Awesome
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).