Gain insights into image data processing and CNNs for image classification using TensorFlow. Delve into CNN architectures and their applications, requiring Python and TensorFlow knowledge.

irml.tar.gz

image-rec

If you want to dive into the technology behind computer vision and self driving cars, this is where to start.

In this course you'll learn how to process data from image files and create convolutional neural networks (CNNs) to classify different types of images. After completing this course, you will have a solid understanding of why CNNs work and which CNN architectures to use for different tasks.

The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites. 

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Image Recognition with Machine Learning

<h2 class="section-title">Chapter Goals:</h2>
<ul>
	<li>Be able to resize pixel data when required</li>
	<li>Understand how resizing works in TensorFlow</li>
</ul>


<h2>A. Basic resizing</h2>

<div id="section1_l3" class="collapse show">
<p>The function we use for resizing pixel data is <code><a href="https://www.tensorflow.org/api_docs/python/tf/image/resize" target="_blank">tf.image.resize</a></code>. It takes in two required arguments: the original image's decoded data and the new size of the image, which is a tuple/list of two integers representing <code>new_height</code> and <code>new_width</code>, in that order.</p>


import tensorflow as tf

with tf.compat.v1.Session() as sess:
    print('Original: {}'.format(
        repr(sess.run(decoded_image))))  # Decoded image data
    resized_img = tf.image.resize(decoded_image, (3, 2))
    print('Resized: {}'.format(
        repr(sess.run(resized_img))))

<p>The function compresses or expands the image (depending on the relationship between the new image dimensions and the old image dimensions) and then returns the pixel data for the resized image, with the same number of channels. Note that if the resized dimensions don't match the same <a href="https://en.wikipedia.org/wiki/Aspect_ratio_(image)" target="_blank">aspect ratio</a> as the original dimensions, the new image data will be distorted.</p>
<p>In the example above, the original image's dimensions were 3x3, while the resized dimensions were 3x2. This resulted in the image data becoming distorted.</p>
</div>


<h2>Chapter Goals:</h2>
<ul>
	<li>Be able to resize pixel data when required</li>
	<li>Understand how resizing works in TensorFlow</li>
</ul>


<h2>A. Basic resizing</h2>

<div id="section1_l3">
<p>The function we use for resizing pixel data is <code><a href="https://www.tensorflow.org/api_docs/python/tf/image/resize" target="_blank">tf.image.resize</a></code>. It takes in two required arguments: the original image's decoded data and the new size of the image, which is a tuple/list of two integers representing <code>new_height</code> and <code>new_width</code>, in that order.</p>


Use TensorFlow to resize images with a variety of different image scaling algorithms.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Resizing

Chapter Goals:

A. Basic resizing