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mediapipe_model_maker.model_util.convert_to_tflite
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Converts the input Keras model to TFLite format.
mediapipe_model_maker.model_util.convert_to_tflite(
model: tf.keras.Model,
quantization_config: Optional[mediapipe_model_maker.quantization.QuantizationConfig
] = None,
supported_ops: Tuple[tf.lite.OpsSet, ...] = (tf.lite.OpsSet.TFLITE_BUILTINS,),
preprocess: Optional[Callable[..., Any]] = None,
allow_custom_ops: bool = False
) -> bytearray
Args |
model
|
Keras model to be converted to TFLite.
|
quantization_config
|
Configuration for post-training quantization.
|
supported_ops
|
A list of supported ops in the converted TFLite file.
|
preprocess
|
A callable to preprocess the representative dataset for
quantization. The callable takes three arguments in order: feature, label,
and is_training.
|
allow_custom_ops
|
A boolean flag to enable custom ops in model conversion.
Default to False.
|
Returns |
bytearray of TFLite model
|
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Last updated 2024-05-07 UTC.
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"type": "thumb-down",
"id": "missingTheInformationINeed",
"label":"Missing the information I need"
},{
"type": "thumb-down",
"id": "tooComplicatedTooManySteps",
"label":"Too complicated / too many steps"
},{
"type": "thumb-down",
"id": "outOfDate",
"label":"Out of date"
},{
"type": "thumb-down",
"id": "samplesCodeIssue",
"label":"Samples / code issue"
},{
"type": "thumb-down",
"id": "otherDown",
"label":"Other"
}]
[{
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"label":"Easy to understand"
},{
"type": "thumb-up",
"id": "solvedMyProblem",
"label":"Solved my problem"
},{
"type": "thumb-up",
"id": "otherUp",
"label":"Other"
}]
{"lastModified": "Last updated 2024-05-07 UTC."}
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