Generative_Image_Rotation (Protoplanetary Disk Image Rotation using Pix2Pix cGAN)

Overview:

This repository hosts Jupyter notebooks dedicated to training and inference of a Conditional Generative Adversarial Network (cGAN) based on the Pix2Pix architecture. The project focuses on generating standardized face-on images of Protoplanetary Disks from source images that are randomly oriented, using a dataset obtained from FARGO3D simulations.

Key Features:

• Training Notebook:

  • Implements a cGAN model with Pix2Pix architecture to transform randomly oriented Protoplanetary Disk images into face-on views.
  • Includes attention mechanisms to enhance image generation quality by focusing on crucial features.
  • Utilizes TensorFlow for model development and training.

• Inference Notebook:

  • Deploys the trained cGAN model to generate face-on images from new, randomly oriented Protoplanetary Disk images.
  • Enables comparison between generated images and corresponding target images for evaluation.

• Dataset Handling:

  • Preprocesses and stores Protoplanetary Disk images in numpy arrays for efficient memory usage during training.
  • Includes 21,000 images sourced from FARGO3D simulations, comprising 30 orientations per image.

• Performance Evaluation:

  • Incorporates periodic model checkpoints and performance visualizations using summarize_performance function.
  • Automatically organizes saved models and plots into uniquely named folders for each training session.

Supervisor:

Dr. Sayantan Auddy

Author:

Dyutiman Santra