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
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:
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.