{"url":"/dataset/emnist","name":"EMNIST","full_name":"Extended MNIST","description_markdown":"**EMNIST** (extended MNIST) has 4 times more data than [MNIST](/dataset/mnist). It is a set of handwritten digits with a 28 x 28 format.\r\n\r\nSource: [Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation](https://arxiv.org/abs/1901.10654)\r\nImage Source: [https://arxiv.org/pdf/1803.01900.pdf](https://arxiv.org/pdf/1803.01900.pdf)","description_withheld":null,"homepage":"https://www.nist.gov/itl/products-and-services/emnist-dataset","introduced_date":"2017-02-17","introduced_date_note":null,"introduced_by":{"paper":"/paper/emnist-an-extension-of-mnist-to-handwritten","title":"EMNIST: an extension of MNIST to handwritten letters","first_author":"Gregory Cohen","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Image Generation","url":"/task/image-generation","datasets_with_task":"/datasets/task/image-generation"},{"name":"Image Clustering","url":"/task/image-clustering","datasets_with_task":"/datasets/task/image-clustering"},{"name":"Fine-Grained Image Classification","url":"/task/fine-grained-image-classification","datasets_with_task":"/datasets/task/fine-grained-image-classification"},{"name":"Dimensionality Reduction","url":"/task/dimensionality-reduction","datasets_with_task":"/datasets/task/dimensionality-reduction"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["EMNIST-Letters","EMNIST-Digits","EMNIST-Balanced","EMNIST","EMNIST-Byclass","EMNIST-Bymerge"],"data_loaders":[{"repo":"https://github.com/pytorch/vision","url":"https://pytorch.org/vision/stable/generated/torchvision.datasets.EMNIST.html","frameworks":["pytorch"]},{"repo":"https://github.com/activeloopai/deeplake","url":"https://datasets.activeloop.ai/docs/ml/datasets/emnist-dataset/","frameworks":["tf","pytorch"]},{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/emnist","frameworks":["tf","jax"]},{"repo":"https://gitlab.com/afagarap/pt-datasets","url":"https://gitlab.com/afagarap/pt-datasets","frameworks":["pytorch"]}],"num_papers_in_archive":264,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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Classification","dataset_variant":"EMNIST-Letters","rows":11,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"WaveMixLite-112/16","paper":"/paper/wavemix-lite-a-resource-efficient-neural","metrics":{"Accuracy":"95.96"},"code_links":[{"title":"pranavphoenix/WaveMix","url":"https://github.com/pranavphoenix/WaveMix"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-classification-on-emnist-digits","task":"Image Classification","dataset_variant":"EMNIST-Digits","rows":7,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"WaveMixLite-112/16","paper":"/paper/wavemix-lite-a-resource-efficient-neural","metrics":{"Accuracy (%)":"99.82"},"code_links":[{"title":"pranavphoenix/WaveMix","url":"https://github.com/pranavphoenix/WaveMix"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/dimensionality-reduction-on-emnist","task":"Dimensionality 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them"},{"leaderboard":"/sota/image-classification-on-emnist-byclass","task":"Image Classification","dataset_variant":"EMNIST-Byclass","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"WaveMixLite-128/7","paper":"/paper/wavemix-lite-a-resource-efficient-neural","metrics":{"Accuracy":"88.43"},"code_links":[{"title":"pranavphoenix/WaveMix","url":"https://github.com/pranavphoenix/WaveMix"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-classification-on-emnist-bymerge","task":"Image Classification","dataset_variant":"EMNIST-Bymerge","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"WaveMixLite-128/16","paper":"/paper/wavemix-lite-a-resource-efficient-neural","metrics":{"Accuracy":"91.80"},"code_links":[{"title":"pranavphoenix/WaveMix","url":"https://github.com/pranavphoenix/WaveMix"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-generation-on-emnist-letters","task":"Image Generation","dataset_variant":"EMNIST-Letters","rows":1,"metrics":["FID"],"first_row_in_archive_order":{"model":"Spiking-Diffusion","paper":"/paper/spiking-diffusion-vector-quantized-discrete","metrics":{"FID":"67.41"},"code_links":[{"title":"Arktis2022/Spiking-Diffusion","url":"https://github.com/Arktis2022/Spiking-Diffusion"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/efficient-global-neural-architecture-search","title":"Efficient Global Neural Architecture Search","date":"2025-02-08","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/regularizing-cross-entropy-loss-via-minimum","title":"Regularizing cross entropy loss via minimum entropy and K-L 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layers","date":"2021-03-21","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/quick-and-robust-feature-selection-the","title":"Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders","date":"2020-12-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/spinalnet-deep-neural-network-with-gradual-1","title":"SpinalNet: Deep Neural Network with Gradual Input","date":"2020-07-07","rows_on_this_dataset":8,"code_links":3,"syntology":null},{"paper":"/paper/improving-k-means-clustering-performance-with","title":"Improving k-Means Clustering Performance with Disentangled Internal Representations","date":"2020-06-05","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/efficient-neural-vision-systems-based-on","title":"Efficient Neural Vision Systems Based on Convolutional Image Acquisition","date":"2020-06-01","rows_on_this_dataset":3,"code_links":0,"syntology":null},{"paper":"/paper/textcaps-handwritten-character-recognition","title":"TextCaps : Handwritten Character Recognition with Very Small Datasets","date":"2019-04-17","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/handwritten-digit-and-letter-recognition","title":"Handwritten digit and letter recognition using hybrid dwt-dct with knn and svm classifier","date":"2018-08-18","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/hybrid-macromicro-level-backpropagation-for","title":"Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks","date":"2018-05-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dynamic-routing-between-capsules","title":"Dynamic Routing Between Capsules","date":"2017-10-26","rows_on_this_dataset":1,"code_links":77,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":118,"samples_ran":18,"samples_unverified":100,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/emnist-an-extension-of-mnist-to-handwritten","title":"EMNIST: an extension of MNIST to handwritten letters","date":"2017-02-17","rows_on_this_dataset":6,"code_links":31,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":1,"samples_unverified":13,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":132,"samples_ran":19,"samples_unverified":113,"pointer_only_for_licence":13,"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."}