Hello,
I have been trying to look for help on something similar to weighted resampling but in the multi-label object detection scenario for my highly imbalanced dataset.
Quick overview:
My dataset consists of images that are broadly classified as good images and bad images (images with defects). Furthermore, the bad images are further classified into seven different (labels 1 through 7) based on the type of defects they have. The good ones which do not have any defects are encoded as all zeros [0,0,ā¦,0] and an image with defects 1 and 2 for example is multi-hot encoded as [1,1,0,ā¦,0] .
Problem:
The good ones are a lot in number and the ones with defects are very less. This calls for weighted re-sampling. I have looked at a discussion thread here: Unbalanced data in multiple object detection and here: Help for Sampling training data for multi-label classification task?!. Was wondering if anyone has a sample implemented. Thanks.