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Introducing the Socket Python SDK
The initial version of the Socket Python SDK is now on PyPI, enabling developers to more easily interact with the Socket REST API in Python projects.
An 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.
Neuromorphic 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 at https://www.garrickorchard.com/datasets/n-mnist
NCaltech101 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 at https://www.garrickorchard.com/datasets/n-caltech101
IBM 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 at http://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 at https://drive.google.com/file/d/0BzvXOhBHjRheYjNWZGYtNFpVRkU/view?usp=sharing
INI 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 at https://dgyblog.com/projects-term/dvs-dataset.html
NTidigits 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 at https://docs.google.com/document/d/1Uxe7GsKKXcy6SlDUX4hoJVAC0-UkH-8kr5UXp0Ndi1M
Prophesee 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 at https://www.prophesee.ai/2018/03/13/dataset-n-cars/
You can install the latest version of this package with:
pip install ebdataset
In the code:
from ebdataset.vision import NMnist
from ebdataset.vision.transforms import ToDense
from ebdataset import ms
# With sparse representation:
for spike_train, label in NMnist(path):
spike_train.x, spike_train.y, spike_train.p, spike_train.ts
break
# Or use the pytorch transforms API for dense tensors
dt = 1*ms
loader = NMnist(path, is_train=True, transforms=ToDense(dt))
for spike_train, label in loader:
spike_train.shape # => (34, 34, 2, duration of sample)
break
Or with the visualization sub-package:
python -m ebdataset.visualization.spike_train_to_vid NMnist path
Feel free to create a pull request if you're interested in this project.
FAQs
An event based dataset loader under one common API.
We found that ebdataset demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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