Unlock the Power of Detectron2 for Object Detection & Segmentation

Detectron2だけでなく、Fast R-CNN等、物体検出の各手法について分かりやすく解説している本です。
物体検出のアルゴリズムがなんとなく理解しているが、もう少し中身を知りたい方におすすめです。
洋書ですが、やさしい英語で書かれていて読みやすかったです。機械学習関連の洋書を読む練習にもなります。

Hands-On Computer Vision with Detectron2 by Van Vung Pham is an excellent book for anyone who wants to learn how to use Detectron2, Facebook's open-source library for object detection and segmentation. The book is well-written and covers a wide range of topics, from the basics of computer vision to the latest advances in Detectron2.

One of the things I liked most about the book is that it takes a hands-on approach. The author doesn't just tell you how to use Detectron2, he shows you. He provides step-by-step instructions on building and training your own object detection and segmentation models. He also includes several exercises that you can use to test your understanding of the material.

Another thing I liked about the book is that it's up-to-date. The author uses the latest version of Detectron2, including coverage of new features such as Cascade R-CNN and Panoptic Segmentation.

Overall, I highly recommend Hands-On Computer Vision with Detectron2 to anyone who wants to learn how to use Detectron2. It's a well-written, comprehensive, and up-to-date book that will teach you everything you need to know about this powerful library.

If you’re figuring out how to use detectron2 or in general you want to figure out computer vision using a tool that’s built on PyTorch (detectron2) then yea get this book. It’s good. It’s helped me build some projects. I recommend using GitHub copilot or a similar code suggestion tool, so you can consult this book and then you can code faster without really needing to check the code in this book. So I use the book to understand what’s possible and what the basics are and how they’re generally implemented and the ideas around them, and then I wing it with the help of copilot and then boom I have a finished project. Also thecodingbug on YouTube has amazing tutorials for getting started on detectron2 and his tutorials are not just Google colaboratory. He actually does it in visual studio or a real IDE. Now I just wish there was a YOLO version of this book, but that would be constantly going out of date I guess.

The book provides a detailed overview of how to use Detectron2 for object detection and segmentation algorithm. However, the reader doesn't have to know Detectron2, but having some prior knowledge of computer vision and deep learning is mandatory. The book covers topics such as the architecture of Detectron2, data preparation, training custom object detection models, fine-tuning models, and deploying Detectron2 models. Some sections on projects helped us apply the skills we learned from the book. As someone who hasn't touched Detectron2 before, the book explains the theory and application very well with visualization. I especially liked the tuning section because it provides thorough explanations which helps people who haven't had too much experience with object detection.

The author provides a wonderful approach in explaining sections of his code by binning snippets of code with appropriate examples! As someone who has read a number of computer vision books, i was expecting it to be explanation first then code after, but with this book, he tackles the code while simultaneously explaining the theory behind the code. Moreover the content within the book talks about the recent breakthroughs in comp vision, as someone who has been following comp vision research i understand that there is an exponential boom on going the past couple of years, however it's still important to understand the fundamental researches that allow us to build new technology

This book guides you through using Detectron2 models for various computer vision tasks, offering insights into the library's architecture and module functionality. Gain practical skills through two real-life projects on object detection and instance segmentation, while learning to deploy Detectron 2 models into production and develop mobile applications. Acquire sound theoretical knowledge and hands-on expertise to tackle advanced computer vision tasks using Detectron2.