Cycle G A N

Introduction
This repository provides an implementation of the CycleGAN model using Keras, specifically applied to the "Horse to Zebra" dataset. The CycleGAN model addresses the challenge of image-to-image translation without the need for paired examples by employing cycle-consistent adversarial networks.

Architecture
CycleGAN is designed to perform image-to-image translation by learning mappings between input and output images. Unlike traditional models that require paired datasets, CycleGAN utilizes unpaired datasets to achieve image transformation through a cycle-consistent adversarial approach.

Training
The training process involves learning two mappings: from the source domain (e.g., horse images) to the target domain (e.g., zebra images), and vice versa. The model leverages cycle consistency to ensure that translations from one domain to another and back result in the original image, thereby reducing the need for paired training data.

Guide: Running Locally

1. Install Dependencies: Ensure that you have Python and necessary libraries such as TensorFlow and Keras installed.
2. Clone Repository: Clone the CycleGAN repository from Hugging Face to your local machine.
3. Prepare Dataset: Download the "Horse to Zebra" dataset from TensorFlow datasets.
4. Run Notebook: Execute the provided Jupyter notebook to train and evaluate the CycleGAN model.
5. Optional: For faster training, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License
This implementation of CycleGAN is licensed under the CC0-1.0 license, allowing for unrestricted use and distribution.