{"url":"/dataset/in-shop","name":"In-Shop","full_name":"In-shop Clothes Retrieval Benchmark","description_markdown":"In-shop Clothes Retrieval Benchmark evaluates the performance of in-shop Clothes Retrieval. This is a large subset of DeepFashion, containing large pose and scale variations. It also has large diversities, large quantities, and rich annotations, including:\r\n\r\n- 7,982 number of clothing items;\r\n- 52,712 number of in-shop clothes images, and ~200,000 cross-pose/scale pairs;\r\n\r\nEach image is annotated by bounding box, clothing type and pose type.","description_withheld":null,"homepage":"https://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/InShopRetrieval.html","introduced_date":"2016-06-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/deepfashion-powering-robust-clothes","title":"DeepFashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations","first_author":"Ziwei Liu","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Retrieval","url":"/task/image-retrieval","datasets_with_task":"/datasets/task/image-retrieval"},{"name":"Metric Learning","url":"/task/metric-learning","datasets_with_task":"/datasets/task/metric-learning"}],"languages":[],"variants":["In-Shop"],"data_loaders":[],"num_papers_in_archive":154,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/metric-learning-on-in-shop-1","task":"Metric Learning","dataset_variant":"In-Shop","rows":15,"metrics":["R@1"],"first_row_in_archive_order":{"model":"Unicom+ViT-L@336px","paper":"/paper/unicom-universal-and-compact-representation","metrics":{"R@1":"96.7"},"code_links":[{"title":"OML-Team/open-metric-learning","url":"https://github.com/OML-Team/open-metric-learning"},{"title":"deepglint/unicom","url":"https://github.com/deepglint/unicom"},{"title":"RocketFlash/easy_metric_learning","url":"https://github.com/RocketFlash/easy_metric_learning/tree/master/tools"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-retrieval-on-in-shop","task":"Image Retrieval","dataset_variant":"In-Shop","rows":7,"metrics":["R@1"],"first_row_in_archive_order":{"model":"CGD (SG/GS)","paper":"/paper/combination-of-multiple-global-descriptors","metrics":{"R@1":"91.9"},"code_links":[{"title":"naver/cgd","url":"https://github.com/naver/cgd"},{"title":"leftthomas/CGD","url":"https://github.com/leftthomas/CGD"},{"title":"clovaai/embedding-expansion","url":"https://github.com/clovaai/embedding-expansion"},{"title":"flyingsheepbin/pet-biometrics","url":"https://github.com/flyingsheepbin/pet-biometrics"},{"title":"puneesh00/loop","url":"https://github.com/puneesh00/loop"},{"title":"PuchatekwSzortach/combination_of_multiple_global_descriptors_for_image_retrieval","url":"https://github.com/PuchatekwSzortach/combination_of_multiple_global_descriptors_for_image_retrieval"},{"title":"Samjoel3101/Self-Supervised-Learning-fastai2","url":"https://github.com/Samjoel3101/Self-Supervised-Learning-fastai2"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/learning-semantic-proxies-from-visual-prompts","title":"Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning","date":"2024-02-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/center-contrastive-loss-for-metric-learning","title":"Center Contrastive Loss for Metric Learning","date":"2023-08-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/stir-siamese-transformer-for-image-retrieval","title":"STIR: Siamese Transformer for Image Retrieval Postprocessing","date":"2023-04-26","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/unicom-universal-and-compact-representation","title":"Unicom: Universal and Compact Representation Learning for Image Retrieval","date":"2023-04-12","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":3,"samples_unverified":3,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fashion-image-retrieval-with-multi-granular-1","title":"Fashion Image Retrieval with Multi-Granular Alignment","date":"2023-02-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/hyperbolic-vision-transformers-combining","title":"Hyperbolic Vision Transformers: Combining Improvements in Metric Learning","date":"2022-03-21","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":4,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dissecting-the-impact-of-different-loss","title":"Dissecting the impact of different loss functions with gradient surgery","date":"2022-01-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/it-takes-two-to-tango-mixup-for-deep-metric","title":"It Takes Two to Tango: Mixup for Deep Metric Learning","date":"2021-06-09","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/hard-negative-examples-are-hard-but-useful","title":"Hard negative examples are hard, but useful","date":"2020-07-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/calibrated-neighborhood-aware-confidence","title":"Calibrated neighborhood aware confidence measure for deep metric learning","date":"2020-06-08","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/proxynca-revisiting-and-revitalizing-proxy","title":"ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis","date":"2020-04-02","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/metric-learning-cross-entropy-vs-pairwise","title":"A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses","date":"2020-03-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cross-batch-memory-for-embedding-learning","title":"Cross-Batch Memory for Embedding Learning","date":"2019-12-14","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/multi-similarity-loss-with-general-pair","title":"Multi-Similarity Loss with General Pair Weighting for Deep Metric Learning","date":"2019-04-14","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/improved-embeddings-with-easy-positive","title":"Improved Embeddings with Easy Positive Triplet Mining","date":"2019-04-08","rows_on_this_dataset":2,"code_links":3,"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/combination-of-multiple-global-descriptors","title":"Combination of Multiple Global Descriptors for Image Retrieval","date":"2019-03-26","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/making-classification-competitive-for-deep","title":"Classification is a Strong Baseline for Deep Metric Learning","date":"2018-11-30","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/attention-based-ensemble-for-deep-metric","title":"Attention-based Ensemble for Deep Metric Learning","date":"2018-04-02","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":10,"samples_harvested":37,"samples_ran":14,"samples_unverified":23,"pointer_only_for_licence":7,"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."}