Multi-Class Semantic Image Segmentation with Keras in Python

Multi-Class Semantic Image Segmentation with Keras in Python, available at $34.99, has an average rating of 3.55, with 30 lectures, based on 22 reviews, and has 1072 subscribers.

You will learn about Understand what multi-class image segmentation is in computer vision Understand the fundamentals of DeepLabv3+ (CNN) Build and train a the multi-class image segmentation model using Keras with Tensorflow as a backend using Google Colab Learn to use the trained model to predict the segmented mask of a new set of image data This course is ideal for individuals who are Beginners starting out to the field of Deep Learning or Industry professionals and aspiring data scientists or People who want to know how to write their image segmentation code It is particularly useful for Beginners starting out to the field of Deep Learning or Industry professionals and aspiring data scientists or People who want to know how to write their image segmentation code.

Enroll now: Multi-Class Semantic Image Segmentation with Keras in Python

Summary

Title: Multi-Class Semantic Image Segmentation with Keras in Python

Price: $34.99

Average Rating: 3.55

Number of Lectures: 30

Number of Published Lectures: 30

Number of Curriculum Items: 30

Number of Published Curriculum Objects: 30

Original Price: ?799

Quality Status: approved

Status: Live

What You Will Learn

Understand what multi-class image segmentation is in computer vision

Understand the fundamentals of DeepLabv3+ (CNN)

Build and train a the multi-class image segmentation model using Keras with Tensorflow as a backend using Google Colab

Learn to use the trained model to predict the segmented mask of a new set of image data

Who Should Attend

Beginners starting out to the field of Deep Learning

Industry professionals and aspiring data scientists

People who want to know how to write their image segmentation code

Target Audiences

Beginners starting out to the field of Deep Learning

Industry professionals and aspiring data scientists

People who want to know how to write their image segmentation code

Welcome to the “Multi-Class Semantic Image Segmentation with Keras in Python” course. In this project, you will learn to build a multi-class image segmentation deep-learning model in Keras with a TensorFlow backend from scratch. You will learn to train the model using the image dataset and perform multi-class image segmentation. By the end of this course, you could build and train the deep learning multi-class image segmentation model. After that, you will also be able to use the trained model to predict segmented masks on new images and visualise them. Please note that you don’t need a high-powered workstation to learn this exciting course. We will carry out the entire project in the Google Colab environment and Google Drive, which is free. You only need an internet connection and a free Gmail account to complete this course. This is a practical course, we will focus on Python programming, and you will understand every part of the program very well. The multi-class image segmentation course applies to many industries, especially the autonomous industry, healthcare, aerial imagery, geo-sensing, precision agriculture, etc. You can add this project to your portfolio, which is essential for your following job interview. This course is designed most straightforwardly to utilise your time wisely. 

Happy learning.

Course Curriculum

Chapter 1: Fundamentals

Lecture 1: Introduction

Lecture 2: Artificial Intelligence

Lecture 3: Machine Learning

Lecture 4: Deep Learning

Lecture 5: Image and Ground Truth/Mask

Lecture 6: What is Image Segmentation?

Lecture 7: How Sematic Segmentation is done?

Lecture 8: How to annotate the ground truth for image segmentation?

Lecture 9: DeepLabv3+ Architecture

Chapter 2: Building, Training and Predicting Image-Segmentation Model

Lecture 1: Download Dataset

Lecture 2: What is inside the Training folder?

Lecture 3: Image Segmentation Python Code

Lecture 4: What is the .h5 file?

Lecture 5: What is inside the Prediction folder?

Lecture 6: What is human_colormap.mat?

Lecture 7: Enabling GPU in Google Colab

Lecture 8: Is GPU connected to Colab notebook?

Lecture 9: Connect Google Colab with Google Drive

Lecture 10: Import Python Libraries

Lecture 11: Training Images

Lecture 12: Validation Images

Lecture 13: Visualizing a Sample Image and Mask

Lecture 14: Preprocess and Prepare Batches of Images

Lecture 15: Build the Model Architecture

Lecture 16: Visualize Model Architecture

Lecture 17: Model Compilation

Lecture 18: Callbacks: Early Stopping

Lecture 19: Callbacks: ModelCheckPoint

Lecture 20: Model Training

Lecture 21: Prediction / Inference

Instructors

Karthik Karunakaran, Ph.D.
Transforming Real-World Problems with the Power of AI-ML

Rating Distribution

1 stars: 3 votes

2 stars: 0 votes

3 stars: 3 votes

4 stars: 6 votes

5 stars: 10 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!