Hands-on computer vision with Detectron2 : develop object detection and segmentation models with a code and visualization approach /

Computer vision is a crucial component of many modern businesses, including automobiles, robotics, and manufacturing, and its market is growing rapidly. This book helps you explore Detectron2, Facebook's next-gen library providing cutting-edge detection and segmentation algorithms. It's us...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Pham, Van Vung (Autor)
Otros Autores: Dang, Tommy (writer of foreword.)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing Ltd., 2023.
Edición:1st edition.
Temas:
Computer vision.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright and Credits
  • Dedications
  • Foreword
  • Contributors
  • Table of Contents
  • Preface
  • Part 1: Introduction to Detectron2
  • Chapter 1: An Introduction to Detectron2 and Computer Vision Tasks
  • Technical requirements
  • Computer vision tasks
  • Object detection
  • Instance segmentation
  • Keypoint detection
  • Semantic segmentation
  • Panoptic segmentation
  • An introduction to Detectron2 and its architecture
  • Introducing Detectron2
  • Detectron2 architecture
  • Detectron2 development environments
  • Cloud development environment for Detectron2 applications
  • Local development environment for Detectron2 applications
  • Connecting Google Colab to a local development environment
  • Summary
  • Chapter 2: Developing Computer Vision Applications Using Existing Detectron2 Models
  • Technical requirements
  • Introduction to Detectron2's Model Zoo
  • Developing an object detection application
  • Getting the configuration file
  • Getting a predictor
  • Performing inferences
  • Visualizing the results
  • Developing an instance segmentation application
  • Selecting a configuration file
  • Getting a predictor
  • Performing inferences
  • Visualizing the results
  • Developing a keypoint detection application
  • Selecting a configuration file
  • Getting a predictor
  • Performing inferences
  • Visualizing the results
  • Developing a panoptic segmentation application
  • Selecting a configuration file
  • Getting a predictor
  • Performing inferences
  • Visualizing the results
  • Developing a semantic segmentation application
  • Selecting a configuration file and getting a predictor
  • Performing inferences
  • Visualizing the results
  • Putting it all together
  • Getting a predictor
  • Performing inferences
  • Visualizing the results
  • Performing a computer vision task
  • Summary
  • Part 2: Developing Custom Object Detection Models
  • Chapter 3: Data Preparation for Object Detection Applications
  • Technical requirements
  • Common data sources
  • Getting images
  • Selecting an image labeling tool
  • Annotation formats
  • Labeling the images
  • Annotation format conversions
  • Converting YOLO datasets to COCO datasets
  • Converting Pascal VOC datasets to COCO datasets
  • Summary
  • Chapter 4: The Architecture of the Object Detection Model in Detectron2
  • Technical requirements
  • Introduction to the application architecture
  • The backbone network
  • Region Proposal Network
  • The anchor generator
  • The RPN head
  • The RPN loss calculation
  • Proposal predictions
  • Region of Interest Heads
  • The pooler
  • The box predictor
  • Summary
  • Chapter 5: Training Custom Object Detection Models
  • Technical requirements
  • Processing data
  • The dataset
  • Downloading and performing initial explorations
  • Data format conversion
  • Displaying samples
  • Using the default trainer
  • Selecting the best model

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