🩸 YOLO11 — Blood Cell Detector (Freeze-10, FP16 ONNX)
This model is a fine-tuned YOLO11 trained for blood cell detection on the
Blood Cell Dataset (Roboflow).
The first 10 layers were frozen to retain pretrained spatial features, and the model was exported to ONNX (FP16) for efficient inference.
⚙️ Configuration
| Attribute | Value |
|---|---|
| Base Model | yolo11n.pt |
| Dataset | Blood Cell (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 | 0.974 |
| mAP50-95 | 0.905 |
| Precision (B) | 0.951 |
| Recall (B) | 0.920 |
| Inference Time (ms) | 33.73 |
| FPS | 29.65 |
| 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.