Image preprocessing of finetune in ResNet
I would try both. Subtracting your mean makes sense because generally one tries to get mean 0. Subtracting image net mean makes sense because you want the network as a feature extractor. If you change something that early in the feature extractor it could be that it doesn't work at all. 
Just like the mean 0 thing, it is generally seen as a desirable property to have features within a fixed range or with a fixed standard deviation. Again, I can't really tell you what is better but you can easily try it. My guess is that there aren't too big differences.

Most important: Make sure you apply the same preprocessing steps to your training / testing / evaluation data.
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?