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AI, Artificial Intelligence, computervision, Convolutional Neural Networks, datascience, deep learning, deeptech, embeddedvision, Neural Networks

14 September 2020

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Sign Language Recognition: Hand Object detection using R-CNN and YOLO

For our introduction to neural networks on FPGAs, we used a variation on the MNIST dataset made for sign language recognition. It keeps the same 28×28 greyscale image style used by the MNIST dataset released in 1999. As we noted in our previous article though, this dataset is very limiting and when trying to apply […]

#embeddedsystems, deeptech, embedded, embeddedvision, fpga, linux, tutorial, vitis, Vitis Vision, xilinx

21 August 2020

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Getting started with Computer Vision for Vitis 2020.1 on Zynq: Part 2 Automation and Scripts

Welcome to the 2020.1 version of getting started with computer vision on Vitis on Zynq. The release of 2020.1 saw significant changes from the old 2019.2 version and we thought it would be useful to update this tutorial to reflect the newer version. We will be covering two different methods: a quick start way and […]

#embeddedsystems, computervision, deeptech, embedded, embeddedvision, fpga, getting started, linux, opencv, software, tutorial, vitis, xilinx

12 August 2020

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Getting started with Computer Vision for Vitis 2020.1 on Zynq: Part 1 Quick Start

Welcome to the 2020.1 version of getting started with computer vision on Vitis on Zynq. The release of 2020.1 saw significant changes from the old 2019.2 version and we thought it would be useful to update this tutorial to reflect the newer version. We will even be covering two different methods: a quick start way […]

AI, Artificial Intelligence, computervision, Convolutional Neural Networks, deeptech, embeddedvision, fpga, Keras, machine learning, Neural Networks, TensorFlow, tutorial, vitis, Vitis AI, xilinx

1 August 2020

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Vitis AI using Tensorflow and Keras Tutorial Part 7

Part 7: Quantising our graph In our previous tutorial we produced our frozen model so now we can optimise it to make it run on our FPGA hardware efficiently, which we can do through quantisation. Quantisation is the process of reducing the number of bits used for our tensors and weights, but exactly why is […]

AI, Artificial Intelligence, computervision, Convolutional Neural Networks, deeptech, embeddedvision, fpga, Keras, machine learning, Neural Networks, TensorFlow, tutorial, vitis, Vitis AI, xilinx

23 July 2020

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Vitis AI using Tensorflow and Keras Tutorial part 6

Part 6: Converting and Freezing our CNN Now we have built a more optimal CNN by handling both under-fitting and over-fitting, we can begin the process of deploying our model on the FPGA itself. The first step in this process is to convert our Keras model to a TensorFlow model and then we need to […]

AI, Artificial Intelligence, Convolutional Neural Networks, deeptech, embedded, fpga, Keras, machine learning, Neural Networks, TensorFlow, tutorial, vitis, Vitis AI, xilinx

16 July 2020

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Vitis AI using Tensorflow and Keras Tutorial Part 5

Part 5: Optimising our CNN In our previous section, we both trained our network on a training set and tested it on a testing set and our accuracy on the training set (0.972) was higher than on our testing set (0.922). In an ideal design the training set should have the same accuracy as the […]

AI, Artificial Intelligence, computervision, Convolutional Neural Networks, deep learning, deeptech, embeddedvision, fpga, Keras, machine learning, TensorFlow, tutorial, vitis, Vitis AI, xilinx

10 July 2020

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Vitis AI using Tensorflow and Keras Tutorial part 4

Part 4: Training the neural network Welcome to Part 4 of our tutorial where we will be focused on training the neural network we built in the previous section Introduction Getting Started Transforming Kaggle Data and Convolutional Neural Networks (CNNs) Training our Neural Network (Current) Optimising our neural network Converting and Freezing our CNN Quanitising […]

Vitis AI using TensorFlow and Keras Tutorial Part 3

Part 3: Extracting Kaggle data and building the Convolutional Neural Network (CNN) Welcome to Part 3 of our tutorial where we will be focused on how to extract our data from the Kaggle set and building our Convolutional Neural Network. Introduction Getting Started Transforming Kaggle Data and Convolutional Neural Networks (CNNs) (Current) Training the neural […]

AI, Artificial Intelligence, computervision, Convolutional Neural Networks, deeptech, embedded, fpga, Keras, Neural Networks, TensorFlow, tutorial, vitis, Vitis AI, xilinx

19 June 2020

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Vitis AI using TensorFlow and Keras Tutorial Part 1

Part 1: Introduction This the first part in our multi-part tutorial on using Vitis AI with Tensorflow and Keras. Other parts of the tutorial can be found here: Introduction (here) Getting Started Transforming Kaggle Data and Convolutional Neural Networks (CNNs) Training the neural network Optimising our neural network Converting and Freezing our CNN Quanitising our […]

AI, Artificial Intelligence, computervision, Convolutional Neural Networks, datascience, deeptech, industry, Neural Networks, tech, technology

7 June 2020

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What is Computer Vision and why are Neural Networks important?

In a nutshell, computer vision is the ability for computers to understand the world around them using visual data. In many ways, we take for granted just how complex computer vision is because our primary sense as humans is sight. Replicating a human’s visual system has proven to be a very difficult challenge and something […]

Sign Language Recognition: Hand Object detection using R-CNN and YOLO

Getting started with Computer Vision for Vitis 2020.1 on Zynq: Part 2 Automation and Scripts

Getting started with Computer Vision for Vitis 2020.1 on Zynq: Part 1 Quick Start

Vitis AI using Tensorflow and Keras Tutorial Part 7

Vitis AI using Tensorflow and Keras Tutorial part 6

Vitis AI using Tensorflow and Keras Tutorial Part 5

Vitis AI using Tensorflow and Keras Tutorial part 4

Vitis AI using TensorFlow and Keras Tutorial Part 3

Vitis AI using TensorFlow and Keras Tutorial Part 1

What is Computer Vision and why are Neural Networks important?

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