Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it 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.