This week you'll learn about the Mean Average Precision (mAP) Using the COCO Evaluator.

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Over the last few weeks, we have introduced you to the world of YOLO, a result of research on revolutionizing the object detection domain. However, how do we know that an object detector is actually good? 

You can have results that look like a million dollars, but without the right metric, those results would mean nothing. Similarly, choosing the wrong metric can make bad results look good and impactful. 

In the world of deep learning, choosing the right metric to judge your research is as crucial as the research itself. For that reason, we will be looking at the Mean Average Precision Metric using the COCO Evaluator today to evaluate our YOLO models.

The big picture: To judge an object detector, we need to assess the predicted bounding box coordinates and compare them with the ground-truth bounding box coordinates. This automatically makes it a much more nuanced task than classification or segmentation, where metrics like confusion matrix, IoU, etc., can be utilized. 

How it works: Object detection requires you to classify not only the object but also find its location. Therefore, simple metrics like precision and recall cannot be used. For that reason, a domain-specific metric called Mean Average Precision (mAP) combines the elements of Intersection over Union (IoU), precision, recall, and the precision-recall curve. 

Our thoughts: To understand mAP, you are also exposed to other metrics, which are key to understanding several other domains in deep learning. 

Yes, but: You need to understand that the YOLO versions we have learned till now have long been surpassed by other versions. If you evaluate primitive versions of YOLO on this evaluator, you will definitely see that by comparison, your model doesn't perform as well as others. 

Stay smart: Do not let that deter you from this journey to master the YOLO family. But before that, it is important to understand the metrics where the COCO evaluator is based. So don't take evaluation metrics lightly!

Click here to read the full tutorial

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