precisely!
My bad for not inserting it, I can’t edit the post anymore.
I would like to analyse what classes it mistakes the most and then potentially try finding a solution for it.
torch.nn.functional.softmax(preds, dim=1).detach().cpu().numpy()

outputs a tensor size:(batch_size, num_classes)
But I cannot decipher how do these probabilities relate to each of the classes!
It isn’t as simple as getting argmax() as often it is making wrong predictions.
The classes are not passed explicitly anywhere in the model (pre-trained resnet50 with finetuning of FC and last conv), and I just cannot connect each prob to its respective class.