Book Image

Hands-On Computer Vision with Detectron2

By : Van Vung Pham
5 (4)
Book Image

Hands-On Computer Vision with Detectron2

5 (4)
By: Van Vung Pham

Overview of this book

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.
Table of Contents (20 chapters)
1
Part 1: Introduction to Detectron2
4
Part 2: Developing Custom Object Detection Models
12
Part 3: Developing a Custom Detectron2 Model for Instance Segmentation Tasks
15
Part 4: Deploying Detectron2 Models into Production

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

anchor ratios

computing 145, 146

hyperparameters 144, 145

setting 140

anchor sizes

generating 144, 145

setting 140

annotation formats 48-55

conversions 58

AugInput class 186, 187

augmentation classes 176

Augmentation class 177

AugmentationList class 186

FixedSizeCrop class 177

MinIoURandomCrop class 184

RandomApply class 178

RandomBrightness class 185

RandomContrast class 185

RandomCrop and CategoryAreaConstraint classes 183

RandomCrop class 178

RandomExtent class 179

RandomFlip class 180

RandomLighting class 185

RandomResize class 181

RandomRotation class 180, 181

RandomSaturation class 185

Resize class 181

ResizeScale class 182

ResizeShortestEdge class 182

average precision (AP) 21, 104, 281

B

...