Visualize and Compress Single Logo Recognition Neural Network

Abstract

Logo recognition by Convolutional Neural Networks (CNNs) on a smartphone requires the network to be both accurate and small. In our previous work [1], we proposed the accompanying dataset method for single logo recognition to increase the recall and precision of the target logo recognition. However, the reason why it works was unclear, thus it was hard to compress the network while maintaining the same accuracy. In this paper, we use DeconvNet [9] to visualize our network’s feature maps and propose a metric to analyze them quantitatively. Finally, we obtain a better understanding of the influences in the network brought by accompanying datasets. Under its guidance, an effective way to compress the network is devised by us. The experiments show that we can reduce the size of the neural network’s first layer by 30% while only lower the recall and precision by 0.014 and 0.01. The training time is also saved by 40% due to the network compression.