Welcome to the GitHub repository for our project on object detection of historical buildings in Intramuros using YOLOv8. In this project, we have developed a model that can accurately identify prominent building attractions in the historical site of Intramuros.
The model is trained and tested to identify the following historical buildings and landmarks within Intramuros:
- Casa Manila
- Fort Santiago
- King Charles IV Monument
- Manila Cathedral
- Palacio de Gobernador
- San Agustin Church
Feel free to add more landmarks to the dataset for further analysis and detection. The dataset is annotated and preprocess using Roboflow.
Before using this repository, make sure you have the following prerequisites installed:
- Python (>=3.9)
- Pip (Python package manager)
To use the repository, follow these steps:
- Clone the repository to your local machine using:
git clone https://github.com/your_username/intramuros-object-detection.git
cd intramuros-object-detection
- To set up the required environment and install dependencies, you can use the provided
requirements.txtfile. Run the following command:
pip install -r requirements.txt
- The dataset is already available in the repository and is preprocessed in YOLOv8 format.
- Train the YOLOv8 model using the
train_colab.ipynbprovided in the repository. - Additionally, you can use the
main.pyscript to display the webcam frames with real-time building detection in Intramuros. For a more interactive experience, you can run theapp.pyscript to access the Streamlit web application for real-time webcam detection of historical buildings. To run it:
streamlit run app.py
We welcome contributions from the community! If you find any issues or want to add improvements, feel free to create a pull request. For major changes, please open an issue to discuss the proposed changes first.
This project is licensed under the MIT License. See the LICENSE file for details.
Feel free to reach out through LinkedIn if you have any questions or need further information.