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

Understanding Detectron2’s solvers

We should try two obvious fine-tuning techniques: changing the backbone network and increasing the batch size. As indicated in the Detectron2 Model Zoo (introduced in Chapter 3), the backbone we selected in Chapter 5 is the simplest one (ResNet50), with a low [email protected] on the pre-trained dataset. It is lightweight and fast to train and infer, so we have been using it for our experiments using a free Google Colab plan. If computation resources are available, selecting a more powerful backbone, such as X101FPN, on the Detectron2 Model Zoo would be beneficial. However, in this section, we will keep this simple backbone model and experiment with the settings and types of optimizers. These are standard hyperparameters because they apply to deep learning in general.

Understanding the available optimizers and their related hyperparameters is essential to understanding the configuration parameters Detectron2 offers for fine-tuning models. This section...