(Searched and didn’t find this asked…)
I’ve been going through the vision tutorial on Colab and I got to the part where it runs
interp.plot_losseses() on the dog & cat breeds.
The heading says "Prediction/Actual/Loss/Probability’,…but should it read “Actual/Prediction” instead? See screenshot below.
The image in the top left is in fact a beagle, not a basset hound. Similarly the bottom left looks more like than a birman than a siamese (given the hair length), and the bottom middle is a boxer, not a saint bernard. (I’m not familiar enough with the other 3 breeds to comment – ok well I don’t know what a japanese chin is but the top middle is not a saint bernard, not even a saint bernard puppy.)
So, is the
plot_top_losses heading reversed from it should be, or are (most of) these dataset labels actually wrong?
I tried checking the source, and…see “EDIT” below.
(I guess one other way to check would be to grab the index values for these images and check their labels, but I haven’t gotten far enough to know how to do that yet.)
EDIT: I tracked further into the source, and it looks like there, the target does in fact come before predicted:
for i,l in enumerate(["target", "predicted", "probabilities", "loss"]):
…i.e., the heading really is reversed from what it should be?