UAV Classification

This repository contains a machine learning project for UAV (Unmanned Aerial Vehicle) classification using deep learning techniques. The project is containerized using Docker and supports experiment tracking with Weights & Biases.

NOTE

The Datasets used in this project are not included in the repository due to their visibility -> We have decided not to open-source the datasets.

If you would like to use the codebase, please use this example directory to store your datasets. and update the config.yaml file to point to your datasets.

Project Structure

├── src/                    # Source code directory
│   ├── main.py            # Main training script
│   ├── script.py          # Utility scripts
│   ├── sweeps.py          # Hyperparameter tuning
│   ├── config.yaml        # Configuration file
│   ├── helper/            # Helper functions
│   ├── notebooks/         # Jupyter notebooks
│   └── run_configs/       # Run configurations
├── .datasets/             # Dataset directory
├── docker-compose.yml     # Docker compose configuration
├── Dockerfile            # Docker build instructions
└── requirements.txt      # Python dependencies

Prerequisites

• Docker and Docker Compose
• Python 3.8+
• CUDA-compatible GPU (recommended)

Setup

1. Clone the repository:

git clone https://github.com/yourusername/UAV_Classification_repo.git
cd UAV_Classification_repo

2. (Optional) Copy the example environment file and configure your variables:

cp .env.example .env

3. Install dependencies (if running locally):

Note: If you are running the container, you can skip this step.

pip install -r requirements.txt

4. Build and using Docker:

docker compose build app

Environment Variables

Create a .env file in the root directory with the following variables:

Note: see setup section for more details

• WANDB_API_KEY: Your Weights & Biases API key
• BOT_TOKEN: Telegram bot token for notifications (optional)
• CHAT_ID: Telegram chat ID for notifications (optional)

Python Usage

1. Configure your experiment in src/config.yaml & orchestrate.yaml

2. Run training:

python src/main.py

Docker Usage

1. Configure your experiment in src/config.yaml & orchestrate.yaml

2. Run training:

docker compose run app

License

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