반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
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다양한 컴퓨팅 환경에서 YOLOv7 모델의 추론 시간 복잡도 분석

YOLOv7 Model Inference Time Complexity Analysis in Different Computing Environments

  • 박천수 (성균관대학교 컴퓨터교육과)
  • 투고 : 2022.07.31
  • 심사 : 2022.09.06
  • 발행 : 2022.09.30
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초록

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.

키워드

과제정보

본 연구는 중소벤처기업부의 연구비지원(S3147433)에 의해 수행되었습니다.

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