Awesome
Efficient Track Anything
[📕Project][🤗Gradio Demo][📕Paper][🤗Checkpoints]
The Efficient Track Anything Model(EfficientTAM) takes a vanilla lightweight ViT image encoder. An efficient memory cross-attention is proposed to further improve the efficiency. Our EfficientTAMs are trained on SA-1B (image) and SA-V (video) datasets. EfficientTAM achieves comparable performance with SAM 2 with improved efficiency. Our EfficientTAM can run >10 frames per second with reasonable video segmentation performance on iPhone 15. Try our demo with a family of EfficientTAMs at [🤗Gradio Demo].
News
[Dec.22 2024] We release 🤗Efficient Track Anything Checkpoints.
[Dec.4 2024] 🤗Efficient Track Anything for segment everything. Thanks to @SkalskiP!
[Dec.2 2024] We release the codebase of Efficient Track Anything.
Online Demo & Examples
Online demo and examples can be found in the project page.
EfficientTAM Video Segmentation Examples
| SAM 2 |  |
| EfficientTAM |  |
EfficientTAM Image Segmentation Examples
Input Image, SAM, EficientSAM, SAM 2, EfficientTAM
| Point-prompt |  |
| Box-prompt |  |
| Segment everything |  |
Model
EfficientTAM checkpoints are available at the Hugging Face Space.
License
Efficient track anything checkpoints and codebase are licensed under Apache 2.0.
Acknowledgement
If you're using Efficient Track Anything in your research or applications, please cite using this BibTeX:
@article{xiong2024efficienttam,
title={Efficient Track Anything},
author={Yunyang Xiong, Chong Zhou, Xiaoyu Xiang, Lemeng Wu, Chenchen Zhu, Zechun Liu, Saksham Suri, Balakrishnan Varadarajan, Ramya Akula, Forrest Iandola, Raghuraman Krishnamoorthi, Bilge Soran, Vikas Chandra},
journal={preprint arXiv:2411.18933},
year={2024}
}