229 lines
14 KiB
Markdown
229 lines
14 KiB
Markdown
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<img src="./resources/mmselfsup_logo.png" width="500"/>
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<div> </div>
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<div align="center">
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<b><font size="5">OpenMMLab 官网</font></b>
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<sup>
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<a href="https://openmmlab.com">
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<i><font size="4">HOT</font></i>
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</a>
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</sup>
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<b><font size="5">OpenMMLab 开放平台</font></b>
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<sup>
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<a href="https://platform.openmmlab.com">
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<i><font size="4">TRY IT OUT</font></i>
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</a>
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</sup>
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</div>
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<div> </div>
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[](https://pypi.org/project/mmselfsup)
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[](https://mmselfsup.readthedocs.io/en/dev-1.x/)
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[](https://github.com/open-mmlab/mmselfsup/actions)
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[](https://codecov.io/gh/open-mmlab/mmselfsup)
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[](https://github.com/open-mmlab/mmselfsup/blob/master/LICENSE)
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[](https://github.com/open-mmlab/mmselfsup/issues)
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[📘使用文档](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/) |
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[🛠️安装教程](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/get_started.html) |
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[👀模型库](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/model_zoo.html) |
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[🆕更新日志](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/notes/changelog.html) |
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[🤔报告问题](https://github.com/open-mmlab/mmselfsup/issues/new/choose)
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</div>
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<div align="center">
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[English](README.md) | 简体中文
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</div>
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## 介绍
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MMSelfSup 是一个基于 PyTorch 实现的开源自监督表征学习工具箱，是 [OpenMMLab](https://openmmlab.com/) 项目成员之一。
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主分支代码支持 **PyTorch 1.6** 及以上的版本。
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### 主要特性
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- **多方法集成**
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MMSelfSup 提供了多种前沿的自监督学习算法，大部分的自监督预训练学习都设置相同，以在基准中获得更加公平的比较。
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- **模块化设计**
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MMSelfSup 遵照 OpenMMLab 项目一贯的设计理念，进行模块化设计，便于用户自定义实现自己的算法。
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- **标准化的性能评测**
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MMSelfSup 拥有丰富的基准进行评估和测试，包括线性评估, 线性特征的 SVM / Low-shot SVM, 半监督分类, 目标检测和语义分割。
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- **兼容性**
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兼容 OpenMMLab 各大算法库，拥有丰富的下游评测任务和预训练模型的应用。
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## 更新
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**v1.0.0rc3** 版本已经在 2022.12.07 发布。
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- 支持 BEiT 和 MILAN 预训练
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- 支持 low-level 重建可视化
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- 更新模型库算法页面文档
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**v1.0.0rc3** 版本已经在 2022.11.01 发布。
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- 支持 MaskFeat
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请参考 [更新日志](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/notes/changelog.html) 获取更多细节和历史版本信息。
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MMSelfSup 1.x 和 0.x 的不同点写在 [迁移文档](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/migration.html) 中。
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## 安装
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MMSelfSup 依赖 [PyTorch](https://pytorch.org/), [MMCV](https://github.com/open-mmlab/mmcv), [MMEngine](https://github.com/open-mmlab/mmengine) 和 [MMClassification](https://github.com/open-mmlab/mmclassification)。
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请参考 [安装文档](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/get_started.html) 获取更详细的安装指南。
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## 快速入门
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我们针对基础使用和进阶用法提供了 [用户指引](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/user_guides/index.html)：
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Pretrain
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- [Config](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/user_guides/1_config.html)
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- [Prepare Dataset](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/user_guides/2_dataset_prepare.html)
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- [Pretrain with Existing Models](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/user_guides/3_pretrain.html)
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Downetream Tasks
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- [Classification](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/user_guides/classification.html)
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- [Detection](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/user_guides/detection.html)
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- [Segmentation](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/user_guides/segmentation.html)
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Useful Tools
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- [Visualization](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/user_guides/visualization.html)
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- [Analysis Tools](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/user_guides/analysis_tools.html)
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我们也提供了 [进阶指引](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/advanced_guides/index.html) 和 [Colab 教程](https://github.com/open-mmlab/mmselfsup/blob/master/demo/mmselfsup_colab_tutorial.ipynb)。
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如果遇到问题，请参考 [常见问题解答](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/notes/faq.html)。
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## 模型库
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请参考 [模型库](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/model_zoo.html) 查看我们更加全面的模型基准结果。
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目前已支持的算法:
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- [x] [Relative Location (ICCV'2015)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/relavive_loc)
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- [x] [Rotation Prediction (ICLR'2018)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/rotation_pred)
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- [x] [DeepCluster (ECCV'2018)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/deepcluster)
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- [x] [NPID (CVPR'2018)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/npid)
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- [x] [ODC (CVPR'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/odc)
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- [x] [MoCo v1 (CVPR'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mocov1)
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- [x] [SimCLR (ICML'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/simclr)
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- [x] [MoCo v2 (arXiv'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/byol)
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- [x] [BYOL (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mocov2)
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- [x] [SwAV (NeurIPS'2020)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/swav)
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- [x] [DenseCL (CVPR'2021)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/densecl)
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- [x] [SimSiam (CVPR'2021)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/simsiam)
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- [x] [Barlow Twins (ICML'2021)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/barlowtwins)
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- [x] [MoCo v3 (ICCV'2021)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mocov3)
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- [x] [BEiT (ICLR'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/beit)
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- [x] [MAE (CVPR'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/mae)
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- [x] [SimMIM (CVPR'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/simmim)
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- [x] [MaskFeat (CVPR'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/maskfeat)
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- [x] [CAE (arXiv'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/cae)
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- [x] [MILAN (arXiv'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/milan)
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- [x] [BEiT v2 (arXiv'2022)](https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/beitv2)
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更多的算法实现已经在我们的计划中。
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## 基准测试
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| 基准测试方法 | 参考设置 |
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| -------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| ImageNet Linear Classification (Multi-head) | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
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| ImageNet Linear Classification (Last) | |
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| ImageNet Semi-Sup Classification | |
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| Places205 Linear Classification (Multi-head) | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
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| iNaturalist2018 Linear Classification (Multi-head) | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
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| PASCAL VOC07 SVM | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
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| PASCAL VOC07 Low-shot SVM | [Goyal2019](http://openaccess.thecvf.com/content_ICCV_2019/papers/Goyal_Scaling_and_Benchmarking_Self-Supervised_Visual_Representation_Learning_ICCV_2019_paper.pdf) |
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| PASCAL VOC07+12 Object Detection | [MoCo](http://openaccess.thecvf.com/content_CVPR_2020/papers/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.pdf) |
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| COCO17 Object Detection | [MoCo](http://openaccess.thecvf.com/content_CVPR_2020/papers/He_Momentum_Contrast_for_Unsupervised_Visual_Representation_Learning_CVPR_2020_paper.pdf) |
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| Cityscapes Segmentation | [MMSeg](configs/benchmarks/mmsegmentation/cityscapes/fcn_r50-d8_769x769_40k_cityscapes.py) |
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| PASCAL VOC12 Aug Segmentation | [MMSeg](configs/benchmarks/mmsegmentation/voc12aug/fcn_r50-d8_512x512_20k_voc12aug.py) |
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## 参与贡献
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我们非常欢迎任何有助于提升 MMSelfSup 的贡献，请参考 [贡献指南](https://mmselfsup.readthedocs.io/zh_CN/dev-1.x/notes/contribution_guide.html) 来了解如何参与贡献。
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## 致谢
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MMSelfSup 是一款由不同学校和公司共同贡献的开源项目，我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户；同时，我们非常感谢 OpenSelfSup 的原开发者和贡献者。
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我们希望该工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现现有算法并开发自己的新模型，从而不断为开源社区提供贡献。
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## 引用
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如果您发现此项目对您的研究有用，请考虑引用：
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```bibtex
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@misc{mmselfsup2021,
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title={{MMSelfSup}: OpenMMLab Self-Supervised Learning Toolbox and Benchmark},
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author={MMSelfSup Contributors},
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howpublished = {\url{https://github.com/open-mmlab/mmselfsup}},
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year={2021}
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}
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```
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## 开源许可证
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该项目采用 [Apache 2.0 开源许可证](LICENSE)。
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## OpenMMLab 的其他项目
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- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
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- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
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- [MMEval](https://github.com/open-mmlab/mmeval): 统一开放的跨框架算法评测库
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- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
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- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
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- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
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- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
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- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
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- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱和基准测试
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- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
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- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具箱
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- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
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- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
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- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
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- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
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- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
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- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
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- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
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- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
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- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
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- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
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- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
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## 欢迎加入 OpenMMLab 社区
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扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=GJP18SjI)，添加OpenMMLab 官方小助手微信，加入 MMSelfSup 微信社区。
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<div align="center">
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<img src="./resources/zhihu_qrcode.jpg" height="400"/> <img src="./resources/qq_group_qrcode.jpg" height="400"/> <img src="./resources/xiaozhushou_weixin_qrcode.jpeg" height="400"/>
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</div>
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我们会在 OpenMMLab 社区为大家
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- 📢 分享 AI 框架的前沿核心技术
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- 💻 解读 PyTorch 常用模块源码
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- 📰 发布 OpenMMLab 的相关新闻
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- 🚀 介绍 OpenMMLab 开发的前沿算法
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- 🏃 获取更高效的问题答疑和意见反馈
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- 🔥 提供与各行各业开发者充分交流的平台
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干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
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