Hello,
I am trying to use the pretrained resnet18 on cifar10 (training only the last fully connected layer):
	model = models.resnet18(pretrained=True)
	for param in model.parameters():
    		param.requires_grad = False

	num_ftrs = model.fc.in_features
	model.fc = torch.nn.Linear(num_ftrs, 10)
	optimizer = optim.Adam(model.fc.parameters())

Since resent expects 224x224 images while cifar10 is 32x32, I added a resize transformation in the data loading:
transform = transforms.Compose([transforms.Resize(224), transforms.ToTensor()])
However, the accuracy remains 10% after a long training. I suspects this is due to the resizing or the fact resnet18 expects images from a different distribution, possibly normalized. If this is the case, what transformation should I use?