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MetaCLIP-2-Age-Range-Estimator

MetaCLIP-2-Age-Range-Estimator is an image classification vision-language encoder model fine-tuned from facebook/metaclip-2-worldwide-s16 for a single-label classification task. It is designed to predict the age range of a person from an image using the MetaClip2ForImageClassification architecture.

MetaCLIP 2: A Worldwide Scaling Recipe : https://huggingface.co/papers/2507.22062

Classification Report:
 precision recall f1-score support

 Child 0-12 0.9763 0.9758 0.9761 2193
 Teenager 13-20 0.9158 0.8437 0.8783 1779
 Adult 21-44 0.9593 0.9779 0.9685 9999
Middle Age 45-64 0.9458 0.9450 0.9454 3785
 Aged 65+ 0.9769 0.9381 0.9571 1260

 accuracy 0.9559 19016
 macro avg 0.9548 0.9361 0.9451 19016
 weighted avg 0.9557 0.9559 0.9556 19016

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The model categorizes images into five age ranges:

• 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
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image

# Model name from Hugging Face Hub
model_name = "prithivMLmods/MetaCLIP-2-Age-Range-Estimator"

# Load processor and model
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)
model.eval()

# Define labels
LABELS = {
 0: "Child (0–12)",
 1: "Teenager (13–20)",
 2: "Adult (21–44)",
 3: "Middle Age (45–64)",
 4: "Aged (65+)"
}

def age_classification(image):
 """Predict 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()

 predictions = {LABELS[i]: round(probs[i], 3) for i in range(len(probs))}
 return predictions

# Build Gradio interface
iface = gr.Interface(
 fn=age_classification,
 inputs=gr.Image(type="numpy", label="Upload Image"),
 outputs=gr.Label(label="Predicted Age Group Probabilities"),
 title="MetaCLIP-2 Age Range Estimator",
 description="Upload a face image to estimate the person's age group using MetaCLIP-2."
)

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

Sample Inference:

👁 Screenshot 2025-11-13 at 01-14-28 MetaCLIP-2 Age Range Estimator
👁 Screenshot 2025-11-13 at 01-15-41 MetaCLIP-2 Age Range Estimator
👁 Screenshot 2025-11-13 at 01-17-31 MetaCLIP-2 Age Range Estimator
👁 Screenshot 2025-11-13 at 01-18-15 MetaCLIP-2 Age Range Estimator
👁 Screenshot 2025-11-13 at 01-18-52 MetaCLIP-2 Age Range Estimator

Intended Use:

The MetaCLIP-2-Age-Range-Estimator model is designed to classify images into five age categories. Potential use cases include:

• Demographic Analysis: Supporting research and business insights into age distribution.
• Health and Fitness Applications: Assisting in age-based health recommendations.
• Security and Access Control: Enabling age verification systems.
• Retail and Marketing: Enhancing personalization and customer profiling.
• Forensics and Surveillance: Supporting age estimation in investigative and security contexts.

Base model