{"url":"/dataset/usps","name":"USPS","full_name":"USPS","description_markdown":"**USPS** is a digit dataset automatically scanned from envelopes by the U.S. Postal Service containing a total of 9,298 16×16 pixel grayscale samples; the images are centered, normalized and show a broad range of font styles.\r\n\r\nSource: [Hallucinating Agnostic Images to Generalize Across Domains](https://arxiv.org/abs/1808.01102)\r\nImage Source: [https://ieeexplore.ieee.org/document/291440](https://ieeexplore.ieee.org/document/291440)","description_withheld":null,"homepage":"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#usps","introduced_date":"1994-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"A Database for Handwritten Text Recognition Research","first_author":null,"url":"https://doi.org/10.1109/34.291440"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"},{"name":"Image Clustering","url":"/task/image-clustering","datasets_with_task":"/datasets/task/image-clustering"},{"name":"Deep Clustering","url":"/task/deep-clustering","datasets_with_task":"/datasets/task/deep-clustering"}],"languages":[],"variants":["USPS","MNIST-to-USPS","USPS-to-MNIST"],"data_loaders":[{"repo":"https://github.com/pytorch/vision","url":"https://pytorch.org/vision/stable/generated/torchvision.datasets.USPS.html","frameworks":["pytorch"]},{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/usps-dataset","frameworks":["tf","pytorch"]}],"num_papers_in_archive":459,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-clustering-on-usps","task":"Image Clustering","dataset_variant":"USPS","rows":16,"metrics":["NMI","Accuracy"],"first_row_in_archive_order":{"model":"SPC","paper":"/paper/selective-pseudo-label-clustering","metrics":{"Accuracy":"0.984","NMI":"0.954"},"code_links":[{"title":"Lou1sM/clustering","url":"https://github.com/Lou1sM/clustering"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/deep-clustering-on-usps","task":"Deep Clustering","dataset_variant":"USPS","rows":1,"metrics":["NMI"],"first_row_in_archive_order":{"model":"DEKM","paper":"/paper/deep-embedded-k-means-clustering","metrics":{"NMI":"82.23"},"code_links":[{"title":"spdj2271/DEKM","url":"https://github.com/spdj2271/DEKM"},{"title":"shyhyawJou/DEKM-Pytorch","url":"https://github.com/shyhyawJou/DEKM-Pytorch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/deep-embedded-k-means-clustering","title":"Deep Embedded K-Means Clustering","date":"2021-09-30","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/selective-pseudo-label-clustering","title":"Selective Pseudo-label Clustering","date":"2021-07-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/scattering-transform-based-image-clustering","title":"Scattering Transform Based Image Clustering using Projection onto Orthogonal Complement","date":"2020-11-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-transformation-invariant-clustering","title":"Deep Transformation-Invariant Clustering","date":"2020-06-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":1,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/interpretable-visualizations-with","title":"Interpretable Visualizations with Differentiating Embedding Networks","date":"2020-06-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/tree-sne-hierarchical-clustering-and","title":"Tree-SNE: Hierarchical Clustering and Visualization Using t-SNE","date":"2020-02-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/n2dnot-too-deep-clustering-via-clustering-the","title":"N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding","date":"2019-08-16","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/balanced-self-paced-learning-for-generative","title":"Balanced Self-Paced Learning for Generative Adversarial Clustering Network","date":"2019-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-clustering-with-a-dynamic-autoencoder","title":"Deep Clustering with a Dynamic Autoencoder: From Reconstruction towards Centroids Construction","date":"2019-01-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-density-based-image-clustering","title":"Deep Density-based Image Clustering","date":"2018-12-11","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/deep-clustering-on-the-link-between","title":"Deep clustering: On the link between discriminative models and K-means","date":"2018-10-09","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-multimodal-subspace-clustering-networks","title":"Deep Multimodal Subspace Clustering Networks","date":"2018-04-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/discriminatively-boosted-image-clustering","title":"Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders","date":"2017-03-23","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/joint-unsupervised-learning-of-deep","title":"Joint Unsupervised Learning of Deep Representations and Image Clusters","date":"2016-04-13","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/graph-degree-linkage-agglomerative-clustering","title":"Graph Degree Linkage: Agglomerative Clustering on a Directed Graph","date":"2012-08-25","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/divide-and-conquer-based-large-scale-spectral","title":"Divide-and-conquer based Large-Scale Spectral Clustering","date":null,"rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":15,"samples_ran":3,"samples_unverified":12,"pointer_only_for_licence":2,"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."}