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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

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788830645
Length 372 pages
Edition 1st Edition
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Authors (2):
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Eliot Andres Eliot Andres
Author Profile Icon Eliot Andres
Eliot Andres
Benjamin Planche Benjamin Planche
Author Profile Icon Benjamin Planche
Benjamin Planche
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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

Introducing anchor boxes

We mentioned that tx, ty, tw, and th are used to compute the bounding box coordinates. Why not ask the network to output the coordinates directly (x, y, w, and h)? In fact, that is how it was done in YOLO v1. Unfortunately, this resulted in a lot of errors because objects vary in size.

Indeed, if most of the objects in the train dataset are big, the network will tend to predict w and h as being very large. And when using the trained model on small objects, it will often fail. To fix this problem, YOLO v2 introduced anchor boxes.

Anchor boxes (also called priors) are a set of bounding box sizes that are decided upon before training the network. For instance, when training a neural network to detect pedestrians, tall and narrow anchor boxes would be picked. An example is shown here:

Figure 5.6: On the left are the three bounding box sizes picked to detect pedestrians. On the right is how we adapt one of the bounding boxes to match a pedestrian

A set of anchor...

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