{"url":"/dataset/sysu-mm01-c","name":"SYSU-MM01-C","full_name":"SYSU-MM01-C","description_markdown":"**SYSU-MM01-C** is an evaluation set that consists of algorithmically generated corruptions applied to the SYSU-MM01 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"},{"name":"Cross-Modal Person Re-Identification","url":null,"datasets_with_task":"/datasets/task/cross-modal-person-re-identification"}],"languages":[],"variants":["SYSU-MM01-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":[{"leaderboard":"/sota/person-re-identification-on-sysu-mm01-c","task":"Person Re-Identification","dataset_variant":"SYSU-MM01-C","rows":2,"metrics":[" Rank-1 (All Search)"," mAP (All Search)"," mINP (All Search)"," Rank-1 (Indoor Search)"," mAP (Indoor Search)"," mINP (Indoor Search)","Rank-1 (All Search)","Rank-1 (Indoor Search)","mAP (All Search)","mAP (Indoor Search)","mINP (All Search)","mINP (Indoor Search)"],"first_row_in_archive_order":{"model":"AGW (ResNet-50)","paper":"/paper/deep-learning-for-person-re-identification-a","metrics":{" Rank-1 (All Search)":"34.42"," Rank-1 (Indoor Search)":"33.80"," mAP (All Search)":"29.99"," mAP (Indoor Search)":"40.98"," mINP (All Search)":"14.73"," mINP (Indoor Search)":"35.39"},"code_links":[{"title":"layumi/Person_reID_baseline_pytorch","url":"https://github.com/layumi/Person_reID_baseline_pytorch"},{"title":"mangye16/ReID-Survey","url":"https://github.com/mangye16/ReID-Survey"},{"title":"zion-king/Deep-Learning-for-Person-Re-identification","url":"https://github.com/zion-king/Deep-Learning-for-Person-Re-identification"},{"title":"pwc-1/Paper-9","url":"https://github.com/pwc-1/Paper-9/tree/main/3/AGW"},{"title":"MindCode-4/code-14","url":"https://github.com/MindCode-4/code-14/tree/main/AGW"},{"title":"2023-MindSpore-1/ms-code-64","url":"https://github.com/2023-MindSpore-1/ms-code-64"},{"title":"Haoyu1004/AGW_mindspore","url":"https://github.com/Haoyu1004/AGW_mindspore"}]},"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/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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":24,"samples_ran":10,"samples_unverified":14,"pointer_only_for_licence":3,"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."}