Preprocessing for finetuned CNN model from pretrained models Is it necessary to preprocess the images the same way as they were during the training of pretrained models in our finetuned model to use it for a different classification task ? Say, I have a pretrained VGG16 model which I am finetuning to use it for a different classification task ? Now, before feeding my dataset to this finetuned model, should I necessarily use vgg16.preprocess_input() for training on this dataset or I can use my own preprocess_input ?
If a network was trained let's say on centered input values (i.e. ranging from -1 to 1) with subtracted mean channel values computed on the ImageNet, it is desirable to keep this preprocessing to make fine-tuning easier. 

From other hand, CNN sometimes can be surprisingly robust to this kind of input fluctuations and, unless you freeze the pre-trained part, can adapt to the changes in the input. So, if you just change mean channel values from default [103.939, 116.779, 123.68] to [128, 128, 128] this should work ok. Otherwise, just compare how the accuracy is improving during fine-tuning using the vgg16 preprocessing and your method.