ZhengPeng7

--- library_name: birefnet tags: - background-removal - mask-generation - Dichotomous Image Segmentation - Camouflaged Object Detection - Salient Object Detection - pytorch_model_hub_mixin - model_hub_mixin - transformers repo_url: https://github.com/ZhengPeng7/BiRefNet pipeline_tag: image-segmentation license: mit --- Bilateral Reference for High-Resolution Dichotomous Image Segmentation

--- library_name: birefnet tags: - background-removal - mask-generation - Dichotomous Image Segmentation - Camouflaged Object Detection - Salient Object Detection - pytorch_model_hub_mixin - model_hub_mixin repo_url: https://github.com/ZhengPeng7/BiRefNet pipeline_tag: image-segmentation license: mit ---

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 Performance: | Dataset | Method | Smeasure | maxFm | meanEm | MSE | maxEm | meanFm | wFmeasure | adpEm | adpFm | HCE | mBA | maxBIoU | meanBIoU | | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | :------: | | TE-P3M-500-NP | BiRefNet-matting--epoch100 | .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 + Many thanks to @freepik for their generous support on GPU resources for training this model!