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IOU(Intersection over Union) is a term used to describe the extent of overlap of two boxes. The greater the region of overlap, the greater the IOU. IOU is mainly used in applications related to object detection, where we train a model to output a box that fits perfectly around an object.

Intersection over Union (IoU) is used when calculating mAP. It is a number from 0 to 1 that specifies the amount of overlap between the predicted and ground truth bounding box. an IoU of 0 means that there is no overlap between the boxes.

Intersect over Union (IoU) is a metric that allows us to evaluate how similar our predicted bounding box is to the ground truth bounding box. The idea is that we want to compare the ratio of the area where the two boxes overlap to the total combined area of the two boxes.

The IOU is a number between 0 and 1, with larger being better. Ideally, the predicted box and the ground-truth have an IOU of 100% but in practice anything over 50% is usually considered to be a correct prediction.

MeanIoU classIOU is defined as follows: IOU = true_positive / (true_positive + false_positive + false_negative). The predictions are accumulated in a confusion matrix, weighted by sample_weight and the metric is then calculated from it. If sample_weight is None , weights default to 1.

An IOU, a phonetic acronym of the words "I owe you," is a document that acknowledges the existence of a debt. An IOU is often viewed as an informal written agreement rather than a legally binding commitment. Dating as far back as the 18th century, at least, IOUs are still very much in use.

The denominator is the area of union, or more simply, the area encompassed by both the predicted bounding box and the ground-truth bounding box. Dividing the area of overlap by the area of union yields our final score the Intersection over Union.