AI engineering glossary · Computer vision
What is precision and recall for detection?
Precision and recall for detection: Detection versions of the standard metrics: a predicted box is a true positive if its IoU with a real object passes a threshold. Precision = TP / (TP + FP) and recall = TP / (TP + FN), so missed objects and false boxes are both counted.
Precision and recall for detection: an example
Of 10 predicted boxes, 8 match real objects and there are 12 real objects: precision = 0.8, recall = 8 / 12 = 0.67.
Related terms
More Computer vision terms
- Pretrained backbone
- R-CNN
- Random crop
- RANSAC
- Receptive field
- Region proposal
- Region proposal network
- ResNet
Where you learn it
Precision and recall for detection is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.