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📘使用文档 | 🛠️安装教程 | 👀模型库 | 🆕更新日志 | 🤔报告问题

English | 简体中文

简介

MMSelfSup 是一个基于 PyTorch 实现的开源自监督表征学习工具箱,是 OpenMMLab 项目成员之一。

主分支代码支持 PyTorch 1.5 及以上的版本。

主要特性
  • 多方法集成

    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。

  • 模块化设计

    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。

  • 标准化的性能评测

    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。

  • 兼容性

    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。

最新进展

💎 稳定版本

最新的 v0.11.0 版本已经在 2022.12.30 发布。

新版本亮点:

  • 支持 InterCLR
  • 修复部分 bugs

请参考 更新日志 获取更多细节和历史版本信息。

MMSelfSup 和 OpenSelfSup 的不同点写在 对比文档 中。

🌟 1.x 预览版本

全新的 v1.0.0rc4 版本已经在 2022.12.07 发布:

新版本亮点:

  • 基于全新的 MMEngineMMCV
  • 完善了新版本 文档
  • 支持了 BEiT v1, BEiT v2, MILAN, MixMIM, EVA
  • 支持了不同训练时间、不同尺寸的 MAE, SimMIM, MoCoV3 的预训练模型。
  • 更加简洁的 APIs。
  • 更丰富的可视化工具。
  • 更加强大的数据管道。
  • 部分模型具有更高的准确率。

1.x 分支 查看更多新特性。 欢迎大家提 Issues 和 PRs!

安装

MMSelfSup 依赖 PyTorch, MMCVMMClassification.

请参考 安装文档 获取更详细的安装指南。

快速入门

请参考 准备数据 准备数据集, 入门指南 获取 MMSelfSup 的基本使用方法和 基准测试 来运行下游任务。

我们也提供了更加全面的教程,包括:

另外,我们提供了 colab 教程

如果遇到问题,请参考 常见问题解答

模型库

请参考 模型库 查看我们更加全面的模型基准结果。

目前已支持的算法:

更多的算法实现已经在我们的计划中。

基准测试

基准测试方法 参考设置
ImageNet Linear Classification (Multi-head) Goyal2019
ImageNet Linear Classification (Last)
ImageNet Semi-Sup Classification
Places205 Linear Classification (Multi-head) Goyal2019
iNaturalist2018 Linear Classification (Multi-head) Goyal2019
PASCAL VOC07 SVM Goyal2019
PASCAL VOC07 Low-shot SVM Goyal2019
PASCAL VOC07+12 Object Detection MoCo
COCO17 Object Detection MoCo
Cityscapes Segmentation MMSeg
PASCAL VOC12 Aug Segmentation MMSeg

参与贡献

我们非常欢迎任何有助于提升 MMSelfSup 的贡献,请参考 贡献指南 来了解如何参与贡献。

致谢

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。

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。

引用

如果您发现此项目对您的研究有用,请考虑引用:

@misc{mmselfsup2021,
    title={{MMSelfSup}: OpenMMLab Self-Supervised Learning Toolbox and Benchmark},
    author={MMSelfSup Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmselfsup}},
    year={2021}
}

开源许可证

该项目采用 Apache 2.0 开源许可证

OpenMMLab 的其他项目

  • MMCV: OpenMMLab 计算机视觉基础库
  • MIM: MIM 是 OpenMMlab 项目、算法、模型的统一入口
  • MMClassification: OpenMMLab 图像分类工具箱
  • MMDetection: OpenMMLab 目标检测工具箱
  • MMDetection3D: OpenMMLab 新一代通用 3D 目标检测平台
  • MMYOLO: OpenMMLab YOLO 系列工具箱和基准测试
  • MMRotate: OpenMMLab 旋转框检测工具箱与测试基准
  • MMSegmentation: OpenMMLab 语义分割工具箱
  • MMOCR: OpenMMLab 全流程文字检测识别理解工具箱
  • MMPose: OpenMMLab 姿态估计工具箱
  • MMHuman3D: OpenMMLab 人体参数化模型工具箱与测试基准
  • MMSelfSup: OpenMMLab 自监督学习工具箱与测试基准
  • MMRazor: OpenMMLab 模型压缩工具箱与测试基准
  • MMFewShot: OpenMMLab 少样本学习工具箱与测试基准
  • MMAction2: OpenMMLab 新一代视频理解工具箱
  • MMTracking: OpenMMLab 一体化视频目标感知平台
  • MMFlow: OpenMMLab 光流估计工具箱与测试基准
  • MMEditing: OpenMMLab 图像视频编辑工具箱
  • MMGeneration: OpenMMLab 图片视频生成模型工具箱
  • MMDeploy: OpenMMLab 模型部署框架

欢迎加入 OpenMMLab 社区

扫描下方的二维码可关注 OpenMMLab 团队的 知乎官方账号,加入 OpenMMLab 团队的 官方交流 QQ 群,添加OpenMMLab 官方小助手微信,加入 MMSelfSup 微信社区。

我们会在 OpenMMLab 社区为大家

  • 📢 分享 AI 框架的前沿核心技术
  • 💻 解读 PyTorch 常用模块源码
  • 📰 发布 OpenMMLab 的相关新闻
  • 🚀 介绍 OpenMMLab 开发的前沿算法
  • 🏃 获取更高效的问题答疑和意见反馈
  • 🔥 提供与各行各业开发者充分交流的平台

干货满满 📘,等你来撩 💗,OpenMMLab 社区期待您的加入 👬

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简介

自监督学习算法库,紧跟前沿算法,并提供丰富多样的下游任务评估标准,对接 OpenMMLab 各类视觉任务,如 MMDetection,MMSegmentation 等。 展开 收起
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