Image preprocessing of finetune in ResNet
In my honest opinion people overstate the impact of image preprocessing. The only truly important thing is that the test data is similar in value scale to the training data. There are some theoretical benefits of having a pre normalized dataset, with the usage of batch normalization, but in practice it never made much of a difference (2-4% Accuracy).
If you have a running model and you are trying to get those last few % better accuracy without having to increase the amount of parameter than I would suggest tweaking this to your use-case. 
In my opinion there is no single method that works for every use-case, but a good starting point is to use the same preprocessing as ImageNet, because the features will be similar to the ones produced for the imagenet classification.
I want to finetune ResNet50 ImageNet pretrained model, and I have a few question about image preprocessing of finetune.   

In ImageNet preprocessing, we need to subtract the mean of pixel ([103.939, 116.779, 123.68]). When I use my dataset to finetune, should I subtract mean of ImageNet or subtract the mean of my data.
I do see many people rescale the data to [0,1], but the pretrained model(ImageNet) use image scale in [0,255]. Why do people do that? Is it reasonable?