In this tutorial, you will learn how to optimally tune dlib’s shape predictor hyperparameters and options to obtain a shape predictor that balances speed, accuracy, and model size. Today is part two in our two-part series on training custom shape predictors with dlib: Part #1: Training custom dlib shape predictors (last week’s tutorial) Part #2: Tuning […]
Search results for "Cloud Computing"
Remote development on the Raspberry Pi (or Amazon EC2)
In this blog post, you will learn how to perform remote development on a Raspberry Pi (or Amazon EC2 server). You will discover how to configure your host machine to connect to these systems and remotely perform Python development. Today’s tutorial is actually a chapter for my upcoming book, Raspberry Pi for Computer Vision. As […]
Auto-Keras and AutoML: A Getting Started Guide
In this tutorial, you will learn how to use Auto-Keras, an open source alternative to Google’s AutoML, for automated machine learning and deep learning. When training a neural network on a dataset there are two primary objectives a deep learning practitioner is trying to optimize and balance: Defining a neural network architecture that lends itself […]
Deep Learning and Medical Image Analysis with Keras
In this tutorial, you will learn how to apply deep learning to perform medical image analysis. Specifically, you will discover how to use the Keras deep learning library to automatically analyze medical images for malaria testing. Such a deep learning + medical imaging system can help reduce the 400,000+ deaths per year caused by malaria. […]
OpenCV OCR and text recognition with Tesseract
By Adrian Rosebrock on September 17, 2018 in Deep Learning, Optical Character Recognition (OCR), Tutorials
In this tutorial, you will learn how to apply OpenCV OCR (Optical Character Recognition). We will perform both (1) text detection and (2) text recognition using OpenCV, Python, and Tesseract. A few weeks ago I showed you how to perform text detection using OpenCV’s EAST deep learning model. Using this model we were able to […]
How to plot accuracy and loss with mxnet
When it comes to high-performance deep learning on multiple GPUs (and not to mention, multiple machines) I tend to use the mxnet library. Part of the Apache Incubator, mxnet is a flexible, efficient, and scalable library for deep learning (Amazon even uses it in their own in-house deep learning). Inside the ImageNet Bundle of my […]
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 […]
OpenCV Track Object Movement
This past Saturday, I was caught in the grips of childhood nostalgia, so I busted out my PlayStation 1 and my original copy of Final Fantasy VII. As a kid in late middle school/early high school, I logged 70+ hours playing through this heartbreaking, inspirational, absolute masterpiece of an RPG. As a kid in middle […]
Zero-parameter, automatic Canny edge detection with Python and OpenCV
Today I’ve got a little trick for you, straight out of the PyImageSearch vault. This trick is really awesome — and in many cases, it completely alleviates the need to tune the parameters to your Canny edge detectors. But before we get into that, let’s discuss the Canny edge detector a bit. Looking for the source code […]
Image Pyramids with Python and OpenCV
It’s too damn cold up in Connecticut — so cold that I had to throw in the towel and escape for a bit. Last week I took a weekend trip down to Orlando, FL just to escape. And while the weather wasn’t perfect (mid-60 degrees Fahrenheit, cloudy, and spotty rain, as you can see from the […]