  We apply convolutional neural networks (CNN) to the problem of image
orientation detection in the context of determining the correct orientation
(from 0, 90, 180, and 270 degrees) of a consumer photo. The problem is
especially important for digitazing analog photographs. We substantially
improve on the published state of the art in terms of the performance on one of
the standard datasets, and test our system on a more difficult large dataset of
consumer photos. We use Guided Backpropagation to obtain insights into how our
CNN detects photo orientation, and to explain its mistakes.
