You're reading from Hands-On Computer Vision with Detectron2

Product type Book

Published in Apr 2023

Publisher Packt

ISBN-13 9781800561625

Pages 318 pages

Edition 1st Edition

Languages

Python

Concepts

Computer Vision

Author (1):

Van Vung Pham

Table of Contents (20) Chapters

Preface

1. Part 1: Introduction to Detectron2

2. Chapter 1: An Introduction to Detectron2 and Computer Vision Tasks

3. Chapter 2: Developing Computer Vision Applications Using Existing Detectron2 Models

4. Part 2: Developing Custom Object Detection Models

5. Chapter 3: Data Preparation for Object Detection Applications

6. Chapter 4: The Architecture of the Object Detection Model in Detectron2

7. Chapter 5: Training Custom Object Detection Models

8. Chapter 6: Inspecting Training Results and Fine-Tuning Detectron2’s Solvers

9. Chapter 7: Fine-Tuning Object Detection Models

10. Chapter 8: Image Data Augmentation Techniques

11. Chapter 9: Applying Train-Time and Test-Time Image Augmentations

12. Part 3: Developing a Custom Detectron2 Model for Instance Segmentation Tasks

13. Chapter 10: Training Instance Segmentation Models

14. Chapter 11: Fine-Tuning Instance Segmentation Models

15. Part 4: Deploying Detectron2 Models into Production

16. Chapter 12: Deploying Detectron2 Models into Server Environments

17. Chapter 13: Deploying Detectron2 Models into Browsers and Mobile Environments

18. Index

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19. Other Books You May Enjoy

Common data sources

Chapter 2 introduced the two most common datasets for the computer vision community. They are ImageNet and Microsoft COCO (Common Objects in Context). These datasets also contain many pre-trained models that can predict various class labels that may meet your everyday needs.

If your task is to detect a less common class label, it might be worth exploring the Large Vocabulary Instance Segmentation (LVIS) dataset. It has more than 1,200 categories and 164,000 images, and it contains many rare categories and about 2 million high-quality instance segmentation masks. Detectron2 also provides pre-trained models for predicting these 1,200+ labels. Thus, you can follow the steps described in Chapter 2 to create a computer vision application that meets your needs. More information about the LVIS dataset is available on its website at https://www.lvisdataset.org.

If you have a task where no existing/pre-trained models can meet your needs, it is time to find existing...