BASNet

BASNet (New Version May 2nd, 2021)

'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.

Salient Object Detection(SOD) Qualitative Comparison

Salient Objects in Clutter(SOC) Qualitative Comparison

Camouflaged Object Detection(COD) Qualitative Comparison

Predicted maps of SOD, SOC and COD datasets

SOD Results will come soon!
SOC Results will come soon!
COD Results

BASNet (CVPR 2019)

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

(2020-May-09) NEWS! Our new Salient Object Detection model (U^2-Net), which is just accepted by Pattern Recognition, is available now!

U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection

Evaluation

Evaluation Code

Required libraries

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.

Usage

1. Clone this repo

git clone https://github.com/NathanUA/BASNet.git

1. Download the pre-trained model basnet.pth from GoogleDrive or baidu extraction code: 6phq, and put it into the dirctory 'saved_models/basnet_bsi/'

2. 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.

Architecture

Quantitative Comparison

Qualitative Comparison

Citation

…

Citation

@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}
}