OpenGVLab/InternVideo sits at 2.4k stars on GitHub, written primarily in Python. [ECCV2024] Video Foundation Models & Data for Multimodal Understanding
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InternVideo: Video Foundation Models for Multimodal Understanding
This repo contains InternVideo series and related works in video foundation models.
InternVideo: general video foundation models via generative and discriminative learning
InternVideo2: scaling video foundation models for multimodal video understanding
InternVideo2.5: empowering video mllms with long and rich context modeling
InternVideo3: multimodal contextual reasoning via efficient long-horizon agents
InternVideo-Next: general video foundation models for genuine world understanding
InternVid: a large-scale video-text dataset for multimodal understanding and generation
Updates
2026.06: InternVideo3 is released with the technical report, 8B instruct model, long-video SFT dataset, evaluation scripts, and an initial video-agent implementation in Vidify.
2025.12: InternVideo-Next is released with the technical report, pretrained model weights in the Hugging Face collection, and pretraining code.
2025.01: InternVideo2.5 is now released! Check out the technical report for detailed insights, and access the model on HuggingFace.
2024.08.12: We provide smaller models, InternVideo2-S/B/L, which are distilled from InternVideo2-1B. We also build smaller VideoCLIP with MobileCLIP.
2024.08: InternVideo2-Stage3-8B and InternVideo2-Stage3-8B-HD are released. 8B indicates the use of InternVideo2-1B and the 7B LLM.
2024.07: The video annotation for InternVid2 (HuggingFace) is released.
2024.06: The full version of the video annotation (230M video-text pairs) for InternVid (OpenDataLab | HuggingFace) is released.
2024.04: The Checkpoints and scripts for InternVideo2 are released.
2024.03: The technical report of InternVideo2 is released.
2024.01: InternVid (a video-text dataset for video understanding and generation) has been accepted for spotlight presentation of ICLR 2024.
2023.07: A video-text dataset InternVid is released at here for facilitating multimodal understanding and generation.
2023.05: Video instruction data are released at here for tuning end-to-end video-centric multimodal dialogue systems like VideoChat.
2023.01: The code & models of InternVideo are released.
2022.12: The technical report of InternVideo is released.
If you have any questions during the trial, running or deployment, feel free to join our WeChat group discussion! If you have any ideas or suggestions for the project, you are also welcome to join our WeChat group discussion!
We are hiring researchers, engineers and interns in General Vision Group, Shanghai AI Lab. If you are interested in working with us on video foundation models and related topics, please contact Yi Wang ([email protected]).
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Is OpenGVLab/InternVideo open source?
Yes — OpenGVLab/InternVideo ships under the Apache-2.0 license, which makes its source code freely readable (and, depending on license terms, forkable and reusable). Source: github.com/OpenGVLab/InternVideo.
What is OpenGVLab/InternVideo?
OpenGVLab/InternVideo (OpenGVLab/InternVideo) is a Python project on GitHub. From the project's own README: [ECCV2024] Video Foundation Models & Data for Multimodal Understanding
What license does OpenGVLab/InternVideo use?
OpenGVLab/InternVideo is released under the Apache-2.0 license. Always verify the LICENSE file directly on GitHub for the authoritative terms — license strings can be edited out of sync with a project's actual stance.
Where do I read more about OpenGVLab/InternVideo?
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Read full README in the tab above.
Is InternVideo worth your time?
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