supertskone / face-generation

Face Generation with Generative Adversarial Networks (GANs)

Welcome to my Face Generation project! In this project, I've built a custom generative adversarial network (GAN) to generate new images of faces, particularly showcasing faces of celebrities from the CelebA dataset.

Project Overview

Introduction

The primary goal of this project is to leverage the power of GANs to create high-quality facial images. After training the GAN model for a few epochs, you'll witness the model's ability to generate realistic and convincing faces.

How to Use

To get started with this project, follow these steps:

1. Open the notebook file, dlnd_face_generation_starter.ipynb, and follow the detailed instructions provided within.

2. The project is organized into several sections:

   • Data Pipeline: Implement a data augmentation function and a custom dataset class to load and preprocess the images.

   • Model Implementation: Build a custom generator and a custom discriminator to create your GAN architecture.

   • Loss Functions and Gradient Penalty: Decide on appropriate loss functions and whether to employ gradient penalty.

   • Training Loop: Implement the training loop and choose a training strategy.

Feel free to revisit any section to fine-tune your model or data pipeline based on the results you obtain. Building a deep learning model, especially a GAN, is an iterative process, so don't hesitate to experiment and improve your project.

Conclusion

Creating a deep learning model capable of generating realistic faces is an exciting challenge. I hope you find this project as engaging and rewarding as I did. Good luck, and happy face generation!