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GSoC 2023 application
CVAT (Computer Vision Annotation Tool, 8.8k stars on GitHub) is an open data annotation platform for Computer Vision use cases. It is used by tens of thousands of users around the world (e.g., CVAT.org had more than 65k registered users).
The simple answer to the question is that GSoC both introduces our org to a new set of people we may not have had a chance to interact with otherwise, and gives our mentors a chance to work on contributions to the open-source product that they wanted to see done but had not found the resources to do. It will be great to be connected with talented students who can help us implement new ideas and features for our open-source community. In many cases, the data annotation can be prohibitively expensive. Our goal is to democratize it and provide a state-of-the-art solution for individuals, teams, and startups.
Coding for CVAT benefits the students with the ability to work on a complex client-server application with a huge community and implemented using a modern software stack (e.g., Django, React, Docker, Kubernetes, OPA).
A successful GSoC program is one where a majority of the projects become accepted pull requests having passed the functionality, unit test, style and linter checks, and documentation. We believe this goal produces the maximal benefit both to the students and to the org.
All our mentors are known contributors to CVAT. Most mentors are graduate students, experienced engineers, or engineering managers that have experience in managing students and interns. We have weekly meetings on progress and track students on shared Google docs. Mentors are required to have a minimum of one weekly meeting with students and email contact is shared and open to admins who monitor contact. The first milestone is to create a pull request and mentors are required to critique it. These pull requests continue throughout the summer until the final pull requests that must be accepted by the mentor if the student is not to fail. Mentors are required to make sure the students pass checks, have google unit tests, examples of use and documentation (https://opencv.github.io/cvat/docs/).
Students are required to fill out a schedule in their application. For every feature, our team will define a minimum valuable product, a set of milestones. We will be always ready to help if the student is stuck. They start the summer with a pull request that must pass all checks and has documentation, tests, and example of use. They must meet with mentors at least once a week (unless prior excuse) and fill out periodic progress logs and students are failed if they do not have an accepted start, middle and finished pull request. In the end, students are required to submit a Youtube video showing their results where each student has their "moment of fame".
Open, ongoing communication is held on a mailing list dedicated to that year's GSoC and Github provides further community chatter. GSoC contributors will be able to communicate with real customers using GitHub Issues, Gitter, Discord, and other public channels: https://github.com/opencv/cvat#contact-us Mainly, students get and stay involved by actual contributed code and documentation. They are required to immediately create a pull request that grows and grows over the summer.
In the spirit of collaboration and working to address the needs of our community, in the future active CVAT development by Intel will now move under the OpenCV umbrella to foster open ecosystems and drive innovation, and we are honored to take on this role. (https://opencv.org/opencv-and-cvat-computer-vision-annotation-tool-intel/)
CVAT.ai Corporation is the primary contributor to the project.
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