How to properly mean-center my data

Question

My task is to estimate a person's age based on a rgb image of the face of that person. I'm using ResNet-50 to that end. At first stage I trained my net on a dataset which is called WIKI-IMDB (after filtering and expanding my dataset by horizontal flipping it contains ~300k images) and I initialized the weights of the network with the weights of a pre-trained ResNet-50 network which trained on ImageNet. When training on the WIKI-IMDB dataset I calculated the mean of each channel (RGB) of the train set input and subtracted it from each example in the batch (I also used the same mean to subtract it from the validation set which I'm using for early stopping). after I finished training my network on WIKI-IMDB I want to test it on another dataset set which is a benchmark for this task. my new dataset which I'm testing on is called CACD and contains ~160k images which I'm dividing to train set and test set. I need to fine-tune my network on the CACD train set and finally report the metric I'm using on the CACD test set.

My question is: when learning on the new CACD train set, should I subtract the mean of the CACD train set from each example in the batch or should I keep subtracting the mean of the WIKI-IMDB train set? and if I need to subtract the new mean of the CACD train set, should I use that mean also when evaluating on the CACD test set?

Answer 1

Each channel of rgb colors has values between 0 and 255. There are many normalization methods. For this case I would just divide the images over 255 without subtracting the mean so that I make sure the output is between 0 and 1

Z = X/255

if you are interested in centering the images around zero, you can subtract 127 (as a mean) and then divide over 255

Z = (X-127)/255

Answer 2

Concerning to use the training summary statistics or those of the complete data set, here are some topics / replies that have good answers to this question:

A previous stack question

On the rapidminer community board

In short, 'peeking' in your test data while training your model is a common but dangerous error.

Answer 3

Before splitting your dataset in training and testing subsets, apply Z-score normalisation, that is subtract the mean and divide the remainder with the variance of the complete dataset.