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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-patch16-224 |
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pipeline_tag: image-classification |
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library_name: transformers |
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tags: |
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- Age |
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- Detection |
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- Siglip2 |
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- ViT |
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- AutoImageProcessor |
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- 0-60+ |
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--- |
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# **Age-Classification-SigLIP2** |
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> **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. |
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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 0.9744 0.9562 0.9652 2193 |
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Teenager 13-20 0.8675 0.7032 0.7768 1779 |
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Adult 21-44 0.9053 0.9769 0.9397 9999 |
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Middle Age 45-64 0.9059 0.8317 0.8672 3785 |
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Aged 65+ 0.9144 0.8397 0.8755 1260 |
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accuracy 0.9109 19016 |
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macro avg 0.9135 0.8615 0.8849 19016 |
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weighted avg 0.9105 0.9109 0.9087 19016 |
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``` |
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The model categorizes images into five age groups: |
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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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# **Run with Transformers🤗** |
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```python |
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!pip install -q transformers torch pillow gradio |
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``` |
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```python |
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import gradio as gr |
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from transformers import AutoImageProcessor |
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from transformers import SiglipForImageClassification |
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from transformers.image_utils import load_image |
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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/Age-Classification-SigLIP2" |
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model = SiglipForImageClassification.from_pretrained(model_name) |
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processor = AutoImageProcessor.from_pretrained(model_name) |
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def age_classification(image): |
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"""Predicts the age group of a person from an 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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labels = { |
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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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predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
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return predictions |
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# Create Gradio interface |
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iface = gr.Interface( |
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fn=age_classification, |
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inputs=gr.Image(type="numpy"), |
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outputs=gr.Label(label="Prediction Scores"), |
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title="Age Group Classification", |
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description="Upload an image to predict the person's age group." |
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) |
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# Launch the app |
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if __name__ == "__main__": |
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iface.launch() |
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``` |
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# **Sample Inference:** |
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# **Intended Use:** |
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The **Age-Classification-SigLIP2** model is designed to classify images into five age categories. Potential use cases include: |
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- **Demographic Analysis:** Helping businesses and researchers analyze age distribution. |
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- **Health & Fitness Applications:** Assisting in age-based health recommendations. |
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- **Security & Access Control:** Implementing age verification in digital systems. |
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- **Retail & Marketing:** Enhancing personalized customer experiences. |
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- **Forensics & Surveillance:** Aiding in age estimation for security purposes. |
