Mastering Computer Vision with TensorFlow 2.x

April 8, 2021
Mastering Computer Vision with TensorFlow 2.x

Apply neural network architectures to buildstate-of-the-art computer vision applications using the Pythonprogramming languageKey FeaturesGain a fundamental understanding of advanced computer visionand neural network models in use todayCover tasks such as low-level vision, image classification, andobject detectionDevelop deep learning models on cloud platforms and optimizethem using TensorFlow Lite and the OpenVINO toolkitBook DescriptionComputer vision allows machines to gain human-levelunderstanding to visualize, process, and analyze images and videos.This book focuses on using TensorFlow to help you learn advancedcomputer vision tasks such as image acquisition, processing, andanalysis. You'll start with the key principles of computer visionand deep learning to build a solid foundation, before coveringneural network architectures and understanding how they work ratherthan using them as a black box. Next, you'll explore architecturessuch as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. Asyou advance, you'll learn to use visual search methods usingtransfer learning. You'll also cover advanced computer visionconcepts such as semantic segmentation, image inpainting withGAN's, object tracking, video segmentation, and action recognition.Later, the book focuses on how machine learning and deep learningconcepts can be used to perform tasks such as edge detection andface recognition. You'll then discover how to develop powerfulneural network models on your PC and on various cloud platforms.Finally, you'll learn to perform model optimization methods todeploy models on edge devices for real-time inference. By the endof this book, you'll have a solid understanding of computer visionand be able to confidently develop models to automate tasks.What you will learnExplore methods of feature extraction and image retrieval andvisualize different layers of the neural network modelUse TensorFlow for various visual search methods for real-worldscenariosBuild neural networks or adjust parameters to optimize theperformance of modelsUnderstand TensorFlow DeepLab to perform semantic segmentationon images and DCGAN for image inpaintingEvaluate your model and optimize and integrate it into yourapplication to operate at scaleGet up to speed with techniques for performing manual andautomated image annotationWho this book is forThis book is for computer vision professionals, image processingprofessionals, machine learning engineers and AI developers whohave some knowledge of machine learning and deep learning and wantto build expert-level computer vision applications. In addition tofamiliarity with TensorFlow, Python knowledge will be required toget started with this book.