I’m currently working on a student multiclass image segmentation project using U-Net.
My dataset has imbalanced class distribution. Some classes are present in almost every image, while others are quite rare. I’m unsure about the best approach for image patching:
Make equal class representation within each patch? In this case for some classes I will have to use data augmentation techniques.
Or to maintain the original imbalanced proportion within patches?