CODEWITHSUNDEEP
pythontensorflowimage-processingkerasconv-neural-network
Arnold Klein
Arnold Klein

Reputation: 3086

Convolutional neural network architectures with an arbitrary number of input channels (more than RGB)

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?

Upvotes: 7

Views: 557

Answers (1)

Simon Schrodi
Simon Schrodi

Reputation: 804

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.

Upvotes: 2

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