ImageClassifier

Instance Constructors

new ImageClassifier(modelPathPrefix: String, inputDescriptors: IndexedSeq[DataDesc], contexts: Array[Context] = Context.cpu(), epoch: Option[Int] = Some(0))

modelPathPrefix
Path prefix from where to load the model artifacts. These include the symbol, parameters, and synset.txt. Example: file://model-dir/resnet-152 (containing resnet-152-symbol.json, resnet-152-0000.params, and synset.txt).
inputDescriptors
Descriptors defining the input node names, shape, layout and type parameters
contexts
Device contexts on which you want to run inference; defaults to CPU
epoch
Model epoch to load; defaults to 0

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
val batch: Int

Attributes
protected[org.apache.mxnet.infer]
val channel: Int

Attributes
protected[org.apache.mxnet.infer]
def classify[T](input: IndexedSeq[Array[T]], topK: Option[Int] = None): IndexedSeq[(String, T)]

Takes flat arrays as input and returns (Label, Score) tuples.
Takes flat arrays as input and returns (Label, Score) tuples.
T
The Scala equivalent of the DType used for the input array and return value
input
Indexed sequence one-dimensional array of floats/doubles
topK
(Optional) How many result (sorting based on the last axis) elements to return. Default returns unsorted output.
returns
Indexed sequence of (Label, Score) tuples

Definition Classes
Classifier → ClassifierBase
def classifyImage(inputImage: BufferedImage, topK: Option[Int] = None, dType: DType.DType = DType.Float32): IndexedSeq[IndexedSeq[(String, Float)]]

To classify the image according to the provided model
To classify the image according to the provided model
inputImage
Path prefix of the input image
topK
Number of result elements to return, sorted by probability
dType
The precision at which to run the inference. specify the DType as DType.Float64 for Double precision. Defaults to DType.Float32
returns
List of list of tuples of (Label, Probability)
def classifyImageBatch(inputBatch: Traversable[BufferedImage], topK: Option[Int] = None, dType: DType.DType = DType.Float32): IndexedSeq[IndexedSeq[(String, Float)]]

To classify batch of input images according to the provided model
To classify batch of input images according to the provided model
inputBatch
Input array of buffered images
topK
Number of result elements to return, sorted by probability
dType
The precision at which to run the inference. specify the DType as DType.Float64 for Double precision. Defaults to DType.Float32
returns
List of list of tuples of (Label, Probability)
def classifyWithNDArray(input: IndexedSeq[NDArray], topK: Option[Int] = None): IndexedSeq[IndexedSeq[(String, Float)]]

Perform multiple classification operations on NDArrays.
Perform multiple classification operations on NDArrays. Also works with batched input.
input
Indexed sequence of NDArrays
topK
(Optional) How many result (sorting based on the last axis) elements to return. Default returns unsorted output.
returns
Traversable sequence of (Label, Score) tuples.

Definition Classes
Classifier → ClassifierBase
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val contexts: Array[Context]

Device contexts on which you want to run inference; defaults to CPU
Device contexts on which you want to run inference; defaults to CPU

Attributes
protected
Definition Classes
Classifier
val epoch: Option[Int]

Model epoch to load; defaults to 0
Model epoch to load; defaults to 0

Attributes
protected
Definition Classes
Classifier
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
val handler: MXNetHandler

Attributes
protected[org.apache.mxnet.infer]
Definition Classes
Classifier
def hashCode(): Int

Definition Classes
AnyRef → Any
val height: Int

Attributes
protected[org.apache.mxnet.infer]
val inputDescriptors: IndexedSeq[DataDesc]

Descriptors defining the input node names, shape, layout and type parameters
Descriptors defining the input node names, shape, layout and type parameters

Attributes
protected
Definition Classes
Classifier
val inputLayout: String

Attributes
protected[org.apache.mxnet.infer]
val inputShape: Shape

Attributes
protected[org.apache.mxnet.infer]
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
def outputShapes: IndexedSeq[(String, Shape)]

Get the names and shapes that would be returns by a classify call
Get the names and shapes that would be returns by a classify call
returns
a list of (name, shape) tuples
val predictor: PredictBase

Attributes
protected[org.apache.mxnet.infer]
Definition Classes
Classifier
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val synset: IndexedSeq[String]

Attributes
protected[org.apache.mxnet.infer]
Definition Classes
Classifier
val synsetFilePath: String

Attributes
protected[org.apache.mxnet.infer]
Definition Classes
Classifier
def toString(): String

Definition Classes
AnyRef → Any
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
val width: Int

Attributes
protected[org.apache.mxnet.infer]

Related Docs: object ImageClassifier | package infer

class ImageClassifier extends Classifier

Instance Constructors

new ImageClassifier(modelPathPrefix: String, inputDescriptors: IndexedSeq[DataDesc], contexts: Array[Context] = Context.cpu(), epoch: Option[Int] = Some(0))

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

val batch: Int

val channel: Int

def classify[T](input: IndexedSeq[Array[T]], topK: Option[Int] = None): IndexedSeq[(String, T)]

def classifyImage(inputImage: BufferedImage, topK: Option[Int] = None, dType: DType.DType = DType.Float32): IndexedSeq[IndexedSeq[(String, Float)]]

def classifyImageBatch(inputBatch: Traversable[BufferedImage], topK: Option[Int] = None, dType: DType.DType = DType.Float32): IndexedSeq[IndexedSeq[(String, Float)]]

def classifyWithNDArray(input: IndexedSeq[NDArray], topK: Option[Int] = None): IndexedSeq[IndexedSeq[(String, Float)]]

def clone(): AnyRef

val contexts: Array[Context]

val epoch: Option[Int]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

val handler: MXNetHandler

def hashCode(): Int

val height: Int

val inputDescriptors: IndexedSeq[DataDesc]

val inputLayout: String

val inputShape: Shape

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

def outputShapes: IndexedSeq[(String, Shape)]

val predictor: PredictBase

final def synchronized[T0](arg0: ⇒ T0): T0

val synset: IndexedSeq[String]

val synsetFilePath: String

def toString(): String

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

val width: Int

Inherited from Classifier

Inherited from ClassifierBase

Inherited from AnyRef

Inherited from Any

Ungrouped