The Video-LLaVA project by PKU-YuanGroup is a Python-based software that focuses on learning united visual representation by alignment before projection. The project is relatively new, created in October 2023, and has been actively maintained with the last push made in November 2023.
The project has gained significant attention with 952 stars, 42 forks, and 11 watchers. It has 90 commits across 2 branches. The project is licensed under the Apache License 2.0.
The README provides a comprehensive overview of the project, including the project's highlights, main results, requirements and installation steps, API usage, training and validation instructions, and citation information. It also includes a demo section with Gradio Web UI and CLI Inference examples.
There are currently 2 open issues, the most recent ones being a licensing question and a request for silicon mac os support. There are 3 closed issues, which were promptly addressed by the maintainers. There is 1 open pull request aimed at enhancing configurability for Gradio and LLaVA, and 4 closed pull requests.
The project seems to be in a healthy state with active development and prompt issue resolution.
The project is active with recent issues and pull requests. There are 2 open issues, primarily concerning licensing and support for Silicon Mac OS. The project maintainers are responsive, as evidenced by the 3 recently closed issues.
The project is showing signs of active maintenance and development. The maintainers are actively working on improving performance, as seen in their response to Issue #6. They are also responsive to bug reports and user feedback, as seen in Issue #3 and Issue #1. This suggests a positive trajectory for the project.
There is currently 1 open pull request, PR #9, which focuses on code optimization and enhanced configurability for Gradio and LLaVA. The changes include the addition of a .gitignore file, removal of unnecessary compiled Python files, and refactoring of code. This pull request is still under review.
There have been 4 closed pull requests recently:
Summary
Detailed Reports
eval_gpt_mmvet.py and was merged.The project seems to be in active development with regular pull requests. The focus appears to be on code optimization, configurability enhancements, and minor fixes. The addition of a Replicate demo link suggests an effort to improve accessibility and user experience. However, the non-merging of PR #8 indicates potential disagreements or issues with proposed changes.
The Video-LLaVA project is a Python-based software project developed by the PKU-YuanGroup. It is designed for learning united visual representation by alignment before projection. The project has gained significant traction with 952 stars, 42 forks, and 11 watchers, indicating its popularity and usefulness within the community.
The project is actively maintained, with the last push made less than a month ago. There are 90 commits spread across 2 branches, and 3 open issues, suggesting ongoing development and user engagement. The project is licensed under the Apache License 2.0, making it free and open-source.
The README provides a comprehensive overview of the project, including its purpose, usage, installation instructions, API details, and citation information. It also includes a demo and results section, showcasing the project's capabilities. The project also provides an API for local model loading and inference for both images and videos.
However, there are a few potential areas of concern. The project has a relatively large size (118111 kB), which might be a barrier for users with limited storage or bandwidth. Additionally, the project requires Python 3.10 and Pytorch 2.0.1, which might not be available or compatible with all systems.
In summary, Video-LLaVA is a promising and active project in the field of visual representation learning. It has a vibrant community and is under active development. However, potential users should be aware of its large size and specific software requirements.