I want to create a deep learning model to classify images. My dataset has around 400 classes and the classes have different number of images..

  • How can I train the deep learning network on unbalanced datasets of images?

I will use data augmentation to increase the amount of data. Also I will apply oversampling..

  • When should I apply oversampling before or after splitting the images into training, testing, validation sets?

  • Should I make oversampling manually?


Unbalanced classes are almost certainly not a problem, and oversampling will not solve a non-problem: Are unbalanced datasets problematic, and (how) does oversampling (purport to) help?

(See here for a general motivation for this answer. See here for a motivation for short answers. Longer answers are always welcome.)

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