CycleGAN-Keras

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Keras implementation of CycleGAN

Implementation using a tensorflow backend. Testing and evaluation done on street view images.

Results - 256x256 pixel images

Day 2 night

Input	Translation	Input	Translation

Night 2 day

Input	Translation	Input	Translation

Model additions as training options

• Identity learning (on different modulus of training iterations)
• PatchGAN in discriminators
• Multi-scale discriminators
• Resize convolution in generators
• Supervised learning with training weight
• Data generator (if using a large dataset)
• Weight on discriminator training labels on real images

Code usage

1. Prepare your dataset under the directory 'data' and set dataset name to parameter 'image_folder' in CycleGAN init function.

• Directory structure on new dataset needed for training and testing:
  • data/Dataset-name/trainA
  • data/Dataset-name/trainB
  • data/Dataset-name/testA
  • data/Dataset-name/testB

2. Set wanted training options, also found in the init function.

3. Train a model by:

python model.py

4. Generate synthetic images by following specifications under:

• generate_images/ReadMe.rtf

The following gif shows an example of the training progression in a translation from day to night.

Left: Input image. Middle: Translated images. Right: Reconstructed images.

More results

Day 2 night - gif

Rainy 2 sunny - gif

Sunny 2 rainy - gif