Convolutional neural network architectures with an arbitrary number of input channels (more than RGB)
If you retrain the models, that's not a problem. Only if you want to use a trained model, you have to keep the input the same.
I am very new to image recognition with CNNs and currently using several standard (pre-trained) architectures available within Keras (VGG and ResNet) for image classification tasks. I am wondering how one can generalise the number of input channels to more than 3 (instead of standard RGB). For example, I have an image which was taken through 5 different (optic) filters and I am thinking about passing these 5 images to the network.
So, conceptually, I need to pass as an input (Height, Width, Depth) = (28, 28, 5), where 28x28 is the image size and 5 - the number of channels.
Any easy way to do it with ResNet or VGG please?