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:
Make/3.15.3-GCCcore-8.3.0 Python/3.7.4-GCCcore-8.3.0 cuDNN/7.0.5-CUDA-9.0.176 (optional?) Graphviz/2.42.2-foss-2019b
Start with a clean module environment and install directory.
ml purge rm -rf $SCRATCH/Detectron2-foss-2019b <pre> Create and activate a Python VE to install into. <pre> ml Python/3.7.4-GCCcore-8.3.0 <pre> === fosscuda/2018b === This build includes a CUDA-enabled OpenMPI for using multiple GPU nodes to speed up processing. <pre> CMake/3.12.1-GCCcore-7.3.0 Python-3.6.6-fosscuda-2018b