TABLE 4.
Pain classification accuracy using different face detectors.
| Subset | AP | Recall | FI Score | AUC | FPR | TPR |
|---|---|---|---|---|---|---|
| YOLOv3-SEG16 | 0.485 | 0.475 | 0.404 | 0.489 | 0.748 | 0.777 |
| YOLOv3-SEG32 | 0.492 | 0.479 | 0.448 | 0.492 | 0.746 | 0.732 |
| Y0L0v3-Equal | 0.623 | 0.595 | 0.585 | 0.607 | 0.557 | 0.772 |
| YOLOv5-SEG32 | 0.656 | 0.657 | 0.656 | 0.655 | 0.307 | 0.617 |
| Y0L0v5-MM | 0.614 | 0.615 | 0.614 | 0.612 | 0.321 | 0.546 |
| Y0L0v5-Equal | 0.689 | 0.688 | 0.685 | 0.683 | 0.229 | 0.596 |
| Y0L0v6* [9] | 0.709 | 0.695 | 0.702 | 0.819 | 0.500 | 0.861 |