I am doing an image segmentation task where there are just two labels ‘0’ and 1(or black and white pixels).
I was wondering if there is a way to give a threshold when using unet_learner, so that a white pixel is chosen only if the value > threshold(say 0.6). I’m assuming by default it takes max of two possible outputs.
I had a similar question @sumeetd . Did you end up finding an answer to your question?
For what it’s worth, I found a nice post on Kaggle using a thresholding approach with binary classification, but I don’t see it build into the fast.ai documentation.
Did either of you figure this out?
You only need to use learn.predict and check third value in returned tuple which is the logits of probabilities. Then you use torch.sigmoid(x) > threshold to get your class prediction.