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Evaluating Object Detection Models Using Mean Average Precision

Evaluating an object detection model using Mean average precision can provide valuable insight to how the model is performing at various confidence values.
Evaluating Object Detection Models Using Mean Average Precision

To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The mAP compares the ground-truth bounding box to the detected box and returns a score. The higher the score, the more accurate the model is in its detections.

Usually, the object detection models are evaluated with different IoU thresholds where each threshold may give different predictions from the other thresholds. Assume that the model is fed by an image that has 10 objects distributed across 2 classes. How to calculate the mAP?

To calculate the mAP, start by calculating the AP for each class. The mean of the APs for all classes is the mAP.

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Evaluating Object Detection Models Using Mean Average Precision - KDnuggets
In this article we will see see how precision and recall are used to calculate the Mean Average Precision (mAP).
https://www.kdnuggets.com/2021/03/evaluating-object-detection-models-using-mean-average-precision.html

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