{"url":"/dataset/imagifilter","name":"ImagiFilter","full_name":null,"description_markdown":"ImagiFilter focusses on photographic and/or natural images, a very common use-case in computer vision research. Annotations for coarse prediction are provided, i.e. photographic vs. non-photographic, and smaller fine-grained prediction tasks where the non-photographic class is broken down into five classes: maps, drawings, graphs, icons, and sketches.\r\n\r\nSource: [ImagiFilter: A resource to enable the semi-automatic mining of images at scale](https://arxiv.org/pdf/2008.09152)","description_withheld":null,"homepage":"https://github.com/houda96/imagi-filter","introduced_date":"2020-08-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/imagifilter-a-resource-to-enable-the-semi","title":"ImagiFilter: A resource to enable the semi-automatic mining of images at scale","first_author":"Houda Alberts","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Natural Language Understanding","url":"/task/natural-language-understanding","datasets_with_task":"/datasets/task/natural-language-understanding"},{"name":"Data Augmentation","url":"/task/data-augmentation","datasets_with_task":"/datasets/task/data-augmentation"}],"languages":[],"variants":["ImagiFilter"],"data_loaders":[{"repo":"https://github.com/houda96/imagi-filter","url":"https://github.com/houda96/imagi-filter","frameworks":[]}],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}