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

Common data sources

Chapter 2 introduced the two most common datasets for the computer vision community. They are ImageNet and Microsoft COCO (Common Objects in Context). These datasets also contain many pre-trained models that can predict various class labels that may meet your everyday needs.

If your task is to detect a less common class label, it might be worth exploring the Large Vocabulary Instance Segmentation (LVIS) dataset. It has more than 1,200 categories and 164,000 images, and it contains many rare categories and about 2 million high-quality instance segmentation masks. Detectron2 also provides pre-trained models for predicting these 1,200+ labels. Thus, you can follow the steps described in Chapter 2 to create a computer vision application that meets your needs. More information about the LVIS dataset is available on its website at https://www.lvisdataset.org.

If you have a task where no existing/pre-trained models can meet your needs, it is time to find existing...