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---
license: apache-2.0
datasets:
- prithivMLmods/Age-Classification-Set
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
library_name: transformers
tags:
- Age
- Detection
- Siglip2
- ViT
- AutoImageProcessor
- 0-60+
---
# **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.
```py
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🤗**
```python
!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. |