Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images
By using machine learning models to extract information from images, organizations today are making breakthroughs in healthcare, manufacturing, retail, and other industries. This practical book shows ML engineers and data scientists how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques.
Google engineers Valliappa Lakshmanan, Martin Garner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You’ll learn how to design, train, evaluate, and predict with models written in TensorFlow/Keras. This book also covers best practices to improve the operationalization of the models using end-to-end ML pipelines.
You’ll learn how to:
- Design ML architecture for computer vision tasks
- Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task
- Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model
- Preprocess images for data augmentation and to support learnability
- Incorporate explainability and responsible AI best practices
- Deploy image models as web services or on edge devices
- Monitor and manage ML models
Preface Who Is This Book For? How to Use This Book Organization of the Book Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments 1. Machine Learning for Computer Vision Machine Learning Deep Learning Use Cases Summary 2. ML Models for Vision A Dataset for Machine Perception 5-Flowers Dataset Reading Image Data Visualizing Image Data Reading the Dataset File A Linear Model Using Keras Keras Model Training the Model A Neural Network Using Keras Neural Networks Deep Neural Networks Summary Glossary 3. Image Vision Pretrained Embeddings Pretrained Model Transfer Learning Fine-Tuning Convolutional Networks Convolutional Filters Stacking Convolutional Layers Pooling Layers AlexNet The Quest for Depth Filter Factorization 1x1 Convolutions VGG19 Global Average Pooling Modular Architectures Inception SqueezeNet ResNet and Skip Connections DenseNet Depth-Separable Convolutions Xception Neural Architecture Search Designs NASNet The MobileNet Family Beyond Convolution: The Transformer Architecture Choosing a Model Performance Comparison Ensembling Recommended Strategy Summary 4. Object Detection and Image Segmentation Object Detection YOLO RetinaNet Segmentation Mask R-CNN and Instance Segmentation U-Net and Semantic Segmentation Summary 5. Creating Vision Datasets Collecting Images Photographs Imaging Proof of Concept Data Types Channels Geospatial Data Audio and Video Manual Labeling Multilabel Object Detection Labeling at Scale Labeling User Interface Multiple Tasks Voting and Crowdsourcing Labeling Services Automated Labeling Labels from Related Data Noisy Student Self-Supervised Learning Bias Sources of Bias Selection Bias Measurement Bias Confirmation Bias Detecting Bias Creating a Dataset Splitting Data TensorFlow Records Reading TensorFlow Records Summary 6. Preprocessing Reasons for Preprocessing Shape Transformation Data Quality Transformation Improving Model Quality Size and Resolution Using Keras Preprocessing Layers Using the TensorFlow Image Module Mixing Keras and TensorFlow Model Training Training-Serving Skew Reusing Functions Preprocessing Within the Model Using tf.transform Data Augmentation Spatial Transformations Color Distortion Information Dropping Forming Input Images Summary 7. Training Pipeline Efficient Ingestion Storing Data Efficiently Reading Data in Parallel Maximizing GPU Utilization Saving Model State Exporting the Model Checkpointing Distribution Strategy Choosing a Strategy Creating the Strategy Serverless ML Creating a Python Package Submitting a Training Job Hyperparameter Tuning Deploying the Model Summary 8. Model Quality and Continuous Evaluation Monitoring TensorBoard Weight Histograms Device Placement Data Visualization Training Events Model Quality Metrics Metrics for Classification Metrics for Regression Metrics for Object Detection Quality Evaluation Sliced Evaluations Fairness Monitoring Continuous Evaluation Summary 9. Model Predictions Making Predictions Exporting the Model Using In-Memory Models Improving Abstraction Improving Efficiency Online Prediction TensorFlow Serving Modifying the Serving Function Handling Image Bytes Batch and Stream Prediction The Apache Beam Pipeline Managed Service for Batch Prediction Invoking Online Prediction Edge ML Constraints and Optimizations TensorFlow Lite Running TensorFlow Lite Processing the Image Buffer Federated Learning Summary 10. Trends in Production ML Machine Learning Pipelines The Need for Pipelines Kubeflow Pipelines Cluster Containerizing the Codebase Writing a Component Connecting Components Automating a Run Explainability Techniques Adding Explainability No-Code Computer Vision Why Use No-Code? Loading Data Training Evaluation Summary 11. Advanced Vision Problems Object Measurement Reference Object Segmentation Rotation Correction Ratio and Measurements Counting Density Estimation Extracting Patches Simulating Input Images Regression Prediction Pose Estimation PersonLab The PoseNet Model Identifying Multiple Poses Image Search Distributed Search Fast Search Better Embeddings Summary 12. Image and Text Generation Image Understanding Embeddings Auxiliary Learning Tasks Autoencoders Variational Autoencoders Image Generation Generative Adversarial Networks GAN Improvements Image-to-Image Translation Super-Resolution Modifying Pictures (Inpainting) Anomaly Detection Deepfakes Image Captioning Dataset Tokenizing the Captions Batching Captioning Model Training Loop Prediction Summary Afterword Index
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