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speed_up_preserving_accuracy.md

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OpenPose - Maximizing the OpenPose Speed

Contents

  1. OpenPose Benchmark
  2. Profiling Speed
  3. Speed Up Preserving Accuracy
  4. Speed Up and Memory Reduction
  5. CPU Version

OpenPose Benchmark

Check the OpenPose Benchmark to discover the approximate expected speed of your graphics card.

Profiling Speed

Check the doc/installation.md#profiling-speed section to measure the bottlenecks in your OpenPose distribution and make sure everything is working as expected.

Speed Up Preserving Accuracy

Some speed tips to maximize the OpenPose runtime speed while preserving the accuracy (do not expect miracles, but it might help a bit boosting the framerate):

1. Enable the `WITH_OPENCV_WITH_OPENGL` flag in CMake to have a much faster GUI display (but you must also compile OpenCV with OpenGL support). Note: Default OpenCV in Ubuntu (from apt-get install) does have OpenGL support included. Nevertheless, default Windows portable binaries do not.
2. Change GPU rendering by CPU rendering to get approximately +0.5 FPS (`--render_pose 1`).
3. Use cuDNN 5.1 (cuDNN 6 is ~10% slower).
4. Use the `BODY_25` model for simultaneously maximum speed and accuracy (both COCO and MPII models are slower and less accurate).

Speed Up and Memory Reduction

Some speed tips to highly maximize the OpenPose speed, but keep in mind the accuracy trade-off:

1. Reduce the `--net_resolution` (e.g., to 320x176) (lower accuracy). Note: For maximum accuracy, follow [doc/quick_start.md#maximum-accuracy-configuration](./quick_start.md#maximum-accuracy-configuration).
2. For face, reduce the `--face_net_resolution`. The resolution 320x320 usually works pretty decently.
3. Points 1-2 will also reduce the GPU memory usage (or RAM memory for CPU version).
4. Use the `BODY_25` model for maximum speed. Use `MPI_4_layers` model for minimum GPU memory usage (but lower accuracy, speed, and number of parts).

CPU Version

The CPU version runs at about 0.3 FPS on the COCO model, and at about 0.1 FPS (i.e., about 15 sec / frame) on the default BODY_25 model. Switch to COCO model and/or reduce the net_resolution as indicated above. Contradictory fact: BODY_25 model is about 5x slower than COCO on CPU-only version, but it is about 40% faster on GPU version.