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Project Overview

Welcome to MunicipalAI's Computer Vision project! This project focuses on Community issue computer vision tasks using multiple models. In this readme, you will find essential information about the project, its objectives, and how to get started.

Project Goals

The primary goals of this project are:

Data Transparency: Allows anyone to view the data used to train the models.

Algorithm Transparency: Allows anyone to view the models and approach used to train.

Feedback: If a mistake was made the community may correct and improve.

Project Structure

The project is organized into the following directories:

models/: model types

    instance/: a directory of each model
    
        model/: files needed to train model

Installation

Before you can run the project, you need to set up your environment. In each of the model folders there is a readme and setup.sh script. In the readme is the docker image we used on https://vast.ai . Once you have launched the docker image you can run the command included in the readme.

Training Models:

The setup.sh script in each model folder will launch the training.

Documentation:

Maintain up-to-date documentation for each model in the corresponding model directory within models/. Include infor 6807 mation on model architecture, training history, and usage instructions.

Contributing

We welcome contributions from the community. If you want to contribute to this project, please follow these steps:

Fork the repository. Create a new branch for your feature or bug fix. Develop your changes and test thoroughly. Submit a pull request with a clear description of your changes.

License

This project is under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License. Please review the license terms before using or contributing to the project.

Contact

If you have any questions or need assistance, please contact the project maintainer at [james.ben.harris@gmail.com].

We hope you find this project exciting and look forward to your contributions!

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