{"url":"/dataset/physion","name":"Physion","full_name":null,"description_markdown":"**Physion** is a visual and physical prediction benchmark to measure the performance of machine learning models on making predictions about commonplace real world physical events. In realistically simulating a wide variety of physical phenomena -- rigid and soft-body collisions, stable multi-object configurations, rolling and sliding, projectile motion -- this dataset presents a more comprehensive challenge than existing benchmarks. Moreover, the dataset also contains human responses for the stimuli so that model predictions can be directly compared to human judgments.","description_withheld":null,"homepage":"https://github.com/cogtoolslab/physics-benchmarking-neurips2021","introduced_date":"2021-06-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/physion-evaluating-physical-prediction-from","title":"Physion: Evaluating Physical Prediction from Vision in Humans and Machines","first_author":"Daniel M. Bear","url":null},"license":{"name":"MIT License","url":"https://github.com/cogtoolslab/physics-benchmarking-neurips2021/blob/master/LICENSE"},"modalities":[{"name":"Environment","url":"/datasets/modality/environment"}],"tasks":[],"languages":[],"variants":["Physion"],"data_loaders":[],"num_papers_in_archive":19,"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."}