{"url":"/dataset/cuhk03-c","name":"CUHK03-C","full_name":"CUHK03-C","description_markdown":"**CUHK03-C** is an evaluation set that consists of algorithmically generated corruptions applied to the CUHK03 test-set.  These corruptions consist of Noise: Gaussian, shot,\r\nimpulse, 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"}],"languages":[],"variants":["CUHK03-C"],"data_loaders":[{"repo":"https://github.com/MinghuiChen43/CIL-ReID","url":"https://github.com/MinghuiChen43/CIL-ReID","frameworks":["pytorch"]}],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/person-re-identification-on-cuhk03-c","task":"Person Re-Identification","dataset_variant":"CUHK03-C","rows":8,"metrics":[" Rank-1"," mAP"," mINP","Rank-1","mAP","mINP"],"first_row_in_archive_order":{"model":"CaceNet","paper":"/paper/devil-s-in-the-detail-graph-based-key-point","metrics":{" Rank-1":"17.04"," mAP":"10.62"," mINP":"2.09"},"code_links":[{"title":"TencentYoutuResearch/PersonReID-CACENET","url":"https://github.com/TencentYoutuResearch/PersonReID-CACENET"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/benchmarks-for-corruption-invariant-person-re","title":"Benchmarks for Corruption Invariant Person Re-identification","date":"2021-11-01","rows_on_this_dataset":1,"code_links":1,"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/devil-s-in-the-detail-graph-based-key-point","title":"Devil's in the Details: Aligning Visual Clues for Conditional Embedding in Person Re-Identification","date":"2020-09-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-diverse-features-with-part-level","title":"Learning Diverse Features with Part-Level Resolution for Person Re-Identification","date":"2020-01-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-learning-for-person-re-identification-a","title":"Deep Learning for Person Re-identification: A Survey and Outlook","date":"2020-01-13","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":22,"samples_ran":8,"samples_unverified":14,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/relation-network-for-person-re-identification","title":"Relation Network for Person Re-identification","date":"2019-11-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/mixed-high-order-attention-network-for-person","title":"Mixed High-Order Attention Network for Person Re-Identification","date":"2019-08-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-coarse-to-fine-pyramidal-model-for-person","title":"Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training","date":"2018-10-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/alignedreid-surpassing-human-level","title":"AlignedReID: Surpassing Human-Level Performance in Person Re-Identification","date":"2017-11-22","rows_on_this_dataset":1,"code_links":15,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":25,"samples_ran":10,"samples_unverified":15,"pointer_only_for_licence":4,"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."}