[email protected] vs. [email protected]:0.95: What’s the Difference?

mAP is an object-detection evaluation metric built from precision, which measures the correctness of positive predictions, and recall, which measures how many real objects are found; confidence thresholds create a precision-recall trade-off, and Average Precision summarizes each class’s precision-recall curve before mean Average Precision averages results across classes. Whether a prediction counts as correct also depends on Intersection over Union (IoU), the overlap between predicted and ground-truth bounding boxes. [email protected] calculates performance using a single, relatively forgiving IoU threshold of 0.50, making it useful when general object detection and approximate localization are sufficient. In contrast, [email protected]:0.95 averages mAP across ten thresholds from 0.50 through 0.95, producing a more stringent and typically lower score that better reflects consistent localization accuracy and aligns with the COCO evaluation protocol. The guide also notes that Roboflow Train and compatible benchmarking workflows can report these metrics, including breakdowns by object-size categories, to compare models and diagnose performance on small, medium, and large bounding boxes.

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