diff --git a/README.md b/README.md index ba1d3a444..9e8b85ba4 100644 --- a/README.md +++ b/README.md @@ -17,7 +17,7 @@
 
- +
[![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) + + + +
English | [简体中文](README_zh-CN.md) +
+ ## 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 +
+Major features - **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). - -## 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). @@ -144,20 +171,21 @@ 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 - -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), 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. - -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. +## FAQ Please refer to [FAQ](docs/en/faq.md) for frequently asked questions. +## Contributing + +We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline. + +## Acknowledgement + +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 If you find this project useful in your research, please consider cite: @@ -171,16 +199,9 @@ If you find this project useful in your research, please consider cite: } ``` -## Contributing +## License -We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline. - -## Acknowledgement - -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 index 864491627..33aa7ad64 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -17,7 +17,7 @@
 
- +
[![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 + + ## 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) +
+Major features + ### 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)。 - -## 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。 @@ -143,20 +168,18 @@ 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 - -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)。 -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)。 - -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)。 +## 常见问题 如果遇到问题，请参考 [常见问题解答](docs/zh_cn/faq.md)。 +## 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 + +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。 @@ -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 +## 开源许可证 -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 - -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