Instructions to use liodon-ai/dental-panoramic-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use liodon-ai/dental-panoramic-detector with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("liodon-ai/dental-panoramic-detector") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Dental Panoramic X-Ray Detector
YOLO11-N model trained to detect dental pathologies on panoramic radiographs.
Classes
| ID | Name | Description |
|---|---|---|
| 0 | caries |
Dental caries (including deep caries) |
| 1 | periapical_lesion |
Periapical lesion / apical periodontitis |
| 2 | impacted_tooth |
Impacted and wisdom teeth |
Performance
Trained on 9,928 panoramic X-rays, validated on DENTEX val set (46 images, 182 boxes).
| Metric | Score |
|---|---|
| mAP50 | 0.622 |
| mAP50-95 | 0.406 |
| Precision | 0.630 |
| Recall | 0.614 |
Training converged at epoch 27/57 (early stopping, patience=30).
Training Data
liodon-ai/dental-panoramic-xray-yolo β combined DENTEX + OralXrays-9 (CVPR 2025), 9,928 train images, 39,715 annotated boxes.
Files
| File | Size | Description |
|---|---|---|
best.pt |
5.2 MB | PyTorch weights (Ultralytics) |
best.onnx |
10.1 MB | ONNX export for framework-agnostic inference |
Recommended Inference Settings
| Parameter | Value | Reason |
|---|---|---|
conf |
0.45 | Filters weak detections β reduces noise without missing high-confidence findings |
iou |
0.35 | Tighter NMS β prevents duplicate boxes on adjacent teeth |
imgsz |
640 | Training resolution |
At conf=0.25 the model over-fires on caries (adjacent teeth flagged together). At conf=0.45 output is clean and clinically readable.
Usage
from ultralytics import YOLO
model = YOLO("best.pt")
results = model("panoramic.jpg", imgsz=640, conf=0.45, iou=0.35)
results[0].show()
Or with ONNX:
import onnxruntime as ort
sess = ort.InferenceSession("best.onnx")
Per-Class Notes
impacted_tooth β highest quality class. Consistently detects impacted wisdom teeth with tight boxes and 0.66β0.84 confidence. Closest to clinical-grade.
periapical_lesion β fires correctly when present, but limited by small val set. Treat as a flag to look closer, not a diagnosis.
caries β directionally correct (right quadrant, right teeth) but recall is limited at panoramic resolution. Use as a screening hint, not a count.
Citation
@model{liodonai2026dentalpanoramic,
title={Dental Panoramic X-Ray Detector},
author={Liodon AI},
year={2026},
publisher={Hugging Face},
url={https://huggingface.co/liodon-ai/dental-panoramic-detector}
}
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