Frequently Asked Questionsīefore you post a new question, please first look at the above Q & A and existing GitHub issues. Training/Test Tipsīest practice for training and testing your models. Datasetsĭownload pix2pix/CycleGAN datasets and create your own datasets. We provide the pre-built Docker image and Dockerfile that can run this code repo. See a list of currently available models at. scripts/test_single.sh for how to apply a model to Facade label maps (stored in the directory facades/testB). If you would like to apply a pre-trained model to a collection of input images (rather than image pairs), please use -model test option. Note that we specified -direction BtoA as Facades dataset's A to B direction is photos to labels. datasets/facades/ -direction BtoA -model pix2pix -name facades_label2photo_pretrained ZeroCostDL4Mic Colab notebook: CycleGAN and pix2pix Other implementations CycleGAN PyTorch Colab notebook: CycleGAN and pix2pix TensorFlow Core pix2pix Tutorial: Google Colab | Code TensorFlow Core CycleGAN Tutorial: Google Colab | Code Please contact the instructor if you would like to adopt it in your course. Roger Grosse for CSC321 "Intro to Neural Networks and Machine Learning" at University of Toronto. Talks and CourseĬycleGAN course assignment code and handout designed by Prof. Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Image-to-Image Translation with Conditional Adversarial Networks. Jun-Yan Zhu*, Taesung Park*, Phillip Isola, Alexei A. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. If you use this code for your research, please cite: To help users better understand and adapt our codebase, we provide an overview of the code structure of this repository.ĮdgesCats Demo | pix2pix-tensorflow | by Christopher Hesse To implement custom models and datasets, check out our templates. You may find useful information in training/test tips and frequently asked questions. Check out the older branch that supports PyTorch 0.1-0.3. Note: The current software works well with PyTorch 1.4. If you would like to reproduce the same results as in the papers, check out the original CycleGAN Torch and pix2pix Torch code in Lua/Torch. This PyTorch implementation produces results comparable to or better than our original Torch software. The code was written by Jun-Yan Zhu and Taesung Park, and supported by Tongzhou Wang. We provide PyTorch implementations for both unpaired and paired image-to-image translation. New: Please check out contrastive-unpaired-translation (CUT), our new unpaired image-to-image translation model that enables fast and memory-efficient training.
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