Spaces:
Runtime error
A newer version of the Gradio SDK is available:
4.42.0
title: Track Anything
emoji: 🐠
colorFrom: purple
colorTo: indigo
sdk: gradio
sdk_version: 3.27.0
app_file: app.py
pinned: false
license: mit
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
![](/spaces/baileyarzate/Track-Anything/resolve/main/assets/track-anything-logo.jpg)
Track-Anything is a flexible and interactive tool for video object tracking and segmentation. It is developed upon Segment Anything, can specify anything to track and segment via user clicks only. During tracking, users can flexibly change the objects they wanna track or correct the region of interest if there are any ambiguities. These characteristics enable Track-Anything to be suitable for:
- Video object tracking and segmentation with shot changes.
- Visualized development and data annnotation for video object tracking and segmentation.
- Object-centric downstream video tasks, such as video inpainting and editing.
![](/spaces/baileyarzate/Track-Anything/resolve/main/assets/avengers.gif)
:rocket: Updates
2023/04/25: We are delighted to introduce Caption-Anything :writing_hand:, an inventive project from our lab that combines the capabilities of Segment Anything, Visual Captioning, and ChatGPT.
2023/04/20: We deployed [DEMO] on Hugging Face :hugs:!
Demo
Multiple Object Tracking and Segmentation (with XMem)
Video Object Tracking and Segmentation with Shot Changes (with XMem)
Video Inpainting (with E2FGVI)
Get Started
Linux
# Clone the repository:
git clone https://github.com/gaomingqi/Track-Anything.git
cd Track-Anything
# Install dependencies:
pip install -r requirements.txt
# Run the Track-Anything gradio demo.
python app.py --device cuda:0 --sam_model_type vit_h --port 12212
Citation
If you find this work useful for your research or applications, please cite using this BibTeX:
@misc{yang2023track,
title={Track Anything: Segment Anything Meets Videos},
author={Jinyu Yang and Mingqi Gao and Zhe Li and Shang Gao and Fangjing Wang and Feng Zheng},
year={2023},
eprint={2304.11968},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Acknowledgements
The project is based on Segment Anything, XMem, and E2FGVI. Thanks for the authors for their efforts.