## What is mAP?

The mAP is a prominent evaluation measure in computer vision for object identification, such as localization and classification. The position of an instance is determined by bounding box coordinates, and categorization informs you what it is.

The mAP is used by several object identification algorithms, including YOLO, Faster R-CNN, and MobileNet SSD to validate their models in preparation for publication.

The abbreviation mAP stands for Mean Average Precision, where precision is a metric for determining how accurate your forecasts are. In other words, the percentage of your forecasts that are right. It determines how many of your model’s predictions were actually right.

You could end up implementing this model in your application based on the mAP score. **That would be a huge mistake.** The average of precision values is not used to compute mAP.

Object detection systems use a bounding box and a class label to create predictions.

We calculate the overlap between the predicted and ground truth bounding boxes for each bounding box. IoU is a metric for this (intersection over union).

We compute Precision and Recall for object detection tasks using the IoU value for specified IoU criteria. For instance, if the IoU criteria is 0.5 and the IoU value for a forecast is 0.9, the prediction is classified as True Positive (TF). If IoU is 0.3, on the other hand, we consider it a False Positive (FP).

## Precision-Recall curve

Precision is a measure of how often your model gets it right when it guesses. The recall is a metric that determines if your model correctly predicted every time it should have predicted. Consider the picture, which has five cats. A model that properly classifies only one of these five as “cat” has perfect accuracy (since every estimate it makes – one – is right) but poor recall (only one of five cats has been found).

Models with a confidence component can vary the amount of confidence required to make a prediction to trade off accuracy for recall. Differently stated, if avoiding false positives—saying a cat is present when the cell was a cat—is more essential than avoiding false negatives, the model’s confidence threshold may be raised to encourage the model to only make high precision predictions at the price of decreasing its coverage (recall).

The precision-recall curve is the process of visualizing the model’s accuracy and recall as a function of the model’s confidence threshold. It’s slanted downward because as confidence falls, more forecasts (which aid recall) and fewer exact predictions are produced (hurting precision).

- The slope is trending downhill as the model loses confidence. If the accuracy and recall curves are slanted upward, the model’s confidence estimation is likely to be flawed.

Metrics are beloved by AI researchers, because the entire precision-recall curve may be recorded in a single measure. The first is F1, which combines precision and recall metrics to determine the best confidence threshold where precision and recall generate the maximum F1 value. Then there’s AUC (Area Under the Curve), which combines the percentage of the plot that lies below the accuracy and recall curves.

Average precision (AP) is the ultimate precision-recall curve statistic, and it’s the one we’re most interested in. The weighted mean of precisions acquired at each threshold is determined, with the increase in recall from the preceding threshold serving as the weight.

The precision-recall curve’s packed form is captured by both AUC and AP. It is a question of choosing whether to use one or the other for object detection, and the research community has decided on AP for interpretability.

## Average precision

Finding the area under the precision-recall curve is the standard definition for the Average Precision (AP). The average of AP is mAP (mean average precision).

In certain cases, the AP for each class is calculated and then averaged to produce the mAP. In other cases, though, they have the same meaning. For example, there is no difference between AP and mAP when evaluating the COCO challenge.

Depending on the different detection problems that exist, the mean Average Precision or mAP score is derived by taking the mean AP over all classes and/or overall IoU thresholds.