| --- |
| license: apache-2.0 |
| tags: |
| - computer-vision |
| - image-segmentation |
| - mask-generation |
| - vision-transformer |
| - onnx |
| - segment-anything |
| - sam |
| pipeline_tag: image-segmentation |
| --- |
| |
| # Segment Anything (SAM) ONNX Weights |
|
|
| This repository contains ONNX-optimized weights for the **Segment Anything Model (SAM)**, a foundation model designed for promptable and automatic mask generation (AMG). |
|
|
| The architecture is decoupled into separate **Image Encoder** and **Mask Decoder** ONNX graphs. This design allows running heavy image encoding once while executing lightweight mask decoding interactively across multiple point prompts on both CPU and GPU without PyTorch dependencies. |
|
|
| ## Available Files |
| * **`vit_b_encoder.onnx` & `vit_b_decoder.onnx`**: ViT-Base backbone, optimized for reduced memory footprint and higher throughput. |
| * **`vit_l_encoder.onnx` & `vit_l_decoder.onnx`**: ViT-Large backbone, balanced representation capacity and inference speed. |
| * **`vit_h_encoder.onnx` & `vit_h_decoder.onnx`**: ViT-Huge backbone, standard high-fidelity segmentation 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 SAM |
| |
| # Initialize SAM automatic mask generator |
| segmentor = SAM(model_variant="vit_h") |
| |
| # Generate instance mask proposals across a uniform grid |
| result = segmentor.generate_masks("image.jpg", points_per_side=32) |
| |
| # Save segmentation overlay |
| result.visualize_mask("sam_output.png") |
| ``` |
|
|
|
|
| ## Original Citation |
| If you use these models in academic work, please cite the original authors: |
| ```bibtex |
| @article{kirillov2023segany, |
| title={Segment Anything}, |
| author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross}, |
| journal={arXiv preprint arXiv:2304.02643}, |
| year={2023} |
| } |
| ``` |
