Data 0.002 (0.003) Loss 2.8143 (2.6181) Prec@1 36.875 (36.606) Prec@5 59.375 (67.251) Epoch: [13][8530/11272] Time 0.884 (0.834) Data 0.003 (0.003) Loss 2.8965 (2.6182) Prec@1 26.875 (36.604) Prec@5 63.125 (67.251) Epoch: [13][8540/11272] Time 0.920 (0.834) Data 0.001 (0.003) Loss 2.8452 (2.6182) Prec@1 28.750 (36.603) Prec@5 63.750 (67.251) Epoch: [13][8550/11272] Time 0.744 (0.834) Data 0.001 (0.003) Loss 2.5518 (2.6183) Prec@1 36.250 (36.601) Prec@5 69.375 (67.248) Epoch: [13][8560/11272] Time 0.750 (0.834) Data 0.001 (0.003) Loss 2.7154 (2.6182) Prec@1 36.875 (36.602) Prec@5 68.125 (67.250) Epoch: [13][8570/11272] Time 0.950 (0.834) Data 0.002 (0.003) Loss 2.5000 (2.6182) Prec@1 35.625 (36.602) Prec@5 71.250 (67.249) Epoch: [13][8580/11272] Time 0.799 (0.834) Data 0.004 (0.003) Loss 2.6623 (2.6183) Prec@1 36.875 (36.601) Prec@5 63.125 (67.247) Epoch: [13][8590/11272] Time 0.741 (0.834) Data 0.001 (0.003) Loss 2.6460 (2.6183) Prec@1 35.000 (36.599) Prec@5 64.375 (67.246) Epoch: [13][8600/11272] Time 0.904 (0.834) Data 0.003 (0.003) Loss 2.6169 (2.6183) Prec@1 35.625 (36.601) Prec@5 68.125 (67.247) Epoch: [13][8610/11272] Time 0.927 (0.834) Data 0.002 (0.003) Loss 2.5210 (2.6182) Prec@1 42.500 (36.602) Prec@5 68.750 (67.248) Epoch: [13][8620/11272] Time 0.739 (0.834) Data 0.001 (0.003) Loss 2.6572 (2.6183) Prec@1 32.500 (36.601) Prec@5 69.375 (67.248) Epoch: [13][8630/11272] Time 0.737 (0.834) Data 0.002 (0.003) Loss 2.6717 (2.6183) Prec@1 33.750 (36.600) Prec@5 68.125 (67.249) Epoch: [13][8640/11272] Time 0.867 (0.834) Data 0.001 (0.003) Loss 2.6078 (2.6184) Prec@1 36.250 (36.598) Prec@5 68.125 (67.248) Epoch: [13][8650/11272] Time 0.837 (0.834) Data 0.001 (0.003) Loss 2.6785 (2.6183) Prec@1 36.250 (36.598) Prec@5 66.875 (67.250) Epoch: [13][8660/11272] Time 0.756 (0.834) Data 0.001 (0.003) Loss 2.5938 (2.6183) Prec@1 34.375 (36.597) Prec@5 71.250 (67.251) Epoch: [13][8670/11272] Time 0.783 (0.834) Data 0.002 (0.003) Loss 2.6729 (2.6184) Prec@1 30.625 (36.593) Prec@5 61.875 (67.250) Epoch: [13][8680/11272] Time 0.839 (0.834) Data 0.001 (0.003) Loss 2.4637 (2.6183) Prec@1 38.125 (36.592) Prec@5 72.500 (67.253) Epoch: [13][8690/11272] Time 0.915 (0.834) Data 0.002 (0.003) Loss 2.2880 (2.6183) Prec@1 45.000 (36.592) Prec@5 72.500 (67.254) Epoch: [13][8700/11272] Time 0.748 (0.834) Data 0.001 (0.003) Loss 2.6763 (2.6183) Prec@1 33.750 (36.592) Prec@5 64.375 (67.253) Epoch: [13][8710/11272] Time 0.905 (0.834) Data 0.002 (0.003) Loss 2.5177 (2.6183) Prec@1 36.875 (36.592) Prec@5 73.125 (67.255) Epoch: [13][8720/11272] Time 0.898 (0.834) Data 0.002 (0.003) Loss 2.3765 (2.6181) Prec@1 38.750 (36.593) Prec@5 72.500 (67.258) Epoch: [13][8730/11272] Time 0.751 (0.834) Data 0.001 (0.003) Loss 2.5306 (2.6181) Prec@1 39.375 (36.595) Prec@5 65.625 (67.258) Epoch: [13][8740/11272] Time 0.737 (0.834) Data 0.001 (0.003) Loss 2.6342 (2.6182) Prec@1 34.375 (36.594) Prec@5 68.750 (67.256) Epoch: [13][8750/11272] Time 0.910 (0.834) Data 0.002 (0.003) Loss 2.7696 (2.6183) Prec@1 31.250 (36.590) Prec@5 65.625 (67.256) Epoch: [13][8760/11272] Time 0.860 (0.834) Data 0.002 (0.003) Loss 2.5474 (2.6182) Prec@1 39.375 (36.592) Prec@5 68.125 (67.257) Epoch: [13][8770/11272] Time 0.770 (0.834) Data 0.001 (0.003) Loss 2.6234 (2.6182) Prec@1 38.750 (36.592) Prec@5 64.375 (67.256) Epoch: [13][8780/11272] Time 0.810 (0.834) Data 0.002 (0.003) Loss 2.9261 (2.6182) Prec@1 30.625 (36.591) Prec@5 64.375 (67.256) Epoch: [13][8790/11272] Time 0.894 (0.834) Data 0.001 (0.003) Loss 2.5427 (2.6182) Prec@1 33.125 (36.592) Prec@5 72.500 (67.257) Epoch: [13][8800/11272] Time 0.899 (0.834) Data 0.002 (0.003) Loss 2.4541 (2.6183) Prec@1 39.375 (36.589) Prec@5 69.375 (67.253) Epoch: [13][8810/11272] Time 0.774 (0.834) Data 0.002 (0.003) Loss 2.4297 (2.6184) Prec@1 40.000 (36.587) Prec@5 71.250 (67.251) Epoch: [13][8820/11272] Time 0.743 (0.834) Data 0.002 (0.003) Loss 2.6900 (2.6184) Prec@1 34.375 (36.585) Prec@5 68.125 (67.249) Epoch: [13][8830/11272] Time 0.894 (0.834) Data 0.002 (0.003) Loss 2.5203 (2.6184) Prec@1 38.750 (36.588) Prec@5 70.000 (67.251) Epoch: [13][8840/11272] Time 0.810 (0.834) Data 0.001 (0.003) Loss 2.5887 (2.6183) Prec@1 36.875 (36.590) Prec@5 70.000 (67.252) Epoch: [13][8850/11272] Time 0.807 (0.834) Data 0.001 (0.003) Loss 2.6059 (2.6183) Prec@1 33.750 (36.589) Prec@5 66.250 (67.252) Epoch: [13][8860/11272] Time 0.913 (0.834) Data 0.002 (0.003) Loss 2.7148 (2.6182) Prec@1 31.250 (36.590) Prec@5 61.250 (67.253) Epoch: [13][8870/11272] Time 0.905 (0.834) Data 0.002 (0.003) Loss 2.6361 (2.6183) Prec@1 38.750 (36.589) Prec@5 69.375 (67.252) Epoch: [13][8880/11272] Time 0.775 (0.834) Data 0.001 (0.003) Loss 2.5487 (2.6184) Prec@1 36.250 (36.588) Prec@5 67.500 (67.252) Epoch: [13][8890/11272] Time 0.777 (0.834) Data 0.002 (0.003) Loss 2.4361 (2.6184) Prec@1 34.375 (36.584) Prec@5 72.500 (67.249) Epoch: [13][8900/11272] Time 0.907 (0.834) Data 0.002 (0.003) Loss 2.7611 (2.6185) Prec@1 33.125 (36.584) Prec@5 65.625 (67.247) Epoch: [13][8910/11272] Time 0.903 (0.834) Data 0.001 (0.003) Loss 2.7209 (2.6186) Prec@1 33.750 (36.583) Prec@5 63.750 (67.248) Epoch: [13][8920/11272] Time 0.734 (0.834) Data 0.001 (0.003) Loss 2.3868 (2.6185) Prec@1 39.375 (36.582) Prec@5 71.875 (67.249) Epoch: [13][8930/11272] Time 0.747 (0.834) Data 0.001 (0.003) Loss 2.3574 (2.6185) Prec@1 39.375 (36.582) Prec@5 70.625 (67.250) Epoch: [13][8940/11272] Time 0.895 (0.834) Data 0.002 (0.003) Loss 2.6706 (2.6185) Prec@1 38.125 (36.582) Prec@5 68.750 (67.251) Epoch: [13][8950/11272] Time 0.869 (0.834) Data 0.002 (0.003) Loss 2.5342 (2.6184) Prec@1 40.000 (36.583) Prec@5 68.750 (67.251) Epoch: [13][8960/11272] Time 0.805 (0.834) Data 0.002 (0.003) Loss 2.7569 (2.6185) Prec@1 31.875 (36.584) Prec@5 60.625 (67.250) Epoch: [13][8970/11272] Time 0.738 (0.834) Data 0.001 (0.003) Loss 2.7411 (2.6185) Prec@1 36.875 (36.582) Prec@5 66.250 (67.252) Epoch: [13][8980/11272] Time 0.852 (0.834) Data 0.001 (0.003) Loss 2.6620 (2.6186) Prec@1 35.625 (36.579) Prec@5 65.000 (67.250) Epoch: [13][8990/11272] Time 0.729 (0.834) Data 0.001 (0.003) Loss 2.5128 (2.6185) Prec@1 40.000 (36.580) Prec@5 69.375 (67.252) Epoch: [13][9000/11272] Time 0.820 (0.834) Data 0.002 (0.003) Loss 2.6391 (2.6186) Prec@1 36.875 (36.579) Prec@5 64.375 (67.250) Epoch: [13][9010/11272] Time 0.910 (0.834) Data 0.001 (0.003) Loss 2.6434 (2.6185) Prec@1 38.750 (36.580) Prec@5 66.875 (67.250) Epoch: [13][9020/11272] Time 0.938 (0.834) Data 0.002 (0.003) Loss 2.6370 (2.6185) Prec@1 37.500 (36.581) Prec@5 68.750 (67.250) Epoch: [13][9030/11272] Time 0.757 (0.834) Data 0.001 (0.003) Loss 2.5193 (2.6185) Prec@1 40.625 (36.582) Prec@5 68.750 (67.252) Epoch: [13][9040/11272] Time 0.788 (0.834) Data 0.002 (0.003) Loss 2.4981 (2.6185) Prec@1 35.000 (36.586) Prec@5 68.125 (67.252) Epoch: [13][9050/11272] Time 0.889 (0.834) Data 0.001 (0.003) Loss 2.4554 (2.6185) Prec@1 40.000 (36.585) Prec@5 68.750 (67.253) Epoch: [13][9060/11272] Time 0.855 (0.834) Data 0.002 (0.003) Loss 2.4569 (2.6185) Prec@1 43.125 (36.584) Prec@5 72.500 (67.253) Epoch: [13][9070/11272] Time 0.781 (0.834) Data 0.002 (0.003) Loss 2.4360 (2.6186) Prec@1 42.500 (36.581) Prec@5 71.250 (67.252) Epoch: [13][9080/11272] Time 0.755 (0.834) Data 0.002 (0.003) Loss 2.7533 (2.6186) Prec@1 32.500 (36.580) Prec@5 65.000 (67.251) Epoch: [13][9090/11272] Time 0.883 (0.834) Data 0.002 (0.003) Loss 2.6789 (2.6187) Prec@1 39.375 (36.578) Prec@5 68.125 (67.250) Epoch: [13][9100/11272] Time 0.887 (0.834) Data 0.001 (0.003) Loss 2.5813 (2.6187) Prec@1 40.625 (36.580) Prec@5 66.875 (67.249) Epoch: [13][9110/11272] Time 0.768 (0.834) Data 0.001 (0.003) Loss 2.7071 (2.6188) Prec@1 35.625 (36.578) Prec@5 68.125 (67.249) Epoch: [13][9120/11272] Time 0.949 (0.834) Data 0.002 (0.003) Loss 2.3213 (2.6187) Prec@1 45.625 (36.579) Prec@5 75.000 (67.250) Epoch: [13][9130/11272] Time 0.978 (0.834) Data 0.001 (0.003) Loss 2.7502 (2.6188) Prec@1 36.250 (36.579) Prec@5 63.750 (67.249) Epoch: [13][9140/11272] Time 0.740 (0.834) Data 0.001 (0.003) Loss 2.4249 (2.6187) Prec@1 38.750 (36.578) Prec@5 68.125 (67.250) Epoch: [13][9150/11272] Time 0.820 (0.834) Data 0.002 (0.003) Loss 2.4575 (2.6187) Prec@1 43.750 (36.577) Prec@5 71.250 (67.250) Epoch: [13][9160/11272] Time 0.883 (0.834) Data 0.001 (0.003) Loss 2.6495 (2.6187) Prec@1 35.000 (36.576) Prec@5 65.625 (67.251) Epoch: [13][9170/11272] Time 0.888 (0.834) Data 0.001 (0.003) Loss 2.7869 (2.6187) Prec@1 34.375 (36.576) Prec@5 66.250 (67.252) Epoch: [13][9180/11272] Time 0.769 (0.834) Data 0.002 (0.003) Loss 2.5872 (2.6187) Prec@1 38.750 (36.577) Prec@5 68.750 (67.253) Epoch: [13][9190/11272] Time 0.735 (0.834) Data 0.002 (0.003) Loss 2.7082 (2.6187) Prec@1 38.125 (36.577) Prec@5 66.875 (67.254) Epoch: [13][9200/11272] Time 0.912 (0.834) Data 0.002 (0.003) Loss 2.6321 (2.6188) Prec@1 38.750 (36.575) Prec@5 69.375 (67.254) Epoch: [13][9210/11272] Time 0.880 (0.834) Data 0.001 (0.003) Loss 2.6135 (2.6187) Prec@1 36.250 (36.577) Prec@5 66.250 (67.255) Epoch: [13][9220/11272] Time 0.783 (0.834) Data 0.002 (0.003) Loss 2.5646 (2.6187) Prec@1 41.875 (36.578) Prec@5 71.875 (67.255) Epoch: [13][9230/11272] Time 0.777 (0.834) Data 0.002 (0.003) Loss 2.6937 (2.6186) Prec@1 34.375 (36.579) Prec@5 65.625 (67.255) Epoch: [13][9240/11272] Time 0.870 (0.834) Data 0.001 (0.003) Loss 2.6059 (2.6187) Prec@1 35.000 (36.577) Prec@5 69.375 (67.253) Epoch: [13][9250/11272] Time 0.791 (0.834) Data 0.003 (0.003) Loss 2.6333 (2.6187) Prec@1 36.250 (36.577) Prec@5 66.250 (67.252) Epoch: [13][9260/11272] Time 0.779 (0.834) Data 0.001 (0.003) Loss 2.5766 (2.6188) Prec@1 39.375 (36.577) Prec@5 66.875 (67.252) Epoch: [13][9270/11272] Time 0.900 (0.834) Data 0.002 (0.003) Loss 2.4260 (2.6188) Prec@1 37.500 (36.577) Prec@5 70.625 (67.253) Epoch: [13][9280/11272] Time 0.875 (0.834) Data 0.002 (0.003) Loss 2.7101 (2.6188) Prec@1 35.625 (36.578) Prec@5 67.500 (67.253) Epoch: [13][9290/11272] Time 0.743 (0.834) Data 0.001 (0.003) Loss 2.7165 (2.6188) Prec@1 39.375 (36.577) Prec@5 64.375 (67.253) Epoch: [13][9300/11272] Time 0.760 (0.834) Data 0.002 (0.003) Loss 2.5886 (2.6188) Prec@1 33.750 (36.577) Prec@5 72.500 (67.255) Epoch: [13][9310/11272] Time 0.909 (0.834) Data 0.002 (0.003) Loss 2.4810 (2.6188) Prec@1 44.375 (36.577) Prec@5 69.375 (67.255) Epoch: [13][9320/11272] Time 0.888 (0.834) Data 0.001 (0.003) Loss 2.6630 (2.6188) Prec@1 42.500 (36.578) Prec@5 67.500 (67.256) Epoch: [13][9330/11272] Time 0.750 (0.834) Data 0.001 (0.003) Loss 2.7924 (2.6188) Prec@1 32.500 (36.579) Prec@5 60.625 (67.255) Epoch: [13][9340/11272] Time 0.758 (0.834) Data 0.002 (0.003) Loss 2.6512 (2.6189) Prec@1 35.000 (36.578) Prec@5 68.125 (67.256) Epoch: [13][9350/11272] Time 0.910 (0.834) Data 0.002 (0.003) Loss 2.6600 (2.6189) Prec@1 36.875 (36.578) Prec@5 65.000 (67.255) Epoch: [13][9360/11272] Time 0.862 (0.834) Data 0.001 (0.003) Loss 2.7906 (2.6189) Prec@1 35.000 (36.578) Prec@5 61.875 (67.255) Epoch: [13][9370/11272] Time 0.789 (0.834) Data 0.002 (0.003) Loss 2.5273 (2.6187) Prec@1 38.125 (36.580) Prec@5 68.125 (67.258) Epoch: [13][9380/11272] Time 0.911 (0.834) Data 0.002 (0.003) Loss 2.6072 (2.6187) Prec@1 35.625 (36.582) Prec@5 68.750 (67.260) Epoch: [13][9390/11272] Time 0.847 (0.834) Data 0.001 (0.003) Loss 2.5046 (2.6186) Prec@1 39.375 (36.582) Prec@5 70.000 (67.261) Epoch: [13][9400/11272] Time 0.777 (0.834) Data 0.002 (0.003) Loss 2.5317 (2.6186) Prec@1 40.625 (36.581) Prec@5 63.125 (67.260) Epoch: [13][9410/11272] Time 0.763 (0.834) Data 0.002 (0.003) Loss 2.8849 (2.6187) Prec@1 33.750 (36.581) Prec@5 61.250 (67.257) Epoch: [13][9420/11272] Time 0.863 (0.834) Data 0.001 (0.003) Loss 2.8166 (2.6188) Prec@1 34.375 (36.578) Prec@5 60.625 (67.255) Epoch: [13][9430/11272] Time 0.943 (0.834) Data 0.002 (0.003) Loss 2.8239 (2.6189) Prec@1 35.000 (36.577) Prec@5 64.375 (67.256) Epoch: [13][9440/11272] Time 0.750 (0.834) Data 0.001 (0.003) Loss 2.6695 (2.6189) Prec@1 35.000 (36.577) Prec@5 66.250 (67.255) Epoch: [13][9450/11272] Time 0.739 (0.834) Data 0.001 (0.003) Loss 2.5506 (2.6189) Prec@1 38.125 (36.578) Prec@5 72.500 (67.257) Epoch: [13][9460/11272] Time 0.878 (0.834) Data 0.001 (0.003) Loss 2.6479 (2.6188) Prec@1 36.250 (36.577) Prec@5 66.250 (67.257) Epoch: [13][9470/11272] Time 0.881 (0.834) Data 0.001 (0.003) Loss 2.1761 (2.6188) Prec@1 43.750 (36.578) Prec@5 73.125 (67.259) Epoch: [13][9480/11272] Time 0.762 (0.834) Data 0.001 (0.003) Loss 2.4810 (2.6188) Prec@1 40.000 (36.577) Prec@5 70.625 (67.257) Epoch: [13][9490/11272] Time 0.759 (0.834) Data 0.002 (0.003) Loss 2.6496 (2.6189) Prec@1 40.625 (36.576) Prec@5 66.250 (67.256) Epoch: [13][9500/11272] Time 0.888 (0.834) Data 0.002 (0.003) Loss 2.8449 (2.6190) Prec@1 37.500 (36.575) Prec@5 63.750 (67.252) Epoch: [13][9510/11272] Time 0.786 (0.834) Data 0.004 (0.003) Loss 2.7292 (2.6190) Prec@1 36.875 (36.576) Prec@5 60.625 (67.254) Epoch: [13][9520/11272] Time 0.756 (0.834) Data 0.002 (0.003) Loss 2.3345 (2.6189) Prec@1 38.125 (36.575) Prec@5 69.375 (67.255) Epoch: [13][9530/11272] Time 0.899 (0.834) Data 0.001 (0.003) Loss 2.5495 (2.6189) Prec@1 38.125 (36.576) Prec@5 70.000 (67.255) Epoch: [13][9540/11272] Time 0.848 (0.834) Data 0.002 (0.003) Loss 2.6313 (2.6189) Prec@1 39.375 (36.575) Prec@5 68.750 (67.254) Epoch: [13][9550/11272] Time 0.790 (0.834) Data 0.002 (0.003) Loss 2.7544 (2.6189) Prec@1 37.500 (36.575) Prec@5 66.250 (67.254) Epoch: [13][9560/11272] Time 0.745 (0.834) Data 0.001 (0.003) Loss 2.5448 (2.6189) Prec@1 33.750 (36.575) Prec@5 67.500 (67.254) Epoch: [13][9570/11272] Time 0.849 (0.834) Data 0.001 (0.003) Loss 2.5607 (2.6190) Prec@1 40.000 (36.575) Prec@5 69.375 (67.254) Epoch: [13][9580/11272] Time 0.882 (0.834) Data 0.002 (0.003) Loss 2.5804 (2.6190) Prec@1 38.750 (36.574) Prec@5 67.500 (67.254) Epoch: [13][9590/11272] Time 0.799 (0.834) Data 0.001 (0.003) Loss 2.6321 (2.6189) Prec@1 34.375 (36.575) Prec@5 65.000 (67.255) Epoch: [13][9600/11272] Time 0.754 (0.834) Data 0.002 (0.003) Loss 2.4997 (2.6190) Prec@1 37.500 (36.576) Prec@5 69.375 (67.254) Epoch: [13][9610/11272] Time 0.904 (0.834) Data 0.001 (0.003) Loss 2.7542 (2.6190) Prec@1 36.250 (36.576) Prec@5 66.250 (67.253) Epoch: [13][9620/11272] Time 0.885 (0.834) Data 0.001 (0.003) Loss 2.6178 (2.6190) Prec@1 40.625 (36.576) Prec@5 64.375 (67.252) Epoch: [13][9630/11272] Time 0.755 (0.834) Data 0.002 (0.003) Loss 2.7060 (2.6189) Prec@1 29.375 (36.576) Prec@5 68.750 (67.253) Epoch: [13][9640/11272] Time 0.874 (0.834) Data 0.001 (0.003) Loss 2.1789 (2.6190) Prec@1 49.375 (36.576) Prec@5 74.375 (67.252) Epoch: [13][9650/11272] Time 0.887 (0.834) Data 0.002 (0.003) Loss 2.6359 (2.6189) Prec@1 40.625 (36.577) Prec@5 68.125 (67.252) Epoch: [13][9660/11272] Time 0.744 (0.834) Data 0.002 (0.003) Loss 2.5705 (2.6190) Prec@1 41.875 (36.577) Prec@5 66.875 (67.250) Epoch: [13][9670/11272] Time 0.746 (0.834) Data 0.002 (0.003) Loss 2.7418 (2.6190) Prec@1 36.875 (36.577) Prec@5 67.500 (67.250) Epoch: [13][9680/11272] Time 0.840 (0.834) Data 0.001 (0.003) Loss 2.7072 (2.6191) Prec@1 30.625 (36.576) Prec@5 68.125 (67.250) Epoch: [13][9690/11272] Time 0.848 (0.834) Data 0.001 (0.003) Loss 2.4001 (2.6190) Prec@1 39.375 (36.575) Prec@5 70.625 (67.250) Epoch: [13][9700/11272] Time 0.771 (0.833) Data 0.002 (0.003) Loss 2.4683 (2.6190) Prec@1 35.625 (36.575) Prec@5 69.375 (67.250) Epoch: [13][9710/11272] Time 0.781 (0.833) Data 0.002 (0.003) Loss 2.3412 (2.6190) Prec@1 48.125 (36.576) Prec@5 73.125 (67.251) Epoch: [13][9720/11272] Time 0.881 (0.833) Data 0.002 (0.003) Loss 2.5896 (2.6190) Prec@1 37.500 (36.577) Prec@5 69.375 (67.252) Epoch: [13][9730/11272] Time 0.892 (0.833) Data 0.001 (0.003) Loss 2.7456 (2.6189) Prec@1 33.750 (36.577) Prec@5 68.125 (67.253) Epoch: [13][9740/11272] Time 0.744 (0.833) Data 0.002 (0.003) Loss 2.5666 (2.6189) Prec@1 34.375 (36.576) Prec@5 65.000 (67.255) Epoch: [13][9750/11272] Time 0.779 (0.833) Data 0.002 (0.003) Loss 2.4529 (2.6188) Prec@1 41.250 (36.577) Prec@5 68.125 (67.257) Epoch: [13][9760/11272] Time 0.945 (0.833) Data 0.001 (0.003) Loss 2.8618 (2.6188) Prec@1 34.375 (36.579) Prec@5 58.125 (67.258) Epoch: [13][9770/11272] Time 0.858 (0.833) Data 0.002 (0.003) Loss 2.4142 (2.6188) Prec@1 40.625 (36.578) Prec@5 70.625 (67.258) Epoch: [13][9780/11272] Time 0.744 (0.833) Data 0.001 (0.003) Loss 2.5846 (2.6188) Prec@1 37.500 (36.578) Prec@5 71.875 (67.259) Epoch: [13][9790/11272] Time 0.897 (0.833) Data 0.002 (0.003) Loss 2.4704 (2.6188) Prec@1 40.000 (36.576) Prec@5 72.500 (67.259) Epoch: [13][9800/11272] Time 0.831 (0.833) Data 0.002 (0.002) Loss 2.5216 (2.6188) Prec@1 41.250 (36.575) Prec@5 70.000 (67.260) Epoch: [13][9810/11272] Time 0.758 (0.833) Data 0.002 (0.002) Loss 2.5337 (2.6187) Prec@1 33.125 (36.576) Prec@5 70.000 (67.261) Epoch: [13][9820/11272] Time 0.756 (0.833) Data 0.002 (0.002) Loss 2.2695 (2.6188) Prec@1 38.750 (36.575) Prec@5 76.250 (67.261) Epoch: [13][9830/11272] Time 0.888 (0.833) Data 0.002 (0.002) Loss 2.7294 (2.6187) Prec@1 31.250 (36.576) Prec@5 67.500 (67.263) Epoch: [13][9840/11272] Time 0.823 (0.833) Data 0.001 (0.002) Loss 2.6837 (2.6188) Prec@1 31.250 (36.572) Prec@5 61.875 (67.262) Epoch: [13][9850/11272] Time 0.744 (0.833) Data 0.001 (0.002) Loss 2.7815 (2.6187) Prec@1 34.375 (36.573) Prec@5 65.000 (67.264) Epoch: [13][9860/11272] Time 0.742 (0.833) Data 0.001 (0.002) Loss 2.7422 (2.6187) Prec@1 34.375 (36.573) Prec@5 63.125 (67.264) Epoch: [13][9870/11272] Time 0.909 (0.833) Data 0.002 (0.002) Loss 2.2624 (2.6187) Prec@1 45.000 (36.574) Prec@5 72.500 (67.265) Epoch: [13][9880/11272] Time 0.881 (0.833) Data 0.001 (0.002) Loss 2.4097 (2.6186) Prec@1 41.875 (36.576) Prec@5 76.250 (67.267) Epoch: [13][9890/11272] Time 0.769 (0.833) Data 0.002 (0.002) Loss 2.3616 (2.6186) Prec@1 42.500 (36.577) Prec@5 74.375 (67.267) Epoch: [13][9900/11272] Time 0.737 (0.833) Data 0.001 (0.002) Loss 2.5259 (2.6186) Prec@1 39.375 (36.577) Prec@5 73.750 (67.269) Epoch: [13][9910/11272] Time 0.878 (0.833) Data 0.002 (0.002) Loss 2.5561 (2.6185) Prec@1 33.750 (36.577) Prec@5 71.250 (67.271) Epoch: [13][9920/11272] Time 0.755 (0.833) Data 0.002 (0.002) Loss 2.3895 (2.6184) Prec@1 43.125 (36.579) Prec@5 73.125 (67.272) Epoch: [13][9930/11272] Time 0.749 (0.833) Data 0.001 (0.002) Loss 2.7628 (2.6185) Prec@1 34.375 (36.579) Prec@5 60.000 (67.270) Epoch: [13][9940/11272] Time 0.951 (0.833) Data 0.002 (0.002) Loss 2.6281 (2.6185) Prec@1 36.250 (36.580) Prec@5 68.125 (67.271) Epoch: [13][9950/11272] Time 0.891 (0.833) Data 0.002 (0.002) Loss 2.4642 (2.6184) Prec@1 40.625 (36.582) Prec@5 70.625 (67.271) Epoch: [13][9960/11272] Time 0.757 (0.833) Data 0.001 (0.002) Loss 2.6138 (2.6185) Prec@1 39.375 (36.583) Prec@5 68.125 (67.271) Epoch: [13][9970/11272] Time 0.734 (0.833) Data 0.002 (0.002) Loss 2.7224 (2.6185) Prec@1 31.250 (36.581) Prec@5 66.875 (67.271) Epoch: [13][9980/11272] Time 0.924 (0.833) Data 0.002 (0.002) Loss 2.5500 (2.6185) Prec@1 40.625 (36.583) Prec@5 71.250 (67.270) Epoch: [13][9990/11272] Time 0.840 (0.833) Data 0.002 (0.002) Loss 2.7339 (2.6186) Prec@1 32.500 (36.582) Prec@5 65.625 (67.269) Epoch: [13][10000/11272] Time 0.744 (0.833) Data 0.002 (0.002) Loss 2.9696 (2.6186) Prec@1 36.250 (36.583) Prec@5 61.875 (67.268) Epoch: [13][10010/11272] Time 0.745 (0.833) Data 0.001 (0.002) Loss 2.7219 (2.6186) Prec@1 31.875 (36.581) Prec@5 58.750 (67.268) Epoch: [13][10020/11272] Time 0.899 (0.833) Data 0.002 (0.002) Loss 2.7282 (2.6186) Prec@1 34.375 (36.581) Prec@5 63.750 (67.266) Epoch: [13][10030/11272] Time 0.884 (0.833) Data 0.002 (0.002) Loss 2.7605 (2.6186) Prec@1 38.125 (36.582) Prec@5 60.625 (67.265) Epoch: [13][10040/11272] Time 0.807 (0.833) Data 0.002 (0.002) Loss 2.6913 (2.6186) Prec@1 31.875 (36.583) Prec@5 68.125 (67.266) Epoch: [13][10050/11272] Time 0.843 (0.833) Data 0.001 (0.002) Loss 2.5318 (2.6187) Prec@1 36.250 (36.580) Prec@5 67.500 (67.265) Epoch: [13][10060/11272] Time 0.871 (0.833) Data 0.002 (0.002) Loss 2.6527 (2.6187) Prec@1 35.625 (36.582) Prec@5 63.750 (67.264) Epoch: [13][10070/11272] Time 0.773 (0.833) Data 0.001 (0.002) Loss 2.6062 (2.6186) Prec@1 33.125 (36.584) Prec@5 68.750 (67.266) Epoch: [13][10080/11272] Time 0.755 (0.833) Data 0.002 (0.002) Loss 2.7048 (2.6186) Prec@1 31.875 (36.583) Prec@5 67.500 (67.265) Epoch: [13][10090/11272] Time 0.875 (0.833) Data 0.001 (0.002) Loss 2.5548 (2.6186) Prec@1 41.250 (36.582) Prec@5 65.625 (67.265) Epoch: [13][10100/11272] Time 0.896 (0.833) Data 0.001 (0.002) Loss 2.6474 (2.6185) Prec@1 33.125 (36.581) Prec@5 68.125 (67.267) Epoch: [13][10110/11272] Time 0.738 (0.833) Data 0.001 (0.002) Loss 2.7156 (2.6186) Prec@1 36.250 (36.581) Prec@5 66.875 (67.266) Epoch: [13][10120/11272] Time 0.776 (0.833) Data 0.002 (0.002) Loss 2.9065 (2.6186) Prec@1 31.875 (36.581) Prec@5 61.875 (67.266) Epoch: [13][10130/11272] Time 0.912 (0.833) Data 0.001 (0.002) Loss 2.5935 (2.6186) Prec@1 35.625 (36.582) Prec@5 67.500 (67.267) Epoch: [13][10140/11272] Time 0.886 (0.833) Data 0.001 (0.002) Loss 2.5678 (2.6187) Prec@1 41.875 (36.581) Prec@5 65.000 (67.266) Epoch: [13][10150/11272] Time 0.759 (0.833) Data 0.001 (0.002) Loss 2.9197 (2.6187) Prec@1 31.875 (36.581) Prec@5 59.375 (67.265) Epoch: [13][10160/11272] Time 0.743 (0.833) Data 0.001 (0.002) Loss 2.7680 (2.6187) Prec@1 33.750 (36.581) Prec@5 65.000 (67.266) Epoch: [13][10170/11272] Time 0.888 (0.833) Data 0.002 (0.002) Loss 2.7252 (2.6188) Prec@1 35.000 (36.580) Prec@5 67.500 (67.264) Epoch: [13][10180/11272] Time 0.774 (0.833) Data 0.003 (0.002) Loss 2.5788 (2.6187) Prec@1 38.125 (36.582) Prec@5 65.625 (67.264) Epoch: [13][10190/11272] Time 0.760 (0.833) Data 0.002 (0.002) Loss 2.6873 (2.6187) Prec@1 39.375 (36.583) Prec@5 67.500 (67.264) Epoch: [13][10200/11272] Time 0.892 (0.833) Data 0.001 (0.002) Loss 2.6707 (2.6187) Prec@1 30.000 (36.582) Prec@5 68.125 (67.262) Epoch: [13][10210/11272] Time 0.911 (0.833) Data 0.002 (0.002) Loss 2.4258 (2.6187) Prec@1 34.375 (36.582) Prec@5 66.250 (67.262) Epoch: [13][10220/11272] Time 0.782 (0.833) Data 0.002 (0.002) Loss 2.6619 (2.6187) Prec@1 34.375 (36.580) Prec@5 68.125 (67.262) Epoch: [13][10230/11272] Time 0.767 (0.833) Data 0.002 (0.002) Loss 2.7832 (2.6187) Prec@1 29.375 (36.579) Prec@5 65.000 (67.261) Epoch: [13][10240/11272] Time 0.852 (0.833) Data 0.002 (0.002) Loss 2.7235 (2.6187) Prec@1 35.000 (36.579) Prec@5 65.000 (67.262) Epoch: [13][10250/11272] Time 0.853 (0.833) Data 0.001 (0.002) Loss 2.7212 (2.6187) Prec@1 36.250 (36.580) Prec@5 65.000 (67.262) Epoch: [13][10260/11272] Time 0.803 (0.833) Data 0.002 (0.002) Loss 2.6149 (2.6186) Prec@1 37.500 (36.579) Prec@5 64.375 (67.262) Epoch: [13][10270/11272] Time 0.767 (0.833) Data 0.002 (0.002) Loss 2.7035 (2.6186) Prec@1 34.375 (36.580) Prec@5 65.625 (67.262) Epoch: [13][10280/11272] Time 0.841 (0.833) Data 0.001 (0.002) Loss 2.7649 (2.6187) Prec@1 36.875 (36.579) Prec@5 65.000 (67.261) Epoch: [13][10290/11272] Time 0.861 (0.833) Data 0.002 (0.002) Loss 2.4995 (2.6187) Prec@1 38.125 (36.579) Prec@5 71.250 (67.259) Epoch: [13][10300/11272] Time 0.781 (0.833) Data 0.002 (0.002) Loss 2.6325 (2.6187) Prec@1 41.250 (36.580) Prec@5 64.375 (67.260) Epoch: [13][10310/11272] Time 0.855 (0.833) Data 0.002 (0.002) Loss 2.8456 (2.6187) Prec@1 31.250 (36.579) Prec@5 63.125 (67.259) Epoch: [13][10320/11272] Time 0.855 (0.833) Data 0.001 (0.002) Loss 2.2989 (2.6186) Prec@1 46.875 (36.581) Prec@5 71.875 (67.260) Epoch: [13][10330/11272] Time 0.784 (0.833) Data 0.001 (0.002) Loss 2.6397 (2.6186) Prec@1 38.125 (36.580) Prec@5 66.250 (67.262) Epoch: [13][10340/11272] Time 0.744 (0.833) Data 0.002 (0.002) Loss 2.8722 (2.6187) Prec@1 36.875 (36.580) Prec@5 61.250 (67.260) Epoch: [13][10350/11272] Time 0.893 (0.833) Data 0.002 (0.002) Loss 2.4602 (2.6186) Prec@1 40.000 (36.580) Prec@5 67.500 (67.261) Epoch: [13][10360/11272] Time 0.886 (0.833) Data 0.002 (0.002) Loss 2.6090 (2.6187) Prec@1 36.875 (36.580) Prec@5 68.125 (67.260) Epoch: [13][10370/11272] Time 0.812 (0.833) Data 0.002 (0.002) Loss 2.7618 (2.6187) Prec@1 31.250 (36.578) Prec@5 66.250 (67.259) Epoch: [13][10380/11272] Time 0.773 (0.833) Data 0.002 (0.002) Loss 2.8242 (2.6187) Prec@1 31.875 (36.575) Prec@5 62.500 (67.258) Epoch: [13][10390/11272] Time 0.852 (0.833) Data 0.001 (0.002) Loss 2.7722 (2.6187) Prec@1 30.625 (36.575) Prec@5 64.375 (67.258) Epoch: [13][10400/11272] Time 0.881 (0.833) Data 0.001 (0.002) Loss 2.8653 (2.6188) Prec@1 33.750 (36.573) Prec@5 61.875 (67.256) Epoch: [13][10410/11272] Time 0.782 (0.833) Data 0.002 (0.002) Loss 2.7035 (2.6189) Prec@1 36.875 (36.572) Prec@5 66.875 (67.254) Epoch: [13][10420/11272] Time 0.746 (0.833) Data 0.001 (0.002) Loss 2.6640 (2.6189) Prec@1 38.750 (36.573) Prec@5 62.500 (67.255) Epoch: [13][10430/11272] Time 0.944 (0.833) Data 0.002 (0.002) Loss 2.5505 (2.6189) Prec@1 36.875 (36.573) Prec@5 65.625 (67.256) Epoch: [13][10440/11272] Time 0.744 (0.833) Data 0.004 (0.002) Loss 2.6527 (2.6189) Prec@1 31.250 (36.572) Prec@5 66.250 (67.254) Epoch: [13][10450/11272] Time 0.743 (0.833) Data 0.002 (0.002) Loss 2.7576 (2.6190) Prec@1 35.000 (36.571) Prec@5 65.000 (67.253) Epoch: [13][10460/11272] Time 0.855 (0.833) Data 0.001 (0.002) Loss 2.6422 (2.6190) Prec@1 31.875 (36.570) Prec@5 65.000 (67.253) Epoch: [13][10470/11272] Time 0.888 (0.833) Data 0.002 (0.002) Loss 2.8827 (2.6190) Prec@1 29.375 (36.570) Prec@5 63.125 (67.252) Epoch: [13][10480/11272] Time 0.748 (0.833) Data 0.002 (0.002) Loss 2.6848 (2.6191) Prec@1 34.375 (36.567) Prec@5 67.500 (67.251) Epoch: [13][10490/11272] Time 0.756 (0.833) Data 0.001 (0.002) Loss 2.3422 (2.6190) Prec@1 43.750 (36.570) Prec@5 75.000 (67.254) Epoch: [13][10500/11272] Time 0.876 (0.833) Data 0.002 (0.002) Loss 2.6854 (2.6190) Prec@1 35.625 (36.568) Prec@5 61.250 (67.253) Epoch: [13][10510/11272] Time 0.848 (0.833) Data 0.001 (0.002) Loss 2.9304 (2.6190) Prec@1 31.250 (36.568) Prec@5 61.250 (67.253) Epoch: [13][10520/11272] Time 0.778 (0.833) Data 0.002 (0.002) Loss 2.4432 (2.6190) Prec@1 41.875 (36.569) Prec@5 71.875 (67.253) Epoch: [13][10530/11272] Time 0.774 (0.833) Data 0.002 (0.002) Loss 2.4393 (2.6190) Prec@1 40.000 (36.567) Prec@5 70.000 (67.252) Epoch: [13][10540/11272] Time 0.844 (0.833) Data 0.001 (0.002) Loss 2.5650 (2.6190) Prec@1 41.250 (36.567) Prec@5 67.500 (67.251) Epoch: [13][10550/11272] Time 0.886 (0.833) Data 0.001 (0.002) Loss 2.6711 (2.6191) Prec@1 31.875 (36.565) Prec@5 71.250 (67.249) Epoch: [13][10560/11272] Time 0.752 (0.832) Data 0.002 (0.002) Loss 2.5851 (2.6191) Prec@1 32.500 (36.565) Prec@5 68.125 (67.249) Epoch: [13][10570/11272] Time 0.908 (0.833) Data 0.002 (0.002) Loss 2.6549 (2.6191) Prec@1 33.750 (36.567) Prec@5 69.375 (67.251) Epoch: [13][10580/11272] Time 0.912 (0.833) Data 0.002 (0.002) Loss 2.7121 (2.6191) Prec@1 33.750 (36.565) Prec@5 65.625 (67.250) Epoch: [13][10590/11272] Time 0.739 (0.832) Data 0.001 (0.002) Loss 2.7964 (2.6192) Prec@1 38.125 (36.566) Prec@5 62.500 (67.250) Epoch: [13][10600/11272] Time 0.794 (0.832) Data 0.002 (0.002) Loss 2.6525 (2.6191) Prec@1 36.250 (36.566) Prec@5 68.750 (67.252) Epoch: [13][10610/11272] Time 0.892 (0.832) Data 0.001 (0.002) Loss 2.3116 (2.6191) Prec@1 40.625 (36.565) Prec@5 71.250 (67.251) Epoch: [13][10620/11272] Time 0.841 (0.832) Data 0.001 (0.002) Loss 2.4559 (2.6191) Prec@1 45.625 (36.567) Prec@5 72.500 (67.252) Epoch: [13][10630/11272] Time 0.769 (0.832) Data 0.001 (0.002) Loss 2.9028 (2.6191) Prec@1 30.625 (36.567) Prec@5 62.500 (67.252) Epoch: [13][10640/11272] Time 0.751 (0.832) Data 0.001 (0.002) Loss 2.5927 (2.6191) Prec@1 41.250 (36.566) Prec@5 66.875 (67.251) Epoch: [13][10650/11272] Time 0.896 (0.832) Data 0.002 (0.002) Loss 2.5330 (2.6191) Prec@1 36.250 (36.566) Prec@5 71.875 (67.252) Epoch: [13][10660/11272] Time 0.946 (0.832) Data 0.002 (0.002) Loss 2.6226 (2.6191) Prec@1 40.625 (36.567) Prec@5 66.875 (67.254) Epoch: [13][10670/11272] Time 0.757 (0.832) Data 0.001 (0.002) Loss 2.5967 (2.6191) Prec@1 35.000 (36.566) Prec@5 72.500 (67.253) Epoch: [13][10680/11272] Time 0.751 (0.832) Data 0.001 (0.002) Loss 2.8764 (2.6192) Prec@1 32.500 (36.566) Prec@5 63.750 (67.251) Epoch: [13][10690/11272] Time 0.890 (0.832) Data 0.001 (0.002) Loss 2.4389 (2.6192) Prec@1 40.625 (36.565) Prec@5 70.000 (67.250) Epoch: [13][10700/11272] Time 0.888 (0.832) Data 0.002 (0.002) Loss 2.7550 (2.6192) Prec@1 31.250 (36.567) Prec@5 61.875 (67.251) Epoch: [13][10710/11272] Time 0.795 (0.832) Data 0.003 (0.002) Loss 2.6844 (2.6191) Prec@1 39.375 (36.570) Prec@5 66.250 (67.254) Epoch: [13][10720/11272] Time 0.962 (0.832) Data 0.002 (0.002) Loss 2.5145 (2.6191) Prec@1 38.750 (36.571) Prec@5 70.000 (67.253) Epoch: [13][10730/11272] Time 0.871 (0.832) Data 0.001 (0.002) Loss 2.4336 (2.6191) Prec@1 36.250 (36.570) Prec@5 71.875 (67.253) Epoch: [13][10740/11272] Time 0.763 (0.832) Data 0.002 (0.002) Loss 2.5076 (2.6191) Prec@1 36.250 (36.570) Prec@5 68.750 (67.253) Epoch: [13][10750/11272] Time 0.756 (0.832) Data 0.002 (0.002) Loss 2.6340 (2.6191) Prec@1 35.000 (36.571) Prec@5 63.125 (67.252) Epoch: [13][10760/11272] Time 0.870 (0.832) Data 0.001 (0.002) Loss 2.9318 (2.6191) Prec@1 28.125 (36.572) Prec@5 60.000 (67.250) Epoch: [13][10770/11272] Time 0.838 (0.832) Data 0.001 (0.002) Loss 2.8301 (2.6191) Prec@1 32.500 (36.571) Prec@5 59.375 (67.250) Epoch: [13][10780/11272] Time 0.743 (0.832) Data 0.001 (0.002) Loss 2.6759 (2.6191) Prec@1 28.750 (36.570) Prec@5 68.125 (67.250) Epoch: [13][10790/11272] Time 0.741 (0.832) Data 0.001 (0.002) Loss 2.9028 (2.6190) Prec@1 31.875 (36.571) Prec@5 58.750 (67.251) Epoch: [13][10800/11272] Time 0.947 (0.832) Data 0.002 (0.002) Loss 2.6101 (2.6190) Prec@1 36.250 (36.571) Prec@5 67.500 (67.251) Epoch: [13][10810/11272] Time 0.877 (0.832) Data 0.001 (0.002) Loss 2.6740 (2.6190) Prec@1 41.250 (36.572) Prec@5 68.750 (67.250) Epoch: [13][10820/11272] Time 0.761 (0.832) Data 0.002 (0.002) Loss 2.6673 (2.6190) Prec@1 38.125 (36.573) Prec@5 66.250 (67.250) Epoch: [13][10830/11272] Time 0.784 (0.832) Data 0.001 (0.002) Loss 2.7977 (2.6190) Prec@1 34.375 (36.572) Prec@5 66.250 (67.249) Epoch: [13][10840/11272] Time 0.899 (0.832) Data 0.002 (0.002) Loss 2.8158 (2.6191) Prec@1 38.125 (36.570) Prec@5 65.000 (67.247) Epoch: [13][10850/11272] Time 0.808 (0.832) Data 0.001 (0.002) Loss 2.6753 (2.6190) Prec@1 36.875 (36.572) Prec@5 67.500 (67.248) Epoch: [13][10860/11272] Time 0.793 (0.832) Data 0.002 (0.002) Loss 2.6053 (2.6190) Prec@1 36.875 (36.574) Prec@5 65.000 (67.248) Epoch: [13][10870/11272] Time 0.928 (0.832) Data 0.002 (0.002) Loss 2.6930 (2.6189) Prec@1 33.750 (36.574) Prec@5 67.500 (67.249) Epoch: [13][10880/11272] Time 0.856 (0.832) Data 0.002 (0.002) Loss 2.7236 (2.6189) Prec@1 34.375 (36.573) Prec@5 63.125 (67.249) Epoch: [13][10890/11272] Time 0.738 (0.832) Data 0.002 (0.002) Loss 2.6464 (2.6189) Prec@1 35.000 (36.573) Prec@5 66.875 (67.248) Epoch: [13][10900/11272] Time 0.787 (0.832) Data 0.002 (0.002) Loss 2.6873 (2.6190) Prec@1 33.125 (36.572) Prec@5 65.625 (67.247) Epoch: [13][10910/11272] Time 0.893 (0.832) Data 0.002 (0.002) Loss 2.5162 (2.6190) Prec@1 38.750 (36.572) Prec@5 65.625 (67.246) Epoch: [13][10920/11272] Time 0.861 (0.832) Data 0.002 (0.002) Loss 2.9737 (2.6190) Prec@1 26.875 (36.572) Prec@5 59.375 (67.248) Epoch: [13][10930/11272] Time 0.753 (0.832) Data 0.002 (0.002) Loss 2.6602 (2.6190) Prec@1 39.375 (36.573) Prec@5 63.750 (67.246) Epoch: [13][10940/11272] Time 0.745 (0.832) Data 0.001 (0.002) Loss 2.6042 (2.6190) Prec@1 31.875 (36.573) Prec@5 61.250 (67.245) Epoch: [13][10950/11272] Time 0.900 (0.832) Data 0.002 (0.002) Loss 2.7051 (2.6190) Prec@1 33.125 (36.574) Prec@5 66.875 (67.245) Epoch: [13][10960/11272] Time 0.873 (0.832) Data 0.001 (0.002) Loss 2.7644 (2.6190) Prec@1 31.875 (36.572) Prec@5 60.000 (67.244) Epoch: [13][10970/11272] Time 0.811 (0.832) Data 0.002 (0.002) Loss 3.0692 (2.6191) Prec@1 33.125 (36.571) Prec@5 56.250 (67.243) Epoch: [13][10980/11272] Time 0.857 (0.832) Data 0.001 (0.002) Loss 2.8809 (2.6191) Prec@1 30.000 (36.570) Prec@5 61.875 (67.243) Epoch: [13][10990/11272] Time 0.895 (0.832) Data 0.002 (0.002) Loss 2.6696 (2.6191) Prec@1 38.750 (36.571) Prec@5 69.375 (67.245) Epoch: [13][11000/11272] Time 0.770 (0.832) Data 0.002 (0.002) Loss 2.7073 (2.6191) Prec@1 38.125 (36.570) Prec@5 66.250 (67.245) Epoch: [13][11010/11272] Time 0.748 (0.832) Data 0.003 (0.002) Loss 2.6844 (2.6192) Prec@1 33.750 (36.568) Prec@5 65.625 (67.243) Epoch: [13][11020/11272] Time 0.940 (0.832) Data 0.002 (0.002) Loss 2.9097 (2.6192) Prec@1 27.500 (36.568) Prec@5 60.625 (67.243) Epoch: [13][11030/11272] Time 0.921 (0.832) Data 0.001 (0.002) Loss 2.6928 (2.6192) Prec@1 41.250 (36.569) Prec@5 66.250 (67.243) Epoch: [13][11040/11272] Time 0.763 (0.832) Data 0.001 (0.002) Loss 2.3251 (2.6193) Prec@1 45.000 (36.569) Prec@5 73.125 (67.242) Epoch: [13][11050/11272] Time 0.768 (0.832) Data 0.001 (0.002) Loss 2.4984 (2.6192) Prec@1 39.375 (36.568) Prec@5 69.375 (67.243) Epoch: [13][11060/11272] Time 0.927 (0.832) Data 0.002 (0.002) Loss 2.4272 (2.6191) Prec@1 40.625 (36.570) Prec@5 72.500 (67.246) Epoch: [13][11070/11272] Time 0.856 (0.832) Data 0.002 (0.002) Loss 2.9424 (2.6192) Prec@1 29.375 (36.570) Prec@5 60.625 (67.244) Epoch: [13][11080/11272] Time 0.750 (0.832) Data 0.001 (0.002) Loss 2.7114 (2.6192) Prec@1 36.250 (36.570) Prec@5 58.750 (67.243) Epoch: [13][11090/11272] Time 0.783 (0.832) Data 0.002 (0.002) Loss 2.6325 (2.6192) Prec@1 34.375 (36.569) Prec@5 65.000 (67.242) Epoch: [13][11100/11272] Time 0.840 (0.832) Data 0.002 (0.002) Loss 2.7549 (2.6193) Prec@1 29.375 (36.569) Prec@5 62.500 (67.241) Epoch: [13][11110/11272] Time 0.745 (0.833) Data 0.003 (0.002) Loss 2.3861 (2.6192) Prec@1 45.625 (36.569) Prec@5 70.000 (67.241) Epoch: [13][11120/11272] Time 0.796 (0.833) Data 0.002 (0.002) Loss 2.7592 (2.6193) Prec@1 31.250 (36.569) Prec@5 66.250 (67.241) Epoch: [13][11130/11272] Time 0.872 (0.833) Data 0.001 (0.002) Loss 2.5300 (2.6193) Prec@1 38.125 (36.566) Prec@5 68.750 (67.241) Epoch: [13][11140/11272] Time 0.932 (0.833) Data 0.002 (0.002) Loss 2.3825 (2.6193) Prec@1 43.750 (36.568) Prec@5 71.250 (67.241) Epoch: [13][11150/11272] Time 0.732 (0.832) Data 0.001 (0.002) Loss 2.6566 (2.6193) Prec@1 36.875 (36.568) Prec@5 69.375 (67.242) Epoch: [13][11160/11272] Time 0.737 (0.832) Data 0.001 (0.002) Loss 2.7538 (2.6193) Prec@1 35.625 (36.567) Prec@5 61.250 (67.241) Epoch: [13][11170/11272] Time 0.894 (0.832) Data 0.002 (0.002) Loss 2.3686 (2.6192) Prec@1 44.375 (36.568) Prec@5 74.375 (67.243) Epoch: [13][11180/11272] Time 0.912 (0.832) Data 0.002 (0.002) Loss 2.6052 (2.6192) Prec@1 38.750 (36.569) Prec@5 68.750 (67.242) Epoch: [13][11190/11272] Time 0.753 (0.832) Data 0.002 (0.002) Loss 2.5669 (2.6193) Prec@1 36.250 (36.569) Prec@5 71.250 (67.242) Epoch: [13][11200/11272] Time 0.786 (0.832) Data 0.001 (0.002) Loss 2.7290 (2.6192) Prec@1 30.625 (36.569) Prec@5 65.625 (67.244) Epoch: [13][11210/11272] Time 0.939 (0.832) Data 0.002 (0.002) Loss 2.8661 (2.6192) Prec@1 35.625 (36.570) Prec@5 63.750 (67.245) Epoch: [13][11220/11272] Time 0.945 (0.832) Data 0.001 (0.002) Loss 2.6549 (2.6192) Prec@1 43.750 (36.570) Prec@5 66.250 (67.245) Epoch: [13][11230/11272] Time 0.772 (0.832) Data 0.001 (0.002) Loss 2.4443 (2.6192) Prec@1 38.750 (36.570) Prec@5 68.750 (67.246) Epoch: [13][11240/11272] Time 0.879 (0.832) Data 0.001 (0.002) Loss 2.7465 (2.6192) Prec@1 33.125 (36.569) Prec@5 62.500 (67.245) Epoch: [13][11250/11272] Time 0.854 (0.832) Data 0.002 (0.002) Loss 2.7010 (2.6193) Prec@1 31.875 (36.566) Prec@5 65.000 (67.243) Epoch: [13][11260/11272] Time 0.750 (0.832) Data 0.002 (0.002) Loss 2.8092 (2.6192) Prec@1 30.000 (36.565) Prec@5 60.625 (67.243) Epoch: [13][11270/11272] Time 0.771 (0.832) Data 0.000 (0.002) Loss 2.7664 (2.6193) Prec@1 33.750 (36.564) Prec@5 64.375 (67.244) Test: [0/229] Time 4.471 (4.471) Loss 1.1072 (1.1072) Prec@1 66.250 (66.250) Prec@5 94.375 (94.375) Test: [10/229] Time 0.518 (0.789) Loss 1.2938 (1.9178) Prec@1 62.500 (50.341) Prec@5 91.875 (82.102) Test: [20/229] Time 0.340 (0.605) Loss 2.6657 (2.2628) Prec@1 36.250 (44.464) Prec@5 67.500 (74.792) Test: [30/229] Time 0.360 (0.539) Loss 2.0977 (2.1206) Prec@1 44.375 (47.077) Prec@5 77.500 (77.036) Test: [40/229] Time 0.418 (0.502) Loss 0.7468 (2.1566) Prec@1 84.375 (46.021) Prec@5 91.250 (76.448) Test: [50/229] Time 0.355 (0.481) Loss 3.1898 (2.2129) Prec@1 21.875 (44.718) Prec@5 56.250 (75.159) Test: [60/229] Time 0.442 (0.467) Loss 3.1902 (2.2194) Prec@1 19.375 (44.416) Prec@5 54.375 (74.877) Test: [70/229] Time 0.385 (0.455) Loss 2.4234 (2.2388) Prec@1 42.500 (43.671) Prec@5 68.125 (74.771) Test: [80/229] Time 0.354 (0.447) Loss 3.1144 (2.2802) Prec@1 21.875 (42.338) Prec@5 56.875 (74.375) Test: [90/229] Time 0.426 (0.441) Loss 1.8893 (2.2722) Prec@1 59.375 (42.562) Prec@5 77.500 (74.650) Test: [100/229] Time 0.360 (0.437) Loss 1.9311 (2.2503) Prec@1 55.625 (43.298) Prec@5 82.500 (75.043) Test: [110/229] Time 0.478 (0.434) Loss 1.9639 (2.2310) Prec@1 41.875 (43.592) Prec@5 82.500 (75.552) Test: [120/229] Time 0.344 (0.430) Loss 3.0337 (2.2543) Prec@1 28.125 (42.898) Prec@5 71.250 (75.372) Test: [130/229] Time 0.385 (0.427) Loss 1.7989 (2.2363) Prec@1 57.500 (43.306) Prec@5 79.375 (75.687) Test: [140/229] Time 0.385 (0.424) Loss 2.3265 (2.2506) Prec@1 37.500 (42.886) Prec@5 70.625 (75.443) Test: [150/229] Time 0.417 (0.423) Loss 2.2389 (2.2837) Prec@1 50.000 (42.148) Prec@5 71.250 (74.884) Test: [160/229] Time 0.378 (0.420) Loss 2.1346 (2.2866) Prec@1 47.500 (42.116) Prec@5 81.250 (74.810) Test: [170/229] Time 0.382 (0.418) Loss 2.5953 (2.3044) Prec@1 36.250 (41.700) Prec@5 76.875 (74.466) Test: [180/229] Time 0.404 (0.417) Loss 3.3117 (2.3109) Prec@1 25.000 (41.775) Prec@5 51.250 (74.292) Test: [190/229] Time 0.348 (0.415) Loss 2.1361 (2.3016) Prec@1 32.500 (41.950) Prec@5 85.000 (74.493) Test: [200/229] Time 0.488 (0.415) Loss 2.3800 (2.2884) Prec@1 40.000 (42.118) Prec@5 73.125 (74.848) Test: [210/229] Time 0.308 (0.414) Loss 1.9864 (2.2813) Prec@1 34.375 (42.222) Prec@5 86.875 (75.083) Test: [220/229] Time 0.369 (0.413) Loss 2.1013 (2.2712) Prec@1 49.375 (42.565) Prec@5 76.875 (75.175) * Prec@1 42.986 Prec@5 75.357 Epoch: [14][0/11272] Time 3.190 (3.190) Data 2.246 (2.246) Loss 2.6923 (2.6923) Prec@1 35.000 (35.000) Prec@5 63.750 (63.750) Epoch: [14][10/11272] Time 0.768 (1.053) Data 0.002 (0.206) Loss 2.7968 (2.5671) Prec@1 35.625 (38.295) Prec@5 59.375 (68.182) Epoch: [14][20/11272] Time 0.863 (0.948) Data 0.001 (0.109) Loss 2.5956 (2.5776) Prec@1 35.625 (37.262) Prec@5 69.375 (68.363) Epoch: [14][30/11272] Time 0.876 (0.902) Data 0.002 (0.074) Loss 2.5793 (2.5900) Prec@1 38.750 (37.339) Prec@5 70.000 (68.185) Epoch: [14][40/11272] Time 0.780 (0.884) Data 0.002 (0.056) Loss 2.8623 (2.6087) Prec@1 32.500 (36.768) Prec@5 62.500 (67.729) Epoch: [14][50/11272] Time 0.924 (0.875) Data 0.002 (0.046) Loss 2.5901 (2.6099) Prec@1 45.000 (37.047) Prec@5 66.250 (67.623) Epoch: [14][60/11272] Time 0.839 (0.866) Data 0.002 (0.038) Loss 2.8367 (2.6247) Prec@1 32.500 (36.578) Prec@5 61.250 (67.398) Epoch: [14][70/11272] Time 0.819 (0.861) Data 0.002 (0.033) Loss 2.5654 (2.6171) Prec@1 34.375 (36.452) Prec@5 66.875 (67.720) Epoch: [14][80/11272] Time 0.830 (0.859) Data 0.001 (0.029) Loss 2.5997 (2.6109) Prec@1 39.375 (36.597) Prec@5 68.125 (67.863) Epoch: [14][90/11272] Time 0.914 (0.858) Data 0.001 (0.026) Loss 2.6229 (2.6086) Prec@1 31.875 (36.655) Prec@5 69.375 (67.754) Epoch: [14][100/11272] Time 0.845 (0.855) Data 0.001 (0.024) Loss 2.6674 (2.6042) Prec@1 33.125 (36.584) Prec@5 66.875 (67.785) Epoch: [14][110/11272] Time 0.781 (0.853) Data 0.002 (0.022) Loss 2.5486 (2.6048) Prec@1 38.125 (36.526) Prec@5 67.500 (67.708) Epoch: [14][120/11272] Time 0.781 (0.851) Data 0.001 (0.020) Loss 2.5807 (2.6055) Prec@1 40.000 (36.601) Prec@5 63.750 (67.645) Epoch: [14][130/11272] Time 0.883 (0.850) Data 0.002 (0.019) Loss 2.8432 (2.6149) Prec@1 31.250 (36.498) Prec@5 61.250 (67.428) Epoch: [14][140/11272] Time 0.881 (0.850) Data 0.002 (0.018) Loss 2.8045 (2.6134) Prec@1 34.375 (36.512) Prec@5 62.500 (67.371) Epoch: [14][150/11272] Time 0.812 (0.849) Data 0.001 (0.017) Loss 2.4359 (2.6112) Prec@1 40.000 (36.635) Prec@5 70.000 (67.442) Epoch: [14][160/11272] Time 0.776 (0.848) Data 0.004 (0.016) Loss 2.3655 (2.6045) Prec@1 41.250 (36.724) Prec@5 71.875 (67.597) Epoch: [14][170/11272] Time 0.914 (0.848) Data 0.002 (0.015) Loss 2.4111 (2.6069) Prec@1 44.375 (36.758) Prec@5 70.000 (67.540) Epoch: [14][180/11272] Time 0.792 (0.847) Data 0.003 (0.014) Loss 2.5951 (2.6075) Prec@1 35.000 (36.678) Prec@5 68.750 (67.503) Epoch: [14][190/11272] Time 0.802 (0.846) Data 0.001 (0.013) Loss 2.5111 (2.6075) Prec@1 36.875 (36.630) Prec@5 67.500 (67.457) Epoch: [14][200/11272] Time 0.903 (0.846) Data 0.002 (0.013) Loss 2.6299 (2.6067) Prec@1 35.000 (36.614) Prec@5 63.750 (67.491) Epoch: [14][210/11272] Time 0.862 (0.846) Data 0.002 (0.012) Loss 2.9451 (2.6090) Prec@1 35.625 (36.605) Prec@5 58.125 (67.438) Epoch: [14][220/11272] Time 0.773 (0.845) Data 0.002 (0.012) Loss 2.4237 (2.6092) Prec@1 42.500 (36.654) Prec@5 70.000 (67.398) Epoch: [14][230/11272] Time 0.761 (0.846) Data 0.002 (0.011) Loss 2.8227 (2.6088) Prec@1 32.500 (36.596) Prec@5 57.500 (67.422) Epoch: [14][240/11272] Time 0.906 (0.845) Data 0.002 (0.011) Loss 2.7464 (2.6097) Prec@1 31.875 (36.527) Prec@5 63.125 (67.414) Epoch: [14][250/11272] Time 0.864 (0.845) Data 0.002 (0.011) Loss 2.6786 (2.6081) Prec@1 29.375 (36.571) Prec@5 71.250 (67.490) Epoch: [14][260/11272] Time 0.775 (0.844) Data 0.002 (0.010) Loss 2.9097 (2.6086) Prec@1 31.875 (36.643) Prec@5 61.250 (67.431) Epoch: [14][270/11272] Time 0.769 (0.844) Data 0.002 (0.010) Loss 2.4283 (2.6109) Prec@1 38.125 (36.626) Prec@5 70.625 (67.389) Epoch: [14][280/11272] Time 0.870 (0.844) Data 0.002 (0.010) Loss 2.5830 (2.6144) Prec@1 35.000 (36.586) Prec@5 73.750 (67.324) Epoch: [14][290/11272] Time 0.859 (0.843) Data 0.002 (0.009) Loss 2.5263 (2.6117) Prec@1 31.875 (36.630) Prec@5 70.000 (67.354) Epoch: [14][300/11272] Time 0.790 (0.843) Data 0.002 (0.009) Loss 2.4439 (2.6101) Prec@1 37.500 (36.632) Prec@5 67.500 (67.390) Epoch: [14][310/11272] Time 0.876 (0.842) Data 0.001 (0.009) Loss 2.5075 (2.6107) Prec@1 41.875 (36.646) Prec@5 70.625 (67.408) Epoch: [14][320/11272] Time 0.914 (0.842) Data 0.001 (0.009) Loss 2.4246 (2.6093) Prec@1 40.625 (36.673) Prec@5 71.250 (67.451) Epoch: [14][330/11272] Time 0.792 (0.842) Data 0.002 (0.009) Loss 2.4203 (2.6088) Prec@1 36.250 (36.658) Prec@5 69.375 (67.449) Epoch: [14][340/11272] Time 0.803 (0.842) Data 0.001 (0.008) Loss 2.4543 (2.6087) Prec@1 41.875 (36.629) Prec@5 68.125 (67.434) Epoch: [14][350/11272] Time 0.924 (0.842) Data 0.002 (0.008) Loss 2.7626 (2.6082) Prec@1 32.500 (36.652) Prec@5 66.875 (67.441) Epoch: [14][360/11272] Time 0.864 (0.842) Data 0.002 (0.008) Loss 2.4213 (2.6091) Prec@1 40.625 (36.657) Prec@5 70.000 (67.422) Epoch: [14][370/11272] Time 0.775 (0.841) Data 0.002 (0.008) Loss 2.7268 (2.6102) Prec@1 36.250 (36.609) Prec@5 66.250 (67.417) Epoch: [14][380/11272] Time 0.790 (0.841) Data 0.005 (0.008) Loss 2.4657 (2.6107) Prec@1 37.500 (36.570) Prec@5 67.500 (67.392) Epoch: [14][390/11272] Time 0.973 (0.841) Data 0.002 (0.008) Loss 2.4043 (2.6104) Prec@1 38.750 (36.565) Prec@5 75.625 (67.396) Epoch: [14][400/11272] Time 0.886 (0.841) Data 0.002 (0.007) Loss 2.4189 (2.6091) Prec@1 43.125 (36.590) Prec@5 70.625 (67.417) Epoch: [14][410/11272] Time 0.779 (0.840) Data 0.002 (0.007) Loss 2.6246 (2.6090) Prec@1 36.250 (36.629) Prec@5 63.125 (67.415) Epoch: [14][420/11272] Time 0.776 (0.840) Data 0.002 (0.007) Loss 2.7994 (2.6096) Prec@1 38.750 (36.612) Prec@5 60.625 (67.399) Epoch: [14][430/11272] Time 0.943 (0.840) Data 0.002 (0.007) Loss 2.6081 (2.6089) Prec@1 36.250 (36.647) Prec@5 70.625 (67.441) Epoch: [14][440/11272] Time 0.882 (0.841) Data 0.002 (0.007) Loss 2.7988 (2.6077) Prec@1 26.875 (36.652) Prec@5 66.875 (67.469) Epoch: [14][450/11272] Time 0.761 (0.841) Data 0.002 (0.007) Loss 2.4208 (2.6077) Prec@1 37.500 (36.653) Prec@5 73.125 (67.493) Epoch: [14][460/11272] Time 0.892 (0.841) Data 0.001 (0.007) Loss 2.6340 (2.6093) Prec@1 36.875 (36.612) Prec@5 64.375 (67.447) Epoch: [14][470/11272] Time 0.819 (0.840) Data 0.001 (0.007) Loss 2.5190 (2.6094) Prec@1 36.875 (36.592) Prec@5 68.750 (67.452) Epoch: [14][480/11272] Time 0.751 (0.840) Data 0.003 (0.006) Loss 2.3831 (2.6098) Prec@1 38.125 (36.551) Prec@5 69.375 (67.466) Epoch: [14][490/11272] Time 0.783 (0.839) Data 0.001 (0.006) Loss 2.6082 (2.6094) Prec@1 35.000 (36.549) Prec@5 66.250 (67.472) Epoch: [14][500/11272] Time 0.875 (0.839) Data 0.001 (0.006) Loss 2.5891 (2.6092) Prec@1 35.000 (36.557) Prec@5 71.250 (67.489) Epoch: [14][510/11272] Time 0.884 (0.839) Data 0.002 (0.006) Loss 2.7863 (2.6106) Prec@1 35.625 (36.544) Prec@5 62.500 (67.457) Epoch: [14][520/11272] Time 0.757 (0.838) Data 0.002 (0.006) Loss 2.4260 (2.6086) Prec@1 39.375 (36.621) Prec@5 72.500 (67.493) Epoch: [14][530/11272] Time 0.765 (0.838) Data 0.002 (0.006) Loss 2.7347 (2.6074) Prec@1 34.375 (36.635) Prec@5 65.625 (67.515) Epoch: [14][540/11272] Time 0.930 (0.838) Data 0.001 (0.006) Loss 2.6368 (2.6065) Prec@1 33.125 (36.654) Prec@5 71.875 (67.524) Epoch: [14][550/11272] Time 0.870 (0.838) Data 0.001 (0.006) Loss 2.7950 (2.6088) Prec@1 33.125 (36.616) Prec@5 64.375 (67.507) Epoch: [14][560/11272] Time 0.793 (0.837) Data 0.002 (0.006) Loss 2.7077 (2.6086) Prec@1 38.750 (36.666) Prec@5 69.375 (67.487) Epoch: [14][570/11272] Time 0.766 (0.837) Data 0.002 (0.006) Loss 2.5763 (2.6089) Prec@1 36.250 (36.662) Prec@5 68.750 (67.491) Epoch: [14][580/11272] Time 0.911 (0.837) Data 0.002 (0.006) Loss 2.6311 (2.6087) Prec@1 33.750 (36.671) Prec@5 63.125 (67.457) Epoch: [14][590/11272] Time 0.734 (0.837) Data 0.001 (0.006) Loss 2.2861 (2.6079) Prec@1 38.750 (36.659) Prec@5 73.125 (67.469) Epoch: [14][600/11272] Time 0.742 (0.837) Data 0.002 (0.005) Loss 2.8415 (2.6087) Prec@1 31.250 (36.630) Prec@5 61.875 (67.429) Epoch: [14][610/11272] Time 0.883 (0.837) Data 0.001 (0.005) Loss 2.7864 (2.6086) Prec@1 33.125 (36.623) Prec@5 63.750 (67.440) Epoch: [14][620/11272] Time 0.879 (0.837) Data 0.002 (0.005) Loss 2.8519 (2.6094) Prec@1 31.250 (36.593) Prec@5 56.875 (67.408) Epoch: [14][630/11272] Time 0.787 (0.837) Data 0.002 (0.005) Loss 2.5813 (2.6105) Prec@1 41.250 (36.584) Prec@5 69.375 (67.397) Epoch: [14][640/11272] Time 0.737 (0.836) Data 0.001 (0.005) Loss 2.5971 (2.6106) Prec@1 35.000 (36.582) Prec@5 66.250 (67.395) Epoch: [14][650/11272] Time 0.964 (0.836) Data 0.002 (0.005) Loss 2.5965 (2.6118) Prec@1 40.625 (36.561) Prec@5 68.125 (67.377) Epoch: [14][660/11272] Time 0.939 (0.836) Data 0.002 (0.005) Loss 2.5786 (2.6114) Prec@1 35.625 (36.554) Prec@5 70.625 (67.384) Epoch: [14][670/11272] Time 0.805 (0.836) Data 0.002 (0.005) Loss 2.4599 (2.6112) Prec@1 42.500 (36.583) Prec@5 65.000 (67.374) Epoch: [14][680/11272] Time 0.798 (0.836) Data 0.002 (0.005) Loss 2.6143 (2.6117) Prec@1 36.875 (36.580) Prec@5 68.125 (67.361) Epoch: [14][690/11272] Time 0.881 (0.836) Data 0.002 (0.005) Loss 2.6576 (2.6118) Prec@1 38.750 (36.584) Prec@5 65.625 (67.351) Epoch: [14][700/11272] Time 0.851 (0.836) Data 0.001 (0.005) Loss 2.4591 (2.6112) Prec@1 35.000 (36.596) Prec@5 71.875 (67.371) Epoch: [14][710/11272] Time 0.764 (0.836) Data 0.002 (0.005) Loss 2.4111 (2.6097) Prec@1 39.375 (36.631) Prec@5 71.250 (67.404) Epoch: [14][720/11272] Time 0.851 (0.836) Data 0.001 (0.005) Loss 2.7207 (2.6098) Prec@1 29.375 (36.618) Prec@5 68.125 (67.404) Epoch: [14][730/11272] Time 0.857 (0.836) Data 0.002 (0.005) Loss 2.7930 (2.6100) Prec@1 32.500 (36.625) Prec@5 68.125 (67.399) Epoch: [14][740/11272] Time 0.751 (0.836) Data 0.002 (0.005) Loss 2.8112 (2.6103) Prec@1 28.125 (36.635) Prec@5 61.250 (67.395) Epoch: [14][750/11272] Time 0.819 (0.836) Data 0.002 (0.005) Loss 2.8128 (2.6100) Prec@1 32.500 (36.639) Prec@5 61.250 (67.398) Epoch: [14][760/11272] Time 0.913 (0.836) Data 0.002 (0.005) Loss 2.5972 (2.6092) Prec@1 36.875 (36.656) Prec@5 71.250 (67.413) Epoch: [14][770/11272] Time 0.873 (0.836) Data 0.002 (0.005) Loss 2.6932 (2.6093) Prec@1 32.500 (36.625) Prec@5 63.125 (67.398) Epoch: [14][780/11272] Time 0.783 (0.835) Data 0.002 (0.005) Loss 2.4446 (2.6075) Prec@1 43.750 (36.657) Prec@5 65.625 (67.411) Epoch: [14][790/11272] Time 0.815 (0.836) Data 0.003 (0.005) Loss 2.8570 (2.6077) Prec@1 29.375 (36.655) Prec@5 62.500 (67.394) Epoch: [14][800/11272] Time 0.877 (0.836) Data 0.001 (0.005) Loss 2.8667 (2.6088) Prec@1 31.250 (36.632) Prec@5 65.000 (67.381) Epoch: [14][810/11272] Time 0.856 (0.835) Data 0.001 (0.005) Loss 2.6780 (2.6075) Prec@1 35.000 (36.672) Prec@5 67.500 (67.385) Epoch: [14][820/11272] Time 0.755 (0.835) Data 0.001 (0.004) Loss 2.3607 (2.6074) Prec@1 43.125 (36.688) Prec@5 74.375 (67.392) Epoch: [14][830/11272] Time 0.756 (0.835) Data 0.002 (0.004) Loss 2.5114 (2.6073) Prec@1 35.625 (36.688) Prec@5 70.625 (67.392) Epoch: [14][840/11272] Time 0.941 (0.835) Data 0.002 (0.004) Loss 2.6930 (2.6080) Prec@1 36.875 (36.684) Prec@5 70.000 (67.375) Epoch: [14][850/11272] Time 0.758 (0.835) Data 0.003 (0.004) Loss 2.5973 (2.6075) Prec@1 43.750 (36.690) Prec@5 69.375 (67.385) Epoch: [14][860/11272] Time 0.795 (0.835) Data 0.002 (0.004) Loss 2.7478 (2.6068) Prec@1 38.750 (36.716) Prec@5 64.375 (67.394) Epoch: [14][870/11272] Time 0.918 (0.835) Data 0.003 (0.004) Loss 2.7015 (2.6067) Prec@1 38.125 (36.719) Prec@5 64.375 (67.406) Epoch: [14][880/11272] Time 0.960 (0.835) Data 0.003 (0.004) Loss 2.8330 (2.6068) Prec@1 35.625 (36.722) Prec@5 61.875 (67.391) Epoch: [14][890/11272] Time 0.824 (0.835) Data 0.002 (0.004) Loss 2.5454 (2.6067) Prec@1 38.750 (36.724) Prec@5 68.750 (67.377) Epoch: [14][900/11272] Time 0.817 (0.835) Data 0.002 (0.004) Loss 2.3858 (2.6059) Prec@1 39.375 (36.747) Prec@5 75.000 (67.392) Epoch: [14][910/11272] Time 0.861 (0.835) Data 0.001 (0.004) Loss 2.4825 (2.6058) Prec@1 41.250 (36.756) Prec@5 70.625 (67.393) Epoch: [14][920/11272] Time 0.850 (0.835) Data 0.002 (0.004) Loss 2.5711 (2.6061) Prec@1 36.875 (36.745) Prec@5 68.125 (67.384) Epoch: [14][930/11272] Time 0.764 (0.835) Data 0.002 (0.004) Loss 2.3284 (2.6054) Prec@1 43.125 (36.763) Prec@5 73.125 (67.396) Epoch: [14][940/11272] Time 0.811 (0.835) Data 0.002 (0.004) Loss 2.5098 (2.6048) Prec@1 41.250 (36.770) Prec@5 69.375 (67.411) Epoch: [14][950/11272] Time 0.818 (0.835) Data 0.001 (0.004) Loss 2.3848 (2.6035) Prec@1 43.125 (36.829) Prec@5 71.875 (67.440) Epoch: [14][960/11272] Time 0.843 (0.834) Data 0.002 (0.004) Loss 2.4725 (2.6037) Prec@1 40.625 (36.830) Prec@5 66.875 (67.425) Epoch: [14][970/11272] Time 0.742 (0.834) Data 0.001 (0.004) Loss 2.6751 (2.6037) Prec@1 35.000 (36.838) Prec@5 65.000 (67.412) Epoch: [14][980/11272] Time 0.954 (0.835) Data 0.002 (0.004) Loss 2.7017 (2.6045) Prec@1 35.000 (36.820) Prec@5 65.000 (67.401) Epoch: [14][990/11272] Time 0.866 (0.834) Data 0.001 (0.004) Loss 2.3314 (2.6040) Prec@1 43.750 (36.839) Prec@5 74.375 (67.412) Epoch: [14][1000/11272] Time 0.744 (0.834) Data 0.001 (0.004) Loss 2.7831 (2.6047) Prec@1 36.875 (36.838) Prec@5 63.750 (67.406) Epoch: [14][1010/11272] Time 0.807 (0.834) Data 0.002 (0.004) Loss 2.4832 (2.6046) Prec@1 39.375 (36.838) Prec@5 71.875 (67.405) Epoch: [14][1020/11272] Time 0.835 (0.834) Data 0.001 (0.004) Loss 2.5530 (2.6042) Prec@1 38.125 (36.838) Prec@5 67.500 (67.416) Epoch: [14][1030/11272] Time 0.946 (0.834) Data 0.001 (0.004) Loss 2.6272 (2.6045) Prec@1 35.625 (36.827) Prec@5 67.500 (67.410) Epoch: [14][1040/11272] Time 0.763 (0.834) Data 0.001 (0.004) Loss 2.5960 (2.6039) Prec@1 40.000 (36.840) Prec@5 70.000 (67.426) Epoch: [14][1050/11272] Time 0.767 (0.834) Data 0.002 (0.004) Loss 2.4138 (2.6031) Prec@1 37.500 (36.860) Prec@5 69.375 (67.441) Epoch: [14][1060/11272] Time 0.834 (0.834) Data 0.001 (0.004) Loss 2.7260 (2.6032) Prec@1 35.000 (36.864) Prec@5 66.250 (67.434) Epoch: [14][1070/11272] Time 0.889 (0.834) Data 0.002 (0.004) Loss 2.7257 (2.6034) Prec@1 35.625 (36.862) Prec@5 65.625 (67.425) Epoch: [14][1080/11272] Time 0.740 (0.834) Data 0.002 (0.004) Loss 2.6741 (2.6035) Prec@1 36.875 (36.865) Prec@5 68.125 (67.416) Epoch: [14][1090/11272] Time 0.751 (0.834) Data 0.002 (0.004) Loss 2.5574 (2.6028) Prec@1 35.625 (36.874) Prec@5 70.000 (67.431) Epoch: [14][1100/11272] Time 0.880 (0.834) Data 0.001 (0.004) Loss 2.3459 (2.6027) Prec@1 40.625 (36.881) Prec@5 72.500 (67.425) Epoch: [14][1110/11272] Time 0.755 (0.834) Data 0.003 (0.004) Loss 2.3711 (2.6026) Prec@1 38.125 (36.878) Prec@5 75.625 (67.423) Epoch: [14][1120/11272] Time 0.831 (0.834) Data 0.002 (0.004) Loss 2.6456 (2.6030) Prec@1 31.875 (36.877) Prec@5 66.875 (67.423) Epoch: [14][1130/11272] Time 0.895 (0.834) Data 0.002 (0.004) Loss 2.6852 (2.6032) Prec@1 36.250 (36.866) Prec@5 67.500 (67.416) Epoch: [14][1140/11272] Time 0.924 (0.834) Data 0.002 (0.004) Loss 2.5920 (2.6031) Prec@1 35.625 (36.862) Prec@5 66.875 (67.416) Epoch: [14][1150/11272] Time 0.821 (0.834) Data 0.001 (0.004) Loss 2.2944 (2.6034) Prec@1 40.625 (36.857) Prec@5 75.625 (67.415) Epoch: [14][1160/11272] Time 0.774 (0.834) Data 0.002 (0.004) Loss 2.3845 (2.6033) Prec@1 40.625 (36.853) Prec@5 68.750 (67.412) Epoch: [14][1170/11272] Time 0.843 (0.834) Data 0.002 (0.004) Loss 2.8479 (2.6032) Prec@1 31.875 (36.849) Prec@5 55.625 (67.417) Epoch: [14][1180/11272] Time 0.885 (0.834) Data 0.002 (0.004) Loss 2.5714 (2.6032) Prec@1 36.875 (36.852) Prec@5 68.125 (67.415) Epoch: [14][1190/11272] Time 0.771 (0.834) Data 0.002 (0.004) Loss 2.4295 (2.6030) Prec@1 43.750 (36.859) Prec@5 67.500 (67.425) Epoch: [14][1200/11272] Time 0.748 (0.834) Data 0.001 (0.004) Loss 2.4598 (2.6034) Prec@1 34.375 (36.854) Prec@5 70.625 (67.419) Epoch: [14][1210/11272] Time 0.912 (0.834) Data 0.002 (0.004) Loss 2.4142 (2.6034) Prec@1 33.750 (36.837) Prec@5 73.750 (67.424) Epoch: [14][1220/11272] Time 0.910 (0.834) Data 0.001 (0.004) Loss 2.3254 (2.6034) Prec@1 46.875 (36.841) Prec@5 71.875 (67.423) Epoch: [14][1230/11272] Time 0.750 (0.834) Data 0.001 (0.004) Loss 2.7728 (2.6037) Prec@1 41.875 (36.839) Prec@5 63.125 (67.424) Epoch: [14][1240/11272] Time 0.946 (0.834) Data 0.001 (0.004) Loss 2.6871 (2.6041) Prec@1 38.750 (36.839) Prec@5 65.000 (67.425) Epoch: [14][1250/11272] Time 0.884 (0.834) Data 0.001 (0.004) Loss 2.5767 (2.6041) Prec@1 32.500 (36.846) Prec@5 72.500 (67.432) Epoch: [14][1260/11272] Time 0.771 (0.834) Data 0.001 (0.004) Loss 2.7093 (2.6044) Prec@1 37.500 (36.828) Prec@5 66.250 (67.426) Epoch: [14][1270/11272] Time 0.769 (0.834) Data 0.001 (0.004) Loss 2.7586 (2.6047) Prec@1 36.875 (36.833) Prec@5 66.875 (67.425) Epoch: [14][1280/11272] Time 0.887 (0.834) Data 0.002 (0.003) Loss 2.4323 (2.6045) Prec@1 39.375 (36.835) Prec@5 68.750 (67.437) Epoch: [14][1290/11272] Time 0.910 (0.834) Data 0.002 (0.003) Loss 2.6685 (2.6044) Prec@1 35.625 (36.840) Prec@5 68.125 (67.439) Epoch: [14][1300/11272] Time 0.750 (0.834) Data 0.002 (0.003) Loss 2.7496 (2.6048) Prec@1 31.250 (36.831) Prec@5 61.250 (67.428) Epoch: [14][1310/11272] Time 0.791 (0.834) Data 0.002 (0.003) Loss 2.8312 (2.6049) Prec@1 35.000 (36.834) Prec@5 60.625 (67.432) Epoch: [14][1320/11272] Time 0.883 (0.834) Data 0.002 (0.003) Loss 2.7671 (2.6052) Prec@1 32.500 (36.825) Prec@5 63.125 (67.434) Epoch: [14][1330/11272] Time 0.837 (0.834) Data 0.001 (0.003) Loss 2.5448 (2.6052) Prec@1 41.250 (36.829) Prec@5 68.125 (67.427) Epoch: [14][1340/11272] Time 0.779 (0.834) Data 0.002 (0.003) Loss 2.7549 (2.6055) Prec@1 35.625 (36.822) Prec@5 62.500 (67.411) Epoch: [14][1350/11272] Time 0.778 (0.834) Data 0.002 (0.003) Loss 2.7774 (2.6056) Prec@1 33.125 (36.819) Prec@5 65.000 (67.404) Epoch: [14][1360/11272] Time 0.886 (0.834) Data 0.001 (0.003) Loss 2.4605 (2.6058) Prec@1 41.875 (36.821) Prec@5 70.000 (67.407) Epoch: [14][1370/11272] Time 0.861 (0.834) Data 0.002 (0.003) Loss 2.6989 (2.6061) Prec@1 38.125 (36.824) Prec@5 66.875 (67.404) Epoch: [14][1380/11272] Time 0.789 (0.833) Data 0.001 (0.003) Loss 2.4537 (2.6059) Prec@1 41.875 (36.827) Prec@5 70.000 (67.403) Epoch: [14][1390/11272] Time 0.907 (0.833) Data 0.002 (0.003) Loss 2.8843 (2.6061) Prec@1 26.875 (36.810) Prec@5 61.250 (67.404) Epoch: [14][1400/11272] Time 0.849 (0.833) Data 0.001 (0.003) Loss 2.6864 (2.6061) Prec@1 31.875 (36.811) Prec@5 69.375 (67.411) Epoch: [14][1410/11272] Time 0.757 (0.833) Data 0.002 (0.003) Loss 2.3483 (2.6060) Prec@1 43.750 (36.815) Prec@5 71.875 (67.414) Epoch: [14][1420/11272] Time 0.736 (0.833) Data 0.001 (0.003) Loss 2.6909 (2.6063) Prec@1 39.375 (36.818) Prec@5 62.500 (67.409) Epoch: [14][1430/11272] Time 0.910 (0.833) Data 0.001 (0.003) Loss 2.4912 (2.6066) Prec@1 38.750 (36.825) Prec@5 70.625 (67.411) Epoch: [14][1440/11272] Time 0.877 (0.833) Data 0.002 (0.003) Loss 2.7513 (2.6070) Prec@1 31.250 (36.810) Prec@5 66.875 (67.406) Epoch: [14][1450/11272] Time 0.750 (0.832) Data 0.001 (0.003) Loss 2.7098 (2.6070) Prec@1 38.750 (36.811) Prec@5 65.000 (67.409) Epoch: [14][1460/11272] Time 0.774 (0.832) Data 0.002 (0.003) Loss 2.2574 (2.6060) Prec@1 43.750 (36.819) Prec@5 71.250 (67.431) Epoch: [14][1470/11272] Time 0.825 (0.832) Data 0.001 (0.003) Loss 2.5768 (2.6056) Prec@1 40.625 (36.826) Prec@5 66.875 (67.431) Epoch: [14][1480/11272] Time 0.810 (0.832) Data 0.002 (0.003) Loss 2.7703 (2.6058) Prec@1 31.875 (36.815) Prec@5 67.500 (67.426) Epoch: [14][1490/11272] Time 0.747 (0.832) Data 0.001 (0.003) Loss 2.6661 (2.6066) Prec@1 33.125 (36.801) Prec@5 65.625 (67.410) Epoch: [14][1500/11272] Time 0.742 (0.832) Data 0.001 (0.003) Loss 2.6072 (2.6064) Prec@1 36.875 (36.812) Prec@5 67.500 (67.416) Epoch: [14][1510/11272] Time 0.922 (0.832) Data 0.002 (0.003) Loss 2.6018 (2.6064) Prec@1 34.375 (36.798) Prec@5 66.875 (67.421) Epoch: [14][1520/11272] Time 0.752 (0.832) Data 0.001 (0.003) Loss 2.5461 (2.6062) Prec@1 33.125 (36.791) Prec@5 68.125 (67.419) Epoch: [14][1530/11272] Time 0.772 (0.832) Data 0.002 (0.003) Loss 2.6539 (2.6065) Prec@1 41.250 (36.790) Prec@5 65.625 (67.410) Epoch: [14][1540/11272] Time 0.862 (0.832) Data 0.001 (0.003) Loss 2.6146 (2.6069) Prec@1 36.250 (36.784) Prec@5 70.000 (67.405) Epoch: [14][1550/11272] Time 0.959 (0.832) Data 0.002 (0.003) Loss 2.2780 (2.6070) Prec@1 44.375 (36.777) Prec@5 71.875 (67.402) Epoch: [14][1560/11272] Time 0.756 (0.831) Data 0.002 (0.003) Loss 2.3080 (2.6068) Prec@1 42.500 (36.764) Prec@5 71.875 (67.404) Epoch: [14][1570/11272] Time 0.745 (0.831) Data 0.001 (0.003) Loss 2.3168 (2.6068) Prec@1 48.750 (36.760) Prec@5 72.500 (67.404) Epoch: [14][1580/11272] Time 0.897 (0.831) Data 0.002 (0.003) Loss 2.3257 (2.6067) Prec@1 43.125 (36.757) Prec@5 71.875 (67.404) Epoch: [14][1590/11272] Time 0.840 (0.831) Data 0.002 (0.003) Loss 2.4134 (2.6062) Prec@1 40.625 (36.771) Prec@5 70.000 (67.421) Epoch: [14][1600/11272] Time 0.760 (0.831) Data 0.002 (0.003) Loss 2.5265 (2.6066) Prec@1 39.375 (36.769) Prec@5 71.250 (67.414) Epoch: [14][1610/11272] Time 0.773 (0.831) Data 0.002 (0.003) Loss 2.6241 (2.6067) Prec@1 36.875 (36.763) Prec@5 66.250 (67.409) Epoch: [14][1620/11272] Time 0.852 (0.831) Data 0.002 (0.003) Loss 2.4735 (2.6064) Prec@1 38.125 (36.762) Prec@5 70.625 (67.414) Epoch: [14][1630/11272] Time 0.865 (0.831) Data 0.002 (0.003) Loss 2.6941 (2.6067) Prec@1 30.625 (36.755) Prec@5 66.875 (67.412) Epoch: [14][1640/11272] Time 0.778 (0.831) Data 0.001 (0.003) Loss 2.6659 (2.6066) Prec@1 35.000 (36.760) Prec@5 63.750 (67.403) Epoch: [14][1650/11272] Time 0.886 (0.831) Data 0.001 (0.003) Loss 2.6497 (2.6068) Prec@1 38.125 (36.764) Prec@5 66.250 (67.398) Epoch: [14][1660/11272] Time 0.923 (0.831) Data 0.002 (0.003) Loss 2.5928 (2.6067) Prec@1 36.250 (36.766) Prec@5 65.625 (67.404) Epoch: [14][1670/11272] Time 0.745 (0.831) Data 0.002 (0.003) Loss 2.5039 (2.6067) Prec@1 39.375 (36.753) Prec@5 71.875 (67.394) Epoch: [14][1680/11272] Time 0.802 (0.831) Data 0.002 (0.003) Loss 2.4603 (2.6070) Prec@1 45.000 (36.753) Prec@5 69.375 (67.396) Epoch: [14][1690/11272] Time 0.873 (0.831) Data 0.001 (0.003) Loss 2.4878 (2.6069) Prec@1 40.625 (36.768) Prec@5 65.000 (67.390) Epoch: [14][1700/11272] Time 0.903 (0.831) Data 0.002 (0.003) Loss 2.8585 (2.6071) Prec@1 30.625 (36.754) Prec@5 61.250 (67.393) Epoch: [14][1710/11272] Time 0.762 (0.831) Data 0.002 (0.003) Loss 2.4741 (2.6068) Prec@1 37.500 (36.760) Prec@5 70.625 (67.394) Epoch: [14][1720/11272] Time 0.753 (0.830) Data 0.002 (0.003) Loss 2.4910 (2.6070) Prec@1 43.750 (36.761) Prec@5 68.750 (67.393) Epoch: [14][1730/11272] Time 0.927 (0.831) Data 0.002 (0.003) Loss 2.2713 (2.6066) Prec@1 40.625 (36.768) Prec@5 73.750 (67.408) Epoch: [14][1740/11272] Time 0.894 (0.830) Data 0.002 (0.003) Loss 2.6631 (2.6066) Prec@1 39.375 (36.763) Prec@5 62.500 (67.404) Epoch: [14][1750/11272] Time 0.782 (0.830) Data 0.002 (0.003) Loss 2.8603 (2.6065) Prec@1 33.125 (36.768) Prec@5 63.125 (67.405) Epoch: [14][1760/11272] Time 0.785 (0.830) Data 0.001 (0.003) Loss 2.5899 (2.6059) Prec@1 31.875 (36.768) Prec@5 65.625 (67.411) Epoch: [14][1770/11272] Time 0.850 (0.830) Data 0.002 (0.003) Loss 2.8099 (2.6060) Prec@1 33.750 (36.764) Prec@5 60.625 (67.409) Epoch: [14][1780/11272] Time 0.716 (0.830) Data 0.001 (0.003) Loss 2.7083 (2.6060) Prec@1 34.375 (36.760) Prec@5 65.625 (67.410) Epoch: [14][1790/11272] Time 0.770 (0.830) Data 0.002 (0.003) Loss 2.5624 (2.6061) Prec@1 37.500 (36.754) Prec@5 67.500 (67.408) Epoch: [14][1800/11272] Time 0.865 (0.830) Data 0.002 (0.003) Loss 2.7033 (2.6061) Prec@1 35.000 (36.754) Prec@5 67.500 (67.416) Epoch: [14][1810/11272] Time 0.879 (0.830) Data 0.001 (0.003) Loss 2.4631 (2.6062) Prec@1 42.500 (36.761) Prec@5 70.000 (67.408) Epoch: [14][1820/11272] Time 0.762 (0.830) Data 0.002 (0.003) Loss 2.7126 (2.6064) Prec@1 38.750 (36.763) Prec@5 65.000 (67.411) Epoch: [14][1830/11272] Time 0.755 (0.830) Data 0.002 (0.003) Loss 2.7060 (2.6064) Prec@1 37.500 (36.761) Prec@5 67.500 (67.411) Epoch: [14][1840/11272] Time 0.948 (0.830) Data 0.002 (0.003) Loss 2.3617 (2.6064) Prec@1 42.500 (36.764) Prec@5 74.375 (67.408) Epoch: [14][1850/11272] Time 0.870 (0.830) Data 0.002 (0.003) Loss 2.4655 (2.6063) Prec@1 38.125 (36.768) Prec@5 70.625 (67.410) Epoch: [14][1860/11272] Time 0.740 (0.830) Data 0.001 (0.003) Loss 2.6616 (2.6064) Prec@1 35.000 (36.763) Prec@5 67.500 (67.404) Epoch: [14][1870/11272] Time 0.760 (0.830) Data 0.002 (0.003) Loss 2.5391 (2.6063) Prec@1 38.750 (36.768) Prec@5 67.500 (67.411) Epoch: [14][1880/11272] Time 0.969 (0.830) Data 0.002 (0.003) Loss 2.3708 (2.6056) Prec@1 41.875 (36.782) Prec@5 72.500 (67.430) Epoch: [14][1890/11272] Time 0.892 (0.830) Data 0.001 (0.003) Loss 2.4758 (2.6060) Prec@1 42.500 (36.777) Prec@5 69.375 (67.417) Epoch: [14][1900/11272] Time 0.771 (0.830) Data 0.002 (0.003) Loss 2.5450 (2.6062) Prec@1 38.750 (36.768) Prec@5 66.875 (67.411) Epoch: [14][1910/11272] Time 0.916 (0.830) Data 0.001 (0.003) Loss 2.5850 (2.6066) Prec@1 37.500 (36.764) Prec@5 67.500 (67.398) Epoch: [14][1920/11272] Time 0.917 (0.830) Data 0.002 (0.003) Loss 2.8658 (2.6065) Prec@1 29.375 (36.768) Prec@5 60.625 (67.401) Epoch: [14][1930/11272] Time 0.795 (0.830) Data 0.002 (0.003) Loss 2.4932 (2.6065) Prec@1 38.125 (36.770) Prec@5 68.750 (67.393) Epoch: [14][1940/11272] Time 0.749 (0.830) Data 0.002 (0.003) Loss 2.3569 (2.6065) Prec@1 41.250 (36.772) Prec@5 71.875 (67.393) Epoch: [14][1950/11272] Time 0.872 (0.830) Data 0.002 (0.003) Loss 2.6263 (2.6064) Prec@1 38.750 (36.771) Prec@5 69.375 (67.400) Epoch: [14][1960/11272] Time 0.884 (0.830) Data 0.002 (0.003) Loss 2.4933 (2.6065) Prec@1 38.750 (36.769) Prec@5 70.625 (67.398) Epoch: [14][1970/11272] Time 0.756 (0.830) Data 0.001 (0.003) Loss 2.4711 (2.6059) Prec@1 41.250 (36.788) Prec@5 68.125 (67.412) Epoch: [14][1980/11272] Time 0.740 (0.830) Data 0.002 (0.003) Loss 2.6102 (2.6057) Prec@1 38.750 (36.788) Prec@5 66.875 (67.420) Epoch: [14][1990/11272] Time 0.890 (0.830) Data 0.002 (0.003) Loss 2.4345 (2.6058) Prec@1 41.250 (36.784) Prec@5 71.875 (67.421) Epoch: [14][2000/11272] Time 0.855 (0.830) Data 0.002 (0.003) Loss 2.4305 (2.6054) Prec@1 37.500 (36.792) Prec@5 69.375 (67.429) Epoch: [14][2010/11272] Time 0.752 (0.830) Data 0.002 (0.003) Loss 2.5542 (2.6051) Prec@1 36.875 (36.796) Prec@5 67.500 (67.436) Epoch: [14][2020/11272] Time 0.751 (0.830) Data 0.002 (0.003) Loss 2.4684 (2.6051) Prec@1 41.250 (36.804) Prec@5 66.875 (67.435) Epoch: [14][2030/11272] Time 0.885 (0.830) Data 0.002 (0.003) Loss 2.6096 (2.6048) Prec@1 41.250 (36.809) Prec@5 65.000 (67.442) Epoch: [14][2040/11272] Time 0.881 (19.667) Data 0.003 (18.839) Loss 2.6990 (2.6049) Prec@1 36.250 (36.809) Prec@5 63.750 (67.433) Epoch: [14][2050/11272] Time 0.757 (19.575) Data 0.001 (18.747) Loss 2.5394 (2.6052) Prec@1 37.500 (36.801) Prec@5 68.125 (67.425) Epoch: [14][2060/11272] Time 0.844 (19.484) Data 0.001 (18.656) Loss 2.7801 (2.6051) Prec@1 30.000 (36.803) Prec@5 59.375 (67.424) Epoch: [14][2070/11272] Time 0.890 (19.394) Data 0.002 (18.566) Loss 2.6466 (2.6050) Prec@1 36.875 (36.804) Prec@5 66.875 (67.430) Epoch: [14][2080/11272] Time 0.724 (19.305) Data 0.002 (18.477) Loss 2.6514 (2.6050) Prec@1 32.500 (36.809) Prec@5 65.625 (67.433) Epoch: [14][2090/11272] Time 0.735 (19.217) Data 0.002 (18.389) Loss 2.7357 (2.6052) Prec@1 36.250 (36.802) Prec@5 65.000 (67.425) Epoch: [14][2100/11272] Time 0.947 (19.129) Data 0.001 (18.301) Loss 2.5447 (2.6054) Prec@1 33.750 (36.797) Prec@5 67.500 (67.418) Epoch: [14][2110/11272] Time 0.852 (19.043) Data 0.001 (18.215) Loss 2.5540 (2.6054) Prec@1 38.125 (36.798) Prec@5 66.250 (67.422) Epoch: [14][2120/11272] Time 0.772 (18.957) Data 0.001 (18.129) Loss 2.7551 (2.6056) Prec@1 33.125 (36.788) Prec@5 62.500 (67.418) Epoch: [14][2130/11272] Time 0.735 (18.872) Data 0.002 (18.044) Loss 2.7222 (2.6056) Prec@1 35.000 (36.782) Prec@5 64.375 (67.419) Epoch: [14][2140/11272] Time 0.864 (18.787) Data 0.002 (17.959) Loss 2.7059 (2.6058) Prec@1 32.500 (36.777) Prec@5 65.625 (67.412) Epoch: [14][2150/11272] Time 0.912 (18.704) Data 0.001 (17.876) Loss 2.5069 (2.6056) Prec@1 38.125 (36.787) Prec@5 69.375 (67.411) Epoch: [14][2160/11272] Time 0.784 (18.621) Data 0.001 (17.793) Loss 3.0115 (2.6058) Prec@1 28.125 (36.783) Prec@5 58.125 (67.411) Epoch: [14][2170/11272] Time 0.842 (18.539) Data 0.001 (17.711) Loss 2.5296 (2.6058) Prec@1 34.375 (36.779) Prec@5 68.750 (67.415) Epoch: [14][2180/11272] Time 0.966 (18.458) Data 0.001 (17.630) Loss 2.5757 (2.6059) Prec@1 38.750 (36.775) Prec@5 68.750 (67.413) Epoch: [14][2190/11272] Time 0.727 (18.377) Data 0.001 (17.550) Loss 2.4591 (2.6060) Prec@1 39.375 (36.777) Prec@5 71.875 (67.411) Epoch: [14][2200/11272] Time 0.777 (18.298) Data 0.001 (17.470) Loss 2.4168 (2.6059) Prec@1 35.625 (36.775) Prec@5 71.250 (67.411) Epoch: [14][2210/11272] Time 0.878 (18.218) Data 0.002 (17.391) Loss 2.8765 (2.6058) Prec@1 30.625 (36.778) Prec@5 63.750 (67.412) Epoch: [14][2220/11272] Time 0.930 (18.140) Data 0.001 (17.312) Loss 2.3615 (2.6062) Prec@1 45.000 (36.773) Prec@5 71.875 (67.401) Epoch: [14][2230/11272] Time 0.761 (18.062) Data 0.001 (17.235) Loss 3.0046 (2.6068) Prec@1 27.500 (36.762) Prec@5 55.625 (67.384) Epoch: [14][2240/11272] Time 0.748 (17.986) Data 0.001 (17.158) Loss 2.6754 (2.6068) Prec@1 33.125 (36.761) Prec@5 65.625 (67.379) Epoch: [14][2250/11272] Time 0.849 (17.909) Data 0.001 (17.082) Loss 2.5038 (2.6066) Prec@1 41.875 (36.768) Prec@5 70.000 (67.380) Epoch: [14][2260/11272] Time 0.903 (17.834) Data 0.002 (17.006) Loss 2.6726 (2.6066) Prec@1 34.375 (36.763) Prec@5 65.000 (67.380) Epoch: [14][2270/11272] Time 0.784 (17.759) Data 0.002 (16.931) Loss 2.4405 (2.6068) Prec@1 44.375 (36.763) Prec@5 75.625 (67.379) Epoch: [14][2280/11272] Time 0.735 (17.684) Data 0.002 (16.857) Loss 2.5921 (2.6070) Prec@1 35.000 (36.751) Prec@5 70.000 (67.379) Epoch: [14][2290/11272] Time 0.854 (17.611) Data 0.001 (16.784) Loss 3.0369 (2.6070) Prec@1 28.125 (36.753) Prec@5 58.125 (67.375) Epoch: [14][2300/11272] Time 0.954 (17.538) Data 0.002 (16.711) Loss 2.8051 (2.6074) Prec@1 32.500 (36.746) Prec@5 62.500 (67.369) Epoch: [14][2310/11272] Time 0.764 (17.466) Data 0.002 (16.638) Loss 2.5855 (2.6073) Prec@1 36.250 (36.743) Prec@5 68.750 (67.374) Epoch: [14][2320/11272] Time 0.880 (17.394) Data 0.001 (16.567) Loss 2.7757 (2.6073) Prec@1 33.125 (36.748) Prec@5 64.375 (67.378) Epoch: [14][2330/11272] Time 0.873 (17.323) Data 0.001 (16.496) Loss 2.8440 (2.6075) Prec@1 30.625 (36.748) Prec@5 68.125 (67.382) Epoch: [14][2340/11272] Time 0.749 (17.252) Data 0.001 (16.425) Loss 2.7981 (2.6078) Prec@1 36.250 (36.749) Prec@5 60.000 (67.371) Epoch: [14][2350/11272] Time 0.777 (17.182) Data 0.001 (16.355) Loss 2.5389 (2.6077) Prec@1 32.500 (36.750) Prec@5 69.375 (67.372) Epoch: [14][2360/11272] Time 0.909 (17.113) Data 0.002 (16.286) Loss 2.4728 (2.6076) Prec@1 42.500 (36.757) Prec@5 70.000 (67.373) Epoch: [14][2370/11272] Time 0.847 (17.044) Data 0.001 (16.217) Loss 2.3317 (2.6073) Prec@1 40.000 (36.759) Prec@5 73.750 (67.379) Epoch: [14][2380/11272] Time 0.746 (16.976) Data 0.001 (16.149) Loss 2.3547 (2.6073) Prec@1 42.500 (36.760) Prec@5 68.125 (67.380) Epoch: [14][2390/11272] Time 0.816 (16.909) Data 0.002 (16.082) Loss 2.7295 (2.6071) Prec@1 38.750 (36.768) Prec@5 65.625 (67.389) Epoch: [14][2400/11272] Time 0.929 (16.842) Data 0.001 (16.015) Loss 2.5766 (2.6069) Prec@1 40.000 (36.767) Prec@5 66.250 (67.391) Epoch: [14][2410/11272] Time 0.825 (16.775) Data 0.002 (15.948) Loss 2.8813 (2.6072) Prec@1 30.000 (36.763) Prec@5 61.875 (67.383) Epoch: [14][2420/11272] Time 0.769 (16.710) Data 0.001 (15.882) Loss 2.5996 (2.6073) Prec@1 36.875 (36.759) Prec@5 68.125 (67.383) Epoch: [14][2430/11272] Time 0.756 (16.644) Data 0.001 (15.817) Loss 2.5554 (2.6073) Prec@1 38.125 (36.760) Prec@5 68.750 (67.386) Epoch: [14][2440/11272] Time 0.855 (16.579) Data 0.001 (15.752) Loss 2.6204 (2.6076) Prec@1 36.875 (36.761) Prec@5 68.750 (67.384) Epoch: [14][2450/11272] Time 0.698 (16.515) Data 0.001 (15.688) Loss 2.5715 (2.6078) Prec@1 37.500 (36.752) Prec@5 69.375 (67.377) Epoch: [14][2460/11272] Time 0.713 (16.451) Data 0.001 (15.624) Loss 2.5875 (2.6079) Prec@1 37.500 (36.748) Prec@5 71.875 (67.377) Epoch: [14][2470/11272] Time 0.826 (16.388) Data 0.001 (15.561) Loss 2.4332 (2.6079) Prec@1 36.250 (36.744) Prec@5 73.125 (67.381) Epoch: [14][2480/11272] Time 0.854 (16.325) Data 0.001 (15.498) Loss 2.6553 (2.6081) Prec@1 33.125 (36.735) Prec@5 65.625 (67.375) Epoch: [14][2490/11272] Time 0.750 (16.263) Data 0.001 (15.436) Loss 2.6216 (2.6080) Prec@1 37.500 (36.740) Prec@5 68.750 (67.378) Epoch: [14][2500/11272] Time 0.756 (16.201) Data 0.001 (15.374) Loss 2.5821 (2.6081) Prec@1 38.125 (36.740) Prec@5 67.500 (67.374) Epoch: [14][2510/11272] Time 0.884 (16.140) Data 0.001 (15.313) Loss 2.7926 (2.6081) Prec@1 30.625 (36.738) Prec@5 66.250 (67.373) Epoch: [14][2520/11272] Time 0.868 (16.079) Data 0.001 (15.252) Loss 2.5378 (2.6084) Prec@1 30.000 (36.731) Prec@5 67.500 (67.362) Epoch: [14][2530/11272] Time 0.774 (16.019) Data 0.001 (15.192) Loss 3.0094 (2.6086) Prec@1 31.250 (36.727) Prec@5 60.000 (67.359) Epoch: [14][2540/11272] Time 0.739 (15.959) Data 0.001 (15.132) Loss 2.6628 (2.6086) Prec@1 38.750 (36.724) Prec@5 65.000 (67.360) Epoch: [14][2550/11272] Time 0.840 (15.899) Data 0.001 (15.073) Loss 2.6813 (2.6085) Prec@1 35.000 (36.727) Prec@5 65.000 (67.363) Epoch: [14][2560/11272] Time 0.872 (15.840) Data 0.001 (15.014) Loss 2.7199 (2.6083) Prec@1 34.375 (36.726) Prec@5 64.375 (67.369) Epoch: [14][2570/11272] Time 0.741 (15.782) Data 0.001 (14.956) Loss 2.2928 (2.6081) Prec@1 40.625 (36.723) Prec@5 73.750 (67.375) Epoch: [14][2580/11272] Time 0.885 (15.724) Data 0.002 (14.898) Loss 2.7697 (2.6082) Prec@1 35.625 (36.722) Prec@5 62.500 (67.371) Epoch: [14][2590/11272] Time 0.865 (15.667) Data 0.002 (14.840) Loss 2.5352 (2.6081) Prec@1 40.000 (36.726) Prec@5 65.000 (67.370) Epoch: [14][2600/11272] Time 0.785 (15.609) Data 0.001 (14.783) Loss 2.6864 (2.6080) Prec@1 35.625 (36.732) Prec@5 67.500 (67.376) Epoch: [14][2610/11272] Time 0.820 (15.553) Data 0.003 (14.727) Loss 2.8264 (2.6081) Prec@1 35.000 (36.727) Prec@5 60.625 (67.373) Epoch: [14][2620/11272] Time 0.863 (15.497) Data 0.001 (14.671) Loss 2.5505 (2.6084) Prec@1 36.250 (36.723) Prec@5 66.250 (67.365) Epoch: [14][2630/11272] Time 0.864 (15.441) Data 0.001 (14.615) Loss 2.6579 (2.6085) Prec@1 36.250 (36.723) Prec@5 67.500 (67.364) Epoch: [14][2640/11272] Time 0.763 (15.385) Data 0.002 (14.560) Loss 2.5073 (2.6083) Prec@1 35.625 (36.725) Prec@5 65.000 (67.367) Epoch: [14][2650/11272] Time 0.742 (15.330) Data 0.001 (14.505) Loss 2.7569 (2.6084) Prec@1 32.500 (36.719) Prec@5 68.125 (67.364) Epoch: [14][2660/11272] Time 0.871 (15.276) Data 0.001 (14.450) Loss 2.6638 (2.6081) Prec@1 38.750 (36.727) Prec@5 65.000 (67.370) Epoch: [14][2670/11272] Time 0.857 (15.222) Data 0.002 (14.396) Loss 2.5235 (2.6079) Prec@1 34.375 (36.729) Prec@5 66.875 (67.368) Epoch: [14][2680/11272] Time 0.751 (15.168) Data 0.002 (14.342) Loss 2.6638 (2.6082) Prec@1 42.500 (36.730) Prec@5 66.875 (67.360) Epoch: [14][2690/11272] Time 0.793 (15.115) Data 0.002 (14.289) Loss 2.5049 (2.6081) Prec@1 38.750 (36.729) Prec@5 68.125 (67.359) Epoch: [14][2700/11272] Time 0.828 (15.062) Data 0.001 (14.236) Loss 2.6792 (2.6082) Prec@1 35.000 (36.728) Prec@5 66.250 (67.364) Epoch: [14][2710/11272] Time 0.730 (15.009) Data 0.003 (14.184) Loss 2.4965 (2.6079) Prec@1 35.000 (36.731) Prec@5 71.875 (67.375) Epoch: [14][2720/11272] Time 0.777 (14.957) Data 0.002 (14.131) Loss 2.6775 (2.6080) Prec@1 32.500 (36.723) Prec@5 65.000 (67.373) Epoch: [14][2730/11272] Time 0.907 (14.905) Data 0.002 (14.080) Loss 2.7084 (2.6080) Prec@1 35.000 (36.726) Prec@5 66.250 (67.370) Epoch: [14][2740/11272] Time 0.850 (14.854) Data 0.001 (14.028) Loss 2.6522 (2.6081) Prec@1 32.500 (36.722) Prec@5 68.750 (67.367) Epoch: [14][2750/11272] Time 0.814 (14.803) Data 0.001 (13.977) Loss 2.6906 (2.6079) Prec@1 35.000 (36.730) Prec@5 68.125 (67.371) Epoch: [14][2760/11272] Time 0.758 (14.752) Data 0.001 (13.927) Loss 2.4010 (2.6080) Prec@1 42.500 (36.734) Prec@5 69.375 (67.367) Epoch: [14][2770/11272] Time 0.855 (14.702) Data 0.002 (13.877) Loss 2.4399 (2.6078) Prec@1 40.000 (36.736) Prec@5 72.500 (67.374) Epoch: [14][2780/11272] Time 0.851 (14.652) Data 0.001 (13.827) Loss 2.5401 (2.6078) Prec@1 36.875 (36.737) Prec@5 70.000 (67.375) Epoch: [14][2790/11272] Time 0.748 (14.602) Data 0.002 (13.777) Loss 2.5464 (2.6075) Prec@1 38.750 (36.742) Prec@5 66.875 (67.382) Epoch: [14][2800/11272] Time 0.757 (14.553) Data 0.002 (13.728) Loss 2.7315 (2.6074) Prec@1 38.125 (36.746) Prec@5 62.500 (67.381) Epoch: [14][2810/11272] Time 0.837 (14.504) Data 0.001 (13.679) Loss 2.2463 (2.6074) Prec@1 45.000 (36.741) Prec@5 72.500 (67.378) Epoch: [14][2820/11272] Time 0.893 (14.456) Data 0.001 (13.631) Loss 2.7328 (2.6074) Prec@1 34.375 (36.739) Prec@5 65.000 (67.377) Epoch: [14][2830/11272] Time 0.737 (14.407) Data 0.001 (13.582) Loss 2.6657 (2.6075) Prec@1 33.750 (36.741) Prec@5 68.125 (67.377) Epoch: [14][2840/11272] Time 0.925 (14.360) Data 0.002 (13.535) Loss 2.7993 (2.6073) Prec@1 34.375 (36.739) Prec@5 66.875 (67.380) Epoch: [14][2850/11272] Time 0.820 (14.312) Data 0.001 (13.487) Loss 2.4737 (2.6075) Prec@1 33.750 (36.734) Prec@5 73.750 (67.376) Epoch: [14][2860/11272] Time 0.733 (14.265) Data 0.001 (13.440) Loss 2.9610 (2.6078) Prec@1 29.375 (36.722) Prec@5 60.625 (67.374) Epoch: [14][2870/11272] Time 0.739 (14.218) Data 0.001 (13.393) Loss 2.6040 (2.6077) Prec@1 36.250 (36.722) Prec@5 70.625 (67.377) Epoch: [14][2880/11272] Time 0.879 (14.171) Data 0.001 (13.347) Loss 2.6116 (2.6078) Prec@1 36.875 (36.720) Prec@5 67.500 (67.376) Epoch: [14][2890/11272] Time 0.851 (14.125) Data 0.001 (13.301) Loss 2.6965 (2.6077) Prec@1 35.625 (36.726) Prec@5 67.500 (67.379) Epoch: [14][2900/11272] Time 0.770 (14.079) Data 0.002 (13.255) Loss 2.7328 (2.6077) Prec@1 29.375 (36.726) Prec@5 66.250 (67.380) Epoch: [14][2910/11272] Time 0.750 (14.034) Data 0.002 (13.209) Loss 2.7709 (2.6080) Prec@1 31.875 (36.721) Prec@5 65.625 (67.377) Epoch: [14][2920/11272] Time 0.903 (13.989) Data 0.001 (13.164) Loss 2.7387 (2.6082) Prec@1 36.250 (36.720) Prec@5 65.625 (67.371) Epoch: [14][2930/11272] Time 0.877 (13.944) Data 0.002 (13.119) Loss 2.8915 (2.6088) Prec@1 35.625 (36.708) Prec@5 66.875 (67.359) Epoch: [14][2940/11272] Time 0.749 (13.899) Data 0.001 (13.074) Loss 2.6957 (2.6090) Prec@1 30.625 (36.704) Prec@5 60.625 (67.352) Epoch: [14][2950/11272] Time 0.727 (13.855) Data 0.001 (13.030) Loss 2.8616 (2.6091) Prec@1 30.625 (36.696) Prec@5 58.125 (67.348) Epoch: [14][2960/11272] Time 0.843 (13.811) Data 0.001 (12.986) Loss 2.9149 (2.6093) Prec@1 31.875 (36.696) Prec@5 61.875 (67.345) Epoch: [14][2970/11272] Time 0.718 (13.767) Data 0.003 (12.942) Loss 2.9739 (2.6095) Prec@1 32.500 (36.690) Prec@5 62.500 (67.343) Epoch: [14][2980/11272] Time 0.772 (13.723) Data 0.001 (12.899) Loss 2.6176 (2.6097) Prec@1 33.125 (36.684) Prec@5 68.750 (67.342) Epoch: [14][2990/11272] Time 0.832 (13.680) Data 0.002 (12.856) Loss 2.4190 (2.6092) Prec@1 39.375 (36.693) Prec@5 66.250 (67.349) Epoch: [14][3000/11272] Time 0.881 (13.637) Data 0.001 (12.813) Loss 2.4371 (2.6092) Prec@1 43.125 (36.695) Prec@5 71.875 (67.349) Epoch: [14][3010/11272] Time 0.749 (13.595) Data 0.001 (12.771) Loss 2.5433 (2.6095) Prec@1 41.875 (36.691) Prec@5 68.750 (67.340) Epoch: [14][3020/11272] Time 0.727 (13.552) Data 0.002 (12.728) Loss 2.7855 (2.6096) Prec@1 31.875 (36.683) Prec@5 63.125 (67.338) Epoch: [14][3030/11272] Time 0.888 (13.510) Data 0.001 (12.686) Loss 2.7646 (2.6097) Prec@1 34.375 (36.682) Prec@5 65.625 (67.336) Epoch: [14][3040/11272] Time 0.869 (13.469) Data 0.001 (12.645) Loss 2.7842 (2.6100) Prec@1 35.625 (36.680) Prec@5 65.625 (67.333) Epoch: [14][3050/11272] Time 0.739 (13.427) Data 0.001 (12.603) Loss 2.6032 (2.6100) Prec@1 35.625 (36.680) Prec@5 66.250 (67.328) Epoch: [14][3060/11272] Time 0.791 (13.386) Data 0.001 (12.562) Loss 2.5664 (2.6099) Prec@1 38.125 (36.680) Prec@5 65.625 (67.329) Epoch: [14][3070/11272] Time 0.793 (13.345) Data 0.001 (12.521) Loss 2.3176 (2.6097) Prec@1 42.500 (36.686) Prec@5 76.250 (67.333) Epoch: [14][3080/11272] Time 0.839 (13.304) Data 0.001 (12.480) Loss 2.5708 (2.6096) Prec@1 41.250 (36.691) Prec@5 72.500 (67.338) Epoch: [14][3090/11272] Time 0.729 (13.264) Data 0.001 (12.440) Loss 2.6593 (2.6095) Prec@1 35.625 (36.694) Prec@5 64.375 (67.335) Epoch: [14][3100/11272] Time 0.891 (13.224) Data 0.002 (12.400) Loss 2.5041 (2.6093) Prec@1 41.250 (36.701) Prec@5 70.625 (67.346) Epoch: [14][3110/11272] Time 0.919 (13.184) Data 0.002 (12.360) Loss 2.8126 (2.6095) Prec@1 35.625 (36.702) Prec@5 60.625 (67.339) Epoch: [14][3120/11272] Time 0.781 (13.144) Data 0.002 (12.321) Loss 2.5221 (2.6095) Prec@1 41.875 (36.702) Prec@5 67.500 (67.337) Epoch: [14][3130/11272] Time 0.793 (13.105) Data 0.001 (12.281) Loss 2.6259 (2.6098) Prec@1 35.000 (36.695) Prec@5 66.875 (67.331) Epoch: [14][3140/11272] Time 0.864 (13.066) Data 0.002 (12.242) Loss 3.0629 (2.6098) Prec@1 25.000 (36.696) Prec@5 61.250 (67.329) Epoch: [14][3150/11272] Time 0.881 (13.027) Data 0.001 (12.203) Loss 2.7854 (2.6100) Prec@1 32.500 (36.693) Prec@5 63.125 (67.328) Epoch: [14][3160/11272] Time 0.839 (12.988) Data 0.001 (12.165) Loss 2.6741 (2.6099) Prec@1 34.375 (36.699) Prec@5 67.500 (67.330) Epoch: [14][3170/11272] Time 0.760 (12.950) Data 0.002 (12.126) Loss 2.7558 (2.6097) Prec@1 38.125 (36.703) Prec@5 63.750 (67.331) Epoch: [14][3180/11272] Time 0.849 (12.912) Data 0.001 (12.088) Loss 2.7200 (2.6097) Prec@1 33.750 (36.707) Prec@5 66.250 (67.335) Epoch: [14][3190/11272] Time 0.884 (12.874) Data 0.002 (12.050) Loss 2.7774 (2.6097) Prec@1 38.125 (36.708) Prec@5 65.625 (67.333) Epoch: [14][3200/11272] Time 0.780 (12.836) Data 0.002 (12.013) Loss 2.5534 (2.6096) Prec@1 36.875 (36.710) Prec@5 65.625 (67.333) Epoch: [14][3210/11272] Time 0.747 (12.799) Data 0.001 (11.975) Loss 2.5988 (2.6098) Prec@1 36.875 (36.708) Prec@5 67.500 (67.327) Epoch: [14][3220/11272] Time 0.909 (12.762) Data 0.002 (11.938) Loss 2.6854 (2.6098) Prec@1 36.250 (36.710) Prec@5 65.625 (67.327) Epoch: [14][3230/11272] Time 0.932 (12.725) Data 0.002 (11.901) Loss 2.8673 (2.6101) Prec@1 30.000 (36.706) Prec@5 64.375 (67.320) Epoch: [14][3240/11272] Time 0.743 (12.688) Data 0.003 (11.864) Loss 2.3365 (2.6099) Prec@1 42.500 (36.706) Prec@5 73.125 (67.323) Epoch: [14][3250/11272] Time 0.814 (12.652) Data 0.002 (11.828) Loss 2.7761 (2.6099) Prec@1 36.250 (36.704) Prec@5 62.500 (67.321) Epoch: [14][3260/11272] Time 0.886 (12.615) Data 0.001 (11.792) Loss 2.6758 (2.6099) Prec@1 29.375 (36.703) Prec@5 72.500 (67.323) Epoch: [14][3270/11272] Time 0.735 (12.579) Data 0.001 (11.756) Loss 2.3567 (2.6098) Prec@1 45.000 (36.708) Prec@5 69.375 (67.323) Epoch: [14][3280/11272] Time 0.728 (12.543) Data 0.001 (11.720) Loss 2.6853 (2.6099) Prec@1 35.000 (36.703) Prec@5 66.250 (67.322) Epoch: [14][3290/11272] Time 0.919 (12.508) Data 0.002 (11.684) Loss 2.6454 (2.6102) Prec@1 39.375 (36.698) Prec@5 64.375 (67.314) Epoch: [14][3300/11272] Time 0.885 (12.472) Data 0.002 (11.649) Loss 2.8681 (2.6105) Prec@1 31.250 (36.693) Prec@5 61.875 (67.306) Epoch: [14][3310/11272] Time 0.786 (12.437) Data 0.001 (11.614) Loss 2.3908 (2.6107) Prec@1 40.625 (36.686) Prec@5 69.375 (67.301) Epoch: [14][3320/11272] Time 0.794 (12.402) Data 0.001 (11.579) Loss 2.8023 (2.6108) Prec@1 31.875 (36.685) Prec@5 64.375 (67.298) Epoch: [14][3330/11272] Time 0.846 (12.368) Data 0.001 (11.544) Loss 2.5013 (2.6107) Prec@1 37.500 (36.688) Prec@5 71.250 (67.301) Epoch: [14][3340/11272] Time 0.888 (12.333) Data 0.001 (11.509) Loss 2.7065 (2.6108) Prec@1 35.625 (36.685) Prec@5 66.250 (67.298) Epoch: [14][3350/11272] Time 0.768 (12.299) Data 0.002 (11.475) Loss 2.6379 (2.6106) Prec@1 37.500 (36.694) Prec@5 68.125 (67.297) Epoch: [14][3360/11272] Time 0.782 (12.265) Data 0.001 (11.441) Loss 2.7035 (2.6105) Prec@1 35.000 (36.696) Prec@5 64.375 (67.299) Epoch: [14][3370/11272] Time 0.874 (12.231) Data 0.001 (11.407) Loss 2.5195 (2.6103) Prec@1 39.375 (36.699) Prec@5 70.000 (67.302) Epoch: [14][3380/11272] Time 0.775 (12.197) Data 0.001 (11.373) Loss 2.6976 (2.6103) Prec@1 35.000 (36.700) Prec@5 65.000 (67.303) Epoch: [14][3390/11272] Time 0.750 (12.163) Data 0.002 (11.340) Loss 2.6405 (2.6105) Prec@1 37.500 (36.700) Prec@5 66.875 (67.302) Epoch: [14][3400/11272] Time 0.897 (12.130) Data 0.001 (11.306) Loss 2.7106 (2.6105) Prec@1 40.625 (36.697) Prec@5 69.375 (67.306) Epoch: [14][3410/11272] Time 0.920 (12.097) Data 0.001 (11.273) Loss 2.4976 (2.6106) Prec@1 38.125 (36.697) Prec@5 70.625 (67.303) Epoch: [14][3420/11272] Time 0.813 (12.064) Data 0.001 (11.240) Loss 2.5035 (2.6106) Prec@1 40.000 (36.699) Prec@5 68.750 (67.301) Epoch: [14][3430/11272] Time 0.744 (12.031) Data 0.002 (11.207) Loss 2.4717 (2.6104) Prec@1 41.875 (36.703) Prec@5 71.875 (67.306) Epoch: [14][3440/11272] Time 0.866 (11.999) Data 0.002 (11.175) Loss 2.6435 (2.6105) Prec@1 33.750 (36.703) Prec@5 68.125 (67.304) Epoch: [14][3450/11272] Time 0.862 (11.966) Data 0.001 (11.143) Loss 2.8192 (2.6105) Prec@1 34.375 (36.705) Prec@5 61.250 (67.303) Epoch: [14][3460/11272] Time 0.734 (11.934) Data 0.002 (11.110) Loss 2.6825 (2.6107) Prec@1 35.000 (36.697) Prec@5 70.000 (67.302) Epoch: [14][3470/11272] Time 0.776 (11.902) Data 0.002 (11.078) Loss 2.4703 (2.6108) Prec@1 38.750 (36.700) Prec@5 68.125 (67.304) Epoch: [14][3480/11272] Time 0.845 (11.870) Data 0.001 (11.047) Loss 2.7289 (2.6109) Prec@1 31.875 (36.691) Prec@5 63.750 (67.301) Epoch: [14][3490/11272] Time 0.823 (11.838) Data 0.001 (11.015) Loss 2.6480 (2.6111) Prec@1 37.500 (36.691) Prec@5 68.125 (67.299) Epoch: [14][3500/11272] Time 0.739 (11.807) Data 0.001 (10.983) Loss 2.4401 (2.6110) Prec@1 36.250 (36.693) Prec@5 71.875 (67.301) Epoch: [14][3510/11272] Time 0.932 (11.776) Data 0.001 (10.952) Loss 2.7450 (2.6110) Prec@1 33.125 (36.693) Prec@5 60.000 (67.302) Epoch: [14][3520/11272] Time 0.883 (11.745) Data 0.001 (10.921) Loss 2.5986 (2.6111) Prec@1 41.250 (36.694) Prec@5 69.375 (67.299) Epoch: [14][3530/11272] Time 0.741 (11.714) Data 0.002 (10.890) Loss 2.5077 (2.6110) Prec@1 35.000 (36.691) Prec@5 70.000 (67.304) Epoch: [14][3540/11272] Time 0.781 (11.683) Data 0.001 (10.859) Loss 2.4804 (2.6110) Prec@1 39.375 (36.688) Prec@5 67.500 (67.305) Epoch: [14][3550/11272] Time 0.855 (11.652) Data 0.002 (10.829) Loss 3.0691 (2.6111) Prec@1 26.250 (36.680) Prec@5 57.500 (67.303) Epoch: [14][3560/11272] Time 0.853 (11.622) Data 0.001 (10.798) Loss 2.6109 (2.6113) Prec@1 35.000 (36.675) Prec@5 68.750 (67.302) Epoch: [14][3570/11272] Time 0.752 (11.592) Data 0.002 (10.768) Loss 2.4011 (2.6113) Prec@1 41.875 (36.677) Prec@5 75.625 (67.305) Epoch: [14][3580/11272] Time 0.758 (11.562) Data 0.001 (10.738) Loss 2.7158 (2.6114) Prec@1 35.625 (36.675) Prec@5 65.625 (67.302) Epoch: [14][3590/11272] Time 0.907 (11.532) Data 0.002 (10.708) Loss 2.5810 (2.6117) Prec@1 40.625 (36.672) Prec@5 66.875 (67.299) Epoch: [14][3600/11272] Time 0.885 (11.502) Data 0.001 (10.678) Loss 2.8087 (2.6117) Prec@1 31.875 (36.676) Prec@5 62.500 (67.297) Epoch: [14][3610/11272] Time 0.840 (11.472) Data 0.001 (10.649) Loss 2.6367 (2.6116) Prec@1 40.625 (36.680) Prec@5 68.125 (67.303) Epoch: [14][3620/11272] Time 0.732 (11.443) Data 0.001 (10.619) Loss 2.8021 (2.6116) Prec@1 34.375 (36.680) Prec@5 65.000 (67.302) Epoch: [14][3630/11272] Time 0.932 (11.414) Data 0.001 (10.590) Loss 2.5609 (2.6116) Prec@1 40.000 (36.679) Prec@5 68.750 (67.303) Epoch: [14][3640/11272] Time 0.736 (11.385) Data 0.003 (10.561) Loss 2.3223 (2.6114) Prec@1 41.250 (36.681) Prec@5 70.625 (67.307) Epoch: [14][3650/11272] Time 0.757 (11.356) Data 0.002 (10.532) Loss 2.4947 (2.6116) Prec@1 40.625 (36.681) Prec@5 70.625 (67.307) Epoch: [14][3660/11272] Time 0.901 (11.327) Data 0.001 (10.503) Loss 2.7771 (2.6116) Prec@1 33.750 (36.677) Prec@5 61.875 (67.306) Epoch: [14][3670/11272] Time 0.825 (11.298) Data 0.001 (10.475) Loss 2.4807 (2.6116) Prec@1 36.875 (36.678) Prec@5 68.125 (67.306) Epoch: [14][3680/11272] Time 0.787 (11.270) Data 0.002 (10.446) Loss 2.6783 (2.6118) Prec@1 31.250 (36.674) Prec@5 66.250 (67.302) Epoch: [14][3690/11272] Time 0.725 (11.242) Data 0.001 (10.418) Loss 2.6409 (2.6116) Prec@1 36.250 (36.675) Prec@5 62.500 (67.305) Epoch: [14][3700/11272] Time 0.853 (11.213) Data 0.001 (10.390) Loss 2.4806 (2.6118) Prec@1 46.250 (36.675) Prec@5 76.250 (67.303) Epoch: [14][3710/11272] Time 0.832 (11.185) Data 0.001 (10.362) Loss 2.5908 (2.6116) Prec@1 37.500 (36.675) Prec@5 66.875 (67.309) Epoch: [14][3720/11272] Time 0.752 (11.158) Data 0.002 (10.334) Loss 2.7077 (2.6115) Prec@1 32.500 (36.676) Prec@5 68.750 (67.310) Epoch: [14][3730/11272] Time 0.738 (11.130) Data 0.001 (10.306) Loss 2.5194 (2.6114) Prec@1 39.375 (36.677) Prec@5 70.625 (67.311) Epoch: [14][3740/11272] Time 0.875 (11.102) Data 0.001 (10.279) Loss 2.6948 (2.6113) Prec@1 35.000 (36.679) Prec@5 65.000 (67.312) Epoch: [14][3750/11272] Time 0.828 (11.075) Data 0.001 (10.251) Loss 2.7073 (2.6113) Prec@1 35.625 (36.679) Prec@5 67.500 (67.316) Epoch: [14][3760/11272] Time 0.757 (11.047) Data 0.001 (10.224) Loss 2.7116 (2.6114) Prec@1 33.750 (36.671) Prec@5 69.375 (67.316) Epoch: [14][3770/11272] Time 0.905 (11.020) Data 0.001 (10.197) Loss 2.5889 (2.6114) Prec@1 36.875 (36.671) Prec@5 66.250 (67.313) Epoch: [14][3780/11272] Time 0.870 (10.993) Data 0.002 (10.170) Loss 2.4414 (2.6114) Prec@1 41.250 (36.673) Prec@5 71.250 (67.314) Epoch: [14][3790/11272] Time 0.808 (10.967) Data 0.001 (10.143) Loss 2.3958 (2.6114) Prec@1 40.000 (36.676) Prec@5 71.875 (67.313) Epoch: [14][3800/11272] Time 0.753 (10.940) Data 0.001 (10.117) Loss 2.7179 (2.6113) Prec@1 33.125 (36.679) Prec@5 63.750 (67.313) Epoch: [14][3810/11272] Time 0.852 (10.913) Data 0.001 (10.090) Loss 2.6650 (2.6115) Prec@1 40.000 (36.677) Prec@5 63.750 (67.304) Epoch: [14][3820/11272] Time 0.919 (10.887) Data 0.001 (10.064) Loss 2.6336 (2.6115) Prec@1 36.875 (36.675) Prec@5 68.750 (67.303) Epoch: [14][3830/11272] Time 0.755 (10.861) Data 0.001 (10.037) Loss 2.4243 (2.6115) Prec@1 35.000 (36.674) Prec@5 70.625 (67.302) Epoch: [14][3840/11272] Time 0.761 (10.834) Data 0.001 (10.011) Loss 2.6424 (2.6116) Prec@1 33.750 (36.672) Prec@5 68.125 (67.304) Epoch: [14][3850/11272] Time 0.839 (10.808) Data 0.001 (9.985) Loss 2.4075 (2.6116) Prec@1 39.375 (36.674) Prec@5 71.250 (67.300) Epoch: [14][3860/11272] Time 0.862 (10.783) Data 0.001 (9.959) Loss 2.7270 (2.6116) Prec@1 30.000 (36.673) Prec@5 69.375 (67.298) Epoch: [14][3870/11272] Time 0.760 (10.757) Data 0.002 (9.934) Loss 2.5887 (2.6114) Prec@1 40.000 (36.683) Prec@5 65.625 (67.304) Epoch: [14][3880/11272] Time 0.734 (10.731) Data 0.001 (9.908) Loss 2.5323 (2.6113) Prec@1 42.500 (36.685) Prec@5 70.000 (67.308) Epoch: [14][3890/11272] Time 0.911 (10.706) Data 0.001 (9.883) Loss 2.6595 (2.6114) Prec@1 32.500 (36.680) Prec@5 65.625 (67.307) Epoch: [14][3900/11272] Time 0.754 (10.680) Data 0.003 (9.857) Loss 2.7122 (2.6115) Prec@1 34.375 (36.678) Prec@5 64.375 (67.305) Epoch: [14][3910/11272] Time 0.759 (10.655) Data 0.002 (9.832) Loss 2.6132 (2.6115) Prec@1 35.625 (36.678) Prec@5 65.000 (67.305) Epoch: [14][3920/11272] Time 0.849 (10.630) Data 0.001 (9.807) Loss 2.6215 (2.6115) Prec@1 37.500 (36.679) Prec@5 64.375 (67.306) Epoch: [14][3930/11272] Time 0.858 (10.605) Data 0.002 (9.782) Loss 2.7360 (2.6113) Prec@1 35.000 (36.682) Prec@5 60.000 (67.307) Epoch: [14][3940/11272] Time 0.739 (10.580) Data 0.001 (9.757) Loss 2.3987 (2.6111) Prec@1 37.500 (36.684) Prec@5 73.125 (67.311) Epoch: [14][3950/11272] Time 0.727 (10.555) Data 0.002 (9.733) Loss 2.5824 (2.6112) Prec@1 40.625 (36.685) Prec@5 70.625 (67.312) Epoch: [14][3960/11272] Time 0.878 (10.531) Data 0.001 (9.708) Loss 2.5254 (2.6110) Prec@1 40.000 (36.688) Prec@5 66.250 (67.316) Epoch: [14][3970/11272] Time 0.830 (10.506) Data 0.001 (9.684) Loss 2.9755 (2.6112) Prec@1 34.375 (36.684) Prec@5 58.750 (67.311) Epoch: [14][3980/11272] Time 0.760 (10.482) Data 0.002 (9.659) Loss 2.7633 (2.6112) Prec@1 40.000 (36.685) Prec@5 65.000 (67.309) Epoch: [14][3990/11272] Time 0.778 (10.458) Data 0.002 (9.635) Loss 2.6954 (2.6113) Prec@1 33.750 (36.682) Prec@5 66.250 (67.311) Epoch: [14][4000/11272] Time 0.897 (10.434) Data 0.002 (9.611) Loss 2.7451 (2.6113) Prec@1 33.750 (36.681) Prec@5 63.125 (67.310) Epoch: [14][4010/11272] Time 0.869 (10.410) Data 0.001 (9.587) Loss 2.5108 (2.6112) Prec@1 36.250 (36.684) Prec@5 68.750 (67.312) Epoch: [14][4020/11272] Time 0.702 (10.386) Data 0.001 (9.563) Loss 2.7362 (2.6113) Prec@1 35.000 (36.679) Prec@5 61.250 (67.309) Epoch: [14][4030/11272] Time 0.906 (10.362) Data 0.001 (9.539) Loss 2.5597 (2.6114) Prec@1 39.375 (36.678) Prec@5 60.625 (67.307) Epoch: [14][4040/11272] Time 0.865 (10.339) Data 0.001 (9.516) Loss 2.5536 (2.6115) Prec@1 35.625 (36.675) Prec@5 71.875 (67.303) Epoch: [14][4050/11272] Time 0.783 (10.315) Data 0.002 (9.492) Loss 2.5800 (2.6117) Prec@1 35.625 (36.673) Prec@5 70.000 (67.299) Epoch: [14][4060/11272] Time 0.808 (10.292) Data 0.001 (9.469) Loss 2.5018 (2.6116) Prec@1 43.125 (36.676) Prec@5 66.250 (67.297) Epoch: [14][4070/11272] Time 0.866 (10.268) Data 0.001 (9.446) Loss 2.5029 (2.6116) Prec@1 36.250 (36.676) Prec@5 65.625 (67.297) Epoch: [14][4080/11272] Time 0.872 (10.245) Data 0.001 (9.423) Loss 2.5079 (2.6115) Prec@1 39.375 (36.679) Prec@5 66.875 (67.300) Epoch: [14][4090/11272] Time 0.760 (10.222) Data 0.002 (9.400) Loss 2.7275 (2.6115) Prec@1 28.125 (36.680) Prec@5 66.875 (67.299) Epoch: [14][4100/11272] Time 0.736 (10.199) Data 0.002 (9.377) Loss 2.5896 (2.6113) Prec@1 34.375 (36.680) Prec@5 63.750 (67.300) Epoch: [14][4110/11272] Time 0.864 (10.176) Data 0.001 (9.354) Loss 2.4116 (2.6112) Prec@1 40.625 (36.685) Prec@5 69.375 (67.305) Epoch: [14][4120/11272] Time 0.885 (10.154) Data 0.002 (9.331) Loss 2.4648 (2.6112) Prec@1 38.125 (36.683) Prec@5 70.000 (67.306) Epoch: [14][4130/11272] Time 0.758 (10.131) Data 0.002 (9.309) Loss 2.5991 (2.6110) Prec@1 36.250 (36.686) Prec@5 63.750 (67.309) Epoch: [14][4140/11272] Time 0.737 (10.109) Data 0.001 (9.286) Loss 2.5197 (2.6111) Prec@1 35.000 (36.685) Prec@5 73.750 (67.308) Epoch: [14][4150/11272] Time 0.870 (10.086) Data 0.001 (9.264) Loss 2.6452 (2.6113) Prec@1 36.250 (36.683) Prec@5 65.625 (67.307) Epoch: [14][4160/11272] Time 0.868 (10.064) Data 0.001 (9.242) Loss 2.5989 (2.6115) Prec@1 36.875 (36.677) Prec@5 70.000 (67.302) Epoch: [14][4170/11272] Time 0.735 (10.042) Data 0.001 (9.219) Loss 2.6489 (2.6116) Prec@1 36.250 (36.676) Prec@5 60.000 (67.297) Epoch: [14][4180/11272] Time 0.853 (10.020) Data 0.002 (9.197) Loss 2.7943 (2.6117) Prec@1 35.000 (36.675) Prec@5 65.000 (67.295) Epoch: [14][4190/11272] Time 0.834 (9.998) Data 0.001 (9.175) Loss 2.7125 (2.6117) Prec@1 35.000 (36.677) Prec@5 67.500 (67.297) Epoch: [14][4200/11272] Time 0.757 (9.976) Data 0.001 (9.154) Loss 2.5995 (2.6116) Prec@1 35.625 (36.676) Prec@5 71.875 (67.303) Epoch: [14][4210/11272] Time 0.774 (9.954) Data 0.002 (9.132) Loss 2.6625 (2.6117) Prec@1 36.250 (36.675) Prec@5 67.500 (67.302) Epoch: [14][4220/11272] Time 0.846 (9.933) Data 0.001 (9.110) Loss 2.5210 (2.6116) Prec@1 38.125 (36.677) Prec@5 70.000 (67.306) Epoch: [14][4230/11272] Time 0.845 (9.911) Data 0.001 (9.089) Loss 3.0762 (2.6115) Prec@1 27.500 (36.682) Prec@5 60.625 (67.307) Epoch: [14][4240/11272] Time 0.762 (9.890) Data 0.001 (9.067) Loss 2.4837 (2.6115) Prec@1 36.250 (36.683) Prec@5 68.750 (67.307) Epoch: [14][4250/11272] Time 0.763 (9.868) Data 0.001 (9.046) Loss 2.4019 (2.6113) Prec@1 41.250 (36.689) Prec@5 73.125 (67.310) Epoch: [14][4260/11272] Time 0.851 (9.847) Data 0.001 (9.025) Loss 2.7706 (2.6115) Prec@1 36.250 (36.684) Prec@5 67.500 (67.309) Epoch: [14][4270/11272] Time 0.867 (9.826) Data 0.002 (9.004) Loss 2.6026 (2.6115) Prec@1 42.500 (36.686) Prec@5 68.125 (67.308) Epoch: [14][4280/11272] Time 0.719 (9.805) Data 0.001 (8.983) Loss 2.5796 (2.6115) Prec@1 33.125 (36.685) Prec@5 71.875 (67.307) Epoch: [14][4290/11272] Time 0.727 (9.784) Data 0.002 (8.962) Loss 2.5668 (2.6116) Prec@1 38.125 (36.683) Prec@5 69.375 (67.305) Epoch: [14][4300/11272] Time 0.849 (9.763) Data 0.002 (8.941) Loss 2.4095 (2.6114) Prec@1 40.625 (36.687) Prec@5 73.125 (67.306) Epoch: [14][4310/11272] Time 0.722 (9.742) Data 0.001 (8.920) Loss 2.6410 (2.6114) Prec@1 37.500 (36.688) Prec@5 67.500 (67.307) Epoch: [14][4320/11272] Time 0.755 (9.722) Data 0.001 (8.899) Loss 2.4713 (2.6114) Prec@1 43.125 (36.688) Prec@5 73.750 (67.308) Epoch: [14][4330/11272] Time 0.898 (9.701) Data 0.001 (8.879) Loss 2.8785 (2.6115) Prec@1 29.375 (36.688) Prec@5 65.000 (67.305) Epoch: [14][4340/11272] Time 0.947 (9.681) Data 0.002 (8.858) Loss 2.7608 (2.6117) Prec@1 36.250 (36.686) Prec@5 65.625 (67.300) Epoch: [14][4350/11272] Time 0.752 (9.660) Data 0.001 (8.838) Loss 2.6772 (2.6118) Prec@1 36.250 (36.684) Prec@5 66.875 (67.299) Epoch: [14][4360/11272] Time 0.809 (9.640) Data 0.002 (8.818) Loss 2.6241 (2.6119) Prec@1 32.500 (36.675) Prec@5 67.500 (67.302) Epoch: [14][4370/11272] Time 0.830 (9.620) Data 0.001 (8.798) Loss 2.2928 (2.6120) Prec@1 44.375 (36.672) Prec@5 73.750 (67.300) Epoch: [14][4380/11272] Time 0.843 (9.600) Data 0.001 (8.778) Loss 2.5488 (2.6119) Prec@1 40.625 (36.671) Prec@5 68.125 (67.301) Epoch: [14][4390/11272] Time 0.805 (9.580) Data 0.002 (8.758) Loss 2.4154 (2.6118) Prec@1 39.375 (36.676) Prec@5 71.875 (67.304) Epoch: [14][4400/11272] Time 0.788 (9.560) Data 0.001 (8.738) Loss 2.4385 (2.6120) Prec@1 42.500 (36.672) Prec@5 71.250 (67.299) Epoch: [14][4410/11272] Time 0.878 (9.540) Data 0.001 (8.718) Loss 2.7873 (2.6123) Prec@1 36.250 (36.670) Prec@5 63.750 (67.294) Epoch: [14][4420/11272] Time 0.885 (9.520) Data 0.002 (8.698) Loss 2.7836 (2.6123) Prec@1 32.500 (36.667) Prec@5 66.250 (67.292) Epoch: [14][4430/11272] Time 0.737 (9.501) Data 0.001 (8.678) Loss 2.4271 (2.6124) Prec@1 43.125 (36.668) Prec@5 71.250 (67.291) Epoch: [14][4440/11272] Time 0.889 (9.481) Data 0.001 (8.659) Loss 2.4913 (2.6126) Prec@1 40.000 (36.666) Prec@5 68.125 (67.288) Epoch: [14][4450/11272] Time 0.893 (9.462) Data 0.001 (8.639) Loss 2.8299 (2.6127) Prec@1 33.125 (36.664) Prec@5 59.375 (67.284) Epoch: [14][4460/11272] Time 0.757 (9.442) Data 0.001 (8.620) Loss 2.4944 (2.6128) Prec@1 36.875 (36.662) Prec@5 71.875 (67.282) Epoch: [14][4470/11272] Time 0.758 (9.423) Data 0.002 (8.601) Loss 2.8615 (2.6129) Prec@1 26.875 (36.663) Prec@5 65.625 (67.283) Epoch: [14][4480/11272] Time 0.877 (9.404) Data 0.002 (8.582) Loss 2.8029 (2.6130) Prec@1 32.500 (36.659) Prec@5 62.500 (67.276) Epoch: [14][4490/11272] Time 0.865 (9.385) Data 0.002 (8.563) Loss 2.8875 (2.6131) Prec@1 35.625 (36.658) Prec@5 60.000 (67.275) Epoch: [14][4500/11272] Time 0.765 (9.366) Data 0.002 (8.544) Loss 2.4869 (2.6131) Prec@1 40.000 (36.657) Prec@5 70.000 (67.274) Epoch: [14][4510/11272] Time 0.798 (9.347) Data 0.001 (8.525) Loss 2.4996 (2.6133) Prec@1 41.875 (36.652) Prec@5 67.500 (67.271) Epoch: [14][4520/11272] Time 0.872 (9.328) Data 0.001 (8.506) Loss 2.6533 (2.6134) Prec@1 35.625 (36.652) Prec@5 65.625 (67.272) Epoch: [14][4530/11272] Time 0.932 (9.309) Data 0.002 (8.487) Loss 2.6545 (2.6133) Prec@1 40.000 (36.650) Prec@5 69.375 (67.275) Epoch: [14][4540/11272] Time 0.722 (9.290) Data 0.001 (8.468) Loss 2.4150 (2.6134) Prec@1 38.125 (36.645) Prec@5 72.500 (67.274) Epoch: [14][4550/11272] Time 0.766 (9.272) Data 0.001 (8.450) Loss 2.5425 (2.6134) Prec@1 38.125 (36.646) Prec@5 66.875 (67.271) Epoch: [14][4560/11272] Time 0.920 (9.253) Data 0.002 (8.431) Loss 2.7905 (2.6135) Prec@1 35.000 (36.647) Prec@5 64.375 (67.267) Epoch: [14][4570/11272] Time 0.764 (9.235) Data 0.003 (8.413) Loss 2.5394 (2.6134) Prec@1 38.750 (36.649) Prec@5 69.375 (67.269) Epoch: [14][4580/11272] Time 0.756 (9.216) Data 0.001 (8.394) Loss 2.4856 (2.6134) Prec@1 40.000 (36.650) Prec@5 68.750 (67.268) Epoch: [14][4590/11272] Time 0.879 (9.198) Data 0.003 (8.376) Loss 2.9323 (2.6135) Prec@1 31.250 (36.649) Prec@5 64.375 (67.266) Epoch: [14][4600/11272] Time 0.849 (9.180) Data 0.002 (8.358) Loss 2.7502 (2.6137) Prec@1 36.875 (36.645) Prec@5 62.500 (67.261) Epoch: [14][4610/11272] Time 0.747 (9.162) Data 0.002 (8.340) Loss 2.5959 (2.6135) Prec@1 38.125 (36.652) Prec@5 65.625 (67.264) Epoch: [14][4620/11272] Time 0.866 (9.144) Data 0.002 (8.322) Loss 2.7570 (2.6133) Prec@1 32.500 (36.656) Prec@5 67.500 (67.269) Epoch: [14][4630/11272] Time 0.868 (9.126) Data 0.001 (8.304) Loss 2.5560 (2.6133) Prec@1 40.625 (36.654) Prec@5 67.500 (67.269) Epoch: [14][4640/11272] Time 0.863 (9.108) Data 0.002 (8.286) Loss 2.5462 (2.6133) Prec@1 41.250 (36.656) Prec@5 69.375 (67.269) Epoch: [14][4650/11272] Time 0.770 (9.090) Data 0.001 (8.268) Loss 2.4332 (2.6135) Prec@1 37.500 (36.648) Prec@5 70.625 (67.268) Epoch: [14][4660/11272] Time 0.752 (9.072) Data 0.002 (8.250) Loss 2.7508 (2.6134) Prec@1 38.125 (36.651) Prec@5 66.250 (67.267) Epoch: [14][4670/11272] Time 0.850 (9.055) Data 0.001 (8.233) Loss 2.2571 (2.6133) Prec@1 38.750 (36.653) Prec@5 78.750 (67.271) Epoch: [14][4680/11272] Time 0.921 (9.037) Data 0.001 (8.215) Loss 2.5888 (2.6134) Prec@1 35.625 (36.649) Prec@5 68.750 (67.269) Epoch: [14][4690/11272] Time 0.756 (9.020) Data 0.001 (8.198) Loss 2.5807 (2.6135) Prec@1 38.125 (36.648) Prec@5 66.250 (67.271) Epoch: [14][4700/11272] Time 0.950 (9.002) Data 0.002 (8.180) Loss 2.6870 (2.6135) Prec@1 38.125 (36.650) Prec@5 62.500 (67.270) Epoch: [14][4710/11272] Time 0.945 (8.985) Data 0.001 (8.163) Loss 2.6939 (2.6136) Prec@1 38.750 (36.651) Prec@5 66.250 (67.269) Epoch: [14][4720/11272] Time 0.776 (8.968) Data 0.001 (8.145) Loss 2.9721 (2.6135) Prec@1 31.875 (36.654) Prec@5 58.125 (67.268) Epoch: [14][4730/11272] Time 0.748 (8.950) Data 0.002 (8.128) Loss 2.8725 (2.6136) Prec@1 28.750 (36.653) Prec@5 63.750 (67.264) Epoch: [14][4740/11272] Time 0.840 (8.933) Data 0.001 (8.111) Loss 2.6179 (2.6136) Prec@1 34.375 (36.655) Prec@5 66.250 (67.263) Epoch: [14][4750/11272] Time 0.950 (8.916) Data 0.002 (8.094) Loss 3.0021 (2.6136) Prec@1 28.125 (36.655) Prec@5 55.625 (67.263) Epoch: [14][4760/11272] Time 0.754 (8.899) Data 0.001 (8.077) Loss 2.8292 (2.6137) Prec@1 34.375 (36.656) Prec@5 60.000 (67.261) Epoch: [14][4770/11272] Time 0.766 (8.882) Data 0.002 (8.060) Loss 2.7194 (2.6136) Prec@1 33.125 (36.658) Prec@5 65.625 (67.264) Epoch: [14][4780/11272] Time 0.942 (8.865) Data 0.002 (8.043) Loss 2.8672 (2.6136) Prec@1 31.250 (36.657) Prec@5 61.250 (67.264) Epoch: [14][4790/11272] Time 0.917 (8.849) Data 0.002 (8.026) Loss 2.6772 (2.6135) Prec@1 38.125 (36.659) Prec@5 66.250 (67.266) Epoch: [14][4800/11272] Time 0.753 (8.832) Data 0.002 (8.010) Loss 2.5447 (2.6136) Prec@1 35.625 (36.659) Prec@5 66.875 (67.265) Epoch: [14][4810/11272] Time 0.751 (8.815) Data 0.002 (7.993) Loss 2.4569 (2.6136) Prec@1 41.250 (36.660) Prec@5 68.125 (67.266) Epoch: [14][4820/11272] Time 0.917 (8.799) Data 0.002 (7.977) Loss 2.5724 (2.6135) Prec@1 41.875 (36.661) Prec@5 66.875 (67.268) Epoch: [14][4830/11272] Time 0.723 (8.782) Data 0.003 (7.960) Loss 2.4129 (2.6135) Prec@1 39.375 (36.661) Prec@5 70.000 (67.269) Epoch: [14][4840/11272] Time 0.756 (8.766) Data 0.001 (7.944) Loss 2.4957 (2.6138) Prec@1 36.250 (36.655) Prec@5 69.375 (67.262) Epoch: [14][4850/11272] Time 0.853 (8.750) Data 0.002 (7.927) Loss 2.6272 (2.6138) Prec@1 38.750 (36.653) Prec@5 66.875 (67.262) Epoch: [14][4860/11272] Time 0.890 (8.733) Data 0.002 (7.911) Loss 2.5761 (2.6139) Prec@1 36.875 (36.651) Prec@5 68.750 (67.260) Epoch: [14][4870/11272] Time 0.751 (8.717) Data 0.001 (7.895) Loss 2.5477 (2.6142) Prec@1 38.125 (36.643) Prec@5 67.500 (67.257) Epoch: [14][4880/11272] Time 0.752 (8.701) Data 0.002 (7.879) Loss 2.8375 (2.6142) Prec@1 29.375 (36.638) Prec@5 63.125 (67.255) Epoch: [14][4890/11272] Time 0.941 (8.685) Data 0.001 (7.862) Loss 2.7538 (2.6141) Prec@1 31.875 (36.639) Prec@5 58.750 (67.258) Epoch: [14][4900/11272] Time 0.828 (8.669) Data 0.001 (7.846) Loss 2.6741 (2.6141) Prec@1 36.875 (36.638) Prec@5 65.625 (67.257) Epoch: [14][4910/11272] Time 0.754 (8.653) Data 0.001 (7.830) Loss 2.5315 (2.6140) Prec@1 38.750 (36.639) Prec@5 70.000 (67.258) Epoch: [14][4920/11272] Time 0.737 (8.637) Data 0.001 (7.814) Loss 2.7599 (2.6141) Prec@1 34.375 (36.637) Prec@5 65.000 (67.255) Epoch: [14][4930/11272] Time 0.883 (8.621) Data 0.001 (7.799) Loss 2.4168 (2.6141) Prec@1 41.875 (36.634) Prec@5 71.250 (67.255) Epoch: [14][4940/11272] Time 0.848 (8.605) Data 0.002 (7.783) Loss 2.5718 (2.6141) Prec@1 39.375 (36.633) Prec@5 64.375 (67.254) Epoch: [14][4950/11272] Time 0.776 (8.589) Data 0.002 (7.767) Loss 2.6494 (2.6141) Prec@1 38.750 (36.632) Prec@5 63.125 (67.255) Epoch: [14][4960/11272] Time 0.913 (8.574) Data 0.001 (7.751) Loss 2.5487 (2.6140) Prec@1 40.000 (36.637) Prec@5 70.000 (67.259) Epoch: [14][4970/11272] Time 0.851 (8.558) Data 0.001 (7.736) Loss 2.6910 (2.6142) Prec@1 35.000 (36.635) Prec@5 66.250 (67.256) Epoch: [14][4980/11272] Time 0.726 (8.543) Data 0.001 (7.720) Loss 2.6493 (2.6141) Prec@1 38.125 (36.634) Prec@5 69.375 (67.257) Epoch: [14][4990/11272] Time 0.717 (8.527) Data 0.001 (7.705) Loss 2.2904 (2.6142) Prec@1 45.000 (36.632) Prec@5 71.250 (67.254) Epoch: [14][5000/11272] Time 0.871 (8.512) Data 0.001 (7.690) Loss 2.6342 (2.6143) Prec@1 35.625 (36.630) Prec@5 65.625 (67.253) Epoch: [14][5010/11272] Time 0.896 (8.496) Data 0.001 (7.674) Loss 2.5353 (2.6143) Prec@1 36.875 (36.630) Prec@5 69.375 (67.254) Epoch: [14][5020/11272] Time 0.773 (8.481) Data 0.001 (7.659) Loss 2.6337 (2.6143) Prec@1 37.500 (36.632) Prec@5 64.375 (67.252) Epoch: [14][5030/11272] Time 0.747 (8.466) Data 0.001 (7.644) Loss 2.8978 (2.6144) Prec@1 35.625 (36.632) Prec@5 61.875 (67.251) Epoch: [14][5040/11272] Time 0.867 (8.451) Data 0.001 (7.628) Loss 2.4887 (2.6143) Prec@1 37.500 (36.634) Prec@5 71.250 (67.250) Epoch: [14][5050/11272] Time 0.851 (8.435) Data 0.002 (7.613) Loss 2.7482 (2.6144) Prec@1 31.250 (36.634) Prec@5 63.750 (67.252) Epoch: [14][5060/11272] Time 0.762 (8.420) Data 0.002 (7.598) Loss 2.6048 (2.6144) Prec@1 41.875 (36.638) Prec@5 66.250 (67.251) Epoch: [14][5070/11272] Time 0.783 (8.405) Data 0.002 (7.583) Loss 2.5950 (2.6141) Prec@1 40.000 (36.645) Prec@5 66.875 (67.255) Epoch: [14][5080/11272] Time 0.872 (8.390) Data 0.002 (7.568) Loss 2.5285 (2.6141) Prec@1 40.625 (36.641) Prec@5 65.625 (67.253) Epoch: [14][5090/11272] Time 0.848 (8.376) Data 0.002 (7.554) Loss 2.7307 (2.6142) Prec@1 40.625 (36.639) Prec@5 66.250 (67.250) Epoch: [14][5100/11272] Time 0.791 (8.361) Data 0.002 (7.539) Loss 2.5072 (2.6143) Prec@1 38.750 (36.637) Prec@5 70.625 (67.248) Epoch: [14][5110/11272] Time 0.946 (8.346) Data 0.001 (7.524) Loss 2.3058 (2.6142) Prec@1 45.000 (36.637) Prec@5 75.625 (67.249) Epoch: [14][5120/11272] Time 0.834 (8.331) Data 0.001 (7.509) Loss 2.7629 (2.6143) Prec@1 33.125 (36.638) Prec@5 64.375 (67.250) Epoch: [14][5130/11272] Time 0.733 (8.317) Data 0.001 (7.495) Loss 2.4675 (2.6143) Prec@1 38.750 (36.638) Prec@5 68.750 (67.249) Epoch: [14][5140/11272] Time 0.790 (8.302) Data 0.001 (7.480) Loss 2.4724 (2.6142) Prec@1 41.250 (36.638) Prec@5 68.125 (67.253) Epoch: [14][5150/11272] Time 0.874 (8.288) Data 0.001 (7.466) Loss 2.7059 (2.6143) Prec@1 36.875 (36.635) Prec@5 68.125 (67.251) Epoch: [14][5160/11272] Time 0.917 (8.273) Data 0.001 (7.451) Loss 2.6760 (2.6144) Prec@1 31.250 (36.634) Prec@5 64.375 (67.247) Epoch: [14][5170/11272] Time 0.763 (8.259) Data 0.001 (7.437) Loss 2.3753 (2.6143) Prec@1 42.500 (36.637) Prec@5 73.125 (67.247) Epoch: [14][5180/11272] Time 0.749 (8.245) Data 0.001 (7.422) Loss 2.4942 (2.6143) Prec@1 39.375 (36.640) Prec@5 68.125 (67.246) Epoch: [14][5190/11272] Time 0.891 (8.230) Data 0.002 (7.408) Loss 2.7912 (2.6144) Prec@1 34.375 (36.638) Prec@5 61.875 (67.243) Epoch: [14][5200/11272] Time 0.883 (8.216) Data 0.002 (7.394) Loss 2.5496 (2.6143) Prec@1 43.125 (36.642) Prec@5 68.125 (67.246) Epoch: [14][5210/11272] Time 0.740 (8.202) Data 0.002 (7.380) Loss 2.4329 (2.6143) Prec@1 39.375 (36.642) Prec@5 70.625 (67.245) Epoch: [14][5220/11272] Time 0.787 (8.188) Data 0.002 (7.366) Loss 2.7396 (2.6144) Prec@1 38.750 (36.643) Prec@5 64.375 (67.244) Epoch: [14][5230/11272] Time 0.863 (8.174) Data 0.001 (7.351) Loss 2.4955 (2.6145) Prec@1 39.375 (36.643) Prec@5 70.000 (67.243) Epoch: [14][5240/11272] Time 0.738 (8.160) Data 0.002 (7.337) Loss 2.5671 (2.6145) Prec@1 36.250 (36.642) Prec@5 70.000 (67.246) Epoch: [14][5250/11272] Time 0.757 (8.146) Data 0.001 (7.323) Loss 2.5903 (2.6145) Prec@1 38.750 (36.642) Prec@5 68.750 (67.245) Epoch: [14][5260/11272] Time 0.858 (8.132) Data 0.001 (7.310) Loss 2.7232 (2.6146) Prec@1 36.875 (36.641) Prec@5 63.750 (67.246) Epoch: [14][5270/11272] Time 0.842 (8.118) Data 0.002 (7.296) Loss 2.9304 (2.6146) Prec@1 31.875 (36.639) Prec@5 61.250 (67.244) Epoch: [14][5280/11272] Time 0.780 (8.104) Data 0.001 (7.282) Loss 2.4934 (2.6145) Prec@1 33.750 (36.638) Prec@5 67.500 (67.245) Epoch: [14][5290/11272] Time 0.760 (8.090) Data 0.002 (7.268) Loss 2.6892 (2.6145) Prec@1 36.250 (36.636) Prec@5 63.125 (67.244) Epoch: [14][5300/11272] Time 0.885 (8.076) Data 0.002 (7.254) Loss 2.8879 (2.6145) Prec@1 34.375 (36.635) Prec@5 60.625 (67.245) Epoch: [14][5310/11272] Time 0.869 (8.063) Data 0.001 (7.241) Loss 2.8985 (2.6147) Prec@1 28.125 (36.631) Prec@5 57.500 (67.241) Epoch: [14][5320/11272] Time 0.759 (8.049) Data 0.001 (7.227) Loss 2.5754 (2.6146) Prec@1 35.625 (36.631) Prec@5 68.125 (67.243) Epoch: [14][5330/11272] Time 0.728 (8.036) Data 0.002 (7.214) Loss 2.5704 (2.6145) Prec@1 40.625 (36.635) Prec@5 70.625 (67.244) Epoch: [14][5340/11272] Time 0.857 (8.022) Data 0.001 (7.200) Loss 2.6646 (2.6146) Prec@1 36.250 (36.635) Prec@5 69.375 (67.244) Epoch: [14][5350/11272] Time 0.866 (8.009) Data 0.001 (7.187) Loss 2.5317 (2.6145) Prec@1 32.500 (36.635) Prec@5 71.875 (67.247) Epoch: [14][5360/11272] Time 0.733 (7.995) Data 0.002 (7.173) Loss 2.7841 (2.6147) Prec@1 32.500 (36.632) Prec@5 63.125 (67.244) Epoch: [14][5370/11272] Time 0.823 (7.982) Data 0.001 (7.160) Loss 2.9196 (2.6147) Prec@1 31.250 (36.631) Prec@5 65.000 (67.245) Epoch: [14][5380/11272] Time 0.938 (7.968) Data 0.001 (7.147) Loss 2.7228 (2.6147) Prec@1 33.125 (36.630) Prec@5 63.750 (67.245) Epoch: [14][5390/11272] Time 0.758 (7.955) Data 0.001 (7.133) Loss 2.4406 (2.6146) Prec@1 40.625 (36.634) Prec@5 71.250 (67.247) Epoch: [14][5400/11272] Time 0.779 (7.942) Data 0.001 (7.120) Loss 2.6124 (2.6146) Prec@1 35.000 (36.632) Prec@5 69.375 (67.246) Epoch: [14][5410/11272] Time 0.870 (7.929) Data 0.001 (7.107) Loss 2.4678 (2.6145) Prec@1 40.625 (36.634) Prec@5 71.875 (67.248) Epoch: [14][5420/11272] Time 0.922 (7.916) Data 0.001 (7.094) Loss 2.5774 (2.6144) Prec@1 34.375 (36.637) Prec@5 68.750 (67.250) Epoch: [14][5430/11272] Time 0.719 (7.903) Data 0.001 (7.081) Loss 2.8097 (2.6144) Prec@1 33.125 (36.636) Prec@5 65.000 (67.249) Epoch: [14][5440/11272] Time 0.731 (7.890) Data 0.001 (7.068) Loss 2.7286 (2.6144) Prec@1 35.625 (36.638) Prec@5 64.375 (67.247) Epoch: [14][5450/11272] Time 0.844 (7.877) Data 0.001 (7.055) Loss 2.8858 (2.6144) Prec@1 26.250 (36.636) Prec@5 59.375 (67.247) Epoch: [14][5460/11272] Time 0.928 (7.864) Data 0.002 (7.042) Loss 2.7748 (2.6146) Prec@1 34.375 (36.635) Prec@5 63.125 (67.244) Epoch: [14][5470/11272] Time 0.765 (7.851) Data 0.002 (7.029) Loss 2.5025 (2.6145) Prec@1 33.125 (36.636) Prec@5 67.500 (67.245) Epoch: [14][5480/11272] Time 0.781 (7.838) Data 0.002 (7.016) Loss 2.6072 (2.6146) Prec@1 35.625 (36.635) Prec@5 65.625 (67.244) Epoch: [14][5490/11272] Time 0.887 (7.825) Data 0.002 (7.003) Loss 2.7348 (2.6146) Prec@1 35.625 (36.634) Prec@5 63.750 (67.243) Epoch: [14][5500/11272] Time 0.719 (7.812) Data 0.003 (6.991) Loss 2.7485 (2.6148) Prec@1 37.500 (36.634) Prec@5 66.250 (67.239) Epoch: [14][5510/11272] Time 0.768 (7.800) Data 0.002 (6.978) Loss 2.5560 (2.6147) Prec@1 37.500 (36.635) Prec@5 63.750 (67.241) Epoch: [14][5520/11272] Time 0.878 (7.787) Data 0.001 (6.965) Loss 2.3221 (2.6146) Prec@1 41.875 (36.639) Prec@5 73.125 (67.242) Epoch: [14][5530/11272] Time 0.874 (7.775) Data 0.002 (6.953) Loss 2.6459 (2.6146) Prec@1 36.875 (36.639) Prec@5 65.000 (67.241) Epoch: [14][5540/11272] Time 0.763 (7.762) Data 0.002 (6.940) Loss 2.6738 (2.6148) Prec@1 33.750 (36.636) Prec@5 66.250 (67.237) Epoch: [14][5550/11272] Time 0.757 (7.749) Data 0.001 (6.928) Loss 2.8249 (2.6148) Prec@1 33.750 (36.636) Prec@5 61.875 (67.236) Epoch: [14][5560/11272] Time 0.852 (7.737) Data 0.002 (6.915) Loss 2.7293 (2.6149) Prec@1 31.875 (36.634) Prec@5 65.625 (67.234) Epoch: [14][5570/11272] Time 0.865 (7.725) Data 0.001 (6.903) Loss 2.8012 (2.6150) Prec@1 33.125 (36.633) Prec@5 61.875 (67.233) Epoch: [14][5580/11272] Time 0.756 (7.712) Data 0.002 (6.891) Loss 2.5222 (2.6151) Prec@1 37.500 (36.630) Prec@5 70.000 (67.234) Epoch: [14][5590/11272] Time 0.750 (7.700) Data 0.001 (6.878) Loss 2.6111 (2.6152) Prec@1 36.250 (36.629) Prec@5 65.625 (67.233) Epoch: [14][5600/11272] Time 0.863 (7.688) Data 0.001 (6.866) Loss 2.6149 (2.6151) Prec@1 36.875 (36.628) Prec@5 67.500 (67.236) Epoch: [14][5610/11272] Time 0.969 (7.675) Data 0.002 (6.854) Loss 2.5953 (2.6150) Prec@1 35.000 (36.630) Prec@5 65.625 (67.237) Epoch: [14][5620/11272] Time 0.733 (7.663) Data 0.001 (6.842) Loss 2.7759 (2.6150) Prec@1 33.750 (36.631) Prec@5 63.125 (67.236) Epoch: [14][5630/11272] Time 0.935 (7.651) Data 0.002 (6.829) Loss 2.6320 (2.6149) Prec@1 39.375 (36.631) Prec@5 63.750 (67.237) Epoch: [14][5640/11272] Time 0.855 (7.639) Data 0.001 (6.817) Loss 2.6521 (2.6149) Prec@1 38.750 (36.630) Prec@5 64.375 (67.235) Epoch: [14][5650/11272] Time 0.765 (7.627) Data 0.001 (6.805) Loss 2.5036 (2.6148) Prec@1 41.250 (36.633) Prec@5 68.125 (67.236) Epoch: [14][5660/11272] Time 0.751 (7.615) Data 0.001 (6.793) Loss 2.7636 (2.6149) Prec@1 35.000 (36.634) Prec@5 66.875 (67.235) Epoch: [14][5670/11272] Time 0.894 (7.603) Data 0.001 (6.781) Loss 2.4654 (2.6149) Prec@1 39.375 (36.634) Prec@5 63.125 (67.233) Epoch: [14][5680/11272] Time 0.884 (7.591) Data 0.002 (6.769) Loss 2.5428 (2.6149) Prec@1 38.750 (36.632) Prec@5 67.500 (67.234) Epoch: [14][5690/11272] Time 0.761 (7.579) Data 0.002 (6.757) Loss 2.3025 (2.6149) Prec@1 41.250 (36.632) Prec@5 70.625 (67.231) Epoch: [14][5700/11272] Time 0.754 (7.567) Data 0.001 (6.746) Loss 2.6299 (2.6149) Prec@1 33.750 (36.631) Prec@5 65.625 (67.231) Epoch: [14][5710/11272] Time 0.933 (7.555) Data 0.002 (6.734) Loss 2.6619 (2.6150) Prec@1 35.625 (36.631) Prec@5 65.625 (67.228) Epoch: [14][5720/11272] Time 0.863 (7.544) Data 0.002 (6.722) Loss 2.5767 (2.6150) Prec@1 38.125 (36.629) Prec@5 68.125 (67.228) Epoch: [14][5730/11272] Time 0.788 (7.532) Data 0.001 (6.710) Loss 2.7083 (2.6150) Prec@1 36.250 (36.630) Prec@5 70.625 (67.229) Epoch: [14][5740/11272] Time 0.723 (7.520) Data 0.002 (6.699) Loss 2.3649 (2.6151) Prec@1 40.625 (36.629) Prec@5 71.875 (67.230) Epoch: [14][5750/11272] Time 0.821 (7.509) Data 0.002 (6.687) Loss 2.5715 (2.6151) Prec@1 34.375 (36.628) Prec@5 72.500 (67.232) Epoch: [14][5760/11272] Time 0.723 (7.497) Data 0.003 (6.675) Loss 2.5400 (2.6150) Prec@1 35.625 (36.629) Prec@5 69.375 (67.234) Epoch: [14][5770/11272] Time 0.750 (7.485) Data 0.002 (6.664) Loss 2.4952 (2.6149) Prec@1 38.750 (36.630) Prec@5 73.125 (67.237) Epoch: [14][5780/11272] Time 0.923 (7.474) Data 0.002 (6.652) Loss 2.5992 (2.6147) Prec@1 36.875 (36.634) Prec@5 68.750 (67.238) Epoch: [14][5790/11272] Time 0.838 (7.462) Data 0.001 (6.641) Loss 2.3703 (2.6147) Prec@1 36.875 (36.634) Prec@5 76.250 (67.240) Epoch: [14][5800/11272] Time 0.767 (7.451) Data 0.002 (6.629) Loss 2.7151 (2.6148) Prec@1 40.000 (36.634) Prec@5 61.250 (67.238) Epoch: [14][5810/11272] Time 0.759 (7.439) Data 0.001 (6.618) Loss 2.5214 (2.6147) Prec@1 40.000 (36.636) Prec@5 66.250 (67.238) Epoch: [14][5820/11272] Time 0.895 (7.428) Data 0.001 (6.607) Loss 2.6295 (2.6148) Prec@1 33.125 (36.634) Prec@5 70.000 (67.237) Epoch: [14][5830/11272] Time 0.948 (7.417) Data 0.002 (6.595) Loss 2.3557 (2.6147) Prec@1 43.125 (36.633) Prec@5 68.750 (67.237) Epoch: [14][5840/11272] Time 0.759 (7.405) Data 0.002 (6.584) Loss 2.6347 (2.6147) Prec@1 36.250 (36.633) Prec@5 68.750 (67.237) Epoch: [14][5850/11272] Time 0.756 (7.394) Data 0.002 (6.573) Loss 2.7657 (2.6148) Prec@1 37.500 (36.633) Prec@5 64.375 (67.236) Epoch: [14][5860/11272] Time 0.881 (7.383) Data 0.002 (6.561) Loss 2.6875 (2.6150) Prec@1 31.250 (36.630) Prec@5 65.000 (67.230) Epoch: [14][5870/11272] Time 0.892 (7.372) Data 0.002 (6.550) Loss 2.5822 (2.6150) Prec@1 36.875 (36.627) Prec@5 70.000 (67.233) Epoch: [14][5880/11272] Time 0.743 (7.361) Data 0.002 (6.539) Loss 2.5642 (2.6151) Prec@1 33.750 (36.626) Prec@5 66.875 (67.233) Epoch: [14][5890/11272] Time 0.863 (7.349) Data 0.001 (6.528) Loss 2.7063 (2.6151) Prec@1 35.625 (36.622) Prec@5 65.000 (67.232) Epoch: [14][5900/11272] Time 0.865 (7.338) Data 0.001 (6.517) Loss 2.5153 (2.6151) Prec@1 35.625 (36.622) Prec@5 68.750 (67.232) Epoch: [14][5910/11272] Time 0.790 (7.327) Data 0.001 (6.506) Loss 2.6814 (2.6151) Prec@1 38.125 (36.621) Prec@5 67.500 (67.232) Epoch: [14][5920/11272] Time 0.794 (7.316) Data 0.002 (6.495) Loss 2.5333 (2.6152) Prec@1 40.625 (36.623) Prec@5 67.500 (67.231) Epoch: [14][5930/11272] Time 0.854 (7.305) Data 0.001 (6.484) Loss 2.6908 (2.6152) Prec@1 36.875 (36.621) Prec@5 66.250 (67.229) Epoch: [14][5940/11272] Time 0.883 (7.295) Data 0.002 (6.473) Loss 2.8213 (2.6152) Prec@1 26.875 (36.618) Prec@5 63.125 (67.228) Epoch: [14][5950/11272] Time 0.767 (7.284) Data 0.001 (6.462) Loss 2.5869 (2.6152) Prec@1 35.625 (36.621) Prec@5 67.500 (67.229) Epoch: [14][5960/11272] Time 0.844 (7.273) Data 0.003 (6.451) Loss 2.5831 (2.6152) Prec@1 39.375 (36.621) Prec@5 68.125 (67.230) Epoch: [14][5970/11272] Time 0.983 (7.262) Data 0.002 (6.441) Loss 2.5499 (2.6152) Prec@1 39.375 (36.618) Prec@5 66.875 (67.229) Epoch: [14][5980/11272] Time 0.896 (7.251) Data 0.002 (6.430) Loss 2.5843 (2.6153) Prec@1 34.375 (36.614) Prec@5 72.500 (67.229) Epoch: [14][5990/11272] Time 0.826 (7.241) Data 0.002 (6.419) Loss 2.4311 (2.6153) Prec@1 41.875 (36.614) Prec@5 70.625 (67.229) Epoch: [14][6000/11272] Time 0.736 (7.230) Data 0.002 (6.408) Loss 2.6091 (2.6154) Prec@1 41.250 (36.614) Prec@5 65.000 (67.227) Epoch: [14][6010/11272] Time 0.888 (7.219) Data 0.001 (6.398) Loss 2.8054 (2.6155) Prec@1 33.125 (36.611) Prec@5 61.875 (67.224) Epoch: [14][6020/11272] Time 0.868 (7.209) Data 0.002 (6.387) Loss 2.7678 (2.6156) Prec@1 32.500 (36.608) Prec@5 72.500 (67.225) Epoch: [14][6030/11272] Time 0.722 (7.198) Data 0.001 (6.377) Loss 2.6911 (2.6156) Prec@1 31.875 (36.609) Prec@5 67.500 (67.224) Epoch: [14][6040/11272] Time 0.895 (7.187) Data 0.001 (6.366) Loss 2.7368 (2.6155) Prec@1 33.125 (36.606) Prec@5 60.625 (67.224) Epoch: [14][6050/11272] Time 0.872 (7.177) Data 0.002 (6.355) Loss 2.8624 (2.6157) Prec@1 25.625 (36.601) Prec@5 61.875 (67.221) Epoch: [14][6060/11272] Time 0.836 (7.166) Data 0.001 (6.345) Loss 2.6552 (2.6157) Prec@1 36.875 (36.602) Prec@5 67.500 (67.221) Epoch: [14][6070/11272] Time 0.761 (7.156) Data 0.001 (6.335) Loss 2.3798 (2.6156) Prec@1 40.625 (36.602) Prec@5 72.500 (67.223) Epoch: [14][6080/11272] Time 0.871 (7.146) Data 0.001 (6.324) Loss 2.5961 (2.6156) Prec@1 36.250 (36.601) Prec@5 64.375 (67.223) Epoch: [14][6090/11272] Time 0.880 (7.135) Data 0.001 (6.314) Loss 2.8885 (2.6157) Prec@1 34.375 (36.602) Prec@5 60.625 (67.222) Epoch: [14][6100/11272] Time 0.742 (7.125) Data 0.001 (6.303) Loss 2.7864 (2.6157) Prec@1 31.875 (36.603) Prec@5 65.625 (67.221) Epoch: [14][6110/11272] Time 0.735 (7.115) Data 0.001 (6.293) Loss 2.6283 (2.6157) Prec@1 38.125 (36.604) Prec@5 68.750 (67.220) Epoch: [14][6120/11272] Time 0.819 (7.104) Data 0.002 (6.283) Loss 2.5647 (2.6159) Prec@1 38.750 (36.601) Prec@5 65.625 (67.216) Epoch: [14][6130/11272] Time 0.892 (7.094) Data 0.001 (6.273) Loss 2.6551 (2.6158) Prec@1 33.125 (36.602) Prec@5 63.750 (67.218) Epoch: [14][6140/11272] Time 0.743 (7.084) Data 0.001 (6.262) Loss 2.5698 (2.6160) Prec@1 37.500 (36.600) Prec@5 71.250 (67.214) Epoch: [14][6150/11272] Time 0.794 (7.074) Data 0.001 (6.252) Loss 2.5180 (2.6160) Prec@1 41.250 (36.602) Prec@5 70.000 (67.216) Epoch: [14][6160/11272] Time 0.831 (7.063) Data 0.001 (6.242) Loss 2.6772 (2.6160) Prec@1 33.750 (36.601) Prec@5 61.875 (67.218) Epoch: [14][6170/11272] Time 0.744 (7.053) Data 0.001 (6.232) Loss 2.6728 (2.6159) Prec@1 38.125 (36.600) Prec@5 64.375 (67.220) Epoch: [14][6180/11272] Time 0.770 (7.043) Data 0.002 (6.222) Loss 2.8142 (2.6161) Prec@1 32.500 (36.598) Prec@5 61.875 (67.217) Epoch: [14][6190/11272] Time 0.890 (7.033) Data 0.001 (6.212) Loss 2.4209 (2.6160) Prec@1 40.625 (36.600) Prec@5 71.250 (67.220) Epoch: [14][6200/11272] Time 0.904 (7.023) Data 0.002 (6.202) Loss 2.8095 (2.6160) Prec@1 31.250 (36.599) Prec@5 64.375 (67.220) Epoch: [14][6210/11272] Time 0.732 (7.013) Data 0.001 (6.192) Loss 2.9242 (2.6159) Prec@1 30.000 (36.601) Prec@5 59.375 (67.221) Epoch: [14][6220/11272] Time 0.721 (7.003) Data 0.002 (6.182) Loss 2.5861 (2.6161) Prec@1 36.250 (36.601) Prec@5 64.375 (67.218) Epoch: [14][6230/11272] Time 0.836 (6.993) Data 0.001 (6.172) Loss 2.6987 (2.6162) Prec@1 38.125 (36.602) Prec@5 66.250 (67.214) Epoch: [14][6240/11272] Time 0.926 (6.983) Data 0.001 (6.162) Loss 2.4359 (2.6162) Prec@1 41.250 (36.601) Prec@5 71.875 (67.215) Epoch: [14][6250/11272] Time 0.784 (6.974) Data 0.002 (6.152) Loss 2.5703 (2.6163) Prec@1 37.500 (36.600) Prec@5 70.625 (67.216) Epoch: [14][6260/11272] Time 0.759 (6.964) Data 0.001 (6.142) Loss 2.8525 (2.6162) Prec@1 37.500 (36.602) Prec@5 64.375 (67.215) Epoch: [14][6270/11272] Time 0.859 (6.954) Data 0.001 (6.133) Loss 2.6736 (2.6162) Prec@1 40.000 (36.602) Prec@5 66.875 (67.216) Epoch: [14][6280/11272] Time 0.951 (6.944) Data 0.002 (6.123) Loss 2.6120 (2.6162) Prec@1 35.625 (36.602) Prec@5 65.625 (67.217) Epoch: [14][6290/11272] Time 0.736 (6.935) Data 0.001 (6.113) Loss 2.6403 (2.6161) Prec@1 37.500 (36.604) Prec@5 66.875 (67.219) Epoch: [14][6300/11272] Time 0.896 (6.925) Data 0.001 (6.103) Loss 2.5105 (2.6161) Prec@1 38.750 (36.606) Prec@5 68.125 (67.217) Epoch: [14][6310/11272] Time 0.900 (6.915) Data 0.001 (6.094) Loss 2.4943 (2.6161) Prec@1 40.000 (36.608) Prec@5 73.750 (67.219) Epoch: [14][6320/11272] Time 0.751 (6.905) Data 0.002 (6.084) Loss 2.4966 (2.6159) Prec@1 41.250 (36.611) Prec@5 70.000 (67.220) Epoch: [14][6330/11272] Time 0.747 (6.896) Data 0.001 (6.074) Loss 2.8328 (2.6160) Prec@1 38.750 (36.611) Prec@5 63.750 (67.219) Epoch: [14][6340/11272] Time 0.865 (6.886) Data 0.001 (6.065) Loss 2.4135 (2.6159) Prec@1 43.125 (36.614) Prec@5 68.125 (67.222) Epoch: [14][6350/11272] Time 0.857 (6.877) Data 0.001 (6.055) Loss 2.5727 (2.6157) Prec@1 35.000 (36.617) Prec@5 68.125 (67.226) Epoch: [14][6360/11272] Time 0.736 (6.867) Data 0.001 (6.046) Loss 2.4893 (2.6157) Prec@1 40.000 (36.619) Prec@5 72.500 (67.227) Epoch: [14][6370/11272] Time 0.773 (6.858) Data 0.002 (6.036) Loss 2.6826 (2.6157) Prec@1 36.875 (36.618) Prec@5 67.500 (67.227) Epoch: [14][6380/11272] Time 0.919 (6.848) Data 0.001 (6.027) Loss 2.9453 (2.6156) Prec@1 30.000 (36.619) Prec@5 64.375 (67.228) Epoch: [14][6390/11272] Time 0.949 (6.839) Data 0.001 (6.017) Loss 2.3702 (2.6156) Prec@1 40.000 (36.618) Prec@5 68.125 (67.228) Epoch: [14][6400/11272] Time 0.738 (6.829) Data 0.001 (6.008) Loss 2.5009 (2.6158) Prec@1 41.875 (36.616) Prec@5 65.625 (67.228) Epoch: [14][6410/11272] Time 0.748 (6.820) Data 0.001 (5.999) Loss 2.7669 (2.6157) Prec@1 36.875 (36.619) Prec@5 63.750 (67.230) Epoch: [14][6420/11272] Time 0.860 (6.811) Data 0.001 (5.989) Loss 2.4625 (2.6156) Prec@1 42.500 (36.621) Prec@5 75.625 (67.233) Epoch: [14][6430/11272] Time 0.748 (6.801) Data 0.004 (5.980) Loss 2.5392 (2.6157) Prec@1 31.875 (36.617) Prec@5 74.375 (67.233) Epoch: [14][6440/11272] Time 0.739 (6.792) Data 0.001 (5.971) Loss 2.4788 (2.6157) Prec@1 40.625 (36.618) Prec@5 66.875 (67.232) Epoch: [14][6450/11272] Time 0.812 (6.783) Data 0.001 (5.961) Loss 2.5818 (2.6156) Prec@1 42.500 (36.621) Prec@5 65.000 (67.233) Epoch: [14][6460/11272] Time 0.855 (6.774) Data 0.002 (5.952) Loss 2.5368 (2.6157) Prec@1 39.375 (36.620) Prec@5 68.750 (67.231) Epoch: [14][6470/11272] Time 0.746 (6.764) Data 0.001 (5.943) Loss 2.4817 (2.6156) Prec@1 37.500 (36.623) Prec@5 68.125 (67.235) Epoch: [14][6480/11272] Time 0.778 (6.755) Data 0.002 (5.934) Loss 2.6492 (2.6155) Prec@1 41.875 (36.625) Prec@5 68.750 (67.238) Epoch: [14][6490/11272] Time 0.821 (6.746) Data 0.001 (5.925) Loss 2.2719 (2.6155) Prec@1 41.250 (36.624) Prec@5 76.250 (67.239) Epoch: [14][6500/11272] Time 0.912 (6.737) Data 0.002 (5.916) Loss 2.6362 (2.6155) Prec@1 35.625 (36.623) Prec@5 66.875 (67.239) Epoch: [14][6510/11272] Time 0.741 (6.728) Data 0.001 (5.907) Loss 2.7447 (2.6155) Prec@1 30.000 (36.624) Prec@5 66.875 (67.239) Epoch: [14][6520/11272] Time 0.741 (6.719) Data 0.001 (5.897) Loss 2.4657 (2.6156) Prec@1 38.125 (36.624) Prec@5 69.375 (67.238) Epoch: [14][6530/11272] Time 0.863 (6.710) Data 0.001 (5.888) Loss 2.2140 (2.6155) Prec@1 39.375 (36.624) Prec@5 78.125 (67.240) Epoch: [14][6540/11272] Time 0.882 (6.701) Data 0.001 (5.879) Loss 2.7820 (2.6155) Prec@1 33.750 (36.625) Prec@5 64.375 (67.242) Epoch: [14][6550/11272] Time 0.739 (6.692) Data 0.002 (5.870) Loss 2.4148 (2.6156) Prec@1 38.750 (36.622) Prec@5 73.125 (67.242) Epoch: [14][6560/11272] Time 0.946 (6.683) Data 0.001 (5.862) Loss 2.6068 (2.6156) Prec@1 36.875 (36.622) Prec@5 66.875 (67.241) Epoch: [14][6570/11272] Time 0.937 (6.674) Data 0.002 (5.853) Loss 2.8562 (2.6156) Prec@1 28.750 (36.623) Prec@5 62.500 (67.240) Epoch: [14][6580/11272] Time 0.743 (6.665) Data 0.001 (5.844) Loss 2.3695 (2.6156) Prec@1 42.500 (36.624) Prec@5 71.875 (67.241) Epoch: [14][6590/11272] Time 0.740 (6.656) Data 0.001 (5.835) Loss 2.6128 (2.6156) Prec@1 38.125 (36.624) Prec@5 64.375 (67.240) Epoch: [14][6600/11272] Time 0.871 (6.647) Data 0.001 (5.826) Loss 2.7649 (2.6156) Prec@1 37.500 (36.624) Prec@5 63.750 (67.240) Epoch: [14][6610/11272] Time 0.831 (6.639) Data 0.001 (5.817) Loss 2.5059 (2.6156) Prec@1 39.375 (36.623) Prec@5 70.625 (67.240) Epoch: [14][6620/11272] Time 0.713 (6.630) Data 0.001 (5.808) Loss 2.6300 (2.6155) Prec@1 42.500 (36.625) Prec@5 67.500 (67.243) Epoch: [14][6630/11272] Time 0.737 (6.621) Data 0.001 (5.800) Loss 2.7899 (2.6156) Prec@1 30.000 (36.622) Prec@5 61.875 (67.240) Epoch: [14][6640/11272] Time 0.874 (6.612) Data 0.001 (5.791) Loss 2.7730 (2.6157) Prec@1 35.000 (36.622) Prec@5 61.250 (67.237) Epoch: [14][6650/11272] Time 0.857 (6.604) Data 0.001 (5.782) Loss 2.8182 (2.6157) Prec@1 29.375 (36.620) Prec@5 66.875 (67.238) Epoch: [14][6660/11272] Time 0.745 (6.595) Data 0.001 (5.774) Loss 2.7741 (2.6157) Prec@1 31.250 (36.619) Prec@5 64.375 (67.239) Epoch: [14][6670/11272] Time 0.789 (6.586) Data 0.002 (5.765) Loss 2.7569 (2.6158) Prec@1 36.875 (36.621) Prec@5 66.250 (67.238) Epoch: [14][6680/11272] Time 0.793 (6.578) Data 0.001 (5.756) Loss 2.6398 (2.6157) Prec@1 38.125 (36.624) Prec@5 64.375 (67.239) Epoch: [14][6690/11272] Time 0.752 (6.569) Data 0.005 (5.748) Loss 2.6446 (2.6156) Prec@1 35.625 (36.625) Prec@5 68.750 (67.239) Epoch: [14][6700/11272] Time 0.748 (6.560) Data 0.002 (5.739) Loss 2.8983 (2.6157) Prec@1 35.000 (36.624) Prec@5 60.625 (67.237) Epoch: [14][6710/11272] Time 0.894 (6.552) Data 0.001 (5.731) Loss 2.7174 (2.6157) Prec@1 31.875 (36.624) Prec@5 63.750 (67.235) Epoch: [14][6720/11272] Time 0.858 (6.543) Data 0.002 (5.722) Loss 2.7216 (2.6159) Prec@1 30.000 (36.620) Prec@5 65.625 (67.232) Epoch: [14][6730/11272] Time 0.718 (6.535) Data 0.002 (5.714) Loss 2.4506 (2.6157) Prec@1 36.875 (36.622) Prec@5 70.625 (67.236) Epoch: [14][6740/11272] Time 0.727 (6.526) Data 0.002 (5.705) Loss 2.7844 (2.6156) Prec@1 32.500 (36.622) Prec@5 66.875 (67.237) Epoch: [14][6750/11272] Time 0.895 (6.518) Data 0.002 (5.697) Loss 2.5788 (2.6156) Prec@1 38.750 (36.625) Prec@5 67.500 (67.238) Epoch: [14][6760/11272] Time 0.859 (6.509) Data 0.001 (5.688) Loss 2.7398 (2.6156) Prec@1 34.375 (36.624) Prec@5 66.250 (67.238) Epoch: [14][6770/11272] Time 0.762 (6.501) Data 0.001 (5.680) Loss 2.4718 (2.6156) Prec@1 36.250 (36.622) Prec@5 68.750 (67.239) Epoch: [14][6780/11272] Time 0.734 (6.493) Data 0.001 (5.671) Loss 2.6982 (2.6154) Prec@1 40.625 (36.625) Prec@5 67.500 (67.242) Epoch: [14][6790/11272] Time 0.885 (6.484) Data 0.001 (5.663) Loss 2.7635 (2.6155) Prec@1 31.875 (36.623) Prec@5 66.250 (67.240) Epoch: [14][6800/11272] Time 0.922 (6.476) Data 0.002 (5.655) Loss 2.7389 (2.6156) Prec@1 30.625 (36.621) Prec@5 63.750 (67.240) Epoch: [14][6810/11272] Time 0.790 (6.468) Data 0.002 (5.646) Loss 2.2235 (2.6155) Prec@1 41.875 (36.622) Prec@5 75.000 (67.242) Epoch: [14][6820/11272] Time 0.867 (6.459) Data 0.002 (5.638) Loss 2.5257 (2.6155) Prec@1 40.000 (36.621) Prec@5 68.125 (67.243) Epoch: [14][6830/11272] Time 0.857 (6.451) Data 0.002 (5.630) Loss 2.6661 (2.6156) Prec@1 37.500 (36.620) Prec@5 69.375 (67.241) Epoch: [14][6840/11272] Time 0.783 (6.443) Data 0.002 (5.622) Loss 2.3054 (2.6155) Prec@1 42.500 (36.620) Prec@5 72.500 (67.243) Epoch: [14][6850/11272] Time 0.740 (6.435) Data 0.001 (5.613) Loss 2.7596 (2.6156) Prec@1 35.000 (36.619) Prec@5 62.500 (67.241) Epoch: [14][6860/11272] Time 0.914 (6.427) Data 0.001 (5.605) Loss 2.5790 (2.6156) Prec@1 40.625 (36.621) Prec@5 66.250 (67.243) Epoch: [14][6870/11272] Time 0.885 (6.418) Data 0.001 (5.597) Loss 2.3665 (2.6154) Prec@1 45.000 (36.622) Prec@5 70.000 (67.244) Epoch: [14][6880/11272] Time 0.723 (6.410) Data 0.001 (5.589) Loss 2.8430 (2.6156) Prec@1 38.750 (36.620) Prec@5 63.750 (67.241) Epoch: [14][6890/11272] Time 0.766 (6.402) Data 0.002 (5.581) Loss 2.3707 (2.6154) Prec@1 43.125 (36.623) Prec@5 70.625 (67.246) Epoch: [14][6900/11272] Time 0.828 (6.394) Data 0.001 (5.573) Loss 2.4845 (2.6153) Prec@1 45.625 (36.625) Prec@5 71.250 (67.247) Epoch: [14][6910/11272] Time 0.875 (6.386) Data 0.002 (5.565) Loss 2.3342 (2.6153) Prec@1 40.625 (36.628) Prec@5 73.750 (67.248) Epoch: [14][6920/11272] Time 0.759 (6.378) Data 0.002 (5.557) Loss 2.5491 (2.6152) Prec@1 42.500 (36.628) Prec@5 70.000 (67.249) Epoch: [14][6930/11272] Time 0.766 (6.370) Data 0.002 (5.549) Loss 2.6211 (2.6152) Prec@1 36.250 (36.628) Prec@5 66.250 (67.252) Epoch: [14][6940/11272] Time 0.909 (6.362) Data 0.001 (5.541) Loss 2.6823 (2.6152) Prec@1 36.875 (36.628) Prec@5 65.000 (67.251) Epoch: [14][6950/11272] Time 0.849 (6.354) Data 0.002 (5.533) Loss 2.8021 (2.6153) Prec@1 32.500 (36.625) Prec@5 60.000 (67.250) Epoch: [14][6960/11272] Time 0.776 (6.346) Data 0.001 (5.525) Loss 2.6088 (2.6152) Prec@1 38.750 (36.626) Prec@5 67.500 (67.252) Epoch: [14][6970/11272] Time 0.952 (6.338) Data 0.001 (5.517) Loss 2.6821 (2.6154) Prec@1 31.250 (36.621) Prec@5 65.000 (67.250) Epoch: [14][6980/11272] Time 0.873 (6.330) Data 0.002 (5.509) Loss 2.6528 (2.6153) Prec@1 35.000 (36.623) Prec@5 63.750 (67.251) Epoch: [14][6990/11272] Time 0.749 (6.322) Data 0.002 (5.501) Loss 2.9222 (2.6153) Prec@1 35.625 (36.626) Prec@5 61.875 (67.251) Epoch: [14][7000/11272] Time 0.789 (6.314) Data 0.002 (5.493) Loss 2.8016 (2.6154) Prec@1 33.125 (36.624) Prec@5 68.125 (67.251) Epoch: [14][7010/11272] Time 0.845 (6.307) Data 0.001 (5.485) Loss 2.7242 (2.6154) Prec@1 34.375 (36.626) Prec@5 66.250 (67.250) Epoch: [14][7020/11272] Time 0.852 (6.299) Data 0.001 (5.478) Loss 2.5785 (2.6154) Prec@1 38.750 (36.625) Prec@5 69.375 (67.250) Epoch: [14][7030/11272] Time 0.758 (6.291) Data 0.002 (5.470) Loss 2.8348 (2.6153) Prec@1 34.375 (36.627) Prec@5 64.375 (67.252) Epoch: [14][7040/11272] Time 0.755 (6.283) Data 0.002 (5.462) Loss 2.5624 (2.6154) Prec@1 40.000 (36.627) Prec@5 67.500 (67.253) Epoch: [14][7050/11272] Time 0.879 (6.276) Data 0.001 (5.454) Loss 2.7254 (2.6154) Prec@1 36.875 (36.628) Prec@5 64.375 (67.254) Epoch: [14][7060/11272] Time 0.793 (6.268) Data 0.002 (5.447) Loss 2.6080 (2.6153) Prec@1 36.250 (36.628) Prec@5 64.375 (67.254) Epoch: [14][7070/11272] Time 0.758 (6.260) Data 0.002 (5.439) Loss 2.4709 (2.6154) Prec@1 43.125 (36.628) Prec@5 70.000 (67.254) Epoch: [14][7080/11272] Time 0.776 (6.252) Data 0.002 (5.431) Loss 2.6975 (2.6155) Prec@1 38.125 (36.626) Prec@5 66.875 (67.252) Epoch: [14][7090/11272] Time 0.854 (6.245) Data 0.001 (5.424) Loss 2.5439 (2.6154) Prec@1 38.750 (36.628) Prec@5 64.375 (67.254) Epoch: [14][7100/11272] Time 0.775 (6.237) Data 0.001 (5.416) Loss 2.8580 (2.6154) Prec@1 36.250 (36.628) Prec@5 60.000 (67.255) Epoch: [14][7110/11272] Time 0.771 (6.229) Data 0.001 (5.408) Loss 2.4327 (2.6153) Prec@1 44.375 (36.631) Prec@5 70.000 (67.257) Epoch: [14][7120/11272] Time 0.815 (6.222) Data 0.001 (5.401) Loss 2.7004 (2.6153) Prec@1 34.375 (36.629) Prec@5 64.375 (67.257) Epoch: [14][7130/11272] Time 0.923 (6.214) Data 0.002 (5.393) Loss 2.5981 (2.6153) Prec@1 35.625 (36.630) Prec@5 66.875 (67.257) Epoch: [14][7140/11272] Time 0.724 (6.207) Data 0.001 (5.386) Loss 2.6118 (2.6152) Prec@1 37.500 (36.631) Prec@5 71.250 (67.258) Epoch: [14][7150/11272] Time 0.742 (6.199) Data 0.001 (5.378) Loss 2.5865 (2.6153) Prec@1 36.875 (36.631) Prec@5 66.250 (67.257) Epoch: [14][7160/11272] Time 0.968 (6.192) Data 0.002 (5.371) Loss 2.5655 (2.6153) Prec@1 38.125 (36.629) Prec@5 69.375 (67.256) Epoch: [14][7170/11272] Time 0.877 (6.184) Data 0.001 (5.363) Loss 2.7198 (2.6154) Prec@1 32.500 (36.626) Prec@5 65.000 (67.256) Epoch: [14][7180/11272] Time 0.760 (6.177) Data 0.002 (5.356) Loss 2.5965 (2.6154) Prec@1 32.500 (36.630) Prec@5 67.500 (67.256) Epoch: [14][7190/11272] Time 0.795 (6.169) Data 0.001 (5.348) Loss 2.5361 (2.6154) Prec@1 36.875 (36.630) Prec@5 66.875 (67.254) Epoch: [14][7200/11272] Time 0.831 (6.162) Data 0.001 (5.341) Loss 2.5374 (2.6155) Prec@1 36.875 (36.629) Prec@5 71.250 (67.252) Epoch: [14][7210/11272] Time 0.868 (6.154) Data 0.001 (5.333) Loss 2.7611 (2.6155) Prec@1 35.000 (36.629) Prec@5 68.125 (67.252) Epoch: [14][7220/11272] Time 0.746 (6.147) Data 0.001 (5.326) Loss 2.7518 (2.6154) Prec@1 34.375 (36.629) Prec@5 65.625 (67.256) Epoch: [14][7230/11272] Time 0.876 (6.140) Data 0.001 (5.319) Loss 2.5241 (2.6154) Prec@1 42.500 (36.630) Prec@5 67.500 (67.254) Epoch: [14][7240/11272] Time 0.905 (6.132) Data 0.001 (5.311) Loss 2.7317 (2.6154) Prec@1 32.500 (36.629) Prec@5 61.250 (67.255) Epoch: [14][7250/11272] Time 0.733 (6.125) Data 0.001 (5.304) Loss 2.3722 (2.6152) Prec@1 40.000 (36.630) Prec@5 74.375 (67.257) Epoch: [14][7260/11272] Time 0.781 (6.118) Data 0.001 (5.297) Loss 2.6009 (2.6153) Prec@1 37.500 (36.628) Prec@5 65.625 (67.257) Epoch: [14][7270/11272] Time 0.877 (6.110) Data 0.001 (5.289) Loss 2.7448 (2.6153) Prec@1 33.750 (36.626) Prec@5 64.375 (67.256) Epoch: [14][7280/11272] Time 0.930 (6.103) Data 0.002 (5.282) Loss 2.5402 (2.6154) Prec@1 42.500 (36.622) Prec@5 66.250 (67.254) Epoch: [14][7290/11272] Time 0.740 (6.096) Data 0.001 (5.275) Loss 2.6202 (2.6155) Prec@1 38.750 (36.621) Prec@5 68.750 (67.252) Epoch: [14][7300/11272] Time 0.739 (6.089) Data 0.002 (5.268) Loss 2.5834 (2.6154) Prec@1 35.000 (36.623) Prec@5 68.750 (67.252) Epoch: [14][7310/11272] Time 0.900 (6.081) Data 0.002 (5.260) Loss 2.4204 (2.6154) Prec@1 43.750 (36.624) Prec@5 65.625 (67.250) Epoch: [14][7320/11272] Time 0.838 (6.074) Data 0.001 (5.253) Loss 2.6385 (2.6156) Prec@1 32.500 (36.620) Prec@5 64.375 (67.248) Epoch: [14][7330/11272] Time 0.740 (6.067) Data 0.002 (5.246) Loss 2.4506 (2.6156) Prec@1 43.750 (36.618) Prec@5 73.125 (67.248) Epoch: [14][7340/11272] Time 0.750 (6.060) Data 0.001 (5.239) Loss 2.8816 (2.6157) Prec@1 28.125 (36.616) Prec@5 64.375 (67.246) Epoch: [14][7350/11272] Time 0.868 (6.053) Data 0.001 (5.232) Loss 2.4135 (2.6157) Prec@1 40.000 (36.615) Prec@5 73.125 (67.246) Epoch: [14][7360/11272] Time 0.721 (6.046) Data 0.002 (5.225) Loss 2.4460 (2.6157) Prec@1 38.750 (36.615) Prec@5 67.500 (67.244) Epoch: [14][7370/11272] Time 0.755 (6.039) Data 0.001 (5.218) Loss 2.7220 (2.6156) Prec@1 30.625 (36.618) Prec@5 65.000 (67.248) Epoch: [14][7380/11272] Time 0.968 (6.031) Data 0.002 (5.211) Loss 2.5676 (2.6155) Prec@1 37.500 (36.617) Prec@5 68.750 (67.248) Epoch: [14][7390/11272] Time 0.852 (6.024) Data 0.002 (5.203) Loss 2.4684 (2.6155) Prec@1 41.875 (36.620) Prec@5 70.625 (67.248) Epoch: [14][7400/11272] Time 0.830 (6.017) Data 0.002 (5.196) Loss 2.6539 (2.6156) Prec@1 38.125 (36.620) Prec@5 66.875 (67.248) Epoch: [14][7410/11272] Time 0.728 (6.010) Data 0.002 (5.189) Loss 2.4077 (2.6155) Prec@1 36.875 (36.622) Prec@5 71.875 (67.248) Epoch: [14][7420/11272] Time 0.838 (6.003) Data 0.001 (5.182) Loss 2.6063 (2.6155) Prec@1 38.750 (36.624) Prec@5 68.750 (67.248) Epoch: [14][7430/11272] Time 0.889 (5.996) Data 0.002 (5.175) Loss 2.4828 (2.6156) Prec@1 43.125 (36.625) Prec@5 70.000 (67.248) Epoch: [14][7440/11272] Time 0.751 (5.989) Data 0.001 (5.169) Loss 2.5726 (2.6155) Prec@1 41.250 (36.627) Prec@5 71.250 (67.248) Epoch: [14][7450/11272] Time 0.806 (5.983) Data 0.002 (5.162) Loss 2.6185 (2.6155) Prec@1 38.750 (36.627) Prec@5 66.250 (67.251) Epoch: [14][7460/11272] Time 0.963 (5.976) Data 0.002 (5.155) Loss 2.5615 (2.6156) Prec@1 39.375 (36.626) Prec@5 69.375 (67.250) Epoch: [14][7470/11272] Time 0.864 (5.969) Data 0.002 (5.148) Loss 2.7254 (2.6156) Prec@1 33.125 (36.626) Prec@5 65.000 (67.249) Epoch: [14][7480/11272] Time 0.746 (5.962) Data 0.002 (5.141) Loss 2.5795 (2.6156) Prec@1 41.875 (36.627) Prec@5 68.125 (67.250) Epoch: [14][7490/11272] Time 0.911 (5.955) Data 0.001 (5.134) Loss 2.4112 (2.6156) Prec@1 45.000 (36.627) Prec@5 76.875 (67.251) Epoch: [14][7500/11272] Time 0.829 (5.948) Data 0.001 (5.127) Loss 2.4474 (2.6154) Prec@1 36.875 (36.630) Prec@5 73.125 (67.256) Epoch: [14][7510/11272] Time 0.761 (5.941) Data 0.001 (5.120) Loss 2.5714 (2.6154) Prec@1 36.875 (36.630) Prec@5 65.000 (67.256) Epoch: [14][7520/11272] Time 0.757 (5.934) Data 0.002 (5.114) Loss 2.3853 (2.6152) Prec@1 40.000 (36.630) Prec@5 74.375 (67.259) Epoch: [14][7530/11272] Time 0.876 (5.928) Data 0.001 (5.107) Loss 2.9407 (2.6154) Prec@1 31.875 (36.627) Prec@5 58.750 (67.256) Epoch: [14][7540/11272] Time 0.858 (5.921) Data 0.001 (5.100) Loss 2.6105 (2.6154) Prec@1 34.375 (36.626) Prec@5 67.500 (67.255) Epoch: [14][7550/11272] Time 0.728 (5.914) Data 0.001 (5.093) Loss 2.6442 (2.6154) Prec@1 33.125 (36.624) Prec@5 66.250 (67.255) Epoch: [14][7560/11272] Time 0.762 (5.907) Data 0.001 (5.087) Loss 3.0371 (2.6154) Prec@1 23.750 (36.622) Prec@5 58.750 (67.254) Epoch: [14][7570/11272] Time 0.876 (5.901) Data 0.002 (5.080) Loss 3.1376 (2.6155) Prec@1 30.000 (36.620) Prec@5 55.625 (67.251) Epoch: [14][7580/11272] Time 0.894 (5.894) Data 0.002 (5.073) Loss 2.3783 (2.6155) Prec@1 33.125 (36.620) Prec@5 72.500 (67.249) Epoch: [14][7590/11272] Time 0.792 (5.887) Data 0.001 (5.066) Loss 2.5932 (2.6155) Prec@1 38.125 (36.619) Prec@5 66.875 (67.249) Epoch: [14][7600/11272] Time 0.758 (5.881) Data 0.002 (5.060) Loss 2.6745 (2.6156) Prec@1 33.125 (36.617) Prec@5 67.500 (67.250) Epoch: [14][7610/11272] Time 0.853 (5.874) Data 0.001 (5.053) Loss 2.6535 (2.6155) Prec@1 36.250 (36.618) Prec@5 68.125 (67.251) Epoch: [14][7620/11272] Time 0.776 (5.867) Data 0.003 (5.046) Loss 2.6575 (2.6155) Prec@1 37.500 (36.617) Prec@5 66.875 (67.250) Epoch: [14][7630/11272] Time 0.750 (5.861) Data 0.001 (5.040) Loss 2.6426 (2.6155) Prec@1 35.000 (36.617) Prec@5 65.625 (67.250) Epoch: [14][7640/11272] Time 0.891 (5.854) Data 0.002 (5.033) Loss 2.4182 (2.6155) Prec@1 36.875 (36.616) Prec@5 73.125 (67.251) Epoch: [14][7650/11272] Time 0.958 (5.848) Data 0.002 (5.027) Loss 2.7322 (2.6155) Prec@1 32.500 (36.616) Prec@5 65.000 (67.252) Epoch: [14][7660/11272] Time 0.739 (5.841) Data 0.001 (5.020) Loss 2.8846 (2.6155) Prec@1 35.625 (36.615) Prec@5 63.125 (67.251) Epoch: [14][7670/11272] Time 0.809 (5.834) Data 0.002 (5.014) Loss 2.6161 (2.6155) Prec@1 38.750 (36.615) Prec@5 68.125 (67.251) Epoch: [14][7680/11272] Time 0.928 (5.828) Data 0.001 (5.007) Loss 2.7550 (2.6154) Prec@1 35.000 (36.617) Prec@5 67.500 (67.251) Epoch: [14][7690/11272] Time 0.902 (5.821) Data 0.001 (5.001) Loss 2.4638 (2.6155) Prec@1 41.250 (36.616) Prec@5 71.250 (67.249) Epoch: [14][7700/11272] Time 0.727 (5.815) Data 0.002 (4.994) Loss 2.5666 (2.6156) Prec@1 39.375 (36.615) Prec@5 68.125 (67.246) Epoch: [14][7710/11272] Time 0.761 (5.808) Data 0.002 (4.988) Loss 2.7639 (2.6155) Prec@1 35.000 (36.617) Prec@5 63.750 (67.248) Epoch: [14][7720/11272] Time 0.869 (5.802) Data 0.001 (4.981) Loss 2.5870 (2.6156) Prec@1 37.500 (36.620) Prec@5 67.500 (67.247) Epoch: [14][7730/11272] Time 0.893 (5.796) Data 0.001 (4.975) Loss 2.7727 (2.6155) Prec@1 33.750 (36.619) Prec@5 65.625 (67.250) Epoch: [14][7740/11272] Time 0.781 (5.789) Data 0.002 (4.968) Loss 2.4509 (2.6154) Prec@1 36.875 (36.620) Prec@5 72.500 (67.251) Epoch: [14][7750/11272] Time 0.939 (5.783) Data 0.001 (4.962) Loss 2.5007 (2.6153) Prec@1 42.500 (36.622) Prec@5 72.500 (67.252) Epoch: [14][7760/11272] Time 0.858 (5.776) Data 0.001 (4.955) Loss 2.3470 (2.6153) Prec@1 36.875 (36.622) Prec@5 76.250 (67.256) Epoch: [14][7770/11272] Time 0.758 (5.770) Data 0.001 (4.949) Loss 2.5513 (2.6153) Prec@1 38.750 (36.622) Prec@5 66.250 (67.256) Epoch: [14][7780/11272] Time 0.732 (5.764) Data 0.001 (4.943) Loss 2.4394 (2.6152) Prec@1 45.625 (36.624) Prec@5 68.125 (67.259) Epoch: [14][7790/11272] Time 0.879 (5.757) Data 0.002 (4.936) Loss 2.5706 (2.6150) Prec@1 35.000 (36.626) Prec@5 67.500 (67.262) Epoch: [14][7800/11272] Time 0.846 (5.751) Data 0.001 (4.930) Loss 2.7242 (2.6151) Prec@1 34.375 (36.624) Prec@5 68.125 (67.261) Epoch: [14][7810/11272] Time 0.772 (5.745) Data 0.002 (4.924) Loss 2.9586 (2.6150) Prec@1 30.000 (36.626) Prec@5 61.875 (67.262) Epoch: [14][7820/11272] Time 0.727 (5.738) Data 0.001 (4.917) Loss 2.3321 (2.6149) Prec@1 37.500 (36.627) Prec@5 71.250 (67.264) Epoch: [14][7830/11272] Time 0.851 (5.732) Data 0.001 (4.911) Loss 2.4548 (2.6148) Prec@1 40.625 (36.629) Prec@5 70.000 (67.265) Epoch: [14][7840/11272] Time 0.856 (5.726) Data 0.002 (4.905) Loss 2.8244 (2.6147) Prec@1 33.125 (36.629) Prec@5 65.625 (67.266) Epoch: [14][7850/11272] Time 0.757 (5.719) Data 0.002 (4.899) Loss 2.4505 (2.6147) Prec@1 40.000 (36.631) Prec@5 70.625 (67.269) Epoch: [14][7860/11272] Time 0.763 (5.713) Data 0.002 (4.892) Loss 2.4737 (2.6147) Prec@1 38.750 (36.632) Prec@5 72.500 (67.268) Epoch: [14][7870/11272] Time 0.861 (5.707) Data 0.002 (4.886) Loss 2.7528 (2.6148) Prec@1 36.250 (36.630) Prec@5 62.500 (67.266) Epoch: [14][7880/11272] Time 0.899 (5.701) Data 0.002 (4.880) Loss 2.4010 (2.6147) Prec@1 42.500 (36.632) Prec@5 73.750 (67.268) Epoch: [14][7890/11272] Time 0.727 (5.695) Data 0.001 (4.874) Loss 2.5677 (2.6147) Prec@1 31.875 (36.630) Prec@5 73.125 (67.269) Epoch: [14][7900/11272] Time 0.832 (5.688) Data 0.001 (4.868) Loss 2.7472 (2.6148) Prec@1 35.625 (36.631) Prec@5 63.125 (67.266) Epoch: [14][7910/11272] Time 0.949 (5.682) Data 0.001 (4.862) Loss 2.3754 (2.6147) Prec@1 42.500 (36.632) Prec@5 72.500 (67.268) Epoch: [14][7920/11272] Time 0.763 (5.676) Data 0.001 (4.855) Loss 2.5203 (2.6146) Prec@1 42.500 (36.634) Prec@5 67.500 (67.268) Epoch: [14][7930/11272] Time 0.748 (5.670) Data 0.002 (4.849) Loss 2.8670 (2.6147) Prec@1 33.750 (36.634) Prec@5 62.500 (67.268) Epoch: [14][7940/11272] Time 0.858 (5.664) Data 0.001 (4.843) Loss 2.8814 (2.6147) Prec@1 29.375 (36.634) Prec@5 60.000 (67.266) Epoch: [14][7950/11272] Time 0.918 (5.658) Data 0.001 (4.837) Loss 2.4248 (2.6147) Prec@1 38.750 (36.637) Prec@5 76.250 (67.266) Epoch: [14][7960/11272] Time 0.743 (5.652) Data 0.001 (4.831) Loss 2.6828 (2.6148) Prec@1 32.500 (36.635) Prec@5 68.125 (67.266) Epoch: [14][7970/11272] Time 0.800 (5.646) Data 0.001 (4.825) Loss 2.5215 (2.6148) Prec@1 36.875 (36.635) Prec@5 70.625 (67.264) Epoch: [14][7980/11272] Time 0.808 (5.640) Data 0.002 (4.819) Loss 2.8696 (2.6149) Prec@1 33.125 (36.636) Prec@5 66.250 (67.263) Epoch: [14][7990/11272] Time 0.897 (5.634) Data 0.002 (4.813) Loss 2.6447 (2.6149) Prec@1 35.625 (36.636) Prec@5 63.750 (67.263) Epoch: [14][8000/11272] Time 0.791 (5.628) Data 0.002 (4.807) Loss 2.7407 (2.6150) Prec@1 39.375 (36.634) Prec@5 62.500 (67.262) Epoch: [14][8010/11272] Time 0.889 (5.622) Data 0.001 (4.801) Loss 2.6519 (2.6150) Prec@1 35.000 (36.633) Prec@5 67.500 (67.263) Epoch: [14][8020/11272] Time 0.832 (5.616) Data 0.001 (4.795) Loss 2.6094 (2.6150) Prec@1 36.875 (36.631) Prec@5 65.625 (67.262) Epoch: [14][8030/11272] Time 0.741 (5.610) Data 0.001 (4.789) Loss 2.8883 (2.6150) Prec@1 33.125 (36.634) Prec@5 65.000 (67.265) Epoch: [14][8040/11272] Time 0.785 (5.604) Data 0.002 (4.783) Loss 2.5692 (2.6150) Prec@1 35.000 (36.632) Prec@5 70.000 (67.264) Epoch: [14][8050/11272] Time 0.851 (5.598) Data 0.001 (4.777) Loss 2.6544 (2.6150) Prec@1 34.375 (36.631) Prec@5 66.875 (67.265) Epoch: [14][8060/11272] Time 0.839 (5.592) Data 0.002 (4.771) Loss 2.9360 (2.6150) Prec@1 33.125 (36.631) Prec@5 58.750 (67.263) Epoch: [14][8070/11272] Time 0.757 (5.586) Data 0.001 (4.765) Loss 2.4949 (2.6151) Prec@1 36.250 (36.633) Prec@5 69.375 (67.264) Epoch: [14][8080/11272] Time 0.749 (5.580) Data 0.001 (4.759) Loss 2.4058 (2.6151) Prec@1 41.250 (36.633) Prec@5 73.750 (67.264) Epoch: [14][8090/11272] Time 0.906 (5.574) Data 0.001 (4.753) Loss 2.8752 (2.6151) Prec@1 32.500 (36.633) Prec@5 65.000 (67.265) Epoch: [14][8100/11272] Time 0.942 (5.568) Data 0.001 (4.748) Loss 2.5594 (2.6151) Prec@1 35.000 (36.632) Prec@5 67.500 (67.264) Epoch: [14][8110/11272] Time 0.774 (5.562) Data 0.002 (4.742) Loss 2.8448 (2.6152) Prec@1 31.875 (36.631) Prec@5 58.750 (67.262) Epoch: [14][8120/11272] Time 0.804 (5.557) Data 0.001 (4.736) Loss 2.6269 (2.6152) Prec@1 36.250 (36.631) Prec@5 70.000 (67.263) Epoch: [14][8130/11272] Time 0.892 (5.551) Data 0.002 (4.730) Loss 2.6195 (2.6152) Prec@1 38.125 (36.632) Prec@5 68.125 (67.264) Epoch: [14][8140/11272] Time 0.894 (5.545) Data 0.001 (4.724) Loss 2.5725 (2.6152) Prec@1 36.875 (36.631) Prec@5 67.500 (67.263) Epoch: [14][8150/11272] Time 0.764 (5.539) Data 0.001 (4.718) Loss 2.7119 (2.6152) Prec@1 31.875 (36.632) Prec@5 63.125 (67.262) Epoch: [14][8160/11272] Time 0.939 (5.533) Data 0.002 (4.713) Loss 2.6682 (2.6151) Prec@1 33.750 (36.634) Prec@5 63.750 (67.265) Epoch: [14][8170/11272] Time 0.919 (5.528) Data 0.001 (4.707) Loss 2.5486 (2.6150) Prec@1 39.375 (36.637) Prec@5 70.000 (67.266) Epoch: [14][8180/11272] Time 0.779 (5.522) Data 0.001 (4.701) Loss 2.7279 (2.6151) Prec@1 35.000 (36.635) Prec@5 65.625 (67.265) Epoch: [14][8190/11272] Time 0.778 (5.516) Data 0.001 (4.695) Loss 2.9230 (2.6150) Prec@1 32.500 (36.638) Prec@5 60.625 (67.266) Epoch: [14][8200/11272] Time 0.898 (5.510) Data 0.001 (4.690) Loss 2.8514 (2.6150) Prec@1 33.750 (36.639) Prec@5 60.000 (67.266) Epoch: [14][8210/11272] Time 0.937 (5.505) Data 0.001 (4.684) Loss 2.6579 (2.6150) Prec@1 33.125 (36.638) Prec@5 66.875 (67.267) Epoch: [14][8220/11272] Time 0.774 (5.499) Data 0.001 (4.678) Loss 2.3829 (2.6149) Prec@1 40.000 (36.638) Prec@5 71.250 (67.268)