Gen AI - Image Generation using GAN

Gen AI is a project focused on generating CIFAR-10-like images using Generative Adversarial Networks (GANs). The custom GAN model consists of a Generator and a Discriminator, trained to produce realistic images and differentiate between real and generated ones.

Table of Contents

• Key Features
• Use Case
• Usage

• Note
• Screenshots
• License

Key Features

• Trains a custom GAN model on the CIFAR-10 dataset.
• Generates images resembling CIFAR-10 images with unique variations.
• Supports training and image generation via Jupyter Notebooks or Google Colab.

Use Case

• Data Augmentation: Augment existing datasets with synthetic images.
• Concept Exploration: Explore novel concepts and artistic expressions.
• Prototyping: Create mock-ups and test interfaces with generated images.

Usage

Setup

• Clone the repository to your local machine.
• Ensure dependencies are installed, including Python, TensorFlow, and Keras.

Training

• Open the provided Jupyter Notebook or upload it to Google Colab.
• Run the notebook to train the GAN model.
• Utilize GPU acceleration for faster training, especially on Google Colab.

Generation

• After training, use the trained model to generate new images.
• Experiment with different latent space values to explore image variations.

Note

• No graphical user interface (UI) is provided. Interact with the project through Jupyter Notebooks or Google Colab.
• GPU acceleration is recommended for efficient training.
• Contributions are welcome! Fork the repository, make improvements, and submit pull requests.

Screenshots

License

For any inquiries or support, please contact abhijeetmaharana77@.com.