In last week’s blog post, you learned how to train a Convolutional Neural Network (CNN) with Keras. Today, we’re going to take this trained Keras model and deploy it to an iPhone and iOS app using what Apple has dubbed “CoreML”, an easy-to-use machine learning framework for Apple applications: To recap, thus far in this three-part series, […]
Keras and Convolutional Neural Networks (CNNs)
Today’s blog post is part two in a three-part series on building a complete end-to-end image classification + deep learning application: Part 1: How to (quickly) build a deep learning image dataset Part 2: Keras and Convolutional Neural Networks (today’s post) Part 3: Running a Keras model on iOS (to be published next week) By […]
How to (quickly) build a deep learning image dataset
When I was a kid, I was a huge Pokemon nerd. I collected the trading cards, played the Game Boy games, and watched the TV show. If it involved Pokemon, I was probably interested in it. Pokemon made a lasting impression on me — and looking back, Pokemon may have even inspired me to study […]
(Faster) Facial landmark detector with dlib
Back in September 2017, Davis King released v19.7 of dlib — and inside the release notes you’ll find a short, inconspicuous bullet point on dlib’s new 5-point facial landmark detector: Added a 5 point face landmarking model that is over 10x smaller than the 68 point model, runs faster, and works with both HOG and […]
An interview with Kwabena Agyeman, co-creator of OpenMV and microcontroller expert
After publishing last week’s blog post on reading barcodes with Python and OpenMV, I received a lot of emails from readers asking questions about embedded computer vision and how microcontrollers can be used for computer vision. Instead of trying to address these questions myself, I thought it would be best to bring in a true […]
An interview with David Austin: 1st place and $25,000 in Kaggle’s most popular image classification competition
In today’s blog post, I interview David Austin, who, with his teammate, Weimin Wang, took home 1st place (and $25,000) in Kaggle’s Iceberg Classifier Challenge. David and Weimin’s winning solution can be practically used to allow safer navigation for ships and boats across hazardous waters, resulting in less damages to ships and cargo, and most […]
My review of Microsoft’s data science virtual machine (DSVM) for deep learning
Over the past few months, I’ve been using Microsoft’s Ubuntu deep learning and data science virtual machine (DSVM) for a few projects I’m working on here at PyImageSearch. At first, I was a bit hesitant (and perhaps even a bit resistant) to giving it a try — I already have a pre-configured Amazon AWS deep […]
Reading barcodes with Python and OpenMV
What if I said that there’s a camera that: Is low cost at $65. Runs MicroPython. And can be expanded with shields just like an Arduino/RPi. Meet OpenMV! I met Kwabena Agyeman, the founder of OpenMV, during the PyImageSearch Gurus Kickstarter campaign in January 2015. At that time, Kwabena and the OpenMV team were running […]
Python, argparse, and command line arguments
Today we are going to discuss a fundamental developer, engineer, and computer scientist skill — command line arguments. Specifically, we’ll be discussing: What are command line arguments Why we use command line arguments How to parse command line arguments with Python Command line arguments are an elementary skill that you must learn how to use, especially […]
The 7 best deep learning books you should be reading right now
In today’s post I’m going to share with you the 7 best deep learning books (in no particular order) I have come across and would personally recommend you read. Some of these deep learning books are heavily theoretical, focusing on the mathematics and associated assumptions behind neural networks and deep learning. Other deep learning books […]