{"url":"/dataset/feta-car-manuals","name":"FETA Car-Manuals","full_name":"FETA Car-Manuals dataset, image-text retrieval for foundation models' expert data performance.","description_markdown":"**FETA** benchmark focuses on text-to-image and image-to-text retrieval in public car manuals and sales catalogue brochures. The FETA Car-Manuals dataset consists of a total of 349 PDF documents from 5 car manufacturers, namely Nissan, Toyota, Mazda, Renault, Chevrolet.","description_withheld":null,"homepage":"","introduced_date":"2022-09-08","introduced_date_note":null,"introduced_by":{"paper":"/paper/feta-towards-specializing-foundation-models","title":"FETA: Towards Specializing Foundation Models for Expert Task Applications","first_author":"Amit Alfassy","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Image Retrieval","url":"/task/image-retrieval","datasets_with_task":"/datasets/task/image-retrieval"},{"name":"Image-to-Text Retrieval","url":"/task/image-to-text-retrieval","datasets_with_task":"/datasets/task/image-to-text-retrieval"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["FETA Car-Manuals"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-retrieval-on-feta-car-manuals","task":"Image Retrieval","dataset_variant":"FETA Car-Manuals","rows":1,"metrics":["R@1","R@10","R@5"],"first_row_in_archive_order":{"model":"FETA's CLIP-MIL (Many-Shot Image-to-text)","paper":"/paper/feta-towards-specializing-foundation-models","metrics":{"R@1":"29","R@10":"72.6","R@5":"59.9"},"code_links":[{"title":"alfassy/FETA","url":"https://github.com/alfassy/FETA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-to-text-retrieval-on-feta-car-manuals","task":"Image-to-Text Retrieval","dataset_variant":"FETA Car-Manuals","rows":1,"metrics":["R@1","R@10","R@5"],"first_row_in_archive_order":{"model":"FETA's CLIP-MIL (Many-Shot Image-to-text)","paper":"/paper/feta-towards-specializing-foundation-models","metrics":{"R@1":"35.5","R@10":"67","R@5":"58.3"},"code_links":[{"title":"alfassy/FETA","url":"https://github.com/alfassy/FETA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/feta-towards-specializing-foundation-models","title":"FETA: Towards Specializing Foundation Models for Expert Task Applications","date":"2022-09-08","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}