| --- |
| 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 |
| --- |
| |
| %3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L18"> | |
|
| # 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 |
| ``` |
|
|
| %3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L43"> | |
|
| --- |
|
|
| ## 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 |
|
|
| %3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L121"> | | %3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L122"> | | %3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L123"> | | %3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L124"> | | %3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L125"> | |
|
| --- |
|
|
| ## 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. |
