Book Image

Hands-On Computer Vision with Detectron2

By : Van Vung Pham
Book Image

Hands-On Computer Vision with Detectron2

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)
Part 1: Introduction to Detectron2
Part 2: Developing Custom Object Detection Models
Part 3: Developing a Custom Detectron2 Model for Instance Segmentation Tasks
Part 4: Deploying Detectron2 Models into Production


This chapter introduced the file formats and their corresponding runtimes supported by PyTorch in general and Detectron2 specifically. It then provided simple code to export PyTorch models into TorchScript using tracing and scripting approaches. It also provided a tutorial on deploying the TorchScript model into the low-latency C++ environment at production time. This chapter then described the main utilities that Detectron2 provides to perform exports of its models to TorchScript. It then provided the steps and code to export a custom Detectron2 model into TorchScript with tracing and scripting approaches using the described utilities.

The server deployment is useful in mass and large system production. However, in many cases, there are requirements to deploy models in different environments, such as web and mobile environments. Therefore, the next chapter discusses the necessary steps to perform these deployment tasks.