<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hrsc2016 on DeepLearning.Earth</title><link>https://deeplearning.earth/tags/hrsc2016/</link><description>Recent content in Hrsc2016 on DeepLearning.Earth</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Sun, 13 Sep 2026 16:00:00 +0700</lastBuildDate><atom:link href="https://deeplearning.earth/tags/hrsc2016/index.xml" rel="self" type="application/rss+xml"/><item><title>HRSC2016 in oriented-det — recipes, trains, and a held-out 90.41%</title><link>https://deeplearning.earth/posts/2026-09-13_hrsc2016_recipes_trains_and_results/</link><pubDate>Sun, 13 Sep 2026 16:00:00 +0700</pubDate><guid>https://deeplearning.earth/posts/2026-09-13_hrsc2016_recipes_trains_and_results/</guid><description>DOTA is the pretrain zoo. HRSC2016 is the small-data ship zoo.
Three oriented-det families now have native HRSC recipes and published 3× Hub weights: Oriented R-CNN 90.41%, Rotated Faster R-CNN 88.77%, Rotated FCOS 88.34% mAP50. Those numbers are make eval-val on ImageSets test. Test is not in training. That sentence is the whole point of this dataset in our stack — DOTA eval-val is leaky; HRSC eval-val is a real holdout.</description></item></channel></rss>