A quick update on the issue.
When I try to train VGGs, AlexNet and Squeezenet1.0 from scratch for some reason they do no learn at all. Keep bouncing around 50% (0.457 0.542). I tried everything, different optimizers (SGD, Adam), learning rates, number of epoch. On the other hand, in exactly the same conditions ResNets (18,34,50), Inception, Densenet seem to be training okay.
I have literally no idea what I am doing wrong. I’ve been following this tutorial - https://pytorch.org/tutorials/beginner/finetuning_torchvision_models_tutorial.html 6 What interesting is if you scroll down, the guy attempts to train a model from scratch and he ends up with the same problem. His model doesn’t learn at all, the accuracy doesn’t change - 0.457. He doesn’t comment on it sadly.
If anyone could explain why this is the case please. Literally have been following the tutorial. On top of that, when I train VGGs [VGG16, VGG19] in sequence, the code crashes on VGG19 and says CUDA is out of memory. BUT, when I train it alone it trains but doesn’t learn anything anyway. At the end of each training session for each model (basic FOR loop) I delete everything to make sure it doesn’t affect the next training sessions: model, trained model, optimizer, loss function etc.
Thanks!