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We perform the classification of ancient Roman Republican coins viarecognizing their reverse motifs where various objects, faces, scenes, animals,and buildings are minted along with legends. Most of these coins are eroded dueto their age and varying degrees of preservation, thereby affecting theirinformative attributes for visual recognition. Changes in the positions ofprincipal symbols on the reverse motifs also cause huge variations among thecoin types. Lastly, in-plane orientations, uneven illumination, and a moderatebackground clutter further make the classification task non-trivial andchallenging.  To this end, we present a novel network model, CoinNet, that employs compactbilinear pooling, residual groups, and feature attention layers. Furthermore,we gathered the largest and most diverse image dataset of the Roman Republicancoins that contains more than 18,000 images belonging to 228 different reversemotifs. On this dataset, our model achieves a classification accuracy of morethan \textbf{98\%} and outperforms the conventional bag-of-visual-words basedapproaches and more recent state-of-the-art deep learning methods. We alsoprovide a detailed ablation study of our network and its generalizationcapability. Models and Datasets available athttps://github.com/saeed-anwar/CoinNet

Deep Ancient Roman Republican Coin Classification via Feature Fusion and Attention