{"url":"/dataset/msmt17-c","name":"MSMT17-C","full_name":"MSMT17-C","description_markdown":"**MSMT17-C** is an evaluation set that consists of algorithmically generated corruptions applied to the MSMT17 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":["MSMT17-C"],"data_loaders":[{"repo":"https://github.com/MinghuiChen43/CIL-ReID","url":"https://github.com/MinghuiChen43/CIL-ReID","frameworks":["pytorch"]}],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/person-re-identification-on-msmt17-c","task":"Person Re-Identification","dataset_variant":"MSMT17-C","rows":5,"metrics":[" Rank-1"," mAP"," mINP","Rank-1","mAP","mINP"],"first_row_in_archive_order":{"model":"SBS (ResNet-50)","paper":"/paper/fastreid-a-pytorch-toolbox-for-real-world","metrics":{" Rank-1":"28.77"," mAP":"7.89"," mINP":"0.05"},"code_links":[{"title":"JDAI-CV/fast-reid","url":"https://github.com/JDAI-CV/fast-reid"},{"title":"linghu8812/tensorrt_tracker","url":"https://github.com/linghu8812/tensorrt_tracker"},{"title":"Vill-Lab/2023-TIFS-DTIBA","url":"https://github.com/Vill-Lab/2023-TIFS-DTIBA"}]},"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/fastreid-a-pytorch-toolbox-for-real-world","title":"FastReID: A Pytorch Toolbox for General Instance Re-identification","date":"2020-06-04","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"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/omni-scale-feature-learning-for-person-re","title":"Omni-Scale Feature Learning for Person Re-Identification","date":"2019-05-02","rows_on_this_dataset":1,"code_links":17,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":26,"samples_ran":2,"samples_unverified":24,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/bags-of-tricks-and-a-strong-baseline-for-deep","title":"Bag of Tricks and A Strong Baseline for Deep Person Re-identification","date":"2019-03-17","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":2,"samples_unverified":6,"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":4,"samples_harvested":58,"samples_ran":14,"samples_unverified":44,"pointer_only_for_licence":6,"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."}