CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation

Please download the following datasets from the corresponding links and save them to ./data folder

Cityscapes: Download leftImg8bit_trainvaltest.zip and gt_trainvaltest.zip from here and extract them to data/cityscapes.

GTA: Download images, labels from here and extract them to data/gta.

Synthia: Download SYNTHIA-RAND-CITYSCAPES from here and extract it to data/synthia.

After saving the dataset, Please make sure that your directory tree looks exactly like the following chart:

CLUDA
├── ...
├── data
│   ├── cityscapes
│   │   ├── leftImg8bit
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── gtFine
│   │   │   ├── train
│   │   │   ├── val
│   ├── gta
│   │   ├── images
│   │   ├── labels
│   ├── synthia
│   │   ├── RGB
│   │   ├── GT
│   │   │   ├── LABELS
├── ...

Testing & Predictions

For testing, please replace 220609_1430_gtaHR2csHR_hrda_s1_5fbff.json in test.sh with the json file generated in the output_dir_name of your experiment and run the following command:

sh test.sh <output_dir_name>

Training

For running the main experiment please do the following:

./run_train.sh <name of the experiments (any name of your choice)>

Note

For reproducing results on DAFormer + CLUDA, please set FD loss hyperparameter (lambda) = 0.009 and Learning rate power = 1.1 while training with Swin-L backbone. For same experiment with SegFormer backbone, the above parameters shall remain same as mentioned in DAFormer.

Acknowledgements

We have used code from following open-source repositories. We thank the authors for making their work public.

• HRDA
• DAFormer
• MMSegmentation
• SegFormer
• DACS
• Exploring Cross-Image Pixel Contrast for Semantic Segmentation