Accuracy
Measure how often predicted class labels match the reference labels.
Testing methods
16AP@0.50
Measure detection average precision at an IoU threshold of 0.50.
AP@0.75
Measure detection average precision at an IoU threshold of 0.75.
Dice
Measure Dice overlap between predicted and reference masks.
IoU variants
Compare predicted and reference boxes using IoU, GIoU, DIoU and CIoU.
Macro precision, recall and F1
Compare macro-averaged precision, recall and F1 for classification.
MAE
Measure the mean absolute difference between predicted and reference values.
mAP
Measure average precision for oriented bounding boxes.
mAP 50–95
Measure detection average precision across IoU thresholds from 0.50 to 0.95.
MAPE
Measure mean absolute error relative to non-zero reference values.
mAR
Measure detection recall with limits of 1, 10 and 100 detections per image.
Mask mAP
Measure instance-mask average precision at the supported IoU thresholds.
Mean IoU
Measure the intersection over union of predicted and reference masks.
Pixel precision and recall
Measure pixel-level precision and recall for segmentation.
R²
Compare regression residuals with variation in the reference values.
RMSE
Measure the square root of the mean squared prediction error.
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