Book Image

Hands-On Computer Vision with Detectron2

By : Van Vung Pham
Book Image

Hands-On Computer Vision with Detectron2

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

Preparing data for training segmentation models

This section introduces several scenarios about data preparation steps that may be necessary for your cases. Specifically, this section first introduces the common tasks to prepare a dataset, including getting images, labeling images, and converting annotations. Additionally, in practice, data may come in various formats that might not be standard, and in this case, we may need to perform the data preparation steps from scratch. Therefore, this section also introduces the steps to prepare the brain tumor dataset for training custom segmentation models using Detectron2.

Getting images, labeling images, and converting annotations

If you do not have a dataset, Chapter 3 introduces common places to obtain data for computer vision applications in general. It would help if you also went to these sources for object instance segmentation data. Also, you can use the same Python script (Detectron2_Chapter03_Download_Images.ipynb) introduced...