The Way to Measure Success in Computer Vision: What is Mean Average Precision (mAP)?

There are many complex terms and concepts used in the world of computer vision. One of these terms is "Mean Average Precision" or "mAP" for short. But what exactly is mAP and why is it important?

1. The Basis of mAP

There are many ways to measure how successful a computer model is. mAP is a metric that measures how accurately the model makes predictions in tasks such as object detection.

2. Precision and Recall

To understand mAP, we first need to know two basic concepts: "precision" and "recall.". Precision refers to the proportion of objects that the model correctly predicts are actually correct. Precision refers to how many objects the model correctly detects and actually captures.

3. How is mAP Calculated?

Precision and sensitivity values are calculated for many different thresholds. A "Precision-Recall" curve is drawn over these values. mAP is the average of the area under this curve. A high mAP value means that the model detects objects with high accuracy.

Low and High Mean Average Precision

4. Mean Average Precision in practice

Let's walk through a cat detection model. This model needs to check whether a cat is present or absent in photos. Imagine that in some images the model correctly detects the cat, but in others it either gets it wrong or misses the cat. This is where mAP comes in. mAP is a value that summarizes the overall success of the model. A high mAP value indicates that the model is able to accurately and consistently detect cats in photos. This gives us important insights into how reliable the model is, especially when we want to implement a cat recognition system in the real world.

Result

In the field of computer vision, a precise and objective assessment of model performance is critical. mAP is one of the central metrics that enables this assessment in object detection tasks. A high mAP value indicates that the model is performing with high accuracy in detection tasks, which means that we can expect reliable and consistent results in practical applications. In this era of accelerating technological advancements, a deep understanding of metrics such as mAP allows us to make more informed decisions about how the model will perform in real-world conditions.