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Advanced Deep Learning with TensorFlow 2 and Keras

You're reading from   Advanced Deep Learning with TensorFlow 2 and Keras Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781838821654
Length 512 pages
Edition 2nd Edition
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Author (1):
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Rowel Atienza Rowel Atienza
Author Profile Icon Rowel Atienza
Rowel Atienza
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Table of Contents (16) Chapters Close

Preface 1. Introducing Advanced Deep Learning with Keras 2. Deep Neural Networks FREE CHAPTER 3. Autoencoders 4. Generative Adversarial Networks (GANs) 5. Improved GANs 6. Disentangled Representation GANs 7. Cross-Domain GANs 8. Variational Autoencoders (VAEs) 9. Deep Reinforcement Learning 10. Policy Gradient Methods 11. Object Detection 12. Semantic Segmentation 13. Unsupervised Learning Using Mutual Information 14. Other Books You May Enjoy
15. Index

13. SSD model validation

After training the SSD model for 200 epochs, the performance can be validated. Three possible metrics for evaluation are used: 1) IoU, 2) Precision, and 3) Recall.

The first metric is mean IoU (mIoU). Given the ground truth test dataset, the IoU between the ground truth bounding box and predicted bounding box is computed. This is done for all ground truth and predicted bounding boxes after performing NMS. The average of all IoUs is computed as mIoU:

(Equation 11.13.1)

where nbox is the number of ground truth bounding boxes bi and npred is the number of predicted bounding boxes dj. Please note that this metric does not validate if the two overlapping bounding boxes belong to the same class. If this is required, then the code can be easily modified. Listing 11.13.1 shows the code implementation.

The second metric is precision as shown in Equation 11.3.2. It is the number of object categories correctly predicted (true positive or TP) divided...

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