{"url":"/dataset/n-caltech-101","name":"N-Caltech 101","full_name":"Neuromorphic-Caltech101","description_markdown":"The Neuromorphic-Caltech101 (N-Caltech101) dataset is a spiking version of the original frame-based Caltech101 dataset. The original dataset contained both a \"Faces\" and \"Faces Easy\" class, with each consisting of different versions of the same images. The \"Faces\" class has been removed from N-Caltech101 to avoid confusion, leaving 100 object classes plus a background class. The N-Caltech101 dataset was captured by mounting the ATIS sensor on a motorized pan-tilt unit and having the sensor move while it views Caltech101 examples on an LCD monitor as shown in the video below. 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.","description_withheld":null,"homepage":"https://www.garrickorchard.com/datasets/n-caltech101","introduced_date":"2015-07-28","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades","first_author":null,"url":null},"license":{"name":"Creative Commons 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"Object Recognition","url":"/task/object-recognition","datasets_with_task":"/datasets/task/object-recognition"},{"name":"Event data classification","url":"/task/event-data-classification","datasets_with_task":"/datasets/task/event-data-classification"}],"languages":[],"variants":["N-Caltech 101"],"data_loaders":[{"repo":"https://github.com/windere/eas-snn","url":"https://github.com/windere/eas-snn","frameworks":["pytorch"]}],"num_papers_in_archive":110,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-classification-on-n-caltech-101","task":"Image Classification","dataset_variant":"N-Caltech 101","rows":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"mMND (STDP)","paper":"/paper/sequence-approximation-using-feedforward","metrics":{"Accuracy":"58.1"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/event-data-classification-on-n-caltech-101","task":"Event data classification","dataset_variant":"N-Caltech 101","rows":2,"metrics":["Accuracy (% )"],"first_row_in_archive_order":{"model":"Event Trojan","paper":"/paper/event-trojan-asynchronous-event-based","metrics":{"Accuracy (% )":"85.61"},"code_links":[{"title":"rfww/eventtrojan","url":"https://github.com/rfww/eventtrojan"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/object-recognition-on-n-caltech-101","task":"Object Recognition","dataset_variant":"N-Caltech 101","rows":2,"metrics":["Accuracy (% )"],"first_row_in_archive_order":{"model":"Spike-VGG11","paper":"/paper/eventrpg-event-data-augmentation-with","metrics":{"Accuracy (% )":"85.62"},"code_links":[{"title":"myuansun/eventrpg","url":"https://github.com/myuansun/eventrpg"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/event-trojan-asynchronous-event-based","title":"Event Trojan: Asynchronous Event-based Backdoor Attacks","date":"2024-07-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/eventrpg-event-data-augmentation-with","title":"EventRPG: Event Data Augmentation with Relevance Propagation Guidance","date":"2024-03-14","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":16,"samples_ran":5,"samples_unverified":11,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/shrinking-your-timestep-towards-low-latency","title":"Shrinking Your TimeStep: Towards Low-Latency Neuromorphic Object Recognition with Spiking Neural Network","date":"2024-01-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/s-tllr-stdp-inspired-temporal-local-learning","title":"S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks","date":"2023-06-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ecsnet-spatio-temporal-feature-learning-for","title":"Ecsnet: Spatio-temporal feature learning for event camera","date":"2022-08-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sequence-approximation-using-feedforward","title":"Sequence Approximation using Feedforward Spiking Neural Network for Spatiotemporal Learning: Theory and Optimization Methods","date":"2021-09-29","rows_on_this_dataset":2,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":17,"samples_ran":6,"samples_unverified":11,"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."}