This project is a requirement under the CS 180 2425.2 course of the University of the Philippines Diliman.
The documentation for the training process is available in model_training.ipynb
The demo version is available in model_demo.ipynb.
Two models were developed for this classification problem.
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The first model is a Convolutional Neural Network (CNN). It can be found on Hugging Face at ktrin-u/KEY-Cnn
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The second model is a Vision Transformer (ViT). It can be found on Hugging Face as well at ktrin-u/KEY-ViT.
The dataset was provided to us by a professor.
The following research papers were referred to during development
- Vision Transformer-Based Framework for AI-Generated Image Detection in Interior Design
- Reducing Complexity of 3D Indoor Object Detection
- Optimization of vision transformer-based detection of lung diseases from chest X-ray images
This project and its dependencies are managed using uv by Astral.
To run the project, follow the steps below.
- Clone the repository
- Install uv by Astral.
- If Python 3.12 is not available within the system, run
uv python install 3.12 - Run
uv venv -p 3.12 - Run
uv sync - Change the current active directory to
web - Run
uv run uvicorn web_app.asgi:applicationNo need to collectstatic, since the staticfiles are included in the repository due to their small size.
For development, consider the following steps after Step 6.
- Ensure that NodeJS is installed
- Run
python manage.py tailwind install