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program:object-detection-tflite-codereef (v1.0.0)
License: BSD 3-clause (code) and CC BY-SA 4.0 (data)
Creation date: 2019-03-29
Source: GitHub
cID: b0ac08fe1d3c2615:2aa4d87983bafb55

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This portable workflow is our attempt to provide a common CLI with Python JSON API and a JSON meta description to automatically detect or install required components (models, data sets, libraries, frameworks, tools), and then build, run, validate, benchmark and auto-tune the associated method (program) across diverse models, datasets, compilers, platforms and environments. Our on-going project is to make the onboarding process as simple as possible via this platform. Please check this CK white paper and don't hesitate to contact us if you have suggestions or feedback!
  • Automation framework: CK
  • Development repository: ck-tensorflow-codereef
  • Source: GitHub
  • Available command lines:
    • ck run program:object-detection-tflite-codereef --cmd_key=default (META)
  • Support for host OS: any
  • Support for target OS: android, linux
  • Tags: tensorflow-lite,object-detection,tflite,lang-cpp
  • Template: Object Detection via TFLite (C++)
  • How to get the stable version via the client:
    pip install cbench
    cb download program:object-detection-tflite-codereef --version=1.0.0 --all
    ck run program:object-detection-tflite-codereef
  • How to get the development version:
    pip install ck
    ck pull repo --url=
    ck run program:object-detection-tflite-codereef

  • CLI and Python API: module:program
  • Dependencies    


    TensorFlow Lite (C++) object detection program

    This program uses a statically linked TensorFlow Lite (C++) library and TensorFlow Lite SSD MobileNet models.

    Note: It uses TensorFlow Lite realisation of Non Max Suppresion for custom operator.



    $ ck pull repo:ck-tensorflow
    $ ck pull repo:ck-mlperf

    TensorFlow Lite library

    $ ck install package:lib-tflite-1.13.1-src-static

    Flatbuffers library

    $ ck install package --tags=lib,flatbuffers

    xOpenme library

    $ ck install package --tags=lib,xopenme

    TensorFlow models API

    $ ck install ck-tensorflow:package:tensorflowmodel-api


    $ ck install package --tags=tensorflowmodel,api

    Python libraries


    $ ck install package --tags=lib,python-package,numpy


    $ ck install package --tags=lib,python-package,pillow


    $ ck install package --tags=lib,python-package,matplotlib

    Python API for COCO-dataset

    $ ck install package --tags=tool,coco

    TensorFlow models

    Install a TensorFlow model SSD MobileNet via:

    $ ck install ck-tensorflow:package:-object-detection-ssd-mobilenet-v1-coco


    $ ck install ck-mlperf:package:model-tflite-mlperf-ssd-mobilenet


    $ ck install package --tags=dataset,object-detection,coco

    NB: If you have previously installed the coco datasets, you should probably renew them:

    $ ck refresh env:{dataset-env-uoa}

    where dataset-env-uoa is one of the env identifiers returned by:

    $ ck show env --tags=dataset,coco


    $ ck compile ck-tensorflow:program:object-detection-tflite


    $ ck run ck-tensorflow:program:object-detection-tflite

    Program parameters


    The number of images to be processed.

    Default: 1

    $ ck run ck-tensorflow:program:object-detection-tflite --env.CK_BATCH_COUNT=100


    The number of threads used by TF Lite library for job.

    Default: 1

    $ ck run ck-tensorflow:program:object-detection-tflite --env.CK_HOST_CPU_NUMBER_OF_PROCESSORS=2


    Show additional info (FULL_REPORT) or complete additional info (VERBOSE)

    Default: no

    $ ck run ck-tensorflow:program:object-detection-tflite --env.FULL_REPORT=yes


    $ ck run ck-tensorflow:program:object-detection-tflite --env.VERBOSE=yes


    Enable tuning model settings (just for custom operator use).

    Note: Are you sure you know to do?

    Default: no

    Available settings are:

    MAX_DETECTIONS (integer, >0)
    DETECTIONS_PER_CLASS (integer, >0)
    NMS_SCORE_THRESHOLD (float, >=0.0)
    NMS_IOU_THRESHOLD (float, >=0.0)
    SCALE_H (float, >0.0)
    SCALE_W (float, >0.0)
    SCALE_X (float, >0.0)
    SCALE_Y (float, >0.0)

    Usage example:

    $ ck run ck-tensorflow:program:object-detection-tflite \
        --env.CK_BATCH_COUNT=10 \
        --env.FULL_REPORT=yes \
        --env.USE_CUSTOM_NMS_SETTINGS=yes \
        --env.MAX_DETECTIONS=100 \
        --env.NMS_SCORE_THRESHOLD=0.25 \




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