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	.978	.996	.990	.003	.996	.987	.989	.962	.980	.000	.825	.934	.888
TE-AM-2k	BiRefNet-matting--epoch_100	.970	.995	.989	.003	.996	.986	.987	.985	.990	.000	.830	.937	.876

Check the main BiRefNet_lite model repo for more info and how to use it:
https://huggingface.co/ZhengPeng7/BiRefNet_lite/blob/main/README.md

Also check the GitHub repo of BiRefNet for all things you may want:
https://github.com/ZhengPeng7/BiRefNet

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}
}

ZhengPeng7/BiRefNet_lite-matting

Bilateral Reference for High-Resolution Dichotomous Image Segmentation

This repo holds the official weights of BiRefNet for general matting.

Training Sets:

Validation Sets:

Performance:

Citation

ZhengPeng7/
BiRefNet_lite-matting