The aim of this project is to develop an efficient computer vision model capable of real-time wildlife detection.

• Datasets
• Project Structure
• Getting Started
• Evaluation
• Web App
• Contirbuting
• Author

The dataset used in this project consists of labeled images of 10 different animal classes: Buffalo, Cheetahs, Deer, Elephant, Fox, Jaguars, Lion, Panda, Tiger, Zebra. You can find the datasets:

• Dataset 1
• Dataset 2
• Dataset 3

├── config
│   └── custom.yaml    
├── data
│   ├── images         
│   └── labels         
├── logs
│   └── log.log      
├── notebooks
│   └── yolov8.ipynb
├── runs
│   └── detect
│       ├── train
│       └── val
├── scripts
│   ├── app.py
│   ├── convert_format.py
│   └── train_test_split.py
├── README.md
└── requirements.txt

Follow theses steps to set up the environment and run the application.

1. Fork the repository here.

2. Clone the forked repository.

   git clone https://github.com/<YOUR-USERNAME>/Animal-Species-Detection
   cd Animal-Species-Detection

3. Create a python virtual environment.

   python3 -m venv venv

4. Activate the virtual environment.

   • On Linux and macOS

   source venv/bin/activate

   • On Windows

   venv\Scripts\activate

5. Install Dependencies

   pip install -r requirements.txt

6. Run the application.

   streamlit run './scripts/app.py'

The performance of the model is evaluated by metrics such as Precision, Recal, and Mean Average Precision (mAP).

The trained model has been deployed on Hugging Face for practical use.

• you can access the deployed web app

Contributions to this project are welcome! If you have any suggestions, improvements, or bug fixes, feel free to open an issue or a pull request.

• Lemi Debele