Downloadfairface_age_image_detection is a Vision Transformer (ViT)-based model fine-tuned to classify the age group of a person in an image. Built on top of google/vit-base-patch16-224-in21k, the model was trained using the FairFace dataset, which provides a diverse set of facial images across multiple age categories. It predicts one of nine age groups, ranging from “0–2” up to “70+”. On evaluation over 10,000 samples, the model achieves an overall accuracy of approximately 59%, with best performance in the “3–9” and “0–2” categories. Though performance in older age brackets like “70+” is lower, it still captures broad demographic trends useful for research or pre-filtering tasks. The model is provided under the Apache 2.0 license and supports deployment via Hugging Face’s Inference API. Users can apply it for age estimation in images where fine-grained precision is not mission-critical.
Features
- Classifies faces into 9 distinct age groups
- Based on vit-base-patch16-224-in21k architecture
- Fine-tuned on the FairFace dataset
- Achieves 59% overall classification accuracy
- Highest performance in younger age groups
- Returns age range predictions (e.g., "20–29")
- Compatible with Hugging Face pipeline and Transformers
- Open-source under Apache 2.0 license
Project Samples
