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 the pre-trained models available on the Detectron2 Model Zoo. Specifically, it specified the vital information to look for while selecting a pre-trained model for a computer vision task. It then provided the code snippets for developing an object detection application, an object instance segmentation application, a keypoint detection application, a panoptic segmentation application, and a semantic segmentation application. The code snippets for these applications are similar. Therefore, the code snippets are abstracted into typical methods for getting a model, performing inferences, and visualizing outputs. These methods can then be reused to develop different computer vision applications with Detectron2 quickly.

The cutting-edge models on Detectron2 are trained with many images and a vast amount of computation resources. Therefore, these models have high accuracies. Additionally, they are also trained on the datasets with class labels for everyday...