DAFormer trains semantic segmentation models on synthetic data and adapts them to real images without requiring manual annotations. It improves upon prior domain adaptive segmentation work by exploring better network architectures and training strategies. The project provides an official PyTorch implementation including training code, pretrained models, and evaluations on standard domain adaptation benchmarks.