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---
license: cc-by-nc-4.0
language:
- en
base_model:
- facebook/metaclip-2-worldwide-s16
pipeline_tag: image-classification
library_name: transformers
tags:
- text-generation-inference
- age-ange-estimator
---
# **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](https://huggingface.co/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.
>[!note]
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
```
---
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**
```python
!pip install -q transformers torch pillow gradio
```
```python
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:**
# **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. |