{"url":"/dataset/shapes-1","name":"SHAPES","full_name":"Swarm Heuristics based Adaptive and Penalized Estimation of Splines","description_markdown":"**SHAPES** is a dataset of synthetic images designed to benchmark systems for understanding of spatial and logical relations among multiple objects. The dataset consists of complex questions about arrangements of colored shapes. The questions are built around compositions of concepts and relations, e.g. Is there a red shape above a circle? or Is a red shape blue?. Questions contain between two and four attributes, object types, or relationships. There are 244 questions and 15,616 images in total, with all questions having a yes and no answer (and corresponding supporting image). This eliminates the risk of learning biases.\r\n\r\nEach image is a 30×30 RGB image depicting a 3×3 grid of objects. Each object is characterized by shape (circle, square, triangle), colour (red, green, blue) and size (small, big).\r\n\r\nSource: [Visual Question Answering: A Survey of Methods and Datasets](https://arxiv.org/abs/1607.05910)\r\nImage Source: [https://github.com/ronghanghu/n2nmn#train-and-evaluate-on-the-shapes-dataset](https://github.com/ronghanghu/n2nmn#train-and-evaluate-on-the-shapes-dataset)","description_withheld":null,"homepage":"https://github.com/ronghanghu/n2nmn#train-and-evaluate-on-the-shapes-dataset","introduced_date":"2016-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/neural-module-networks","title":"Neural Module Networks","first_author":"Jacob Andreas","url":null},"license":{"name":"Unknown","url":null},"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":"Time Series Classification","url":"/task/time-series-classification","datasets_with_task":"/datasets/task/time-series-classification"},{"name":"Visual Reasoning","url":"/task/visual-reasoning","datasets_with_task":"/datasets/task/visual-reasoning"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["SHAPES"],"data_loaders":[{"repo":"https://github.com/ronghanghu/n2nmn","url":"https://github.com/ronghanghu/n2nmn","frameworks":["tf"]}],"num_papers_in_archive":120,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/time-series-classification-on-shapes","task":"Time Series Classification","dataset_variant":"SHAPES","rows":9,"metrics":["Accuracy","NLL"],"first_row_in_archive_order":{"model":"GP-Sig","paper":"/paper/variational-gaussian-processes-with-signature","metrics":{"Accuracy":"1.000","NLL":"0.011"},"code_links":[{"title":"tgcsaba/GPSig","url":"https://github.com/tgcsaba/GPSig"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/seq2tens-an-efficient-representation-of","title":"Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections","date":"2020-06-12","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/variational-gaussian-processes-with-signature","title":"Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances","date":"2019-06-19","rows_on_this_dataset":6,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multivariate-lstm-fcns-for-time-series","title":"Multivariate LSTM-FCNs for Time Series Classification","date":"2018-01-14","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"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":8,"samples_ran":2,"samples_unverified":6,"pointer_only_for_licence":2,"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."}