  In this paper we propose a method for logo recognition using deep learning.
Our recognition pipeline is composed of a logo region proposal followed by a
Convolutional Neural Network (CNN) specifically trained for logo
classification, even if they are not precisely localized. Experiments are
carried out on the FlickrLogos-32 database, and we evaluate the effect on
recognition performance of synthetic versus real data augmentation, and image
pre-processing. Moreover, we systematically investigate the benefits of
different training choices such as class-balancing, sample-weighting and
explicit modeling the background class (i.e. no-logo regions). Experimental
results confirm the feasibility of the proposed method, that outperforms the
methods in the state of the art.
