metadata

title: FastSAM
emoji: 🐠
colorFrom: pink
colorTo: indigo
sdk: gradio
sdk_version: 4.36.1
app_file: app_gradio.py
pinned: false
license: apache-2.0

Fast Segment Anything

Official PyTorch Implementation of the .

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.

Local Setup (Anaconda Environment Recommended)

Create a new conda environment

conda create -n fastsam python=3.11

Install PyTorch 2.5.0 with CUDA 12.4

conda install pytorch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 pytorch-cuda=12.4 -c pytorch -c nvidia

Install rest of the requirements

pip install -r requirements.txt

License

The model is licensed under the Apache 2.0 license.

Acknowledgement

Segment Anything provides the SA-1B dataset and the base codes.
YOLOv8 provides codes and pre-trained models.
YOLACT provides powerful instance segmentation method.
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}
}