---
license: cc-by-nc-4.0
tags:
  - dental
  - object-detection
  - image-classification
  - dinov3
  - vit-b16
  - yolo
  - pytorch
language:
  - en
library_name: pytorch
pipeline_tag: object-detection
---

# DINOv3 ToothVLM: Dental Disease Detection & Periodontal Classification (Sonata)

Models fine-tuned on the **Sonata** dental dataset, built on a **DINOv3 (ViT-B/16)** backbone.

> **License notice**: these weights are derived from the DINOv3 pretrained
> backbone (`dinov3_vitb16_pretrain_lvd1689m`), which is released under the
> [DINOv3 License](https://github.com/facebookresearch/dinov3) (non-commercial).
> By using these models you agree to the original DINOv3 license terms.

## Repository Layout

```
weights/
├── detection/best_map.pth         # Multi-disease detector (1.1 GB)
└── classification/best_val_acc.pth # Periodontal classifier (661 MB)
```

---

## 1. Detector — Multi-Disease Detection (`weights/detection/best_map.pth`)

DINOv3 ViT-B/16 backbone + YOLOv10-style end-to-end detection head (NMS-free),
trained on the Sonata `All_Diseases` COCO split (mixed training of all disease
categories).

### Classes (4)
`caries` · `calculus` · `mouth_ulcer` · `tooth_discoloration`

### Performance (COCO bbox, validation)
| Metric | Value |
|---|---|
| mAP@0.5:0.95 | **0.444** |
| mAP@0.5 | **0.699** |
| mAP@0.75 | 0.477 |
| mAP small / medium / large | 0.360 / 0.454 / 0.419 |
| Best mAP (training) | 0.451 |

### Training setup
| Hyperparameter | Value |
|---|---|
| Backbone | DINOv3 ViT-B/16 (`lvd1689m` pretrained) |
| Epochs | 50 |
| Batch size | 8 |
| Base LR / backbone LR | 0.001 / 0.0001 |
| Warmup epochs | 5 |
| Unfrozen transformer blocks | 6 |
| Loss | E2E (YOLOv10) |

### Usage
```python
import torch

ckpt = torch.load("weights/detection/best_map.pth", map_location="cpu")
# State dict contains the DINOv3 backbone + v10Detect head.
# See train_detector_sonata.py in the source repo for the exact architecture,
# preprocessing (resize to 1024, normalization) and inference (e2e, no NMS) code.
```

---

## 2. Classifier — Periodontal Disease Classification (`weights/classification/best_val_acc.pth`)

DINOv3 ViT-B/16 backbone + CNN classification head
(Conv+BN+ReLU → GAP → Dropout → FC), trained on the Sonata
`Periodontal_Disease` subset with a stratified 70/15/15 train/val/test split.

### Classes (3)
`gingival_diseases` · `non_periodontal_disease` · `periodontitis`

### Performance (test set, n = 353)
| Metric | Value |
|---|---|
| Accuracy | **0.771** |
| Macro-F1 | **0.772** |

Per-class F1:
| Class | Precision | Recall | F1 |
|---|---|---|---|
| gingival_diseases | 0.788 | 0.741 | 0.764 |
| non_periodontal_disease | 0.641 | 0.750 | 0.691 |
| periodontitis | 0.883 | 0.838 | 0.860 |

### Training setup
Same backbone fine-tuning strategy as the detector (frozen backbone,
last 6 transformer blocks unfrozen, backbone LR 0.0001).

### Usage
```python
import torch

ckpt = torch.load("weights/classification/best_val_acc.pth", map_location="cpu")
# See train_classifier_periodontal.py for the model definition,
# image preprocessing (resize 512, ImageNet normalization) and inference.
```

---

## Citation
If you use these models in your research, please cite the DINOv3 paper:

```bibtex
@article{oquab2025dinov3,
  title={DINOv3: Learning Robust Visual Features without Pretraining},
  author={Oquab, Maxime and others},
  journal={arXiv preprint arXiv:2505.18478},
  year={2025}
}
```