{"id":21635285,"url":"https://github.com/sarthak-0-sach/ad-creatives","last_synced_at":"2026-04-08T11:32:02.019Z","repository":{"id":259181912,"uuid":"871017189","full_name":"SartHak-0-Sach/Ad-creatives","owner":"SartHak-0-Sach","description":"Ad-Creatives: An Image Classification Model for Advertisements: This project involves training a machine learning model to classify whether a given image is an advertisement or not. 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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.\n\n### The challenge\nThe goal of this project is to build a machine learning model that can automatically classify images into advertisements or non-advertisements. The challenge involves:\n\n- **Training the model** using labeled data (advertisements vs. non-advertisements).\n- **Fine-tuning the model** to optimize performance and accuracy.\n- **Deploying the solution** for real-time use in detecting ad content in images.\n\n### Features\n- **Advertisement Detection:** Classifies whether an image is an advertisement or not.\n- **Pre-trained Model:** A trained model that can be used immediately to predict new images.\n- **Customizable:** Can be further trained or tuned with new datasets to improve accuracy.\n- **Notebook Implementation:** Jupyter notebook (`ad-creatives.ipynb`) for easy understanding and modification.\n\n### File Structure\n```\n/root-directory\n|-- .github/workflows/           # GitHub workflows for CI/CD\n|-- .gitignore                   # Files to be ignored by Git\n|-- LICENSE                      # Project license\n|-- README.md                    # Project description and instructions\n|-- ad-creatives.ipynb            # Jupyter Notebook with model training and testing\n|-- info.txt                     # Project information and code description\n|-- requirements.txt             # Python dependencies required for the project\n```\n\n### Technologies Used\n- **Python**\n- **TensorFlow/Keras** for machine learning model development\n- **Jupyter Notebook** for code execution and visualization\n- **OpenCV** for image processing\n- **Scikit-learn** for model evaluation\n\n## Setup Instructions\n\n### Prerequisites\n- Python 3.x\n- Jupyter Notebook\n- Basic knowledge of machine learning and image processing\n\n### Installation\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/yourusername/ad-creatives.git\n   ```\n2. Navigate to the project directory:\n   ```bash\n   cd ad-creatives\n   ```\n3. Install the required dependencies:\n   ```bash\n   pip install -r requirements.txt\n   ```\n4. Open the Jupyter notebook:\n   ```bash\n   jupyter notebook ad-creatives.ipynb\n   ```\n\n### Usage\n1. **Model Training:** The Jupyter notebook walks through the steps to load the dataset, train the model, and evaluate its performance.\n2. **Model Prediction:** Once the model is trained, you can use it to predict whether a given image is an advertisement.\n\n### Future Improvements\n- Add more complex image augmentation techniques to improve model robustness.\n- Incorporate more advanced architectures (e.g., transfer learning using pre-trained models).\n- Deploy the model as a web application for real-time image classification.\n\n### Useful resources\n\n- [TensorFlow Keras Documentation](https://www.tensorflow.org/guide/keras) - This was immensely helpful in building the model architecture and fine-tuning it for classification tasks.\n- [OpenCV Image Processing](https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html) - A great guide to getting started with image manipulation techniques using OpenCV.\n- [Scikit-learn Model Evaluation](https://scikit-learn.org/stable/modules/model_evaluation.html) - Helped me understand different metrics for evaluating my model’s performance.\n\n## Author\n\n\u003cb\u003e\u003cstrong\u003eSarthak Sachdev\u003c/strong\u003e\u003c/b\u003e\n- Website - [Sarthak Sachdev](https://itsmesarthak.netlify.app/)\n- LinkedIn - [Sarthak Sachdev](https://www.linkedin.com/in/sarthak2004/)\n- Twitter - [@sarthak_sach69](https://www.twitter.com/sarthak_sach69)\n\n## Acknowledgments\n\nI’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.\n\n## Got feedback for me?\n\nI’d love to hear your thoughts! Please feel free to email me at saarsaach30[at]gmail[dot]com with any feedback or suggestions.\n\n## Contributing\nContributions are welcome! Please fork this repository and submit a pull request for any new features or fixes.\n\n## License📃\nThis project is licensed under the BSD 3-Clause License.\n\nCopyright (c) 2024, Sarthak Sachdev\n\n**Happy coding!** 😊🚀\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsarthak-0-sach%2Fad-creatives","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsarthak-0-sach%2Fad-creatives","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsarthak-0-sach%2Fad-creatives/lists"}