{"url":"/dataset/tallyqa","name":"TallyQA","full_name":null,"description_markdown":"TallyQA is a large-scale dataset for open-ended counting.\r\n\r\nSource: [TallyQA: Answering Complex Counting Questions](/paper/tallyqa-answering-complex-counting-questions)","description_withheld":null,"homepage":"http://www.manojacharya.com/tallyqa.html","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/tallyqa-answering-complex-counting-questions","title":"TallyQA: Answering Complex Counting Questions","first_author":"Manoj Acharya","url":null},"license":{"name":"Unknown","url":null},"modalities":[],"tasks":[{"name":"Object Counting","url":"/task/object-counting","datasets_with_task":"/datasets/task/object-counting"}],"languages":[],"variants":["TallyQA","TallyQA-Simple","TallyQA-Complex"],"data_loaders":[],"num_papers_in_archive":33,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/object-counting-on-tallyqa-complex","task":"Object Counting","dataset_variant":"TallyQA-Complex","rows":6,"metrics":["Accuracy","RMSE"],"first_row_in_archive_order":{"model":"SMoLA-PaLI-X Specialist","paper":"/paper/omni-smola-boosting-generalist-multimodal","metrics":{"Accuracy":"77.1"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/object-counting-on-tallyqa-simple","task":"Object Counting","dataset_variant":"TallyQA-Simple","rows":6,"metrics":["Accuracy","RMSE"],"first_row_in_archive_order":{"model":"SMoLA-PaLI-X Specialist","paper":"/paper/omni-smola-boosting-generalist-multimodal","metrics":{"Accuracy":"86.3"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/visual-program-distillation-distilling-tools","title":"Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models","date":"2023-12-05","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/omni-smola-boosting-generalist-multimodal","title":"Omni-SMoLA: Boosting Generalist Multimodal Models with Soft Mixture of Low-rank Experts","date":"2023-12-01","rows_on_this_dataset":4,"code_links":0,"syntology":null},{"paper":"/paper/revisiting-modulated-convolutions-for-visual","title":"MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond","date":"2020-04-24","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/tallyqa-answering-complex-counting-questions","title":"TallyQA: Answering Complex Counting Questions","date":"2018-10-29","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"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."}