README.md

---
library_name: birefnet
tags:
- background-removal
- mask-generation
- Dichotomous Image Segmentation
- Camouflaged Object Detection
- Salient Object Detection
- pytorch_model_hub_mixin
- model_hub_mixin
repo_url: https://github.com/ZhengPeng7/BiRefNet
pipeline_tag: image-segmentation
license: mit
---

> This BiRefNet was trained with images in `512x512` for faster and more accurate lower resolution inference.
### Performance:
> All tested in FP16 mode.

|    Dataset    |                Method               | Resolution | maxFm | wFmeasure | MAE  | Smeasure | meanEm | HCE  | maxEm | meanFm | adpEm | adpFm | mBA  | maxBIoU | meanBIoU |
|     :------:    | :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |  :------: |
| DIS-VD | [**BiRefNet_512x512**-epoch_216](https://huggingface.co/ZhengPeng7/BiRefNet_512x512) |   512x512  |  .879 |    .840   | .040 |   .888   |  .931  | 1526 |  .941 |  .864  |  .938 |  .857 | .732 |   .747  |   .726   |
| DIS-VD | [**BiRefNet**-general-epoch_244](https://huggingface.co/ZhengPeng7/BiRefNet) |  512x512 |  .834 |    .789   | .050 |   .860   |  .891  | 1589 |  .905 |  .817  |  .902 |  .816 | .708 |   .698  |   .669   |
| DIS-VD | [**BiRefNet_HR**-general-epoch_130](https://huggingface.co/ZhengPeng7/BiRefNet_HR) |  512x512 |  .540 |    .409   | .112 |   .634   |  .565  | 1647 |  .682 |  .428  |  .690 |  .576 | .585 |   .384  |   .309   |

<h1 align="center">Bilateral Reference for High-Resolution Dichotomous Image Segmentation</h1>

<div align='center'>
    <a href='https://scholar.google.com/citations?user=TZRzWOsAAAAJ' target='_blank'><strong>Peng Zheng</strong></a><sup> 1,4,5,6</sup>,&thinsp;
    <a href='https://scholar.google.com/citations?user=0uPb8MMAAAAJ' target='_blank'><strong>Dehong Gao</strong></a><sup> 2</sup>,&thinsp;
    <a href='https://scholar.google.com/citations?user=kakwJ5QAAAAJ' target='_blank'><strong>Deng-Ping Fan</strong></a><sup> 1*</sup>,&thinsp;
    <a href='https://scholar.google.com/citations?user=9cMQrVsAAAAJ' target='_blank'><strong>Li Liu</strong></a><sup> 3</sup>,&thinsp;
    <a href='https://scholar.google.com/citations?user=qQP6WXIAAAAJ' target='_blank'><strong>Jorma Laaksonen</strong></a><sup> 4</sup>,&thinsp;
    <a href='https://scholar.google.com/citations?user=pw_0Z_UAAAAJ' target='_blank'><strong>Wanli Ouyang</strong></a><sup> 5</sup>,&thinsp;
    <a href='https://scholar.google.com/citations?user=stFCYOAAAAAJ' target='_blank'><strong>Nicu Sebe</strong></a><sup> 6</sup>
</div>

<div align='center'>
    <sup>1 </sup>Nankai University&ensp;  <sup>2 </sup>Northwestern Polytechnical University&ensp;  <sup>3 </sup>National University of Defense Technology&ensp; <sup>4 </sup>Aalto University&ensp;  <sup>5 </sup>Shanghai AI Laboratory&ensp;  <sup>6 </sup>University of Trento&ensp; 
</div>

<div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;">
  <a href='https://www.sciopen.com/article/pdf/10.26599/AIR.2024.9150038.pdf'><img src='https://img.shields.io/badge/Journal-Paper-red'></a>&ensp; 
  <a href='https://arxiv.org/pdf/2401.03407'><img src='https://img.shields.io/badge/arXiv-BiRefNet-red'></a>&ensp; 
  <a href='https://drive.google.com/file/d/1aBnJ_R9lbnC2dm8dqD0-pzP2Cu-U1Xpt/view?usp=drive_link'><img src='https://img.shields.io/badge/中文版-BiRefNet-red'></a>&ensp; 
  <a href='https://www.birefnet.top'><img src='https://img.shields.io/badge/Page-BiRefNet-red'></a>&ensp; 
  <a href='https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM'><img src='https://img.shields.io/badge/Drive-Stuff-green'></a>&ensp; 
  <a href='LICENSE'><img src='https://img.shields.io/badge/License-MIT-yellow'></a>&ensp; 
  <a href='https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF%20Spaces-BiRefNet-blue'></a>&ensp; 
  <a href='https://huggingface.co/ZhengPeng7/BiRefNet'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF%20Models-BiRefNet-blue'></a>&ensp; 
  <a href='https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba?usp=drive_link'><img src='https://img.shields.io/badge/Single_Image_Inference-F9AB00?style=for-the-badge&logo=googlecolab&color=525252'></a>&ensp; 
  <a href='https://colab.research.google.com/drive/1MaEiBfJ4xIaZZn0DqKrhydHB8X97hNXl#scrollTo=DJ4meUYjia6S'><img src='https://img.shields.io/badge/Inference_&_Evaluation-F9AB00?style=for-the-badge&logo=googlecolab&color=525252'></a>&ensp; 
</div>


|            *DIS-Sample_1*        |             *DIS-Sample_2*        |
| :------------------------------: | :-------------------------------: |
| <img src="https://drive.google.com/thumbnail?id=1ItXaA26iYnE8XQ_GgNLy71MOWePoS2-g&sz=w400" /> |  <img src="https://drive.google.com/thumbnail?id=1Z-esCujQF_uEa_YJjkibc3NUrW4aR_d4&sz=w400" /> |

This repo is the official implementation of "[**Bilateral Reference for High-Resolution Dichotomous Image Segmentation**](https://arxiv.org/pdf/2401.03407.pdf)" (___CAAI AIR 2024___). 



**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

## Acknowledgement:

+ Many thanks to @freepik for their generous support on GPU resources for training this model!


## Citation

```
@article{zheng2024birefnet,
  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},
  volume = {3},
  pages = {9150038},
  year={2024}
}
```