<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Flask on Infinite Script</title><link>https://www.infinitescript.com/tags/flask/</link><description>Recent content in Flask on Infinite Script</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Tue, 18 Feb 2020 23:23:00 +0000</lastBuildDate><atom:link href="https://www.infinitescript.com/tags/flask/index.xml" rel="self" type="application/rss+xml"/><item><title>DataNinja</title><link>https://www.infinitescript.com/project/dataninja/</link><pubDate>Tue, 18 Feb 2020 23:23:00 +0000</pubDate><guid>https://www.infinitescript.com/project/dataninja/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;&#10;&lt;p&gt;&lt;strong&gt;DataNinja&lt;/strong&gt; is a full-stack machine learning workbench designed for object detection tasks. It provides a browser-based interface to manage datasets, configure and train detection models, evaluate results, and, most distinctively, push trained models to embedded hardware in a single workflow.&lt;/p&gt;&#10;&lt;p&gt;The motivation behind the project was to bridge the gap between the server side (where GPUs live and models are trained) and the edge side (where inference actually runs, often on low-power embedded SoCs or FPGAs). Most open-source ML tools at the time handled one or the other; DataNinja treats them as a single, connected pipeline.&lt;/p&gt;</description></item></channel></rss>