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Revision as of 16:40, 12 August 2020 by J-perdue (talk | contribs) (foss/2019b)
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"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 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


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

Modules used include:

(optional?) Graphviz/2.42.2-foss-2019b

Start with a clean module environment and install directory.

ml purge
rm -rf $SCRATCH/Detectron2-foss-2019b
Create and activate a Python VE to install into.
ml Python/3.7.4-GCCcore-8.3.0


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