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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

iNaturalist2018

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
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
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THE SOFTWARE.

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}
}