[Enhancement] Refine projects (#2586)

## Motivation Make projects contribution more clear ## Modification 1. Add description on project/README 2. Modify comments to reference in example_project/README 3. Add faq for projects ## BC-breaking (Optional) No
open-mmlab · Feb 22, 2023 · 2d38bc8 · 2d38bc8
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diff --git a/README.md b/README.md
@@ -101,7 +101,8 @@ To migrate from MMSegmentation 1.x, please refer to [migration](docs/en/migratio
 
 Results and models are available in the [model zoo](docs/en/model_zoo.md).
 
-Supported backbones:
+<details open>
+<summary>Supported backbones:</summary>
 
 - [x] ResNet (CVPR'2016)
 - [x] ResNeXt (CVPR'2017)
@@ -117,7 +118,10 @@ Supported backbones:
 - [x] [MAE (CVPR'2022)](configs/mae)
 - [x] [PoolFormer (CVPR'2022)](configs/poolformer)
 
-Supported methods:
+</details>
+
+<details open>
+<summary>Supported methods:</summary>
 
 - [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn)
 - [x] [ERFNet (T-ITS'2017)](configs/erfnet)
@@ -156,7 +160,10 @@ Supported methods:
 - [x] [MaskFormer (NeurIPS'2021)](configs/maskformer)
 - [x] [Mask2Former (CVPR'2022)](configs/mask2former)
 
-Supported datasets:
+</details>
+
+<details open>
+<summary>Supported datasets:</summary>
 
 - [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#cityscapes)
 - [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#pascal-voc)
@@ -175,8 +182,14 @@ Supported datasets:
 - [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#isprs-vaihingen)
 - [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#isaid)
 
+</details>
+
 Please refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions.
 
+## Projects
+
+[Here](projects/README.md) are some implementations of SOTA models and solutions built on MMSegmentation, which are supported and maintained by community users. These projects demonstrate the best practices based on MMSegmentation for research and product development. We welcome and appreciate all the contributions to OpenMMLab ecosystem.
+
 ## Contributing
 
 We appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
@@ -205,7 +218,7 @@ If you find this project useful in your research, please consider cite:
 
 This project is released under the [Apache 2.0 license](LICENSE).
 
-## Projects in OpenMMLab
+## OpenMMLab Family
 
 - [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.

diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -82,7 +82,8 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 
 测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
 
-已支持的骨干网络：
+<details open>
+<summary>已支持的骨干网络：</summary>
 
 - [x] ResNet (CVPR'2016)
 - [x] ResNeXt (CVPR'2017)
@@ -98,7 +99,10 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 - [x] [MAE (CVPR'2022)](configs/mae)
 - [x] [PoolFormer (CVPR'2022)](configs/poolformer)
 
-已支持的算法：
+</details>
+
+<details open>
+<summary>已支持的算法：</summary>
 
 - [x] [FCN (CVPR'2015/TPAMI'2017)](configs/fcn)
 - [x] [ERFNet (T-ITS'2017)](configs/erfnet)
@@ -137,7 +141,10 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 - [x] [MaskFormer (NeurIPS'2021)](configs/maskformer)
 - [x] [Mask2Former (CVPR'2022)](configs/mask2former)
 
-已支持的数据集：
+</details>
+
+<details open>
+<summary>已支持的数据集：</summary>
 
 - [x] [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#cityscapes)
 - [x] [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#pascal-voc)
@@ -156,15 +163,22 @@ 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/1.x/docs/zh_cn/dataset_prepare.md#isprs-vaihingen)
 - [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/zh_cn/dataset_prepare.md#isaid)
 
+</details>
+
 如果遇到问题，请参考 [常见问题解答](docs/zh_cn/notes/faq.md)。
 
+## 社区项目
+
+[这里](projects/README.md)有一些由社区用户支持和维护的基于 MMSegmentation 的 SOTA 模型和解决方案的实现。这些项目展示了基于 MMSegmentation 的研究和产品开发的最佳实践。
+我们欢迎并感谢对 OpenMMLab 生态系统的所有贡献。
+
 ## 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。
+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
 

diff --git a/projects/README.md b/projects/README.md
@@ -1,9 +1,19 @@
 # Projects
 
-Implementing new models and features into OpenMMLab's algorithm libraries could be troublesome due to the rigorous requirements on code quality, which could hinder the fast iteration of SOTA models and might discourage our members from sharing their latest outcomes here.
+The OpenMMLab ecosystem can only grow through the contributions of the community.
+Everyone is welcome to post their implementation of any great ideas in this folder! If you wish to start your own project, please go through the [example project](example_project/) for the best practice. For common questions about projects, please read our [faq](faq.md).
 
-And that's why we have this `Projects/` folder now, where some experimental features, frameworks and models are placed, only needed to satisfy the minimum requirement on the code quality, and can be used as standalone libraries. Users are welcome to use them if they [use MMSegmentation from source](https://mmsegmentation.readthedocs.io/en/dev-1.x/get_started.html#best-practices).
+## External Projects
 
-Everyone is welcome to post their implementation of any great ideas in this folder! If you wish to start your own project, please go through the [example project](example_project/) for the best practice.
+There are also selected external projects released in the community that use MMSegmentation:
 
-Note: The core maintainers of MMSegmentation only ensure the results are reproducible and the code quality meets its claim at the time each project was submitted, but they may not be responsible for future maintenance. The original authors take responsibility for maintaining their own projects.
+- [SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://github.com/visual-attention-network/segnext)
+- [Vision Transformer Adapter for Dense Predictions](https://github.com/czczup/ViT-Adapter)
+- [UniFormer: Unifying Convolution and Self-attention for Visual Recognition](https://github.com/Sense-X/UniFormer)
+- [Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation](https://github.com/facebookresearch/HRViT)
+- [ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias](https://github.com/ViTAE-Transformer/ViTAE-Transformer)
+- [DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation](https://github.com/lhoyer/DAFormer)
+- [MPViT : Multi-Path Vision Transformer for Dense Prediction](https://github.com/youngwanLEE/MPViT)
+- [TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation](https://github.com/hustvl/TopFormer)
+
+Note: These projects are supported and maintained by their own contributors. The core maintainers of MMSegmentation only ensure the results are reproducible and the code quality meets its claim at the time each project was submitted, but they may not be responsible for future maintenance.
diff --git a/projects/example_project/README.md b/projects/example_project/README.md
@@ -1,20 +1,26 @@
 # Dummy ResNet Wrapper
 
-This is an example README for community `projects/`. We have provided detailed explanations for each field in the form of html comments, which are visible when you read the source of this README file. If you wish to submit your project to our main repository, then all the fields in this README are mandatory for others to understand what you have achieved in this implementation. For more details, read our [contribution guide](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/.github/CONTRIBUTING.md) or approach us in [Discussions](https://github.com/open-mmlab/mmsegmentation/discussions).
+> A README.md template for releasing a project.
+>
+> All the fields in this README are **mandatory** for others to understand what you have achieved in this implementation.
+> Please read our [Projects FAQ](../faq.md) if you still feel unclear about the requirements, or raise an [issue](https://github.com/open-mmlab/mmsegmentation/issues) to us!
 
 ## Description
 
-<!-- Share any information you would like others to know. For example:
+> Share any information you would like others to know. For example:
+>
+> Author: @xxx.
+>
+> This is an implementation of \[XXX\].
 
-Author: @xxx.
-
-This is an implementation of \[XXX\]. -->
+Author： @xxx.
 
 This project implements a dummy ResNet wrapper, which literally does nothing new but prints "hello world" during initialization.
 
 ## Usage
 
-<!-- For a typical model, this section should contain the commands for training and testing. You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`. -->
+> For a typical model, this section should contain the commands for training and testing.
+> You are also suggested to dump your environment specification to env.yml by `conda env export > env.yml`.
 
 ### Prerequisites
 
@@ -47,17 +53,16 @@ mim train mmsegmentation configs/fcn_dummy-r50-d8_4xb2-40k_cityscapes-512x1024.p
 mim test mmsegmentation configs/fcn_dummy-r50-d8_4xb2-40k_cityscapes-512x1024.py --work-dir work_dirs/dummy_resnet --checkpoint ${CHECKPOINT_PATH}
 ```
 
-<!-- List the results as usually done in other model's README. [Example](https://github.com/open-mmlab/mmsegmentation/tree/dev-1.x/configs/fcn#results-and-models)
-
-You should claim whether this is based on the pre-trained weights, which are converted from the official release; or it's a reproduced result obtained from retraining the model in this project. -->
+> List the results as usually done in other model's README. \[Example\](https://github.com/open-mmlab/mmsegmentation/tree/dev-1.x/configs/fcn#results-and-models
+> You should claim whether this is based on the pre-trained weights, which are converted from the official release; or it's a reproduced result obtained from retraining the model in this project
 
 | Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
 | ------ | -------- | --------- | ------: | -------- | -------------- | ----: | ------------: | ------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | FCN | R-50-D8 | 512x1024 | 40000 | 5.7 | 4.17 | 72.25 | 73.36 | [config](configs/fcn_dummy-r50-d8_4xb2-40k_cityscapes-512x1024.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608-efe53f0d.pth) \| [log](https://download.openmmlab.com/mmsegmentation/v0.5/fcn/fcn_r50-d8_512x1024_40k_cityscapes/fcn_r50-d8_512x1024_40k_cityscapes_20200604_192608.log.json) |
 
 ## Citation
 
-<!-- You may remove this section if not applicable. -->
+> You may remove this section if not applicable.
 
 ```bibtex
 @misc{mmseg2020,
@@ -72,58 +77,58 @@ You should claim whether this is based on the pre-trained weights, which are con
 
 Here is a checklist illustrating a usual development workflow of a successful project, and also serves as an overview of this project's progress.
 
-<!-- The PIC (person in charge) or contributors of this project should check all the items that they believe have been finished, which will further be verified by codebase maintainers via a PR.
+> The PIC (person in charge) or contributors of this project should check all the items that they believe have been finished, which will further be verified by codebase maintainers via a PR.
 
-OpenMMLab's maintainer will review the code to ensure the project's quality. Reaching the first milestone means that this project suffices the minimum requirement of being merged into 'projects/'. But this project is only eligible to become a part of the core package upon attaining the last milestone.
+> OpenMMLab's maintainer will review the code to ensure the project's quality. Reaching the first milestone means that this project suffices the minimum requirement of being merged into 'projects/'. But this project is only eligible to become a part of the core package upon attaining the last milestone.
 
-Note that keeping this section up-to-date is crucial not only for this project's developers but the entire community, since there might be some other contributors joining this project and deciding their starting point from this list. It also helps maintainers accurately estimate time and effort on further code polishing, if needed.
+> Note that keeping this section up-to-date is crucial not only for this project's developers but the entire community, since there might be some other contributors joining this project and deciding their starting point from this list. It also helps maintainers accurately estimate time and effort on further code polishing, if needed.
 
-A project does not necessarily have to be finished in a single PR, but it's essential for the project to at least reach the first milestone in its very first PR. -->
+> A project does not necessarily have to be finished in a single PR, but it's essential for the project to at least reach the first milestone in its very first PR.
 
 - [ ] Milestone 1: PR-ready, and acceptable to be one of the `projects/`.
 
  - [ ] Finish the code
 
- <!-- The code's design shall follow existing interfaces and convention. For example, each model component should be registered into `mmseg.registry.MODELS` and configurable via a config file. -->
+> The code's design shall follow existing interfaces and convention. For example, each model component should be registered into `mmseg.registry.MODELS` and configurable via a config file.
 
- - [ ] Basic docstrings & proper citation
+- [ ] Basic docstrings & proper citation
 
- <!-- Each major object should contain a docstring, describing its functionality and arguments. If you have adapted the code from other open-source projects, don't forget to cite the source project in docstring and make sure your behavior is not against its license. Typically, we do not accept any code snippet under GPL license. [A Short Guide to Open Source Licenses](https://medium.com/nationwide-technology/a-short-guide-to-open-source-licenses-cf5b1c329edd) -->
+> Each major object should contain a docstring, describing its functionality and arguments. If you have adapted the code from other open-source projects, don't forget to cite the source project in docstring and make sure your behavior is not against its license. Typically, we do not accept any code snippet under GPL license. [A Short Guide to Open Source Licenses](https://medium.com/nationwide-technology/a-short-guide-to-open-source-licenses-cf5b1c329edd)
 
- - [ ] Test-time correctness
+- [ ] Test-time correctness
 
- <!-- If you are reproducing the result from a paper, make sure your model's inference-time performance matches that in the original paper. The weights usually could be obtained by simply renaming the keys in the official pre-trained weights. This test could be skipped though, if you are able to prove the training-time correctness and check the second milestone. -->
+> If you are reproducing the result from a paper, make sure your model's inference-time performance matches that in the original paper. The weights usually could be obtained by simply renaming the keys in the official pre-trained weights. This test could be skipped though, if you are able to prove the training-time correctness and check the second milestone.
 
- - [ ] A full README
+- [ ] A full README
 
- <!-- As this template does. -->
+> As this template does.
 
 - [ ] Milestone 2: Indicates a successful model implementation.
 
  - [ ] Training-time correctness
 
- <!-- If you are reproducing the result from a paper, checking this item means that you should have trained your model from scratch based on the original paper's specification and verified that the final result matches the report within a minor error range. -->
+> If you are reproducing the result from a paper, checking this item means that you should have trained your model from scratch based on the original paper's specification and verified that the final result matches the report within a minor error range.
 
 - [ ] Milestone 3: Good to be a part of our core package!
 
  - [ ] Type hints and docstrings
 
- <!-- Ideally *all* the methods should have [type hints](https://www.pythontutorial.net/python-basics/python-type-hints/) and [docstrings](https://google.github.io/styleguide/pyguide.html#381-docstrings). [Example](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/utils/io.py#L9) -->
+> Ideally *all* the methods should have [type hints](https://www.pythontutorial.net/python-basics/python-type-hints/) and [docstrings](https://google.github.io/styleguide/pyguide.html#381-docstrings). [Example](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/mmseg/utils/io.py#L9)
 
- - [ ] Unit tests
+- [ ] Unit tests
 
- <!-- Unit tests for each module are required. [Example](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/tests/test_utils/test_io.py#L14) -->
+> Unit tests for each module are required. [Example](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/tests/test_utils/test_io.py#L14)
 
- - [ ] Code polishing
+- [ ] Code polishing
 
- <!-- Refactor your code according to reviewer's comment. -->
+> Refactor your code according to reviewer's comment.
 
- - [ ] Metafile.yml
+- [ ] Metafile.yml
 
- <!-- It will be parsed by MIM and Inferencer. [Example](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn.yml) -->
+> It will be parsed by MIM and Inferencer. [Example](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/fcn.yml)
 
 - [ ] Move your modules into the core package following the codebase's file hierarchy structure.
 
- <!-- In particular, you may have to refactor this README into a standard one. [Example](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/README.md) -->
+> In particular, you may have to refactor this README into a standard one. [Example](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/configs/fcn/README.md)
 
 - [ ] Refactor your modules into the core package following the codebase's file hierarchy structure.