| --- |
| 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. |
