Awesome
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, Tengyu Ma
This is the official implementation of LDAM-DRW in the paper Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss in PyTorch.
Dependency
The code is built with following libraries:
Dataset
- Imbalanced CIFAR. The original data will be downloaded and converted by
imbalancec_cifar.py
. - The paper also reports results on Tiny ImageNet and iNaturalist 2018. We will update the code for those datasets later.
Training
We provide several training examples with this repo:
- To train the ERM baseline on long-tailed imbalance with ratio of 100
python cifar_train.py --gpu 0 --imb_type exp --imb_factor 0.01 --loss_type CE --train_rule None
- To train the LDAM Loss along with DRW training on long-tailed imbalance with ratio of 100
python cifar_train.py --gpu 0 --imb_type exp --imb_factor 0.01 --loss_type LDAM --train_rule DRW
Reference
If you find our paper and repo useful, please cite as
@inproceedings{cao2019learning,
title={Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss},
author={Cao, Kaidi and Wei, Colin and Gaidon, Adrien and Arechiga, Nikos and Ma, Tengyu},
booktitle={Advances in Neural Information Processing Systems},
year={2019}
}