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YOLOv7 Model Inference Time Complexity Analysis in Different Computing Environments  

Park, Chun-Su (Computer Education, Sungkyunkwan University)
Publication Information
Journal of the Semiconductor & Display Technology / v.21, no.3, 2022 , pp. 7-11 More about this Journal
Abstract
Object detection technology is one of the main research topics in the field of computer vision and has established itself as an essential base technology for implementing various vision systems. Recent DNN (Deep Neural Networks)-based algorithms achieve much higher recognition accuracy than traditional algorithms. However, it is well-known that the DNN model inference operation requires a relatively high computational power. In this paper, we analyze the inference time complexity of the state-of-the-art object detection architecture Yolov7 in various environments. Specifically, we compare and analyze the time complexity of four types of the Yolov7 model, YOLOv7-tiny, YOLOv7, YOLOv7-X, and YOLOv7-E6 when performing inference operations using CPU and GPU. Furthermore, we analyze the time complexity variation when inferring the same models using the Pytorch framework and the Onnxruntime engine.
Keywords
YOLOv7; Deep neural networks; Pytorch; Onnxruntime; GPU acceleration;
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