{"url":"/dataset/figureqa","name":"FigureQA","full_name":null,"description_markdown":"FigureQA is a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images. The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts. \r\n\r\nSource: [FigureQA: An Annotated Figure Dataset for Visual Reasoning](/paper/figureqa-an-annotated-figure-dataset-for)","description_withheld":null,"homepage":"https://www.microsoft.com/en-us/research/project/figureqa-dataset/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/figureqa-an-annotated-figure-dataset-for","title":"FigureQA: An Annotated Figure Dataset for Visual Reasoning","first_author":"Samira Ebrahimi Kahou","url":null},"license":{"name":"Custom","url":"https://www.microsoft.com/en-us/research/project/figureqa-dataset/#!download"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Question Answering","url":"/task/question-answering","datasets_with_task":"/datasets/task/question-answering"},{"name":"Visual Question Answering (VQA)","url":"/task/visual-question-answering","datasets_with_task":"/datasets/task/visual-question-answering"},{"name":"Visual Reasoning","url":"/task/visual-reasoning","datasets_with_task":"/datasets/task/visual-reasoning"},{"name":"Chart Question Answering","url":"/task/chart-question-answering","datasets_with_task":"/datasets/task/chart-question-answering"}],"languages":[],"variants":["FigureQA","FigureQA - test 1"],"data_loaders":[],"num_papers_in_archive":61,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/visual-question-answering-on-figureqa-test-1","task":"Visual Question Answering (VQA)","dataset_variant":"FigureQA - test 1","rows":3,"metrics":["1:1 Accuracy"],"first_row_in_archive_order":{"model":"PReFIL","paper":"/paper/answering-questions-about-data-visualizations","metrics":{"1:1 Accuracy":"94.88"},"code_links":[{"title":"kushalkafle/PREFIL","url":"https://github.com/kushalkafle/PREFIL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/classification-regression-for-chart","title":"Classification-Regression for Chart Comprehension","date":"2021-11-29","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/answering-questions-about-data-visualizations","title":"Answering Questions about Data Visualizations using Efficient Bimodal Fusion","date":"2019-08-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/figureqa-an-annotated-figure-dataset-for","title":"FigureQA: An Annotated Figure Dataset for Visual Reasoning","date":"2017-10-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":0,"samples_unverified":12,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":13,"samples_ran":1,"samples_unverified":12,"pointer_only_for_licence":12,"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."}