EfficientNet4Lite

class EfficientNet4Lite : ONNXModels.CV<OnnxInferenceModel>

This model is a neural network for image classification that take images as input and classify the major object in the image into a set of 1000 different classes (labels are available via org.jetbrains.kotlinx.dl.api.core.util.loadImageNetClassLabels method).

EfficientNet-Lite 4 is the largest variant and most accurate of the set of EfficientNet-Lite model. It is an integer-only quantized model that produces the highest accuracy of all of the EfficientNet models. It achieves 80.4% ImageNet top-1 accuracy, while still running in real-time (e.g. 30ms/image) on a Pixel 4 CPU.

The model have

  • an input with the shape (1x224x224x3)

  • an output with the shape (1x1000)

See also

Constructors

EfficientNet4Lite
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fun EfficientNet4Lite()

Functions

model
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open fun model(modelHub: ModelHub): OnnxInferenceModel
preInit
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open override fun preInit(): InferenceModel
preprocessInput
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open fun preprocessInput(imageFile: File, preprocessing: Preprocessing): FloatArray
open override fun preprocessInput(data: FloatArray, tensorShape: LongArray): FloatArray
pretrainedModel
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open override fun pretrainedModel(modelHub: ModelHub): ImageRecognitionModel

Properties

channelsFirst
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open override val channelsFirst: Boolean
modelRelativePath
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open override val modelRelativePath: String