Must-Do Top Open-Source CycleGAN Python Projects Before 2021
1. CycleGAN Tensorflow

CycleGAN Tensorflow is the implementation of the paper CycleGAN using Python, Tensorflow. It is implemented using Tensorflow 1.0.0 and Python 3.6.

It covers all the steps discussed in the paper including:

Data Preparation for training

Training of the model

Exporting model from a checkpoint to a standalone GraphDef file

After exporting the model, how to use it for inference

How to load pre-trained models

2. Pytorch CycleGAN

Pytorch CycleGAN is a clean and readable Pytorch implementation of CycleGAN paper. The code is written in Python 3.6.x.

The repository contains steps in details to:

setup the dataset for the training

how to start a training session using the images dataset

testing the images and saving the output

3. VON

Visual Object Networks(VON) is an end-to-end adversarial learning framework that jointly models 3D shapes and 2D images. The code is tested in Python 3.6 and above using Pytorch.

The model can synthesize a 3D shape, its intermediate 2.5D depth representation, and a 2D image all at once. VON not only generates realistic images but also enables several 3D operations.

VON allows several 3D applications such as (left) changing the viewpoint, texture, or shape independently, and (right) interpolating between two objects in shape space, texture space, or both.

VON can transfer the texture of a real image to different shapes and viewpoints.

4. Voice Converter CycleGAN

Cycle-consistent adversarial networks (CycleGAN) has been widely used for image conversions but it turns out that it could also be used for voice conversion. This is an implementation of CycleGAN on human speech conversions. The neural network utilized a 1D gated convolution neural network (Gated CNN) for the generator and 2D Gated CNN for the discriminator.

The model takes Mel-cepstral coefficients (MCEPs) (for spectral envelope) as input for voice conversions. The code is written in Python 3.5 using Tensorflow 1.8 and the dataset used for training can be downloaded here: VCC2016 dataset.

The model can be trained on GPU or for the conversion of voices pre-trained models can also be used.

Conversions: The result of voice conversation using Voice Converter CycleGAN can be found here.

5. Pix2Depth

Pix2Depth is a depth map estimation from monocular images. Pix2Depth uses several strategies to extract depth from RGB images. Of all the strategies used, Pix2pix and CycleGAN give the best results.

Pix2Depth is trained on the NYU Depth Dataset. Pix2Depth is also trained to predict RGB images from the depth map.

The web demo for Pix2Depth can be found here.

The web demo has three sections:
['Mrinal Walia']
['Python', 'Open Source', 'Projects', 'Gans', 'Cyclegan']