I would like to fine tune the VGG16 model using my own grayscale images. I know I can fine tune/add my own top layers by doing something like:
base_model = keras.applications.vgg16.VGG16(include_top=False, weights='imagenet', input_tensor=None, input_shape=(im_height,im_width,channels))
but only when channels = 3 according to the documentation. I have thought of simply adding two redundant channels to my image, but this seems like a waste of computation/could make the classification worse. I could also replicate the same image across three channels, but I am similarly unsure of how it would preform.
Keras pre-trained models have trained on color images and if you want to use their full power, you should use color images for fine-tuning. However, if you have grayscale images you can still use these pre-trained models by repeating your grayscale image over three channels. But obviously, it will not as well as using color images as input.