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In this unofficial part two of the object detection series, the video discusses how to evaluate bounding box predictions for objects. The focus is on quantifying the accuracy of a predicted bounding box compared to a target bounding box. The key metric introduced is Intersection over Union (IoU), which helps measure the quality of the prediction. The tutorial aims to implement this metric using PyTorch. After an introduction, the video explores how to assess the effectiveness of bounding boxes, starting with an example involving a car in an image and the corresponding target and predicted bounding boxes.
