track-anything
"--- 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"
Best Scenarios
Technical Constraints
🕸️ Neural Graph Explorer
v15.13📈 Interest Trend
* Real-time activity index across HuggingFace, GitHub and Research citations.
🕸️ Neural Graph Explorer
v15.13📈 Interest Trend
* Real-time activity index across HuggingFace, GitHub and Research citations.
Benchmark integration for interactive spaces is in preview.
🔬Deep Dive
Expand Details [+]▾
🛠️ Technical Profile
⚡ Hardware & Scale
🌐 Cloud & Rights
🎮 Demo Preview
💻 Usage
pip install gradio git clone https://huggingface.co/spaces/VIPLab/track-anything Space Overview
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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.
:rocket: Updates
- 2023/04/25: We are delighted to introduce Caption-Anything :writing_hand:, an inventive project from our lab that combines the capa
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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.
: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]](https://huggingface.co/spaces/watchtowerss/Track-Anything) on Hugging Face :hugs:!
Demo
https://user-images.githubusercontent.com/28050374/232842703-8395af24-b13e-4b8e-aafb-e94b61e6c449.MP4
Multiple Object Tracking and Segmentation (with XMem)
https://user-images.githubusercontent.com/39208339/233035206-0a151004-6461-4deb-b782-d1dbfe691493.mp4
Video Object Tracking and Segmentation with Shot Changes (with XMem)
https://user-images.githubusercontent.com/30309970/232848349-f5e29e71-2ea4-4529-ac9a-94b9ca1e7055.mp4
Video Inpainting (with E2FGVI)
https://user-images.githubusercontent.com/28050374/232959816-07f2826f-d267-4dda-8ae5-a5132173b8f4.mp4
Get Started
#### Linux
<h1 class="text-2xl font-bold mt-8 mb-4 text-gray-900 dark:text-white">Clone the repository:</h1>
git clone https://github.com/gaomingqi/Track-Anything.git
cd Track-Anything<h1 class="text-2xl font-bold mt-8 mb-4 text-gray-900 dark:text-white">Install dependencies: </h1>
pip install -r requirements.txt
<h1 class="text-2xl font-bold mt-8 mb-4 text-gray-900 dark:text-white">Run the Track-Anything gradio demo.</h1>
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.
3,852 characters total