Can you give me an overall picture of the problem that you’re trying to solve? You have mentioned that every example has an average of 130 class labels.

Do you think detection could help in such a case?(but annotating 80K samples can be expensive)
Can you use existing detectors to run on the train set, extract individual class objects and add them(along with augmentation) to your train set? This may help in tackling class imbalance.