Packages

class ViTForImageClassification extends AnnotatorModel[ViTForImageClassification] with HasBatchedAnnotateImage[ViTForImageClassification] with HasImageFeatureProperties with WriteTensorflowModel with HasEngine

Vision Transformer (ViT) for image classification.

ViT is a transformer based alternative to the convolutional neural networks usually used for image recognition tasks.

Pretrained models can be loaded with pretrained of the companion object:

val imageClassifier = ViTForImageClassification.pretrained()
  .setInputCols("image_assembler")
  .setOutputCol("class")

The default model is "image_classifier_vit_base_patch16_224", if no name is provided.

For available pretrained models please see the Models Hub.

Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. The Spark NLP Workshop example shows how to import them https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see ViTImageClassificationTestSpec.

References:

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Paper Abstract:

While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.

Example

import com.johnsnowlabs.nlp.annotator._
import com.johnsnowlabs.nlp.ImageAssembler
import org.apache.spark.ml.Pipeline

val imageDF: DataFrame = spark.read
  .format("image")
  .option("dropInvalid", value = true)
  .load("src/test/resources/image/")

val imageAssembler = new ImageAssembler()
  .setInputCol("image")
  .setOutputCol("image_assembler")

val imageClassifier = ViTForImageClassification
  .pretrained()
  .setInputCols("image_assembler")
  .setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier))
val pipelineDF = pipeline.fit(imageDF).transform(imageDF)
Ordering
  1. Grouped
  2. Alphabetic
  3. By Inheritance
Inherited
  1. ViTForImageClassification
  2. HasEngine
  3. WriteTensorflowModel
  4. HasImageFeatureProperties
  5. HasBatchedAnnotateImage
  6. AnnotatorModel
  7. CanBeLazy
  8. RawAnnotator
  9. HasOutputAnnotationCol
  10. HasInputAnnotationCols
  11. HasOutputAnnotatorType
  12. ParamsAndFeaturesWritable
  13. HasFeatures
  14. DefaultParamsWritable
  15. MLWritable
  16. Model
  17. Transformer
  18. PipelineStage
  19. Logging
  20. Params
  21. Serializable
  22. Serializable
  23. Identifiable
  24. AnyRef
  25. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new ViTForImageClassification()

    Annotator reference id.

    Annotator reference id. Used to identify elements in metadata or to refer to this annotator type

  2. new ViTForImageClassification(uid: String)

    uid

    required uid for storing annotator to disk

Type Members

  1. type AnnotationContent = Seq[Row]

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    Attributes
    protected
    Definition Classes
    AnnotatorModel
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  11. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  12. def batchAnnotate(batchedAnnotations: Seq[Array[AnnotationImage]]): Seq[Seq[Annotation]]

    Takes a document and annotations and produces new annotations of this annotator's annotation type

    Takes a document and annotations and produces new annotations of this annotator's annotation type

    batchedAnnotations

    Annotations that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every input annotation. Not necessary one to one relationship

    Definition Classes
    ViTForImageClassificationHasBatchedAnnotateImage
  13. def batchProcess(rows: Iterator[_]): Iterator[Row]
    Definition Classes
    HasBatchedAnnotateImage
  14. val batchSize: IntParam

    Size of every batch (Default depends on model).

    Size of every batch (Default depends on model).

    Definition Classes
    HasBatchedAnnotateImage
  15. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  16. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  17. final def clear(param: Param[_]): ViTForImageClassification.this.type
    Definition Classes
    Params
  18. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  19. val configProtoBytes: IntArrayParam

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  20. def copy(extra: ParamMap): ViTForImageClassification

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  21. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  22. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  23. val doNormalize: BooleanParam

    Whether or not to normalize the input with mean and standard deviation

    Whether or not to normalize the input with mean and standard deviation

    Definition Classes
    HasImageFeatureProperties
  24. val doResize: BooleanParam

    Whether to resize the input to a certain size

    Whether to resize the input to a certain size

    Definition Classes
    HasImageFeatureProperties
  25. val engine: Param[String]

    This param is set internally once via loadSavedModel.

    This param is set internally once via loadSavedModel. That's why there is no setter

    Definition Classes
    HasEngine
  26. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  28. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  29. def explainParams(): String
    Definition Classes
    Params
  30. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  31. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  32. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  33. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  34. val featureExtractorType: Param[String]

    Name of model's architecture for feature extraction

    Name of model's architecture for feature extraction

    Definition Classes
    HasImageFeatureProperties
  35. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  36. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  37. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  38. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  39. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  40. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  41. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  42. def getBatchSize: Int

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotateImage
  43. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  44. def getClasses: Array[String]

    Returns labels used to train this model

  45. def getConfigProtoBytes: Option[Array[Byte]]

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  46. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  47. def getDoNormalize: Boolean

    Definition Classes
    HasImageFeatureProperties
  48. def getDoResize: Boolean

    Definition Classes
    HasImageFeatureProperties
  49. def getEngine: String

    Definition Classes
    HasEngine
  50. def getFeatureExtractorType: String

    Definition Classes
    HasImageFeatureProperties
  51. def getImageMean: Array[Double]

    Definition Classes
    HasImageFeatureProperties
  52. def getImageStd: Array[Double]

    Definition Classes
    HasImageFeatureProperties
  53. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  54. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  55. def getModelIfNotSet: TensorflowViTClassifier

  56. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  57. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  58. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  59. def getResample: Int

    Definition Classes
    HasImageFeatureProperties
  60. def getSignatures: Option[Map[String, String]]

  61. def getSize: Int

    Definition Classes
    HasImageFeatureProperties
  62. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  63. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  64. def hasParent: Boolean
    Definition Classes
    Model
  65. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  66. val imageMean: DoubleArrayParam

    The sequence of means for each channel, to be used when normalizing images

    The sequence of means for each channel, to be used when normalizing images

    Definition Classes
    HasImageFeatureProperties
  67. val imageStd: DoubleArrayParam

    The sequence of standard deviations for each channel, to be used when normalizing images

    The sequence of standard deviations for each channel, to be used when normalizing images

    Definition Classes
    HasImageFeatureProperties
  68. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  69. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : IMAGE

    Input annotator type : IMAGE

    Definition Classes
    ViTForImageClassificationHasInputAnnotationCols
  71. final val inputCols: StringArrayParam

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  72. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  73. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  74. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  75. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  76. val labels: MapFeature[String, BigInt]

    Labels used to decode predicted IDs back to string tags

  77. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  78. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  79. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  86. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  88. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  89. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  90. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  91. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  92. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  93. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  94. def onWrite(path: String, spark: SparkSession): Unit
  95. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  96. val outputAnnotatorType: AnnotatorType

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    ViTForImageClassificationHasOutputAnnotatorType
  97. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  98. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  99. var parent: Estimator[ViTForImageClassification]
    Definition Classes
    Model
  100. val resample: IntParam

    An optional resampling filter.

    An optional resampling filter. This can be one of PIL.Image.NEAREST, PIL.Image.BOX, PIL.Image.BILINEAR, PIL.Image.HAMMING, PIL.Image.BICUBIC or PIL.Image.LANCZOS. Only has an effect if do_resize is set to True

    Definition Classes
    HasImageFeatureProperties
  101. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  102. def set[T](feature: StructFeature[T], value: T): ViTForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  103. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): ViTForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  104. def set[T](feature: SetFeature[T], value: Set[T]): ViTForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  105. def set[T](feature: ArrayFeature[T], value: Array[T]): ViTForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  106. final def set(paramPair: ParamPair[_]): ViTForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  107. final def set(param: String, value: Any): ViTForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  108. final def set[T](param: Param[T], value: T): ViTForImageClassification.this.type
    Definition Classes
    Params
  109. def setBatchSize(size: Int): ViTForImageClassification.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotateImage
  110. def setConfigProtoBytes(bytes: Array[Int]): ViTForImageClassification.this.type

    ConfigProto from tensorflow, serialized into byte array.

    ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()

  111. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): ViTForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  112. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): ViTForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  113. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): ViTForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  114. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): ViTForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  115. final def setDefault(paramPairs: ParamPair[_]*): ViTForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  116. final def setDefault[T](param: Param[T], value: T): ViTForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  117. def setDoNormalize(value: Boolean): ViTForImageClassification.this.type

    Definition Classes
    HasImageFeatureProperties
  118. def setDoResize(value: Boolean): ViTForImageClassification.this.type

    Definition Classes
    HasImageFeatureProperties
  119. def setFeatureExtractorType(value: String): ViTForImageClassification.this.type

    Definition Classes
    HasImageFeatureProperties
  120. def setImageMean(value: Array[Double]): ViTForImageClassification.this.type

    Definition Classes
    HasImageFeatureProperties
  121. def setImageStd(value: Array[Double]): ViTForImageClassification.this.type

    Definition Classes
    HasImageFeatureProperties
  122. final def setInputCols(value: String*): ViTForImageClassification.this.type
    Definition Classes
    HasInputAnnotationCols
  123. def setInputCols(value: Array[String]): ViTForImageClassification.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  124. def setLabels(value: Map[String, BigInt]): ViTForImageClassification.this.type

  125. def setLazyAnnotator(value: Boolean): ViTForImageClassification.this.type
    Definition Classes
    CanBeLazy
  126. def setModelIfNotSet(spark: SparkSession, tensorflow: TensorflowWrapper, imageMean: Array[Double], imageStd: Array[Double], resample: Int, size: Int): ViTForImageClassification.this.type

  127. final def setOutputCol(value: String): ViTForImageClassification.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  128. def setParent(parent: Estimator[ViTForImageClassification]): ViTForImageClassification
    Definition Classes
    Model
  129. def setResample(value: Int): ViTForImageClassification.this.type

    Definition Classes
    HasImageFeatureProperties
  130. def setSignatures(value: Map[String, String]): ViTForImageClassification.this.type

  131. def setSize(value: Int): ViTForImageClassification.this.type

    Definition Classes
    HasImageFeatureProperties
  132. val signatures: MapFeature[String, String]

    It contains TF model signatures for the laded saved model

  133. val size: IntParam

    Resize the input to the given size.

    Resize the input to the given size. If a tuple is provided, it should be (width, height). If only an integer is provided, then the input will be resized to (size, size). Only has an effect if do_resize is set to True.

    Definition Classes
    HasImageFeatureProperties
  134. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  135. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  136. final def transform(dataset: Dataset[_]): DataFrame

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    dataset

    Dataset[Row]

    Definition Classes
    AnnotatorModel → Transformer
  137. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  138. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  139. final def transformSchema(schema: StructType): StructType

    requirement for pipeline transformation validation.

    requirement for pipeline transformation validation. It is called on fit()

    Definition Classes
    RawAnnotator → PipelineStage
  140. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  141. val uid: String
    Definition Classes
    ViTForImageClassification → Identifiable
  142. def validate(schema: StructType): Boolean

    takes a Dataset and checks to see if all the required annotation types are present.

    takes a Dataset and checks to see if all the required annotation types are present.

    schema

    to be validated

    returns

    True if all the required types are present, else false

    Attributes
    protected
    Definition Classes
    RawAnnotator
  143. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  144. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  145. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  146. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  147. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  148. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  149. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  150. def writeTensorflowModelV2(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None, savedSignatures: Option[Map[String, String]] = None): Unit
    Definition Classes
    WriteTensorflowModel

Inherited from HasEngine

Inherited from WriteTensorflowModel

Inherited from HasImageFeatureProperties

Inherited from CanBeLazy

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[ViTForImageClassification]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.

Annotator types

Required input and expected output annotator types

Members

Parameter setters

Parameter getters