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 discussed the steps to explore, process, and prepare a custom dataset for training object detection models using Detectron2. After processing the dataset, it is relatively easy to register the train, test, and evaluation data (if there is any) with Detectron2 and start training object detection models using the default trainer. The training process may result in many models. Therefore, this chapter provided the standard evaluation metrics and approaches for selecting the best model. The default trainer may meet the most common training requirements. However, in several cases, a custom trainer may be necessary to incorporate more customizations into the training process. This chapter provided code snippets to build a custom trainer that incorporates evaluations on the test set during training. It also provided a code snippet for a custom hook that extracts the evaluation metrics and stores the best model during training.

The next chapter, Chapter 6, uses TensorBoard...