| | --- |
| | license: apache-2.0 |
| | datasets: |
| | - prithivMLmods/Age-Classification-Set |
| | language: |
| | - en |
| | base_model: |
| | - google/siglip2-base-patch16-224 |
| | pipeline_tag: image-classification |
| | library_name: transformers |
| | tags: |
| | - Age |
| | - Detection |
| | - Siglip2 |
| | - ViT |
| | - AutoImageProcessor |
| | - 0-60+ |
| | --- |
| |  |
| |
|
| | # **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. |
| |
|
| | ```py |
| | 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 |
| | ``` |
| |
|
| |  |
| |
|
| |
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|
| | 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🤗** |
| |
|
| | ```python |
| | !pip install -q transformers torch pillow gradio |
| | ``` |
| |
|
| | ```python |
| | 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.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() |
| | |
| | labels = { |
| | "0": "Child 0-12", |
| | "1": "Teenager 13-20", |
| | "2": "Adult 21-44", |
| | "3": "Middle Age 45-64", |
| | "4": "Aged 65+" |
| | } |
| | predictions = {labels[str(i)]: round(probs[i], 3) for i in range(len(probs))} |
| | |
| | return predictions |
| | |
| | # Create Gradio interface |
| | iface = gr.Interface( |
| | fn=age_classification, |
| | inputs=gr.Image(type="numpy"), |
| | outputs=gr.Label(label="Prediction Scores"), |
| | title="Age Group Classification", |
| | description="Upload an image to predict the person's age group." |
| | ) |
| | |
| | # Launch the app |
| | if __name__ == "__main__": |
| | iface.launch() |
| | ``` |
| | # **Sample Inference:** |
| |
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| |  |
| |
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| |  |
| |
|
| | # **Intended Use:** |
| |
|
| | The **Age-Classification-SigLIP2** model is designed to classify images into five age categories. Potential use cases include: |
| |
|
| | - **Demographic Analysis:** Helping businesses and researchers analyze age distribution. |
| | - **Health & Fitness Applications:** Assisting in age-based health recommendations. |
| | - **Security & Access Control:** Implementing age verification in digital systems. |
| | - **Retail & Marketing:** Enhancing personalized customer experiences. |
| | - **Forensics & Surveillance:** Aiding in age estimation for security purposes. |
