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--- |
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title: FastSAM |
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emoji: 🐠 |
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colorFrom: pink |
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colorTo: indigo |
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sdk: gradio |
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sdk_version: 4.36.1 |
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app_file: app_gradio.py |
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pinned: false |
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license: apache-2.0 |
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--- |
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# Fast Segment Anything |
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Official PyTorch Implementation of the <a href="https://github.com/CASIA-IVA-Lab/FastSAM">. |
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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 |
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the SAM method at **50× higher run-time speed**. |
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## Local Setup (Anaconda Environment Recommended) |
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* Create a new conda environment |
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``` |
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conda create -n fastsam python=3.11 |
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``` |
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* Install PyTorch 2.5.0 with CUDA 12.4 |
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``` |
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conda install pytorch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 pytorch-cuda=12.4 -c pytorch -c nvidia |
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``` |
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* Install rest of the requirements |
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``` |
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pip install -r requirements.txt |
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``` |
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## License |
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The model is licensed under the [Apache 2.0 license](LICENSE). |
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## Acknowledgement |
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- [Segment Anything](https://segment-anything.com/) provides the SA-1B dataset and the base codes. |
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- [YOLOv8](https://github.com/ultralytics/ultralytics) provides codes and pre-trained models. |
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- [YOLACT](https://arxiv.org/abs/2112.10003) provides powerful instance segmentation method. |
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- [Grounded-Segment-Anything](https://huggingface.co/spaces/yizhangliu/Grounded-Segment-Anything) provides a useful web demo template. |
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## Citing FastSAM |
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If you find this project useful for your research, please consider citing the following BibTeX entry. |
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``` |
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@misc{zhao2023fast, |
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title={Fast Segment Anything}, |
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author={Xu Zhao and Wenchao Ding and Yongqi An and Yinglong Du and Tao Yu and Min Li and Ming Tang and Jinqiao Wang}, |
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year={2023}, |
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eprint={2306.12156}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV} |
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} |
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``` |
