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

The Detectron2 data loader

Applying augmentations in Detectron2 can be straightforward and complicated at the same time. It is relatively easy to use the declarative approach and apply existing transformations and augmentations provided by Detectron2, which should meet the most common needs. However, adding custom augmentations that require multiple data samples (e.g., MixUp and Mosaic) is a little complicated. This section describes how Detectron2 loads data and how to incorporate existing and custom data augmentations into training Detectron2 models. Figure 9.1 illustrates the steps and main components of the Detectron2 data loading system.

Figure 9.1: Loading data and data augmentations in Detectron2

Figure 9.1: Loading data and data augmentations in Detectron2

There are classes for Dataset, Sampler, Mapper, and Loader. The Dataset component normally stores a list of data items in JSON format. The Sampler component helps to randomly select one data item (dataset_dict) from the dataset. The selected data item has...