AGNet: Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images

Code for ICANN 2022 paper "Attention Guided Network for Salient Object Detection in Optical Remote Sensing Images", by Yuhan Lin, Han Sun, Ningzhong Liu, Yetong Bian, Jun Cen, and Huiyu Zhou

Requirement

python-3.6
pytorch-1.8.1
torchvision
numpy
tqdm
cv2

Usage

• Clone this repo into your workstation git clone https://github.com/NuaaYH/AGNet.git
• The datasets used in this paper can be download from BaiduYun: https://pan.baidu.com/s/1lwnw99tq9Uu10rrsX0rivg (code:u6st)
• Set the project format as follows:
  ./AGNet
  ./Dataset
• Create the folders in AGNet as shown below:
  ./Outputs/pred/AGNet/EORSSD(or ORSSD)/Test
  ./Checkpoints/trained

training

1. Comment out line 79 of run.py like #self.net.load_state_dict......
2. Comment out line 171 of run.py like #run.test() and ensure that the run.train() statement is executable
3. python run.py

testing

1. Put the model weights in ./Checkpoints/trained and ensure that line 79 of run.py is executable
2. Comment out line 170 of run.py like #run.train() and ensure that the run.test() statement is executable
3. python run.py

evaluation

The evaluation code can be available at https://github.com/zyjwuyan/SOD_Evaluation_Metrics.

Results

• The results of ours and the comparison methods in our paper can be download from BaiduYun:
  link：https://pan.baidu.com/s/1KV-Yf6ZdzU6FKmoqLBV7BA code：a617
• The pre-trained model can be download from BaiduYun:
  link：https://pan.baidu.com/s/19B7eu1DeurfkoECwqVZ5cA code：bpmp