makeitcolor

MakeItColor: Image Colorization Model

Overview

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.

This model builds upon the work of DDColor, utilizing a dual-encoder approach and trained on the ImageNet-Val5k dataset.

Features

• Task: Image Colorization
• Framework: PyTorch, ModelScope
• Architecture: Convolutional Neural Network (CNN)
• Input: Grayscale images (single-channel)
• Output: Colorized images (RGB format)

Installation

Ensure you have Python 3.7+ installed. Then, install the required dependencies:

pip install opencv-python
pip install modelscope==1.12.0 
pip install datasets==2.14.7
pip install pillow
pip install numpy

Usage

ModelScope Pipeline

import cv2
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from huggingface_hub import snapshot_download

# Download the model files to a local directory
snapshot_download(repo_id="muhammadnoman76/makeitcolor", local_dir="./makeitcolor", repo_type="model")

# Initialize the colorization pipeline
img_colorization = pipeline(Tasks.image_colorization, model='./makeitcolor')

# Load a grayscale image
img_path = 'input.jpg'

# Run colorization
result = img_colorization(img_path)

# Save the colorized image
cv2.imwrite('result.png', result['output_img'])

Note:

• Ensure that the input image (input.jpg) is a proper grayscale (single-channel) image.
• The output (result.png) will be a standard RGB image.

Google Colab

For an interactive demonstration, try our Google Colab notebook.

Model Files

The repository contains:

• pytorch_model.pt: Pre-trained model weights
• configuration.json: Model configuration file for ModelScope integration
• README.md: Documentation

Requirements

Hardware

• CPU (supported)
• GPU (recommended for faster inference)

Software Dependencies

• modelscope
• opencv-python
• torch

Input Format

• Grayscale images (.png, .jpg, etc.)

Example Workflow

1. Prepare a grayscale image (e.g., input.jpg)
2. Run the provided example code
3. Check the output file (result.png) for the colorized result

Limitations

• May struggle with highly complex, ambiguous, or abstract grayscale images
• Performance and output quality depend on the clarity and details of the input
• Primarily optimized for natural images; results may vary for synthetic or artistic inputs

Credits

This work builds upon and was inspired by the DDColor project.
MakeItColor leverages a dual-encoder strategy from DDColor and is trained on the ImageNet-Val5k dataset.

Special thanks to the creators of DDColor for their foundational contributions.

License

This project is licensed under the Apache License 2.0.

Contact

For issues, questions, or feedback:

• Open an issue on the Hugging Face repository
• Contact the maintainer directly at: [email protected]

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Developed by Muhammad Noman