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

Training custom segmentation models

The previous section described the architecture of an object segmentation application that extends the object detection application. Therefore, the training steps and configurations of these two applications are similar. This section utilizes the source code provided in the Putting it all together section in swswswswswswswswsw. Ideally, we should perform all the steps in that chapter to get a better set of hyperparameters for this specific dataset. However, for simplicity, this section reuses all the source code as it is and focuses on the differences for training object segmentation applications. The following code snippet downloads the dataset prepared in the previous steps with train and test sets:

!wget -q <url_to/>
!unzip -q

Once the dataset is extracted, the source code for Detectron2 installation, registering datasets, getting a training configuration, building a custom trainer with...