cityscape-adverse

Cityscape‑Adverse A benchmark for evaluating semantic segmentation robustness under realistic adverse conditions. Overview Cityscape‑Adverse extends the original Cityscapes dataset by applying eight realistic environmental modifications—rainy, foggy, spring, autumn, snowy, sunny, night, and dawn—using diffusion‑based image editing. All transformations preserve the original 2048×1024 semantic labels, enabling direct evaluation of model robustness in out‑of‑distribution… See the full description on the dataset page:

Source: Hugging Face Hub (naufalso/cityscape-adverse). Metadata imported from the dataset’s Hub tags.