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

Deploying Detectron2 Models into Server Environments

This chapter walks you through the steps of the export process to convert Detectron2 models into deployable artifacts. Specifically, it describes the standard file formats of deep learning models such as TorchScript and the corresponding runtimes for these formats, such as PyTorch and C++. This chapter then provides the steps to convert Detectron2 models to the standard file formats and deploy them to the corresponding runtimes.

By the end of this chapter, you will understand the standard file formats and runtimes that Detectron2 supports. You can perform steps to export Detectron2 models into TorchScript format using tracing or scripting method. Additionally, you can create a C++ application to load and execute the exported models.

In this chapter, we will cover the following topics:

  • Supported file formats and runtimes for PyTorch models
  • Deploying custom Detectron2 models