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GunithJakka/image_to_image_translation_cyclegan

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image_to_image_translation_cyclegan


Setup

  1. Download the Monet2Photos dataset as follows:
wget https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/monet2photo.zip
  1. Make a new directory (e.g. 'data'), and make it the current directory
mkdir data
cd data
  1. Unzip the zip file containing the dataset inside the 'data' directory
unzip monet2photo.zip
  1. Install the keras_contrib library as follows:
pip install git+https://www.github.com/keras-team/keras-contrib.git
  1. Create a new directory (e.g. 'results') to store the original, generated and reconstructed images
mkdir results

About the execution modes

A) Train mode

  • Vanilla execution mode
  • Images will be loaded, and the generator and discriminator networks will be trained
  • Generator will generate images from the given input, discriminator will distinguish between real and fake images
  • The adversarial model is responsible for reaching an optimum value of the objective function
  • After every epoch, the weights are updated to tune both the networks to reach maximum efficiency.

B) Predict mode

  • The discriminator network isn't involved here - only the weights of the generator network are loaded, and the generator network is trained
  • The generator generates images for the input images it is provided
  • No networks are optimized, the generator just generates images for the entire batch of inputs.

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  • Python 100.0%