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--- |
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license: apache-2.0 |
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datasets: |
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- prithivMLmods/Age-Classification-Set |
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language: |
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- en |
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base_model: |
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- google/siglip2-base-patch16-512 |
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library_name: transformers |
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pipeline_tag: image-classification |
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tags: |
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- Age-Detection |
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- SigLIP2 |
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- Image |
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--- |
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# open-age-detection |
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> `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. |
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> \[!note] |
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> *SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features* |
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> [https://arxiv.org/pdf/2502.14786](https://arxiv.org/pdf/2502.14786) |
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```py |
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Classification Report: |
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precision recall f1-score support |
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Child 0-12 0.9827 0.9859 0.9843 2193 |
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Teenager 13-20 0.9663 0.8713 0.9163 1779 |
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Adult 21-44 0.9669 0.9884 0.9775 9999 |
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Middle Age 45-64 0.9665 0.9538 0.9601 3785 |
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Aged 65+ 0.9737 0.9706 0.9722 1260 |
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accuracy 0.9691 19016 |
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macro avg 0.9713 0.9540 0.9621 19016 |
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weighted avg 0.9691 0.9691 0.9688 19016 |
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``` |
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--- |
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## Label Space: 5 Age Groups |
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``` |
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Class 0: Child 0–12 |
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Class 1: Teenager 13–20 |
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Class 2: Adult 21–44 |
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Class 3: Middle Age 45–64 |
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Class 4: Aged 65+ |
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``` |
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--- |
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## Install Dependencies |
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```bash |
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pip install -q transformers torch pillow gradio hf_xet |
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``` |
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--- |
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## Inference Code |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor, SiglipForImageClassification |
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from PIL import Image |
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import torch |
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# Load model and processor |
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model_name = "prithivMLmods/open-age-detection" # Updated model name |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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# Updated label mapping |
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id2label = { |
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"0": "Child 0-12", |
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"1": "Teenager 13-20", |
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"2": "Adult 21-44", |
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"3": "Middle Age 45-64", |
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"4": "Aged 65+" |
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} |
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def classify_image(image): |
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image = Image.fromarray(image).convert("RGB") |
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inputs = processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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logits = outputs.logits |
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probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist() |
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prediction = { |
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id2label[str(i)]: round(probs[i], 3) for i in range(len(probs)) |
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} |
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return prediction |
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# Gradio Interface |
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iface = gr.Interface( |
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fn=classify_image, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(num_top_classes=5, label="Age Group Detection"), |
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title="open-age-detection", |
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description="Upload a facial image to estimate the age group: Child, Teenager, Adult, Middle Age, or Aged." |
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) |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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--- |
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## Demo Inference |
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--- |
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## Intended Use |
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`open-age-detection` is designed for: |
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* **Demographic Analysis** – Estimate age groups for statistical or analytical applications. |
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* **Smart Personalization** – Age-based content or product recommendation. |
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* **Access Control** – Assist systems requiring age verification. |
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* **Social Research** – Study age-related trends in image datasets. |
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* **Surveillance and Security** – Profile age ranges in monitored environments. |
