Check_ Good Bad_ Teeth

Introduction

The "Check_GoodBad_Teeth" model is an image classification model designed to distinguish between images of good and bad teeth. It is built using PyTorch and is part of the Hugging Face model repository. The model was autogenerated by HuggingPics, a tool for creating image classifiers.

Architecture

The model leverages the Vision Transformer (ViT) architecture, which is known for its effectiveness in image classification tasks. It utilizes the PyTorch framework, enabling efficient model training and deployment.

Training

The model's primary metric for evaluation is accuracy, with a reported value of 1. This indicates that, during testing, the model achieved perfect accuracy on its validation set. The model is trained using the HuggingPics framework, which allows users to create image classifiers easily.

Guide: Running Locally

To run the "Check_GoodBad_Teeth" model locally, follow these steps:

1. Clone the Repository: Download the model files from the Hugging Face model hub.
2. Install Dependencies: Ensure that PyTorch and other necessary libraries (e.g., transformers, huggingpics) are installed.
3. Run the Model: Use a script or interactive environment to load and run the model on your dataset.
4. Use Cloud GPUs: For optimal performance, consider using cloud GPU services like Google Colab, which offers free GPU access and can efficiently run the notebook available here.

License

The model and its associated code are subject to the licensing terms provided by Hugging Face and the HuggingPics repository. Users are encouraged to review these terms to ensure compliance.