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)
Part 1: Introduction to Detectron2
Part 2: Developing Custom Object Detection Models
Part 3: Developing a Custom Detectron2 Model for Instance Segmentation Tasks
Part 4: Deploying Detectron2 Models into Production

Image augmentation techniques

Image augmentation techniques help greatly improve the robustness and accuracy of computer vision deep learning models. Detectron2 and many other modern computer vision architectures use image augmentation. Therefore, it is essential to understand image augmentation techniques and how Detectron2 uses them. This section covers what image augmentations are, why they are important, and introduces popular methods to perform them (how). The next two sections explain how Detectron2 uses them during training and inferencing.

Why image augmentations?

Deep learning architectures with a small number of weights may not be accurate (bias issue). Therefore, modern architectures tend to be complex and have huge numbers of weights. Training these models often involves passing through the training datasets for several epochs; one epoch means the whole training dataset is passed through the model once. Therefore, the huge numbers of weights may mean the models tend...