Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Computer Vision with TensorFlow 2

You're reading from   Hands-On Computer Vision with TensorFlow 2 Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras

Arrow left icon
Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788830645
Length 372 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Eliot Andres Eliot Andres
Author Profile Icon Eliot Andres
Eliot Andres
Benjamin Planche Benjamin Planche
Author Profile Icon Benjamin Planche
Benjamin Planche
Arrow right icon
View More author details
Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision FREE CHAPTER
2. Computer Vision and Neural Networks 3. TensorFlow Basics and Training a Model 4. Modern Neural Networks 5. Section 2: State-of-the-Art Solutions for Classic Recognition Problems
6. Influential Classification Tools 7. Object Detection Models 8. Enhancing and Segmenting Images 9. Section 3: Advanced Concepts and New Frontiers of Computer Vision
10. Training on Complex and Scarce Datasets 11. Video and Recurrent Neural Networks 12. Optimizing Models and Deploying on Mobile Devices 13. Migrating from TensorFlow 1 to TensorFlow 2 14. Assessments 15. Other Books You May Enjoy

Faster R-CNN – a powerful object detection model

The main benefit of YOLO is its speed. While it can achieve very good results, it is now outperformed by more complex networks. Faster Region with Convolutional Neural Networks (Faster R-CNN) is considered state of the art at the time of writing. It is also quite fast, reaching 4-5 FPS on a modern GPU. In this section, we will explore its architecture.

The Faster R-CNN architecture was engineered over several years of research. More precisely, it was built incrementally from two architectures—R-CNN and Fast R-CNN. In this section, we will focus on the latest architecture, Faster R-CNN:

  • Faster R-CNN: towards real-time object detection with region proposal networks (2015), Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun

This paper draws a lot of knowledge from the two previous designs. Therefore, some of the...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at £16.99/month. Cancel anytime