README.md

This project provides the code and results for 'MAGNet: Multi-scale Awareness and Global Fusion Network for RGB-D Salient Object Detection'

Environments

conda create -n magnet python=3.9.18
conda activate magnet
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge
conda install -c conda-forge opencv-python==4.7.0
pip install timm==0.6.5
conda install -c conda-forge tqdm
conda install yacs

Data Preparation

• Download the RGB-D raw data from baidu / Google drive
  
• Download the RGB-T raw data from baidu / Google drive
  

Note that the depth maps of the raw data above are foreground is white.

Training & Testing

• Train the MAGNet:
  1. download the pretrained SMT pth from baidu / Google drive, and put it under ckps/smt/.
  2. modify the rgb_root depth_root gt_root in train_Net.py according to your own data path.
  3. run python train_Net.py
• Test the MAGNet:
  1. modify the test_path pth_path in test_Net.py according to your own data path.
  2. run python test_Net.py

Evaluate tools

• You can select one of toolboxes to get the metrics CODToolbox / SOD_Evaluation_Metrics

Saliency Maps

We provide the saliency maps of DUT, LFSD, NJU2K, NLPR, SIP, SSD, STERE datasets.

• RGB-D baidu / Google drive
  

We provide the saliency maps of VT821, VT1000, VT5000 datasets.

• RGB-T baidu / Google drive
  

Trained Models

• RGB-D baidu / Google drive
  

Acknowledgement

The implement of this project is based on the codebases bellow.

• SeaNet
  
• LSNet
  
• Fps/speed test MobileSal
• Evaluate tools CODToolbox / SOD_Evaluation_Metrics
  

Contact

Feel free to contact me if you have any questions: (mingyu6346 at 163 dot com)