| | --- |
| | title: FastSAM |
| | emoji: 🐠 |
| | 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 **50× 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} |
| | } |
| | ``` |
