██████╗ ██████╗ ██╗ ██████╗ ██████╗ ██╗███████╗███████╗ ██╔════╝██╔═══██╗██║ ██╔═══██╗██╔══██╗██║╚══███╔╝██╔════╝ ██║ ██║ ██║██║ ██║ ██║██████╔╝██║ ███╔╝ █████╗ ██║ ██║ ██║██║ ██║ ██║██╔══██╗██║ ███╔╝ ██╔══╝ ╚██████╗╚██████╔╝███████╗╚██████╔╝██║ ██║██║███████╗███████╗ ╚═════╝ ╚═════╝ ╚══════╝ ╚═════╝ ╚═╝ ╚═╝╚═╝╚══════╝╚══════╝ DESCRIPTION Grayscale image colorization using a conditional DCGAN USAGE First make sure that you have every packages in the requirements.txt file. The input image resolution should be either 32x32 or 256x256. To train using DCGAN change the config/train.json file to put the correct parameters and launch the training using: python train.py --config config/train.json To test the trained model, change the config/colorize.json file to put the correct parameters and launch the inference using: python colorize.py --config config/colorize.json REFERENCES Colorful Image Colorization (https://arxiv.org/abs/1603.08511) Image Colorization with Generative Adversarial Networks (https://arxiv.org/abs/1803.05400) CONTRIBUTORS Hussem Ben Belgacem
hbenbel/Colorization
Grayscale image colorization using a conditional DCGAN in Python and Pytorch
PythonMIT