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Aerial Image Segmentation with PyTorch
Parth Dhameliya
Parth Dhameliya
In this 2-hour project-based course, you will be able to :
Read more
In this 2-hour project-based course, you will be able to :
Read more
In this 2-hour project-based course, you will be able to :
- Understand the Massachusetts Roads Segmentation Dataset and you will write a custom dataset class for Image-mask dataset. Additionally, you will apply segmentation domain augmentations to augment images as well as its masks. For image-mask augmentation you will use albumentation library. You will plot the image-Mask pair.
- Load a pretrained state of the art convolutional neural network for segmentation problem(for e.g, Unet) using segmentation model pytorch library.
- Create train function and evaluator function which will helpful to write training loop. Moreover, you will use training loop to train the model.
- Finally, we will use best trained segementation model for inference.
Register for this course and see more details by visiting:
OpenCourser.com/course/l5via1/aerial
What's inside
Syllabus
Project Overview
Understand the Segmentation Dataset and you will write a custom dataset class for Image-mask dataset. Additionally, you will apply segmentation augmentation to augment images as well as its masks. For image-mask augmentation you will use albumentation library. You will plot the image-Mask pair. Load a pretrained state of the art convolutional neural network for segmentation problem(for e.g, Unet) using segmentation model pytorch library. Create train function and evaluator function which will helpful to write training loop. Moreover, you will use training loop to train the model.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners, introducing core concepts and techniques for segmentation using deep learning
Taught by Parth Dhameliya, a well-regarded expert in computer vision and deep learning
Provides hands-on experience through a project-based approach, allowing learners to apply concepts and techniques directly
Leverages segmentation domain augmentations to enhance the training dataset, improving model performance
Covers state-of-the-art convolutional neural networks for segmentation, such as Unet, ensuring learners are up-to-date with industry practices
Provides insights into customizing dataset classes for image-mask datasets, allowing learners to tailor models to specific requirements
Save this course
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Aerial Image Segmentation with PyTorch with these
activities:
Review Deep Learning with Python
Show steps
Read this book to strengthen your foundational knowledge of deep learning concepts and their implementation in Python.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Review Linear Algebra and Calculus
Show steps
Refreshing your knowledge of these foundational concepts will enhance your understanding of the mathematical underpinnings of deep learning.
Browse courses on
Linear Algebra
Show steps
-
Review key concepts from linear algebra, such as vectors, matrices, and transformations.
-
Brush up on essential calculus concepts, including derivatives and integrals.
-
Apply these concepts to solve problems related to deep learning.
Gather Resources on Image Segmentation
Show steps
Organizing resources will provide you with a valuable reference for further learning and exploration of image segmentation.
Browse courses on
Image Segmentation
Show steps
-
Identify and collect relevant resources, such as articles, tutorials, and datasets.
-
Categorize and organize the resources for easy access.
-
Share the compilation with others to contribute to the learning community.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice Image-Mask Augmentation Techniques
Show steps
Practice applying image-mask augmentation techniques to solidify your understanding of data augmentation for segmentation tasks.
Show steps
-
Install albumentation library
-
Define a custom dataset class for image-mask dataset
-
Apply image-mask augmentation techniques using albumentation library
-
Plot the image-mask pair to visualize the effects of augmentation
Build a Convolutional Neural Network for Image Segmentation
Show steps
This project will provide hands-on experience in designing and implementing a CNN for image segmentation, reinforcing your understanding of the concepts covered in the course.
Browse courses on
CNN
Show steps
-
Gather and prepare the image dataset.
-
Design and implement a CNN architecture.
-
Train and evaluate the CNN model.
Solve Image Segmentation Challenges on Kaggle
Show steps
Solving challenges on Kaggle will test your skills in image segmentation and provide exposure to real-world datasets.
Browse courses on
Kaggle
Show steps
-
Identify and select a suitable challenge.
-
Develop a solution using the techniques learned in the course.
-
Submit your solution and compare it to others.
Create a Tutorial on Image Segmentation with albumentation
Show steps
Creating a tutorial will help you solidify your understanding of image segmentation and albumentation by explaining the concepts to others.
Show steps
-
Identify the key concepts to be covered.
-
Write a clear and concise tutorial.
-
Illustrate the concepts with examples.
Volunteer for an Image Segmentation Project
Show steps
Volunteering on a project will provide you with practical experience in image segmentation and the opportunity to contribute to a real-world cause.
Browse courses on
Image Segmentation
Show steps
-
Identify organizations or projects that are working on image segmentation.
-
Contact the organization and inquire about volunteer opportunities.
-
Participate in the project and contribute your skills to the team's efforts.
Build a Mobile Application for Image Segmentation
Show steps
Building a mobile application will not only enhance your knowledge of image segmentation but also provide you with valuable software development experience.
Browse courses on
Image Segmentation
Show steps
-
Choose a mobile platform (e.g., iOS, Android).
-
Design the user interface and functionality.
-
Integrate the image segmentation model.
-
Test and deploy the application.
Review Deep Learning with Python
Show steps
Read this book to strengthen your foundational knowledge of deep learning concepts and their implementation in Python.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
Review Linear Algebra and Calculus
Show steps
Refreshing your knowledge of these foundational concepts will enhance your understanding of the mathematical underpinnings of deep learning.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review key concepts from linear algebra, such as vectors, matrices, and transformations.
- Brush up on essential calculus concepts, including derivatives and integrals.
- Apply these concepts to solve problems related to deep learning.
Gather Resources on Image Segmentation
Show steps
Organizing resources will provide you with a valuable reference for further learning and exploration of image segmentation.
Browse courses on
Image Segmentation
Show steps
Show steps
Show steps
- Identify and collect relevant resources, such as articles, tutorials, and datasets.
- Categorize and organize the resources for easy access.
- Share the compilation with others to contribute to the learning community.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Practice Image-Mask Augmentation Techniques
Show steps
Practice applying image-mask augmentation techniques to solidify your understanding of data augmentation for segmentation tasks.
Show steps
Show steps
Show steps
- Install albumentation library
- Define a custom dataset class for image-mask dataset
- Apply image-mask augmentation techniques using albumentation library
- Plot the image-mask pair to visualize the effects of augmentation
Build a Convolutional Neural Network for Image Segmentation
Show steps
This project will provide hands-on experience in designing and implementing a CNN for image segmentation, reinforcing your understanding of the concepts covered in the course.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Gather and prepare the image dataset.
- Design and implement a CNN architecture.
- Train and evaluate the CNN model.
Solve Image Segmentation Challenges on Kaggle
Show steps
Solving challenges on Kaggle will test your skills in image segmentation and provide exposure to real-world datasets.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Identify and select a suitable challenge.
- Develop a solution using the techniques learned in the course.
- Submit your solution and compare it to others.
Create a Tutorial on Image Segmentation with albumentation
Show steps
Creating a tutorial will help you solidify your understanding of image segmentation and albumentation by explaining the concepts to others.
Show steps
Show steps
Show steps
- Identify the key concepts to be covered.
- Write a clear and concise tutorial.
- Illustrate the concepts with examples.
Volunteer for an Image Segmentation Project
Show steps
Volunteering on a project will provide you with practical experience in image segmentation and the opportunity to contribute to a real-world cause.
Browse courses on
Image Segmentation
Show steps
Show steps
Show steps
- Identify organizations or projects that are working on image segmentation.
- Contact the organization and inquire about volunteer opportunities.
- Participate in the project and contribute your skills to the team's efforts.
Build a Mobile Application for Image Segmentation
Show steps
Building a mobile application will not only enhance your knowledge of image segmentation but also provide you with valuable software development experience.
Browse courses on
Image Segmentation
Show steps
Show steps
Show steps
- Choose a mobile platform (e.g., iOS, Android).
- Design the user interface and functionality.
- Integrate the image segmentation model.
- Test and deploy the application.
Career center
Learners who complete Aerial Image Segmentation with PyTorch will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They work closely with Data Scientists and other engineers to ensure that machine learning models are scalable, efficient, and accurate. This course can be helpful for Machine Learning Engineers who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers design and develop computer vision systems that can see and interpret the world around them. They work on a variety of applications, such as object recognition, facial recognition, and medical imaging. This course can be helpful for Computer Vision Engineers who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Data Scientist
Data Scientist
Data Scientists build and apply machine learning models to make data-driven decisions. They are responsible for the entire data science lifecycle, from data collection and preparation to model building and evaluation. This course can be helpful for Data Scientists who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Civil Engineer
Civil Engineer
Civil Engineers design and build infrastructure, such as roads, bridges, and buildings. They work on a variety of projects, from small residential projects to large-scale infrastructure projects. This course can be helpful for Civil Engineers who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Geospatial Analyst
Geospatial Analyst
Geospatial Analysts use geographic information systems (GIS) to analyze and visualize data. They work on a variety of projects, from land use planning to environmental modeling. This course can be helpful for Geospatial Analysts who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Urban Planner
Urban Planner
Urban Planners design and plan cities and towns. They work on a variety of projects, from land use planning to transportation planning. This course can be helpful for Urban Planners who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Environmental Scientist
Environmental Scientist
Environmental Scientists study the environment and its interactions with humans. They work on a variety of projects, from pollution control to climate change mitigation. This course can be helpful for Environmental Scientists who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Cartographer
Cartographer
Cartographers create maps and other visual representations of geographic data. They work on a variety of projects, from navigation charts to land use maps. This course can be helpful for Cartographers who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
GIS Analyst
GIS Analyst
GIS Analysts use geographic information systems (GIS) to analyze and visualize data. They work on a variety of projects, from land use planning to environmental modeling. This course can be helpful for GIS Analysts who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Geographer
Geographer
Geographers study the Earth's surface and its human and physical features. They work on a variety of projects, from mapping to climate change research. This course can be helpful for Geographers who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Remote Sensing Scientist
Remote Sensing Scientist
Remote Sensing Scientists use satellite imagery and other data to study the Earth's surface. They work on a variety of projects, from land cover mapping to deforestation monitoring. This course can be helpful for Remote Sensing Scientists who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Land Surveyor
Land Surveyor
Land Surveyors measure and map the Earth's surface. They work on a variety of projects, from boundary surveys to construction projects. This course can be helpful for Land Surveyors who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Photogrammetrist
Photogrammetrist
Photogrammetrists use aerial and satellite imagery to create maps and other visual representations of the Earth's surface. They work on a variety of projects, from land use planning to disaster response. This course can be helpful for Photogrammetrists who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They work on a variety of projects, from small personal projects to large enterprise systems. This course can be helpful for Software Engineers who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help businesses make informed decisions. They work on a variety of projects, from customer segmentation to fraud detection. This course can be helpful for Data Analysts who are interested in learning about aerial image segmentation, a specialized field of image segmentation that is used in a variety of applications, such as remote sensing and land use planning.
For more career information including salaries, visit:
OpenCourser.com/course/l5via1/aerial
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Aerial Image Segmentation with PyTorch .
This classic textbook provides a comprehensive overview of remote sensing and image interpretation. It covers topics such as image acquisition, image processing, and image interpretation, and valuable resource for anyone working in the field of remote sensing.
Provides a comprehensive overview of remote sensing of the environment. It covers topics such as image acquisition, image processing, and image interpretation, and valuable resource for anyone working in the field of remote sensing.
This classic textbook provides a comprehensive overview of digital image processing. It covers topics such as image enhancement, image restoration, and image analysis, and valuable resource for anyone working in the field of image processing.
Save
This comprehensive textbook covers the fundamental algorithms and techniques used in computer vision. It provides a solid foundation for understanding the principles behind image processing, feature extraction, and object recognition.
Provides a comprehensive overview of image processing and data analysis using ERDAS IMAGINE, a leading software package for geospatial data processing. It covers topics such as image enhancement, image classification, and change detection, and valuable resource for anyone working with geospatial data.
This textbook provides a comprehensive overview of pattern recognition and machine learning. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning, and valuable resource for anyone interested in the theoretical foundations of machine learning.
This textbook provides a comprehensive overview of computer vision, covering topics such as image formation, feature extraction, and object recognition. It valuable resource for anyone interested in the theoretical foundations of computer vision.
This practical guide introduces the latest deep learning techniques for computer vision tasks. It covers topics such as image classification, object detection, and semantic segmentation, and provides code examples in Python.
Practical guide to deep learning with Python. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, and provides code examples in Keras.
Provides a comprehensive introduction to geographic information systems (GIS). It covers topics such as data acquisition, data management, and data analysis, and valuable resource for anyone working with GIS.
Provides a comprehensive introduction to Python for data analysis. It covers topics such as data manipulation, visualization, and machine learning, and valuable resource for anyone working with data in Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/l5via1/aerial
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Language
English
Good to know
Builds a strong foundation for beginners, introducing core concepts and techniques for segmentation using deep learning
Taught by Parth Dhameliya, a well-regarded expert in computer vision and deep learning
Provides hands-on experience through a project-based approach, allowing learners to apply concepts and techniques directly
Leverages segmentation domain augmentations to enhance the training dataset, improving model performance
Covers state-of-the-art convolutional neural networks for segmentation, such as Unet, ensuring learners are up-to-date with industry practices
Provides insights into customizing dataset classes for image-mask datasets, allowing learners to tailor models to specific requirements
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