Retinanet Dockerfile with GPU support – Gary Sieling

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Retinanet is an object detection model that is supposed to be suitable for tagging objects in videos. To use this, you need to install the keras-retinanet project from github.The following example shows how to train this, taken from the excellent pyimagesearch book:retinanet-train \ --batch-size 4 \ --steps 349 \ --epochs 50 \ --weights logos/resnet50_coco_best_v2.1.0.h5 \ --snapshot-path $DIR/snapshots \ csv logos/retinanet_train.csv \ logos/retinanet_classes.csvUnder the hood this uses Tensorflow, which is easy to get running against a CPU (and the default install doesn’t use Intel vector instructions, so even there it’s slower than it needs to be)When I ran the above command it estimated 5 hours per epoch – 50 training epochs would take around ten days. Based on my laptop’s electricity consumption, this would consume around $5.25 of electricity ($0.21/kWh).The “easy” answer is to use a GPU (1080 Ti), which will bring the time to 5 minutes per epoch (4.1 hours fo…