Bilateral Reference for High-Resolution Dichotomous Image Segmentation
Peng Zheng 1,4,5,6,โ
Dehong Gao 2,โ
Deng-Ping Fan 1*,โ
Li Liu 3,โ
Jorma Laaksonen 4,โ
Wanli Ouyang 5,โ
Nicu Sebe 6
1 Nankai Universityโ 2 Northwestern Polytechnical Universityโ 3 National University of Defense Technologyโ 4 Aalto Universityโ 5 Shanghai AI Laboratoryโ 6 University of Trentoโ
This repo holds the official weights of BiRefNet for general matting.
Training Sets:
- P3M-10k (except TE-P3M-500-P)
- TR-humans
Validation Sets:
- TE-P3M-500-P
Performance:
Dataset | Method | Smeasure | maxFm | meanEm | MAE | maxEm | meanFm | wFmeasure | adpEm | adpFm | HCE |
---|---|---|---|---|---|---|---|---|---|---|---|
TE-P3M-500-P | BiRefNet-portrai--epoch_150 | .983 | .996 | .991 | .006 | .997 | .988 | .990 | .933 | .965 | .000 |
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 @fal for their generous support on GPU resources for training this BiRefNet for portrait matting.
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}
}
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