Methods for increasing accuracying of a CNN for image classification
I'm currently working on a image classification task, involving a large datasets of grayscale images of cartoons and my CNN needs to classify them. Atm my model has a test accuracy of about 88% but I know a higher accuracy is possible.
I've tried:

improving / changing the actual model / architecture
using different meta parameters
different loss functions from the pytorch libraries
a bunch of  different transforms
different optimizes from torch.optim

I've also tried a bunch of the standard models included in torchvision.models and am still getting sub 90% accuracy on the test set.
Do I just need to keep trying the above things to squeeze out better accuracy or are there any other avenues I can try? Would really appreciate any suggestions, the only other thing I can think of would be making my own custom loss function specific for the data set but I'm not exactly sure how much that would help?

From what you've described, it sounds like it might be worth spending some time on the data preparation. Here is a good article on how to do that for images. Some ideas you could try are:

Resizing all your images to a fixed size
Subtracting mean pixel values, i.e. normalizing the dataset

I don't really know the context of what you're doing but I would also consider adding additional features that may be relevant and seeing if that helps.
