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Full Description
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
Who This Book Is For
Data scientists and machine learning engineers interested in building computer vision projects and solving business problems
Contents
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.
