{"url":"/dataset/nus-wide","name":"NUS-WIDE","full_name":null,"description_markdown":"The **NUS-WIDE** dataset contains 269,648 images with a total of 5,018 tags collected from Flickr. These images are manually annotated with 81 concepts, including objects and scenes.\r\n\r\nSource: [Parallel Grid Pooling for Data Augmentation](https://arxiv.org/abs/1803.11370)\r\n\r\nImage Source: [Li et al](https://www.researchgate.net/publication/273063258_Data_Clustering_Using_Side_Information_Dependent_Chinese_Restaurant_Processes)","description_withheld":null,"homepage":"https://github.com/NExTplusplus/NUS-WIDE/","introduced_date":"2009-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"NUS-WIDE: a real-world web image database from National University of Singapore","first_author":null,"url":"https://doi.org/10.1145/1646396.1646452"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Retrieval","url":"/task/image-retrieval","datasets_with_task":"/datasets/task/image-retrieval"},{"name":"Multi-Label Classification","url":"/task/multi-label-classification","datasets_with_task":"/datasets/task/multi-label-classification"},{"name":"Cross-Modal Retrieval","url":"/task/cross-modal-retrieval","datasets_with_task":"/datasets/task/cross-modal-retrieval"},{"name":"Multiview Clustering","url":"/task/multiview-clustering","datasets_with_task":"/datasets/task/multiview-clustering"},{"name":"Multi-label zero-shot learning","url":"/task/multi-label-zero-shot-learning","datasets_with_task":"/datasets/task/multi-label-zero-shot-learning"}],"languages":[],"variants":["NUS-WIDE"],"data_loaders":[],"num_papers_in_archive":348,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multi-label-zero-shot-learning-on-nus-wide","task":"Multi-label zero-shot learning","dataset_variant":"NUS-WIDE","rows":10,"metrics":["mAP"],"first_row_in_archive_order":{"model":"MKT(CLIP)","paper":"/paper/open-vocabulary-multi-label-classification","metrics":{"mAP":"42.7"},"code_links":[{"title":"sunanhe/mkt","url":"https://github.com/sunanhe/mkt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multi-label-classification-on-nus-wide","task":"Multi-Label Classification","dataset_variant":"NUS-WIDE","rows":9,"metrics":["MAP"],"first_row_in_archive_order":{"model":"Q2L-CvT(resolution 384, ImageNet-21K pretrained)","paper":"/paper/query2label-a-simple-transformer-way-to-multi","metrics":{"MAP":"70.1"},"code_links":[{"title":"SlongLiu/query2labels","url":"https://github.com/SlongLiu/query2labels"},{"title":"curt-tigges/query2label","url":"https://github.com/curt-tigges/query2label"},{"title":"averyfallson/rmffn","url":"https://github.com/averyfallson/rmffn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-retrieval-on-nus-wide","task":"Image Retrieval","dataset_variant":"NUS-WIDE","rows":1,"metrics":["MAP"],"first_row_in_archive_order":{"model":"DTQ","paper":"/paper/deep-triplet-quantization","metrics":{"MAP":"0.801"},"code_links":[{"title":"thulab/DeepHash","url":"https://github.com/thulab/DeepHash"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/combining-metric-learning-and-attention-heads","title":"Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification","date":"2022-09-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-dual-modality-approach-for-zero-shot-multi","title":"Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features","date":"2022-08-19","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/open-vocabulary-multi-label-classification","title":"Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer","date":"2022-07-05","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":3,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ml-decoder-scalable-and-versatile","title":"ML-Decoder: Scalable and Versatile Classification Head","date":"2021-11-25","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":2,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/discriminative-region-based-multi-label-zero","title":"Discriminative Region-based Multi-Label Zero-Shot Learning","date":"2021-08-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":7,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/query2label-a-simple-transformer-way-to-multi","title":"Query2Label: A Simple Transformer Way to Multi-Label Classification","date":"2021-07-22","rows_on_this_dataset":3,"code_links":3,"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/multi-layered-semantic-representation-network","title":"Multi-layered Semantic Representation Network for Multi-label Image Classification","date":"2021-06-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/semantic-diversity-learning-for-zero-shot","title":"Semantic Diversity Learning for Zero-Shot Multi-label Classification","date":"2021-05-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/generative-multi-label-zero-shot-learning","title":"Generative Multi-Label Zero-Shot Learning","date":"2021-01-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/asymmetric-loss-for-multi-label","title":"Asymmetric Loss For Multi-Label Classification","date":"2020-09-29","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":4,"samples_unverified":8,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-shared-multi-attention-framework-for-multi","title":"A Shared Multi-Attention Framework for Multi-Label Zero-Shot Learning","date":"2020-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cross-modality-attention-with-semantic-graph","title":"Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification","date":"2019-12-17","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-triplet-quantization","title":"Deep Triplet Quantization","date":"2019-02-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multi-label-image-classification-via","title":"Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection","date":"2018-09-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-spatial-regularization-with-image","title":"Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification","date":"2017-02-20","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/fast-zero-shot-image-tagging","title":"Fast Zero-Shot Image Tagging","date":"2016-05-31","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":6,"samples_harvested":40,"samples_ran":18,"samples_unverified":22,"pointer_only_for_licence":7,"papers_with_no_sample_that_ran":1,"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."}