Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective
Zhengzhuo Xu, Zenghao Chai, Chun Yuan
This is the PyTorch implementation of our paper in NeurIPS 2021.
Environment settings
- PyTorch >= 1.4
- Scikit-learn
- Matplotlib
Training
To train the model, please select a config file path or customize by yourself. For example:
python train.py config/cifar10_100.pyThe result will be saved in ./result.
Inferance model
| Dataset | log | Model |
|---|---|---|
| CIFAR-10-LT-50 | link | link |
| CIFAR-10-LT-100 | link | link |
| CIFAR-10-LT-200 | link | link |
| CIFAR-100-LT-50 | link | link |
| CIFAR-100-LT-100 | link | link |
| CIFAR-100-LT-200 | link | link |
Note
- Some settings may be a little different from the paper reported. Because we make further optimization considering reviewers' suggestions.
- Some bugs need to be fixed when imb factor = 0.1
- Need to solve the test-agnostic situation.
Reference
Welcome to cite:
@inproceedings{PriorLT,
title={Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective},
author={Xu, Zhengzhuo and Chai, Zenghao and Yuan, Chun},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021}
}Acknowledgement
We adopt the code from the following repos. We thank them for providing their awesome code.