Object Detection in the Image Recognition Process Using Transformers

Modern object detection methods in the image recognition process us-ing transformer technology are analyzed.The various methods advantages and disadvantages are identified. An own network was created based on the DETR transformer from the FAIR team, and its operation was analyzed. A comparison of the transformer networks perfor-mance with optimized architectures of convolutional neural networks is made.The cloud computing tools, graphics processors, Internet of Things clusters or embedded microprocessor systems were used in the research process.To ensure high object detector accuracy and real-time detection results on different types of devices, an efficient object detector and model scaling technique are required.The transformer model learning is illustrated step-by-step process.