Downloadvit-age-classifier is a Vision Transformer (ViT) model fine-tuned by nateraw to classify a person's age based on their facial image. Trained on the FairFace dataset, the model predicts age group categories using facial features with high accuracy. It leverages the robust image representation capabilities of ViT for fine-grained facial analysis. With 85.8 million parameters, the model operates efficiently for image classification tasks on faces. The model outputs probabilities for predefined age classes and is compatible with Hugging Face’s transformers library using ViTFeatureExtractor. It's suitable for integration into pipelines for demographic analysis, social science research, or personalized UI experiences. However, users should be aware of dataset bias and ethical implications when deploying facial analysis models.
Features
- Fine-tuned on the FairFace dataset
- Predicts age group from facial images
- Based on the Vision Transformer (ViT) architecture
- 85.8 million parameters
- Outputs class probabilities and predicted age group
- Easy integration with Hugging Face Transformers API
- Supports inference with PyTorch
- Suitable for real-time image classification tasks
Project Samples
