Introduction to Machine Learning 2024 - Project

Table of Contents

1. Introduction
2. Getting Started
   • Project Structure
3. Usage
   • Prerequisites
   • Installation
   • Running the Project
     • Frontend
     • Testing
     • Scripts
4. Project Description
5. Authors
6. License

Introduction:

This repository contains the code for the Introduction to Machine Learning 2024 project. The project task is to develop CNN model for intercom device detecting authorised and unauthorised people.

Getting Started:

Project Structure:

├── examples
├── reports
└── src
    ├── cnn
    ├── frontend
    ├── scripts
    ├── pipeline
    └── test

• examples: Contains example code using jupyter notebooks.
• reports: Contains reports and documentation created for project milestones etc.
• src: Contains the source code for the project.
  • cnn: Contains code for the CNN model.
  • frontend: Contains the code for the frontend of the project.
  • scripts: Collection of standalone scripts used in the project.
  • pipelines: Contains the code for the data processing pipelines. Including the code for the audio processing part of the project.
  • test: Contains the code for testing the project.

Usage:

Prerequisites:

To set up the project, ensure you have the following dependencies:

• Python 3.12 or higher
• Python virtual environment (virtualenv) for dependency management (recommended)

Installation:

1. First, clone the repository:

git clone https://github.com/Jlisowskyy/intro-ml-2024

2. Navigate to the project directory:

cd intro-ml-2024

3. Initialize the virtual environment:

python -m venv .venv

4. Activate the virtual environment:

On Unix or macOS:

source .venv/bin/activate

On Windows:

.venv\Scripts\activate

5. Install the required dependencies:

pip install -r requirements.txt

Running the project:

Running the project is easy as never! Simply run:

python main.py

To start our CLI (interactive mode and argument mode) to get detailed description on running specific functionalities.

Project Description:

Authors:

• Łukasz Kryczka
• Michał Kwiatkowski
• Jakub Lisowski
• Tomasz Mycielski
• Kuba Pietrzak

License:

Licensed under the MIT License. See LICENSE for more information.