---
title: VideoMamba
canonical_url: "https://www.modelscope.cn/models/Shanghai_AI_Laboratory/VideoMamba"
md_url: "https://www.modelscope.cn/models/Shanghai_AI_Laboratory/VideoMamba.md"
repository: Shanghai_AI_Laboratory/VideoMamba
last_updated: 2024-12-30
license: apache-2.0
library_name:
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 1103
stars: 0
---

# VideoMamba

> VideoMamba - Shanghai_AI_Laboratory 在 ModelScope 开源的模型。VideoMamba is a purely SSM-based model for video understanding.

Shanghai_AI_Laboratory/VideoMamba 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可。

- **Repository**: Shanghai_AI_Laboratory/VideoMamba
- **License**: apache-2.0
- **Downloads**: 1103
- **Stars**: 0
- **Last updated**: 2024-12-30

Source: https://www.modelscope.cn/models/Shanghai_AI_Laboratory/VideoMamba

---

<br>

# VideoMamba

## Model Details

VideoMamba is a purely SSM-based model for video understanding.

- **Developed by:** [OpenGVLab](https://github.com/OpenGVLab)
- **Model type:** An efficient backbone based on the bidirectional state space model.
- **License:** Non-commercial license


### Model Sources

- **Repository:** https://github.com/OpenGVLab/VideoMamba
- **Paper:** https://arxiv.org/abs/2403.06977

## Uses

The primary use of VideoMamba is research on image and video tasks, e.g., image classification, action recognition, long-term video understanding, and video-text retrieval, with an SSM-based backbone.
The primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.

## How to Get Started with the Model

- You can replace the backbone for video tasks with the proposed VideoMamba: https://github.com/OpenGVLab/VideoMamba/blob/main/videomamba/video_sm/models/videomamba.py
- Then you can load this checkpoint and start training.


### Citation Information

```
@misc{li2024videomamba,
      title={VideoMamba: State Space Model for Efficient Video Understanding}, 
      author={Kunchang Li and Xinhao Li and Yi Wang and Yinan He and Yali Wang and Limin Wang and Yu Qiao},
      year={2024},
      eprint={2403.06977},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```