Ecosystem

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This project is used to collect the information of all open-source projects built with the OpenMMLab projects. The collected projects will be displayed in the official OpenMMLab Ecosystem Page.

File Structure

The data collected in this repo is used for internal analysis and homepage display. The repo has the following file structure:

ecosystem
|-----.pre-commit-config.yaml # configuration of the pre-commit hooks
|-----LICENSE #  license of the project
|-----projects_index.yaml # store the detailed information of each project
|-----README.md # readme file

How to Contribute

We appreciate all contributions to add new projects into OpenMMLab ecosystem.

Workflow

1. fork and pull the latest OpenMMLab repository (ecosystem)
2. checkout a new branch (do not use master branch for PRs)
3. commit your changes
4. create a PR

Add New Project into YAML

If you want to add one project into the ecosystem, please edit the project_index.yaml and add the related information of the specific project.

The following keys are required to :

• repo_url: Github link of the project
• paper_url: Paper link of the project(Only add one if have > 1 papers, use "" if no paper)
• type: Type of the ecosystem projects(Choose one from Type Table)
• mmrepos: Which OpenMMLab projects have been adopted in this project(Choose one or more from the MMRepos Table, please pay attention to capitalization)
• tags: Tags of this project(<= 5 item)
• summary: One sentence summary of the project(English and Chinese)

Please add the information of the specific project with the following example:

- repo_url: https://github.com/NVlabs/FAN # repo url
  paper_url: https://arxiv.org/abs/2204.12451 # paper url
  type: Official Implementation # type of the projects
  mmrepos: # used projects of OpenMMLab
    - MMDetection
    - MMSegmentation
  tags: # tags of this projects
    - ICML
    - Vision Transformer
  summary: # Engish/Chinese summary of this projects with one sentence
    zh: Official PyTorch implementation of Fully Attentional Networks
    en: 基于PyTorch的Fully Attentional Networks官方实现

Validity Check

We use pre-commit hook that checks and formats for trailing whitespaces, check-yaml, and use our openmmlab pre-commit hook for check-ecosystem-validity. The config for a pre-commit hook is stored in .pre-commit-config.

After you clone the repository, you will need to install initialize pre-commit hook.

pip install -U pre-commit

From the repository folder

pre-commit install

After this on every commit check code linters and formatter will be enforced.

Before you create a PR, make sure that your code lints and is formatted by yapf.

Type Table

Chinese	English	含义	Meaning
官方实现	Official Implementation	论文的官方实现	Official Implementation of the Research Work
第三方实现	Community Implementation	论文的第三方实现	Community Implementation of the Research Work
比赛代码	Competition	比赛代码	Open-source Code of the Competition
算法框架	Library	在 OpenMMLab 之上开发的第三方代码库	Algorithm Library Built on OpenMMLab
服务	Service	第三方服务项目，例如 wandb	Service Project, e.g., wandb
教程	Tutorial	基于OpenMMLab开发的教程	Tutorial Built on OpenMMLab
示例	Demo	基于OpenMMLab设计的Demo	Demo Built on OpenMMLab
其他	Others	不在以上分类中的其他项目	Projects not in the above Types

MMRepos

• MMCV: OpenMMLab foundational library for computer vision.
• MMClassification: OpenMMLab image classification toolbox and benchmark.
• MMDetection: OpenMMLab detection toolbox and benchmark.
• MMDetection3D: OpenMMLab's next-generation platform for general 3D object detection.
• MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
• MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
• MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
• MMPose: OpenMMLab pose estimation toolbox and benchmark.
• MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
• MMSelfSup: OpenMMLab self-supervised learning toolbox and benchmark.
• MMRazor: OpenMMLab model compression toolbox and benchmark.
• MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
• MMAction2: OpenMMLab's next-generation action understanding toolbox and benchmark.
• MMTracking: OpenMMLab video perception toolbox and benchmark.
• MMFlow: OpenMMLab optical flow toolbox and benchmark.
• MMEditing: OpenMMLab image and video editing toolbox.
• MMGeneration: OpenMMLab image and video generative models toolbox.
• MMDeploy: OpenMMLab model deployment framework.

License

This project is released under the Apache 2.0 license.