Today’s blog post on reading barcodes and QR codes with OpenCV is inspired by a question I received from PyImageSearch reader, Hewitt: Hey Adrian, I really love the PyImageSearch blog. I look forward to your emails each week. Keep doing what you’re doing. I have a question for you: Does OpenCV have any modules that […]
A gentle guide to deep learning object detection
Today’s blog post is inspired by PyImageSearch reader Ezekiel, who emailed me last week and asked: Hey Adrian, I went through your previous blog post on deep learning object detection along with the followup tutorial for real-time deep learning object detection. Thanks for those. I’ve been using your source code in my example projects but […]
Multi-label classification with Keras
Today’s blog post on multi-label classification with Keras was inspired from an email I received last week from PyImageSearch reader, Switaj. Switaj writes: Hi Adrian, thanks for the PyImageSearch blog and sharing your knowledge each week. I’m building an image fashion search engine and need help. Using my app a user will upload a photo […]
A fun, hands-on deep learning project for beginners, students, and hobbyists
Today’s blog post is a “bonus tutorial” in our most recent series on building a complete, end-to-end deep learning application: Part 1: How to (quickly) build a deep learning image dataset Part 2: Keras and Convolutional Neural Networks Part 3: Running Keras models on iOS with CoreML In this series we have been fulfilling a […]
Running Keras models on iOS with CoreML
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 […]