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Tag: tutorial

# 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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#000 How to access and edit pixel values in OpenCV with Python?

Highlight: Welcome to another datahacker.rs post series! We are going to talk about digital image processing using OpenCV in Python. In this series, you will be introduced to the basic concepts of OpenCV and you will be able to start writing your first scripts in Python. Our first post will provide you with an introduction to the OpenCV library and some basic concepts that are necessary for building your computer vision applications. You will learn…
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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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#012 TF Transfer Learning in TensorFlow 2.0

Highlights: In this post we are going to show how to build a computer vision model without building it from scratch. The idea behind transfer learning is that a neural network that has been trained on a large dataset can apply its knowledge to a dataset that it has never seen before. That is, why it’s called a transfer learning; we transfer the learning of an existing model to a new dataset. Tutorial Overview: Introduction Transfer…
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#011 TF How to improve the model performance with Data Augmentation?

Highlights: In this post we will show the benefits of data augmentation techniques as a way to improve performance of a model. This method will be very beneficial when we do not have enough data at our disposal. Tutorial Overview: Training without data augmentation What is data augmentation? Training with data augmentation Visualization 1. Training without data augmentation A familiar question is “why should we use data augmentation?”. So, let’s see the answer. In order…
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