Age Classification SigLIP2 EU AI Act Compliance Profile

Age-Classification-SigLIP2

Age-Classification-SigLIP2 is an image classification vision-language encoder model fine-tuned from google/siglip2-base-patch16-224 for a single-label classification task. It is designed to predict the age group of a person from an image using the SiglipForImageClassification architecture.

Classification Report:
                  precision    recall  f1-score   support

      Child 0-12     0.9744    0.9562    0.9652      2193
  Teenager 13-20     0.8675    0.7032    0.7768      1779
     Adult 21-44     0.9053    0.9769    0.9397      9999
Middle Age 45-64     0.9059    0.8317    0.8672      3785
        Aged 65+     0.9144    0.8397    0.8755      1260

        accuracy                         0.9109     19016
       macro avg     0.9135    0.8615    0.8849     19016
    weighted avg     0.9105    0.9109    0.9087     19016

The model categorizes images into five 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+"

Run with Transformers🤗

!pip install -q transformers torch pillow gradio

import gradio as gr
from transformers import AutoImageProcessor
from transformers import SiglipForImageClassification
from transformers.image_utils import load_image
from PIL import Image
import torch

# Load model and processor
model_name = "prithivMLmods/Age-Classification-SigLIP2"
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)

def age_classification(image):
    """Predicts the age group of a person from an image."""
    image = Image.froma

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Base Model