DMPNet

Pytorch implementation for DMPNet: Dynamic Message Propagation Network for RGB-D Salient Object Detection.

Requirements

• Python 3.7
  
• Pytorch 1.8.0
  
• Torchvision 0.9.0
  
• Cuda 11.1

Usage

This is the Pytorch implementation of DMPNet. It has been trained and tested on Linux (Ubuntu20 + Cuda 11.1 + Python 3.7 + Pytorch 1.8), and it can also work on Windows.

To Train

• Download the pre-trained ImageNet backbone, password: bd0z
  (resnet101/resnet50, densenet161, vgg16 and vgg_conv1, whereas the latter already exists in the folder), and put it in the 'pretrained' folder.

• Download the training dataset, password: uw24
  and modify the 'train_root' and 'train_list' in the main.py.

• Start to train with

python main.py --mode=train --arch=resnet --network=resnet101 --train_root=xx/dataset/RGBDcollection --train_list=xx/dataset/RGBDcollection/train.lst 

To Test

• Download the testing dataset, password: uw24
  and have it in the 'dataset/test/' folder.
• Download the already-trained DMPNet pytorch model, password: kas7
  and modify the 'model' to its saving path in the main.py.
• Start to test with

python main.py --mode=test --arch=resnet --network=resnet101 --model=xx/JLDCF_resnet101.pth --sal_mode=LFSD  --test_folder=test/LFSD