¹Nankai University ²Northwestern Polytechnical University ³National University of Defense Technology ⁴Aalto University ⁵Shanghai AI Laboratory ⁶University 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	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}
}

frankjoshua/BiRefNet-matting

Bilateral Reference for High-Resolution Dichotomous Image Segmentation