[Fix] README for mmseg 1.x (open-mmlab#2009)

* [Fix] README for mmseg 1.x

* typo

* link and refine

.github/CONTRIBUTING.md

@@ -1,23 +1,21 @@
-# Contributing to mmsegmentation
+# Contributing to MMSegmentation 1.x
 
 All kinds of contributions are welcome, including but not limited to the following.
 
-- Fixes (typo, bugs)
-- New features and components
+- Fix typo or bugs
+- Add documentation or translate the documentation into other languages
+- Add new features and components
 
 ## Workflow
 
-1. fork and pull the latest mmsegmentation
-2. checkout a new branch (do not use master branch for PRs)
+1. fork and pull the latest MMSegmentation repository
+2. checkout a new branch from 'dev-1.x' (do not use master branch for PRs)
 3. commit your changes
 4. create a PR
 
-:::{note}
-
-- If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
-- If you are the author of some papers and would like to include your method to mmsegmentation,
-  please contact Kai Chen (chenkaidev\[at\]gmail\[dot\]com). We will much appreciate your contribution.
-  :::
+```{note}
+If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first.
+```
 
 ## Code style
 
@@ -27,15 +25,18 @@ We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code
 
 We use the following tools for linting and formatting:
 
-- [flake8](http://flake8.pycqa.org/en/latest/): linter
-- [yapf](https://github.com/google/yapf): formatter
-- [isort](https://github.com/timothycrosley/isort): sort imports
+- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.
+- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
+- [yapf](https://github.com/google/yapf): A formatter for Python files.
+- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.
+- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.
+- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
 
-Style configurations of yapf and isort can be found in [setup.cfg](../setup.cfg) and [.isort.cfg](../.isort.cfg).
+Style configurations of yapf and isort can be found in [setup.cfg](./setup.cfg).
 
-We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`,
-fixes `end-of-files`, sorts `requirments.txt` automatically on every commit.
-The config for a pre-commit hook is stored in [.pre-commit-config](../.pre-commit-config.yaml).
+We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
+fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
+The config for a pre-commit hook is stored in [.pre-commit-config](./.pre-commit-config.yaml).
 
 After you clone the repository, you will need to install initialize pre-commit hook.
 

README.md

@@ -22,14 +22,14 @@
 
 [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mmsegmentation)](https://pypi.org/project/mmsegmentation/)
 [![PyPI](https://img.shields.io/pypi/v/mmsegmentation)](https://pypi.org/project/mmsegmentation)
-[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmsegmentation.readthedocs.io/en/latest/)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmsegmentation.readthedocs.io/en/1.x/)
 [![badge](https://github.com/open-mmlab/mmsegmentation/workflows/build/badge.svg)](https://github.com/open-mmlab/mmsegmentation/actions)
 [![codecov](https://codecov.io/gh/open-mmlab/mmsegmentation/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmsegmentation)
-[![license](https://img.shields.io/github/license/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/blob/master/LICENSE)
+[![license](https://img.shields.io/github/license/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/blob/1.x/LICENSE)
 [![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/1.x/
 
 English | [简体中文](README_zh-CN.md)
 
@@ -38,7 +38,7 @@ English | [简体中文](README_zh-CN.md)
 MMSegmentation is an open source semantic segmentation toolbox based on PyTorch.
 It is a part of the OpenMMLab project.
 
-The master branch works with **PyTorch 1.5+**.
+The 1.x branch works with **PyTorch 1.6+**.
 
 ![demo image](resources/seg_demo.gif)
 
@@ -60,14 +60,29 @@ The master branch works with **PyTorch 1.5+**.
 
   The training speed is faster than or comparable to other codebases.
 
-## License
+## What's New
 
-This project is released under the [Apache 2.0 license](LICENSE).
+v1.0.0rc0 was released in 31/8/2022.
+Please refer to [changelog.md](docs/en/notes/changelog.md) for details and release history.
+
+- Unifies interfaces of all components based on MMEngine.
+- Faster training and testing speed with complete support of mixed precision training.
+- Refactored and more flexible architecture.
+
+## Installation
+
+Please refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/user_guides/dataset_prepare.md#prepare-datasets) for dataset preparation.
+
+## Get Started
 
-## Changelog
+Please see [Overview](docs/en/overview.md) for the general introduction of MMSegmentation.
 
-v0.24.1 was released in 5/1/2022.
-Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
+Please see [user guides](https://mmsegmentation.readthedocs.io/en/1.x/user_guides/index.html#) for the basic usage of MMSegmentation.
+There are also [advanced tutorials](https://mmsegmentation.readthedocs.io/en/dev-1.x/advanced_guides/index.html) for in-depth understanding of mmseg design and implementation .
+
+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/1.x/demo/MMSegmentation_Tutorial.ipynb) on Colab.
+
+To migrate from MMSegmentation 1.x, please refer to [migration](docs/en/migration.md).
 
 ## Benchmark and model zoo
 
@@ -127,36 +142,35 @@ Supported methods:
 
 Supported datasets:
 
-- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#cityscapes)
-- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-voc)
-- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#ade20k)
-- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-context)
-- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-10k)
-- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-164k)
-- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#chase-db1)
-- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#drive)
-- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#hrf)
-- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#stare)
-- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#dark-zurich)
-- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#nighttime-driving)
-- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#loveda)
-- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#isprs-potsdam)
-- [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.
+- [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#cityscapes)
+- [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#pascal-voc)
+- [x] [ADE20K](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#ade20k)
+- [x] [Pascal Context](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#pascal-context)
+- [x] [COCO-Stuff 10k](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#coco-stuff-10k)
+- [x] [COCO-Stuff 164k](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#coco-stuff-164k)
+- [x] [CHASE_DB1](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#chase-db1)
+- [x] [DRIVE](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#drive)
+- [x] [HRF](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#hrf)
+- [x] [STARE](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#stare)
+- [x] [Dark Zurich](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#dark-zurich)
+- [x] [Nighttime Driving](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#nighttime-driving)
+- [x] [LoveDA](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#loveda)
+- [x] [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#isprs-potsdam)
+- [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#isprs-vaihingen)
+- [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/dataset_prepare.md#isaid)
+
+Please refer to [FAQ](docs/en/notes/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
 
@@ -171,19 +185,13 @@ 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
 
+- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models
 - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
 - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
 - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.