BiRefNet_lite-2K

<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>^1,4,5,6,&thinsp; <a href='https://scholar.google.com/citations?user=0uPb8MMAAAAJ' target='_blank'><strong>Dehong Gao</strong></a>²,&thinsp; <a href='https://scholar.google.com/citations?user=kakwJ5QAAAAJ' target='_blank'><strong>Deng-Ping Fan</strong></a>^1*,&thinsp; <a href='https://scholar.google.com/citations?user=9cMQrVsAAAAJ' target='_blank'><strong>Li Liu</strong></a>³,&thinsp; <a href='https://scholar.google.com/citations?user=qQP6WXIAAAAJ' target='_blank'><strong>Jorma Laaksonen</strong></a>⁴,&thinsp; <a href='https://scholar.google.com/citations?user=pw_0Z_UAAAAJ' target='_blank'><strong>Wanli Ouyang</strong></a>⁵,&thinsp; <a href='https://scholar.google.com/citations?user=stFCYOAAAAAJ' target='_blank'><strong>Nicu Sebe</strong></a>⁶ </div> <div align='center'> ¹Nankai University&ensp; ²Northwestern Polytechnical University&ensp; ³National University of Defense Technology&ensp; ⁴Aalto University&ensp; ⁵Shanghai AI Laboratory&ensp; ⁶University of Trento&ensp; </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>&ensp; <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>&ensp; <a href='https://www.birefnet.top'><img src='https://img.shields.io/badge/Page-BiRefNet-red'></a>&ensp; <a href='https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM'><img src='https://img.shields.io/badge/Drive-Stuff-green'></a>&ensp; <a href='LICENSE'><img src='https://img.shields.io/badge/License-MIT-yellow'></a>&ensp; <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>&ensp; <a href='https://huggingface.co/ZhengPeng7/BiRefNet'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF%20Models-BiRefNet-blue'></a>&ensp; <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>&ensp; <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>&ensp; </div>

This repo holds the official weights of BiRefNet_lite trained in 2K resolution.

Training Sets:

• DIS5K (except DIS-VD)
• HRS10K
• UHRSD
• 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

HR samples selection:

size_h, size_w = 1440, 2560
ratio = 0.8
h, w = image.shape[:2]
h >= size_h and w >= size_w or (h > size_h * ratio and w > size_w * ratio)

Validation Sets:

• DIS-VD
• TE-P3M-500-NP

Performance:

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

Remember to set the resolution of input images to 2K (2560, 1440) for better results when using this model.

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}
}