[Doc] Format readme (#1635)

谢昕辰 · web-flow · commit 092b3578bbf3 · 2022-06-02T20:07:14.000+08:00
* quick links

* reorganize readme

* move licence

* modify README_zh-CN
diff --git a/README.md b/README.md
@@ -17,7 +17,7 @@
     </sup>
   </div>
   <div>&nbsp;</div>
-</div>
+
 <br />
 
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmsegmentation)](https://pypi.org/project/mmsegmentation/)
@@ -29,20 +29,31 @@
 [![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues)
 [![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues)
 
-Documentation: https://mmsegmentation.readthedocs.io/
+[📘Documentation](https://mmsegmentation.readthedocs.io/en/latest/) |
+[🛠️Installation](https://mmsegmentation.readthedocs.io/en/latest/get_started.html) |
+[👀Model Zoo](https://mmsegmentation.readthedocs.io/en/latest/model_zoo.html) |
+[🆕Update News](https://mmsegmentation.readthedocs.io/en/latest/changelog.html) |
+[🤔Reporting Issues](https://github.com/open-mmlab/mmsegmentation/issues/new/choose)
+
+</div>
+
+<div align="center">
 
 English | [简体中文](README_zh-CN.md)
 
+</div>
+
 ## Introduction
 
 MMSegmentation is an open source semantic segmentation toolbox based on PyTorch.
-It is a part of the OpenMMLab project.
+It is a part of the [OpenMMLab](https://openmmlab.com/) project.
 
 The master branch works with **PyTorch 1.5+**.
 
 ![demo image](resources/seg_demo.gif)
 
-### Major features
+<details open>
+<summary>Major features</summary>
 
 - **Unified Benchmark**
 
@@ -60,15 +71,31 @@ The master branch works with **PyTorch 1.5+**.
 
   The training speed is faster than or comparable to other codebases.
 
-## License
-
-This project is released under the [Apache 2.0 license](LICENSE).
+</details>
 
-## Changelog
+## What's New
 
 v0.24.1 was released in 5/1/2022.
 Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
+## Installation
+
+Please refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/dataset_prepare.md#prepare-datasets) for dataset preparation.
+
+## Get Started
+
+Please see [train.md](docs/en/train.md) and [inference.md](docs/en/inference.md) for the basic usage of MMSegmentation.
+There are also tutorials for:
+
+- [customizing dataset](docs/en/tutorials/customize_datasets.md)
+- [designing data pipeline](docs/en/tutorials/data_pipeline.md)
+- [customizing modules](docs/en/tutorials/customize_models.md)
+- [customizing runtime](docs/en/tutorials/customize_runtime.md)
+- [training tricks](docs/en/tutorials/training_tricks.md)
+- [useful tools](docs/en/useful_tools.md)
+
+A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb) on Colab.
+
 ## Benchmark and model zoo
 
 Results and models are available in the [model zoo](docs/en/model_zoo.md).
@@ -144,19 +171,20 @@ Supported datasets:
 - [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-vaihingen)
 - [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isaid)
 
-## Installation
+## FAQ
 
-Please refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/dataset_prepare.md#prepare-datasets) for dataset preparation.
+Please refer to [FAQ](docs/en/faq.md) for frequently asked questions.
 
-## Get Started
+## Contributing
 
-Please see [train.md](docs/en/train.md) and [inference.md](docs/en/inference.md) for the basic usage of MMSegmentation.
-There are also tutorials for [customizing dataset](docs/en/tutorials/customize_datasets.md), [designing data pipeline](docs/en/tutorials/data_pipeline.md), [customizing modules](docs/en/tutorials/customize_models.md), and [customizing runtime](docs/en/tutorials/customize_runtime.md).
-We also provide many [training tricks](docs/en/tutorials/training_tricks.md) for better training and [useful tools](docs/en/useful_tools.md) for deployment.
+We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
 
-A Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb) on Colab.
+## Acknowledgement
 
-Please refer to [FAQ](docs/en/faq.md) for frequently asked questions.
+MMSegmentation is an open source project that welcome any contribution and feedback.
+We wish that the toolbox and benchmark could serve the growing research
+community by providing a flexible as well as standardized toolkit to reimplement existing methods
+and develop their own new semantic segmentation methods.
 
 ## Citation
 
@@ -171,16 +199,9 @@ If you find this project useful in your research, please consider cite:
 }
 ```
 
-## Contributing
-
-We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
-
-## Acknowledgement
+## License
 
-MMSegmentation is an open source project that welcome any contribution and feedback.
-We wish that the toolbox and benchmark could serve the growing research
-community by providing a flexible as well as standardized toolkit to reimplement existing methods
-and develop their own new semantic segmentation methods.
+This project is released under the [Apache 2.0 license](LICENSE).
 
 ## Projects in OpenMMLab
 
diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -17,7 +17,7 @@
     </sup>
   </div>
   <div>&nbsp;</div>
-</div>
+
 <br />
 
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmsegmentation)](https://pypi.org/project/mmsegmentation/)
@@ -29,10 +29,16 @@
 [![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues)
 [![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues)
 
-文档: https://mmsegmentation.readthedocs.io/zh_CN/latest
+[📘使用文档](https://mmsegmentation.readthedocs.io/en/latest/) |
+[🛠️安装指南](https://mmsegmentation.readthedocs.io/en/latest/get_started.html) |
+[👀模型库](https://mmsegmentation.readthedocs.io/en/latest/model_zoo.html) |
+[🆕更新日志](https://mmsegmentation.readthedocs.io/en/latest/changelog.html) |
+[🤔报告问题](https://github.com/open-mmlab/mmsegmentation/issues/new/choose)
 
 [English](README.md) | 简体中文
 
+</div>
+
 ## 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。
@@ -41,6 +47,9 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 
 ![示例图片](resources/seg_demo.gif)
 
+<details open>
+<summary>Major features</summary>
+
 ### 主要特性
 
 - **统一的基准平台**
@@ -59,15 +68,31 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 
   训练速度比其他语义分割代码库更快或者相当。
 
-## 开源许可证
-
-该项目采用 [Apache 2.0 开源许可证](LICENSE)。
+</details>
 
-## 更新日志
+## 最新进展
 
 最新版本 v0.24.1 在 2022.5.1 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](docs/en/changelog.md)。
 
+## 安装
+
+请参考[快速入门文档](docs/zh_cn/get_started.md#installation)进行安装，参考[数据集准备](docs/zh_cn/dataset_prepare.md)处理数据。
+
+## 快速入门
+
+请参考[训练教程](docs/zh_cn/train.md)和[测试教程](docs/zh_cn/inference.md)学习 MMSegmentation 的基本使用。
+我们也提供了一些进阶教程，内容覆盖了:
+
+- [增加自定义数据集](docs/zh_cn/tutorials/customize_datasets.md)
+- [设计新的数据预处理流程](docs/zh_cn/tutorials/data_pipeline.md)
+- [增加自定义模型](docs/zh_cn/tutorials/customize_models.md)
+- [增加自定义的运行时配置](docs/zh_cn/tutorials/customize_runtime.md)。
+- [训练技巧说明](docs/zh_cn/tutorials/training_tricks.md)
+- [有用的工具](docs/zh_cn/useful_tools.md)。
+
+同时，我们提供了 Colab 教程。你可以在[这里](demo/MMSegmentation_Tutorial.ipynb)浏览教程，或者直接在 Colab 上[运行](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb)。
+
 ## 基准测试和模型库
 
 测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
@@ -143,19 +168,17 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 - [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isprs-vaihingen)
 - [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/zh_cn/dataset_prepare.md#isaid)
 
-## 安装
+## 常见问题
 
-请参考[快速入门文档](docs/zh_cn/get_started.md#installation)进行安装，参考[数据集准备](docs/zh_cn/dataset_prepare.md)处理数据。
+如果遇到问题，请参考 [常见问题解答](docs/zh_cn/faq.md)。
 
-## 快速入门
+## 贡献指南
 
-请参考[训练教程](docs/zh_cn/train.md)和[测试教程](docs/zh_cn/inference.md)学习 MMSegmentation 的基本使用。
-我们也提供了一些进阶教程，内容覆盖了[增加自定义数据集](docs/zh_cn/tutorials/customize_datasets.md)，[设计新的数据预处理流程](docs/zh_cn/tutorials/data_pipeline.md)，[增加自定义模型](docs/zh_cn/tutorials/customize_models.md)，[增加自定义的运行时配置](docs/zh_cn/tutorials/customize_runtime.md)。
-除此之外，我们也提供了很多实用的[训练技巧说明](docs/zh_cn/tutorials/training_tricks.md)和模型部署相关的[有用的工具](docs/zh_cn/useful_tools.md)。
+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](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
 
-同时，我们提供了 Colab 教程。你可以在[这里](demo/MMSegmentation_Tutorial.ipynb)浏览教程，或者直接在 Colab 上[运行](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb)。
+## 致谢
 
-如果遇到问题，请参考 [常见问题解答](docs/zh_cn/faq.md)。
+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
 
@@ -170,13 +193,9 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 }
 ```
 
-## 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](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。
-
-## 致谢
+## 开源许可证
 
-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。
+该项目采用 [Apache 2.0 开源许可证](LICENSE)。
 
 ## OpenMMLab 的其他项目
 
