DSRNet

Prerequisites

Ubuntu 18.04
Python==3.8.3
Torch==1.8.2+cu111
Torchvision==0.9.2+cu111\

Dataset

For all datasets, they should be organized in below's fashion:

|__dataset_name
   |__images: xxx.jpg ...
   |__GT : xxx.png ...

Suppose we use DUTS-TR for training, the overall folder structure should be:

|__dataset
    |__DUTS
       |__DUTS-TR
          |__DUTS-TR-Image: xxx.jpg ...
          |__DUTS-TR-Mask : xxx.png ...
   |__benchmark
      |__ECSSD
         |__images: xxx.jpg ...
         |__GT : xxx.png ...
      |__HKU-IS
         |__images: xxx.jpg ...
         |__GT : xxx.png ...
      ...

ECSSD || HKU-IS || DUTS-TE || DUT-OMRON || PASCAL-S

Train & Test

Firstly, make sure you have enough GPU RAM.
With default setting (batchsize=8), 24GB RAM is required, but you can always reduce the batchsize to fit your hardware.

Default values in option.py are already set to the same configuration as our paper, so
after setting the --dataset_root flag in option.py, to train the model (default dataset: DUTS-TR), simply:

python main.py --GPU_ID 0

to test the model located in the ckpt folder (default dataset: DUTS-TE), simply:

python main.py --test_only --pretrain "best.pt" --GPU_ID 0

If you want to train/test with different settings, please refer to option.py for more control options.
Currently only support training on single GPU.

Pretrain Model & Pre-calculated Saliency Map

Pre-calculated saliency map: [Baidu 8a3a]

Pre-trained model on ECSSD: [Baidu xhcp]

Evaluation

Firstly, obtain predictions via

python main.py --test_only --pretrain "xxx/best.pt" --GPU_ID 0 --save_result --save_msg "result"

Output will be saved in ./output/abc if you specified the save_msg flag.

For PR curve and F curve, we use the code provided by this repo: [PySODEvalToolkit]
For MAE, F measure and S score, we use the code provided by this repo: [PySODEvalToolkit]

Evaluation Results

Quantitative Evaluation

Qualitative Evaluation