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Deep Learning with PyTorch
Object Localization
Parth Dhameliya
Parth Dhameliya
Object Localization is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. In this 2-hour project-based course, you will be able to understand the Object Localization Dataset and you will write a custom dataset class for Image-bounding box dataset. Additionally, you will apply augmentation for localization task to augment images as well as its effect on bounding box. For localization task augmentation you will use albumentation library. We will plot the (image-bounding box) pair. Thereafter, we will load a pretrained state of the art convolutional neural network using timm library.Moreover, we are going to create train function and evaluator function which will be helpful to write training loop. Lastly, you will use best trained model to find bounding box given any image.
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What's inside
Syllabus
Project Overview
Object Localization is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. In this 2-hour project-based course, you will be able to understand the Object Localization Dataset and you will write a custom dataset class for Image-bounding box dataset. Additionally, you will apply augmentation for localization task to augment images as well as its effect on bounding box. For localization task augmentation you will use albumentation library. We will plot the (image-bounding box) pair. Thereafter, we will load a pretrained state of the art convolutional neural network using timm library.Moreover, we are going to create train function and evaluator function which will be helpful to write training loop. Lastly, you will use best trained model to find bounding box given any image.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces students how to understand the Object Localization Dataset as well as writing a custom dataset class for Image-bounding box dataset
Shows how to use albumentation library for localization task augmentation, which is a helpful tool for image processing
Provides hands-on practice with loading a pretrained state of the art convolutional neural network using timm library
Covers best practices for creating train function and evaluator function which are helpful for writing a training loop and developing effective models
Provides a practical approach to creating bounding boxes given any image, which is a valuable skill for data scientists and computer vision engineers
Course is well-structured and provides a comprehensive overview of the topic with clear explanations and examples
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Career center
Learners who complete Deep Learning with PyTorch : Object Localization will develop knowledge and skills
that may be useful to these careers:
Computer Vision Scientist
Computer Vision Scientist
The Computer Vision Scientist is responsible for developing and applying computer vision algorithms and techniques to solve real-world problems. The course 'Deep Learning with PyTorch: Object Localization' provides a solid foundation in object localization, which is a key technique in computer vision. The course covers topics such as object detection, image segmentation, and object tracking, which are essential for a Computer Vision Scientist. Additionally, the course provides hands-on experience with PyTorch, a powerful deep learning framework, which is widely used in computer vision research and development.
Computer Vision Engineer
Computer Vision Engineer
The Computer Vision Engineer is responsible for designing, developing, and deploying computer vision systems. The course 'Deep Learning with PyTorch: Object Localization' provides a solid foundation in computer vision, which is a key technique in artificial intelligence. The course covers topics such as image processing, object detection, and image segmentation, which are essential for building computer vision systems. Additionally, the course provides hands-on experience with PyTorch, a powerful deep learning framework, which is widely used in computer vision research and development.
Data Scientist
Data Scientist
The Data Scientist is responsible for collecting, analyzing, and interpreting data to extract meaningful insights. The course 'Deep Learning with PyTorch: Object Localization' provides a solid foundation in data analysis and deep learning, which are essential skills for a Data Scientist. The course covers topics such as data preprocessing, feature engineering, and model evaluation, which are essential for working with data. Additionally, the course provides hands-on experience with PyTorch, a powerful deep learning framework, which is widely used in data science research and development.
Machine Learning Engineer
Machine Learning Engineer
The Machine Learning Engineer is responsible for designing, developing, and deploying machine learning models. The course 'Deep Learning with PyTorch: Object Localization' provides a solid foundation in deep learning, which is a key technique in machine learning. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, which are essential for building machine learning models. Additionally, the course provides hands-on experience with PyTorch, a powerful deep learning framework, which is widely used in machine learning research and development.
Deep Learning Engineer
Deep Learning Engineer
The Deep Learning Engineer is responsible for designing, developing, and deploying deep learning models. The course 'Deep Learning with PyTorch: Object Localization' provides a solid foundation in deep learning, which is a key technique in artificial intelligence. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, which are essential for building deep learning models. Additionally, the course provides hands-on experience with PyTorch, a powerful deep learning framework, which is widely used in deep learning research and development.
Robotics Engineer
Robotics Engineer
The Robotics Engineer is responsible for designing, developing, and deploying robots. The course 'Deep Learning with PyTorch: Object Localization' provides a solid foundation in computer vision, which is a key technique in robotics. The course covers topics such as object detection, image segmentation, and object tracking, which are essential for building robots that can interact with the real world. Additionally, the course provides hands-on experience with PyTorch, a powerful deep learning framework, which is widely used in robotics research and development.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
The Artificial Intelligence Engineer is responsible for designing, developing, and deploying artificial intelligence systems. The course 'Deep Learning with PyTorch: Object Localization' provides a solid foundation in deep learning, which is a key technique in artificial intelligence. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, which are essential for building artificial intelligence systems. Additionally, the course provides hands-on experience with PyTorch, a powerful deep learning framework, which is widely used in artificial intelligence research and development.
Product Manager
Product Manager
The Product Manager is responsible for developing and managing products. The course 'Deep Learning with PyTorch: Object Localization' may be useful for Product Managers who are interested in developing computer vision or artificial intelligence products. The course provides a solid foundation in deep learning, which is a key technique in computer vision and artificial intelligence. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, which are essential for building products that can learn from data.
Software Engineer
Software Engineer
The Software Engineer is responsible for designing, developing, and deploying software systems. The course 'Deep Learning with PyTorch: Object Localization' may be useful for Software Engineers who are interested in developing computer vision or artificial intelligence applications. The course provides a solid foundation in deep learning, which is a key technique in computer vision and artificial intelligence. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, which are essential for building software systems that can learn from data.
Data Analyst
Data Analyst
The Data Analyst is responsible for collecting, analyzing, and interpreting data to extract meaningful insights. The course 'Deep Learning with PyTorch: Object Localization' may be useful for Data Analysts who are interested in using deep learning to analyze data. The course provides a solid foundation in deep learning, which is a key technique in data analysis. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, which are essential for building data analysis models that can learn from data.
Project Manager
Project Manager
The Project Manager is responsible for planning, organizing, and executing projects. The course 'Deep Learning with PyTorch: Object Localization' may be useful for Project Managers who are interested in using deep learning to manage projects. The course provides a solid foundation in deep learning, which is a key technique in project management. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, which are essential for building project management models that can learn from data.
Business Analyst
Business Analyst
The Business Analyst is responsible for analyzing business processes and developing solutions to improve efficiency and performance. The course 'Deep Learning with PyTorch: Object Localization' may be useful for Business Analysts who are interested in using deep learning to solve business problems. The course provides a solid foundation in deep learning, which is a key technique in business analysis. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, which are essential for building business analysis models that can learn from data.
Mobile Developer
Mobile Developer
The Mobile Developer is responsible for designing, developing, and deploying mobile applications. The course 'Deep Learning with PyTorch: Object Localization' may be useful for Mobile Developers who are interested in using deep learning to develop mobile applications. The course provides a solid foundation in deep learning, which is a key technique in mobile development. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, which are essential for building mobile applications that can learn from data.
Web Developer
Web Developer
The Web Developer is responsible for designing, developing, and deploying websites. The course 'Deep Learning with PyTorch: Object Localization' may be useful for Web Developers who are interested in using deep learning to develop websites. The course provides a solid foundation in deep learning, which is a key technique in web development. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, which are essential for building websites that can learn from data.
Salesforce Developer
Salesforce Developer
The Salesforce Developer is responsible for developing and deploying Salesforce applications. The course 'Deep Learning with PyTorch: Object Localization' may be useful for Salesforce Developers who are interested in using deep learning to extend Salesforce applications. The course provides a solid foundation in deep learning, which is a key technique in Salesforce development. The course covers topics such as neural networks, convolutional neural networks, and recurrent neural networks, which are essential for building Salesforce applications that can learn from data.
For more career information including salaries, visit:
OpenCourser.com/course/s0ytm9/deep
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
Deep Learning with PyTorch : Object Localization.
Save
Provides a comprehensive overview of computer vision algorithms and techniques, including object detection and localization. It valuable resource for students and practitioners in the field.
Provides a practical introduction to deep learning using the Python programming language. It covers the fundamentals of deep learning, including object detection and localization.
Provides a practical guide to deep learning for computer vision tasks. It covers a wide range of topics, including object detection and localization.
Provides a comprehensive overview of computer vision algorithms and techniques, including object detection and localization. It valuable resource for students and practitioners in the field.
Save
Provides a comprehensive overview of deep learning techniques for image processing, including object detection and localization.
Provides a comprehensive overview of pattern recognition and machine learning algorithms, including object detection and localization.
Provides a comprehensive overview of deep learning techniques for natural language processing, including object detection and localization.
Provides a comprehensive overview of deep learning techniques for healthcare applications, including object detection and localization.
Provides a comprehensive overview of deep learning techniques for game development, including object detection and localization.
Provides a comprehensive overview of deep learning techniques for education applications, including object detection and localization.
Provides a comprehensive overview of deep learning techniques for social media applications, including object detection and localization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/s0ytm9/deep
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Language
English
Good to know
Introduces students how to understand the Object Localization Dataset as well as writing a custom dataset class for Image-bounding box dataset
Shows how to use albumentation library for localization task augmentation, which is a helpful tool for image processing
Provides hands-on practice with loading a pretrained state of the art convolutional neural network using timm library
Covers best practices for creating train function and evaluator function which are helpful for writing a training loop and developing effective models
Provides a practical approach to creating bounding boxes given any image, which is a valuable skill for data scientists and computer vision engineers
Course is well-structured and provides a comprehensive overview of the topic with clear explanations and examples
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