{"url":"/dataset/two4two","name":"Two4Two","full_name":"A Synthetic Dataset For Controlled Experiments","description_markdown":"Two4Two is a library to create synthetic image data crafted for human evaluations of interpretable ML approaches (esp. image classification). The synthetic images show two abstract animals: Peaky (arms inwards) and Stretchy (arms outwards). They are similar-looking, abstract animals, made of eight blocks. The core functionality of this library is that one can correlate different parameters with an animal type to create bias in the data.","description_withheld":null,"homepage":"https://github.com/mschuessler/two4two/","introduced_date":"2022-04-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/do-users-benefit-from-interpretable-vision-a-1","title":"Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset","first_author":"Leon Sixt","url":null},"license":{"name":"MIT","url":"https://github.com/mschuessler/two4two/blob/master/LICENSE"},"modalities":[{"name":"Actions","url":"/datasets/modality/actions"}],"tasks":[{"name":"Action Generation","url":"/task/action-generation","datasets_with_task":"/datasets/task/action-generation"}],"languages":[],"variants":["Two4Two"],"data_loaders":[],"num_papers_in_archive":1,"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."}