This project focuses on converting cartoonish images to realistic images using Generative Adversarial Networks (GANs). The implementation involves training a GAN model on a dataset of cartoonish drawings and their corresponding real photos, followed by evaluating the model's performance using both qualitative and quantitative metrics.
This project aims to transform non-realistic, cartoonish images into more realistic-looking images using GANs. The focus is on ensuring the generated images maintain high fidelity and realism.
The dataset consists of cartoonish drawings of people (grayscale) and real photos of people (RGB). Both image types are sized 200x250.
Images are preprocessed to normalize pixel values and resize to a consistent shape. Data augmentation techniques are applied to increase the diversity of the training set.