How does Labelbox handle multi-class evaluation metric calculations (e.g. precision and recall) for object detection use cases?
Specifically, for example, if I have 100 images with 8 different classes (i.e. 8 different bounding box detections) in EACH image, how do we calculate precision, recall, etc.? Do we use any sort of average/weighted average scheme across all the detections across all the images?