A Cascaded Refined RGB-D Salient Object Detection Network Based on the Attention Mechanism

pytorch==1.8.0

torchvision==0.9.0

tensorboardX==2.5

opencv-python==4.5.5.64

If you want to retrain our network, we recommend that you follow these steps.

1.Download the dataset and place it in the CR_dataset folder.[code:0617]https://pan.baidu.com/s/1PzjLo43pigzhCm-CxTyDIw

2.Modify the training parameters of the model in options.py. Such as batchsize, gpu_id.

3.Open a terminal and run python3 CRNet_train.py. The trained parameter model will be saved in the CRNet_cpts folder.

If you would like to reproduce our results, please follow these steps.

1.We provide a link to download the parameters of the trained model, put it in the model_pths folder.[code:0617]https://pan.baidu.com/s/1FoAmEL10jNoKCVT74p1xXA

2.Open a terminal and run python3 CRNet_test.py.

3.We also provide links to download the results of our experiments.[code:0617]https://pan.baidu.com/s/1LkruTi2hTGtbbX4HKr4wkA

If you would like to evaluate our entire model parameters through quantitative metrics, please follow these steps.

1.Download the results of our experiments and place them in any path.

2.The evaluation metric code has been placed in the eval_code folder, please use MATLAB to open it.

3.Modify the path to the dataset in main.m.

4.run main.m.