Mean Average Precision (mAP) is a widely used metric to evaluate the performance of object detection and segmentation algorithms. It provides a comprehensive measure by combining precision and recall across different classes and thresholds, offering insights into how well a model detects and localizes objects. By averaging the precision values for each class at various intersection-over-union (IoU) thresholds, mAP helps to assess the overall effectiveness of an object detection system.
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