Complete interpretation of YOLOv5 network structure [source code + hand-drawn network structure + module structure]

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Detailed explanation of YOLOv5 network structure
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Preface

Although I have used YOLOv5 to run several data sets before the winter vacation, I have not delved into its network structure and characteristics. For more than a week after school started, I was full of food and had nothing to do. So I set a flag for myself three days ago: understand the network structure of YOLOv5 and record my learning process. That’s why I have a simple and half-understood detailed explanation of YOLO in the past few days. If you have any questions while reading, please feel free to share them in the comment area.

Overall network structure

1 YOLO anchor settings

First, the anchor generation and detection process is analyzed, and the anchor source code that can be brought into your own data set is given.
YOLO anchor settings

2 YOLO Backbone

Starting from the network configuration file of yolo, we carefully analyze the composition of the backbone and the propagation process of the feature map, and analyze the composition of each component, such as CSP, CBS, SSPF, and Bottleneck. For the bottleneck module Bottleneck, we introduce it with Resnet and give The difference between resnet and CPS.
Detailed explanation of Backbone of YOLOv5

3 Neck by YOLO

The classic FPN+PAN design is a top-down + bottom-up feature extraction method.
Neck design of YOLOv5

4 YOLO’s head

Mainly the understanding of the Detect module
Detailed explanation of YOLOv5 head

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