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{} |
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--- |
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# Dataset Card for DLC Speed Benchmarking ZIP |
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This supports the dlc-live benchmarking [zip](https://github.com/DeepLabCut/DeepLabCut-live/blob/427c12609307ef685e4193c91026567308820cbe/dlclive/benchmark.py#L40) formally hosted on our Harvard Rowland server. |
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All information can be found in our publication: |
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Real-time, low-latency closed-loop feedback using markerless posture tracking |
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Gary A Kane, Gonçalo Lopes, Jonny L Saunders, Alexander Mathis, Mackenzie W Mathis |
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https://elifesciences.org/articles/61909 |
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### Direct Use |
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```python |
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""" |
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DeepLabCut Toolbox (deeplabcut.org) |
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© A. & M. Mathis Labs |
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Licensed under GNU Lesser General Public License v3.0 |
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""" |
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# Script for running the official benchmark from Kane et al, 2020. |
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# Please share your results at https://github.com/DeepLabCut/DLC-inferencespeed-benchmark |
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import os, pathlib |
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import glob |
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from dlclive import benchmark_videos, download_benchmarking_data |
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datafolder = os.path.join( |
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pathlib.Path(__file__).parent.absolute(), "Data-DLC-live-benchmark" |
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) |
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if not os.path.isdir(datafolder): # only download if data doesn't exist! |
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# Downloading data.... this takes a while (see terminal) |
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download_benchmarking_data(datafolder) |
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n_frames = 10000 # change to 10000 for testing on a GPU! |
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pixels = [2500, 10000, 40000, 160000, 320000, 640000] |
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dog_models = glob.glob(datafolder + "/dog/*[!avi]") |
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dog_video = glob.glob(datafolder + "/dog/*.avi")[0] |
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mouse_models = glob.glob(datafolder + "/mouse_lick/*[!avi]") |
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mouse_video = glob.glob(datafolder + "/mouse_lick/*.avi")[0] |
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this_dir = os.path.dirname(os.path.realpath(__file__)) |
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# storing results in /benchmarking/results: (for your PR) |
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out_dir = os.path.normpath(this_dir + "/results") |
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if not os.path.isdir(out_dir): |
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os.mkdir(out_dir) |
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for m in dog_models: |
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benchmark_videos(m, dog_video, output=out_dir, n_frames=n_frames, pixels=pixels) |
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for m in mouse_models: |
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benchmark_videos(m, mouse_video, output=out_dir, n_frames=n_frames, pixels=pixels) |
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``` |
