A story about me competing against top-performing individuals and teams from domain-expert businesses, crunching 2.5Tb of dataset, figuring out how to deal with label noise, changing competition metric, and finally, winning.
The main objective of xView 2 challenge was to build a solution, capable of processing a pair of satellite images of same region (called hereinafter “pre” and “post” images) to detect buildings and classify level of damage dealt to them.
In this post I describe my solution to this problem and explain few know-how ideas, that resulted in a top-performing solution.
This article is a quintessence of my all experience I’ve got for years working as a computer vision consultant. I hope you will find this interesting and useful. My goal was to create set of rules I follow personally on daily basis.
Here’s an open-source ready to use bootstrap project written in Node.js that lets you to quickly build a REST service to host your image processing and computer vision code in a cloud environment. Please welcome: cloudcv-bootstrap.
While working on CloudCV I encountered problems in node.js addon written in native code. For CloudCV I use node.js with C++ Addon to separate high-performance algorithms (C++) from high-level networking API which node provides.
In this tutorial I’m going to reveal best practices on debugging C++ Addons for Node.js (0.12) using Visual Studio 2013.
Continue reading if you want to read in details why this works.