Keras VGG16 preprocess_input modes

The mode here is not about the backend, but rather about on what framework the model was trained on and ported from. In the keras link to VGG16, it is stated that:

These weights are ported from the ones released by VGG at Oxford

So the VGG16 and VGG19 models were trained in Caffe and ported to TensorFlow, hence mode == 'caffe' here (range from 0 to 255 and then extract the mean [103.939, 116.779, 123.68]).

Newer networks, like MobileNet and ShuffleNet were trained on TensorFlow, so mode is 'tf' for them and the inputs are zero-centered in the range from -1 to 1.

In my experience in training VGG16 in Keras, the inputs should be from 0 to 255, subtracting the mean [103.939, 116.779, 123.68]. I've tried transfer learning (freezing the bottom and stack a classifier on top) with inputs centering from -1 to 1, and the results are much worse than 0..255 - [103.939, 116.779, 123.68].