| Summary: | The article discusses the solution to the problem of choosing the architecture of a convolutional neural network for use in the computer vision of a smart vending refrigerator. Comparative tests decided the architectures of convolutional neural networks YOLOv2, YOLOv3, YOLOv4, Mask R-CNN, and YOLACT ++ on a standard MS COCO dataset, and then on datasets formed from images of typical smart refrigerator products. As a result of comparative tests, the best performance was demonstrated by the YOLOv3 architecture, trained based on a normalized dataset, supplemented with examples with complex intersections of samples without preprocessing examples. The obtained results substantiated the architecture used in computer vision of serially produced "smart" vending machines.
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