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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

Detecting objects using TensorFlow on Google Cloud

The following instruction describes how to use the TensorFlow object detection API on Google Cloud to detect an object. In order to do that, you must have a Gmail and Google Cloud account. Depending on the region, Google Cloud can give free access for a limited time once you submit your credit card information. The exercise listed here should be covered in this free access. Follow the following steps to create a virtual machine (VM) instance in Google Cloud Console. A VM is needed to run the TensorFlow object detection API and make an inference with it:

  1. Log in to your Gmail account and go to https://cloud.google.com/solutions/creating-object-detection-application-tensorflow.
  1. Create a project as shown in the screenshot below. Here, R-CNN-trainingpack is the name of my project. Your project name will likely be different.
  2. Follow...
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