Archive | Deep Learning

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, […]

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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 […]

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Object detection with deep learning and OpenCV

A couple weeks ago we learned how to classify images using deep learning and OpenCV 3.3’s deep neural network ( dnn ) module. While this original blog post demonstrated how we can categorize an image into one of ImageNet’s 1,000 separate class labels it could not tell us where an object resides in image. In order to obtain […]

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Deep Learning with OpenCV

Two weeks ago OpenCV 3.3 was officially released, bringing with it a highly improved deep learning ( dnn ) module. This module now supports a number of deep learning frameworks, including Caffe, TensorFlow, and Torch/PyTorch. Furthermore, this API for using pre-trained deep learning models is compatible with both the C++ API and the Python bindings, making it […]

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ImageNet: VGGNet, ResNet, Inception, and Xception with Keras

A few months ago I wrote a tutorial on how to classify images using Convolutional Neural Networks (specifically, VGG16) pre-trained on the ImageNet dataset with Python and the Keras deep learning library. The pre-trained networks inside of Keras are capable of recognizing 1,000 different object categories, similar to objects we encounter in our day-to-day lives with high […]

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I’m writing a book on Deep Learning and Convolutional Neural Networks (and I need your advice).

You may have heard me mention it in a passing comment on the PyImageSearch blog… Maybe I even hinted at it in a 1-on-1 email… Or perhaps you simply saw the writing on the wall due to the recent uptick in Deep Learning/Neural Network tutorials here on the blog… But I’m here today to tell […]

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Installing Keras with TensorFlow backend

A few months ago I demonstrated how to install the Keras deep learning library with a Theano backend. In today’s blog post I provide detailed, step-by-step instructions to install Keras using a TensorFlow backend, originally developed by the researchers and engineers on the Google Brain Team. I’ll also (optionally) demonstrate how you can integrate OpenCV into […]

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Stochastic Gradient Descent (SGD) with Python

In last week’s blog post, we discussed gradient descent, a first-order optimization algorithm that can be used to learn a set of classifier coefficients for parameterized learning. However, the “vanilla” implementation of gradient descent can be prohibitively slow to run on large datasets — in fact, it can even be considered computationally wasteful. Instead, we should apply Stochastic […]

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