Day 1: Fundamentals and Tools

Session 1: Introduction to Image Segmentation

• Overview of segmentation techniques: Semantic and instance segmentation
• Applications of segmentation in various industries

Session 2: Annotation Tools for Data Preparation

• Introduction to tools like LabelImg, CVAT, and VIA
• Hands-on lab: Annotating images with LabelImg and CVAT

Session 3: Data Preprocessing for Segmentation

• Techniques for cleaning and preparing annotated datasets
• Hands-on lab: Preparing a dataset for segmentation tasks

Day 2: Implementing Segmentation Models

Session 1: Semantic Segmentation Techniques

• Understanding U-Net, DeepLab, and other architectures
• Hands-on lab: Training a semantic segmentation model with U-Net

Session 2: Instance Segmentation Techniques

• Exploring Mask R-CNN and its applications
• Hands-on lab: Implementing an instance segmentation pipeline

Session 3: Evaluating Segmentation Models

• Metrics for segmentation performance: IoU, Dice coefficient, etc.
• Hands-on lab: Evaluating a trained segmentation model

Day 3: Applications and Advanced Techniques

Session 1: Real-World Applications of Segmentation

• Case studies in healthcare, retail, and autonomous systems
• Group discussion: Identifying segmentation use cases in your domain

Session 2: Advanced Annotation Techniques

• Annotating complex datasets and managing large-scale projects
• Hands-on lab: Collaborative annotation workflows with CVAT

Session 3: Final Project and Review

• Hands-on lab: Developing an end-to-end segmentation pipeline for a real-world problem
• Feedback and discussion: Best practices and future trends in segmentation