{"url":"/sota/image-retrieval-on-par6k","task":{"name":"Image Retrieval","url":"/task/image-retrieval","note":null},"dataset":{"name":"Par6k","url":null},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**Image Retrieval** is a fundamental and long-standing computer vision task that involves finding images similar to a given query from a large database. It is often considered a form of fine-grained, instance-level classification. The task is integral to image recognition alongside [classification](/task/image-classification) and [cross-modal retrieval](/task/cross-modal-retrieva). By leveraging visual similarity and other criteria, image retrieval enables users to efficiently discover relevant images, making it a crucial tool in applications such as search and recommendation.\r\n\r\n<span class=\"description-source\">[Extending CLIP for Category-to-image Retrieval in E-commerce](https://arxiv.org/abs/2112.11294)</span>\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [DELF](https://github.com/tensorflow/models/tree/master/research/delf) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["mAP"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"mAP":"higher"}},"counts":{"rows":7,"rows_with_code":7,"rows_with_paper_page":7,"rows_dated":7,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"Offline Diffusion","metrics":{"mAP":"97.8%"},"uses_additional_data":false,"paper_date":"2018-11-27","paper":"/paper/efficient-image-retrieval-via-decoupling","paper_url":"http://arxiv.org/abs/1811.10907v2","paper_title":"Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing","code":"https://github.com/fyang93/diffusion","n_code_links":2,"syntology":null},{"rank_in_archive_order":2,"model":"DELF+FT+ATT+DIR+QE","metrics":{"mAP":"95.7%"},"uses_additional_data":false,"paper_date":"2016-12-19","paper":"/paper/large-scale-image-retrieval-with-attentive","paper_url":"http://arxiv.org/abs/1612.06321v4","paper_title":"Large-Scale Image Retrieval with Attentive Deep Local Features","code":"https://github.com/tensorflow/models/tree/master/research/delf","n_code_links":13,"syntology":{"n_ran":3,"n_unverified":9,"n_samples":12,"n_pointer_only_licence":0}},{"rank_in_archive_order":3,"model":"DIR+QE*","metrics":{"mAP":"93.8%"},"uses_additional_data":false,"paper_date":"2016-04-05","paper":"/paper/deep-image-retrieval-learning-global","paper_url":"http://arxiv.org/abs/1604.01325v2","paper_title":"Deep Image Retrieval: Learning global representations for image search","code":"https://github.com/tensorflow/models/tree/master/research/delf","n_code_links":3,"syntology":{"n_ran":0,"n_unverified":9,"n_samples":9,"n_pointer_only_licence":0}},{"rank_in_archive_order":4,"model":"R-MAC+R+QE","metrics":{"mAP":"86.5%"},"uses_additional_data":false,"paper_date":"2015-11-18","paper":"/paper/particular-object-retrieval-with-integral-max","paper_url":"http://arxiv.org/abs/1511.05879v2","paper_title":"Particular object retrieval with integral max-pooling of CNN activations","code":"https://github.com/almazan/deep-image-retrieval","n_code_links":6,"syntology":{"n_ran":0,"n_unverified":4,"n_samples":4,"n_pointer_only_licence":0}},{"rank_in_archive_order":5,"model":"siaMAC+QE*","metrics":{"mAP":"85.6%"},"uses_additional_data":false,"paper_date":"2016-04-08","paper":"/paper/cnn-image-retrieval-learns-from-bow","paper_url":"http://arxiv.org/abs/1604.02426v3","paper_title":"CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples","code":"https://github.com/filipradenovic/cnnimageretrieval-pytorch","n_code_links":5,"syntology":{"n_ran":0,"n_unverified":2,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":6,"model":"DELF+FT+ATT","metrics":{"mAP":"85.0%"},"uses_additional_data":false,"paper_date":"2016-12-19","paper":"/paper/large-scale-image-retrieval-with-attentive","paper_url":"http://arxiv.org/abs/1612.06321v4","paper_title":"Large-Scale Image Retrieval with Attentive Deep Local Features","code":"https://github.com/tensorflow/models/tree/master/research/delf","n_code_links":13,"syntology":{"n_ran":3,"n_unverified":9,"n_samples":12,"n_pointer_only_licence":0}},{"rank_in_archive_order":7,"model":"R-MAC","metrics":{"mAP":"83.0%"},"uses_additional_data":false,"paper_date":"2015-11-18","paper":"/paper/particular-object-retrieval-with-integral-max","paper_url":"http://arxiv.org/abs/1511.05879v2","paper_title":"Particular object retrieval with integral max-pooling of CNN activations","code":"https://github.com/almazan/deep-image-retrieval","n_code_links":6,"syntology":{"n_ran":0,"n_unverified":4,"n_samples":4,"n_pointer_only_licence":0}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. 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