In the context of R-CNN and general object detection datasets, ground truth labels play a crucial role in training and evaluating models. These ground truth labels consist of annotations that provide detailed information about the objects present in an image. Each annotation includes a category identifier (category_id) that links to a specific class of object (e.g., "Face" as one possible category), and the bounding box (bbox) which just like the proposed candidate boxes, specifies the object's location within the image through coordinates $[x, y]$ along with its width and height $[w, h]$.

 ...
"annotations": [
  {# e.g., the label for face
	  "category_id": int
	  "bbox": [x,y,w,h],
  },
  {# e.g., the label for rocket
	  "category_id": int
	  "bbox": [x,y,w,h],
  },
    ...
],
...

The bounding box is particularly important as it defines the precise area where the object is located, allowing the detection model to learn where objects tend to appear within the visual field and their typical dimensions. The category ID enables the model to not only detect the presence of objects but also classify them into predefined categories.