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---
license: apache-2.0
datasets:
- strangerguardhf/Tooth-Agenesis-6_Types
language:
- en
base_model:
- google/siglip2-base-patch16-512
pipeline_tag: image-classification
library_name: transformers
tags:
- tooth
- SigLIP2
- chemistry
- biology
- medical
- Calculus
- Mouth Ulcer
- hypodontia
- Tooth Discoloration
- Gingivitis
- tooth-agenesis
---
# tooth-agenesis-siglip2
> tooth-agenesis-siglip2 is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for **multi-class image classification**. It is trained to detect various **dental anomalies and conditions** such as **Calculus**, **Caries**, **Gingivitis**, **Mouth Ulcer**, **Tooth Discoloration**, and **Hypodontia**. The model uses the `SiglipForImageClassification` architecture.
> \[!note]
> SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)
```py
Classification Report:
precision recall f1-score support
Calculus 0.6640 0.7623 0.7098 1296
Caries 0.9525 0.9558 0.9541 2601
Gingivitis 0.8496 0.7842 0.8156 2349
Mouth Ulcer 0.9939 0.9893 0.9916 2806
Tooth Discoloration 0.9314 0.9757 0.9530 2017
hypodontia 0.9983 0.9161 0.9554 1251
accuracy 0.9096 12320
macro avg 0.8983 0.8972 0.8966 12320
weighted avg 0.9132 0.9096 0.9105 12320
```
---
## Label Space: 6 Classes
```
Class 0: Calculus
Class 1: Caries
Class 2: Gingivitis
Class 3: Mouth Ulcer
Class 4: Tooth Discoloration
Class 5: hypodontia
```
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio hf_xet
```
---
## Inference Code
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/tooth-agenesis-siglip2" # Update with actual model name on Hugging Face
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated label mapping
id2label = {
"0": "Calculus",
"1": "Caries",
"2": "Gingivitis",
"3": "Mouth Ulcer",
"4": "Tooth Discoloration",
"5": "hypodontia"
}
def classify_image(image):
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=6, label="Dental Condition Classification"),
title="Tooth Agenesis Detection",
description="Upload a dental image to detect conditions such as Calculus, Caries, Gingivitis, Mouth Ulcer, Tooth Discoloration, or Hypodontia."
)
if __name__ == "__main__":
iface.launch()
```
---
## Intended Use
`tooth-agenesis-siglip2` is designed for:
* **Dental Diagnosis Support** – Assists dentists and clinicians in identifying common dental conditions from images.
* **Oral Health Monitoring** – A tool for regular monitoring of dental health in clinical or remote settings.
* **Tele-dentistry** – Enables automated screening in virtual consultations and rural healthcare setups.
* **Research and Education** – Useful for academic institutions and training platforms for demonstrating AI in dental diagnostics.
* **Early Detection** – Helps identify oral health issues early to prevent progression. |