ResNet18v2

class ResNet18v2 : 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).

This model has 18 layers with ResNetv2 architecture.

The model have

an input with the shape (1x3x224x224)
an output with the shape (1x1000)

NOTE: ResNet v2 uses pre-activation function whereas ResNet v1 uses post-activation for the residual blocks.

See also

Official ResNet model from ONNX Github.

Constructors

ResNet18v2

fun ResNet18v2()

Functions

model

open fun model(modelHub: ModelHub): OnnxInferenceModel

preInit

open override fun preInit(): InferenceModel

preprocessInput

open fun preprocessInput(imageFile: File, preprocessing: Preprocessing): FloatArray

open override fun preprocessInput(data: FloatArray, tensorShape: LongArray): FloatArray

pretrainedModel

open override fun pretrainedModel(modelHub: ModelHub): ImageRecognitionModel

Properties

channelsFirst

open override val channelsFirst: Boolean

modelRelativePath

open override val modelRelativePath: String