A few weeks ago I demonstrated how to perform real-time object detection using deep learning and OpenCV on a standard laptop/desktop. After the post was published I received a number of emails from PyImageSearch readers who were curious if the Raspberry Pi could also be used for real-time object detection. The short answer is “kind […]
Optimizing OpenCV on the Raspberry Pi
This tutorial is meant for advanced Raspberry Pi users who are looking to milk every last bit of performance out of their Pi for computer vision and image processing using OpenCV. I’ll be assuming: You have worked through my previous Raspberry Pi + OpenCV install tutorials (ideally multiple times). You are comfortable with the command […]
Deep learning on the Raspberry Pi with OpenCV
I’ve received a number of emails from PyImageSearch readers who are interested in performing deep learning in their Raspberry Pi. Most of the questions go something like this: Hey Adrian, thanks for all the tutorials on deep learning. You’ve really made deep learning accessible and easy to understand. I have a question: Can I do […]
macOS for deep learning with Python, TensorFlow, and Keras
In today’s tutorial, I’ll demonstrate how you can configure your macOS system for deep learning using Python, TensorFlow, and Keras. This tutorial is the final part of a series on configuring your development environment for deep learning. I created these tutorials to accompany my new book, Deep Learning for Computer Vision with Python; however, you […]
Setting up Ubuntu 16.04 + CUDA + GPU for deep learning with Python
Welcome back! This is the fourth post in the deep learning development environment configuration series which accompany my new book, Deep Learning for Computer Vision with Python. Today, we will configure Ubuntu + NVIDIA GPU + CUDA with everything you need to be successful when training your own deep learning networks on your GPU. Links […]
Configuring Ubuntu for deep learning with Python
When it comes to learning new technology such as deep learning, configuring your development environment tends to be half the battle. Different operating systems, hardware, dependencies, and the actual libraries themselves can lead to many headaches before you’re even able to get started studying deep learning. These issues are further compounded by the speed of […]
Getting started with Deep Learning for Computer Vision with Python
This blog post is intended for readers who have purchased a copy of my new book, Deep Learning for Computer Vision with Python. Inside this tutorial you’ll learn how to: Download the books, code, datasets, and any extras associated with your purchase. Obtain your email receipt and invoice. Access the companion website associated with Deep Learning for […]
Your deep learning + Python Ubuntu virtual machine
When it comes to working with deep learning + Python I highly recommend that you use a Linux environment. Deep learning tools can be more easily configured and installed on Linux, allowing you to develop and run neural networks quickly. Of course, configuring your own deep learning + Python + Linux development environment can be […]
Pre-configured Amazon AWS deep learning AMI with Python
The Ubuntu VirtualBox virtual machine that comes with my book, Deep Learning for Computer Vision with Python, includes all the necessary deep learning and computer vision libraries you need (such as Keras, TensorFlow, scikit-learn, scikit-image, OpenCV, etc.) pre-installed. However, while the deep learning virtual machine is easy to use, it also has a number of drawbacks, […]
Real-time object detection with deep learning and OpenCV
Today’s blog post was inspired by PyImageSearch reader, Emmanuel. Emmanuel emailed me after last week’s tutorial on object detection with deep learning + OpenCV and asked: “Hi Adrian, I really enjoyed last week’s blog post on object detection with deep learning and OpenCV, thanks for putting it together and for making deep learning with OpenCV […]