Cut and paste curriculum learning with hard negative mining for point-of-sale systems

Although point-of-sale (POS) systems generally use barcodes, progress in automation in recent years has come to require real-time performance. Since these systems use machine learning models to detect products from images, the models need to be retrained frequently to support the continual release of new products. Thus, methods for efficiently training a model from a limited amount of data are needed. Curriculum learning was developed to achieve this kind of efficient machine learning. However, curriculum learning in general has the problem that early learning progress is slow. Therefore, we developed a new curriculum learning method using hard negative mining to boost the learning progress. This method provides a remarkable learning effect through simple cut and paste. We test our method on various test data, and the proposed method is found to achieve better performance at the same learning epoch compared with conventional cut and paste methods. We expect our method to contribute to the realization of real-time and easy-to-operate POS systems.