Mean Average Precision (mAP) Explained: Everything You Need to Know

The mAP is calculated by finding Average Precision(AP) for each class and then average over a number of classes. The mAP incorporates the trade-off between precision and recall and considers both false positives (FP) and false negatives (FN). This property makes mAP a suitable metric for most detection applications. Precision-Recall curve is obtained by plotting the model's precision and recall values as a function of the model's confidence score threshold. Precision is a measure of when ""your model predicts how often does it predicts correctly?""