Vision_Transformer: Image Regression Project

Overview

The Vision_Transformer project is a computer vision project focused on utilizing the Vision Transformer (ViT) architecture for image regression tasks. This README provides instructions on getting started with the project, including prerequisites, installation, running tests, deployment, and other essential information.

Getting Started

These instructions will guide you through setting up and running the project on your local machine for development and testing purposes. Follow the steps below to get started.

Prerequisites

Before you begin, ensure you have the following prerequisites:

• Python 3.6 or higher
• Pip 21.0 or higher
• Git 2.17 or higher

Run the following command to install the required packages:

pip install numpy pandas matplotlib seaborn scikit-learn torch torchvision

Installing

To install and set up the project, follow these steps:

1) Clone the repository to your local machine:

git clone https://github.com/your-username/vision-transformer-project.git

2) Navigate to the project directory:

cd vision-transformer-project

3) Run the project-specific setup script (if applicable) to install additional dependencies or configure the environment.

4) Ensure that your dataset is prepared and organized as required by the project code. You may need to adapt the code to your dataset structure.

5) Start running the project by executing the provided Jupyter Notebook or Python scripts.

Running the Tests

To ensure the correctness and code quality of this project, we have included automated tests that you can run. There are two types of tests available:

End-to-End Tests

End-to-end tests cover the entire functionality of the system. They are important to verify that all components of the project work together seamlessly. Below is an example of how to run end-to-end tests:

python run_tests.py --type end_to_end

Running these tests will simulate real-world scenarios and verify that the entire pipeline, from data processing to model prediction, functions correctly.

Coding Style Tests

Coding style tests check the code for adherence to coding standards, readability, and maintainability. They are essential for maintaining a clean and consistent codebase. Here's how to run coding style tests:

python run_tests.py --type coding_style

These tests often include tools like linters or formatters to ensure that the code follows the established coding style guidelines.

Deployment

• Environment Configuration: Set up dependencies and configurations on the server.

• Thoroughly test the deployed project for functionality.

• Monitor the project for performance and errors.

Built With

• Dropwizard - The web framework used
• Maven - Dependency Management
• ROME - Used to generate RSS Feeds

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

Versioning

We use SemVer for versioning. For the versions available, see the tags on this repository.

Authors

• Billie Thompson - Initial work - PurpleBooth

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• Hat tip to anyone whose code was used
• Inspiration
• etc

Built With

• jupyter-notebook