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ResLT: Residual Learning for Long-tailed Recognition

This repository contains the implementation code for paper:
Residual Learning for Long-tailed Recognition https://arxiv.org/abs/2101.10633

If you find this code or idea useful, please consider citing our work:

@article{cui2021reslt,
  title={ResLT: Residual Learning for Long-tailed Recognition},
  author={Cui, Jiequan and Liu, Shu and Tian, Zhuotao and Zhong, Zhisheng and Jia, Jiaya},
  journal={arXiv preprint arXiv:2101.10633},
  year={2021}
}

Overview

In this paper, we proposed a residual learning method to address long-tailed recognition, which contains a Residual Fusion Module and a Parameter Specialization Mechanism. With extensive ablation studies, we demonstrate the effectiveness of our method.

image

Get Started

ResLT Training

For CIFAR, due to the small data size, different experimental environment can have a big difference. To achieve the reported results, you may need to slightly tune the $\alpha$.

bash sh/CIFAR100/CIFAR100LT_imf0.01_resnet32sx1_beta0.9950.sh

For ImageNet-LT,

bash sh/X50.sh

For iNaturalist 2018,

bash sh/R50.sh

Results and Models

CIFAR

Model Download
CIFAR-10-imb0.01 -
CIFAR-10-imb0.02 -
CIFAR-10-imb0.1 -
CIFAR-100-imb0.01 -
CIFAR-100-imb0.02 -
CIFAR-100-imb0.1 -

ImageNet-LT

Model Download log
ResNet-10 model log
ResNeXt-50 model log
ResNeXt-101 model log

iNatualist 2018

Model Download log
ResNet-50 model log

Places-LT

Model Download log
ResNet-152 - -

Acknowledgements

This code is partly based on the open-source implementations from offical PyTorch examples and LDAM-DRW.

Contact

If you have any questions, feel free to contact us through email (jiequancui@link.cuhk.edu.hk) or Github issues. Enjoy!