get-ml-model-resnet50
Automatically generated README for this automation recipe: get-ml-model-resnet50
Category: AI/ML models
License: Apache 2.0
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Notes from the authors, contributors and users: README-extra
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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
Print CM help from the command line
cmr "get raw ml-model resnet50 ml-model-resnet50 image-classification" --help
Run this script
Run this script via CLI
cm run script --tags=get,raw,ml-model,resnet50,ml-model-resnet50,image-classification[,variations]
Run this script via CLI (alternative)
cmr "get raw ml-model resnet50 ml-model-resnet50 image-classification [variations]"
Run this script from Python
import cmind
r = cmind.access({'action':'run'
'automation':'script',
'tags':'get,raw,ml-model,resnet50,ml-model-resnet50,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 resnet50 ml-model-resnet50 image-classification[variations]"
Variations
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No group (any combination of variations can be selected)
Click here to expand this section.
_batch_size.#- ENV variables:
- CM_ML_MODEL_BATCH_SIZE:
#
- CM_ML_MODEL_BATCH_SIZE:
- ENV variables:
_batch_size.1- ENV variables:
- CM_ML_MODEL_BATCH_SIZE:
1
- CM_ML_MODEL_BATCH_SIZE:
- ENV variables:
_fix-input-shape_from-tf_huggingface_default- ENV variables:
- CM_PACKAGE_URL:
https://huggingface.co/ctuning/mlperf-inference-resnet50-onnx-fp32-imagenet2012-v1.0/resolve/main/resnet50_v1.onnx
- CM_PACKAGE_URL:
- ENV variables:
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Group "framework"
Click here to expand this section.
_ncnn- ENV variables:
- CM_ML_MODEL_FRAMEWORK:
ncnn
- CM_ML_MODEL_FRAMEWORK:
- ENV variables:
_onnx(default)- Aliases:
_onnxruntime - ENV variables:
- CM_ML_MODEL_DATA_LAYOUT:
NCHW - CM_ML_MODEL_FRAMEWORK:
onnx - CM_ML_MODEL_INPUT_LAYERS:
input_tensor:0 - CM_ML_MODEL_INPUT_LAYER_NAME:
input_tensor:0 - CM_ML_MODEL_INPUT_SHAPES:
\"input_tensor:0\": (BATCH_SIZE, 3, 224, 224) - CM_ML_MODEL_OUTPUT_LAYERS:
softmax_tensor:0 - CM_ML_MODEL_OUTPUT_LAYER_NAME:
softmax_tensor:0 - CM_ML_MODEL_STARTING_WEIGHTS_FILENAME:
<<<CM_PACKAGE_URL>>> - CM_ML_MODEL_VER:
1.5
- CM_ML_MODEL_DATA_LAYOUT:
- Aliases:
_pytorch- ENV variables:
- CM_ML_MODEL_DATA_LAYOUT:
NCHW - CM_ML_MODEL_FRAMEWORK:
pytorch - CM_ML_MODEL_GIVEN_CHANNEL_MEANS:
? - CM_ML_MODEL_INPUT_LAYER_NAME:
input_tensor:0 - CM_ML_MODEL_INPUT_SHAPES:
\"input_tensor:0\": [BATCH_SIZE, 3, 224, 224] - CM_ML_MODEL_OUTPUT_LAYERS:
output - CM_ML_MODEL_OUTPUT_LAYER_NAME:
? - CM_ML_STARTING_WEIGHTS_FILENAME:
<<<CM_PACKAGE_URL>>>
- CM_ML_MODEL_DATA_LAYOUT:
- ENV variables:
_tensorflow- Aliases:
_tf - ENV variables:
- CM_ML_MODEL_ACCURACY:
76.456 - CM_ML_MODEL_DATA_LAYOUT:
NHWC - CM_ML_MODEL_FRAMEWORK:
tensorflow - CM_ML_MODEL_GIVEN_CHANNEL_MEANS:
123.68 116.78 103.94 - CM_ML_MODEL_INPUT_LAYERS:
input_tensor - CM_ML_MODEL_INPUT_LAYER_NAME:
input_tensor - CM_ML_MODEL_INPUT_SHAPES:
\"input_tensor:0\": (BATCH_SIZE, 3, 224, 224) - CM_ML_MODEL_NORMALIZE_DATA:
0 - CM_ML_MODEL_OUTPUT_LAYERS:
softmax_tensor - CM_ML_MODEL_OUTPUT_LAYER_NAME:
softmax_tensor - CM_ML_MODEL_STARTING_WEIGHTS_FILENAME:
<<<CM_PACKAGE_URL>>> - CM_ML_MODEL_SUBTRACT_MEANS:
YES - CM_PACKAGE_URL:
https://zenodo.org/record/2535873/files/resnet50_v1.pb
- CM_ML_MODEL_ACCURACY:
- Aliases:
_tflite- ENV variables:
- CM_ML_MODEL_ACCURACY:
76.456 - CM_ML_MODEL_DATA_LAYOUT:
NHWC - CM_ML_MODEL_FRAMEWORK:
tflite - CM_ML_MODEL_GIVEN_CHANNEL_MEANS:
123.68 116.78 103.94 - CM_ML_MODEL_INPUT_LAYERS:
input_tensor - CM_ML_MODEL_INPUT_LAYER_NAME:
input_tensor - CM_ML_MODEL_INPUT_SHAPES:
\"input_tensor 2\": (BATCH_SIZE, 224, 224, 3) - CM_ML_MODEL_NORMALIZE_DATA:
0 - CM_ML_MODEL_OUTPUT_LAYERS:
softmax_tensor - CM_ML_MODEL_OUTPUT_LAYER_NAME:
softmax_tensor - CM_ML_MODEL_STARTING_WEIGHTS_FILENAME:
<<<CM_PACKAGE_URL>>> - CM_ML_MODEL_SUBTRACT_MEANS:
YES
- CM_ML_MODEL_ACCURACY:
- ENV variables:
-
Group "model-output"
Click here to expand this section.
_argmax(default)- ENV variables:
- CM_ML_MODEL_OUTPUT_LAYER_ARGMAX:
yes
- CM_ML_MODEL_OUTPUT_LAYER_ARGMAX:
- ENV variables:
_no-argmax- ENV variables:
- CM_ML_MODEL_OUTPUT_LAYER_ARGMAX:
no
- CM_ML_MODEL_OUTPUT_LAYER_ARGMAX:
- ENV variables:
-
Group "opset-version"
Click here to expand this section.
_opset-11- ENV variables:
- CM_ML_MODEL_ONNX_OPSET:
11
- CM_ML_MODEL_ONNX_OPSET:
- ENV variables:
_opset-8- ENV variables:
- CM_ML_MODEL_ONNX_OPSET:
8
- CM_ML_MODEL_ONNX_OPSET:
- ENV variables:
-
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
- CM_ML_MODEL_INPUT_DATA_TYPES:
- ENV variables:
_int8- ENV variables:
- CM_ML_MODEL_INPUT_DATA_TYPES:
int8 - CM_ML_MODEL_PRECISION:
int8 - CM_ML_MODEL_WEIGHT_DATA_TYPES:
int8
- CM_ML_MODEL_INPUT_DATA_TYPES:
- ENV variables:
_uint8- ENV variables:
- CM_ML_MODEL_INPUT_DATA_TYPES:
uint8 - CM_ML_MODEL_PRECISION:
uint8 - CM_ML_MODEL_WEIGHT_DATA_TYPES:
uint8
- CM_ML_MODEL_INPUT_DATA_TYPES:
- ENV variables:
Default variations
_argmax,_fp32,_onnx
Native script being run
No run file exists for Windows
Script output
cmr "get raw ml-model resnet50 ml-model-resnet50 image-classification [variations]" -j