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

Data Preparation for Object Detection Applications

This chapter discusses the steps to prepare data for training models using Detectron2. Specifically, it provides tools to label images if you have some datasets at hand. Otherwise, it points you to places with open datasets so that you can quickly download and build custom applications for computer vision tasks. Additionally, this chapter covers the techniques to convert standard annotation formats to the data format required by Detectron2 if the existing datasets come in different formats.

By the end of this chapter, you will know how to label data for object detection tasks and how to download existing datasets and convert data of different formats to the format supported by Detectron2. Specifically, this chapter covers the following:

  • Common data sources
  • Getting images
  • Selecting image labeling tools
  • Annotation formats
  • Labeling the images
  • Annotation format conversions