n-jaro / segmentation_model_tutorial

Image Segmentation Tutorial using Segmentation Model Library

Author: Nattapon Jaroenchai, University of Illinois Urbana-Champaign

Welcome to the Image Segmentation Tutorial using the Segmentation Model Library!

Explore the world of image segmentation through this comprehensive tutorial utilizing the powerful Segmentation Models library in Python. Whether you're a beginner or an experienced developer, this tutorial is your gateway to mastering the art of constructing advanced image segmentation models. Developed by Nattapon Jaroenchai from the University of Illinois Urbana-Champaign, this tutorial offers a clear and insightful guide to leveraging the capabilities of the Segmentation Models library.

Image segmentation, a pivotal technique in the field of computer vision, involves partitioning digital images into meaningful segments to facilitate analysis and interpretation. The Segmentation Models library simplifies the creation and training of cutting-edge segmentation models, freeing you to focus on unleashing your creative vision.

Key Steps Covered in the Tutorial:

• Importing essential libraries
• Loading and preparing your dataset
• Crafting a model using the Unet architecture with an ImageNet backbone
• Compiling and executing the model's training process
• Visualizing the training history

By the tutorial's conclusion, you'll possess a robust grasp of how to harness the Segmentation Models library to craft your own segmentation models. Whether your goal is to dive into image analysis, enhance your computer vision projects, or expand your machine learning skill set, this tutorial equips you with the knowledge and tools to embark on your journey. Let's embark on this enriching experience together!