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Mastering Computer Vision with TensorFlow 2.x

You're reading from   Mastering Computer Vision with TensorFlow 2.x Build advanced computer vision applications using machine learning and deep learning techniques

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Product type Paperback
Published in May 2020
Publisher Packt
ISBN-13 9781838827069
Length 430 pages
Edition 1st Edition
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Author (1):
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Krishnendu Kar Krishnendu Kar
Author Profile Icon Krishnendu Kar
Krishnendu Kar
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Introduction to Computer Vision and Neural Networks
2. Computer Vision and TensorFlow Fundamentals FREE CHAPTER 3. Content Recognition Using Local Binary Patterns 4. Facial Detection Using OpenCV and CNN 5. Deep Learning on Images 6. Section 2: Advanced Concepts of Computer Vision with TensorFlow
7. Neural Network Architecture and Models 8. Visual Search Using Transfer Learning 9. Object Detection Using YOLO 10. Semantic Segmentation and Neural Style Transfer 11. Section 3: Advanced Implementation of Computer Vision with TensorFlow
12. Action Recognition Using Multitask Deep Learning 13. Object Detection Using R-CNN, SSD, and R-FCN 14. Section 4: TensorFlow Implementation at the Edge and on the Cloud
15. Deep Learning on Edge Devices with CPU/GPU Optimization 16. Cloud Computing Platform for Computer Vision 17. Other Books You May Enjoy

Object Detection Using YOLO

In the previous chapter, we discussed, in detail, the various neural network image classification and object detection architectures that utilize multiple steps for the object detection, classification, and refinement of a bounding box. In this chapter, we will be introducing two single-stage, fast object detection methods—You Only Look Once (YOLO) and RetinaNet. We will be discussing the architectures of each model and then perform inference in real images and videos using YOLO v3. We will show you how to optimize configuration parameters and train your own custom images using YOLO v3.

The topics covered in this chapter are as follows:

  • An overview of YOLO
  • An introduction to Darknet for object detection
  • Real-time prediction using Darknet and Tiny Darknet
  • Comparing YOLOs – YOLO versus YOLO v2 versus YOLO v3
  • When to train a model?
  • Training...
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