Sign Language Recognition: Hand Object detection u...

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

Getting started with Computer Vision for Vitis 202...

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. In this tutorial we will be covering

Improving Convolutional Neural Networks: The weakn...

Introduction In our previous tutorial series, we looked at sign language recognition using the sign language MNIST dataset based off the original 1999 MNIST dataset, which is considered the “Hello World” of machine learning. We did this because we wanted a practical example that could be trained on a standard

Getting started with Computer Vision for Vitis 202...

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:

Vitis AI using Tensorflow and Keras Tutorial Part ...

Part 9: Running our code on the DPU We now have our compiled model ready to run on our board. In this tutorial we will look at running our DPU and exploring the code that interacts with the DPU API. Introduction Getting Started Transforming Kaggle Data and Convolutional Neural Networks

Getting started with Computer Vision for Vitis 202...

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

Vitis AI using Tensorflow and Keras Tutorial Part ...

Part 8: Compiling our CNN We now have our complete model and must make it ready to be run on the FPGA. To do this, we must compile our model with the Vitis AI compiler which will convert and optimise our model into a format that is runnable on the

Vitis AI using Tensorflow and Keras Tutorial Part ...

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

Vitis AI using Tensorflow and Keras Tutorial part ...

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

Vitis AI using Tensorflow and Keras Tutorial Part ...

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

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