@lugiavn I evaluated the torchvision pretrained models in eval mode using pytorch/examples/imagenet code on ILSVRC2012 val dataset (50,000 images), and got similar error-rate as the current benchmark.




model
benchmark_top1
local_top1
benchmark_top5
local_top5




AlexNet
43.45
43.48
20.91
20.93


ResNet-50
23.85
23.87
7.13
7.14


ResNet-101
22.63
22.63
6.44
6.45



The way how to ran the eval code was: python main.py -a resnet101 --pretrained -e data
System Info:
PyTorch version: 0.4.0
Python version: 3.6.4
CUDA/cuDNN version: cuda/9.0, cudnn/v7.0-cuda.9.0
GPU: Quadro GP100 X2
Let me know if you cannot reproduce these results, or have any question.