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README.md
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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***Track-Anything*** is a flexible and interactive tool for video object tracking and segmentation. It is developed upon [Segment Anything](https://github.com/facebookresearch/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:
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- Video object tracking and segmentation with shot changes.
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- Object-centric downstream video tasks, such as video inpainting and editing.
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## Demo
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https://user-images.githubusercontent.com/28050374/232842703-8395af24-b13e-4b8e-aafb-e94b61e6c449.MP4
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# Install dependencies:
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pip install -r requirements.txt
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# Install dependencies for inpainting:
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pip install -U openmim
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mim install mmcv
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# Install dependencies for editing
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pip install madgrad
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# Run the Track-Anything gradio demo.
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python app.py --device cuda:0 --sam_model_type vit_h --port 12212
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```
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## Acknowledgements
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The project is based on [Segment Anything](https://github.com/facebookresearch/segment-anything), [XMem](https://github.com/hkchengrex/XMem), and [E2FGVI](https://github.com/MCG-NKU/E2FGVI). Thanks for the authors for their efforts.
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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<!-- ![](./assets/track-anything-logo.jpg) -->
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<div align=center>
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<img src="./assets/track-anything-logo.jpg"/>
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</div>
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<br/>
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<div align=center>
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<a src="https://img.shields.io/badge/%F0%9F%93%96-Open_in_Spaces-informational.svg?style=flat-square" href="https://arxiv.org/abs/2304.11968">
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<img src="https://img.shields.io/badge/%F0%9F%93%96-Arxiv_2304.11968-red.svg?style=flat-square">
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</a>
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<a src="https://img.shields.io/badge/%F0%9F%A4%97-Open_in_Spaces-informational.svg?style=flat-square" href="https://huggingface.co/spaces/watchtowerss/Track-Anything">
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<img src="https://img.shields.io/badge/%F0%9F%A4%97-Open_in_Spaces-informational.svg?style=flat-square">
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</a>
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<a src="https://img.shields.io/badge/%F0%9F%9A%80-SUSTech_VIP_Lab-important.svg?style=flat-square" href="https://zhengfenglab.com/">
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<img src="https://img.shields.io/badge/%F0%9F%9A%80-SUSTech_VIP_Lab-important.svg?style=flat-square">
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</a>
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</div>
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***Track-Anything*** is a flexible and interactive tool for video object tracking and segmentation. It is developed upon [Segment Anything](https://github.com/facebookresearch/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:
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- Video object tracking and segmentation with shot changes.
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- Visualized development and data annnotation for video object tracking and segmentation.
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- Object-centric downstream video tasks, such as video inpainting and editing.
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<div align=center>
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<img src="./assets/avengers.gif"/>
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</div>
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<!-- ![avengers]() -->
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## :rocket: Updates
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- 2023/04/25: We are delighted to introduce [Caption-Anything](https://github.com/ttengwang/Caption-Anything) :writing_hand:, an inventive project from our lab that combines the capabilities of Segment Anything, Visual Captioning, and ChatGPT.
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- 2023/04/20: We deployed [[DEMO]](https://huggingface.co/spaces/watchtowerss/Track-Anything) on Hugging Face :hugs:!
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## Demo
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https://user-images.githubusercontent.com/28050374/232842703-8395af24-b13e-4b8e-aafb-e94b61e6c449.MP4
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# Install dependencies:
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pip install -r requirements.txt
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# Run the Track-Anything gradio demo.
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python app.py --device cuda:0 --sam_model_type vit_h --port 12212
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```
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## Citation
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If you find this work useful for your research or applications, please cite using this BibTeX:
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```bibtex
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@misc{yang2023track,
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title={Track Anything: Segment Anything Meets Videos},
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author={Jinyu Yang and Mingqi Gao and Zhe Li and Shang Gao and Fangjing Wang and Feng Zheng},
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year={2023},
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eprint={2304.11968},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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## Acknowledgements
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The project is based on [Segment Anything](https://github.com/facebookresearch/segment-anything), [XMem](https://github.com/hkchengrex/XMem), and [E2FGVI](https://github.com/MCG-NKU/E2FGVI). Thanks for the authors for their efforts.
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