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license: apache-2.0 |
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# MakeItColor: Image Colorization Model |
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[](https://colab.research.google.com/drive/10raIuCBUhKCPqIuL_HiSQmkJJ9jbu2VC?usp=sharing) |
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## Overview |
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**MakeItColor** is a deep learning model designed for automatic image colorization. It transforms grayscale images into vivid, realistic colorized outputs using a PyTorch-based Convolutional Neural Network (CNN) architecture integrated with the ModelScope framework. |
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This model builds upon the work of [DDColor](https://github.com/piddnad/DDColor), utilizing a dual-encoder approach and trained on the **ImageNet-Val5k** dataset. |
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## Features |
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- **Task**: Image Colorization |
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- **Framework**: PyTorch, ModelScope |
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- **Architecture**: Convolutional Neural Network (CNN) |
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- **Input**: Grayscale images (single-channel) |
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- **Output**: Colorized images (RGB format) |
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## Installation |
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Ensure you have **Python 3.7+** installed. Then, install the required dependencies: |
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```bash |
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pip install opencv-python |
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pip install modelscope==1.12.0 |
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pip install datasets==2.14.7 |
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pip install pillow |
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pip install numpy |
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``` |
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## Usage |
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### ModelScope Pipeline |
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```python |
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import cv2 |
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from modelscope.pipelines import pipeline |
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from modelscope.utils.constant import Tasks |
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from huggingface_hub import snapshot_download |
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# Download the model files to a local directory |
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snapshot_download(repo_id="muhammadnoman76/makeitcolor", local_dir="./makeitcolor", repo_type="model") |
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# Initialize the colorization pipeline |
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img_colorization = pipeline(Tasks.image_colorization, model='./makeitcolor') |
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# Load a grayscale image |
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img_path = 'input.jpg' |
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# Run colorization |
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result = img_colorization(img_path) |
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# Save the colorized image |
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cv2.imwrite('result.png', result['output_img']) |
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``` |
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> **Note**: |
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> - Ensure that the input image (`input.jpg`) is a proper grayscale (single-channel) image. |
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> - The output (`result.png`) will be a standard RGB image. |
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## Google Colab |
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For an interactive demonstration, try our [Google Colab notebook](https://colab.research.google.com/drive/10raIuCBUhKCPqIuL_HiSQmkJJ9jbu2VC?usp=sharing). |
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## Model Files |
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The repository contains: |
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- `pytorch_model.pt`: Pre-trained model weights |
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- `configuration.json`: Model configuration file for ModelScope integration |
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- `README.md`: Documentation |
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## Requirements |
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### Hardware |
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- CPU (supported) |
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- GPU (recommended for faster inference) |
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### Software Dependencies |
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- `modelscope` |
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- `opencv-python` |
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- `torch` |
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## Input Format |
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- Grayscale images (`.png`, `.jpg`, etc.) |
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### Example Workflow |
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1. Prepare a grayscale image (e.g., `input.jpg`) |
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2. Run the provided example code |
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3. Check the output file (`result.png`) for the colorized result |
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## Limitations |
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- May struggle with highly complex, ambiguous, or abstract grayscale images |
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- Performance and output quality depend on the clarity and details of the input |
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- Primarily optimized for natural images; results may vary for synthetic or artistic inputs |
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## Credits |
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This work builds upon and was inspired by the [DDColor project](https://github.com/piddnad/DDColor). |
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**MakeItColor** leverages a dual-encoder strategy from DDColor and is trained on the **ImageNet-Val5k** dataset. |
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Special thanks to the creators of DDColor for their foundational contributions. |
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## License |
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This project is licensed under the **Apache License 2.0**. |
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## Contact |
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For issues, questions, or feedback: |
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- Open an issue on the Hugging Face repository |
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- Contact the maintainer directly at: [muhammadnomanshafiq76@gmail.com](mailto:muhammadnomanshafiq76@gmail.com) |
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--- |
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**Developed by Muhammad Noman** |
