Today’s blog post is inspired by a question from PyImageSearch reader, Leonard Bogdonoff. After I published my previous post on Face recognition with OpenCV and deep learning, Leonard wrote in and asked: Hey Adrian, can you go into identity clustering? I have a dataset of photos and I can’t seem to pinpoint how I would […]
Tag Archives | machine learning
Raspberry Pi Face Recognition
In last week’s blog post you learned how to perform Face recognition with Python, OpenCV, and deep learning. But as I hinted at in the post, in order to perform face recognition on the Raspberry Pi you first need to consider a few optimizations — otherwise, the face recognition pipeline would fall flat on its […]
Face recognition with OpenCV, Python, and deep learning
In today’s blog post you are going to learn how to perform face recognition in both images and video streams using: OpenCV Python Deep learning As we’ll see, the deep learning-based facial embeddings we’ll be using here today are both (1) highly accurate and (2) capable of being executed in real-time. To learn more about […]
How to build a custom face recognition dataset
In the next couple of blog posts we are going to train a computer vision + deep learning model to perform facial recognition… …but before we can train our model to recognize faces in images and video streams we first need to gather the dataset of faces itself. If you are already using a pre-curated […]
Keras: Multiple outputs and multiple losses
A couple weeks ago we discussed how to perform multi-label classification using Keras and deep learning. Today we are going to discuss a more advanced technique called multi-output classification. So, what’s the difference between the two? And how are you supposed to keep track of all these terms? While it can be a bit confusing, […]
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 […]