Package: ebdataset
ebdatasetAn event based dataset loader under one common python (>=3.5) API built on top of numpy record arrays for sparse representation and PyTorch for dense representation.Supported datasetsNeuromorphic Mnist dataset from
Orchard, G.; Cohen, G.; Jayawant, A.; and Thakor, N.
“Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades",
Frontiers in Neuroscience, vol.9, no.437, Oct. 2015. Available for download athttps://www.garrickorchard.com/datasets/n-mnistNCaltech101 dataset from
Orchard, G.; Cohen, G.; Jayawant, A.; and Thakor, N.
“Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades",
Frontiers in Neuroscience, vol.9, no.437, Oct. 2015. Available for download athttps://www.garrickorchard.com/datasets/n-caltech101IBM DVS128 Gesture dataset from
A. Amir, B. Taba, D. Berg, T. Melano, J. McKinstry, C. Di Nolfo, T. Nayak, A. Andreopoulos, G. Garreau, M. Mendoza, J. Kusnitz, M. Debole, S. Esser, T. Delbruck, M. Flickner, and D. Modha,
"A Low Power, Fully Event-Based Gesture Recognition System,"
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017. Available for download athttp://research.ibm.com/dvsgesture/INI Roshambo17 dataset from
I.-A. Lungu, F. Corradi, and T. Delbruck,
Live Demonstration: Convolutional Neural Network Driven by Dynamic Vision Sensor Playing RoShamBo,
in 2017 IEEE Symposium on Circuits and Systems (ISCAS 2017), Baltimore, MD, USA, 2017. Available for download athttps://drive.google.com/file/d/0BzvXOhBHjRheYjNWZGYtNFpVRkU/view?usp=sharingINI UCF-50 dataset from:
Hu, Y., Liu, H., Pfeiffer, M., and Delbruck, T. (2016).
DVS Benchmark Datasets for Object Tracking, Action Recognition and Object Recognition.
Front. Neurosci. 10, 405. doi:10.3389/fnins.2016.00405. Available for download athttps://dgyblog.com/projects-term/dvs-dataset.htmlNTidigits dataset from:
Anumula, Jithendar, et al. “Feature Representations for Neuromorphic Audio Spike Streams.”
Frontiers in Neuroscience, vol. 12, Feb. 2018, p. 23. DOI.org (Crossref), doi:10.3389/fnins.2018.00023. Available for download athttps://docs.google.com/document/d/1Uxe7GsKKXcy6SlDUX4hoJVAC0-UkH-8kr5UXp0Ndi1MProphesee N-Cars dataset from:
Amos Sironi, Manuele Brambilla, Nicolas Bourdis, Xavier Lagorce, Ryad Benosman
“HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification”.
To appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. Available for download athttps://www.prophesee.ai/2018/03/13/dataset-n-cars/InstallationYou can install the latest version of this package with:pipinstallebdatasetGetting startedIn the code:fromebdataset.visionimportNMnistfromebdataset.vision.transformsimportToDensefromebdatasetimportms# With sparse representation:forspike_train,labelinNMnist(path):spike_train.x,spike_train.y,spike_train.p,spike_train.tsbreak# Or use the pytorch transforms API for dense tensorsdt=1*msloader=NMnist(path,is_train=True,transforms=ToDense(dt))forspike_train,labelinloader:spike_train.shape# => (34, 34, 2, duration of sample)breakOr with the visualization sub-package:python-mebdataset.visualization.spike_train_to_vidNMnistpathContributingFeel free to create a pull request if you're interested in this project.