vit age classifier

Introduction

The VIT-AGE-CLASSIFIER is a vision transformer model fine-tuned to classify the age of a person's face. It utilizes the FairFace dataset and is implemented using PyTorch and Transformers libraries.

Architecture

The model is based on the Vision Transformer (ViT) architecture, which is effective for image classification tasks. It processes images as sequences of patches, allowing it to model relationships between different parts of an image.

Training

The model is fine-tuned using the FairFace dataset, which includes diverse examples to improve age classification accuracy across different demographics. Fine-tuning involves adapting a pre-trained ViT model to specific tasks, such as age classification.

Guide: Running Locally

To run the model locally, follow these steps:

1. Install Dependencies: Ensure you have Python installed along with the transformers, torch, and PIL libraries.

   pip install transformers torch pillow
   

2. Download and Initialize the Model:

   import requests
   from PIL import Image
   from io import BytesIO
   from transformers import ViTFeatureExtractor, ViTForImageClassification
   
   # Fetch an example image
   r = requests.get('https://github.com/dchen236/FairFace/blob/master/detected_faces/race_Asian_face0.jpg?raw=true')
   im = Image.open(BytesIO(r.content))
   
   # Initialize model and feature extractor
   model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
   transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')
   
   # Process the image and obtain predictions
   inputs = transforms(im, return_tensors='pt')
   output = model(**inputs)
   
   # Extract predictions
   proba = output.logits.softmax(1)
   preds = proba.argmax(1)
   

3. Suggested Cloud GPU: For faster processing, consider using cloud GPU services such as AWS EC2 with GPU instances, Google Cloud GPU, or Azure NV-series.

License

The model and associated resources are available under the same license as the FairFace dataset. Ensure compliance with any additional licensing terms related to the use of the Transformers and PyTorch libraries.