{"url":"/dataset/cars196","name":"CARS196","full_name":null,"description_markdown":"CARS196  is composed of 16,185 car images of 196 classes.","description_withheld":null,"homepage":"https://ai.stanford.edu/~jkrause/cars/car_dataset.html","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Image Retrieval","url":"/task/image-retrieval","datasets_with_task":"/datasets/task/image-retrieval"},{"name":"Image Clustering","url":"/task/image-clustering","datasets_with_task":"/datasets/task/image-clustering"},{"name":"Metric Learning","url":"/task/metric-learning","datasets_with_task":"/datasets/task/metric-learning"}],"languages":[],"variants":["CARS196"],"data_loaders":[],"num_papers_in_archive":43,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/metric-learning-on-cars196","task":"Metric Learning","dataset_variant":"CARS196","rows":36,"metrics":["R@1"],"first_row_in_archive_order":{"model":"Unicom+ViT-L@336px","paper":"/paper/unicom-universal-and-compact-representation","metrics":{"R@1":"98.2"},"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-cars196","task":"Image Retrieval","dataset_variant":"CARS196","rows":8,"metrics":["R@1","R@8"],"first_row_in_archive_order":{"model":"CGD (MG/SG)","paper":"/paper/combination-of-multiple-global-descriptors","metrics":{"R@1":"94.8"},"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"},{"leaderboard":"/sota/image-classification-on-cars196","task":"Image Classification","dataset_variant":"CARS196","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"µ2Net+ (ViT-L/16)","paper":"/paper/a-continual-development-methodology-for-large","metrics":{"Accuracy":"87.18"},"code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/muNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-clustering-on-cars196","task":"Image Clustering","dataset_variant":"CARS196","rows":1,"metrics":["NMI"],"first_row_in_archive_order":{"model":"MES-Loss","paper":"/paper/mes-loss-mutually-equidistant-separation","metrics":{"NMI":"74.82"},"code_links":[]},"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/mes-loss-mutually-equidistant-separation","title":"MES-Loss: Mutually equidistant separation metric learning loss function","date":"2023-08-01","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"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/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/a-continual-development-methodology-for-large","title":"A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems","date":"2022-09-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/das-densely-anchored-sampling-for-deep-metric","title":"DAS: Densely-Anchored Sampling for Deep Metric Learning","date":"2022-07-30","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":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/attributable-visual-similarity-learning","title":"Attributable Visual Similarity Learning","date":"2022-03-28","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hyperbolic-vision-transformers-combining","title":"Hyperbolic Vision Transformers: Combining Improvements in Metric Learning","date":"2022-03-21","rows_on_this_dataset":3,"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/non-isotropy-regularization-for-proxy-based","title":"Non-isotropy Regularization for Proxy-based Deep Metric Learning","date":"2022-03-16","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/integrating-language-guidance-into-vision","title":"Integrating Language Guidance into Vision-based Deep Metric Learning","date":"2022-03-16","rows_on_this_dataset":1,"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/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/recall-k-surrogate-loss-with-large-batches","title":"Recall@k Surrogate Loss with Large Batches and Similarity Mixup","date":"2021-08-25","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":0,"samples_unverified":11,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/towards-interpretable-deep-metric-learning","title":"Towards Interpretable Deep Metric Learning with Structural Matching","date":"2021-08-12","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":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/learning-intra-batch-connections-for-deep","title":"Learning Intra-Batch Connections for Deep Metric Learning","date":"2021-02-15","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/s2sd-simultaneous-similarity-based-self","title":"S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning","date":"2020-09-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"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/diva-diverse-visual-feature-aggregation","title":"DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning","date":"2020-04-28","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":2,"samples_unverified":3,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/proxy-anchor-loss-for-deep-metric-learning","title":"Proxy Anchor Loss for Deep Metric Learning","date":"2020-03-31","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/pads-policy-adapted-sampling-for-visual","title":"PADS: Policy-Adapted Sampling for Visual Similarity Learning","date":"2020-03-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":0,"samples_unverified":10,"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/circle-loss-a-unified-perspective-of-pair","title":"Circle Loss: A Unified Perspective of Pair Similarity Optimization","date":"2020-02-25","rows_on_this_dataset":1,"code_links":16,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/the-group-loss-for-deep-metric-learning","title":"The Group Loss for Deep Metric Learning","date":"2019-12-01","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":3,"samples_unverified":15,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mic-mining-interclass-characteristics-for","title":"MIC: Mining Interclass Characteristics for Improved Metric Learning","date":"2019-09-25","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/softtriple-loss-deep-metric-learning-without","title":"SoftTriple Loss: Deep Metric Learning Without Triplet Sampling","date":"2019-09-11","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/metric-learning-with-horde-high-order","title":"Metric Learning With HORDE: High-Order Regularizer for Deep Embeddings","date":"2019-08-07","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/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":3,"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/hardness-aware-deep-metric-learning","title":"Hardness-Aware Deep Metric Learning","date":"2019-03-13","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"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/deep-metric-learning-with-hierarchical","title":"Deep Metric Learning with Hierarchical Triplet Loss","date":"2018-10-16","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"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},{"paper":"/paper/sampling-matters-in-deep-embedding-learning","title":"Sampling Matters in Deep Embedding Learning","date":"2017-06-23","rows_on_this_dataset":2,"code_links":6,"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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":25,"samples_harvested":117,"samples_ran":27,"samples_unverified":90,"pointer_only_for_licence":15,"papers_with_no_sample_that_ran":13,"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."}