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Machine Learning for OpenCV 4

You're reading from   Machine Learning for OpenCV 4 Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn

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Product type Paperback
Published in Sep 2019
Publisher Packt
ISBN-13 9781789536300
Length 420 pages
Edition 2nd Edition
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Authors (4):
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Aditya Sharma Aditya Sharma
Author Profile Icon Aditya Sharma
Aditya Sharma
Michael Beyeler (USD) Michael Beyeler (USD)
Author Profile Icon Michael Beyeler (USD)
Michael Beyeler (USD)
Vishwesh Ravi Shrimali Vishwesh Ravi Shrimali
Author Profile Icon Vishwesh Ravi Shrimali
Vishwesh Ravi Shrimali
Michael Beyeler Michael Beyeler
Author Profile Icon Michael Beyeler
Michael Beyeler
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Fundamentals of Machine Learning and OpenCV FREE CHAPTER
2. A Taste of Machine Learning 3. Working with Data in OpenCV 4. First Steps in Supervised Learning 5. Representing Data and Engineering Features 6. Section 2: Operations with OpenCV
7. Using Decision Trees to Make a Medical Diagnosis 8. Detecting Pedestrians with Support Vector Machines 9. Implementing a Spam Filter with Bayesian Learning 10. Discovering Hidden Structures with Unsupervised Learning 11. Section 3: Advanced Machine Learning with OpenCV
12. Using Deep Learning to Classify Handwritten Digits 13. Ensemble Methods for Classification 14. Selecting the Right Model with Hyperparameter Tuning 15. Using OpenVINO with OpenCV 16. Conclusion 17. Other Books You May Enjoy

Using OpenVINO Model Zoo with OpenCV

In the previous sections, we briefly discussed OpenVINO Model Zoo and how we can use OpenVINO IE with OpenCV. In this section, we will learn more about Model Zoo and what it offers.

OpenVINO Model Zoo is a collection of optimized pre-trained models that can be directly imported into OpenVINO for inference. The importance of this feature lies in the fact that one of the major reasons behind OpenVINO's speedup is the optimized model file that it takes for inference. The underlying inference principle is still the same as most deep learning inference toolkits and languages, such as OpenCV. OpenCV's dnn module uses this speedup principle of OpenVINO by using it as the default backend for all inference tasks.

While it is possible to convert the model files into .xml and .bin files, it's not very easy. There are mainly two problems...

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