Image Segmentation
BiRefNet
Safetensors
background-removal
mask-generation
Image Matting
pytorch_model_hub_mixin
model_hub_mixin
custom_code
Instructions to use frankjoshua/BiRefNet-matting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BiRefNet
How to use frankjoshua/BiRefNet-matting with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("frankjoshua/BiRefNet-matting", trust_remote_code=True)# Option 2: use with BiRefNet # Install from https://github.com/ZhengPeng7/BiRefNet from models.birefnet import BiRefNet model = BiRefNet.from_pretrained("frankjoshua/BiRefNet-matting") - Notebooks
- Google Colab
- Kaggle
| library_name: birefnet | |
| tags: | |
| - background-removal | |
| - mask-generation | |
| - Image Matting | |
| - pytorch_model_hub_mixin | |
| - model_hub_mixin | |
| repo_url: https://github.com/ZhengPeng7/BiRefNet | |
| pipeline_tag: image-segmentation | |
| <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>,  | |
| <a href='https://scholar.google.com/citations?user=0uPb8MMAAAAJ' target='_blank'><strong>Dehong Gao</strong></a><sup> 2</sup>,  | |
| <a href='https://scholar.google.com/citations?user=kakwJ5QAAAAJ' target='_blank'><strong>Deng-Ping Fan</strong></a><sup> 1*</sup>,  | |
| <a href='https://scholar.google.com/citations?user=9cMQrVsAAAAJ' target='_blank'><strong>Li Liu</strong></a><sup> 3</sup>,  | |
| <a href='https://scholar.google.com/citations?user=qQP6WXIAAAAJ' target='_blank'><strong>Jorma Laaksonen</strong></a><sup> 4</sup>,  | |
| <a href='https://scholar.google.com/citations?user=pw_0Z_UAAAAJ' target='_blank'><strong>Wanli Ouyang</strong></a><sup> 5</sup>,  | |
| <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  <sup>2 </sup>Northwestern Polytechnical University  <sup>3 </sup>National University of Defense Technology  <sup>4 </sup>Aalto University  <sup>5 </sup>Shanghai AI Laboratory  <sup>6 </sup>University of Trento  | |
| </div> | |
| <div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;"> | |
| <a href='https://arxiv.org/pdf/2401.03407'><img src='https://img.shields.io/badge/arXiv-BiRefNet-red'></a>  | |
| <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>  | |
| <a href='https://www.birefnet.top'><img src='https://img.shields.io/badge/Page-BiRefNet-red'></a>  | |
| <a href='https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM'><img src='https://img.shields.io/badge/Drive-Stuff-green'></a>  | |
| <a href='LICENSE'><img src='https://img.shields.io/badge/License-MIT-yellow'></a>  | |
| <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>  | |
| <a href='https://huggingface.co/ZhengPeng7/BiRefNet'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF%20Models-BiRefNet-blue'></a>  | |
| <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>  | |
| <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>  | |
| </div> | |
| ## This repo holds the official weights of BiRefNet for general matting. | |
| ### Training Sets: | |
| + P3M-10k (except TE-P3M-500-NP) | |
| + TR-humans | |
| + AM-2k | |
| + AIM-500 | |
| + Human-2k (synthesized with BG-20k) | |
| + Distinctions-646 (synthesized with BG-20k) | |
| + HIM2K | |
| + PPM-100 | |
| ### Validation Sets: | |
| + TE-P3M-500-NP | |
| ### Performance: | |
| | Dataset | Method | Smeasure | maxFm | meanEm | MSE | maxEm | meanFm | wFmeasure | adpEm | adpFm | HCE | mBA | maxBIoU | meanBIoU | | |
| | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | | |
| | TE-P3M-500-NP | BiRefNet-matting--epoch_100 | .979 | .996 | .988 | .003 | .997 | .986 | .988 | .864 | .885 | .000 | .830 | .940 | .888 | | |
| **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} | |
| } | |
| ``` | |