Product data set generation network based on SAM and pix2pix

Aiming at the cumbersome process of collection and labeling of commodity data set caused by rapid change of commodity packaging, this paper designs a commodity data set generation network based on Segment Anything Model (SAM) and Pixel to Pixel (pix2pix). The network uses multi-angle images of a single commodity as input to generate a data set similar to the actual settlement scene. The data set generation test was carried out on Retail Product Checkout Dataset(RPC) set, and the improvement of the generated data set on target detection effect was further verified on YOLOv7, Fast R-CNN and AlexNet target detection networks. The experimental results show that the generated data set can effectively improve the accuracy of commodity recognition, and has better substitution compared with the actual data set. Compared with the original data set, the recognition accuracy of the three networks generated by fusion data set is improved by 7.3%, 4.9% and 7.8%, respectively. Through this method, the efficiency and practicability of model training are significantly improved, and the manpower and material input required for traditional commodity data collection and labeling is reduced.