Age-Classification-SigLIP2 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to predict the age group of a person from an image using the SiglipForImageClassification architecture.

Classification Report:
                  precision    recall  f1-score   support

      Child 0-12     0.9744    0.9562    0.9652      2193
  Teenager 13-20     0.8675    0.7032    0.7768      1779
     Adult 21-44     0.9053    0.9769    0.9397      9999
Middle Age 45-64     0.9059    0.8317    0.8672      3785
        Aged 65+     0.9144    0.8397    0.8755      1260

        accuracy                         0.9109     19016
       macro avg     0.9135    0.8615    0.8849     19016
    weighted avg     0.9105    0.9109    0.9087     19016

The model categorizes images into five age groups:

Class 0: "Child 0-12"
Class 1: "Teenager 13-20"
Class 2: "Adult 21-44"
Class 3: "Middle Age 45-64"
Class 4: "Aged 65+"

Run with Transformers🤗

!pip install -q transformers torch pillow gradio

python

import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Age-Classification-SigLIP2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def age_classification(image):
    """Predicts the age group of a person from an 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()
    
    labels = {
        "0": "Child 0-12", 
        "1": "Teenager 13-20", 
        "2": "Adult 21-44", 
        "3": "Middle Age 45-64", 
        "4": "Aged 65+"
    }
    predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))}
    
    return predictions

# Create Gradio interface
iface = gr.Interface(
    fn=age_classification,
    inputs=gr.Image(type="numpy"),
    outputs=gr.Label(label="Prediction Scores"),
    title="Age Group Classification",
    description="Upload an image to predict the person's age group."
)

# Launch the app
if __name__ == "__main__":
    iface.launch()

Sample Inference:

Intended Use:

The Age-Classification-SigLIP2 model is designed to classify images into five age categories. Potential use cases include:

Demographic Analysis: Helping businesses and researchers analyze age distribution.
Health & Fitness Applications: Assisting in age-based health recommendations.
Security & Access Control: Implementing age verification in digital systems.
Retail & Marketing: Enhancing personalized customer experiences.
Forensics & Surveillance: Aiding in age estimation for security purposes.