View Source Evision.DNN.ClassificationModel (Evision v0.1.8)

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Functions

Raising version of classify/2.

Positional Arguments
Return
  • classId: int
  • conf: float

Has overloading in C++

Variant 1:

Create model from deep learning network.

Create classification model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.

Get enable/disable softmax post processing option.

Set enable/disable softmax post processing option.

Link to this section Functions

Raising version of classify/2.

Positional Arguments
Return
  • classId: int
  • conf: float

Has overloading in C++

Python prototype (for reference):

classify(frame) -> classId, conf
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dnn_ClassificationModel!(network)

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Raising version of dnn_ClassificationModel/1.

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dnn_ClassificationModel!(model, opts)

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Raising version of dnn_ClassificationModel/2.

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dnn_ClassificationModel(network)

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Variant 1:

Create model from deep learning network.

Positional Arguments
  • network: Net.

    Net object.

Python prototype (for reference):

ClassificationModel(network) -> <dnn_ClassificationModel object>

Variant 2:

Create classification model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.

Positional Arguments
  • model: String.

    Binary file contains trained weights.

Keyword Arguments
  • config: String.

    Text file contains network configuration.

Python prototype (for reference):

ClassificationModel(model[, config]) -> <dnn_ClassificationModel object>
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dnn_ClassificationModel(model, opts)

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Create classification model from network represented in one of the supported formats. An order of @p model and @p config arguments does not matter.

Positional Arguments
  • model: String.

    Binary file contains trained weights.

Keyword Arguments
  • config: String.

    Text file contains network configuration.

Python prototype (for reference):

ClassificationModel(model[, config]) -> <dnn_ClassificationModel object>
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getEnableSoftmaxPostProcessing!(self)

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Raising version of getEnableSoftmaxPostProcessing/1.

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getEnableSoftmaxPostProcessing(self)

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Get enable/disable softmax post processing option.

This option defaults to false, softmax post processing is not applied within the classify() function.

Python prototype (for reference):

getEnableSoftmaxPostProcessing() -> retval
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setEnableSoftmaxPostProcessing!(self, enable)

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Raising version of setEnableSoftmaxPostProcessing/2.

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setEnableSoftmaxPostProcessing(self, enable)

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Set enable/disable softmax post processing option.

Positional Arguments
  • enable: bool.

    Set enable softmax post processing within the classify() function.

If this option is true, softmax is applied after forward inference within the classify() function to convert the confidences range to [0.0-1.0]. This function allows you to toggle this behavior. Please turn true when not contain softmax layer in model.

Python prototype (for reference):

setEnableSoftmaxPostProcessing(enable) -> retval