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

Region Proposal Network

Faster R-CNN is called a two-stage technique. The first stage proposes the regions (bounding boxes) and whether an object falls within that region (objectness). Notably, at this stage, it only predicts whether an object is in the proposed box and does not classify it into a specific class. The second stage then continues to fine-tune the proposed regions and classify objects in the proposed bounding boxes into particular labels. The RPN performs the first stage. This section inspects the details of the RPN and its related components in Faster R-CNN architecture, implemented in Detectron2, as in Figure 4.6.

Figure 4.6: The Region Proposal Network and its components

Figure 4.6: The Region Proposal Network and its components

Continuing from the previous code example, the following code snippet displays the RPN (proposal_generator):

rpn = predictor.model.proposal_generator

This snippet should print out the following:


The following...