Splitting unbalanced classes of images into training, validation and testing sets

Hi there,

I have 3 classes of images, Covid, Normal and Viral Pneumonia(VP) in three different folders. There are roughly 3,600; 12,000; and 1,600 images in each folder respectively. As this set is imbalanced, I would like to split it into training, validation and testing subsets while maintaining the proportion of each class in each of the subsets.

I have been doing some research and tried this answer. I loaded all the images using ImageFolder, got an array of the classes (represented as 0, 1 and 2) and retrieved the indexes in a balanced way:

img_dataset = datasets.ImageFolder(
                              root = first_model_im_path,
                              transform = image_transforms["train"]

train_idx, test_idx= train_test_split(

So far so good, but I have hit the wall at this point. The next step would of course be taking that train_idx set of indices and splitting it further, but I also have to guarantee that it remains balanced. Does anyone have any idea how to proceed? Or indeed, is there a better way to achieve what I am attempting?

I appreciate the help, thanks so much in advance,

You could call train_test_split again on train_idx instead of the np.arange input with stratify=class_array[train_idx] to create the train and validation indices. Let me know, if this works.

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Thanks so much for your help!