  We perform the classification of ancient Roman Republican coins via
recognizing their reverse motifs where various objects, faces, scenes, animals,
and buildings are minted along with legends. Most of these coins are eroded due
to their age and varying degrees of preservation, thereby affecting their
informative attributes for visual recognition. Changes in the positions of
principal symbols on the reverse motifs also cause huge variations among the
coin types. Lastly, in-plane orientations, uneven illumination, and a moderate
background clutter further make the classification task non-trivial and
challenging.
  To this end, we present a novel network model, CoinNet, that employs compact
bilinear pooling, residual groups, and feature attention layers. Furthermore,
we gathered the largest and most diverse image dataset of the Roman Republican
coins that contains more than 18,000 images belonging to 228 different reverse
motifs. On this dataset, our model achieves a classification accuracy of more
than \textbf{98\%} and outperforms the conventional bag-of-visual-words based
approaches and more recent state-of-the-art deep learning methods. We also
provide a detailed ablation study of our network and its generalization
capability. Models and Datasets available at
https://github.com/saeed-anwar/CoinNet
