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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 traffic signs using a deep learning model (with PyTorch)

In this recipe, you will learn how to train a custom neural network from scratch using PyTorch and use the model's predictions to classify traffic signs. We shall use the German Traffic Sign Recognition Benchmark (GTSRB) dataset (https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html) as the input images for training/testing. The images are labeled with 43 different traffic signs. The dataset contains 39,209 training and 12,630 test images. For this recipe, it is also recommended that you use a computer with GPU(s) in it to make the training process faster.

Getting ready

First, download the compressed pickled dataset...

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