| --- |
| 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 |
| ``` |
|
|
|  |
|
|
|
|
|
|
| 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:** |
|
|
|  |
|
|
|  |
|
|
| # **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. |
