When working on a lot of classes (and images) it is hard to analyze our model by using simple confusion matrix and stuff already provided.
I was trying to classify 1k types of plants (PlantClef 2016 dataset) and I needed further insight.
With others help I’ve managed to write plot_top_confused which takes 2 classes as arguments and outputs images that got missclassified (i.e. image class A, was labeled as B).
Using the outputs I was able to decide whenever to train model further or not, because those classes seems to have visible differences.
Also written plot_most_correct, so after outputting most_confused I could tell what typical example of given class looks like.
I could do a PR adding those plots (with better quality code than my drafts) to Classification Interpretation