Hi,
I was looking for customized data augmentation for image data set that I have and found this:

  
    
    
    How to augment the minority class only in an unbalanced dataset vision
  
  
    I have an unbalanced image dataset with the positive class being 1/10 of the entire dataset. Classification models trained on this dataset tend to be biased toward the majority class (small false negative rate and bigger false positive rate). I would like to do some augmentation only on the minority class to deal with this. The way I understand, using transforms (random rotation, etc.) when creating torchvision.dataset will augment all of the data so the imbalance remains the same. 
What is the …
  


This would be great to start as @ptrblck pointed. However, I am looking to the clean method to augment data to the certain number of images, let’s say 5000 images per each class. I have various distribution of data  across all classes, from 200 to 1000, so I have to augment some classes more than others. Is there any way to do so?
Thanks