VisDrone Object Detection with YOLOv8

This project implements an object detection system using YOLOv8 on the VisDrone dataset. It includes data preprocessing, model training, evaluation, and visualization components.

Project Structure

project_root/
│
├── data_preprocessing.py
├── augmentation.py
├── model.py
├── evaluation.py
├── visualization.py
├── utils.py
├── config.py
├── main.py
├── requirements.txt
└── README.md

• data_preprocessing.py: Handles dataset preparation and annotation conversion.
• augmentation.py: Implements data augmentation techniques.
• model.py: Contains functions for loading, training, and running inference with the YOLOv8 model.
• evaluation.py: Provides metrics calculation and model evaluation utilities.
• visualization.py: Includes functions for visualizing results and metrics.
• utils.py: Contains utility functions used across the project.
• config.py: Stores configuration parameters and paths.
• main.py: The entry point of the program, orchestrating the entire pipeline.

Setup

1. Clone the repository:

   git clone https://github.com/realjules/drone_vision.git
   cd drone_vision
   

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Download the VisDrone dataset and place it in a dataset folder in the project root. The structure should be:

   dataset/
   ├── train/
   │   ├── annotations/
   │   └── sequences/
   └── val/
       ├── annotations/
       └── sequences/
   

Usage

1. Configure the parameters in config.py if needed.

2. Run the main script:

   python main.py
   

   This will:

   • Preprocess the dataset
   • Train the YOLOv8 model
   • Evaluate the model
   • Generate visualizations

3. Check the output folder for results and visualizations.

Customization

• Modify config.py to change dataset paths, model parameters, or output locations.
• Adjust augmentation techniques in augmentation.py.
• Extend evaluation metrics or visualization options in their respective files.

Contributing

Contributions to improve the project are welcome. Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Make your changes and commit (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature-branch)
5. Create a new Pull Request

License

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

Acknowledgments

• VisDrone dataset: VisDrone Project
• YOLOv8: Ultralytics YOLOv8