Cross Validation Voting for Improving CNN Classification in Grocery Products

The development of deep neural networks that has been carried out in recent years allows solving highly complex computer vision classification problems. Often, although the results obtained with these classifiers are high, there are certain sectors that seek greater accuracy from these systems. Increasing the accuracy of neural networks can be achieved through ensemble learning, which combines different classifiers with the aim of selecting a winner based on different criteria about them. These techniques have traditionally shown good results although they involve training models of different nature and can even produce an overfitting with respect to the training data, so datasets must be chosen to correctly evaluate the result. In this paper, a Cross-Validation-Voting (CVV) technique for grocery product classification is presented. This technique improves several single state-of-the-art classifiers without combining different ones and avoids the problems of overfitting with respect to the training set. The single classifiers are trained multiple times against distributed sets to show how the results obtained to date from the classification of a well-known dataset are improved. In this dataset, an extensive test set was previously selected by the authors to show comparable results with other papers in the literature. The technique is valid not only for vision nets and can be used to solve numerous problems with different kinds of neural networks and classifiers.