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Tag: CNN

# TF Implementing a VGG-19 network in TensorFlow 2.0

Highlights: In this post we will show how to implement a fundamental Convolutional Neural Network like \(VGG-19\) in TensorFlow. The VGG-19 architecture was design by Visual Geometry Group, Department of Engineering Science, University of Oxford. It competed in the ImageNet Large Scale Visual Recognition Challenge in 2014. Tutorial Overview: Theory recapitulation Implementation in TensorFlow 1. Theory recapitulation With ConvNets becoming more of a popular in the computer vision field, a number of attempts have been made to improve the…
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#015 TF Implementing AlexNet in TensorFlow 2.0

Highlights: In this post we will show how to implement a fundamental Convolutional Neural Network \(AlexNet\) in TensorFlow 2.0. The AlexNet architecture is designed by Alex Krizhevsky and published with Ilya Sutskever and Geoffrey Hinton. It competed in the ImageNet Large Scale Visual Recognition Challenge in 2012. Tutorial Overview: Review of the Theory Implementation in TensorFlow 2.0 1. Review of the Theory Real life Computer Vision problems requires big amount of quality data to be trained on. In the past, people…
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#009 TF An implementation of a Convolutional Neural Network in tf.keras – MNIST dataset

In this post we will see how we can classify handwritten digits using Convolutional Neural Network implemented in TensorFlow 2.0. Required packages: Numpy Matplotlib Tensorflow Sklearn Seaborn Table of Contents: Load the digit dataset Implementing a Neural Network Visualization and Testing 1. Load the digit dataset Let start with importing all necessary libraries. After imports, we can use imported module to load mnist data. The load_data() function will automatically download and split our data into…
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#OD1 YOLO Object Detection

YOU ONLY LOOK ONCE Highlights: In this post we will learn about the YOLO Object Detection system, and how to implement such a system with Keras. About Yolo: Our unified architecture is extremely fast. Our base YOLO model processes imagesin real-time at 45 frames per second. A smaller version of the network, Fast YOLO,processes an astounding 155 frames per second … — You Only Look Once: Unified, Real-Time Object Detection, 2015 Tutorial Overview: This post…
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#FA 002 Face Detection with OpenCV in Images

Face detection represents the ability of a computer technology to locate peoples’s faces within digital images. Face detection applications employ algorithms focused on detecting human faces within larger images that also contain other objects such as landscapes, houses, cars and others. Table of Contents: Import required packages Select the network Preprocess the image, standardise, mean subtraction Process the image with a Neural Network Analyze detections The importance of face detection can be seen as: The…
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