Deep Learning for Image Segmentation with Python Pytorch

Deep Learning for Image Segmentation with Python & Pytorch, available at $49.99, has an average rating of 4.17, with 37 lectures, based on 152 reviews, and has 593 subscribers.

You will learn about Learn Image Semantic Segmentation Complete Pipeline and its Real-world Applications with Python & PyTorch Deep Learning Architectures for Semantic Segmentation (UNet, DeepLabV3, PSPNet, PAN, UNet++, MTCNet etc.) Segmentation with Pretrained Pytorch Models (FCN, DeepLabV3) on COCO Dataset Perform Image Segmentation with Deep Learning Models on Custom Datasets Datasets and Data Annotations Tool for Semantic Segmentation Data Augmentation and Data Loaders Implementation in PyTorch Learn Performance Metrics (IOU, etc.) for Segmentation Models Evaluation Transfer Learning and Pretrained Deep Resnet Architecture Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, UNet++) in PyTorch using different Encoder and Decoder Architectures Learn to Optimize Hyperparameters for Segmentation Models to Improve the Performance during Training on Custom Dataset Test Segmentation Trained Model and Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall and F-score Visualize Segmentation Results and Generate RGB Predicted Output Segmentation Map This course is ideal for individuals who are This course is designed for individuals who are interested in learning how to apply Deep Learning techniques to solve Semantic Segmentation problems in real-world using the Python programming language and the PyTorch Deep Learning Framework or This course is designed for a wide range of Students and Professionals, including but not limited to: Machine Learning Engineers, Deep Learning Engineers, Data Scientists, Computer Vision Engineers, and Researchers who want to learn how to use PyTorch to build and train deep learning models for Semantic Segmentation or In general, the course is for anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Semantic Segmentation using Python and PyTorch It is particularly useful for This course is designed for individuals who are interested in learning how to apply Deep Learning techniques to solve Semantic Segmentation problems in real-world using the Python programming language and the PyTorch Deep Learning Framework or This course is designed for a wide range of Students and Professionals, including but not limited to: Machine Learning Engineers, Deep Learning Engineers, Data Scientists, Computer Vision Engineers, and Researchers who want to learn how to use PyTorch to build and train deep learning models for Semantic Segmentation or In general, the course is for anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Semantic Segmentation using Python and PyTorch.

Enroll now: Deep Learning for Image Segmentation with Python & Pytorch

Summary

Title: Deep Learning for Image Segmentation with Python & Pytorch

Price: $49.99

Average Rating: 4.17

Number of Lectures: 37

Number of Published Lectures: 37

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will Learn

Learn Image Semantic Segmentation Complete Pipeline and its Real-world Applications with Python & PyTorch

Deep Learning Architectures for Semantic Segmentation (UNet, DeepLabV3, PSPNet, PAN, UNet++, MTCNet etc.)

Segmentation with Pretrained Pytorch Models (FCN, DeepLabV3) on COCO Dataset

Perform Image Segmentation with Deep Learning Models on Custom Datasets

Datasets and Data Annotations Tool for Semantic Segmentation

Data Augmentation and Data Loaders Implementation in PyTorch

Learn Performance Metrics (IOU, etc.) for Segmentation Models Evaluation

Transfer Learning and Pretrained Deep Resnet Architecture

Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, UNet++) in PyTorch using different Encoder and Decoder Architectures

Learn to Optimize Hyperparameters for Segmentation Models to Improve the Performance during Training on Custom Dataset

Test Segmentation Trained Model and Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall and F-score

Visualize Segmentation Results and Generate RGB Predicted Output Segmentation Map

Who Should Attend

This course is designed for individuals who are interested in learning how to apply Deep Learning techniques to solve Semantic Segmentation problems in real-world using the Python programming language and the PyTorch Deep Learning Framework

This course is designed for a wide range of Students and Professionals, including but not limited to: Machine Learning Engineers, Deep Learning Engineers, Data Scientists, Computer Vision Engineers, and Researchers who want to learn how to use PyTorch to build and train deep learning models for Semantic Segmentation

In general, the course is for anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Semantic Segmentation using Python and PyTorch

Target Audiences

This course is designed for individuals who are interested in learning how to apply Deep Learning techniques to solve Semantic Segmentation problems in real-world using the Python programming language and the PyTorch Deep Learning Framework

This course is designed for a wide range of Students and Professionals, including but not limited to: Machine Learning Engineers, Deep Learning Engineers, Data Scientists, Computer Vision Engineers, and Researchers who want to learn how to use PyTorch to build and train deep learning models for Semantic Segmentation

In general, the course is for anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Semantic Segmentation using Python and PyTorch

This course is designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to Semantic Image Segmentation problems. Are you ready to take your understanding of deep learning to the next level and learn how to apply it to real-world problems? In this course, you’ll learn how to use the power of Deep Learning to segment images and extract meaning from visual data. You’ll start with an introduction to the basics of Semantic Segmentation using Deep Learning, then move on to implementing and training your own models for Semantic Segmentation with Python and PyTorch.
This course is designed for a wide range of students and professionals, including but not limited to:

Machine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Image Segmentation tasks

Computer Vision Engineers and Researchers who want to learn how to use PyTorch to build and train Deep Learning models for Semantic Segmentation

Developers who want to incorporate Semantic Segmentation capabilities into their projects

Graduatesand Researchers in Computer Science, Electrical Engineering, and other related fields who want to learn about the latest advances in Deep Learning for Semantic Segmentation

In general, the course is for Anyonewho wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Semantic Segmentation using Python and PyTorch

The course covers the complete pipeline with hands-on experience of Semantic Segmentation using Deep Learning with Python and PyTorch as follows:

Semantic Image Segmentation and its Real-World Applications in Self Driving Cars or Autonomous Vehicles etc.

Deep Learning Architectures for Semantic Segmentation including Pyramid Scene Parsing Network (PSPNet), UNet, UNet++, Pyramid Attention Network (PAN),  Multi-Task Contextual Network (MTCNet), DeepLabV3, etc.

Datasets and Data annotations Tool for Semantic Segmentation

Google Colab for Writing Python Code

Data Augmentation and Data Loading in PyTorch

Performance Metrics (IOU) for Segmentation Models Evaluation

Transfer Learning and Pretrained Deep Resnet Architecture

Segmentation Models Implementation in PyTorch using different Encoder and Decoder Architectures

Hyperparameters Optimization and Training of Segmentation Models

Test Segmentation Model and Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall and F-score

Visualize Segmentation Results and Generate RGB Predicted Segmentation Map

By the end of this course, you’ll have the knowledge and skills you need to start applying Deep Learning to Semantic Segmentation problems in your own work or research. Whether you’re a Computer Vision Engineer, Data Scientist, or Developer, this course is the perfect way to take your understanding of Deep Learning to the next level. Let’s get started on this exciting journey of Deep Learning for Semantic Segmentation with Python and PyTorch.

Course Curriculum

Chapter 1: Introduction to Course

Lecture 1: Introduction

Chapter 2: Semantic Segmentation and its Real-world Applications

Lecture 1: What is Semantic Image Segmentation?

Lecture 2: Semantic Segmentation Real-world Applications

Chapter 3: Deep Learning Architectures for Segmentation (UNet, PSPNet, PAN, MTCNet)

Lecture 1: Pyramid Scene Parsing Network (PSPNet) for Segmentation

Lecture 2: UNet Architecture for Segmentation

Lecture 3: Pyramid Attention Network (PAN) for Segmentation

Lecture 4: Multi-Task Contextual Network (MTCNet) for Segmentation

Chapter 4: Datasets and Data Annotations Tool for Semantic Segmentation

Lecture 1: Explore Datasets for Semantic Segmentation

Lecture 2: Data Annotations Tool for Semantic Segmentation

Lecture 3: Dataset for Semantic Segmentation

Chapter 5: Google Colab Setting-up for Writing Python Code

Lecture 1: Set-up Google Colab for Writing Segmentation with Python and PyTorch Code

Lecture 2: Connect Google Colab with Google Drive to Read and Write Data

Lecture 3: Python Code

Chapter 6: Segmentation with Pretrained Pytorch Models on COCO Dataset

Lecture 1: Segmentation with Pretrained Pytorch Models

Lecture 2: Colab Notebook: Segmentation with Pretrained Pytorch Models

Chapter 7: Customized Dataset Class Implementation in PyTorch for Data Loading

Lecture 1: Data Loading with PyTorch Customized Dataset Class

Lecture 2: Data Loading for Segmentation with Python and PyTorch Code

Chapter 8: Data Augmentation with Albumentations

Lecture 1: Perform Data Augmentation using Albumentations with different Transformations

Lecture 2: Data Augmentation with Python and PyTorch Code

Chapter 9: Data Loaders Implementation in Pytorch

Lecture 1: Learn to Implement Data Loaders with Pytorch

Chapter 10: Performance Metrics (IOU) for Segmentation Models Evaluation

Lecture 1: Performance Metrics (IOU, Pixel Accuracy) for Segmentation Models Evaluation

Lecture 2: Intersection over Union IOU, Pixel Accuracy with Python and PyTorch

Chapter 11: Transfer Learning and Pretrained Deep Resnet Architecture

Lecture 1: Learn Transfer Learning and Pretrained Deep Resnet Architecture

Chapter 12: Encoders for Segmentation in PyTorch

Lecture 1: Pretrained Encoders for Semantic Image Segmentation with PyTorch

Chapter 13: Decoders for Segmentation in PyTorch

Lecture 1: Decoders for Semantic Segmentation using PyTorch

Chapter 14: Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, UNet++) using PyTorch

Lecture 1: UNet, PSPNet, DeepLab, PAN, UNet++, Segmentation Models with PyTorch and Python

Lecture 2: Segmentation with Python and PyTorch Code

Chapter 15: Hyperparameters Optimization of Segmentation Models

Lecture 1: Learn to Optimize Hyperparameters for Semantic Segmentation Models

Lecture 2: Hyperparameters Optimization for Segmentation with Python and PyTorch Code

Chapter 16: Training of Segmentation Models

Lecture 1: Semantic Image Segmentation Models with Pytorch Training

Lecture 2: Segmentation with Python and PyTorch Training

Chapter 17: Test Segmentation Models & Calculate IOU, Class-wise IOU, Pixel Accuracy Metrics

Lecture 1: Run & Test Segmentation Models and Calculate Class-wise IOU, Accuracy, Fscore

Lecture 2: Deploy Models of Segmentation with PyTorch and Python Code

Chapter 18: Visualize Segmentation Results and Generate RGB Output Segmentation Map

Lecture 1: Visualize Segmentation Results and Generate RGB Predicted Segmentation Map

Lecture 2: Image Segmentation with PyTorch and Python Results Visualization Code

Chapter 19: Bonus Resources: Complete Code and Dataset of Segmentation with Deep Learning

Lecture 1: Please Find Attached Complete Code & Dataset of Segmentation with Deep Learning

Chapter 20: Bonus Lecture

Lecture 1: Bonus Lecture: Video Segmentation and Video Object Detection with Python

Instructors

Dr. Mazhar Hussain
Deep Learning, Computer Vision, AI & Python | CS Lecturer

AI & Computer Science School
Learn AI, Deep Learning, & Computer Vision with Python

Rating Distribution

1 stars: 8 votes

2 stars: 7 votes

3 stars: 15 votes

4 stars: 26 votes

5 stars: 96 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!