You can deploy this model and start earning money today!

jessicanono/
filparty_colorization

Name: filparty_colorization
Availability: InStock

Views

No views yet

README for Image Colorization Model

Welcome to the GitHub repository for our Image Colorization model! This model uses deep learning to add vibrant colors to grayscale images, particularly focusing on historical photographs. Below you'll find instructions on how to set up and use this model.

Requirements

To use this model, you need the following:

Python 3.8 or later
PyTorch
torchvision
PIL (Pillow)
scikit-image
matplotlib
numpy

You can install the necessary libraries using pip:

pip install torch torchvision Pillow scikit-image matplotlib numpy

Model Architecture

ColorizationNet is a neural network model built using PyTorch. It combines the power of ResNet and an upsampling network to colorize grayscale images.

Setup

First, clone this repository and navigate to the directory:

bash

git clone [repo-link]
cd [repo-directory]

Download the trained model weights (colorization_md1.pth) and place them in the repository's root directory.

Usage

To colorize an image, follow these steps:

Prepare the Image: Ensure your image is in grayscale format. You can use any standard image format like JPG or PNG.
Run the Model: Use the following code to colorize your image.

python
1from colorize import ColorizationNet, colorize_single_image 2import torch 3import torch.nn as nn 4 5# Load the model 6model = ColorizationNet() 7model_path = 'colorization_md1.pth' 8pretrained = torch.load(model_path, map_location=lambda storage, loc: storage) 9model.load_state_dict(pretrained) 10model.eval() # Set the model to evaluation mode 11 12# Set the path to your image 13image_path = 'path_to_your_image.jpg' 14save_dir = 'dir_to_save_colorized_image' 15criterion = nn.MSELoss() 16 17# Colorize the image 18use_gpu = torch.cuda.is_available() 19colorize_single_image(image_path, model, criterion, save_dir, epoch=0, use_gpu=use_gpu)
View the Results: The colorized image will be saved in the specified save_path. You will find both the original grayscale and the colorized version there.

Contributions and Issues

Feel free to contribute to this project by submitting pull requests. If you encounter any issues or have questions, please submit them in the issues section of this repository.

License

Enjoy exploring the vibrant colors of history with our model! 🎨📸

jessicanono/filparty_colorization