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

Summary

This chapter described the steps to apply image augmentation techniques using Detectron2 at both train time and test time (inferencing time). Detectron2 provides a declarative approach to applying existing augmentations conveniently. However, the current system supports augmentations on a single input, while several modern image augmentations require data from different inputs. Therefore, this chapter described the Detectron2 data loader system and provided steps to modify several Detectron2 data loader components to enable applying modern image augmentation techniques such as MixUp and Mosaic that require multiple inputs. Lastly, this chapter also described the features in Detectron2 that allow for performing test-time augmentations.

Congratulations! You now understand the Detectron2 architecture for object detection models and should have mastered the steps to prepare data, train, and fine-tune Detectron2 object detection models. The following part of this book has a similar...