Welcome to OpenMMLab!
OpenMMLab is a collection of repositories for various computer vision tasks.
Some demos:
OpenMMLab consists of dozens of projects for various computer vision tasks. The vision of OpenMMLab is becoming a leader of open-source algorithm platforms for computer vision and widely supporting both academic research and industry applications.
Since our first release in 2018, more than 800 contributors have contributed to OpenMMLab. Contributors’ efforts are the key to the rich features and excellent capability of OpenMMLab projects.
In this post, we present a step-by-step tutorial, the first post of our series named [Becoming an OpenMMLab Contributor]. In the future, we will release some small feature requests occasionally based on OpenMMLab repositories. Whether you are a newbie or a senior developer, you can easily participate and win the prizes.
NOW! We sincerely invite you to join OpenMMLab as a contributor!
Before we start the tutorial:
Try our repositories out if you haven’t done this. OpenMMLab projects cover a wide range of computer tasks. There are many impressive features.
[MMCV]: OpenMMLab foundational library for computer vision.
[MIM]: MIM Installs OpenMMLab Packages.
[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.
[MMAction2]: OpenMMLab’s next-generation action understanding toolbox and benchmark.
[MMTracking]: OpenMMLab video perception toolbox and benchmark.
[MMPose]: OpenMMLab pose estimation toolbox and benchmark.
[MMEditing]: OpenMMLab image and video editing toolbox.
[MMOCR]: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
[MMGeneration]: OpenMMLab image and video generative models toolbox.
[MMFlow]: OpenMMLab optical flow toolbox and benchmark.
[MMFewShot]: OpenMMLab FewShot Learning 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.
[MMDeploy]: OpenMMLab Model Deployment Framework.
Here are several approaches to contribute to the OpenMMLab project:
1. Report Issues
In the ‘Issues’ Tab on the repositories homepage on GitHub, you can see a list of existing issues, including feature requests, error reports, general questions, etc.. You can:
1.1 Report New Issues
If you would like to report an unexpected behavior, raise a feature request, or share your ideas with our community, the best way is to open an issue under the corresponding repository. Once you’ve identified an issue and cannot find a similar one in the list of existing issues, feel free to open a new one.
1.2 Involve in Issue discussions
Please comment on the corresponding issue directly if you have some thoughts on existing issues. Issues without the ‘pending’ tag are identified bugs, or new issues need to be solved. Welcome to submit PRs to help us resolve those issues!
2. Pull Request
If you want to fix existing bugs or implement some new features, you can submit a PR to the corresponding repository.
Here are some tips:
- Fork the repository and clone it to local, add ‘upstream’ as a remote repository, then you can keep sync with the ‘upstream’ when submitting PR. It will save a lot of time for conflict solving.
- Create a branch of your own to fix a bug or implement new features.
- Read the docs, follow the code style of OpenMMLab projects.
- Test your code before the code-review stage.
- Read Apache guidelines before getting started.
Once everything is ready, you can click ‘create pull request’ to submit a PR. After we merge the PR, you will be an OpenMMLab contributor!
3. Improve Documentation & Tutorials
Well-written documentation is of great importance for a repository. We look forward to your contribution to delivering high-quality documents with us. Adding new docs and polishing existing docs are both welcome.
4. Share your stories
If OpenMMLab has supported you in your own projects (academic research or applications), or you want to share your awesome OpenMMLab based projects with other people, we strongly encourage you to write a post and share your stories with our community.
5. Participating in Community Discussion
We warmly welcome users to join us and actively participate in the discussions. Your expertise and ideas are helpful for others in the community.
We cherish all efforts from our community and users. We offer various prizes for active users, developers, and contributors.
