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Python Image Processing Cookbook

You're reading from   Python Image Processing Cookbook Over 60 recipes to help you perform complex image processing and computer vision tasks with ease

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781789537147
Length 438 pages
Edition 1st Edition
Languages
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Author (1):
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Sandipan Dey Sandipan Dey
Author Profile Icon Sandipan Dey
Sandipan Dey
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Toc

Table of Contents (11) Chapters Close

Preface 1. Image Manipulation and Transformation 2. Image Enhancement FREE CHAPTER 3. Image Restoration 4. Binary Image Processing 5. Image Registration 6. Image Segmentation 7. Image Classification 8. Object Detection in Images 9. Face Recognition, Image Captioning, and More 10. Other Books You May Enjoy

Classifying images with VGG19/Inception V3/MobileNet/ResNet101 (with PyTorch)

In this recipe, you are going to learn how to use torchvision's pretrained (on Imagenet) deep learning models for a few famous models. ImageNet is an image database organized as per the WordNet hierarchy. Hundreds/thousands of images belong to each node in the hierarchy.

The following plot shows the top-1 accuracy achieved by a few popular deep neural nets participated in the ImageNet challenge, starting from AlexNet (Krizhevsky et al., 2012) on the far left, to the best performing Inception-v4 (Szegedy et al., 2016) on the far right:

The top-1 accuracy is defined as the average number of times the correct label for an image was the highest probability class predicted by the CNN for that image. At the other end of the scale, the top-1 error shows the error that occurs when the model-predicted...

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