I was curious whether anyone has any code snippets of examples of working with 3 band images for classification. I’ve successfully repurposed the MNIST-Spiking example for other datasets, but had to convert the images to greyscale to properly load them. I am particularly interested in how to: format the data, configure the nengo input_node, and adding the time dimension to the data.
If I understand it correctly, the MNIST-spiking example is storing and feeding the image data as an array, and adding a time dimension to the array. But how should this be changed for 3 band imagery? Right now, the data is an array of arrays that contain 3 values for R,G,B channels. This is in contrast to just an array of single values like in the MNIST examples. Maybe an array of 3 large arrays, each representing a color channel, or an large array of arrays for each pixel value, containing 3 values?