This is the official repository for Vista-VID dataset.

(back to top)

• Python
• Gemini

(back to top)

Vista-VID is developed in Python, with usage of Gemini for creating data synthetic.

Make sure your system meets the following minimum requirements:

• Python: Version 3.12+

• uv: Simplify Python environment and dependency management. uv automatically creates a virtual environment in the root directory and installs all required packages for you—no need to manually install Python environments.

• Gemini API LLM Provider from Google with many features for image, video understanding we built on top of them.

# Install dependencies, uv will take care of the python interpreter and venv creation, and install the required packages
uv sync

cp .env.example .env

(back to top)

Place the Gemini API Key in your .env file.

GOOGLE_API_KEY=YOUR_API_KEY

(back to top)

uv run main.py

• Dataset:

  • Video Level-based Description Dataset
  • Video Captioning Dataset
  • Video QA Dataset

• Training recipes

• Benchmark

• Support other LLM providers

See the open issues for a full list of proposed features (and known issues).

(back to top)

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

(back to top)

Distributed under the Unlicense License. See LICENSE.txt for more information.

(back to top)

Bui Van Hop - @hopbui3 - vanhop3499@gmail.com

Project Link: https://github.com/hllj/Vista-VID

(back to top)

Vista-VID is built upon the incredible work of the open-source community. We are deeply grateful to all the projects and contributors whose efforts have made Vista-VID possible. Truly, we stand on the shoulders of giants.

We would like to extend our sincere appreciation to the following projects for their invaluable contributions:

• Gemini Cookbook
• LLaVA-Video
• InternVid

(back to top)