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

The Architecture of the Object Detection Model in Detectron2

This chapter dives deep into the architecture of Detectron2 for the object detection task. The object detection model in Detectron2 is the implementation of Faster R-CNN. Specifically, this architecture includes the backbone network, the region proposal network, and the region of interest heads. This chapter is essential for understanding common terminology when designing deep neural networks for vision systems. Deep understanding helps to fine-tune and customize models for better accuracy while training with the custom datasets.

By the end of this chapter, you will understand Detectron2’s typical architecture in detail. You also know where to customize your Detectron2 model (what configuration parameters to set, how to set them, and where to add/remove layers) to improve performance. Specifically, this chapter covers the following topics:

  • Introduction to the application architecture
  • The backbone network...