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README
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README
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██████╗ ██████╗ ██╗ ██████╗ ██████╗ ██╗███████╗███████╗
██╔════╝██╔═══██╗██║ ██╔═══██╗██╔══██╗██║╚══███╔╝██╔════╝
██║ ██║ ██║██║ ██║ ██║██████╔╝██║ ███╔╝ █████╗
██║ ██║ ██║██║ ██║ ██║██╔══██╗██║ ███╔╝ ██╔══╝
╚██████╗╚██████╔╝███████╗╚██████╔╝██║ ██║██║███████╗███████╗
╚═════╝ ╚═════╝ ╚══════╝ ╚═════╝ ╚═╝ ╚═╝╚═╝╚══════╝╚══════╝
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