The next step is to start scrolling! Figure 4: Keep scrolling through the Google Image search results until the results are no longer relevant. This will enable you to execute JavaScript in a REPL-like manner. The next step is to use a tiny bit of JavaScript to gather the image URLs (which we can then download using Python later in this tutorial).įire up the JavaScript console (I’ll assume you are using the Chrome web browser, but you can use Firefox as well) by clicking View => Developer => JavaScript Console : Figure 2: Opening Google Chrome’s JavaScript Console from the menu bar prior to performing the hack.įrom there, click the Console tab: Figure 3: We will enter JavaScript in the Google Chrome JavaScript Console which is displayed in this figure. Santa Claus is visiting our computer screen!Īs you can see from the example image above we have our search results. In this case we’ll be using the query term “santa clause”: Figure 1: The first step to downloading images from Google Image Search is to enter your query and let the pictures load in your browser. The first step in using Google Images to gather training data for our Convolutional Neural Network is to head to Google Images and enter a query. I’m going to elaborate on these steps and provide further instructions on how you can use this technique to quickly gather training data for deep learning models using Google Images, JavaScript, and a bit of Python. RBM’s team developed the code update for this blog post. A “Real Big” thanks goes out to my friends and JavaScript experts at RealBigMarketing. I thank Michael for the original inspiration of this blog post. Updated April 20, 2020: Michael’s method no longer works with updates to both web browsers and the HTML/CSS used by Google Images to serve search results. He discussed the exact same technique I’m about to share with you in a blog post of his earlier this year. The method I’m about to share with you for gathering Google Images for deep learning is from a fellow deep learning practitioner and friend of mine, Michael Sollami. Let’s go ahead and get started! Using Google Images for training data and machine learning models Part #3: Deploy our trained deep learning model to the Raspberry Pi.Part #2: Train our Not Santa detector using deep learning, Python, and Keras.Part #1: Gather Santa Clause training data using Google Images (this post).In order to keep the magic of ole’ Saint Nicholas alive, we’re going to spend the next three blog posts building our Not Santa detector using deep learning: Without him I don’t think this time of year would mean as much to me (and I certainly wouldn’t be the person I am today). Looking back on my childhood, my dad always went out well of his way to ensure Christmas was a magical time. Today’s blog post is part one of a three part series on a building a Not Santa app, inspired by the Not Hotdog app in HBO’s Silicon Valley (Season 4, Episode 4).Īs a kid Christmas time was my favorite time of the year - and even as an adult I always find myself happier when December rolls around. Looking for the source code to this post? Jump Right To The Downloads Section Deep learning and Google Images for training data Updated April 20, 2020: The JavaScript in this post has been updated because the previous method was no-longer working. In the remainder of today’s blog post I’ll be demonstrating how you can use Google Images to quickly (and easily) gather training data for your deep learning models. So is there a way to leverage the power of Google Images to quickly gather training images and thereby cut down on the time it takes to build your dataset? How in the world do you gather enough images when training deep learning models?ĭeep learning algorithms, especially Convolutional Neural Networks, can be data hungry beasts.Īnd to make matters worse, manually annotating an image dataset can be a time consuming, tedious, and even expensive process. José has a point - without enough training data, your deep learning and machine learning models can’t learn the underlying, discriminative patterns required to make robust classifications. I’m confident I can solve this project, but I need more data. I’ve found a small dataset (~100 images per class), but my models are quick to overfit and far from accurate. I’m working on a project where I need to classify the scenes of outdoor photographs into four distinct categories: cities, beaches, mountains, and forests. This is by far the best resource I’ve seen for deep learning. Hey Adrian, thanks for putting together Deep Learning for Computer Vision with Python.
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