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:
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Machine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Image Segmentation tasks
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Computer Vision Engineers and Researchers who want to learn how to use PyTorch to build and train Deep Learning models for Semantic Segmentation
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Developers who want to incorporate Semantic Segmentation capabilities into their projects
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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
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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:
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Semantic Image Segmentation and its Real-World Applications in Self Driving Cars or Autonomous Vehicles etc.
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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.
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Datasets and Data annotations Tool for Semantic Segmentation
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Google Colab for Writing Python Code
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Data Augmentation and Data Loading in PyTorch
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Performance Metrics (IOU) for Segmentation Models Evaluation
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Transfer Learning and Pretrained Deep Resnet Architecture
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Segmentation Models Implementation in PyTorch using different Encoder and Decoder Architectures
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Hyperparameters Optimization and Training of Segmentation Models
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Test Segmentation Model and Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall and F-score
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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
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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
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