I took the resnet50 PyTorch model from torchvision and exported to ONNX. When I ran it using image-classifier on first 1000 images of imagenet data set, i am seeing almost 20% accuracy loss from the resnet50 caffe2 model (on same 1000 images). It makes me wonder if the options i am using for running pytorch model is not correct. I am using “-use-imagenet-normalization” “-compute-softmax” (pytorch model does not softmax in the end) and “-image-mode=0to1”. Does input to pytorch model gets normalized in the same way as caffe2 model?
What can I check to debug this further?