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get-ml-model-tiny-resnet

Automatically generated README for this automation recipe: get-ml-model-tiny-resnet

Category: AI/ML models

License: Apache 2.0

  • CM meta description for this script: _cm.json
  • Output cached? True

Reuse this script in your project

Install MLCommons CM automation meta-framework

Pull CM repository with this automation recipe (CM script)

cm pull repo mlcommons@cm4mlops

cmr "get raw ml-model resnet pretrained tiny model ic ml-model-tiny-resnet image-classification" --help

Run this script

Run this script via CLI
cm run script --tags=get,raw,ml-model,resnet,pretrained,tiny,model,ic,ml-model-tiny-resnet,image-classification[,variations]
Run this script via CLI (alternative)
cmr "get raw ml-model resnet pretrained tiny model ic ml-model-tiny-resnet image-classification [variations]"
Run this script from Python
import cmind

r = cmind.access({'action':'run'
              'automation':'script',
              'tags':'get,raw,ml-model,resnet,pretrained,tiny,model,ic,ml-model-tiny-resnet,image-classification'
              'out':'con',
              ...
              (other input keys for this script)
              ...
             })

if r['return']>0:
    print (r['error'])
Run this script via Docker (beta)
cm docker script "get raw ml-model resnet pretrained tiny model ic ml-model-tiny-resnet image-classification[variations]"

Variations

  • No group (any combination of variations can be selected)

    Click here to expand this section.

    • _batch_size.#
      • ENV variables:
        • CM_ML_MODEL_BATCH_SIZE: #

  • Group "framework"

    Click here to expand this section.

    • _onnx
      • ENV variables:
        • CM_TMP_ML_MODEL_TF2ONNX: yes
    • _tflite (default)
      • ENV variables:
        • CM_ML_MODEL_ACCURACY: 85
        • CM_ML_MODEL_DATA_LAYOUT: NHWC
        • CM_ML_MODEL_FRAMEWORK: tflite
        • CM_ML_MODEL_GIVEN_CHANNEL_MEANS: ``
        • CM_ML_MODEL_INPUT_LAYERS: ``
        • CM_ML_MODEL_INPUT_LAYER_NAME: ``
        • CM_ML_MODEL_INPUT_SHAPES: ``
        • CM_ML_MODEL_NORMALIZE_DATA: 0
        • CM_ML_MODEL_OUTPUT_LAYERS: ``
        • CM_ML_MODEL_OUTPUT_LAYER_NAME: ``
        • CM_ML_MODEL_STARTING_WEIGHTS_FILENAME: <<<CM_PACKAGE_URL>>>
        • CM_ML_MODEL_SUBTRACT_MEANS: YES

  • Group "precision"

    Click here to expand this section.

    • _fp32
      • ENV variables:
        • CM_ML_MODEL_INPUT_DATA_TYPES: fp32
        • CM_ML_MODEL_PRECISION: fp32
        • CM_ML_MODEL_WEIGHT_DATA_TYPES: fp32
    • _int8 (default)
      • ENV variables:
        • CM_ML_MODEL_INPUT_DATA_TYPES: int8
        • CM_ML_MODEL_PRECISION: int8
        • CM_ML_MODEL_WEIGHT_DATA_TYPES: int8
    • _uint8
      • ENV variables:
        • CM_ML_MODEL_INPUT_DATA_TYPES: uint8
        • CM_ML_MODEL_PRECISION: uint8
        • CM_ML_MODEL_WEIGHT_DATA_TYPES: uint8
Default variations

_int8,_tflite

Native script being run

No run file exists for Windows

Script output

cmr "get raw ml-model resnet pretrained tiny model ic ml-model-tiny-resnet image-classification [variations]"  -j