You are welcome :-). With text we have fairly discrete and granular units, i.e. "words", and we see sentences as compositions of words. For images however, it'

You are welcome :-). With text we have fairly discrete and granular units, i.e. "words", and we see sentences as compositions of words. For images however, it's not so simple. We present the entire image (array of pixels times the number of channels), and all the various layers within the CNN automatically extract "discrete" units like edges, bends etc, but what we get at the end is a semantic encoding of this entire image. But, you could in principle take the encoding of an image using e.g. VGG16 or ResNet or whatever, and then project that encoding onto randomly initialized higher-dimensional space (such as a very high dimensional Echo State Network), and then use that output in your downstream task, like classifying obstacles on the road. I suspect the same principles as what I wrote in the Jupyter Notebook would also apply here, and you will get a performance boost. Not sure if this was done already (computer vision is not really my focus) ;-)