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

The backbone network

Chapter 2 discusses the typical backbone networks for Detectron2. They include ResNet50, ResNet101, ResNeXt101, and their variants. This section inspects the ResNet50 architecture as an example. However, the idea remains the same for other base models (backbone networks). Figure 4.3 summarizes the steps to inspect the backbone network. Specifically, we pass a tensor of data for a single image to the backbone, and the backbone (ResNet50, in this case) gives out a tensor. This output tensor is the extracted salient feature of the input image.

Figure 4.3: The backbone network

Figure 4.3: The backbone network

Specifically, from the default Detectron2’s predictor, we can access the backbone network using the following code snippet:

backbone = predictor.model.backbone

This code snippet should print out the following:


The following code snippet reveals the backbone’s architecture: