I'm currently working on a common image classification with CNN.
I would like to use both normalization (substract mean / divide by std per channel) and data augmentation (rotation, color, blur, ...) but I don't know how to use them together.
Which order should I use ?
First normalize with parameters based only on original images and then augment it (augment a normalized image is relevant ? should I ban some type of augmentation like color ?)
Augment data and apply normalization based on all image (compute mean/ std with augmented images) which seems to be counterintuitive.
Augment data and apply normalization based on only original image which means that data are not really normalized
Or don't use both methods