<?xml-stylesheet type="text/xsl" href="https://community.element14.com/cfs-file/__key/system/syndication/rss.xsl" media="screen"?><rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:slash="http://purl.org/rss/1.0/modules/slash/" xmlns:wfw="http://wellformedweb.org/CommentAPI/"><channel><title>PYNQ-Z2 Dev Kit - CIFAR-10 Convolutional Neural Network</title><link>/products/roadtest/b/blog/posts/pynq-z2-dev-kit---cifar-10-convolutional-neural-network</link><description>The first neural network implementation that I&amp;#39;m going to look at is for CIFAR-10 (Canadian Institute For Advanced Research). CIFAR-10 is a computer vision dataset used to train and test neural networks for object recognition. The CIFAR-1...</description><dc:language>en-US</dc:language><generator>Telligent Community 12</generator><item><title>RE: PYNQ-Z2 Dev Kit - CIFAR-10 Convolutional Neural Network</title><link>https://community.element14.com/products/roadtest/b/blog/posts/pynq-z2-dev-kit---cifar-10-convolutional-neural-network</link><pubDate>Tue, 06 Apr 2021 09:42:38 GMT</pubDate><guid isPermaLink="false">93d5dcb4-84c2-446f-b2cb-99731719e767:f9c9fc2f-28ef-4bac-9a33-92e9ff707b2d</guid><dc:creator>mohitajais</dc:creator><slash:comments>0</slash:comments><description>&lt;p&gt;This is really very interesting. I am interested in doing its real time implementation using cifar10 dataset. I want to know how should I&amp;nbsp; integrate base overlay and cnv overlay using vivado IP integrator for real time implementation using PYNQ board&lt;/p&gt;&lt;img src="https://community.element14.com/aggbug?PostID=7545&amp;AppID=14&amp;AppType=Weblog&amp;ContentType=0" width="1" height="1"&gt;</description></item><item><title>RE: PYNQ-Z2 Dev Kit - CIFAR-10 Convolutional Neural Network</title><link>https://community.element14.com/products/roadtest/b/blog/posts/pynq-z2-dev-kit---cifar-10-convolutional-neural-network</link><pubDate>Tue, 13 Aug 2019 11:04:18 GMT</pubDate><guid isPermaLink="false">93d5dcb4-84c2-446f-b2cb-99731719e767:f9c9fc2f-28ef-4bac-9a33-92e9ff707b2d</guid><dc:creator>dubbie</dc:creator><slash:comments>0</slash:comments><description>&lt;p&gt;This was a very interesting blog. Having done some work with Artificial Neural Networks (but only a very little) I could understand the issues involved. It was also fun to see that this example would recognise cats. For my CatDogFox &lt;a class="jive-link-blog-small" href="/challengesprojects/project14/remote-monitoring-control-devices/b/blog/posts/catdogfoxbot-6-trying-out-an-artificial-neural-network"&gt;CatDogFoxBot #6 : Trying out an Artificial Neural Network&lt;/a&gt;&amp;nbsp; Project 14 Remote Monitoring and Control activity I have used a much simpler ANN and set of images to try and recognise a cat using data from a GridEye thermopile array sensor. It works, sort of, but as you indicated, more training and more data should produce a better result.&lt;/p&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;Dubbie&lt;/p&gt;&lt;img src="https://community.element14.com/aggbug?PostID=7545&amp;AppID=14&amp;AppType=Weblog&amp;ContentType=0" width="1" height="1"&gt;</description></item></channel></rss>