This is an old event-based dataset based on an even older frame-based dataset.
It can be useful for playing with new ideas or teaching, but for anything more serious I suggest looking for the latest datasets on the Event-based Vision Resources page.
The Neuromorphic-MNIST (N-MNIST) dataset is a spiking version of the original frame-based MNIST dataset. It consists of the same 60 000 training and 10 000 testing samples as the original MNIST dataset, and is captured at the same visual scale as the original MNIST dataset (28x28 pixels). The N-MNIST dataset was captured by mounting the ATIS sensor on a motorized pan-tilt unit and having the sensor move while it views MNIST examples on an LCD monitor as shown in this video. 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.
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 (open access Frontiers link)
The dataset is released under the Creative Commons Attribution-ShareAlike 4.0 license.
The dataset is derived from original MNIST dataset, modified as shown in the videos below.
All original copyrights are preserved.
The dataset can be downloaded through any of the links below:
Dropbox (High traffic generated through this link results in it frequently being suspended)
A readme and example Matlab function for reading the files is included in the download.
Further Matlab and Python code for reading and working with the datasets is available on the code page.
Each example is a separate binary file consisting of a list of events. Each event occupies 40 bits as described below:
bit 39 - 32: Xaddress (in pixels)
bit 31 - 24: Yaddress (in pixels)
bit 23: Polarity (0 for OFF, 1 for ON)
bit 22 - 0: Timestamp (in microseconds)
The videos below show the conversion process in action and some of the resulting recordings.