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

Summary

This chapter dives deep into the components of Detectron2’s implementation of Faster R-CNN for object detection tasks. This model is a two-stage technique: region proposal stage and region of interest extraction stage. Both of these stages use the features extracted from a backbone network. This backbone network can be any state-of-the-art convolutional neural network to extract salient features from the input images. The extracted features and information to generate a set of initial anchors (sizes and ratios; Chapter 7 explains more about how to customize these sizes and ratios) are then passed to the region proposal neural network to predict a fixed number of proposals with objectness scores (if there is an object in a proposal) and location deltas (location differences between the predicted proposals and the raw anchors). The selected proposals are then passed to the second stage with the region of interest heads to predict the final object classification and localization...