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

Part 2: Developing Custom Object Detection Models

The second part is about getting your hands busy developing and fine-tuning custom detection models with data preparation, training, and fine-tuning steps. The data preparation step introduces common computer vision datasets and the code to download freely available images. Additionally, it discusses tools to label data, common annotation formats, and the code to convert from different formats to the one Detectron2 supports. It then goes into further detail about the architecture of a Detetron2 application using visualizations and code. After training a model, this part illustrates the steps to utilize TensorBoard to find insights about training before fine-tuning the trained models. For fine-tuning, this section provides a primer on deep-learning optimizers and steps to fine-tune Detectron2 solvers. For optimizing detection models specifically, this part includes the code to compute the suitable sizes and ratios parameters for generating...