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Deep Learning for Computer Vision

You're reading from   Deep Learning for Computer Vision Expert techniques to train advanced neural networks using TensorFlow and Keras

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788295628
Length 310 pages
Edition 1st Edition
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Author (1):
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Rajalingappaa Shanmugamani Rajalingappaa Shanmugamani
Author Profile Icon Rajalingappaa Shanmugamani
Rajalingappaa Shanmugamani
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Table of Contents (12) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Image Classification 3. Image Retrieval 4. Object Detection 5. Semantic Segmentation 6. Similarity Learning 7. Image Captioning 8. Generative Models 9. Video Classification 10. Deployment 11. Other Books You May Enjoy

Developing real-world applications

Recognizing cats and dogs is a cool problem but less likely a problem of importance. Real-world applications of image classification used in products may be different. You may have different data, targets, and so on. In this section, you will learn the tips and tricks to tackle such different settings. The factors that should be considered when approaching a new problem are as follows:

  • The number of targets. Is it a 10 class problem or 10,000 class problem?
  • How vast is the intra-class variance? For example, does the different type of cats have to be identified under one class label?
  • How vast is the inter-class variance? For example, do the different cats have to be identified?
  • How big is the data?
  • How balanced is the data? 
  • Is there already a model that is trained with a lot of images?
  • What is the requisite for deployment inference...
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