Hello trend,
This is Satya Mallick from LearnOpenCV.com.
Mean Average Precision (mAP) is a performance metric used for evaluating machine learning models. It is the most popular metric that is used by benchmark challenges such as PASCAL VOC, COCO, ImageNET challenge, Google Open Image Challenge, etc.
The term Mean Average Precision (mAP) can be tricky. In PASCAL VOC, Mean Average Precision (mAP) and Average Precision (AP) have distinct meanings. They are calculated for a single IoU threshold, i.e., 0.5. However, in MS COCO, mAP and AP are used interchangeably and it is calculated for a set of IoU thresholds (0.5:.0.05.0.95). Continue reading the article to find a detailed explanation and more interesting facts.
In today's blog post, you will learn about:
- Model Evaluation Metrics in brief.
- How to calculate Precision and Recall.
- How to calculate Average Precision and Mean Average Precision using 11-point and 101-point interpolation methods.
- Different Object Detection Challenges (ImageNet, PASCAL VOC, COCO, etc) and the evaluation metrics used by them.
Without further ado, let's get started.
mAP in Object Detection |
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