Image Segmentation in PyTorch Online Course

Image Segmentation in PyTorch Online Course

This course equips you to master image segmentation by combining theory with hands-on practice. Starting with PyTorch fundamentals, tensors, datasets, and neural networks, you'll build a solid foundation before advancing to semantic segmentation with architectures like UNet and Feature Pyramid Network. Learn key techniques such as upsampling, loss functions, and evaluation metrics, while applying your skills to real-world projects like satellite image segmentation. With practical guidance on training loops, hyperparameter tuning, and model saving, you’ll gain the expertise needed to confidently tackle complex segmentation tasks.

Who should take this course?

This course is designed for AI developers, computer vision enthusiasts, and machine learning practitioners who want to learn image segmentation techniques using PyTorch. It’s also ideal for students and professionals aiming to build real-world applications in autonomous systems, medical imaging, and object recognition.

What you will learn

• Build image segmentation models using PyTorch efficiently
• Apply preprocessing techniques to prepare image datasets for ML
• Implement neural networks for complex segmentation tasks
• Optimize model performance with hyperparameter tuning methods
• Evaluate segmentation models using IoU and pixel accuracy metrics
• Debug and enhance model training pipelines for accuracy gains

Course Outline

Course Overview and Setup

• Image Segmentation (101)
• Course Scope
• System Setup
• How to Get the Material
• Conda Environment Setup

PyTorch Introduction (Refresher)

• Modelling Section Overview
• PyTorch Introduction (101)
• Tensor Introduction
• From Tensors to Computational Graphs (101)
• Tensor (Coding)
• Linear Regression from Scratch (Coding, Model Training)
• Linear Regression from Scratch (Coding, Model Evaluation)
• Model Class (Coding)
• Exercise - Learning Rate and Number of Epochs
• Solution - Learning Rate and Number of Epochs
• Batches (101)
• Batches (Coding)
• Datasets and Dataloaders (101)
• Datasets and Dataloaders (Coding)
• Saving and Loading Models (101)
• Saving and Loading Models (Coding)
• Model Training (101)
• Hyperparameter Tuning (101)
• Hyperparameter Tuning (Coding)

Convolutional Neural Networks (Refresher)

• CNN Introduction (101)
• CNN (Interactive)
• Image Preprocessing (101)
• Image Preprocessing (Coding)
• Layer Calculations (101)
• Layer Calculations (Coding)

Semantic Segmentation

• Architecture (101)
• Upsampling (101)
• Loss Functions (101)
• Evaluation Metrics (101)
• Coding Introduction (101)
• Data Prep Introduction (101)
• Data Prep I - Create Folders (Coding)
• Data Prep II - Patches Function (Coding)
• Data Prep III - Create All Patch-Images (Coding)
• Modelling - Dataset (Coding)
• Modelling - Model Setup (Coding)
• Modelling - Training Loop (Coding)
• Modelling - Losses and Saving (Coding)
• Model Evaluation - Calc Metrics (Coding)
• Model Evaluation - Check Prediction (Coding)