Mean average precision (mAP) is a metric used to evaluate the performance of object detection models by measuring the accuracy of predicted bounding boxes against ground truth annotations. It combines precision and recall into a single value, providing insight into how well a model detects and localizes objects across different categories. This metric is particularly important in the realm of object detection and segmentation techniques, as it reflects both the correctness of detections and their relevance to the actual objects in an image.
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