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In this video tutorial, the focus is on evaluating bounding box predictions in object detection. The presenter aims to measure how accurately a predicted bounding box aligns with a target bounding box for an object. To achieve this, the tutorial introduces the metric known as Intersection over Union (IoU). Viewers will learn how to quantify the quality of bounding box predictions by calculating IoU and implementing it using PyTorch. The session begins with an introduction and a brief discussion on the problem of screen tearing before diving into the implementation of the IoU metric.