Hello everyone,
I’m kinda new to the Machine Learning world (I only did some basic models during my master) and I would like to receive some suggestions about some good approaches that I could take for my supervised learning task. My data is structured as following: the input is a 3-channels image and my output is a one channel image. Each channel in the input represents a snapshot of a certain quantity in a physical system. Each pixel in the image can be thought as a site in a lattice. The output contains the probability that a given pixel is active. The output image comes from a physical evolution of the input state. What are some good architectures for this kind of problem? Do you have any paper\article treating this kind of problem? Thanks in advance.
Hi Vincenzo!
If I understand your description, you wish to perform binary (that is to say
two-class) semantic segmentation.
U-Net is a well-established semantic-segmentation architecture.
Pytorch’s torchvision includes some pre-built semantic-segmentation
models (but not U-Net), such as fcn_resnet50.
Best.
K. Frank
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Thanks for the tip, this might be the way to go!