{"url":"/dataset/image-aesthetics-dataset","name":"Image Aesthetics dataset","full_name":null,"description_markdown":"The image aesthetic benchmark [18] consists of 10800\r\nFlickr photos of four categories, i.e., “animals”, “urban”,\r\n“people” and “nature”, and is constructed originally to retrieve beautiful yet unpopular images in social networks.\r\nThe ground truths of the photos in the benchmark are five\r\naesthetic grades: “Unacceptable” - images with extremely\r\nlow quality, out of focus or underexposed, “Flawed” - images with some technical flaws and without any artistic\r\nvalue, “Ordinary” - standard quality images without technical flaws, “Professional” - professional-quality images\r\nwith some artistic value, and “Exceptional” - very appealing images showing both outstanding professional quality\r\nand high artistic value. \r\n\r\nImage Source: [OrdinalCLIP](https://arxiv.org/pdf/2206.02338.pdf)","description_withheld":null,"homepage":"http://di.unito.it/beautyicwsm15","introduced_date":"2015-05-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/an-image-is-worth-more-than-a-thousand","title":"An Image is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures","first_author":"Rossano Schifanella","url":null},"license":null,"modalities":[],"tasks":[{"name":"Aesthetics Quality Assessment","url":"/task/aesthetics-quality-assessment","datasets_with_task":"/datasets/task/aesthetics-quality-assessment"}],"languages":[],"variants":["Image Aesthetics dataset"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/aesthetics-quality-assessment-on-image","task":"Aesthetics Quality Assessment","dataset_variant":"Image Aesthetics dataset","rows":4,"metrics":["Accuracy","MAE"],"first_row_in_archive_order":{"model":"OrdinalCLIP","paper":"/paper/ordinalclip-learning-rank-prompts-for","metrics":{"Accuracy":"73.05","MAE":"0.280"},"code_links":[{"title":"xk-huang/OrdinalCLIP","url":"https://github.com/xk-huang/OrdinalCLIP"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/ordinalclip-learning-rank-prompts-for","title":"OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression","date":"2022-06-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-probabilistic-ordinal-embeddings-for","title":"Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression","date":"2021-03-25","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":8,"samples_unverified":0,"pointer_only_for_licence":8,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/soft-labels-for-ordinal-regression","title":"Soft Labels for Ordinal Regression","date":"2019-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-constrained-deep-neural-network-for-ordinal","title":"A Constrained Deep Neural Network for Ordinal Regression","date":"2018-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":8,"samples_ran":8,"samples_unverified":0,"pointer_only_for_licence":8,"papers_with_no_sample_that_ran":0,"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."}