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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.

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786

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

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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

pip install -q transformers torch pillow gradio hf_xet

Inference Code

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.

Base model