Thanks for your reply!
Yes, I understand that I can always normalize your data so that they have 0 mean and std 1. However, for example, when Resnet50 is trained, the train set ranges from(0, 1), I wonder it is “good” to do the same normalization on my dataset, which ranges from (-25, 0).
My dataset has a small data size(2000), I hope to apply transfer learning to reduce overfitting. Is there a practical way in such scenario?