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| title: FastSAM | |
| emoji: ๐ | |
| colorFrom: pink | |
| colorTo: indigo | |
| sdk: gradio | |
| sdk_version: 5.16.2 | |
| 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} | |
| } | |
| ``` |