Instance Segmentation on Wound Analysis Dataset
88.57PrecisionYOLOv11
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| YOLOv11Features=Image classification; Object detection with bounding boxes; Pixel-level segmentation masks; Requires moderate to heavy computational power2026.03 | 88.57 | 86.79 | 87.67 | |
| UNetFeatures=Image classification; Object detection with bounding boxes; Pixel-level segmentation masks; Requires moderate to heavy computational power2026.03 | 77.22 | 84.53 | 80.71 | |
| Mask R-CNNFeatures=Image classification; Object detection with bounding boxes; Pixel-level segmentation masks; Requires moderate to heavy computational power2026.03 | 77 | 72 | 75 | |
| DeeplabV3Features=Image classification; Object detection with bounding boxes; Pixel-level segmentation masks; Requires moderate to heavy computational power2026.03 | 75.33 | 90.82 | 82.35 |