{"url":"/dataset/regdb-c","name":"RegDB-C","full_name":"RegDB-C","description_markdown":"RegDB-C is an evaluation set that consists of algorithmically generated corruptions applied to the RegDB test-set (color images). These corruptions consist of Noise: Gaussian, shot, impulse, and speckle; Blur: defocus, frosted glass, motion, zoom, and Gaussian; Weather: snow, frost, fog, brightness, spatter, and rain; Digital: contrast, elastic, pixel, JPEG compression, and saturate. Each corruption has five severity levels, resulting in 100 distinct corruptions.","description_withheld":null,"homepage":"https://github.com/MinghuiChen43/CIL-ReID","introduced_date":"2021-11-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/benchmarks-for-corruption-invariant-person-re","title":"Benchmarks for Corruption Invariant Person Re-identification","first_author":"Minghui Chen","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Person Re-Identification","url":"/task/person-re-identification","datasets_with_task":"/datasets/task/person-re-identification"},{"name":"Generalizable Person Re-identification","url":"/task/generalizable-person-re-identification","datasets_with_task":"/datasets/task/generalizable-person-re-identification"},{"name":"Cross-Modal  Person Re-Identification","url":"/task/cross-view-person-re-identification","datasets_with_task":"/datasets/task/cross-view-person-re-identification"},{"name":"Cross-Modal Person Re-Identification","url":null,"datasets_with_task":"/datasets/task/cross-modal-person-re-identification"}],"languages":[],"variants":["RegDB-C"],"data_loaders":[{"repo":"https://github.com/MinghuiChen43/CIL-ReID","url":"https://github.com/MinghuiChen43/CIL-ReID","frameworks":["pytorch"]}],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}