Animal Detection with Custom Trained YOLOv8

Overview

Welcome to the Animal Detection with Custom Trained YOLOv5 project! This application enables real-time animal detection using a custom-trained YOLOv5 model integrated with OpenCV. Whether you're monitoring wildlife or studying animal behavior, this tool provides accurate and efficient detection capabilities.

Key Features

• Real-time animal detection using the webcam feed.
• Support for multiple animal species with customizable class labels.
• Integration with OpenCV for seamless execution and visualization.
• Efficient inference leveraging hardware acceleration platforms.

Installation

To run the Animal Detection with Custom Trained YOLOv5 project, follow these steps:

1. Clone this repository to your local machine.
2. Install Python 3.9 and create a virtual environment.
3. Install required dependencies: pip install -r requirements.txt.
4. Download the YOLOv8 model weights and place them in the specified directory.
5. Run the main.py script.

Folder Structure

📂 animal_detection_yolov5/
├── 📁 data/
│   ├── 📁 train/
│   │   ├── 📁 images/
│   │   └── 📁 labels/
│   └── 📁 valid/
│       ├── 📁 images/
│       └── 📁 labels/
├── 📂 runs/
│   └── 📂 detect/
│       └── 📂 train/
│           └── 📂 weights/
│               └── 📄 best.pt
├── 📄 main.py
├── 📄 config.yaml
├── 📄 model.py
└── 📄 requirements.txt

Usage

python main.py

Screenshots

Contributing

Contributions are welcome! If you have any ideas for improvements or new features, feel free to submit a pull request.

License

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