This project aims to develop a machine learning framework to automate the conversion of textual advertisement descriptions into visually compelling storyboards. This process enhances creativity and efficiency in digital advertising campaigns.
storyboard/
│
├── data/ # Data files
│ ├── raw/ # Raw data
│ └── processed/ # Processed data
│
├── notebooks/ # Jupyter notebooks for exploration and analysis
│ └── EDA.ipynb
│
├── src/ # Source code
│ ├── init.py # Makes src a module
│ ├── data_loader.py # Code for loading and processing data
│ ├── models.py # Code for defining ML models
│ ├── train.py # Code for training models
│ ├── evaluate.py # Code for evaluating models
│ └── utils.py # Utility functions
│
├── tests/ # Unit tests
│ ├── init.py
│ └── test_data_loader.py
│ └── test_models.py
│
├── venv/ # Virtual environment directory
│
├── .gitignore # Git ignore file
├── README.md # Project description
└── requirements.txt # List of required packages
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Clone the repository:
git clone https://github.com/dev-abuke/Automated_Ad_Storyboard_Synthesis.git
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Navigate to the project directory:
cd Automated_Ad_Storyboard_Synthesis -
Set up the virtual environment:
python3 -m venv venv source venv/bin/activate -
Install the dependencies:
pip install -r requirements.txt
- Implement more advanced image segmentation models.
- Integrate additional text analysis techniques.
- Develop a more sophisticated evaluation framework.
- Optimize the performance of the ML models.
- Expand the dataset with more diverse advertisement examples.
This project is licensed under the MIT License. See the LICENSE file for details.