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get-ml-model-retinanet

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

Category: AI/ML models

License: Apache 2.0

  • Notes from the authors, contributors and users: README-extra

  • 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 ml-model raw resnext50 retinanet object-detection" --help

Run this script

Run this script via CLI
cm run script --tags=get,ml-model,raw,resnext50,retinanet,object-detection[,variations]
Run this script via CLI (alternative)
cmr "get ml-model raw resnext50 retinanet object-detection [variations]"
Run this script from Python
import cmind

r = cmind.access({'action':'run'
              'automation':'script',
              'tags':'get,ml-model,raw,resnext50,retinanet,object-detection'
              '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 ml-model raw resnext50 retinanet object-detection[variations]"

Variations

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

    Click here to expand this section.

    • _no-nms
      • ENV variables:
        • CM_TMP_ML_MODEL_RETINANET_NO_NMS: yes
        • CM_ML_MODEL_RETINANET_NO_NMS: yes
        • CM_QAIC_PRINT_NODE_PRECISION_INFO: yes
    • _weights
      • ENV variables:
        • CM_MODEL_WEIGHTS_FILE: yes

  • Group "framework"

    Click here to expand this section.

    • _onnx (default)
      • ENV variables:
        • CM_ML_MODEL_DATA_LAYOUT: NCHW
        • CM_ML_MODEL_FRAMEWORK: onnx
    • _pytorch
      • ENV variables:
        • CM_ML_MODEL_DATA_LAYOUT: NCHW
        • CM_ML_MODEL_FRAMEWORK: pytorch

  • Group "precision"

    Click here to expand this section.

    • _fp32 (default)
      • ENV variables:
        • CM_ML_MODEL_INPUT_DATA_TYPES: fp32
        • CM_ML_MODEL_PRECISION: fp32
        • CM_ML_MODEL_WEIGHT_DATA_TYPES: fp32
Default variations

_fp32,_onnx

Native script being run

No run file exists for Windows

Script output

cmr "get ml-model raw resnext50 retinanet object-detection [variations]"  -j