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---
license: apache-2.0
datasets:
- prithivMLmods/Age-Classification-Set
language:
- en
base_model:
- google/siglip2-base-patch16-512
library_name: transformers
pipeline_tag: image-classification
tags:
- Age-Detection
- SigLIP2
- Image
---
# 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.
> \[!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
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
```
---
## 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
```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/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
---
## 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. |