<?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 Constant Factor</title><link>https://0x1za.github.io/tags/computer-vision/</link><description>Recent content in Computer-Vision on The Constant Factor</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Thu, 11 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://0x1za.github.io/tags/computer-vision/index.xml" rel="self" type="application/rss+xml"/><item><title>Why your document parser's mAP is lying to you</title><link>https://0x1za.github.io/blog/cote-score/</link><pubDate>Thu, 11 Jun 2026 00:00:00 +0000</pubDate><guid>https://0x1za.github.io/blog/cote-score/</guid><description>Your document-layout model scores an mAP of 0.38. Is that good? Bad? Production-ready? You genuinely can&amp;rsquo;t tell — and that&amp;rsquo;s not your fault. The metric itself is the problem. This is a 10-minute tour of why, and of a small library that gives you an answer you can act on.
The metrics everyone already uses Train a layout model today and three numbers come out of the box, all inherited from general object detection:</description></item></channel></rss>