vit-age-classifier
| Property | Value |
|---|---|
| Parameter Count | 85.8M |
| Model Type | Vision Transformer |
| Tensor Type | F32 |
| Downloads | 1.37M |
What is vit-age-classifier?
vit-age-classifier is a specialized Vision Transformer (ViT) model fine-tuned for age classification from facial images. Developed by nateraw, this model leverages the powerful ViT architecture to analyze facial features and determine age categories with high accuracy. The model has gained significant traction with over 1.3 million downloads, demonstrating its reliability and usefulness in real-world applications.
Implementation Details
The model is implemented using PyTorch and the Transformers library, utilizing the ViT architecture as its backbone. It processes input images through a ViTFeatureExtractor and returns probability distributions across age categories. The model works with standard image formats and can be easily integrated into existing pipelines.
- Built on PyTorch framework
- Uses ViT architecture for feature extraction
- Trained on the FairFace dataset
- Supports batch processing of images
- Returns probability distributions for age categories
Core Capabilities
- Facial age classification from images
- Real-time processing capability
- Integration with Hugging Face's Transformers library
- Support for various image formats
- Probability-based age category prediction
Frequently Asked Questions
Q: What makes this model unique?
This model combines the power of Vision Transformers with specialized age classification capabilities, trained on the diverse FairFace dataset. Its high download count and implementation flexibility make it a reliable choice for age classification tasks.
Q: What are the recommended use cases?
The model is ideal for applications requiring automated age classification from facial images, such as demographic analysis, content filtering, or age-verification systems. It's particularly useful in scenarios where batch processing of multiple images is needed.
