Visual Quality Control - Demonstrator

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Green-AI Hub Mittelstand

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About The Project

This repository contains the code for the visual quality control demonstrator, which uses an AI (two combined YOLO models) to check whether a product is faulty. This demosntrator was developed as part of the Green-AI Hub Initiative, funded by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection.

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Table of Contents

1. About The Project
2. Table of Contents
3. Getting Started
   • Prerequisites
   • Installation
4. Usage
5. Contributing
6. License
7. Contact

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Getting Started

Clone this repository, navigate with your terminal into this repository and execute the following steps.

Prerequisites

This is an example of how to list things you need to use the software and how to install them.

pip install -r requirements.txt

Installation

To use the environment, you have to install this repository as a pip package. Alternativly you can open a branch of this repository and implement changes directly in this repo.

1. Navigate to the repository with your terminal.
2. Install the repository as a pip package

   pip install .

3. Check whether the installation was successful

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Usage

To train your own yolo detection models, refer to the yolo_error_detect.ipynb notebook. Afterwards your trained models have to be copied to the models folder and the paths inside of app.py have to be updated accordingly.

If you want to use the web interface to use the models. Follow the following steps:

1. Navigate to the repository with your terminal
2. Ensure that you have all libraries installed
3. Start the web interface with the following command:

 python3 app.py

Important: By executing these steps, you start the web app with the debugging server of flask. This is not safe for production. Please refer to the official flask documentation for deployments.

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE.txt for more information.

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Contact

Green-AI Hub Mittelstand - info@green-ai-hub.de

Project Link: https://github.com/Green-AI-Hub-Mittelstand/repository_name

Get in touch »

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