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Readme zh-CN #262
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| <div align="center"> | ||
| <img src="resources/mmediting-logo.png" width="500px"/> | ||
| </div> | ||
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| ## Introduction | ||
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| [English](/README.md) | 简体中文 | ||
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| [](https://mmediting.readthedocs.io/en/latest/?badge=latest) | ||
| [](https://github.com/open-mmlab/mmediting/actions) | ||
| [](https://codecov.io/gh/open-mmlab/mmediting) | ||
| [](https://pypi.org/project/mmediting/) | ||
| [](https://github.com/open-mmlab/mmediting/blob/master/LICENSE) | ||
| [](https://github.com/open-mmlab/mmediting/issues) | ||
| [](https://github.com/open-mmlab/mmediting/issues) | ||
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| MMEditing 是基于 PyTorch 的图像&视频编辑开源工具箱。是 [OpenMMLab](https://open-mmlab.github.io/) 项目的成员之一。 | ||
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| 主分支代码目前支持 **PyTorch 1.3~1.6** 。 | ||
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| 文献资料: https://mmediting.readthedocs.io/en/latest/. | ||
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| <div align="left"> | ||
| <img src="resources/mmediting-demo.jpg"/> | ||
| </div> | ||
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| ### 主要特性 | ||
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| - **模块化设计** | ||
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| MMEditing 将编辑框架分解为不同的组件，并且可以通过组合不同的模块轻松地构建自定义的编辑器模型。 | ||
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| - **支持多种编辑任务** | ||
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| MMEditing 支持*绘画*、*抠图*、*超分辨率*、*生成*等多种主流编辑任务。 | ||
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| - **SOTA** | ||
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| MMEditing 提供绘画/抠图/超分辨率/生成等任务最先进的算法. | ||
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| ## 许可证 | ||
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| 本项目开源自 [Apache 2.0 license](LICENSE)。 | ||
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| ## 更新记录 | ||
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| v0.6.0 版本已于 2021 年 3 月 31 日发布. | ||
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| 需要注意的是 **MMSR** 已作为 MMEditing 的一部分并入本仓库。 | ||
| MMEditing 缜密地设计新的框架并将其精心实现，希望能够为您带来更好的体验。 | ||
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| ## 基准和模型库 | ||
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| 请参考[模型库](https://mmediting.readthedocs.io/en/latest/modelzoo.html)了解详情。 | ||
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| ## 安装 | ||
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| 请参考[安装指南](docs/install.md)进行安装。 | ||
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| ## 开始使用 | ||
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| 请参考[使用教程](docs/getting_started.md)获取MMEditing的基本用法。 | ||
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| ## 引用 | ||
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| 如果您觉得 MMEditing 对您的研究有所帮助，请考虑引用它： | ||
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| ```bibtex | ||
| @misc{mmediting2020, | ||
| title={OpenMMLab Editing Estimation Toolbox and Benchmark}, | ||
| author={MMEditing Contributors}, | ||
| howpublished = {\url{https://github.com/open-mmlab/mmediting}}, | ||
| year={2020} | ||
| } | ||
| ``` | ||
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| ## 参与贡献 | ||
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| 感谢您为改善 MMEditing 所做的所有贡献。请参阅 [CONTRIBUTING.md in MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/.github/CONTRIBUTING.md) 以获取贡献准则。 | ||
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| ## 致谢 | ||
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| MMEditing 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。 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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| ## OpenMMLab 的其他项目 | ||
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| - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库 | ||
| - [MMClassification](https://github.com/open-mmlab/mmclassification): 图像分类工具箱与测试基准 | ||
| - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 检测工具箱与测试基准 | ||
| - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用3D目标检测平台 | ||
| - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): 语义分割工具箱与测试基准 | ||
| - [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱与测试基准 | ||
| - [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台 | ||
| - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱与测试基准 | ||
| - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱 | ||
| - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包 | ||
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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=aCvMxdr3) | ||
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| <div align="center"> | ||
| <img src="resources/zhihu_qrcode.jpg" height="400" /> <img src="resources/qq_group_qrcode.jpg" height="400" /> | ||
| </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 | ||
| - 💻 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 | ||
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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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https://pypi.org/project/mmedit/