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 discussed advanced CV tasks, including object detection, instance segmentation, keypoint detection, semantic segmentation, and panoptic segmentation, and when to use them. Detectron2 is a framework that helps implement cutting-edge algorithms for these CV tasks with the advantages of being faster, more accurate, modular, customizable, and built on top of PyTorch. Its architecture has four main parts: input data, backbone, region proposal, and region of interest heads. Each of these components is replaceable with a custom implementation. This chapter also provided the steps to set up a cloud development environment using Google Colab, a local development environment, or to connect Google Colab to a local runtime if needed.

You now understand the leading CV tasks Detectron2 can help develop and have set up a development environment. The next chapter (Chapter 2) will guide you through the steps to build CV applications for all the listed CV tasks using the cutting-edge models provided in the Detectron2 Model Zoo.