vietanhdev/segment-anything-onnx-models

Segment Anything (SAM) — ONNX Models

ONNX exports of Meta's original Segment Anything family, plus MobileSAM, packaged for direct use with onnxruntime and AnyLabeling.

Why this repo exists

Running SAM through the original PyTorch checkpoint is heavy on a CPU laptop or an edge device. ONNX gives you a portable, dependency-light runtime that works in Python, C++, JavaScript, and most embedded targets. These exports are the ones AnyLabeling consumes for its smart-labeling features.

Variants

Each .zip bundles the encoder + decoder ONNX files for that backbone.

File	Backbone	Size	Notes
mobile_sam_20230629.zip	MobileSAM	35 MB	Smallest — best for mobile / low-power
mobile_sam_20230629_quant.zip	MobileSAM	10.5 MB	Quantized MobileSAM
sam_vit_b_01ec64.zip	ViT-B	332 MB	Base
sam_vit_b_01ec64_quant.zip	ViT-B	72 MB	Quantized base
sam_vit_l_0b3195.zip	ViT-L	1.1 GB	Large
sam_vit_l_0b3195_quant.zip	ViT-L	213 MB	Quantized large
sam_vit_h_4b8939.zip	ViT-H	2.3 GB	Huge — best quality
sam_vit_h_4b8939_quant.zip	ViT-H	422 MB	Quantized huge

Quick start

pip install huggingface_hub onnxruntime

from huggingface_hub import hf_hub_download
import zipfile, onnxruntime as ort

zip_path = hf_hub_download(repo_id="vietanhdev/segment-anything-onnx-models",
                           filename="sam_vit_b_01ec64_quant.zip")
with zipfile.ZipFile(zip_path) as z:
    z.extractall("./sam_vit_b_quant")

session = ort.InferenceSession("./sam_vit_b_quant/encoder.onnx",
                               providers=["CPUExecutionProvider"])
# Inspect expected inputs:
print([(i.name, i.shape, i.type) for i in session.get_inputs()])

For the full image → mask pipeline (encoder + decoder + prompt handling), see how AnyLabeling wires it: https://github.com/vietanhdev/anylabeling

Use with AnyLabeling

These models drop into AnyLabeling's auto-labeling backend without conversion. See the AnyLabeling docs for the model-config wiring.

Source weights

• Original SAM weights & license: https://github.com/facebookresearch/segment-anything
• MobileSAM: https://github.com/ChaoningZhang/MobileSAM

This repo redistributes the same weights in ONNX format. License unchanged from upstream releases (Apache 2.0).

Citation

@misc{nguyen2026sam_onnx,
  author = {Nguyen, Viet-Anh and {Neural Research Lab}},
  title  = {Segment Anything ONNX Models},
  year   = {2026},
  url    = {https://huggingface.co/vietanhdev/segment-anything-onnx-models}
}

For the underlying model, cite Meta's original SAM paper:

@article{kirillov2023sam,
  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:2304.02643},
  year    = {2023}
}

Acknowledgments

Thanks to Meta AI Research for releasing the SAM family, and to the MobileSAM team for their efficient distillation. This repo packages their work for edge inference.