RoyalVane/CLAN

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1289 stars · 44 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Official PyTorch implementation of CLAN, a CVPR 2019 (oral) / TPAMI 2022 method for unsupervised domain adaptation in semantic segmentation using category-level adversaries with semantics-consistent alignment. It includes training, evaluation, and IoU computation scripts for adapting segmentation models from synthetic datasets (GTA5, SYNTHIA) to the real-world Cityscapes dataset, plus pretrained model checkpoints.

Use cases

• adapt a semantic segmentation model from synthetic images to real street scenes
• reproduce CLAN CVPR 2019 results on GTA5-to-Cityscapes
• benchmark my segmentation model against a domain adaptation baseline
• run unsupervised domain adaptation without source labels at test time
• compute mIoU for segmentation predictions on Cityscapes validation
• evaluate many training checkpoints in bulk to find the best epoch

When to choose

• you need the exact reference implementation for a domain adaptation paper or comparison baseline
• you are doing research on adversarial alignment for semantic segmentation and want a starting codebase
• you need pretrained GTA5/SYNTHIA-to-Cityscapes segmentation models to evaluate or build on

When to avoid

• you need a maintained, general-purpose domain adaptation library for production workloads
• you require a modern PyTorch version, extensive docs, or APIs beyond the paper's experiments
• your task is not semantic segmentation or you lack a GPU with at least 11GB memory

Facets

library · maturity stable

machine-learning deep-learning computer-vision image-processing computer-vision machine-learning deep-learning image-processing python semantic-segmentation domain-adaptation adversarial-learning transfer-learning pytorch deep-learning-research gta5 synthia cityscapes scene-understanding pretrained-models research-code gpu linux

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