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

Developing custom image augmentation techniques

Suppose the custom augmentation requires annotations loaded from one data sample (dataset_dict). In that case, it is relatively simple to implement the custom augmentation or transformation and incorporate it using the declarative approach described in the previous section. This section focuses on more complicated augmentation types that require loading and combining inputs from multiple samples. In these cases, we need to rewrite several parts of the Detectron2 data loader system. Thus, this section describes modifications to the Detectron2 data loader system and develops two custom image augmentations (MixUp and Mosaic) for illustration purposes.

Modifying the existing data loader

The following code snippet imports some required packages and creates an extended version (ExtendedAugInput) of the AugInput class that also enables passing a dataset_dict parameter. The reason is that augmentations such as MixUp and Mosaic may add annotations...