ResNet101v2

object ResNet101v2 : ONNXModels.CV

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.impl.dataset.Imagenet.V1k.labels method).

This model has 101 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.

Functions

model

jvm

open fun model(modelHub: ModelHub): OnnxInferenceModel

pretrainedModel

jvm

open override fun pretrainedModel(modelHub: ModelHub): ImageRecognitionModel

Properties

inputShape

jvm

open val inputShape: LongArray?

Shape of the input accepted by this model, without batch size.

modelRelativePath

jvm

open override val modelRelativePath: String

preprocessor

jvm

open override val preprocessor: Operation<Pair<FloatArray, TensorShape>, Pair<FloatArray, TensorShape>>