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

Detectron2’s image augmentation system

Detectron2’s image augmentation system has three main groups of classes: Transformation, Augmentation, and AugInput. These components help augment images and their related annotations (for example, bounding boxes, segment masks, and key points). Additionally, this system allows you to apply a sequence of declarative augmentation statements and enables augmenting custom data types and custom operations. Figure 8.4 shows a simplified class diagram of Detectron2’s augmentation system:

Figure 8.4: Simplified class diagram of Detectron2’s augmentation system

Figure 8.4: Simplified class diagram of Detectron2’s augmentation system

The Transform and Augmentation classes are the bases for all the classes in their respective groups. Notably, the data format for boxes is in XYXY_ABS mode, which dictates the boxes to be in (x_min, y_min, x_max, y_max), specified in absolute pixels. Generally, subclasses of the Transform base class perform the deterministic changes of the...