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Hands-On Vision and Behavior for Self-Driving Cars

You're reading from   Hands-On Vision and Behavior for Self-Driving Cars Explore visual perception, lane detection, and object classification with Python 3 and OpenCV 4

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781800203587
Length 374 pages
Edition 1st Edition
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Authors (2):
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Krishtof Korda Krishtof Korda
Author Profile Icon Krishtof Korda
Krishtof Korda
Luca Venturi Luca Venturi
Author Profile Icon Luca Venturi
Luca Venturi
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: OpenCV and Sensors and Signals
2. Chapter 1: OpenCV Basics and Camera Calibration FREE CHAPTER 3. Chapter 2: Understanding and Working with Signals 4. Chapter 3: Lane Detection 5. Section 2: Improving How the Self-Driving Car Works with Deep Learning and Neural Networks
6. Chapter 4: Deep Learning with Neural Networks 7. Chapter 5: Deep Learning Workflow 8. Chapter 6: Improving Your Neural Network 9. Chapter 7: Detecting Pedestrians and Traffic Lights 10. Chapter 8: Behavioral Cloning 11. Chapter 9: Semantic Segmentation 12. Section 3: Mapping and Controls
13. Chapter 10: Steering, Throttle, and Brake Control 14. Chapter 11: Mapping Our Environments 15. Assessments 16. Other Books You May Enjoy

A bigger model

Training your own neural network is an art; you need intuition, some luck, a lot of patience, and all the knowledge and help that you can find. You will also need money and time to either buy a faster GPU, use clusters to test more configurations, or pay to get a better dataset.

But there are no real recipes. That said, we will divide our journey into two phases, as explained in Chapter 5, Deep Learning Workflow:

  • Overfitting the training dataset
  • Improving generalization

We will start from where we left off in Chapter 4, Deep Learning with Neural Networks, with our basic model reaching 66% validation accuracy on CIFAR-10, and then we will improve it significantly, first to make it faster, and then to make it more precise.

The starting point

The following is the model that we developed in Chapter 4, Deep Learning with Neural Networks, a model that overfits the dataset because it achieves a high training accuracy value at relatively low validation...

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