Currently, I am reading Rethinking Model Scaling for Convolutional Neural Networks. The authors are talking about a different way of scaling convolutional neural networks by scaling all dimensions simultaneously and relative to each dimension. I understand the scaling methods regarding the depth of a network (# layers) and the resolution (size of the input image).
What I was stumbling is the concept of the network's width (# channels). What is meant by the width or the number of channels of a network? I don't think it is the number of color channels, or is this the case? The number of color channels was the only link I found regarding the terms "ConvNets" and "number of channels".