'Boundary-Aware Segmentation Network for Mobile and Web Applications', Xuebin Qin, Deng-Ping Fan, Chenyang Huang, Cyril Diagne, Zichen Zhang, Adria Cabeza Sant’Anna, Albert Suarez, Martin Jagersand, and Ling Shao.
[SOD Results will come soon!]() \ [SOC Results will come soon!]() \ COD Results
Code for CVPR 2019 paper 'BASNet: Boundary-Aware Salient Object Detection code', Xuebin Qin, Zichen Zhang, Chenyang Huang, Chao Gao, Masood Dehghan and Martin Jagersand.
Contact: xuebin[at]ualberta[dot]ca
U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Python 3.6
numpy 1.15.2
scikit-image 0.14.0
PIL 5.2.0
PyTorch 0.4.0
torchvision 0.2.1
glob
The SSIM loss is adapted from pytorch-ssim.
Clone this repo
git clone https://github.com/NathanUA/BASNet.git
Download the pre-trained model basnet.pth from GoogleDrive or baidu extraction code: 6phq, and put it into the dirctory 'saved_models/basnet_bsi/'
Cd to the directory 'BASNet', run the training or inference process by command: python basnet_train.py
or python basnet_test.py
respectively.
We also provide the predicted saliency maps (GoogleDrive,Baidu) for datasets SOD, ECSSD, DUT-OMRON, PASCAL-S, HKU-IS and DUTS-TE.
@article{DBLP:journals/corr/abs-2101-04704,
author = {Xuebin Qin and
Deng{-}Ping Fan and
Chenyang Huang and
Cyril Diagne and
Zichen Zhang and
Adri{\`{a}} Cabeza Sant'Anna and
Albert Su{\`{a}}rez and
Martin J{\"{a}}gersand and
Ling Shao},
title = {Boundary-Aware Segmentation Network for Mobile and Web Applications},
journal = {CoRR},
volume = {abs/2101.04704},
year = {2021},
url = {https://arxiv.org/abs/2101.04704},
archivePrefix = {arXiv},
}
@InProceedings{Qin_2019_CVPR,
author = {Qin, Xuebin and Zhang, Zichen and Huang, Chenyang and Gao, Chao and Dehghan, Masood and Jagersand, Martin},
title = {BASNet: Boundary-Aware Salient Object Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}