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OpenCV Zoo Benchmark

Benchmarking the speed of OpenCV DNN inferring different models in the zoo. Result of each model includes the time of its preprocessing, inference and postprocessing stages.

Data for benchmarking will be downloaded and loaded in data based on given config.

Preparation

  1. Install python >= 3.6.
  2. Install dependencies: pip install -r requirements.txt.
  3. Download data for benchmarking.
    1. Download all data: python download_data.py
    2. Download one or more specified data: python download_data.py face text. Available names can be found in download_data.py.
    3. You can also download all data from https://pan.baidu.com/s/18sV8D4vXUb2xC9EG45k7bg (code: pvrw). Please place and extract data packages under ./data.

Benchmarking

Linux:

export PYTHONPATH=$PYTHONPATH:.. 
python benchmark.py --cfg ./config/face_detection_yunet.yaml

Windows:

  • CMD

    set PYTHONPATH=%PYTHONPATH%;..
python benchmark.py --cfg ./config/face_detection_yunet.yaml

  • PowerShell

    $env:PYTHONPATH=$env:PYTHONPATH+";.."
python benchmark.py --cfg ./config/face_detection_yunet.yaml