Computer Vision Projects with PyTorch: Design and Develop Production-Grade Models
- Length: 362 pages
- Edition: 1
- Language: English
- Publisher: Apress
- Publication Date: 2022-08-22
- ISBN-10: 1484282728
- ISBN-13: 9781484282724
- Sales Rank: #7311770 (See Top 100 Books)
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
Table of Contents About the Authors About the Technical Reviewer Introduction Chapter 1: The Building Blocks of Computer Vision What Is Computer Vision Applications Classification Object Detection and Localization Image Segmentation Anomaly Detection Video Analysis Channels Convolutional Neural Networks Receptive Field Local Receptive Field Global Receptive Field Pooling Max Pooling Average Pooling Global Average Pooling Calculation: Feature Map and Receptive Fields Kernel Stride Pooling Padding Input and Output Calculation of Receptive Field Understanding the CNN Architecture Type Understanding Types of Architecture AlexNet VGG ResNet Inception Architectures Working with Deep Learning Model Techniques Batch Normalization Dropouts Data Augmentation Techniques Introduction to PyTorch Installation Basic Start Summary Chapter 2: Image Classification Topics to Cover Defining the Problem Overview of the Approach Creating an Image Classification Pipeline First Basic Model Data Data Exploration Data Loader Define the Model The Training Process The Second Variation of Model The Third Variation of the Model The Fourth Variation of the Model Summary Chapter 3: Building an Object Detection Model Object Detection Using Boosted Cascade R-CNN The Region Proposal Network Fast Region-Based Convolutional Neural Network How the Region Proposal Network Works The Anchor Generation Layer The Region Proposal Layer Mask R-CNN Prerequisites YOLO YOLO V2/V3 Project Code Snippets Step 1: Getting Annotated Data Step 2: Fixing the Configuration File and Training The Model File Summary Chapter 4: Building an Image Segmentation Model Image Segmentation Pretrained Support from PyTorch Semantic Segmentation Instance Segmentation Fine-Tuning the Model Summary Chapter 5: Image-Based Search and Recommendation System Problem Statement Approach and Methodology Implementation The Dataset Installing and Importing Libraries Importing and Understanding the Data Feature Engineering ResNet18 Calculating Similarity and Ranking Visualizing the Recommendations Taking Image Input from Users and Recommending Similar Products Summary Chapter 6: Pose Estimation Top-Down Approach Bottom-Up Approach OpenPose Branch-1 Branch-2 HRNet (High-Resolution Net) Higher HRNet PoseNet How Does PoseNet Work? Single Person Pose Estimation Multi-Person Pose Estimation Pros and Cons of PoseNet Applications of Pose Estimation Test Cases Performed Retail Store Videos Implementation Step 1: Identify the List of Human Keypoints to Track Step 2: Identify the Possible Connections Between the Keypoints Step 3: Load the Pretrained Model from the PyTorch Library Step 4: Input Image Preprocessing and Modeling Step 5: Build Custom Functions to Plot the Output Step 6: Plot the Output on the Input Image Summary
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