<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Architecture on DeepLearning.Earth</title><link>https://deeplearning.earth/tags/architecture/</link><description>Recent content in Architecture on DeepLearning.Earth</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><lastBuildDate>Mon, 02 Jan 2023 15:11:48 +0000</lastBuildDate><atom:link href="https://deeplearning.earth/tags/architecture/index.xml" rel="self" type="application/rss+xml"/><item><title>How to choose a deep learning architecture to detect aircrafts in satellite imagery?</title><link>https://deeplearning.earth/posts/2023-01-02_how-to-choose-a-deep-learning-architecture-to-detect-aircrafts-in-satellite-imagery/</link><pubDate>Mon, 02 Jan 2023 15:11:48 +0000</pubDate><guid>https://deeplearning.earth/posts/2023-01-02_how-to-choose-a-deep-learning-architecture-to-detect-aircrafts-in-satellite-imagery/</guid><description>Context Link to heading In recent years, artificial intelligence has made great strides in the field of computer vision. One area that has seen particularly impressive progress is object detection, with a variety of deep learning models achieving high levels of accuracy. However, this abundance of choice can be overwhelming for practitioners who are looking to implement an object detection system.
On top of this, most public models and academic research are benchmarked on COCO which are dataset made of photographs.</description></item></channel></rss>