alt.freeweights.vision / BiRefNet_lite-ONNX
From: onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.visionSubject:

BiRefNet_lite-ONNX

Date: 2026-11-15Commit: de15b22Signed: 2DC28CC7B26A283B ✓

Image segmentation model. Onnx weights, 339 MB in all. Published by onnx-community under MIT.

vision339MBBiRefNet_lite-ONNXonnxpermissive
FactsParamsnot statedSize339 MBFormatsonnxTaskimage segmentationLicenseMITFrom sourcepermissive
$ fw get onnx-community/BiRefNet_lite-ONNX
# manifest sha256 0bd820f2a386… · 339 MB · 6 files
$ fw verify ./BiRefNet_lite-ONNX onnx-community/BiRefNet_lite-ONNX
From sourceHosted (signed)onnx-community/BiRefNet_lite-ONNXLast check: 2026-11-15 04:40 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

Quantized of BiRefNet_lite.

Works with

Runs with

Ranked by fit to this model (onnx), then by GitHub stars.

Convert with

Scan with

Overview

Size
339 MB
Weights
115 MB (smallest of 2 ONNX variants)
Parameters
not stated
Formats
onnx
Task
image segmentation
License
MIT (permissive)
Files
6, each with a sha256
Publisher
onnx-community
Added
2026-11-15
Section
alt.freeweights.vision
Lineage

Original release from the publisher.

Base model: ZhengPeng7/BiRefNet_lite

Where these bytes come from
Publisher at the pinned commit→You

Sources are tried in order. A file counts only when its size and sha256 match the manifest. Sources are checked when you ask for them, not on a timer.

Sources
BadgeHostLast checkResult
From sourcehuggingface.co2026-11-15 04:40 UTCpassed

Model card

From the publisher at commit de15b22. Shown as plain text with links; images open at the publisher.

Bilateral Reference for High-Resolution Dichotomous Image Segmentation#

Peng Zheng 1,4,5,6,&thinsp; Dehong Gao 2,&thinsp; Deng-Ping Fan 1*,&thinsp; Li Liu 3,&thinsp; Jorma Laaksonen 4,&thinsp; Wanli Ouyang 5,&thinsp; Nicu Sebe 6

1 Nankai University&ensp; 2 Northwestern Polytechnical University&ensp; 3 National University of Defense Technology&ensp; 4 Aalto University&ensp; 5 Shanghai AI Laboratory&ensp; 6 University of Trento&ensp;

DIS-Sample_1DIS-Sample_2
Image: imageImage: image

For more information, check out the official repository.

Usage (Transformers.js)#

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

npm i @huggingface/transformers

You can then use the model for image matting, as follows:

import { AutoModel, AutoProcessor, RawImage } from '@huggingface/transformers';

// Load model and processor
const model_id = 'onnx-community/BiRefNet_lite';
const model = await AutoModel.from_pretrained(model_id, { dtype: 'fp32' });
const processor = await AutoProcessor.from_pretrained(model_id);

// Load image from URL
const url = 'https://images.pexels.com/photos/5965592/pexels-photo-5965592.jpeg?auto=compress&cs=tinysrgb&w=1024';
const image = await RawImage.fromURL(url);

// Pre-process image
const { pixel_values } = await processor(image);

// Predict alpha matte
const { output_image } = await model({ input_image: pixel_values });

// Save output mask
const mask = await RawImage.fromTensor(output_image[0].sigmoid().mul(255).to('uint8')).resize(image.width, image.height);
mask.save('mask.png');
Input imageOutput mask
Image: image/pngImage: image/png

Citation#

@article{BiRefNet,
  title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation},
  author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu},
  journal={CAAI Artificial Intelligence Research},
  year={2024}
}

Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

Files

6 files · 339 MBWeights 115 MB (smallest of 2 ONNX variants). Sizes and sha256 from the signed manifest.
Hide all 6 files with their sha256
FileSizesha256
.gitattributes1.52 KB11ad7efa24975ee4b0c3c3a38ed18737f0658a5f75a0a96787b576a78a023361
README.md4.29 KB95be7ac786fc40352e1405c397767af132e3e44f7eb6b924391bb145a2446542
config.json81 B50acd2d700ee3d5facdcf3aab2312bb586372726dce8bdd5741bdae0ca6eb84f
onnx/model.onnx224 MB5600024376f572a557870a5eb0afb1e5961636bef4e1e22132025467d0f03333
onnx/model_fp16.onnx115 MBd39b897ceb16ae654c1731f3dba0cf9b368d9cae74b5a57459b455cc8bfec402
preprocessor_config.json391 B447d9ccb2c4129ab0ca045fc293e7d79dbc45e293ac1e94818d865db77f65a54
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
de15b22ba1 pinned now2026-11-15 04:40 UTC6 files · 339 MB

Use it

$ fw get onnx-community/BiRefNet_lite-ONNX
# manifest sha256 0bd820f2a386… · 339 MB · 6 files
$ fw verify ./BiRefNet_lite-ONNX onnx-community/BiRefNet_lite-ONNX
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/onnx-community/BiRefNet_lite-ONNX.json --dir ./BiRefNet_lite-ONNX $ python3 fw.py verify ./BiRefNet_lite-ONNX ./onnx-community__BiRefNet_lite-ONNX.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/BiRefNet_lite-ONNX --revision de15b22ba131738a16dff04aab8bdf8dc32e3ac1 --local-dir ./BiRefNet_lite-ONNX

License

Name
MIT License
Id
MIT
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
95be7ac786fc40352e1405c397767af132e3e44f7eb6b924391bb145a2446542
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.visionSubject: BiRefNet_lite-ONNXDate: 2026-11-15Commit: de15b22Signed: 2DC28CC7B26A283B ✓
Upstream
onnx-community/BiRefNet_lite-ONNX
Pinned commit
de15b22ba131738a16dff04aab8bdf8dc32e3ac1
Manifest
BiRefNet_lite-ONNX.json · .minisig
Manifest sha256
0bd820f2a3863148a04f5468bd19e4c4f45a69b3ded3614581f694a4debbff02
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 04:40 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/onnx-community/BiRefNet_lite-ONNX.json $ curl -O https://freeweights.allternit.com/manifests/onnx-community/BiRefNet_lite-ONNX.json.minisig $ minisign -Vm BiRefNet_lite-ONNX.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

Parent

ZhengPeng7/BiRefNet_lite · this model is a quantization of it

No model in the catalog names this one as its parent.

Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download onnx-community/BiRefNet_lite-ONNX
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. BiRefNet_lite-ONNX.json. Provenance. Takedown history: none.
From: dima806 <huggingface.co/dima806>Subject: ai_vs_real_image_detection
From: legekka <huggingface.co/legekka>Subject: AI-Anime-Image-Detector-ViT
From: onnx-community <huggingface.co/onnx-community>Subject: AI-Anime-Image-Detector-ViT-ONNX
From: AICodexLab <huggingface.co/AICodexLab>Subject: answerdotai-ModernBERT-base-ai-detector
From: onnx-community <huggingface.co/onnx-community>Subject: answerdotai-ModernBERT-base-ai-detector-ONNX
From: onnx-community <huggingface.co/onnx-community>Subject: bart-large-cnn-ONNX