What does an object detection model typically output?

An object detection model is designed to identify and locate objects within an image. The primary output it provides includes both bounding boxes, which are rectangular coordinates that specify the positions of detected objects, and class labels that categorize these objects according to predefined classes, such as "car," "person," or "dog." This dual output allows applications to not only recognize what is present in the image but also to understand where those objects are located.

While descriptions of the scene, detected objects' names, or the background context may be beneficial in certain contexts, they do not align with the primary purpose of an object detection model. The focus of such a model is specifically on spatially locating various objects within an image and classifying them, making the provided output of bounding boxes and class labels essential for its functionality. It allows for further applications, such as tracking objects over time or enabling other analysis tasks in computer vision.