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This repo is the official implementation of "Bilateral Reference for High-Resolution Dichotomous Image Segmentation" (CAAI AIR 2024). Visit our GitHub repo: https://github.com/ZhengPeng7/BiRefNet for more details -- codes, docs, and model zoo!
Parameters221M
Context—
Weights444.5 MB
Licensemit
AccessOpen weights
Monthly Downloads976k
Runs On
What it takes to serve BiRefNet (221M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.4 GB | 0.5 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.2 GB | 0.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 18, 2026.
SAVRN's Notes on BiRefNet
Cutting a subject cleanly out of a high-resolution image is the job here, and at 221 million parameters it is among the smallest things we would put on a GPU. The 16-bit weights are 0.4 GB and need about 0.5 GB of memory; the 4-bit build runs in 0.1 GB. The cheapest Index listing is one MI300X with 192 GB at $1.85 an hour, far more card than this needs. It belongs as one tenant on hardware already doing other work.
MIT is as short as licenses get: commercial use, modification and redistribution are allowed as long as the copyright and permission notices travel with the files. Access is open. Before committing, confirm the 444 MB safetensors download matches the arXiv:2401.03407 version you tested, since the repository was updated on February 4, 2026, and plan cost as GPU hours, because a segmentation model has no per-token price.
Model Card
By Peng Zheng, published under mit, revision e2bf8e4460fc.
Bilateral Reference for High-Resolution Dichotomous Image Segmentation
Peng Zheng 1,4,5,6,
Dehong Gao 2,
Deng-Ping Fan 1*,
Li Liu 3,
Jorma Laaksonen 4,
Wanli Ouyang 5,
Nicu Sebe 6
1 Nankai University 2 Northwestern Polytechnical University 3 National University of Defense Technology 4 Aalto University 5 Shanghai AI Laboratory 6 University of Trento
| DIS-Sample_1 | DIS-Sample_2 |
|---|---|
This repo is the official implementation of "Bilateral Reference for High-Resolution Dichotomous Image Segmentation" (CAAI AIR 2024).
Visit our GitHub repo: https://github.com/ZhengPeng7/BiRefNet for more details -- codes, docs, and model zoo!
How to use
0. Install Packages:
pip install -qr https://raw.githubusercontent.com/ZhengPeng7/BiRefNet/main/requirements.txt
1. Load BiRefNet:
Use codes + weights from HuggingFace
Only use the weights on HuggingFace -- Pro: No need to download BiRefNet codes manually; Con: Codes on HuggingFace might not be latest version (I'll try to keep them always latest).
Configuration
- Architecture
- BiRefNet
Identity and Version
- Repository
- ZhengPeng7/BiRefNet
- Publisher
- Peng Zheng
- Task
- Image segmentation
- Modality
- Image
- Library
- birefnet
- Parameters
- 221M parameters
- Languages
- Not stated by the source
- Revision
- e2bf8e4460fc8fa32bba5ea4d94b3233d367b0e4
- First published
- 2024-07-12
- Last updated
- 2026-02-04
Files and Weights
9 files, 444.6 MB in total. The weights are 1 file totalling 444.5 MB in safetensors.
Weights1 file · 444.5 MB
Configuration4 files · 97.4 KB
Documentation1 file · 10.0 KB
Other1 file · 149 B
Repository2 files · 3.4 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 444.5 MB | 9ab37426bf4d |
| BiRefNet_config.py | Configuration | 298 B | — |
| birefnet.py | Configuration | 91.9 KB | — |
| config.json | Configuration | 405 B | — |
| handler.py | Configuration | 4.8 KB | — |
| README.md | Documentation | 10.0 KB | — |
| requirements.txt | Other | 149 B | — |
| .gitattributes | Repository | 1.5 KB | — |
| .gitignore | Repository | 1.9 KB | — |
License and Download
- License
- mit
- Access
- Open weights, no gate
- Download size
- 444.5 MB
Download from Peng Zheng
Released by Peng Zheng through its official repository on Hugging Face. Read the license.
Built From
- Described by arXiv:2401.03407
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 444.5 MB |
| 16-bit | 0.4 GB |
| 8-bit | 0.2 GB |
| 4-bit | 0.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About BiRefNet
How much GPU memory does BiRefNet need?
About 0.5 GB at 16-bit and 0.1 GB at 4-bit: the weights (221M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run BiRefNet on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use BiRefNet commercially?
Yes. BiRefNet is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.
Similar Models
This repo is the official implementation of "Bilateral Reference for High-Resolution Dichotomous Image Segmentation" (CAAI AIR 2024). Visit our GitHub repo: https://github.com/ZhengPeng7/BiRefNet for more details -- codes, docs, and model zoo! This repo contains the weights of BiRefNet proposed in our paper, which has achieved the SOTA performance on three tasks (DIS, HRSOD, and COD). Go to my GitHub page for BiRefNet codes and the latest updates: https://github.com/ZhengPeng7/BiRefNet:) + Online Image Inference on Colab: + Online Inference with GUI on Hugging Face with adjustable resolutions: + Inference and evaluation of your given weights: + Many thanks to @freepik for their generous…
This repo is the official implementation of "Bilateral Reference for High-Resolution Dichotomous Image Segmentation" (CAAI AIR 2024). Visit our GitHub repo: https://github.com/ZhengPeng7/BiRefNet for more details -- codes, docs, and model zoo! This repo contains the weights of BiRefNet proposed in our paper, which has achieved the SOTA performance on three tasks (DIS, HRSOD, and COD). Go to my GitHub page for BiRefNet codes and the latest updates: https://github.com/ZhengPeng7/BiRefNet:) + Online Image Inference on Colab: + Online Inference with GUI on Hugging Face with adjustable resolutions: + Inference and evaluation of your given weights: + Many thanks to @freepik for their generous…
Check the main BiRefNet model repo for more info and how to use it: https://huggingface.co/ZhengPeng7/BiRefNet/blob/main/README.md Also check the GitHub repo of BiRefNet for all things you may want: https://github.com/ZhengPeng7/BiRefNet + Many thanks to @fal for their generous support on GPU resources for training this BiRefNet for portrait matting.
For performance of different epochs, check the evalresults-xxx folder for it on my google drive. This repo is the official implementation of "Bilateral Reference for High-Resolution Dichotomous Image Segmentation" (CAAI AIR 2024). Visit our GitHub repo: https://github.com/ZhengPeng7/BiRefNet for more details -- codes, docs, and model zoo! This repo contains the weights of BiRefNet proposed in our paper, which has achieved the SOTA performance on three tasks (DIS, HRSOD, and COD). Go to my GitHub page for BiRefNet codes and the latest updates: https://github.com/ZhengPeng7/BiRefNet:) + Online Image Inference on Colab: + Online Inference with GUI on Hugging Face with adjustable resolutions: +…
Check the main BiRefNet model repo for more info and how to use it: https://huggingface.co/ZhengPeng7/BiRefNet/blob/main/README.md Also check the GitHub repo of BiRefNet for all things you may want: https://github.com/ZhengPeng7/BiRefNet + Many thanks to @freepik for their generous support on GPU resources for training this model!