Pretrained CNN Features for Semantic Segmentation Using Random Forest

Learn how to leverage pretrained VGG16 imagenet weights for feature extraction and train a Random Forest model for semantic segmentation in this 22-minute tutorial. Explore the process of extracting features using VGG16 and utilizing them to create a robust segmentation model that can outperform U-net, especially with limited training data. Discover the steps involved, including importing necessary libraries, setting up the VGG16 model, creating a feature extractor, and organizing data into a dataframe. Access the code and dataset provided to follow along and implement the technique in your own projects. Gain insights into image annotation and learn how to run the code as a workflow online using APEER, a free platform for individuals, students, researchers, and non-profits.

Introduction
What is VGG16
What are labels
Import libraries
Import VGG16
VGG Model
Feature extractor
Dataframe
Saving the model