The Neuromorphic-Caltech101 (N-Caltech101) dataset is a spiking version of the original frame-based Caltech101 dataset. The original dataset contained both a "Faces" and "Faces Easy" class, with each consisting of different versions of the same images. The "Faces" class has been removed from N-Caltech101 to avoid confusion, leaving 100 object classes plus a background class. The N-Caltech101 dataset was captured by mounting the ATIS sensor on a motorized pan-tilt unit and having the sensor move while it views Caltech101 examples on an LCD monitor as shown in the video below. 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 data is available for download below:
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.