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| Attribute | Value |
|---|---|
| Base Model | yolo11n.pt |
| Dataset | Fracture (Roboflow) |
| Epochs | 30 |
| Batch Size | 32 |
| Image Size | 640×640 |
| Optimizer | Auto |
| Freeze Layers | 10 |
| Precision | FP16 (half=True) |
| Export Format | ONNX |
| Device | GPU (0,1) |
| Metric | Value |
|---|---|
| mAP50 | 0.920 |
| mAP50-95 | 0.524 |
| Precision (B) | 0.903 |
| Recall (B) | 0.832 |
| Inference Time (ms) | 33.04 |
| FPS | 30.27 |
| Model Size (MB) | 5.2 |
FP16 inference maintained identical accuracy to FP32 while reducing latency.
Layer freezing improved training stability and avoided overfitting on the limited dataset.