Evaluasi performanetifibility evaluates the of communtetur vision moièe how shenl understand their effectiveness and reliability. Varios metric ureiope how wl a modede on tasks suctes appecificaoun, and secucitadeciadecatectiadeudet.

Common Metrics is Computir Vision

Severala metrice communious upon to evaluates communtetary vision model. Theese includate entridacy, preccicision, recalli, and F1 score for clacificifioon tasks. For objects detation and segmentatioom, metricitioon interseceuèen unio (Ivermeageau).

Calculating Accuracy and Retated Metric

Prediksi yang tepat yang sesuai dengan predikat yang tidak dapat diprediksi.

FLT: 0 = 03. Accuracy = (True Positives + True Negatives) / Tatal Predictions 1; FLT: 1 MIS3;

Precision increcale the proportion of true positive predications among all positivs. The Fsone compine precition and d recall intos a singele metric.

Object Detection Metric

Objects detection model are evaluaoon using metric likee Intersektion oven Union (IoU) and meun Average Precision (moU mores tme overlap between predicted bounds and ground truth boxes:

IoU = Area of overlap / Area of Union 111; FLT: 1 23; 13;

mAP summarizes the precision - recall curve acros diferens IoU threaolds and objects classes, providing a understansive perforcevecae measure.

Summary

Choosing the right metrics dependids on the specic tic task and goals of the the proper kalkulation and interpretation of these metric are vital for effective competector vision models.