This project provides the code and results for 'MAGNet: Multi-scale Awareness and Global Fusion Network for RGB-D Salient Object Detection'
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
- 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.
- Train the MAGNet:
- download the pretrained SMT pth from baidu / Google drive, and put it under
ckps/smt/
. - modify the
rgb_root
depth_root
gt_root
intrain_Net.py
according to your own data path. - run
python train_Net.py
- download the pretrained SMT pth from baidu / Google drive, and put it under
- Test the MAGNet:
- modify the
test_path
pth_path
intest_Net.py
according to your own data path. - run
python test_Net.py
- modify the
- You can select one of toolboxes to get the metrics CODToolbox / SOD_Evaluation_Metrics
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
- RGB-D baidu / Google drive
The implement of this project is based on the codebases bellow.
- SeaNet
- LSNet
- Fps/speed test MobileSal
- Evaluate tools CODToolbox / SOD_Evaluation_Metrics
Feel free to contact me if you have any questions: (mingyu6346 at 163 dot com)