When augmenting a dataset subdivided in train, validation e test set, are the augmented images guaranteed to remain in the same set? E.g. the train set augmented images remain in the train set, and so on for the ones of the validation and test set.
Does all the augmentation methods retains the annotations (instance segmentation masks in my case) in the augmented images? I’ve tried a few and it seem that the answer is yes, but I don’t know if there are exceptions.