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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.

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786

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

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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

pip install -q transformers torch pillow gradio hf_xet

Inference Code

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.

Base model