An efficient deep learning model using network pruning for fake banknote recognition

Abstract

In recent years, pruning methods have been proposed to reduce the size of image classification models based on CNNs and to shorten their inference times. However, most of them are based on setting the less important parameters of the model to zero, but not on reshaping the network. Therefore, in this paper we propose a pruning methodology for sequential CNNs that modifies the shape of the network (both convolutional and fully connected layers) and was applied to four sequential networks (a custom, AlexNet, VGG11 and VGG16) for the “Original and counterfeit Colombian peso banknotes” dataset. The pruned models were evaluated quantitatively and qualitatively. First, in terms of accuracy against FLOPs and parameter reduction. Second, using HiResCAM to explain the patterns on which the model is based for decision making. Quantitative results show a reduction in parameters and FLOPs of about 75% for a reduction in accuracy of up to 0.5% across all models in this study. With larger reductions of about 95%, the AlexNet-pruned and VGG16-pruned models significantly reduced their accuracy (38.1% and 21.9%, respectively), while the custom-pruned model and the VGG11-pruned model reduced their accuracy by only 0.3% and 0.9%, respectively. Furthermore, using HiResCAM, it was observed that the custom-pruned model and the VGG11-pruned model better preserved the original model activations, even at high pruning percentages, and achieved lower accuracy reductions than their competitors. With the proposed methodology, any sequential model can be pruned and reshaped while largely retaining the accuracy of the unpruned model, or even improving it for low pruning percentages.