{"url":"/dataset/mnist-1d","name":"MNIST-1D","full_name":null,"description_markdown":"A minimalist, low-memory, and low-compute alternative to classic deep learning benchmarks. The training examples are 20 times smaller than MNIST examples yet they differentiate more clearly between linear, nonlinear, and convolutional models which attain 32, 68, and 94% accuracy respectively (these models obtain 94, 99+, and 99+% on MNIST).\r\n\r\nSource: [Scaling down Deep Learning](/paper/scaling-down-deep-learning)","description_withheld":null,"homepage":"https://github.com/greydanus/mnist1d","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/scaling-down-deep-learning","title":"Scaling Down Deep Learning with MNIST-1D","first_author":"Sam Greydanus","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["MNIST-1D"],"data_loaders":[{"repo":"https://github.com/greydanus/mnist1d","url":"https://github.com/greydanus/mnist1d","frameworks":["pytorch"]}],"num_papers_in_archive":6,"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."}