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Computer Vision Projects with OpenCV and Python 3

You're reading from   Computer Vision Projects with OpenCV and Python 3 Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow

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Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789954555
Length 182 pages
Edition 1st Edition
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Author (1):
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Matthew Rever Matthew Rever
Author Profile Icon Matthew Rever
Matthew Rever
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Table of Contents (9) Chapters Close

Preface 1. Setting Up an Anaconda Environment FREE CHAPTER 2. Image Captioning with TensorFlow 3. Reading License Plates with OpenCV 4. Human Pose Estimation with TensorFlow 5. Handwritten Digit Recognition with scikit-learn and TensorFlow 6. Facial Feature Tracking and Classification with dlib 7. Deep Learning Image Classification with TensorFlow 8. Other Books You May Enjoy

Using Inception for image classification

In this section, we're going to use a pre-trained model, Inception, from Google to perform image classification. We'll then move on and build our own model—or, at least do some retraining on the model in order to train on our own images and classify our own objects.

For now, we want to see what we can do with a model that's already trained, which would take a lot of time to reproduce from scratch. Let's get started with the code.

Let's go back to Jupyter Notebook. The Notebook file can be found at https://github.com/PacktPublishing/Computer-Vision-Projects-with-OpenCV-and-Python-3/Chapter04.

In order to run the code, we're going to need to download a file from TensorFlow's website, from the following link: http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz. This is the...

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