ChromaFusion is an innovative model designed for colorizing grayscale images by combining convolutional layers with Vision Transformer (ViT) blocks. It leverages semi-supervised learning to achieve high accuracy even with limited labeled data. Additionally, ChromaFusion allows user interaction for enhanced colorization, enabling users to specify areas to color and choose colors interactively. This user-guided approach provides flexibility and improves the model's performance in achieving desired colorization results.
- Overview
- Key Features
- Demo or Examples
- Installation
- Usage
- Model Architecture
- Training Details
- Results
- Contributing
- License
- Citation
The inspiration for developing ChromaFusion comes from a personal passion for historical war images and a desire to bring new life to my grandparents' cherished black and white photographs. Many of these images, some over 100 years old, capture significant moments in history but have lost their vibrancy over time. Colorizing these images not only enhances their visual appeal but also helps in better understanding and connecting with the past.
Combining convolutional layers with Vision Transformer (ViT) blocks allows ChromaFusion to leverage the strengths of both architectures:
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Convolutional Layers: These are excellent at capturing fine-grained local details and textures in images, making them ideal for tasks involving spatial hierarchies.
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Vision Transformers (ViT): ViTs excel at modeling long-range dependencies and understanding the global context of an image. They can effectively capture relationships between distant pixels, which is crucial for holistic image comprehension.
By integrating these two approaches, ChromaFusion can simultaneously capture detailed local features and the broader context, resulting in more accurate and realistic colorization of grayscale images.
Semi-supervised learning is a key component of ChromaFusion, enabling it to perform well even with limited labeled data. Here’s how it benefits the model:
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Leveraging Unlabeled Data: Historical images often lack corresponding color labels. Semi-supervised learning allows the model to learn from both labeled and unlabeled data, significantly improving its generalization capabilities.
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Improved Accuracy: The inclusion of unlabeled data helps the model to learn more robust features, leading to better colorization results. It enhances the model's ability to understand various contexts and structures within the images.
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User Interaction: ChromaFusion also supports user interaction, allowing users to specify areas to color and choose colors interactively. This user-guided approach provides additional supervision, making the colorization process more precise and customizable.
In summary, ChromaFusion combines the best of convolutional networks and Vision Transformers to effectively capture both local details and global context in images. By utilizing semi-supervised learning and enabling user interaction, it addresses the challenges of limited labeled data and provides a powerful tool for bringing historical black and white images back to life.
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Semi-Supervised Learning (SSL): Utilizes SSL to improve accuracy even with limited labeled data, making it effective in scenarios with scarce color references.
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User-Guided Interaction: Allows users to interact with the model by specifying areas to color and choosing colors, providing additional control and customization.
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Flexible Framework: Designed to be easily extendable or modifiable for various image manipulation tasks, beyond just colorization.
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Scalability: Capable of handling larger image resolutions and diverse datasets, ensuring applicability across different use cases.
Still in progress... Stay tuned for updates!
Still in progress... Stay tuned for updates!
Still in progress... Stay tuned for updates!
Still in progress... Stay tuned for updates!
Still in progress... Stay tuned for updates!
Still in progress... Stay tuned for updates!
We welcome contributions to the ChromaFusion project! Here are some ways you can contribute:
If you encounter any bugs, have feature requests, or need help, please open an issue on our GitHub Issues page. Provide as much detail as possible to help us understand and resolve the issue.
If you'd like to contribute code, follow these steps:
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Fork the Repository: Click the "Fork" button at the top right of the repository page to create a copy of the repository on your GitHub account.
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Clone the Forked Repository: Clone your forked repository to your local machine using:
git clone https://github.com//ChromaFusion.git
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Create a New Branch: Create a new branch for your feature or bug fix:
git checkout -b feature-name
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Make Your Changes: Implement your changes in the new branch. Make sure to follow the project's coding standards and guidelines.
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Commit Your Changes: Commit your changes with a descriptive commit message:
git commit -m "Description of changes" -
Push to Your Fork: Push your changes to your forked repository:
git push origin feature-name
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Create a Pull Request: Go to the original repository on GitHub and click the "New Pull Request" button. Select your branch and provide a detailed description of your changes.
- Style Guide: Follow the project's style guide for code formatting and comments.
- Testing: Ensure that your code passes all tests and does not break existing functionality. Add new tests if applicable.
- Documentation: Update documentation to reflect any changes or new features added.
By participating in this project, you agree to abide by our Code of Conduct. We are committed to providing a welcoming and inclusive environment for everyone.
We value your feedback! Feel free to suggest improvements or share your thoughts on the project's direction.
Thank you for contributing to ChromaFusion. Your efforts help make this project better for everyone!
This project is licensed under the MIT License.
Still in progress... Stay tuned for updates!