mAP Scores as Performance Metrics
Explore how mAP scores serve as key performance metrics for YOLO object detection models. Understand the role of true positives, false positives, and false negatives, and learn how mAP is calculated across different IoU thresholds, class averages, and object sizes to assess model accuracy and reliability.
The mAP is used in object detection to compare the performance of a model. It combines two scores, precision and recall, to calculate how our model performs with respect to FN and FP.
How YOLO calculates TPs and FPs?
In object detection tasks performed by YOLO, evaluating the performance of the model involves concepts of true positives (TPs) and false positives (FPs). Here are some definitions of these concepts:
True positives (TPs): A true positive is an instance where the model correctly predicts the object—that is, the predicted bounding box correctly identifies and localizes an object in the image.
False positives (FPs): A false positive is an instance where the model incorrectly predicts the ...