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README.md
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@ -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
</details>
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

65
README_zh-CN.md
View File

@ -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
</details>
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