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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing Ltd.,
2023.
|
| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Pham, Van Vung, |e author. | |
| 245 | 1 | 0 | |a Hands-on computer vision with Detectron2 : |b develop object detection and segmentation models with a code and visualization approach / |c Van Vung Pham ; foreword by Tommy Dang. |
| 250 | |a 1st edition. | ||
| 264 | 1 | |a Birmingham, UK : |b Packt Publishing Ltd., |c 2023. | |
| 300 | |a 1 online resource (318 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 500 | |a Includes index. | ||
| 520 | |a 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 used in research and practical projects at Facebook to support computer vision tasks, and its models can be exported to TorchScript or ONNX for deployment. The book provides you with step-by-step guidance on using existing models in Detectron2 for computer vision tasks (object detection, instance segmentation, key-point detection, semantic detection, and panoptic segmentation). You'll get to grips with the theories and visualizations of Detectron2's architecture and learn how each module in Detectron2 works. As you advance, you'll build your practical skills by working on two real-life projects (preparing data, training models, fine-tuning models, and deployments) for object detection and instance segmentation tasks using Detectron2. Finally, you'll deploy Detectron2 models into production and develop Detectron2 applications for mobile devices. By the end of this deep learning book, you'll have gained sound theoretical knowledge and useful hands-on skills to help you solve advanced computer vision tasks using Detectron2. | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Computer vision. | |
| 650 | 7 | |a Computer vision. |2 fast |0 (OCoLC)fst00872687 | |
| 700 | 1 | |a Dang, Tommy, |e writer of foreword. | |
| 776 | 0 | 8 | |i Print version: |a Pham, Van Vung |t Hands-On Computer Vision with Detectron2 |d Birmingham : Packt Publishing, Limited,c2023 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781800561625/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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