🎗️ YOLO11 — Breast Cancer Detection (Freeze-10, FP16 ONNX)
This model fine-tunes YOLO11n for breast cancer cell detection using the
Breast Cancer Detection Dataset (Roboflow).
The first 10 layers were frozen to retain pretrained detection features, and the model was exported to ONNX (FP16) for deployment.
⚙️ Configuration
| Attribute | Value |
|---|---|
| Base Model | yolo11n.pt |
| Dataset | Breast Cancer Detection (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) |
🩺 Example Detection
📈 Results
| Metric | Value |
|---|---|
| mAP50 | 0.957 |
| mAP50-95 | 0.735 |
| Precision (B) | 0.948 |
| Recall (B) | 0.898 |
| Inference Time (ms) | 33.57 |
| FPS | 29.79 |
| 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.