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

Deploying Detectron2 models using ONNX

ONNX is an open source format for representing and sharing deep learning models between different frameworks. The models can then be deployed in various platforms (e.g., servers or mobile devices) that support these frameworks. The following sections introduce ONNX and its supported frameworks and platforms, export a PyTorch model to ONNX format, and load the exported model into the browser environment.

Introduction to ONNX

ONNX aims to be a universal standard for deep learning models, allowing for interoperability between different tools, libraries, and frameworks. Microsoft and Facebook initiated the ONNX project in 2017. However, this project is currently an open source project managed by the ONNX community, which includes contributors from a wide range of organizations. This means that the project has great potential and support. The format is designed to be flexible and extensible, supporting a wide range of deep learning models, including...