Does Tensorflow use specific image preprocessing normalization for each keras.application network?
Tensorflow provides the preprocessing function for models in keras.applications, called preprocess_input. For example, an image can be preprocessed for InceptionV3 using tf.keras.applications.inception_v3.preprocess_input.
I'm trying to understand what kind of image preprocessing is required when using one of the base networks provided by keras.application whith tensorflow compat.v1 module
In particular, I'm interested about the functions that converts each pixel channel value in the range [-1,1] or similar. I have digged in the code and it seems Tensorflow relies on Keras which, on other hand, should have 3 differents functions: one for tf, one for caffe and the last for torch, meaning not specific ones for each base network
Up until now I have just re-implemented the function for tensorflow (value = value/127.5 - 1) but I also read about others discussing something else (e.g. value = value/255), nothing "official" tho. I have started to have some doubts regarding what I'm doing because, after switching to ResNet50, I can't seem to obtain decent results in contrast to several papers I'm following. I would like to have a definitive idea about the topic, any help would be much appreciated