GitHub

Image-to-image translation in PyTorch  #machinelearning

  • Download the CycleGAN datasets using the following script:

    To train a model on your own datasets, you need to create a data folder with two subdirectories and that contain images from domain A and B.

  • Download the pix2pix datasets using the following script:

    We provide a python script to generate pix2pix training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene.

  • Corresponding images in a pair {A,B} must be the same size and have the same filename, e.g., is considered to correspond to .
  • Once the data is formatted this way, call:

    This will combine each pair of images (A,B) into a single image file, ready for training.

  • CycleGAN: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

    pix2pix: Image-to-image translation with conditional adversarial nets

    iGAN: Interactive Image Generation via Generative Adversarial Networks

    If you love cats, and love reading cool graphics, vision, and learning papers, please check out the Cat Paper Collection:

    [Github] [Webpage]

pytorch-CycleGAN-and-pix2pix – Image-to-image translation in PyTorch (e.g. horse2zebra, edges2cats, and more)
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