Unsupervised domain adaptation is a promising technique for semantic segmentation and other computer vision tasks for which large-scale data annotation is costly and time-consuming. In semantic segmentation particularly, it is attractive to train models on annotated images from a simulated (source) domain and deploy them on real (target) domains. In this work, we present a novel framework for unsupervised domain adaptation based on the notion of target-domain consistency training. Intuitively, our work is based on the insight that in order to perform well on the target domain, a model’s output should be consistent with respect to small perturbations of inputs in the target domain. Specifically, we introduce a new loss term to enforce pixelwise consistency between the model's predictions on a target image and perturbed version of the same image. In comparison to popular adversarial adaptation methods, our approach is simpler, easier to implement, and more memory-efficient during training. Experiments and extensive ablation studies demonstrate that our simple approach achieves remarkably strong results on two challenging synthetic-to-real benchmarks, GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes.

• PyTorch (tested on version 1.7.1, but should work on any version)
• Hydra 1.1: pip install hydra-core --pre
• Other: pip install albumentations tqdm tensorboard
• WandB (optional): pip install wandb

We use Hydra for configuration and Weights and Biases for logging. With Hydra, you can specify a config file (found in configs/) with --config-name=myconfig.yaml. You can also override the config from the command line by specifying the overriding arguments (without --). For example, you can disable Weights and Biases with wandb=False and you can name the run with name=myname.

We have prepared example configs for GTA5 and SYNTHIA in configs/gta5.yaml and configs/synthia.yaml.

To run on GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes, you need to download the respective datasets. Once they are downloaded, you can either modify the config files directly, or organize/symlink the data in the datasets/ directory as follows:

datasets
├── cityscapes
│   ├── gtFine
│   │   ├── train
│   │   │   ├── aachen
│   │   │   └── ...
│   │   └── val
│   └── leftImg8bit
│       ├── train
│       └── val
├── GTA5
│   ├── images
│   ├── labels
│   └── list
├── SYNTHIA
│   └── RAND_CITYSCAPES
│       ├── Depth
│       │   └── Depth
│       ├── GT
│       │   ├── COLOR
│       │   └── LABELS
│       ├── RGB
│       └── synthia_mapped_to_cityscapes
├── city_list
├── gta5_list
└── synthia_list

• For GTA5-to-Cityscapes, we start with a model pretrained on the source (GTA5): Download
• For SYNTHIA-to-Cityscapes, we start with a model pretrained on ImageNet: Download

To run a baseline PixMatch model with standard data augmentations, we can use a command such as:

python main.py --config-name=synthia lam_aug=0.10 name=synthia_baseline

It is also easy to run a model with multiple augmentations:

python main.py --config-name=synthia lam_aug=0.00 lam_fourier=0.10 lam_cutmix=0.10 name=synthia_fourier_and_cutmix

python main.py --config-name=synthia lam_aug=0.10 name=gta5_baseline

To evaluate, simply set the train argument to False:

python main.py train=False

• GTA5-to-Cityscapes: Download
• SYNTHIA-to-Cityscapes: Download

To evaluate a pretrained/trained model, you can run:

# GTA (default)
CUDA_VISIBLE_DEVICES=3 python main.py train=False wandb=False model.checkpoint=$(pwd)/pretrained/GTA5-to-Cityscapes-checkpoint.pth

# SYNTHIA
CUDA_VISIBLE_DEVICES=3 python main.py --config-name synthia train=False wandb=False model.checkpoint=$(pwd)/pretrained/GTA5-to-Cityscapes-checkpoint.pth

@inproceedings{melaskyriazi2021pixmatch,
  author    = {Melas-Kyriazi, Luke and Manrai, Arjun},
  title     = {PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training},
  booktitle = cvpr,
  year      = {2021}
}