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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.

Note that these instructions are for building from source using a Python vitual environment so we can get optimizations for the current machine. [b]We explicitly do NOT use Anaconda (Python for children) which uses precompiled binaries for CPU architectures from over a decade ago.[/b]


We'll start with single-node (no MPI) Detectron, the predecessor to Detecron2, since we've successfully built it on ada (terra test to come). The instructions for Detectron2 (not written/tested) will be added later.

These steps come from the Detectron's INSTALL.md and from the Caffe2 instructions for building from source.

Download, via Git, the needed sources. Note: we used the system git here (no module), but if you have problems you may try loading a Git module.

mkdir $SCRATCH/tmp
cd $SCRATCH/tmp
git clone https://github.com/facebookresearch/Detectron.git
git clone https://github.com/pytorch/pytorch.git # for caffe2
cd pytorch
git submodule update --init --recursive

Clean the module environment and install directory.

ml purge
rm -rf $SCRATCH/Detectron-foss-2019b # remove previous attempt, if there was one.

Create and activate a Python VE to install into.

ml Python/3.7.4-GCCcore-8.3.0
python -m venv $SCRATCH Detectron-foss-2019b


"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
python -m venv $SCRATCH Detectron2-foss-2019b


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