| --- |
| 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 <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**. |
|
|
| ## 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](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} |
| } |
| ``` |
