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

Inspecting Training Results and Fine-Tuning Detectron2’s Solvers

This chapter covers how to use TensorBoard to inspect training histories. It utilizes the code and visualization approach to explain the concepts behind Detectron2’s solvers and their hyperparameters. The related concepts include gradient descent, stochastic gradient descent, momentum, and variable learning rate optimizers. This chapter also provides code to help you find the standard hyperparameters for Detectron2’s solvers.

By the end of this chapter, you will be able to use TensorBoard to analyze training results and find insights. You will also have a deep understanding of the essential hyperparameters for Detectron2’s solvers. Additionally, you will be able to use code to generate appropriate values for these hyperparameters on your custom dataset. Specifically, this chapter covers the following topics:

  • Inspecting training histories with TensorBoard
  • Understanding Detectron2...