  Recently, there has been a flurry of industrial activity around logo
recognition, such as Ditto's service for marketers to track their brands in
user-generated images, and LogoGrab's mobile app platform for logo recognition.
However, relatively little academic or open-source logo recognition progress
has been made in the last four years. Meanwhile, deep convolutional neural
networks (DCNNs) have revolutionized a broad range of object recognition
applications. In this work, we apply DCNNs to logo recognition. We propose
several DCNN architectures, with which we surpass published state-of-art
accuracy on a popular logo recognition dataset.
