I have adapted the “Cats and Dog” example workflow for image-classification with transfer learning from a pretrained VGG16, and so I am using a similar pre-processing for my images (downscaling and 0-1 intensity scaling), and it seems to work just fine so far
However, I have noticed that the keras library has a preprocessing function specific to the VGG16 model
keras.applications.vgg16.preprocess_input which seems to do additional steps like subtracting the average RGB channel values of the original ImageNet training set.
I was wondering if that could be beneficial to the training.
I could put a python node to call this method but that would slow down the workflow due to the image conversion right ?
Any suggestion ?
PS: I am using the Keras integration so I have almost no code in my workflow