I’ve just finished lesson 2. Does anyone know if it’s possible to obtain the second-best prediction, as well as the first, for an image classifier? If my model is making a prediction with e.g. <60% confidence, it might also be useful to show the next-best guess. I looked at the learner.predict documentation, but couldn’t parse the source code.
Assuming a simple image classification problem, you could do something like:
_, _, probs = learn.predict(img)
_, ind = torch.topk(probs, k=2)
cat = dls.vocab[ind[1]]
The first line calculates the probabilities of img
belonging to each class, torch.topk
returns the k
largest values in probs
(in a descending order) alongside their indices, and we finally extract the name of the category.
Good luck!
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