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)
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

Developing a semantic segmentation application

A semantic segmentation task does not detect specific instances of objects but classifies each pixel in an image into some classes of interest. For instance, a model for this task classifies regions of images into pedestrians, roads, cars, trees, buildings, and the sky in a self-driving car application. The following sections detail the steps to develop a semantic segmentation application using Detectron2 pre-trained models.

Selecting a configuration file and getting a predictor

Semantic segmentation is a byproduct of panoptic segmentation. For instance, it groups detected objects of the same class into one if they are in the same region instead of providing segmentation data for every detected object. Therefore, the model for semantic segmentation is the same as that for panoptic segmentation. Therefore, the configuration file, the weights, and the code snippet for getting a predictor are the same as those for the previous panoptic...