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?