Jesteban247/yolo11-fracture-onnx
By Jesteban247 · ↓ 0 · ♥ 0 · Updated 2025-10-23
What's in this repo
This page lists the files in Jesteban247/yolo11-fracture-onnx, including best.onnx.
- 1 ONNX models (.onnx files)
- 6 files, including 1 .png, 1 .md, 1 .yaml, 1 .onnx, 1 .csv
- Last updated 2025-10-23
ONNX files
| File | Size | Download |
|---|---|---|
| best.onnx | — | Download |
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
🦴 YOLO11 — Fracture Detection (Freeze-10, FP16 ONNX)
This model fine-tunes YOLO11n for fracture detection using the
Fracture 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 | 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) |
🩺 Example Detection
📈 Results
| 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.
Model info
Created: 2025-10-23
Updated: 2025-10-23
View on Hugging Face