autotrain_fashion_mnist_vit_base

Introduction

The autotrain_fashion_mnist_vit_base model is designed for image classification tasks, specifically targeting the Fashion MNIST dataset. The model uses Vision Transformer (ViT) architecture and was developed using the AutoTrain platform, a tool that simplifies the process of training machine learning models.

Architecture

The model leverages the Vision Transformer (ViT) architecture, which is well-suited for image classification tasks. This architecture transforms images into sequences of patches, which are then processed similarly to tokens in NLP models. The model is integrated with the PyTorch library, enabling efficient training and inference.

Training

Training was conducted using the Fashion MNIST dataset, focusing on multi-class classification. The AutoTrain platform facilitated the training process, optimizing the model for high accuracy. Key metrics achieved include:

• Accuracy: 94.73%
• Macro F1 Score: 94.73%
• Loss: 0.1678
• CO2 Emissions: 0.2439 grams

These metrics indicate a robust model performance with low environmental impact.

Guide: Running Locally

To run this model locally, follow these steps:

1. Set up your environment: Ensure you have Python and PyTorch installed. Use a virtual environment for better management.
2. Clone the repository: Download the model files from the Hugging Face repository.
3. Install dependencies: Run pip install -r requirements.txt if a requirements file is provided.
4. Load the model: Use the Hugging Face Transformers library to load the model with PyTorch.
5. Run inference: Prepare your input data, and use the model to make predictions.

For efficient processing, it is advisable to use cloud GPUs such as AWS EC2 instances or Google Cloud GPUs.

License

The model and its associated files are released under the Apache 2.0 License, allowing for broad use and distribution, provided that proper credit is given and any modifications are documented.