{"url":"/dataset/n-mnist","name":"N-MNIST","full_name":"Neuromorphic-MNIST","description_markdown":"Brief Description\r\nThe Neuromorphic-MNIST (N-MNIST) dataset is a spiking version of the original frame-based MNIST dataset. It consists of the same 60 000 training and 10 000 testing samples as the original MNIST dataset, and is captured at the same visual scale as the original MNIST dataset (28x28 pixels). The N-MNIST dataset was captured by mounting the ATIS sensor on a motorized pan-tilt unit and having the sensor move while it views MNIST examples on an LCD monitor as shown in this video. A full description of the dataset and how it was created can be found in the paper below. Please cite this paper if you make use of the dataset.\r\n\r\nOrchard, G.; Cohen, G.; Jayawant, A.; and Thakor, N.  “Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades\", Frontiers in Neuroscience, vol.9, no.437, Oct. 2015","description_withheld":null,"homepage":"https://www.garrickorchard.com/datasets/n-mnist","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"}],"languages":[],"variants":["N-MNIST"],"data_loaders":[],"num_papers_in_archive":14,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-classification-on-n-mnist","task":"Image Classification","dataset_variant":"N-MNIST","rows":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"STS-ResNet","paper":"/paper/convolutional-spiking-neural-networks-for","metrics":{"Accuracy":"99.6"},"code_links":[{"title":"aa-samad/conv_snn","url":"https://github.com/aa-samad/conv_snn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/document-image-classification-on-noisy-mnist","task":"Document Image Classification","dataset_variant":"n-MNIST","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Pixel-level RC","paper":"/paper/pixel-level-reconstruction-and-classification","metrics":{"Accuracy":"97.62"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/incomplete-multi-view-clustering-on-noisy","task":"Incomplete multi-view clustering","dataset_variant":"n-MNIST","rows":1,"metrics":["NMI"],"first_row_in_archive_order":{"model":"COMPLETER","paper":"/paper/completer-incomplete-multi-view-clustering","metrics":{"NMI":"75.23"},"code_links":[{"title":"XLearning-SCU/2021-CVPR-Completer","url":"https://github.com/XLearning-SCU/2021-CVPR-Completer"},{"title":"XLearning-SCU/2022-TPAMI-DCP","url":"https://github.com/XLearning-SCU/2022-TPAMI-DCP"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/partially-view-aligned-multi-view-learning-on","task":"Partially View-aligned Multi-view Learning","dataset_variant":"n-MNIST","rows":1,"metrics":["NMI"],"first_row_in_archive_order":{"model":"MvCLN","paper":"/paper/partially-view-aligned-representation","metrics":{"NMI":"93.09"},"code_links":[{"title":"XLearning-SCU/2021-CVPR-MvCLN","url":"https://github.com/XLearning-SCU/2021-CVPR-MvCLN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sneaky-spikes-uncovering-stealthy-backdoor","title":"Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data","date":"2023-02-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/accelerating-spiking-neural-network-training","title":"Robust and accelerated single-spike spiking neural network training with applicability to challenging temporal tasks","date":"2022-05-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/sparse-spiking-gradient-descent","title":"Sparse Spiking Gradient Descent","date":"2021-05-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/completer-incomplete-multi-view-clustering","title":"COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction","date":"2021-03-22","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/partially-view-aligned-representation","title":"Partially View-aligned Representation Learning with Noise-robust Contrastive Loss","date":"2021-03-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/convolutional-spiking-neural-networks-for","title":"Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extraction","date":"2020-03-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pixel-level-reconstruction-and-classification","title":"Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters","date":"2018-06-21","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"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."}