<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Computer-Vision on The 3TC</title><link>https://the-3tc.github.io/tags/computer-vision/</link><description>Recent content in Computer-Vision on The 3TC</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Fri, 23 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://the-3tc.github.io/tags/computer-vision/index.xml" rel="self" type="application/rss+xml"/><item><title>The MOTe error: Decomposable evaluation for Document Layout Analysis</title><link>https://the-3tc.github.io/blog/mote-error/</link><pubDate>Thu, 11 Jun 2026 00:00:00 +0000</pubDate><guid>https://the-3tc.github.io/blog/mote-error/</guid><description>&lt;link rel="stylesheet" href="https://the-3tc.github.io/css/mote.css"&gt;
&lt;p&gt;When using Document Layout Analysis (DLA) models, model performance is usually given as the mean Average Precision across the images in the test dataset. This value is somewhere between 0.0 and 1.0, with 1.0 being a perfect match with the ground truth.&lt;/p&gt;
&lt;p&gt;But is a score of 0.9 good? What about 0.8, or 0.7? What if the &lt;strong&gt;kind&lt;/strong&gt; of error being made impacts tasks being performed later in the pipeline? These questions are not something the mAP and its underlying metrics, the F1 and IoU (Intersection over Union), are able to answer. And start pointing to the more fundemental question &amp;ldquo;&lt;strong&gt;What does good mean?&lt;/strong&gt;&amp;rdquo;.&lt;/p&gt;</description></item></channel></rss>