Метод синтеза данных для повышения эффективности обучения нейронных сетей

Abstract

The article is devoted to the formation of the initial data set for training neural networks. A proven method for creating synthetic data based on a graphics processor using a graphics pipeline is presented. A distinctive feature of this method is its modular architecture, which makes it easy to modify, delete or add individual stages to the synthetic image generation pipeline. A one-stage automatic detector based on a convolutional neural network of the Yolo type has been trained, the quality of the trained model and the operation of the recognition algorithm were also evaluated. Conclusions are drawn regarding the possibility of using this approach when creating representative samples of a large volume and their further use for training neural networks in pattern recognition.