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文档: https://mmsegmentation.readthedocs.io/zh_CN/main

English | 简体中文

简介

MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 OpenMMLab 项目的一部分。

main 分支代码目前支持 PyTorch 1.6 以上的版本。

🎉 MMSegmentation v1.0.0 简介 🎉

我们非常高兴地宣布 MMSegmentation 最新版本的正式发布!在这个新版本中,主要分支是 main 分支,开发分支是 dev-1.x。而之前版本的稳定分支保留为 0.x 分支。请注意,master 分支将只在有限的时间内维护,然后将被删除。我们鼓励您在使用过程中注意分支选择和更新。感谢您一如既往的支持和热情,让我们共同努力,使 MMSegmentation 变得更加健壮和强大!💪

MMSegmentation v1.x 在 0.x 版本的基础上有了显著的提升,提供了更加灵活和功能丰富的体验。为了更好使用 v1.x 中的新功能,我们诚挚邀请您查阅我们详细的 📚 迁移指南,以帮助您无缝地过渡您的项目。您的支持对我们来说非常宝贵,我们热切期待您的反馈!

示例图片

主要特性

  • 统一的基准平台

    我们将各种各样的语义分割算法集成到了一个统一的工具箱,进行基准测试。

  • 模块化设计

    MMSegmentation 将分割框架解耦成不同的模块组件,通过组合不同的模块组件,用户可以便捷地构建自定义的分割模型。

  • 丰富的即插即用的算法和模型

    MMSegmentation 支持了众多主流的和最新的检测算法,例如 PSPNet,DeepLabV3,PSANet,DeepLabV3+ 等.

  • 速度快

    训练速度比其他语义分割代码库更快或者相当。

更新日志

最新版本 v1.0.0 在 2023.04.06 发布。 如果想了解更多版本更新细节和历史信息,请阅读更新日志

安装

请参考快速入门文档进行安装,参考数据集准备处理数据。

快速入门

请参考概述对 MMSegmetation 进行初步了解

请参考用户指南了解 mmseg 的基本使用,以及进阶指南深入了解 mmseg 设计和代码实现。

同时,我们提供了 Colab 教程。你可以在这里浏览教程,或者直接在 Colab 上运行

若需要将 0.x 版本的代码迁移至新版,请参考迁移文档

基准测试和模型库

测试结果和模型可以在模型库中找到。

已支持的骨干网络:
已支持的算法:
已支持的数据集:

如果遇到问题,请参考 常见问题解答

社区项目

这里有一些由社区用户支持和维护的基于 MMSegmentation 的 SOTA 模型和解决方案的实现。这些项目展示了基于 MMSegmentation 的研究和产品开发的最佳实践。 我们欢迎并感谢对 OpenMMLab 生态系统的所有贡献。

贡献指南

我们感谢所有的贡献者为改进和提升 MMSegmentation 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

MMSegmentation 是一个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,从而不断为开源社区提供贡献。

引用

如果你觉得本项目对你的研究工作有所帮助,请参考如下 bibtex 引用 MMSegmentation。

@misc{mmseg2020,
    title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},
    author={MMSegmentation Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmsegmentation}},
    year={2020}
}

开源许可证

该项目采用 Apache 2.0 开源许可证

OpenMMLab 的其他项目

  • MMEngine: OpenMMLab 深度学习模型训练库
  • 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
  • MMEval: 统一开放的跨框架算法评测库
  • 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
  • MMYOLO: OpenMMLab YOLO 系列工具箱与测试基准
  • MMSegmentation: OpenMMLab 语义分割工具箱
  • MMOCR: OpenMMLab 全流程文字检测识别理解工具包
  • 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 以及 MMSegmentation 的 QQ 群。

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

基于 Pytorch 和 MMCV 语义分割开源算法库,也是语义分割算法性能评估框架,已提供超过420个语义分割算法模型,并支持多种分割任务数据集,包括自然图像、遥感图像等。 展开 收起
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