This document lists and describes common performance evaluation metrics used for image classification, object detection, and semantic segmentation. These include similarity index, correct detection ratio, segmentation errors, Hausdorff distance, average surface distance, accuracy, recall, precision, F-measure, Matthews correlation coefficient, Dice coefficient, Jaccard index, specificity, and sensitivity.

1. Best performance evaluation metrics for image Classification, Detection, Segmentation?
1. Similarity Index = 2*TP/(2*TP+FP+FN)
2. Correct detection Ratio = TP/TP+FN
3. Segmentation errors (OSE, USE, TSE)
4. Hausdorff Distance
5. Average Surface distance
6. Accuracy = (TP+TN)/(FN+FP+TP+TN);
7. Recall = TP/(TP+FN);
8. Precision = TP/(TP+FP);
9. F-measure = 2*TP/(2*TP+FP+FN);
10. MCC = (TP*TN-FP*FN)/sqrt((TP+FP)*(TP+FN)*(TN+FP)*(TN+FN));
11. Dice = 2*TP/(2*TP+FP+FN);
12. Jaccard = Dice/(2-Dice);
13. Specitivity = TN/(TN+FP);
14. Sensitivity = TP/(TP+FN);