The Structure of YOLO (Backbone, Neck, and Head)

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.

• The features extracted by the backbone network are represented as feature maps. These are numerical matrices that encapsulate the patterns found in the image.

• Interestingly, the backbone network extracts ...