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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Tools for movie and video research
| Date | Stars |
|---|---|
| 2026-07-24 | 312 |
| 2026-07-25 | 312 |
| 2026-07-28 | 313 |
| 2026-07-30 | 313 |
| 2026-08-06 | 313 |
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**Updates**
[MovieNet Official Website](http://movienet.site/) is online now!
# Introduction
movienet-tools is an open source movie analysis toolbox based on PyTorch.
It's part of [MovieNet](http://movienet.site/) project maintained by MovieNet Team from [MMLab, CUHK](http://mmlab.ie.cuhk.edu.hk).
And it is also one of [OpenMMLab](https://open-mmlab.github.io/index.html) projects.
## Features
- Basic video processing tools.
- Holistic semantic video feature extractors.
- All-in-one movie info web crawler.
## Installation
Please refer to [INSTALL.md](docs/INSTALL.md) for installation and dataset preparation. Pretrained models and dataset are also explanined here.
## Get Started
Please see [GETTING_STARTED.md](docs/GETTING_STARTED.md) for the wiki of movienet data and the basic usage of this toolbox.
## Acknowledgement
The structure of ``movienet-tools`` follows that of codebased in [openmmlab](https://github.com/open-mmlab).
The part of character detection are modified from [mmdetection](https://github.com/open-mmlab/mmdetection)
and the part of action feature extraction are modified from [mmaction](https://github.com/open-mmlab/mmaction).
Many thanks to these open-source codebases.
## Citation
If you use MovieNet dataset or this toolbox or benchmarks in your research, please cite this project.
```
@inproceedings{huang2020movie,
title={MovieNet: A Holistic Dataset for Movie Understanding},
author={Huang, Qingqiu and Xiong, Yu and Rao, Anyi and Wang, Jiaze and Lin, Dahua},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year={2020}
}
```
Excerpt of 1,606 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3e26ca41c31d4f9c, topic:computer-vision, readme:computer vision
matched fp:3e26ca41c31d4f9c, topic:deep-learning