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open-age-detection

open-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 classify the estimated age group of a person from an image. 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

 Child 0-12 0.9827 0.9859 0.9843 2193
 Teenager 13-20 0.9663 0.8713 0.9163 1779
 Adult 21-44 0.9669 0.9884 0.9775 9999
Middle Age 45-64 0.9665 0.9538 0.9601 3785
 Aged 65+ 0.9737 0.9706 0.9722 1260

 accuracy 0.9691 19016
 macro avg 0.9713 0.9540 0.9621 19016
 weighted avg 0.9691 0.9691 0.9688 19016

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Label Space: 5 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+

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/open-age-detection" # Updated model name
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

# Updated label mapping
id2label = {
 "0": "Child 0-12",
 "1": "Teenager 13-20",
 "2": "Adult 21-44",
 "3": "Middle Age 45-64",
 "4": "Aged 65+"
}

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=5, label="Age Group Detection"),
 title="open-age-detection",
 description="Upload a facial image to estimate the age group: Child, Teenager, Adult, Middle Age, or Aged."
)

if __name__ == "__main__":
 iface.launch()

Demo Inference

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Intended Use

open-age-detection is designed for:

• Demographic Analysis – Estimate age groups for statistical or analytical applications.
• Smart Personalization – Age-based content or product recommendation.
• Access Control – Assist systems requiring age verification.
• Social Research – Study age-related trends in image datasets.
• Surveillance and Security – Profile age ranges in monitored environments.

Base model