What is Mean Average Precision (mAP)?
The Computer Vision Research Committee uses an average benchmark for evaluating the performance of computer vision, this is known as Mean Average Precision or mAP. It analyses and evaluates the validity of object detection models. The accuracy of the prediction is measured by precision and (mAP) analyses and calculates the bargain between recall and precision. This results in the maximization of the effect of both metrics.
It helps to compute the average Precision of the values that is more than 0 to 1. It is based on two major concepts, that is:
Precision: It measures the accuracy of your predictions and evaluates the percentage of your correct prediction.
P = TP/(TP + FP)
= TP / Total Predictions
Recall: It measures the positives. The more you evaluate the positive the percentage will keep increasing.
R = TP / (TP + FN)
= TP / Total Ground Truths
How to calculate Average Precision-recall (AP) manually?
When you want to calculate the Average Precision manually out of an image then you can follow these simple steps for your convenience:
- Record every object detected in every class label along with the confidence score.
- Calculate Precision and recall respectively.
- Plot the Precision-recall curve on a graph.
- Use any point interpretation method to calculate the average precision.
- Plot the final interpolated graph and calculate the average Precision for each class.
A mathematical explanation of Mean Average Precision (mAP)
Mean Average Precision (mAP) is the average of every Average Precision (AP) throughout all the detected class labels.
The formula for calculating mean average precision is:
mAP= 1/n * sum (AP), where n is the number of classes.
For instance, an image has 5 class labels. In this case, the Mean Average Precision will be as follows:
mAP = 1/5 * (0.349 + 0.545 + 0 + 1 + 0.5)
= 47.88 %
This result only arrives when you have the Average Precision for every class label separately.
It is important to identify the presence of objects in an image that is relevant to each other and are also separately identified into different class labels. Various object detection models do this classification automatically so that manual resources can be saved. For doing this, various researchers have trained several object detection models to do this work on their own without human intervention. Object detection and image processing thus work hand in hand. The model detects the objects and the bounding boxes surrounding those objects in a particular image. That is where the concept of confidence score in relation to Precision and recall comes into play.
Understanding of Precision-recall curve
The Precision-recall curve is drawn by pointing the model's Precision and recall values at the graph with the final confidence score. The values of Precision and recall are plotted so that they can be joined and be made a curve on a graph. It is a downward sloping curve where the confidence score keeps on decreasing as the values of Precision and recall are marked on the graph.
If you make more predictions the recall is increased and out of the predictions if only a few are correct then it decreases precision. This object detection technique is connected with artificial Intelligence and machine learning and the researchers have constantly thrived to combine Precision and recall as a single metric model.
Object detection with machine learning is a very important concept and for performing the activity Mean Average Precision (mAP) plays a vital role. It reduces human efforts and performs the function of putting several objects in the same class labels and calculating the mean average precision of those several classes automatically.