| --- |
| license: gpl-3.0 |
| tags: |
| - computer-vision |
| - image-segmentation |
| - instance-segmentation |
| - yolo |
| - onnx |
| - fastsam |
| pipeline_tag: image-segmentation |
| --- |
| |
| # FastSAM ONNX Weights |
|
|
| This repository contains ONNX-optimized weights for **FastSAM (Fast Segment Anything Model)**, a real-time CNN-based instance segmentation model built upon the YOLOv8-Seg architecture. |
| By converting the PyTorch weights to standard ONNX format, these models enable real-time object proposal generation and mask decoding on both CPU and GPU edge devices without requiring the PyTorch runtime. |
|
|
| ## Available Files |
|
|
| * **`FastSAM-s.onnx`**: Small YOLOv8-Seg backbone variant (`"s"`), optimized for high frame rate and edge deployment. |
| * **`FastSAM-x.onnx`**: Extra-large YOLOv8-Seg backbone variant (`"x"`), higher capacity proposal generation model. |
|
|
| --- |
| ## How to Use |
|
|
| The easiest way to load and run these models is through the **[spatialhub](https://github.com/spatialhub-ai/spatialhub)** Python library: |
|
|
| ```python |
| from spatialhub import FastSAM |
| # Initialize FastSAM segmentor |
| segmentor = FastSAM(model_variant="x") |
| # Generate mask proposals |
| result = segmentor.generate_masks("scene.png", conf_threshold=0.25) |
| # Save mask visualization |
| result.visualize_mask("fastsam_output.png") |
| ``` |
|
|
| ## Original Citation |
|
|
| If you use these models in academic work, please cite the original authors: |
|
|
| ```bibtex |
| @article{zhao2023fast, |
| title={Fast Segment Anything}, |
| author={Zhao, Xu and Ding, Wenyu and An, Jiaqi and Du, Yufan and Zhao, Tao and Lu, Mengling and Shen, Shaohua}, |
| journal={arXiv preprint arXiv:2306.12156}, |
| year={2023} |
| } |
| ``` |
