The Structure of YOLO (Backbone, Neck, and Head)
Learn the meaning of the terms commonly used in YOLO: backbone, neck, and head.
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The YOLO model consists of the following three main components:
Backbone: It extracts features from the input image.
Neck: It collects features that form the backbone for further transformation.
Head: It is responsible for final predictions.
Backbone
The term backbone in YOLO refers to a CNN that extracts features from the input image. These extracted features are subsequently utilized by later layers in the network for making predictions. Generally, a pretrained model such as a ResNet is used as a backbone. Here are some key features of the backbone network:
The architecture of the backbone network plays a critical role in an object detection model because it significantly influences the quality of the generated feature maps. ...