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PyCIL: A Python Toolbox for Class-Incremental Learning
| Date | Stars |
|---|---|
| 2026-07-24 | 1096 |
| 2026-07-25 | 1096 |
| 2026-07-28 | 1096 |
| 2026-07-30 | 1096 |
| 2026-08-06 | 1096 |
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# PyCIL: A Python Toolbox for Class-Incremental Learning
---
<p align="center">
<a href="#Introduction">Introduction</a> •
<a href="#Methods-Reproduced">Methods Reproduced</a> •
<a href="#Reproduced-Results">Reproduced Results</a> •
<a href="#how-to-use">How To Use</a> •
<a href="#license">License</a> •
<a href="#Acknowledgments">Acknowledgments</a> •
<a href="#Contact">Contact</a>
</p>
<div align="center">
<img src="./resources/logo_v2.png" width="800px">
</div>
---
<div align="center">
[](https://github.com/yaoyao-liu/class-incremental-learning/blob/master/LICENSE)[](https://www.python.org/) [](https://pytorch.org/) []() [](https://paperswithcode.com/task/incremental-learning)
</div>
Welcome to PyCIL, perhaps the toolbox for class-incremental learning with the **most** implemented methods. This is the code repository for "PyCIL: A Python Toolbox for Class-Incremental Learning" [[paper]](https://arxiv.org/abs/2112.12533) in PyTorch. If you use any content of this repo for your work, please cite the following bib entries:
@article{zhou2023pycil,
author = {Da-Wei Zhou and Fu-Yun Wang and Han-Jia Ye and De-Chuan Zhan},
title = {PyCIL: a Python toolbox for class-incremental learning},
journal = {SCIENCE CHINA Information Sciences},
year = {2023},
volume = {66},
number = {9},
pages = {197101},
doi = {https://doi.org/10.1007/s11432-022-3600-y}
}
@article{zhou2024class,
author = {Zhou, Da-Wei and Wang, Qi-Wei and Qi, Zhi-Hong and Ye, Han-Jia and Zhan, De-Chuan and Liu, Ziwei},
title = {Class-Incremental Learning: A Survey},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume={46},
number={12},
pages={9851--9873},
year = {2024}
}
@inproceedings{zhou2024continual,
title={Continual learning with pre-trained models: A survey},
author={Zhou, Da-Wei and Sun, Hai-Long and Ning, Jingyi and Ye, Han-Jia and Zhan, De-Chuan},
booktitle={IJCAI},
pages={8363-8371},
year={2024}
}
## What's New
- [2026-01]🌟 We have released [C3Box](https://github.com/LAMDA-CL/C3Box), a CLIP-based Class-Incremental Learning Toolbox. Have a try!
- [2025-07]🌟 Check out our [latest work](https://arxiv.org/abs/2503.08510) on class-incremental learning with CLIP (**ICCV 2025**)!
- [2025-07]🌟 Check out our [latest work](https://arxiv.org/abs/2508.08165) on pre-trained model-based class-incremental learning (**ICCV 2025**)!
- [2025-07]🌟 Check out our [latest work](https://openreview.net/forum?id=dwjwvTwV3V¬eId=HVZe95quYK) on domain-incremental learning with PTM (**ICML 2025**)!
- [2025-04]🌟 Add [TagFex](https://arxiv.org/abs/2503.00823). State-of-the-art method of 2025!
- [2025-03]🌟 Check out our [latest work](https://arxiv.org/abs/2503.00823) on class-incremental learning (**CVPR 2025**)!
- [2025-02]🌟 Check out our [latest work](https://arxiv.org/abs/2410.00911) on pre-trained model-based domain-incremental learning (**CVPR 2025**)!
- [2025-02]🌟 Check out our [latest work](https://arxiv.org/abs/2305.19270) on class-incremental learning with vision-language models (**TPAMI 2025**)!
- [2024-12]🌟 Check out our [latest work](https://arxiv.org/abs/2412.09441) on pre-trained model-based class-incremental learning (**AAAI 2025**)!
- [2024-08]🌟 Check out our [latestExcerpt of 19,837 characters
Read on GitHub
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Ray Sun · Newcastle University · United Kingdom
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Wang Yabin · HiT
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:06446a010e2be768, topic:deep-learning, topic:pytorch
matched fp:06446a010e2be768, topic:representation-learning