my problem is quite difficult:
I have multiclass classification problem.
my images are:
- very different shapes and sizes, which I want to keep
- the data is unbalanced
different image sizes work when I fintune with “resnet18” for example, but I cannot use batches.
so my idea was to use “virtual” batches. meaning, train 1 by 1 but calculate the gradients every 16 iterations, which will create the effect of training batched. (in this case I am deactivating batchnorm btw).
My training works ok (meaning Loss curves behave normal), but my results are poor.
the other thing I tried doing is adding weights to my cross entropy loss to deal with the unbalanced data, but the results are EXACTLY the same as training without any weights at all.