I’m new to machine learning, and I’d like to do image segmentation on small matrix (1x32x32 patterns). Unfortunately it seems that most networks (Unet, ResNet, etc) are designed to handle large pictures (3x256x256 and more) and I have no idea how to scale them down to my size and if that’s even a good idea.
What would be the simplest network to start with for such small patterns ? Basically I’m trying to discriminate noise from feature in those patterns, say which are noise pixels and which are feature pixels (so 1x32x32 as input, 1x32x32 as output). I can randomly generate as many noise+feature patterns and masks as needed for the training. I’m using the C++ version of PyTorch, libtorch.
thanks !