It means that the lengths/values aren’t equal. I’m not 100% sure that command matters,but don’t take my word for it. Try anyway and see if the rest of the code works.
As for the ‘interp.plot_top_losses(9, figsize=(15,11)),’ it may be because, if you are using a different dataset, there may not be enough images or are not fitted to that size. What I suggest you do is play around with those numbers until something works, like making the numbers smaller.
To add onto @DArXToRm24 (which btw, foliar_learner was simply his variable name for the Learner generated from cnn_learner), when you first began fitting you didn’t find a learning rate to use, and this would be the first step to start at. Begin by doing an lr_find() and then pick your learning rate based on those results. I’d imagine you’ll get a better accuracy with this
Alright. At this point, I’m just going to shoot off suggestions that I don’t know will work, but they’re my best guesses. It’s good the we got those other things out of the way.
One thing you should do, for sure, is fix those mislabeled images. Go through the top losses and find those, because a model can’t run on ‘broken’ data. The loss is killing the model. Also, take out images that aren’t helpful.
My next suggestion is increase the learning rate, especially if fixing the data doesn’t work.
It’s good that you went through the data and found the issue. Good luck!