ATSA

This codebase implements the system described in the paper:

Asymmetric Two-Stream Architecture for Accurate RGB-D Saliency Detection

Miao Zhang, Sun Xiao Fei, Jie Liu, Shuang Xu, Yongri Piao, Huchuan Lu. In ECCV 2020.

Prerequisites

Ubuntu 18
PyTorch 1.3.1
CUDA 10.1
Cudnn 7.5.1
Python 3.7
Numpy 1.17.3

Training and Testing Datasets

Training dataset

Download Link. Code: nx8x

Testing dataset

Download Link. Code: qqsf

Train/Test

test

Firstly, you need to download the 'Testing dataset' and the pretraind checpoint we provided (Baidu Pan. Code: d2o0). Then, you need to set dataset path and checkpoint name correctly. and set the param '--phase' as "test" and '--param' as 'True' in demo.py.

python demo.py

train

Once the train-augment dataset are prepared,you need to set dataset path and checkpoint name correctly. and set the param '--phase' as "train" and '--param' as 'True'(loading checkpoint) or 'False'(do not load checkpoint) in demo.py.

python demo.py

Contact Us

If you have any questions, please contact us (xiaofeisun@mail.dlut.edu.cn; 1605721375@mail.dlut.edu.cn).

Jack960317/ATSA