{"url":"/dataset/n-imagenet","name":"N-ImageNet","full_name":"Large-Scale Dataset for Event-Based Object Recognition","description_markdown":"The N-ImageNet dataset is an event-camera counterpart for the ImageNet dataset. The dataset is obtained by moving an event camera around a monitor displaying images from ImageNet. N-ImageNet contains approximately 1,300k training samples and 50k validation samples. In addition, the dataset also contains variants of the validation dataset recorded under a wide range of lighting or camera trajectories. Additional details about the dataset are explained in the paper available through this [link](https://arxiv.org/abs/2112.01041). Please cite this paper if you make use of the dataset.","description_withheld":null,"homepage":"https://github.com/82magnolia/n_imagenet","introduced_date":"2021-12-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/n-imagenet-towards-robust-fine-grained-object-1","title":"N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras","first_author":"Junho Kim","url":null},"license":null,"modalities":[],"tasks":[{"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-based vision","url":"/task/event-based-vision","datasets_with_task":"/datasets/task/event-based-vision"},{"name":"Robust classification","url":"/task/robust-classification","datasets_with_task":"/datasets/task/robust-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["N-ImageNet","N-ImageNet (mini)"],"data_loaders":[{"repo":"https://github.com/82magnolia/n_imagenet","url":"https://github.com/82magnolia/n_imagenet","frameworks":["pytorch"]}],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/classification-on-n-imagenet","task":"Classification","dataset_variant":"N-ImageNet","rows":9,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"Event Spike Tensor","paper":"/paper/n-imagenet-towards-robust-fine-grained-object-1","metrics":{"Accuracy (%)":"48.93"},"code_links":[{"title":"82magnolia/n_imagenet","url":"https://github.com/82magnolia/n_imagenet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/classification-on-n-imagenet-mini","task":"Classification","dataset_variant":"N-ImageNet (mini)","rows":6,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"Event Imge","paper":"/paper/n-imagenet-towards-robust-fine-grained-object-1","metrics":{"Accuracy (%)":"61.42"},"code_links":[{"title":"82magnolia/n_imagenet","url":"https://github.com/82magnolia/n_imagenet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/n-imagenet-towards-robust-fine-grained-object-1","title":"N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras","date":"2021-12-02","rows_on_this_dataset":15,"code_links":1,"syntology":null}],"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."}