path = Path('some path to data in image-net style folder format')
data = ImageDataBunch.from_folder(path, size=224, num_workers=4)
After training my model, I created confusion matrix and now want to find images that were classified incorrectly
interp = ClassificationInterpretation.from_learner(learn)
losses, idxs = interp.top_losses(50)
data.valid_ds[idxs] - I`ll have incorrect image and can even plot it, but my dataset has couple thousand images and I do not want visually check all of them to find incorrectly classified.
My dataset is not very clean and I want to delete some misclassified examples that are in real were put to wrong directory
If you go onto lesson 2, Jeremy mentions FileDeleter, which does exactly that If you use google Colab, I’m working on a few plugins and porting FileDeleter to there, but for now there are other ways to get around it