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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

Retraining the captioning model

So, now that we have seen image captioning code in action, we are going to retrain the image captioner on our own desired data. However, we need to know that it will be very time consuming and will need over 100 GB of hard drive space for computations if we want it to process in a reasonable time. Even with a good GPU, it may take a few days or a week to complete the computation. Since we are inclined toward implementing it and have the resources, let's start retraining the model.

In the Notebook, the first step is to download the pre-trained Inception model. The webbrowser module will make it easy to open the URL and to download the file:

# First download pretrained Inception (v3) model

import webbrowser
webbrowser.open("http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz")

# Completely unzip tar.gz file to get inception_v3...
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