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program:image-classification-armnn-tf (v1.0.0)
Copyright: See COPYRIGHT.txt for copyright details
License: See LICENSE.txt for licensing details
Creation date: 2019-03-11
Source: GitHub
cID: b0ac08fe1d3c2615:d815e1c7562c4c1a

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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: armnn-mlperf
  • Source: GitHub
  • Available command lines:
    • ck run program:image-classification-armnn-tf --cmd_key=default (META)
  • Support for host OS: any
  • Support for target OS: android, linux
  • Tags: image-classification,tf,armnn,lang-cpp
  • Template: image classification via TF (C++)
  • How to get the stable version via the client:
    pip install cbench
    cb download program:image-classification-armnn-tf --version=1.0.0 --all
    ck run program:image-classification-armnn-tf
  • How to get the development version:
    pip install ck
    ck pull repo --url=
    ck run program:image-classification-armnn-tf

  • CLI and Python API: module:program
  • Dependencies    


    TensorFlow Lite image classification program

    This program uses a statically linked TensorFlow Lite library.

    Compile (depending on desired backend)

    $ ck compile program:image-classification-armnn-tflite
    $ ck compile program:image-classification-armnn-tflite --env.USE_NEON
    $ ck compile program:image-classification-armnn-tflite --env.USE_OPENCL
    $ ck compile program:image-classification-armnn-tflite --env.USE_NEON --env.USE_OPENCL

    Run (assuming the same options for the backend)

    $ ck run program:image-classification-armnn-tflite  --env.CK_BATCH_COUNT=5 --env.USE_NEON

    Here: - CK_BATCH_COUNT - file's count to evaluate (default: 1) - USE_NEON - asking for Cpu acceleration backend support - USE_OPENCL - asking for Gpu acceleration backend support




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