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yolo11-blood_cell-onnx
Jesteban247/yolo11-blood_cell-onnx
yolo11-blood_cell-onnx is a object detection model from Jesteban247. Use it when you need objects located in an image. It is set up for ultralytics. The card lists the license as apache-2.0.
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)…
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Updated Oct 23, 2025
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At a glance
- Task
- Object Detection
- Library
- ultralytics
- License
- apache-2.0
- Access
- Public
- Created
- Oct 23, 2025
- Updated
- Oct 23, 2025
- SHA
- 6847f0ee
Base models
Model card
From the Hugging Face model README
🩸 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.
- Task
- Object Detection
- Library
- ultralytics
- License
- apache-2.0
- Languages
- en
- Created
- Oct 23, 2025
- Updated
- Oct 23, 2025