Performance of Prediction Library


We compare the prediction performance of HyperPose with OpenPose 1.6 and TF-Pose. We implement the OpenPose algorithms with different configurations in HyperPose.

The test-bed has Ubuntu18.04, 1070Ti GPU, Intel i7 CPU (12 logic cores).

HyperPose Configuration DNN Size Input Size HyperPose Baseline
OpenPose (VGG) 209.3MB 656 x 368 27.32 FPS 8 FPS (OpenPose)
OpenPose (TinyVGG) 34.7 MB 384 x 256 124.925 FPS N/A
OpenPose (MobileNet) 17.9 MB 432 x 368 84.32 FPS 8.5 FPS (TF-Pose)
OpenPose (ResNet18) 45.0 MB 432 x 368 62.52 FPS N/A
OpenPifPaf (ResNet50) 97.6 MB 97 x 129 178.6 FPS 35.3

Environment: System@Ubuntu18.04, GPU@1070Ti, CPU@i7(12 logic cores).

Tested Video Source: Crazy Updown Funk(resolution@640x360, frame_count@7458, source@YouTube)

OpenPose performance is not tested with batch processing as it seems not to be implemented. (see here)