{"url":"/dataset/dwd","name":"DWD","full_name":"Diverse Weather Dataset","description_markdown":"Urban-scene detection dataset that consists of five different weather conditions: daytime-sunny, night-sunny, dusk-rainy, daytime-foggy, and night-rainy. The images are collected from diverse weather datasets: Cityscapes, BDD-100k, FoggyCityscapes, and Adverse-Weather. The dataset is used to evaulate the model performance on the single-domain generalized object detection (Single-DGOD). \r\n\r\n- Source paper: [Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation](https://openaccess.thecvf.com/content/CVPR2022/html/Wu_Single-Domain_Generalized_Object_Detection_in_Urban_Scene_via_Cyclic-Disentangled_Self-Distillation_CVPR_2022_paper.html)\r\n- Source page: [AmingWu/Single-DGOD](https://github.com/AmingWu/Single-DGOD)\r\n- Source dataset: [DWD (Diverse Weather Dataset)](https://drive.google.com/drive/folders/1IIUnUrJrvFgPzU8D6KtV0CXa8k1eBV9B)","description_withheld":null,"homepage":"https://github.com/AmingWu/Single-DGOD","introduced_date":"2022-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/single-domain-generalized-object-detection-in","title":"Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation","first_author":"Aming Wu","url":null},"license":{"name":"https://github.com/AmingWu/Single-DGOD?tab=MIT-1-ov-file#readme","url":null},"modalities":[],"tasks":[{"name":"Robust Object Detection","url":"/task/robust-object-detection","datasets_with_task":"/datasets/task/robust-object-detection"}],"languages":[],"variants":["DWD"],"data_loaders":[],"num_papers_in_archive":12,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/robust-object-detection-on-dwd","task":"Robust Object Detection","dataset_variant":"DWD","rows":12,"metrics":["mPC [AP50]"],"first_row_in_archive_order":{"model":"GDD (SD-1.5 Backbone)","paper":"/paper/generalized-diffusion-detector-mining-robust","metrics":{"mPC [AP50]":"40.5"},"code_links":[{"title":"heboyong/Generalized-Diffusion-Detector","url":"https://github.com/heboyong/Generalized-Diffusion-Detector"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/generalized-diffusion-detector-mining-robust","title":"Generalized Diffusion Detector: Mining Robust Features from Diffusion Models for Domain-Generalized Detection","date":"2025-03-03","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/physaug-a-physical-guided-and-frequency-based","title":"PhysAug: A Physical-guided and Frequency-based Data Augmentation for Single-Domain Generalized Object Detection","date":"2024-12-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/object-aware-domain-generalization-for-object","title":"Object-Aware Domain Generalization for Object Detection","date":"2023-12-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/vltseg-simple-transfer-of-clip-based-vision","title":"Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer Learning","date":"2023-12-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/srcd-semantic-reasoning-with-compound-domains","title":"SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection","date":"2023-07-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/style-hallucinated-dual-consistency-learning","title":"Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation","date":"2022-04-06","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/single-domain-generalized-object-detection-in","title":"Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation","date":"2022-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/robustnet-improving-domain-generalization-in","title":"RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening","date":"2021-03-29","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/switchable-whitening-for-deep-representation","title":"Switchable Whitening for Deep Representation Learning","date":"2019-04-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":2,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/iterative-normalization-beyond","title":"Iterative Normalization: Beyond Standardization towards Efficient Whitening","date":"2019-04-06","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":3,"samples_unverified":11,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/two-at-once-enhancing-learning-and","title":"Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net","date":"2018-07-25","rows_on_this_dataset":1,"code_links":25,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":16,"samples_ran":4,"samples_unverified":12,"pointer_only_for_licence":0,"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":5,"samples_harvested":44,"samples_ran":13,"samples_unverified":31,"pointer_only_for_licence":2,"papers_with_no_sample_that_ran":0,"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."}