Modern computer vision with PyTorch

• Verfasst von: Ayyadevara, V Kishore [VerfasserIn]
• Verfasst von: Reddy, Yeshwanth [VerfasserIn]
• Titel: Modern computer vision with PyTorch
• Titelzusatz: a practical roadmap from deep learning fundamentals to advanced applications and Generative AI
• Verf.angabe: V. Kishore Ayyadevara, Yeshwanth Reddy
• Ausgabe: Second edition.
• Verlagsort: Birmingham, UK
• Verlag: Packt Publishing Ltd.
• Jahr: 2024
• Umfang: 1 online resource (746 pages)
• Illustrationen: illustrations
• Gesamttitel/Reihe: Expert insight
• Fussnoten: Includes index
• ISBN: 978-1-80323-133-4
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Vision par ordinateur
• Sach-SW: Apprentissage automatique
• K10plus-PPN: 1892756714
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext
• Lokale URL UB: Bibliothek der Medizinischen Fakultät Mannheim der Universität Heidelberg
• Bibliothek/Idn: UW / m4544412501
• Lokale URL Inst.: Zum Volltext

Abstract

Whether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks. The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion. You’ll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You’ll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you’ll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you’ll learn best practices for deploying a model to production. By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.