Hello again!
Both the training and the validation images are processed using the same functions.
Your first point, however, might be of concern. I did not use model.eval() but I thought passing my data using “with torch.no_grad():” would do the same job. Can it be that this is not the case?
So to be clear, instead of this in my forward pass:
with torch.no_grad():
            output_1 = pretrained_model(input_1.float())
            output_2 = pretrained_model(input_2.float())
            output_3 = pretrained_model(input_3.float())

you suggest I do this when downloading the pretrained model?
model = models.resnet18(pretrained=True)
model = torch.nn.Sequential(*(list(model.children())[:-1]))
model.eval()
model = model.to(device)