IOU Replace Mark For Free

DIoU(Distance- Intersection over union) loss function incorporates the normalized distance between the predicted box and the target box.

Similarly, IoU, in its native form, also has meaningless gradients and cannot be used as a loss function. However, extensions to IoU that preserve gradients exist and can be effectively used as a loss function for training.

Unlike GIoU loss, DIoU loss directly minimizes the distance between predicted and ground truth-bound boxes and converges much faster than GIoU even when the ground truth boxes have horizontal and vertical orientations.

Alternatives to IoU Average Precision (AP) or mean Average Precision (mAP) are common alternatives, both of which are used to evaluate models such as Faster RCNN, Mask RCNN, and YOLO. AP is calculated for every single class, meaning the number of classes and AP values should be equal.

Intersection over Union (IoU) is a commonly used performance metric in object detection tasks in machine learning. It measures the overlap between the predicted bounding box of an object and the ground truth bounding box in an image.

(3) WIoU loss Intersection over union (IoU) is a common evaluation metric in target detection [30] ; it calculates the loss by evaluating the distance between the model's prediction box B and the true box B gt .

Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes. IOU is defined as follows: IOU = true_positive / (true_positive + false_positive + false_negative).

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