ChakradharG / Colorize

Colorize

Conditional GAN for colorizing grayscale images. Uses UNet architecture for generator and Patch-Discriminator for discriminator.

Getting Started

• Clone this repository
• cd Colorize
• Download the 2014 COCO validation dataset and extract it to data/images
• Run python data/split.py to split the dataset into training and validation sets

How to Use

• Change the hyperparameters in config.py if needed
• (Optional) Run python pretrain_generator.py to pretrain the generator (L1 Loss)
• If using pretrained generator, change the pretrained_gen parameter in config.py to true otherwise false
• Run python train_GAN.py to train and save the model
• Store the images to be colorized in ip folder
• Update inference.py with the path to the checkpoint
• Run python inference.py to colorize the images