SDABN

Code of the paper Synergy Between Semantic Segmentation and Image Denoising via Alternate Boosting

Prepare data

Cityscapes

You need to download the Cityscapes. Your directory tree should be look like this: (data_root is specified by DATASET.ROOT in experiment/cityscapes/xxx.yaml)

$data_root
└── cityscapes
   ├── gtFine
   │   ├── test
   │   ├── train
   │   └── val
   └── leftImg8bit
       ├── test
       ├── train
       └── val

OutdoorSeg

You can download the OutdoorSeg.

Train

For one SDABN, train the models following the order of Seg₁ --> Dn₁ --> Seg₂ --> Dn₂ --> Seg₃ --> Dn₃ -->....

Example of training scipt on Cityscapes dataset

# train s1 and get s1.pth
cd cityscapes/seg/
python train.py --stage 1 --resume_s1 \path\to\seg_model\on\clean

# train d1 and get d1.pth
cd cityscapes/dn/
python train.py --stage 2 --resume_seg1 \path\to\s1.pth

# train s2 and get s2.pth
cd cityscapes/seg/
python train.py --stage 3 --resume_s1 \path\to\s1.pth --resume_d1 \path\to\d1.pth --resume_s2 \path\to\s1.pth

# train d2 and get d2.pth
cd cityscapes/dn/
python train.py --stage 4 --resume_seg1 \path\to\s1.pth --resume_d1 \path\to\d1.pth --resume_seg2 \path\to\s2.pth

# train s3 and get s3.pth
cd cityscapes/seg/
python train.py --stage 5 --resume_s1 \path\to\s1.pth --resume_d1 \path\to\d1.pth --resume_s2 \path\to\s2.pth --resume_d2 \path\to\d2.pth --resume_s3 \path\to\s2.pth

# train d3 and get d3.pth
cd cityscapes/dn/
python train.py --stage 6 --resume_seg1 \path\to\s1.pth --resume_d1 \path\to\d1.pth --resume_seg2 \path\to\s2.pth --resume_d2 \path\to\d2.pth --resume_seg3 \path\to\s3.pth

Model link

OneDrive (For example, sds.pth is the model of Seg₂ and sdsdsd.pth is the model of Dn_3.)