This project is focused on building and training a machine learning model that can distinguish between advertisements and non-advertisement images. By leveraging advanced image processing techniques and machine learning algorithms, the model can be used for various applications, such as filtering advertisement images from non-advertisement content.
The goal of this project is to build a machine learning model that can automatically classify images into advertisements or non-advertisements. The challenge involves:
- Training the model using labeled data (advertisements vs. non-advertisements).
- Fine-tuning the model to optimize performance and accuracy.
- Deploying the solution for real-time use in detecting ad content in images.
- Advertisement Detection: Classifies whether an image is an advertisement or not.
- Pre-trained Model: A trained model that can be used immediately to predict new images.
- Customizable: Can be further trained or tuned with new datasets to improve accuracy.
- Notebook Implementation: Jupyter notebook (
ad-creatives.ipynb) for easy understanding and modification.
/root-directory
|-- .github/workflows/ # GitHub workflows for CI/CD
|-- .gitignore # Files to be ignored by Git
|-- LICENSE # Project license
|-- README.md # Project description and instructions
|-- ad-creatives.ipynb # Jupyter Notebook with model training and testing
|-- info.txt # Project information and code description
|-- requirements.txt # Python dependencies required for the project
- Python
- TensorFlow/Keras for machine learning model development
- Jupyter Notebook for code execution and visualization
- OpenCV for image processing
- Scikit-learn for model evaluation
- Python 3.x
- Jupyter Notebook
- Basic knowledge of machine learning and image processing
- Clone the repository:
git clone https://github.com/yourusername/ad-creatives.git
- Navigate to the project directory:
cd ad-creatives - Install the required dependencies:
pip install -r requirements.txt
- Open the Jupyter notebook:
jupyter notebook ad-creatives.ipynb
- Model Training: The Jupyter notebook walks through the steps to load the dataset, train the model, and evaluate its performance.
- Model Prediction: Once the model is trained, you can use it to predict whether a given image is an advertisement.
- Add more complex image augmentation techniques to improve model robustness.
- Incorporate more advanced architectures (e.g., transfer learning using pre-trained models).
- Deploy the model as a web application for real-time image classification.
- TensorFlow Keras Documentation - This was immensely helpful in building the model architecture and fine-tuning it for classification tasks.
- OpenCV Image Processing - A great guide to getting started with image manipulation techniques using OpenCV.
- Scikit-learn Model Evaluation - Helped me understand different metrics for evaluating my model’s performance.
Sarthak Sachdev
- Website - Sarthak Sachdev
- LinkedIn - Sarthak Sachdev
- Twitter - @sarthak_sach69
I’d like to acknowledge the amazing open-source libraries and tools like TensorFlow, OpenCV, and Scikit-learn, which made this project possible. Additionally, a huge thanks to the helpful communities on GitHub and StackOverflow for always providing insightful solutions and advice.
I’d love to hear your thoughts! Please feel free to email me at saarsaach30[at]gmail[dot]com with any feedback or suggestions.
Contributions are welcome! Please fork this repository and submit a pull request for any new features or fixes.
This project is licensed under the BSD 3-Clause License.
Copyright (c) 2024, Sarthak Sachdev
Happy coding! 😊🚀