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Hands-On Deep Learning for IoT

You're reading from  Hands-On Deep Learning for IoT

Product type Book
Published in Jun 2019
Publisher Packt
ISBN-13 9781789616132
Pages 308 pages
Edition 1st Edition
Languages
Authors (2):
Dr. Mohammad Abdur Razzaque Dr. Mohammad Abdur Razzaque
Profile icon Dr. Mohammad Abdur Razzaque
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
View More author details
Toc

Table of Contents (15) Chapters close

Preface 1. Section 1: IoT Ecosystems, Deep Learning Techniques, and Frameworks
2. The End-to-End Life Cycle of the IoT 3. Deep Learning Architectures for IoT 4. Section 2: Hands-On Deep Learning Application Development for IoT
5. Image Recognition in IoT 6. Audio/Speech/Voice Recognition in IoT 7. Indoor Localization in IoT 8. Physiological and Psychological State Detection in IoT 9. IoT Security 10. Section 3: Advanced Aspects and Analytics in IoT
11. Predictive Maintenance for IoT 12. Deep Learning in Healthcare IoT 13. What's Next - Wrapping Up and Future Directions 14. Other Books You May Enjoy

Evaluating models

We can evaluate the models from three different aspects:

  • Learning/(re)training time
  • Storage requirement
  • Performance (accuracy)

The Mobilnet V1's retraining and validation process using the retrain.py module took less than an hour on a desktop (Intel Xenon CPU E5-1650 v3@3.5GHz and 32 GB RAM) with GPU support.

The storage/memory requirement of a model is an essential consideration for resource-constrained IoT devices. To evaluate the storage/memory footprint of the Mobilenet V1, we compared its storage requirement to another two similar networks' (the Incentive V3 and CIFAR-10 CNN) storage requirements. The following screenshot presents the storage requirements for the three models. As shown, Mobilenet V1 requires only 17.1 MB, less than one-fifth of the Incentive V3 (87.5 MB) and CIFAR-10 CNN (91.1 MB). In terms of storage requirements, Mobilenet...

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