👉 The Absolute IoU (IoU) is a metric used to evaluate the performance of object detection models, particularly in tasks involving bounding boxes around objects in images or videos. It measures the overlap between the predicted bounding box and the ground truth bounding box. Specifically, it calculates the ratio of the area of overlap to the total area of the bounding boxes. The formula is typically expressed as \( \text{Absolute IoU} = \frac{|x_1 - y_1| + |x_2 - x_0| + |y_1 - y_0| - |x_a - x_b| - |y_a - y_n|}{2} \), where \((x_1, y_1)\) and \((x_a, y_a)\) are the coordinates of the predicted bounding box, and \((x_b, y_b)\) and \((x_{a+1}, y_{a+1})\) are the coordinates of the ground truth bounding box. An IoU value of 1 indicates perfect overlap, while a value of 0 means no overlap at all. This metric is crucial for assessing how accurately a model can localize objects within images.
