Overview
- Includes a variety of hands-on computer vision projects using transfer learning and PyTorch
- Explains image similarity and anomaly detection models in computer vision
- Covers explainable AI for computer vision using GradCAM (Gradient-weighted Class Activation Mapping)
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Table of contents (10 chapters)
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Front Matter
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Back Matter
Keywords
About this book
The book begins with the fundamentals of computer vision: convolutional neural nets, RESNET, YOLO, data augmentation, and other regularization techniques used in the industry. And then it gives you a quick overview of the PyTorch libraries used in the book. After that, it takes you through the implementation of image classification problems, object detection techniques, and transfer learning while training and running inference. The book covers image segmentation and an anomaly detection model. And it discusses the fundamentals of video processing for computer vision tasks putting images into videos. The book concludes with an explanation of the complete model building process for deep learning frameworks using optimized techniques with highlights on model AI explainability.
After reading this book, you will be able to build your own computer vision projects using transfer learning and PyTorch.
What You Will Learn
- Solve problems in computer vision with PyTorch.
- Implement transfer learning and perform image classification, object detection, image segmentation, and other computer vision applications
- Design and develop production-grade computer vision projects for real-world industry problems
- Interpret computer vision models and solve business problems
Who This Book Is For
Data scientists and machine learning engineers interested in building computer vision projects and solving business problems
Authors and Affiliations
About the authors
Adarsha Shivananda is a senior data scientist on Indegene's product and technology team where he works on building machine learning and artificial intelligence (AI) capabilitiesfor pharma products. He aims to build a pool of exceptional data scientists within and outside of the organization to solve problems through training programs, and always wants to stay ahead of the curve. Previously, he worked with Tredence Analytics and IQVIA. He has worked extensively in the pharma, healthcare, retail, and marketing domains. He lives in Bangalore and loves to read and teach data science.
Nitin Ranjan Sharma is a manager at Novartis, involved in leading a team to develop products using multi-modal techniques. He has been a consultant developing solutions for Fortune 500 companies, involved in solving complex business problems using machine learning and deep learning frameworks. His major focus area and core expertise are computer vision and solving some of the challenging business problems dealing with images and video data. Before Novartis, he was part of the data science team at Publicis Sapient, EY, and TekSystems Global Services. Heis a regular speaker at data science communities and meet-ups and also an open-source contributor. He has also been training and mentoring data science enthusiasts.
Bibliographic Information
Book Title: Computer Vision Projects with PyTorch
Book Subtitle: Design and Develop Production-Grade Models
Authors: Akshay Kulkarni, Adarsha Shivananda, Nitin Ranjan Sharma
DOI: https://doi.org/10.1007/978-1-4842-8273-1
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Akshay Kulkarni, Adarsha Shivananda, and Nitin Ranjan Sharma 2022
Softcover ISBN: 978-1-4842-8272-4Published: 19 July 2022
eBook ISBN: 978-1-4842-8273-1Published: 18 July 2022
Edition Number: 1
Number of Pages: XVI, 346
Number of Illustrations: 154 b/w illustrations
Topics: Machine Learning, Python, Artificial Intelligence