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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.