<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>Annotation - Tag - vo.rs</title><link>https://vo.rs/tags/annotation/</link><description>Annotation - Tag - vo.rs</description><generator>Hugo -- gohugo.io</generator><language>en</language><copyright>This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</copyright><lastBuildDate>Thu, 25 Dec 2025 10:00:00 +0000</lastBuildDate><atom:link href="https://vo.rs/tags/annotation/" rel="self" type="application/rss+xml"/><item><title>Label Studio: Self-Hosted Data Annotation for Training Your Own Models</title><link>https://vo.rs/story/label-studio-self-hosted-data-annotation-for-training-your-own-models/</link><description>&lt;p&gt;There&amp;rsquo;s a comforting lie in machine learning circles that the model is the hard part. It isn&amp;rsquo;t. The model is the bit with the nice papers and the GitHub stars. The hard part — the part that determines whether your classifier works or quietly humiliates you in production — is the labels. Garbage labels, garbage model, no exceptions. And labelling is tedious, error-prone, and almost always done in some horror of a spreadsheet that loses your work when the browser crashes.&lt;/p&gt;</description><pubDate>Thu, 25 Dec 2025 10:00:00 +0000</pubDate></item></channel></rss>