0
$\begingroup$

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?

$\endgroup$
1
$\begingroup$

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.)

| cite | improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.