I would like to use CNN to make classification with 5 classes, but 4 of these classes only have between 16 and 60 images, while the last one has more than 1300. I know 16 or 60 images are not enough, so I want to use transfer learning, fine tuning and data augmentation. However, I have several questions.
As the data augmentation must only be used for the training data, I will have very few images from the 4 classes for the validation set, would it be a problem?
Is it needed to split it in training/validation/test, or is training/validation enough?
Another problem is the unbalanced data: with such a difference between the number of images in each class, would oversampling or undersampling be a good solution?
For transfer learning and fine tuning, should I freeze all the convolutional layers or should I only train a FC layer?