Face Emotion Model

Introduction

The Face Emotion model is a fine-tuned version of Google's vit-base-patch16-224-in21k. It is designed for image classification, particularly focusing on identifying facial expressions from images. The model is trained on the FER2013 dataset, which includes various facial expression images.

Architecture

The model is based on the Vision Transformer (ViT) architecture. Specifically, it uses the vit-base-patch16-224-in21k as its base model, which is fine-tuned to classify images into four categories of facial expressions.

Training

The model achieved a training accuracy of 83.27% and a validation accuracy of 76.52%. This indicates moderate proficiency in classifying facial expressions, though users should be aware of potential biases and limitations inherent in the dataset and model.

Guide: Running Locally

To run the model locally, follow these basic steps:

1. Clone the Repository: Start by cloning the repository from Hugging Face.
2. Set Up Environment: Install the necessary dependencies, which typically include Python, PyTorch, and Hugging Face's transformers library.
3. Load the Model: Use the provided code snippet to load the model into your environment.
4. Test with an Image: Prepare an image to test the model's classification capabilities.

Cloud GPUs

For improved performance, consider using cloud GPUs such as those provided by AWS, Google Cloud Platform, or Azure. These resources can significantly speed up the processing time when working with large models or datasets.

License

The Face Emotion model is released under the MIT License, allowing for open use and modification. Users must adhere to the terms of this license when utilizing the model.