Intersection over Union (IoU), a crucial performance metric, is widely employed to assess the accuracy of annotation, segmentation, and object detection algorithms. It precisely quantifies the degree of overlap between the predicted bounding box or segmented region and the ground truth bounding box or annotated region within a dataset. By doing so, IoU offers a reliable measure of how closely a predicted object corresponds to the actual object annotation, facilitating the evaluation of model accuracy and serving as a basis for fine-tuning algorithms to achieve better performance.
