{"url":"/dataset/cifar10-dvs","name":"CIFAR10-DVS","full_name":"CIFAR10-DVS","description_markdown":"**CIFAR10-DVS** is an event-stream dataset for object classification. 10,000 frame-based images that come from CIFAR-10 dataset are converted into 10,000 event streams with an event-based sensor, whose resolution is 128×128 pixels. The dataset has an intermediate difficulty with 10 different classes. The repeated closed-loop smooth (RCLS) movement of frame-based images is adopted to implement the conversion. Due to the transformation, they produce rich local intensity changes in continuous time which are quantized by each pixel of the event-based camera.\n\nSource: [Structure-Aware Network for Lane Marker Extraction with Dynamic Vision Sensor](https://arxiv.org/abs/2008.06204)\nImage Source: [https://www.frontiersin.org/articles/10.3389/fnins.2017.00309/full](https://www.frontiersin.org/articles/10.3389/fnins.2017.00309/full)","description_withheld":null,"homepage":"https://figshare.com/articles/CIFAR10-DVS_New/4724671/2","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"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":["CIFAR10-DVS"],"data_loaders":[],"num_papers_in_archive":23,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/event-data-classification-on-cifar10-dvs-1","task":"Event data classification","dataset_variant":"CIFAR10-DVS","rows":7,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"tdBN + NDA (VGG11)","paper":"/paper/neuromorphic-data-augmentation-for-training","metrics":{"Accuracy":"81.7"},"code_links":[{"title":"intelligent-computing-lab-yale/nda_snn","url":"https://github.com/intelligent-computing-lab-yale/nda_snn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/object-recognition-on-cifar10-dvs","task":"Object Recognition","dataset_variant":"CIFAR10-DVS","rows":2,"metrics":["Accuracy (% )"],"first_row_in_archive_order":{"model":"Spike-VGG11","paper":"/paper/eventrpg-event-data-augmentation-with","metrics":{"Accuracy (% )":"85.55"},"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/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/im-loss-information-maximization-loss-for","title":"IM-Loss: Information Maximization Loss for Spiking Neural Networks","date":"2022-10-31","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/online-training-through-time-for-spiking","title":"Online Training Through Time for Spiking Neural Networks","date":"2022-10-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/a-synapse-threshold-synergistic-learning","title":"A Synapse-Threshold Synergistic Learning Approach for Spiking Neural Networks","date":"2022-06-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/neuromorphic-data-augmentation-for-training","title":"Neuromorphic Data Augmentation for Training Spiking Neural Networks","date":"2022-03-11","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/differentiable-spike-rethinking-gradient","title":"Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks","date":"2021-12-01","rows_on_this_dataset":1,"code_links":0,"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}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":21,"samples_ran":8,"samples_unverified":13,"pointer_only_for_licence":3,"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."}