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

Selecting an image labeling tool

Computer vision applications are developing rapidly. Therefore, there are many tools for labeling images for computer vision applications. These tools range from free and open source to fully commercial or commercial with free trials (which means there are some limitations regarding available features). The labeling tools may be desktop applications (require installations), online web applications, or locally hosted web applications. The online applications may even provide cloud storage, utilities to support collaborative team labeling, and pre-trained models that help to label quicker (generally, with some cost).

One of the popular image labeling tools is labelImg. It is available at https://github.com/heartexlabs/labelImg/blob/master/README.rst. It is an open source, lightweight, fast, easy-to-use, Python-based application with a short learning curve.

Its limitation is that it supports only the creation of rectangle bounding boxes. It is currently...