Visual Analytics

Object Detection from 9 FPS to 650 FPS in 6 Steps

September 30, 2020
Visual Analytics

Making code run fast on GPUs requires a very different approach to making code run fast on CPUs because the hardware architecture is fundamentally different. Machine learning engineers of all kinds should care about squeezing performance from their models and hardware — not just for production purposes, but also for research and training. In research as in development, a fast iteration loop leads to faster improvement. This article is a practical deep dive into making a specific deep learning model (Nvidia’s SSD300) run fast on a powerful GPU server, but the general principles apply to all GPU programming.

A Simple and Flexible Pytorch Video Pipeline

September 23, 2020
Visual Analytics

Taking machine learning models into production for video analytics doesn’t have to be hard. A pipeline with reasonable efficiency can be created very quickly just by plugging together the right libraries. In this post we’ll create a video pipeline with a focus on flexibility and simplicity using two main libraries: Gstreamer and Pytorch.


© Paul Bridger 2020