{"url":"/dataset/market-1501-c","name":"Market-1501-C","full_name":"Market-1501-C","description_markdown":"**Market-1501-C** is an evaluation set that consists of algorithmically generated corruptions applied to the Market-1501 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":["Market-1501-C"],"data_loaders":[{"repo":"https://github.com/MinghuiChen43/CIL-ReID","url":"https://github.com/MinghuiChen43/CIL-ReID","frameworks":["pytorch"]}],"num_papers_in_archive":22,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/person-re-identification-on-market-1501-c","task":"Person Re-Identification","dataset_variant":"Market-1501-C","rows":22,"metrics":[" Rank-1"," mAP"," mINP","Rank-1","mAP","mINP"],"first_row_in_archive_order":{"model":"TransReID","paper":"/paper/transreid-transformer-based-object-re","metrics":{" Rank-1":"53.19"," mAP":"27.38"," mINP":"1.98"},"code_links":[{"title":"heshuting555/TransReID","url":"https://github.com/heshuting555/TransReID"},{"title":"damo-cv/transreid","url":"https://github.com/damo-cv/transreid"},{"title":"ChristmasStory/TransReID-main","url":"https://github.com/ChristmasStory/TransReID-main"},{"title":"darrishabh/coviprox","url":"https://github.com/darrishabh/coviprox"}]},"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/transreid-transformer-based-object-re","title":"TransReID: Transformer-based Object Re-Identification","date":"2021-02-08","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/a-person-re-identification-data-augmentation","title":"A Person Re-identification Data Augmentation Method with Adversarial Defense Effect","date":"2021-01-21","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/unsupervised-pre-training-for-person-re","title":"Unsupervised Pre-training for Person Re-identification","date":"2020-12-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/top-db-net-top-dropblock-for-activation","title":"Top-DB-Net: Top DropBlock for Activation Enhancement in Person Re-Identification","date":"2020-10-12","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":0,"samples_unverified":15,"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/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/abd-net-attentive-but-diverse-person-re","title":"ABD-Net: Attentive but Diverse Person Re-Identification","date":"2019-08-03","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":1,"samples_unverified":8,"pointer_only_for_licence":0,"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/joint-discriminative-and-generative-learning","title":"Joint Discriminative and Generative Learning for Person Re-identification","date":"2019-04-15","rows_on_this_dataset":1,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":19,"samples_ran":3,"samples_unverified":16,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/perceive-where-to-focus-learning-visibility","title":"Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identification","date":"2019-04-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":0,"samples_unverified":8,"pointer_only_for_licence":0,"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."}},{"paper":"/paper/batch-feature-erasing-for-person-re","title":"Batch DropBlock Network for Person Re-identification and Beyond","date":"2018-11-17","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":3,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/devil-in-the-details-towards-accurate-single","title":"Devil in the Details: Towards Accurate Single and Multiple Human Parsing","date":"2018-09-17","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-discriminative-features-with","title":"Learning Discriminative Features with Multiple Granularities for Person Re-Identification","date":"2018-04-04","rows_on_this_dataset":1,"code_links":16,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":30,"samples_ran":2,"samples_unverified":28,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/beyond-part-models-person-retrieval-with","title":"Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)","date":"2017-11-26","rows_on_this_dataset":1,"code_links":29,"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":12,"samples_harvested":153,"samples_ran":24,"samples_unverified":129,"pointer_only_for_licence":8,"papers_with_no_sample_that_ran":3,"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."}