DRANet

"Augmentation Matters: Dual-threshold Guided Reliability Aware Network for Semi-Supervised Image Semantic Segmentation".

Running DRANet

Prepare datasets

Please download the Pascal and Cityscapes, and set up the path to them properly in the configuration files.

• Pascal: JPEGImages | SegmentationClass

• Cityscapes: leftImg8bit | gtFine

• Splitall: included.

Here is our adopted way，

├── ./data
    ├── splitsall
    	├── cityscapes
    	├── pascal
    ├── VOC2012
    	├── JPEGImages
    	└── SegmentationClassAug
    └── cityscapes
        ├── gtFine
    	└── leftImg8bit

Prepare pre-trained encoder

Please download the pretrained models, and set up the path to these models properly in the file of config_xxx.yaml .

ResNet-50 | ResNet-101

ResNet-50 | ResNet-101

Here is our adopted way，

├── ./pretrained
    ├── resnet50.pth
    └── resnet101.pth

Prepare running Envs

Nothing special

• python: 3.7.13
• pytorch: 1.7.1
• cuda11.0.221_cudnn8.0.5_0
• torchvision: 0.8.2

Ready to Run

Basically, you are recommanded to config the experimental runnings in a ".yaml" file firstly. We include various configuration files under the directory of "exps".

# 1) configure your yaml file in a running script
vim ./scripts/run_abls_citys.sh

# 2) run directly
sh ./scripts/run_abls_citys.sh