Performance of Prediction Library¶
Result¶
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)