What is Mean Average Precision (mAP)?
Mean Average Precision (mAP) is a widely used metric in the field of Object Detection, which falls under the broader category of Computer Vision in Artificial Intelligence. It provides a comprehensive evaluation of a model's performance in detecting and classifying objects within images.
Understanding mAP
mAP is calculated by taking the Average Precision (AP) across multiple classes and then averaging these values. Average Precision itself is derived from the precision-recall curve, which plots precision against recall for various threshold settings.
Precision and Recall
Precision measures the accuracy of the positive predictions made by the model, while Recall indicates the ability to identify all relevant instances. A model with high precision but low recall may miss many objects, whereas one with high recall may include false positives.
Calculation of mAP
To compute mAP, the steps include:
- Compute precision and recall for each detected object.
- Draw the precision-recall curve.
- Calculate the area under the curve, yielding the Average Precision for each class.
- Averaging these values across all classes gives the final mAP score.
Importance in Object Detection
mAP is crucial for benchmarking different object detection models as it accounts for both the correctness and completeness of detections. The higher the mAP score, the better the model performs, making it an essential metric in the development and evaluation of AI systems in Computer Vision.