metadata
library_name: BiRefNet
tags:
- background-removal
- mask-generation
- Image Matting
- pytorch_model_hub_mixin
- model_hub_mixin
repo_url: https://github.com/ZhengPeng7/BiRefNet-matting
pipeline_tag: image-segmentation
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
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 | maxFm | wFmeasure | MSE | Smeasure | meanEm | HCE | maxEm | meanFm | adpEm | adpFm | 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}
}