Mean Average Precision (mAP)
- Average Precision (AP) is a metric that summarizes the precision-recall curve for a specific class. Precision measures the accuracy of the positive predictions, while recall measures the ability to find all relevant instances.
- Mean Average Precision (mAP) is the average of the AP scores for all classes in the dataset.
Intersection over Union (IoU)
- IoU is a metric used to evaluate the accuracy of an object detector on a particular dataset. It measures the overlap between the predicted bounding box and the ground truth bounding box: where is the predicted bounding box and is the ground truth bounding box.
mAP50 vs. mAP50-95
- mAP50: This measures the mean average precision at a fixed IoU threshold of 0.5. It means that a detection is considered correct if the IoU between the predicted bounding box and the ground truth is greater than or equal to 0.5[1][3][5].
- mAP50-95: This is a more comprehensive metric that averages the mean average precision across multiple IoU thresholds, specifically from 0.50 to 0.95 in increments of 0.05. This means it calculates the AP at IoU thresholds of 0.50, 0.55, 0.60, ..., up to 0.95, and then averages these values[2][3][5].
Why mAP50-95 is Important
- Comprehensive Evaluation: By averaging over multiple IoU thresholds, mAP50-95 provides a more thorough evaluation of the model's performance, as it considers both easier (lower IoU) and more challenging (higher IoU) detection scenarios.
- Higher Scrutiny: It imposes a higher level of scrutiny on what counts as a "precise detection," making it a stricter and more reliable metric for model evaluation[4].
Formula
The formula for mAP50-95 can be written as:
Where:
- is the mean Average Precision over IoU thresholds from 0.50 to 0.95
- is the Average Precision at IoU threshold
- The sum is taken over 10 evenly spaced IoU thresholds from 0.50 to 0.95 (inclusive) with a step of 0.05
To expand this further:
Here's a breakdown of the formula:
- We calculate the Average Precision (AP) at each IoU threshold (0.50, 0.55, ..., 0.95).
- We sum these AP values.
- We divide by 10 (the number of thresholds) to get the mean.
This formula provides a single number that summarizes the model's performance across a range of IoU thresholds, giving a more comprehensive evaluation of the object detection model's accuracy.
In summary, mAP50-95 is a robust metric for assessing the performance of object detection models, offering a more detailed and stringent evaluation compared to mAP50.
Citations:
[1] https://typeset.io/questions/what-is-the-difference-between-map50-and-map50-95-5asxtut1cj
[2] https://datascience.stackexchange.com/questions/16797/what-does-the-notation-map-5-95-mean
[3] https://docs.ultralytics.com/guides/yolo-performance-metrics/
[4] https://www.reddit.com/r/computervision/comments/1ahsfto/training_a_yolo_model_how_to_tell_if_model_is/
[5] https://www.reddit.com/r/computervision/comments/162ss9x/trouble_understanding_map50_metric/