Deep Learning for Image Segmentation with Python & Pytorch

Image Semantic Segmentation for Computer Vision with PyTorch & Python to Train & Deploy YOUR own Models (UNet, DeepLab)

4.53 (118 reviews)
Udemy
platform
English
language
Data Science
category
instructor
449
students
3.5 hours
content
Mar 2024
last update
$49.99
regular price

What you will learn

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

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

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

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

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

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

Description

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

  • Graduates and 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 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

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.

Content

Introduction to Course

Introduction

Semantic Segmentation and its Real-world Applications

What is Semantic Image Segmentation?
Semantic Segmentation Real-world Applications

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

Pyramid Scene Parsing Network (PSPNet) for Segmentation
UNet Architecture for Segmentation
Pyramid Attention Network (PAN) for Segmentation
Multi-Task Contextual Network (MTCNet) for Segmentation

Datasets and Data Annotations Tool for Semantic Segmentation

Explore Datasets for Semantic Segmentation
Data Annotations Tool for Semantic Segmentation

Google Colab Setting-up for Writing Python Code

Set-up Google Colab for Writing Segmentation with Python and PyTorch Code
Connect Google Colab with Google Drive to Read and Write Data
Python Code

Customized Dataset Class Implementation in PyTorch for Data Loading

Data Loading with PyTorch Customized Dataset Class
Data Loading for Segmentation with Python and PyTorch Code

Data Augmentation with Albumentations

Perform Data Augmentation using Albumentations with different Transformations
Data Augmentation with Python and PyTorch Code

Data Loaders Implementation in Pytorch

Learn to Implement Data Loaders with Pytorch

Performance Metrics (IOU) for Segmentation Models Evaluation

Performance Metrics (IOU, Pixel Accuracy) for Segmentation Models Evaluation
Intersection over Union IOU, Pixel Accuracy with Python and PyTorch

Transfer Learning and Pretrained Deep Resnet Architecture

Learn Transfer Learning and Pretrained Deep Resnet Architecture

Encoders for Segmentation in PyTorch

Pretrained Encoders for Semantic Image Segmentation with PyTorch

Decoders for Segmentation in PyTorch

Decoders for Semantic Segmentation using PyTorch

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

UNet, PSPNet, DeepLab, PAN, UNet++, Segmentation Models with PyTorch and Python
Segmentation with Python and PyTorch Code

Hyperparameters Optimization of Segmentation Models

Learn to Optimize Hyperparameters for Semantic Segmentation Models
Hyperparameters Optimization for Segmentation with Python and PyTorch Code

Training of Segmentation Models

Semantic Image Segmentation Models with Pytorch Training
Segmentation with Python and PyTorch Training

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

Run & Test Segmentation Models and Calculate Class-wise IOU, Accuracy, Fscore
Deploy Models of Segmentation with PyTorch and Python Code

Visualize Segmentation Results and Generate RGB Output Segmentation Map

Visualize Segmentation Results and Generate RGB Predicted Segmentation Map
Image Segmentation with PyTorch and Python Results Visualization Code

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

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

Screenshots

Deep Learning for Image Segmentation with Python & Pytorch - Screenshot_01Deep Learning for Image Segmentation with Python & Pytorch - Screenshot_02Deep Learning for Image Segmentation with Python & Pytorch - Screenshot_03Deep Learning for Image Segmentation with Python & Pytorch - Screenshot_04

Reviews

Michelle
October 6, 2023
Now I am able to apply the deep learning knowledge to my project to segment the medical images after learning from this course, Thumps up.
Valeria
July 18, 2023
The course covered a broad range of knowledge relating to image segmentation with pytorch coding and the instructor explanations were precise and easy to understand.
Hellen
July 13, 2023
The explanation is really fast. I missed when the instructor said he would provide the code at the end, I was trying to follow the code sections with my computer but some parts were not working for me, so it would be helpful to have the code at the beginning. It is not a bad idea to repeat and highlight this kind of information.
Julia
June 26, 2023
Regrettably, the course was a disappointing investment. It left much to be desired as the instructor appeared to be merely reading from a Wikipedia article. A prime illustration of the course's shallowness and the instructor's attempt to elongate the content can be found in one particular video. The topic of this video was supposedly "hands-on" decoders and encoders, but it amounted to the instructor simply stating, "Now we will try different decoders and encoders." He proceeded to open the notebook and interchange various encoder names within a single line of code, such as "ENCODER = 'resnet101'." Astonishingly, this mundane task occupied a staggering 20 minutes in a supposedly practical 3.5-hour course on Image Segmentation. Undoubtedly, this course has been the most frustrating experience I have encountered on Udemy. To offer a point of comparison, a vastly more critical subject like "Hyperparameter Optimization" merited a mere 9 minutes of attention, with a disproportionate focus on definitions, leaving only a minute to showcase a paltry five lines of code, hastily emphasizing its significance before swiftly moving on to the next video.
Stephen
June 15, 2023
Very interesting course, with clever use of Python software. My only complaint is that parameters were often chosen for various functions without fully explaining the effect of the parameters and why the particular values were chosen. Fully explaining the effect of all the parameter choices would probably make the class too long, so a good compromise would be to include a reference to find out more about the various functions and the available parameter choices.
Shaheer
May 30, 2023
Overall course structure is good. Explanations can be better. Presenter can practice taking more legible pauses between sentences that will make delivery more effective.
Francesco
May 18, 2023
This course start with an introduction to the basics of segmentation using deep learning and then move on to implementing, training and deploying segmentation models with Python. I really recommend this course to learn segmentation from basics to advanced.
Cheng
April 8, 2023
Course is well structured with clear explanations and practical examples. This helps me to gain comprehensive understanding of image segmentation using deep learning techniques in python.
Michael
January 30, 2023
Mazhar explains a broad scope of Image segmentation using deep learning from basics to advance very expertly. I think this course is much more than what I expected with practicals.
Temitayo
January 25, 2023
Course material is well structured and easy to follow. The instructor is knowledgeable and provided clear demonstrations of the concepts. Hands-on coding exercises with python and pytorch helped solidify my understanding and gave me confidence to apply what I learned to real world projects. Instructor also provided resources for further learning. Overall, I highly recommend this course to anyone interested in diving into the world of semantic image segmentation with deep learning.

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5092818
udemy ID
1/17/2023
course created date
1/28/2023
course indexed date
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