Open loucif01 opened 2 years ago
You may try to write your own sampler or reweighted loss or something else.
I personally used dataset level techniques (the "bag of freebies" augmentations from the YOLOv4 whitepaper) to combat the imbalance of example counts between labels. You will have to train longer (dataset grows after augmentation), but the performance improves across labels, which reflects overall performance.
Hi, I want to know if there is any changes that we should set before training on imbalanced dataset?
Thank you.