Compressing YOLOv7
B. van Zwienen (TU Delft - Mechanical Engineering)
Martijn Wisse – Mentor (TU Delft - Robot Dynamics)
Holger Caesar – Graduation committee member (TU Delft - Intelligent Vehicles)
C. Zhang – Graduation committee member (TU Delft - Learning & Autonomous Control)
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Abstract
In the literature, neural network compression can significantly reduce the number of floating-point operations (FLOPs) of a neural network with limited accuracy loss. At the same time, it is common to manually design smaller networks instead of using modern compression techniques. This thesis will compare the two approaches for the object detection network YOLOv7. YOLOv7 can run in real time on a desktop GPU. For edge GPUs a smaller version, called YOLOv7-tiny, was manually designed by the authors of YOLOv7. This thesis answers the question: Can a state-of-the-art compression of YOLOv7 achieve higher accuracy than YOLOv7-tiny at the same number of floating-point operations?
First, two state-of-the-art compression methods are selected and compared on YOLOv7-tiny. Then the best performing method, GBIP, is used to compress YOLOv7 till it has the same number of FLOPs as YOLOv7-tiny. From the experiments it is determined that GBIP is not able to achieve higher accuracy than YOLOv7-tiny at the same number of FLOPs.