Both validation loss and accuracy are increasing using a pre-trained VGG-16 - deep-learning

Question

So, I'm doing a 4 label x-ray images classification on around 12600 images:
Class1:4000
Class2:3616
Class3:1345
Class4:4000
I'm using VGG-16 architecture pertained on the imageNet dataset with cross-entrpy and SGD and a batch size of 32 and a learning rate of 1e-3 running on pytorch
[[749., 6., 50., 2.],
[ 5., 707., 9., 1.],
[ 56., 8., 752., 0.],
[ 4., 1., 0., 243.]]
I know since both train loss/acc are relatively 0/1 the model is overfitting, though I'm surprised that the val acc is still around 0.9!
How to properly interpret that and what causing it and how to prevent it?
I know it's something like because the accuracy is the argmax of softmax like the actual predictions are getting lower and lower but the argmax always stays the same, but I'm really confused about it! I even let it train for +64 epochs same results flat acc while loss increases gradually!
PS. I have seen other questions with answers and didn't really get an explanation

Answer 1

I think your question already says about what is going on. Your model is overfitting as you have also figured out. Now, as you are training more your model slowly becoming more specialized to the train set and loosing the the capability to generalize gradually. So the softmax probabilities are getting more and more flat. But still it is showing more or less the same accuracy for validation set as still now the correct class has at least slightly more probability than the others. So in my opinion there can be some possible reasons for this:
Your train set and validation set may not be from the same distribution.
Your validation set doesn't cover all cases need to be evaluated, it probably contains similar types of images but they do not differ too much. So, when the model can identify one, it can identify many of them from the validation set. If you add more heterogeneous images in validation set, you will no longer see such a large accuracy in validation set.
Similarly, we can say your train set has images which are heterogeneous i.e, they have a lot of variations, and the validation set is covering only a few varieties, so as training goes on, those minorities are getting less priority as the model yet to have many things to learn and generalize. This can happen if you augment your train-set and your model finds the validation set is relatively easier initially (until overfitting), but as training goes on the model gets lost itself while learning a lot of augmented varieties available in the train set. In this case don't make the augmentation too much wild. Think, if the augmented images are still realistic or not. Do augmentation on images as long as they remain realistic and each type of these images' variations occupy enough representative examples in the train set. Don't include unnecessary situations in augmentation those will never occur in reality, as these unrealistic examples will just increase burden on the model than doing any help.