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---
license: apache-2.0
datasets:
- prithivMLmods/Face-Age-10K
language:
- en
base_model:
- google/siglip2-base-patch16-512
pipeline_tag: image-classification
library_name: transformers
tags:
- age-detection
- SigLIP2
- biology
---
# facial-age-detection
> facial-age-detection is a vision-language encoder model fine-tuned from `google/siglip2-base-patch16-512` for **multi-class image classification**. It is trained to detect and classify human faces into **age groups** ranging from early childhood to elderly adults. The model uses the `SiglipForImageClassification` architecture.
> \[!note]
> SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786)
```py
Classification Report:
precision recall f1-score support
age 01-10 0.9614 0.9669 0.9641 2474
age 11-20 0.8418 0.8467 0.8442 1181
age 21-30 0.8118 0.8326 0.8220 1523
age 31-40 0.6937 0.6683 0.6808 1010
age 41-55 0.7106 0.7528 0.7311 1181
age 56-65 0.6878 0.6646 0.6760 799
age 66-80 0.7949 0.7596 0.7768 653
age 80 + 0.9349 0.8343 0.8817 344
accuracy 0.8225 9165
macro avg 0.8046 0.7907 0.7971 9165
weighted avg 0.8226 0.8225 0.8223 9165
```
---
## Label Space: 8 Classes
```
Class 0: age 01-10
Class 1: age 11-20
Class 2: age 21-30
Class 3: age 31-40
Class 4: age 41-55
Class 5: age 56-65
Class 6: age 66-80
Class 7: age 80 +
```
---
## Install Dependencies
```bash
pip install -q transformers torch pillow gradio hf_xet
```
---
## Inference Code
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/facial-age-detection" # Update with actual model name on Hugging Face
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
# Updated label mapping
id2label = {
"0": "age 01-10",
"1": "age 11-20",
"2": "age 21-30",
"3": "age 31-40",
"4": "age 41-55",
"5": "age 56-65",
"6": "age 66-80",
"7": "age 80 +"
}
def classify_image(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()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(num_top_classes=8, label="Age Group Classification"),
title="Facial Age Detection",
description="Upload a face image to estimate the age group: 01–10, 11–20, 21–30, 31–40, 41–55, 56–65, 66–80, or 80+."
)
if __name__ == "__main__":
iface.launch()
```
---
## Intended Use
`facial-age-detection` is designed for:
* **Demographic Analytics** – Estimate age distributions in image datasets for research and commercial analysis.
* **Access Control & Verification** – Enforce age-based access in digital or physical environments.
* **Retail & Marketing** – Understand customer demographics in retail spaces through camera-based analytics.
* **Surveillance & Security** – Enhance people classification systems by integrating age detection.
* **Human-Computer Interaction** – Adapt experiences and interfaces based on user age. |