Explore techniques for enhancing U-Net semantic segmentation performance through ensemble learning of multiple trained networks. Learn to implement a weighted ensemble approach using ResNet34, Inception V3, and VGG16 architectures. Dive into preprocessing steps, model compilation, and prediction methods for multiclass semantic segmentation tasks. Discover how to combine results using nested loops and analyze the improved segmentation outcomes. Access provided resources for dataset download, code samples, and additional tools like APEER for image annotation. Follow along with practical demonstrations and gain insights into advanced segmentation strategies for microscopy and other image analysis applications.
Improving Semantic Segmentation - U-Net Performance via Ensemble of Multiple Trained Networks
DigitalSreeni via YouTube
Overview
Syllabus
Introduction
Prerequisites
Converting labels
Expanding mask dimensions
Multiclass semantic segmentation
Defining models
Compile model
Save model
Load model
Variable Explorer
Preprocessing
Weighted ensemble
Weighted ensemble prediction
Results
Combining results
Nested loop
Ensemble prediction
Data analysis
Taught by
DigitalSreeni
