Spaces:
Running
on
T4
Running
on
T4
title: FastSAM | |
emoji: ๐ | |
colorFrom: pink | |
colorTo: indigo | |
sdk: gradio | |
sdk_version: 3.35.2 | |
app_file: app_gradio.py | |
pinned: false | |
license: apache-2.0 | |
# Fast Segment Anything | |
Official PyTorch Implementation of the <a href="https://github.com/CASIA-IVA-Lab/FastSAM">. | |
The **Fast Segment Anything Model(FastSAM)** is a CNN Segment Anything Model trained by only 2% of the SA-1B dataset published by SAM authors. The FastSAM achieve a comparable performance with | |
the SAM method at **50ร higher run-time speed**. | |
## License | |
The model is licensed under the [Apache 2.0 license](LICENSE). | |
## Acknowledgement | |
- [Segment Anything](https://segment-anything.com/) provides the SA-1B dataset and the base codes. | |
- [YOLOv8](https://github.com/ultralytics/ultralytics) provides codes and pre-trained models. | |
- [YOLACT](https://arxiv.org/abs/2112.10003) provides powerful instance segmentation method. | |
- [Grounded-Segment-Anything](https://huggingface.co/spaces/yizhangliu/Grounded-Segment-Anything) provides a useful web demo template. | |
## Citing FastSAM | |
If you find this project useful for your research, please consider citing the following BibTeX entry. | |
``` | |
@misc{zhao2023fast, | |
title={Fast Segment Anything}, | |
author={Xu Zhao and Wenchao Ding and Yongqi An and Yinglong Du and Tao Yu and Min Li and Ming Tang and Jinqiao Wang}, | |
year={2023}, | |
eprint={2306.12156}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.CV} | |
} | |
``` |