{"url":"/dataset/cuhk03","name":"CUHK03","full_name":"Chinese University of Hong Kong Re-identification","description_markdown":"The **CUHK03** consists of 14,097 images of 1,467 different identities, where 6 campus cameras were deployed for image collection and each identity is captured by 2 campus cameras. This dataset provides two types of annotations, one by manually labelled bounding boxes and the other by bounding boxes produced by an automatic detector. The dataset also provides 20 random train/test splits in which 100 identities are selected for testing and the rest for training\r\n\r\nSource: [Attention Driven Person Re-identification](https://arxiv.org/abs/1810.05866)\r\n\r\nImage Source: [Person Re-Identification Techniques for Intelligent Video Surveillance Systems\r\n](https://www.researchgate.net/publication/324031366_Person_Re-Identification_Techniques_for_Intelligent_Video_Surveillance_Systems)","description_withheld":null,"homepage":"http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html","introduced_date":"2014-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/deepreid-deep-filter-pairing-neural-network","title":"DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification","first_author":"Wei Li","url":null},"license":{"name":"Unknown","url":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":"Face Sketch Synthesis","url":"/task/face-sketch-synthesis","datasets_with_task":"/datasets/task/face-sketch-synthesis"},{"name":"Generalizable Person Re-identification","url":"/task/generalizable-person-re-identification","datasets_with_task":"/datasets/task/generalizable-person-re-identification"},{"name":"Defocus Blur Detection","url":"/task/defocus-blur-detection","datasets_with_task":"/datasets/task/defocus-blur-detection"},{"name":"Defocus Estimation","url":"/task/defocus-estimation","datasets_with_task":"/datasets/task/defocus-estimation"}],"languages":[],"variants":["CUHK03-NP (detected)","CUHK - Blur Detection Dataset","CUHK","CUHK03 labeled","CUHK03 detected","CUHK03 (detected)","CUHK03"],"data_loaders":[],"num_papers_in_archive":419,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/person-re-identification-on-cuhk03-labeled","task":"Person Re-Identification","dataset_variant":"CUHK03 labeled","rows":21,"metrics":["MAP","Rank-1"],"first_row_in_archive_order":{"model":"Weakly Pre-training (ResNet101+RK)","paper":null,"metrics":{"MAP":"89.29","Rank-1":"87.86"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/person-re-identification-on-cuhk03","task":"Person Re-Identification","dataset_variant":"CUHK03","rows":19,"metrics":["MAP","Rank-1","Rank-5","Rank-10"],"first_row_in_archive_order":{"model":"Proposed SGGNN","paper":"/paper/person-re-identification-with-deep-similarity","metrics":{"MAP":"94.3"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/person-re-identification-on-cuhk03-detected","task":"Person Re-Identification","dataset_variant":"CUHK03 detected","rows":19,"metrics":["MAP","Rank-1"],"first_row_in_archive_order":{"model":"Top-DB-Net + RK","paper":"/paper/top-db-net-top-dropblock-for-activation","metrics":{"MAP":"86.9","Rank-1":"85.7"},"code_links":[{"title":"RQuispeC/top-dropblock","url":"https://github.com/RQuispeC/top-dropblock"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/defocus-estimation-on-cuhk-blur-detection","task":"Defocus Estimation","dataset_variant":"CUHK - Blur Detection Dataset","rows":5,"metrics":["Blur Segmentation Accuracy","MAE","F-measure"],"first_row_in_archive_order":{"model":"DMENet (BDCS)","paper":"/paper/deep-defocus-map-estimation-using-domain","metrics":{"Blur Segmentation Accuracy":"87.35"},"code_links":[{"title":"codeslake/DMENet","url":"https://github.com/codeslake/DMENet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/generalizable-person-re-identification-on-22","task":"Generalizable Person Re-identification","dataset_variant":"CUHK03-NP (detected)","rows":5,"metrics":["ClonedPerson->mAP","ClonedPerson->Rank-1","RandPerson->mAP","RandPerson->Rank-1","MSMT17->mAP","MSMT17->Rank-1","MSMT17-All->mAP","MSMT17-All->Rank-1","Market-1501->mAP","Market-1501->Rank-1"],"first_row_in_archive_order":{"model":"TransMatcher","paper":"/paper/transformer-based-deep-image-matching-for","metrics":{"ClonedPerson->Rank-1":"25.4","ClonedPerson->mAP":"24.4","MSMT17->Rank-1":"23.7","MSMT17->mAP":"22.5","MSMT17-All->Rank-1":"31.9","MSMT17-All->mAP":"30.7","Market-1501->Rank-1":"22.2","Market-1501->mAP":"21.4","RandPerson->Rank-1":"17.1","RandPerson->mAP":"16.0"},"code_links":[{"title":"shengcailiao/QAConv","url":"https://github.com/shengcailiao/QAConv"},{"title":"ShengcaiLiao/TransMatcher","url":"https://github.com/ShengcaiLiao/TransMatcher"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/defocus-blur-detection-on-cuhk","task":"Defocus Blur Detection","dataset_variant":"CUHK","rows":2,"metrics":["MAE","Mean absolute error"],"first_row_in_archive_order":{"model":"D-DFFNet","paper":"/paper/depth-and-dof-cues-make-a-better-defocus-blur","metrics":{"MAE":"0.036"},"code_links":[{"title":"yuxinjin-whu/d-dffnet","url":"https://github.com/yuxinjin-whu/d-dffnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/face-sketch-synthesis-on-cuhk","task":"Face Sketch Synthesis","dataset_variant":"CUHK","rows":2,"metrics":["FSIM","SSIM"],"first_row_in_archive_order":{"model":"Residual net + Pseudo Sketch Feature Loss + LSGAN","paper":"/paper/semi-supervised-learning-for-face-sketch","metrics":{"FSIM":"74.23%","SSIM":"63.28%"},"code_links":[{"title":"chaofengc/Face-Sketch-Wild","url":"https://github.com/chaofengc/Face-Sketch-Wild"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/person-re-identification-on-cuhk03-detected-1","task":"Person Re-Identification","dataset_variant":"CUHK03 (detected)","rows":2,"metrics":["MAP","Rank-1"],"first_row_in_archive_order":{"model":"PAN+re-rank","paper":"/paper/pedestrian-alignment-network-for-large-scale","metrics":{"MAP":"43.8","Rank-1":"41.9"},"code_links":[{"title":"layumi/Pedestrian_Alignment","url":"https://github.com/layumi/Pedestrian_Alignment"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/unleashing-the-potential-of-pre-trained","title":"Unleashing the Potential of Pre-Trained Diffusion Models for Generalizable Person Re-Identification","date":"2025-02-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/remix-training-generalized-person-re","title":"ReMix: Training Generalized Person Re-identification on a Mixture of Data","date":"2024-10-29","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/rethinking-person-re-identification-from-a","title":"Rethinking Person Re-identification from a Projection-on-Prototypes Perspective","date":"2023-08-21","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/depth-and-dof-cues-make-a-better-defocus-blur","title":"Depth and DOF Cues Make A Better Defocus Blur Detector","date":"2023-06-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unihcp-a-unified-model-for-human-centric","title":"UniHCP: A Unified Model for Human-Centric Perceptions","date":"2023-03-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":7,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/distill-dbdgan-knowledge-distillation-and","title":"Distill-DBDGAN: Knowledge Distillation and Adversarial Learning Framework for Defocus Blur Detection","date":"2023-02-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dip-learning-discriminative-implicit-parts","title":"DiP: Learning Discriminative Implicit Parts for Person Re-Identification","date":"2022-12-24","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/large-scale-pre-training-for-person-re","title":"Large-Scale Pre-training for Person Re-identification with Noisy Labels","date":"2022-03-30","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":5,"samples_unverified":0,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fpb-feature-pyramid-branch-for-person-re","title":"FPB: Feature Pyramid Branch for Person Re-Identification","date":"2021-08-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/transformer-based-deep-image-matching-for","title":"TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification","date":"2021-05-30","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":10,"samples_unverified":11,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-sampling-based-deep-metric-learning-for","title":"Graph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification","date":"2021-04-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-miner-a-deep-and-multi-branch-network","title":"Deep Miner: A Deep and Multi-branch Network which Mines Rich and Diverse Features for Person Re-identification","date":"2021-02-18","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/lightweight-multi-branch-network-for-person","title":"Lightweight Multi-Branch Network for Person Re-Identification","date":"2021-01-26","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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":4,"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/multi-task-learning-with-coarse-priors-for","title":"Multi-task Learning with Coarse Priors for Robust Part-aware Person Re-identification","date":"2020-03-18","rows_on_this_dataset":2,"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":2,"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/enhancing-diversity-of-defocus-blur-detectors","title":"Enhancing Diversity of Defocus Blur Detectors via Cross-Ensemble Network","date":"2019-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/defusionnet-defocus-blur-detection-via","title":"DeFusionNET: Defocus Blur Detection via Recurrently Fusing and Refining Multi-Scale Deep Features","date":"2019-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-defocus-map-estimation-using-domain","title":"Deep Defocus Map Estimation Using Domain Adaptation","date":"2019-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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":2,"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/deep-constrained-dominant-sets-for-person-re","title":"Deep Constrained Dominant Sets for Person Re-identification","date":"2019-04-25","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/interpretable-and-generalizable-deep-image","title":"Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting","date":"2019-04-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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/adaptively-connected-neural-networks","title":"Adaptively Connected Neural Networks","date":"2019-04-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/auto-reid-searching-for-a-part-aware-convnet","title":"Auto-ReID: Searching for a Part-aware ConvNet for Person Re-Identification","date":"2019-03-23","rows_on_this_dataset":2,"code_links":3,"syntology":null},{"paper":"/paper/unsupervised-tracklet-person-re","title":"Unsupervised Tracklet Person Re-Identification","date":"2019-03-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/semi-supervised-learning-for-face-sketch","title":"Semi-Supervised Learning for Face Sketch Synthesis in the Wild","date":"2018-12-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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":2,"code_links":1,"syntology":null},{"paper":"/paper/fd-gan-pose-guided-feature-distilling-gan-for","title":"FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification","date":"2018-10-06","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/person-re-identification-with-deep-similarity","title":"Person Re-identification with Deep Similarity-Guided Graph Neural Network","date":"2018-07-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/defocus-blur-detection-via-multi-stream","title":"Defocus Blur Detection via Multi-Stream Bottom-Top-Bottom Fully Convolutional Network","date":"2018-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/resource-aware-person-re-identification","title":"Resource Aware Person Re-identification across Multiple Resolutions","date":"2018-05-22","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":1,"samples_unverified":17,"pointer_only_for_licence":0,"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":2,"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/harmonious-attention-network-for-person-re","title":"Harmonious Attention Network for Person Re-Identification","date":"2018-02-22","rows_on_this_dataset":3,"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/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},{"paper":"/paper/high-quality-facial-photo-sketch-synthesis","title":"High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks","date":"2017-10-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pedestrian-alignment-network-for-large-scale","title":"Pedestrian Alignment Network for Large-scale Person Re-identification","date":"2017-07-03","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/a-unified-approach-of-multi-scale-deep-and","title":"A Unified Approach of Multi-scale Deep and Hand-crafted Features for Defocus Estimation","date":"2017-04-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/in-defense-of-the-triplet-loss-for-person-re","title":"In Defense of the Triplet Loss for Person Re-Identification","date":"2017-03-22","rows_on_this_dataset":1,"code_links":31,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":46,"samples_ran":6,"samples_unverified":40,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/svdnet-for-pedestrian-retrieval","title":"SVDNet for Pedestrian Retrieval","date":"2017-03-16","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/re-ranking-person-re-identification-with-k","title":"Re-ranking Person Re-identification with k-reciprocal Encoding","date":"2017-01-29","rows_on_this_dataset":9,"code_links":0,"syntology":null},{"paper":"/paper/unlabeled-samples-generated-by-gan-improve","title":"Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro","date":"2017-01-26","rows_on_this_dataset":1,"code_links":8,"syntology":null},{"paper":"/paper/a-discriminatively-learned-cnn-embedding-for","title":"A Discriminatively Learned CNN Embedding for Person Re-identification","date":"2016-11-17","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/joint-detection-and-identification-feature","title":"Joint Detection and Identification Feature Learning for Person Search","date":"2016-04-07","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":14,"samples_harvested":230,"samples_ran":42,"samples_unverified":188,"pointer_only_for_licence":9,"papers_with_no_sample_that_ran":4,"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."}