<?xml version="1.0" encoding="utf-8" standalone="yes"?><?xml-stylesheet type="text/xsl" href="https://itohi.com/feed.xsl"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Quantization on ITOHI — Open-Source Hardware, Embedded Systems &amp; DIY Builds</title><link>https://itohi.com/tags/quantization/</link><description>Recent content in Quantization on ITOHI — Open-Source Hardware, Embedded Systems &amp; DIY Builds</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>2022 @ ITOHI - All rights reserved</copyright><lastBuildDate>Mon, 13 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://itohi.com/tags/quantization/index.xml" rel="self" type="application/rss+xml"/><item><title>Training a Neural Network to Detect Shaheds and Quads from Audio</title><link>https://itohi.com/fpv/drone-detector-nn/</link><pubDate>Mon, 13 Jul 2026 00:00:00 +0000</pubDate><guid>https://itohi.com/fpv/drone-detector-nn/</guid><description>
&lt;p&gt;&lt;em&gt;Live demo: &lt;a href="https://drone-detector.sintra.site"&gt;drone-detector.sintra.site&lt;/a&gt;&lt;/em&gt;&lt;/p&gt;
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alt="Drone Audio Detector — live inference in browser, IDLE state. FP32 model, 10 classes, per-class calibrated thresholds. RMS −40.6 dB / Peak −18.2 dB from office ambient. Spectrogram shows dual ARGMAX/SOFTMAX output."
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&lt;p&gt;A note before the technical content: the staggered training protocol described in this article was developed with significant help from &lt;a href="https://sintra.ai"&gt;Sintra AI&lt;/a&gt;. What started as a series of questions about why the naive P1→P2 schedule kept plateauing turned into a structured debugging conversation that identified the calibrated checkpointing problem and shaped the cycle logic.&lt;/p&gt;</description></item></channel></rss>