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Age-Classification-SigLIP2

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

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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

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:

👁 Screenshot 2025-03-28 at 12-25-46 Age Group Classification.png

👁 Screenshot 2025-03-28 at 12-36-49 Age Group Classification.png

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.

Base model

google/siglip2-base-patch16-224

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