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Computer vision projects with PyTorchdesign and develop production-grade models /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Computer vision projects with PyTorchby Akshay Kulkarni, Adarsha Shivananda, Nitin Ranjan Sharma.
其他題名:	design and develop production-grade models /
作者:	Kulkarni, Akshay.
其他作者:	Shivananda, Adarsha.
出版者:	Berkeley, CA :Apress :2022.
面頁冊數:	xvi, 346 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Computer vision.
電子資源:	https://doi.org/10.1007/978-1-4842-8273-1
ISBN:	9781484282731$q(electronic bk.)

Computer vision projects with PyTorchdesign and develop production-grade models /
Kulkarni, Akshay.

Computer vision projects with PyTorchdesign and develop production-grade models /[electronic resource] :by Akshay Kulkarni, Adarsha Shivananda, Nitin Ranjan Sharma. - Berkeley, CA :Apress :2022. - xvi, 346 p. :ill., digital ;24 cm.

Chapter 1: The Building Blocks of Computer Vision -- Chapter 2: Image Classification -- Chapter 3: Building Object Detection Model -- Chapter 4: Building Image Segmentation Model -- Chapter 5: Image-Based Search and Recommendation System -- Chapter 6: Pose Estimation -- Chapter 7: Image Anomaly Detection -- Chapter 8: Image Super-Resolution -- Chapter 9: Video Analytics -- Chapter 10: Explainable AI for Computer Vision.

Design and develop end-to-end, production-grade computer vision projects for real-world industry problems. This book discusses computer vision algorithms and their applications using PyTorch. 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.

ISBN: 9781484282731$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-8273-1doiSubjects--Topical Terms:

200113
Computer vision.

LC Class. No.: TA1634 / .K85 2022

Dewey Class. No.: 006.37

Computer vision projects with PyTorchdesign and develop production-grade models /
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520 $a Design and develop end-to-end, production-grade computer vision projects for real-world industry problems. This book discusses computer vision algorithms and their applications using PyTorch. 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.
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