|
|
--- |
|
|
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. |
