An object detection model typically consists of three essential components: a head, a neck, and a backbone. The backbone serves as the fundamental "base" classification model upon which the entire object detection model is built. It plays a pivotal role in extracting the core features from the input data, which are then further processed by the head and neck components to accurately identify and localize objects within the image or other types of data.
