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