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📘使用文档 | 🛠️安装教程 | 👀模型库 | 🤔报告问题

English | 简体中文

说明

MMRazor是一个可用于模型瘦身和AutoML的模型压缩工具箱,包含了3种主流的技术:

  • 网络结构搜索 (NAS)
  • 模型剪枝
  • 知识蒸馏 (KD)
  • 量化 (下个版本发布)

MMRazor是OpenMMLab项目的一部分。

主要特性

  • 兼容性

    MMRazor和OpenMMLab有着类似的架构设计,并且实现了轻量化算法和视觉任务间轻耦合,因此很容易应用于OpenMMLab中其他的项目。

  • 灵活性

    多种轻量化算法可以以一种即插即用的方式来组合使用,从而搭建出功能更强大的系统。

  • 便利性

    得益于更好的模块化设计,开发者仅用修改少量代码,甚至只用修改配置文件即可实现新的轻量化算法。

下面是MMRazor设计和实现的概括图, 如果想了解更多的细节,请参考 tutorials


更新日志

💎 稳定版

MMRazor v0.3.1 版本已经在 2022.5.4 发布。

🌟 1.x预览版

MMRazor v1.0.0rc1版本在2022.10.27发布。

  • 基于MMEngine统一接口
  • 全新的剪枝框架
  • 支持更多的蒸馏/网络结构搜索算法

基准测试和模型库

测试结果可以在 模型库 中找到。

已经支持的算法:

Neural Architecture Search
Pruning
Knowledge Distillation

安装

MMRazor 依赖 PyTorchMMCV

请参考get_started.md获取更详细的安装指南。

快速入门

请参考 train.mdtest.md 学习 MMRazor 的基本使用。 我们也提供了一些进阶教程:

贡献指南

我们感谢所有的贡献者为改进和提升 MMRazor 所作出的努力。 请参考贡献指南来了解参与项目贡献的相关指引。

致谢

MMRazor 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。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。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具,供用户复现已有算法并开发自己的新模型压缩算法,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{2021mmrazor,
    title={OpenMMLab Model Compression Toolbox and Benchmark},
    author={MMRazor Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmrazor}},
    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
  • 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

深度学习模型压缩算法库,支持网络结构搜索、剪枝、蒸馏等主流技术方向,为 OpenMMLab 其他算法库提供即插即用、可自由组合的模型压缩算法,让用户更简单更快速地实现模型轻量化。 展开 收起
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