Automatically infuse grayscale images with vivid colors using CycleGAN, a powerful Generative Adversarial Network (GAN) architecture. This project leverages Python, TensorFlow, and Keras to achieve high-quality image colorization and seamless conversion between grayscale and color versions.

CycleGAN is a type of GAN that can learn to translate images from one domain to another without the need for paired training data. In this project, we train the CycleGAN model on a dataset of grayscale images and their corresponding color versions. Once trained, the model can take any grayscale image as input and produce a realistic colorized version as output.

1. Clone this repository to your local machine.
2. Install the required libraries and dependencies using pip install -r requirements.txt.

1. Ensure you have the grayscale images you want to colorize in a separate folder.
2. Use Inference.ipynb to Convert them

Special thanks to the developers of CycleGAN and the TensorFlow community for their invaluable contributions.

Enjoy transforming your grayscale images into vibrant, colorful masterpieces!