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

Fine-Tuning Object Detection Models

Detectron2 utilizes the concepts of anchors to improve its object detection accuracy by allowing object detection models to predict from a set of anchors instead of from scratch. The set of anchors has various sizes and ratios to reflect the shapes of the objects to be detected. Detectron2 uses two sets of hyperparameters called sizes and ratios to generate the initial set of anchors. Therefore, this chapter explains how Detectron2 processes its inputs and provides code to analyze the ground-truth boxes from a training dataset and find appropriate values for these anchor sizes and ratios.

Additionally, input image pixels’ means and standard deviations are crucial in training Detectron2 models. Specifically, Detectron2 uses these values to normalize the input images during training. Calculating these hyperparameters over the whole dataset at once is often impossible for large datasets. Therefore, this chapter provides the code to calculate...