  Convolutional Neural Networks have become state of the art methods for image
classification over the last couple of years. By now they perform better than
human subjects on many of the image classification datasets. Most of these
datasets are based on the notion of concrete classes (i.e. images are
classified by the type of object in the image). In this paper we present a
novel image classification dataset, using abstract classes, which should be
easy to solve for humans, but variations of it are challenging for CNNs. The
classification performance of popular CNN architectures is evaluated on this
dataset and variations of the dataset that might be interesting for further
research are identified.
