Hprc banner tamu.png

SW:R-CNN

From TAMU HPRC
Revision as of 16:10, 12 August 2020 by J-perdue (talk | contribs) (Installing Detectron2 in a Python virtual environment on HPRC clusters)
Jump to: navigation, search

"Region Based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision and specifically object detection." -- Wikipedia

The page above mentions a number of packages available for using R-CNNs. For now, this page will concentrate on Detectron2.

Detectron2

"Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark." --Detectron2 site

It also includes support for Fast R-CNN, Faster R-CNN and other R-CNNs.

See the Directron2 site for using and training. For now, this page will only cover installation.

Installing Detectron2 in a Python virtual environment on HPRC clusters

foss/2019b

This is a basic/starter build. Note that this build does not include a CUDA-enabled OpenMPI so is limited to the CPUs/GPUs on a single node.

Modules used include:

Make/3.15.3-GCCcore-8.3.0
Python/3.7.4-GCCcore-8.3.0


fosscuda/2018b

This build includes a CUDA-enabled OpenMPI for using multiple GPU nodes to speed up processing.