<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tutorial on DeepLearning.Earth</title><link>https://deeplearning.earth/tags/tutorial/</link><description>Recent content in Tutorial on DeepLearning.Earth</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Wed, 24 Jun 2026 09:00:00 +0700</lastBuildDate><atom:link href="https://deeplearning.earth/tags/tutorial/index.xml" rel="self" type="application/rss+xml"/><item><title>Oriented object detection on macOS, in pure Python</title><link>https://deeplearning.earth/posts/2026-06-25_oriented_object_detection_on_macos_in_pure_python/</link><pubDate>Wed, 24 Jun 2026 09:00:00 +0700</pubDate><guid>https://deeplearning.earth/posts/2026-06-25_oriented_object_detection_on_macos_in_pure_python/</guid><description>If you&amp;rsquo;ve ever tried to detect ships in a harbor or planes on a runway from an aerial photo, you&amp;rsquo;ve probably hit the same wall: axis-aligned boxes are a poor fit. Objects in satellite and drone imagery sit at arbitrary angles. You want rotated boxes — oriented bounding boxes — not rectangles forced to align with the image edges.
For years, the go-to stack for this was MMRotate: powerful, but heavy.</description></item></channel></rss>