naver-ai / cmo

The Majority Can Help the Minority: Context-rich Minority Oversampling for Long-tailed Classification (CVPR, 2022)

by Seulki Park¹, Youngkyu Hong², Byeongho Heo², Sangdoo Yun², Jin Young Choi¹

¹ Seoul National University, ² NAVER AI Lab

This is the official implementation of Context-rich Minority Oversampling for Long-tailed Classification in PyTorch.

Paper | Bibtex | Video | Slides

Requirements

All codes are written by Python 3.7 with

• PyTorch (>= 1.6)
• torchvision (>= 0.7)
• NumPy

Training

We provide several training examples:

CIFAR-100-LT

• CE-DRW + CMO

python cifar_train.py --dataset cifar100 --loss_type CE --train_rule DRW --epochs 200 --data_aug CMO

• BS + CMO

python cifar_train.py --dataset cifar100 --loss_type BS --epochs 200 --data_aug CMO

• BS + CMO (400 epochs, AutoAug)

python cifar_train.py --dataset cifar100 --loss_type BS --epochs 400 --data_aug CMO --use_randaug

ImageNet-LT

root: location of Imagenet dataset. (Assume ImageNet data is located at data/ILSVRC/)

At least 4 GPUs are used in the experiments.

• BS + CMO

python imagenet_train.py -a resnet50 --root data/ILSVRC/ --dataset Imagenet-LT --loss_type BS \
--data_aug CMO --epochs 100 --num_classes 1000 --workers 12 --print_freq 100

• BS + CMO (400 epochs, RandAug)

python imagenet_train.py -a resnet50 --root data/ILSVRC/ --dataset Imagenet-LT --loss_type BS \
--data_aug CMO --epochs 400 --num_classes 1000 --workers 12 --print_freq 100  --wd 5e-4 --lr 0.02 \
--cos --use_randaug

iNaturalist2018

root: location of iNaturalist2018 dataset. (Assume data is located at data/iNat2018/)

At least 4 GPUs are used in the experiments.

• BS + CMO

python inat_train.py -a resnet50 --root data/iNat2018/ --dataset iNat18 --loss_type BS --data_aug CMO \
--epochs 100 --num_classes 8142 --workers 12 --print_freq 100 -b 256 

• BS + CMO (400 epochs, RandAug)

python inat_train.py -a resnet50 --root data/iNat2018/ --dataset iNat18 --loss_type BS --data_aug CMO \
--epochs 400 --num_classes 8142 --workers 12 --print_freq 100 --wd 1e-4 --lr 0.02 --cos --use_randaug

Results and Pretrained models

Test

python test.py -a resnet50 --root data/iNat2018/ --dataset iNat18 --loss_type CE --train_rule DRW  \
--resume ckpt.best.pth.tar 

ImageNet-LT

Method	Model	Top-1 Acc(%)	link
BS + CMO	ResNet-50	52.3	download
BS + CMO (400 epochs)	ResNet-50	58.0	download

iNaturalist2018

Method	Model	Top-1 Acc(%)	link
CE-DRW + CMO	ResNet-50	70.9	download
BS + CMO (400 epochs)	ResNet-50	74.0	download

License

This project is distributed under MIT license, except util/moco_loader.py which is adopted from https://github.com/facebookresearch/moco.

Copyright (c) 2022-present NAVER Corp.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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The above copyright notice and this permission notice shall be included in
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Citation

If you find our paper and repo useful, please cite our paper.

@inproceedings{park2021cmo,
  title={The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification},
  author={Park, Seulki and Hong, Youngkyu and Heo, Byeongho and Yun, Sangdoo and Choi, Jin Young},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  year={2022}
}