{"url":"/dataset/acdc-adverse-conditions-dataset-with","name":"ACDC (Adverse Conditions Dataset with Correspondences)","full_name":"Adverse Conditions Dataset with Correspondences","description_markdown":"We introduce ACDC, the Adverse Conditions Dataset with Correspondences for training and testing semantic segmentation methods on adverse visual conditions. It comprises a large set of 4006 images which are evenly distributed between fog, nighttime, rain, and snow. Each adverse-condition image comes with a high-quality fine pixel-level semantic annotation, a corresponding image of the same scene taken under normal conditions and a binary mask that distinguishes between intra-image regions of clear and uncertain semantic content.\r\n\r\nACDC supports two tasks:\r\n1. standard semantic segmentation\r\n2. uncertainty-aware semantic segmentation","description_withheld":null,"homepage":"","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"},{"name":"Unsupervised Semantic Segmentation","url":"/task/unsupervised-semantic-segmentation","datasets_with_task":"/datasets/task/unsupervised-semantic-segmentation"},{"name":"Domain Generalization","url":"/task/domain-generalization","datasets_with_task":"/datasets/task/domain-generalization"},{"name":"Source-Free Domain Adaptation","url":"/task/source-free-domain-adaptation","datasets_with_task":"/datasets/task/source-free-domain-adaptation"},{"name":"Foggy Scene Segmentation","url":"/task/foggy-scene-segmentation","datasets_with_task":"/datasets/task/foggy-scene-segmentation"}],"languages":[],"variants":["ACDC (Adverse Conditions Dataset with Correspondences)","Cityscapes to ACDC"],"data_loaders":[],"num_papers_in_archive":31,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/domain-adaptation-on-cityscapes-to-acdc","task":"Domain Adaptation","dataset_variant":"Cityscapes to ACDC","rows":16,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"SoRA","paper":"/paper/sora-singular-value-decomposed-low-rank","metrics":{"mIoU":"78.8"},"code_links":[{"title":"ysj9909/DG-SoRA","url":"https://github.com/ysj9909/DG-SoRA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/source-free-domain-adaptation-on-cityscapes","task":"Source-Free Domain Adaptation","dataset_variant":"Cityscapes to ACDC","rows":2,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"CMA","paper":"/paper/contrastive-model-adaptation-for-cross","metrics":{"mIoU":"69.1"},"code_links":[{"title":"brdav/cma","url":"https://github.com/brdav/cma"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/domain-generalization-on-cityscapes-to-acdc","task":"Domain Generalization","dataset_variant":"Cityscapes to ACDC","rows":1,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"ADSI","paper":"/paper/domain-generalization-through-attenuation-of","metrics":{"mIoU":"70.21"},"code_links":[{"title":"ReijiSoftmaxSaito/ADSI","url":"https://github.com/ReijiSoftmaxSaito/ADSI"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/foggy-scene-segmentation-on-acdc-adverse","task":"Foggy Scene Segmentation","dataset_variant":"ACDC (Adverse Conditions Dataset with Correspondences)","rows":1,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"BWG","paper":"/paper/learning-generalized-segmentation-for-foggy","metrics":{"mIoU":"76.7"},"code_links":[{"title":"BiQiWHU/BWG","url":"https://github.com/BiQiWHU/BWG"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unsupervised-semantic-segmentation-on-acdc","task":"Unsupervised Semantic Segmentation","dataset_variant":"ACDC (Adverse Conditions Dataset with Correspondences)","rows":1,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"Segmenter ViT-S/16","paper":"/paper/drive-segment-unsupervised-semantic","metrics":{"mIoU":"16.7"},"code_links":[{"title":"vobecant/DriveAndSegment","url":"https://github.com/vobecant/DriveAndSegment"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/domain-generalization-through-attenuation-of","title":"Domain Generalization through Attenuation of Domain-Specific Information","date":"2025-04-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sora-singular-value-decomposed-low-rank","title":"SoRA: Singular Value Decomposed Low-Rank Adaptation for Domain Generalizable Representation Learning","date":"2024-12-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/coda-instructive-chain-of-domain-adaptation","title":"CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning","date":"2024-03-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-generalized-segmentation-for-foggy","title":"Learning Generalized Segmentation for Foggy-scenes by Bi-directional Wavelet Guidance","date":"2024-02-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/stronger-fewer-superior-harnessing-vision","title":"Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation","date":"2023-12-07","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/learning-content-enhanced-mask-transformer","title":"Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation","date":"2023-07-01","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/hyperbolic-active-learning-for-semantic","title":"Hyperbolic Active Learning for Semantic Segmentation under Domain Shift","date":"2023-06-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":3,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/contrastive-model-adaptation-for-cross","title":"Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation","date":"2023-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":5,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mic-masked-image-consistency-for-context","title":"MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation","date":"2022-12-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/vblc-visibility-boosting-and-logit-constraint","title":"VBLC: Visibility Boosting and Logit-Constraint Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions","date":"2022-11-22","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/refign-align-and-refine-for-adaptation-of","title":"Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions","date":"2022-07-14","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hrda-context-aware-high-resolution-domain","title":"HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation","date":"2022-04-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/drive-segment-unsupervised-semantic","title":"Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation","date":"2022-03-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/daformer-improving-network-architectures-and","title":"DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation","date":"2021-11-29","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":6,"samples_unverified":0,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dannet-a-one-stage-domain-adaptation-network","title":"DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation","date":"2021-04-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dacs-domain-adaptation-via-cross-domain-mixed","title":"DACS: Domain Adaptation via Cross-domain Mixed Sampling","date":"2020-07-17","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/map-guided-curriculum-domain-adaptation-and","title":"Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation","date":"2020-05-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fda-fourier-domain-adaptation-for-semantic","title":"FDA: Fourier Domain Adaptation for Semantic Segmentation","date":"2020-04-11","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":8,"samples_harvested":40,"samples_ran":23,"samples_unverified":17,"pointer_only_for_licence":14,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}