{"url":"/dataset/mnist-large-scale-dataset","name":"MNIST Large Scale dataset","full_name":null,"description_markdown":"The **MNIST Large Scale dataset** is based on the classic [MNIST dataset](/dataset/mnist), but contains large scale variations up to a factor of 16. The motivation behind creating this dataset was to enable testing the ability of different algorithms to learn in the presence of large scale variability and specifically the ability to generalise to new scales not present in the training set over wide scale ranges.\r\n\r\nThe dataset contains training data for each one of the relative size factors 1, 2 and 4 relative to the original MNIST dataset and testing data for relative scaling factors between 1/2 and 8, with a ratio of $\\sqrt[4]{2}$ between adjacent scales.","description_withheld":null,"homepage":"http://doi.org/10.5281/zenodo.3820247","introduced_date":"2020-06-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/exploring-the-ability-of-cnns-to-generalise","title":"Exploring the ability of CNNs to generalise to previously unseen scales over wide scale ranges","first_author":"Ylva Jansson","url":null},"license":{"name":"Available at Zenodo after request","url":"http://doi.org/10.5281/zenodo.3820247"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Scale Generalisation","url":"/task/scale-generalisation","datasets_with_task":"/datasets/task/scale-generalisation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["MNIST Large Scale dataset"],"data_loaders":[{"repo":"https://github.com/spacemir/MNISTLargeScaleDataset","url":"http://doi.org/10.5281/zenodo.3820247","frameworks":[]}],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/scale-generalisation-on-mnist-large-scale","task":"Scale Generalisation","dataset_variant":"MNIST Large Scale dataset","rows":2,"metrics":["Average Accuracy"],"first_row_in_archive_order":{"model":"FovAvg Single-scale training","paper":"/paper/scale-invariant-scale-channel-networks-deep","metrics":{"Average Accuracy":"99.32"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/scale-invariant-scale-channel-networks-deep","title":"Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales","date":"2021-06-11","rows_on_this_dataset":2,"code_links":0,"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."}