Astronomy Image Colorization Using Machine Learning (Gans)

Colorize Black & White Astronomical Images Using Python, PyTorch, and FastAPI

What you'll learn

Discover the fundamentals of Generative Adversarial Networks (GANs) and understand their architecture, loss functions, and optimization challenges.

Generate galaxies using GANs by setting up and training a model from scratch with hands-on coding in Kaggle Notebooks.

Dive deeper into Wasserstein GAN with Gradient Penalty (WGAN-GP), learning about the algorithm and its implementation for more stable training.

Implement WGAN-GP to generate realistic galaxy images and compare generated images with real astronomical data.

Master Image-to-Image Translation GANs (Pix2Pix) and explore how they can be used for transforming images in the context of astronomy.

Colorize black-and-white astronomical images using UNET architecture, PyTorch, and advanced GAN models to recreate realistic, vivid space images.

Get introduced to FastAPI and Streamlit, learn to build APIs and create a frontend for your machine learning models.

Create and deploy your own Image Colorization App using FastAPI, bringing all your learning together in a real-world project.

Requirements

Basic knowledge of Python programming.

Familiarity with machine learning concepts is recommended, but not mandatory.

Enthusiasm to learn GANs, WGANs, and image processing techniques!

Description

Are you fascinated by the beauty of the universe but curious about how machine learning can be used to bring astronomical images to life? Welcome to Astronomy Image Colorization using Machine Learning (GANs), where you will dive deep into the world of Generative Adversarial Networks (GANs) and their applications in astronomical image processing.In this course, you will learn how to leverage machine learning techniques to generate galaxies and colorize black-and-white images from space. You will gain practical knowledge by building end-to-end projects, from understanding GANs to creating your own image colorization app using FastAPI and Streamlit.What You’ll Learn:Module 1: Discover the fundamentals of Generative Adversarial Networks (GANs) and understand their architecture, loss functions, and optimization challenges.Module 2: Generate galaxies using GANs by setting up and training a model from scratch with hands-on coding in Kaggle Notebooks.Module 3: Dive deeper into Wasserstein GAN with Gradient Penalty (WGAN-GP), learning about the algorithm and its implementation for more stable training.Module 4: Implement WGAN-GP to generate realistic galaxy images and compare generated images with real astronomical data.Module 5: Master Image-to-Image Translation GANs (Pix2Pix) and explore how they can be used for transforming images in the context of astronomy.Module 6: Colorize black-and-white astronomical images using UNET architecture, PyTorch, and advanced GAN models to recreate realistic, vivid space images.Module 7: Get introduced to FastAPI and Streamlit, learn to build APIs and create a frontend for your machine learning models.Module 8: Create and deploy your own Image Colorization App using FastAPI, bringing all your learning together in a real-world project.Course Highlights:Real-world Astronomy Applications: Work with real astronomical data to train your models.Project-Based Learning: Build multiple projects, including a Galaxy Generation project and a colorization web app.Hands-on with GANs: Deep dive into the technical details of GANs, WGANs, and Pix2Pix with step-by-step coding exercises.PyTorch & FastAPI: Learn how to use PyTorch for model building and FastAPI to deploy your models in production.Who This Course is For:Data science enthusiasts interested in Generative Adversarial Networks (GANs).Machine learning engineers looking to enhance their skills in computer vision and image generation.Astronomy buffs who want to apply machine learning to space image processing.Developers interested in building real-world ML apps using FastAPI and Streamlit.Requirements:Basic knowledge of Python programming.Familiarity with machine learning concepts is recommended, but not mandatory.Enthusiasm to learn GANs, WGANs, and image processing techniques!FAQs Section:What tools and libraries will we use in this course?You'll use Python libraries like PyTorch for model building, FastAPI for backend development, and Streamlit for frontend interfaces. We'll also leverage Kaggle Notebooks for coding exercises.Do I need prior experience with GANs?No prior experience with GANs is necessary, but basic Python programming knowledge and a basic understanding of machine learning would be beneficial.

Overview

Section 1: Introduction to Course and Generative Adversarial Networks

Lecture 1 What you're going to build?

Lecture 2 Course Introduction

Lecture 3 Prerequisites

Lecture 4 Intro to Module 1

Lecture 5 Generative Models

Lecture 6 Example of GANs

Lecture 7 Introduction to GANs

Lecture 8 GAN Architecture

Lecture 9 Loss Function

Lecture 10 Balanced Training

Lecture 11 Optimizers

Lecture 12 Problem with GANs

Lecture 13 Module 1 Conclusion

Section 2: Generate Galaxies using GAN

Lecture 14 Module 2 Introduction

Lecture 15 Problem Statement

Lecture 16 Setting up Kaggle Notebook

Lecture 17 Importing Libraries

Lecture 18 Load and Analyse the Image

Lecture 19 Function to preprocess the Image

Lecture 20 Create Dataset Pipeline

Lecture 21 Visualise the Data

Lecture 22 Build the Generator Model

Lecture 23 Build the Discriminator Model

Lecture 24 Define Losses and Optimizers

Lecture 25 Setup Checkpoints

Lecture 26 Function to generate and save images

Lecture 27 Function to define a training step

Lecture 28 Function to Train the GAN model

Lecture 29 Train the Model

Lecture 30 Discuss Final Results

Lecture 31 Module 2 Conclusion

Section 3: WGAN with Losses and Gradient Penalty

Lecture 32 Module 3 Intro

Lecture 33 Quick Recap on GAN

Lecture 34 Introduction to Wasserstein GAN (WGAN)

Lecture 35 Lipschitz Constraint

Lecture 36 Loss Functions for WGAN

Lecture 37 WGAN Algorithm

Lecture 38 WGAN-GP Algorithm

Lecture 39 Understanding WGAN-GP

Lecture 40 Module 3 Conclusion

Section 4: Generating Galaxies using WGAN-GP

Lecture 41 Module 4 Introduction

Lecture 42 Importing Dependencies

Lecture 43 Setting Memory Growth for each GPU

Lecture 44 Project Setup

Lecture 45 Dataset Preparation

Lecture 46 Building the Generator and the Critic

Lecture 47 Learning Rate Setup

Lecture 48 Building the Checkpoints

Lecture 49 Function to generate and save images

Lecture 50 Build the training step for the Generator and the Critic

Lecture 51 Training WGAN-GP

Lecture 52 Compare the real data with the generated data

Lecture 53 Module 4 Conclusion

Section 5: Image to Image Translation GANs

Lecture 54 Module 5 Introduction

Lecture 55 Block Diagram of Pix2Pix

Lecture 56 UNET Architecture for Generator

Lecture 57 PatchGAN for Discriminator

Lecture 58 Training of Generator

Lecture 59 Training of Discriminator

Lecture 60 Quick Paper Walkthrough

Lecture 61 Module 5 Conclusion

Section 6: Colorizing black and white Astronomical Images

Lecture 62 Module 6 Introduction

Lecture 63 PyTorch for Final Project

Lecture 64 Adding Dataset to the Notebook

Lecture 65 Unzip the Training Images

Lecture 66 Importing Libraries and Modules

Lecture 67 Visualizing Images in the Dataset

Lecture 68 Understanding LAB color space

Lecture 69 DataLoader for Training and Validation Sets

Lecture 70 Revisiting UNET

Lecture 71 Understand the Basics of UNET with Pytorch

Lecture 72 Highly Modular UNET for Generator Implementation

Lecture 73 70x70 PatchGAN for Discriminator Implementation

Lecture 74 Binary Cross Entropy Loss for Generator and Discriminator

Lecture 75 Initializing Weights and Model with those Weights

Lecture 76 Main Model Definition for Training

Lecture 77 Define Utility Functions for Training

Lecture 78 Training Outputs

Lecture 79 Human Eye Validation

Lecture 80 Calculating PSNR and SSIM on Input Data

Lecture 81 Final Outputs and Conclusion

Lecture 82 Module 6 Conclusion

Section 7: Introduction to FastAPI and Streamlit

Lecture 83 Module 7 Introduction

Lecture 84 What’s an API?

Lecture 85 RESTful API

Lecture 86 FastAPI Introduction

Lecture 87 Setting up the Demo for FastAPI

Lecture 88 Get Method - App Working

Lecture 89 Post Method - Create Mission

Lecture 90 Get Method - Retrieve Mission(s)

Lecture 91 Streamlit Frontend

Lecture 92 Module 7 Conclusion

Section 8: Image Colorization App

Lecture 93 Module 8 Introduction

Lecture 94 Download the Project file

Lecture 95 Setup the Project

Lecture 96 Experience the Webapp

Lecture 97 Model file model.py

Lecture 98 Main app file

Lecture 99 frontend.py using chatGPT

Lecture 100 Module 8 Conclusion

Lecture 101 Course Conclusion

Data science enthusiasts interested in Generative Adversarial Networks (GANs).,Machine learning engineers looking to enhance their skills in computer vision and image generation.,Astronomy buffs who want to apply machine learning to space image processing.,Developers interested in building real-world ML apps using FastAPI and Streamlit.