class SwinForImageClassification extends ViTForImageClassification with HasRescaleFactor

SwinImageClassification is an image classifier based on Swin.

The Swin Transformer was proposed in Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.

It is basically a hierarchical Transformer whose representation is computed with shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection.

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

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

The default model is "image_classifier_swin_base_patch4_window7_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 🚀. To see which models are compatible and how to import them see https://github.com/JohnSnowLabs/spark-nlp/discussions/5669 and to see more extended examples, see SwinForImageClassificationTest.

References:

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Paper Abstract:

This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with Shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test- dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the- art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures.

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 = SwinForImageClassification
  .pretrained()
  .setInputCols("image_assembler")
  .setOutputCol("class")

val pipeline = new Pipeline().setStages(Array(imageAssembler, imageClassifier))
val pipelineDF = pipeline.fit(imageDF).transform(imageDF)

pipelineDF
  .selectExpr("reverse(split(image.origin, '/'))[0] as image_name", "class.result")
  .show(truncate = false)
+-----------------+----------------------------------------------------------+
|image_name       |result                                                    |
+-----------------+----------------------------------------------------------+
|palace.JPEG      |[palace]                                                  |
|egyptian_cat.jpeg|[tabby, tabby cat]                                        |
|hippopotamus.JPEG|[hippopotamus, hippo, river horse, Hippopotamus amphibius]|
|hen.JPEG         |[hen]                                                     |
|ostrich.JPEG     |[ostrich, Struthio camelus]                               |
|junco.JPEG       |[junco, snowbird]                                         |
|bluetick.jpg     |[bluetick]                                                |
|chihuahua.jpg    |[Chihuahua]                                               |
|tractor.JPEG     |[tractor]                                                 |
|ox.JPEG          |[ox]                                                      |
+-----------------+----------------------------------------------------------+
Linear Supertypes
HasRescaleFactor, ViTForImageClassification, HasEngine, WriteOnnxModel, WriteTensorflowModel, HasImageFeatureProperties, HasBatchedAnnotateImage[ViTForImageClassification], AnnotatorModel[ViTForImageClassification], CanBeLazy, RawAnnotator[ViTForImageClassification], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[ViTForImageClassification], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
ConvNextForImageClassification
Ordering
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Inherited
  1. SwinForImageClassification
  2. HasRescaleFactor
  3. ViTForImageClassification
  4. HasEngine
  5. WriteOnnxModel
  6. WriteTensorflowModel
  7. HasImageFeatureProperties
  8. HasBatchedAnnotateImage
  9. AnnotatorModel
  10. CanBeLazy
  11. RawAnnotator
  12. HasOutputAnnotationCol
  13. HasInputAnnotationCols
  14. HasOutputAnnotatorType
  15. ParamsAndFeaturesWritable
  16. HasFeatures
  17. DefaultParamsWritable
  18. MLWritable
  19. Model
  20. Transformer
  21. PipelineStage
  22. Logging
  23. Params
  24. Serializable
  25. Serializable
  26. Identifiable
  27. AnyRef
  28. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new SwinForImageClassification()

    Annotator reference id.

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

  2. new SwinForImageClassification(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
    SwinForImageClassification → ViTForImageClassification → HasBatchedAnnotateImage
  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[_]): SwinForImageClassification.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()

    Definition Classes
    ViTForImageClassification
  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 doRescale: BooleanParam

    Whether to rescale the image values by rescaleFactor.

    Whether to rescale the image values by rescaleFactor.

    Definition Classes
    HasRescaleFactor
  25. val doResize: BooleanParam

    Whether to resize the input to a certain size

    Whether to resize the input to a certain size

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

    Override for additional custom schema checks

    Override for additional custom schema checks

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

    Name of model's architecture for feature extraction

    Name of model's architecture for feature extraction

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

    Size of every batch.

    Size of every batch.

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

    Returns labels used to train this model

    Returns labels used to train this model

    Definition Classes
    ViTForImageClassification
  46. def getConfigProtoBytes: Option[Array[Byte]]

    ConfigProto from tensorflow, serialized into byte array.

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

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

    Definition Classes
    HasImageFeatureProperties
  49. def getDoRescale: Boolean

    Definition Classes
    HasRescaleFactor
  50. def getDoResize: Boolean

    Definition Classes
    HasImageFeatureProperties
  51. def getEngine: String

    Definition Classes
    HasEngine
  52. def getFeatureExtractorType: String

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

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

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

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  56. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  57. def getModelIfNotSet: ViTClassifier

    Definition Classes
    ViTForImageClassification
  58. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  59. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

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

    Definition Classes
    HasImageFeatureProperties
  62. def getRescaleFactor: Double

    Definition Classes
    HasRescaleFactor
  63. def getSignatures: Option[Map[String, String]]

    Definition Classes
    ViTForImageClassification
  64. def getSize: Int

    Definition Classes
    HasImageFeatureProperties
  65. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  66. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  67. def hasParent: Boolean
    Definition Classes
    Model
  68. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  69. 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
  70. 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
  71. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  72. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : IMAGE

    Input annotator type : IMAGE

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

    Labels used to decode predicted IDs back to string tags

    Labels used to decode predicted IDs back to string tags

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

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    ViTForImageClassification → HasOutputAnnotatorType
  100. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  101. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  102. var parent: Estimator[ViTForImageClassification]
    Definition Classes
    Model
  103. 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
  104. val rescaleFactor: DoubleParam

    Factor to scale the image values (Default: 1 / 255.0).

    Factor to scale the image values (Default: 1 / 255.0).

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

    Size of every batch.

    Size of every batch.

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

    ConfigProto from tensorflow, serialized into byte array.

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

    Definition Classes
    ViTForImageClassification
  115. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): SwinForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  116. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): SwinForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  117. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): SwinForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  118. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): SwinForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  119. final def setDefault(paramPairs: ParamPair[_]*): SwinForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  120. final def setDefault[T](param: Param[T], value: T): SwinForImageClassification.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  121. def setDoNormalize(value: Boolean): SwinForImageClassification.this.type

    Definition Classes
    HasImageFeatureProperties
  122. def setDoRescale(value: Boolean): SwinForImageClassification.this.type

    Definition Classes
    HasRescaleFactor
  123. def setDoResize(value: Boolean): SwinForImageClassification.this.type

    Definition Classes
    HasImageFeatureProperties
  124. def setFeatureExtractorType(value: String): SwinForImageClassification.this.type

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

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

    Definition Classes
    HasImageFeatureProperties
  127. final def setInputCols(value: String*): SwinForImageClassification.this.type
    Definition Classes
    HasInputAnnotationCols
  128. def setInputCols(value: Array[String]): SwinForImageClassification.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

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

    Definition Classes
    ViTForImageClassification
  130. def setLazyAnnotator(value: Boolean): SwinForImageClassification.this.type
    Definition Classes
    CanBeLazy
  131. def setModelIfNotSet(spark: SparkSession, tensorflowWrapper: Option[TensorflowWrapper], onnxWrapper: Option[OnnxWrapper], preprocessor: Preprocessor): SwinForImageClassification.this.type

    Definition Classes
    ViTForImageClassification
  132. final def setOutputCol(value: String): SwinForImageClassification.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

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

    Definition Classes
    HasImageFeatureProperties
  135. def setRescaleFactor(value: Double): SwinForImageClassification.this.type

    Definition Classes
    HasRescaleFactor
  136. def setSignatures(value: Map[String, String]): SwinForImageClassification.this.type

    Definition Classes
    ViTForImageClassification
  137. def setSize(value: Int): SwinForImageClassification.this.type

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

    It contains TF model signatures for the laded saved model

    It contains TF model signatures for the laded saved model

    Definition Classes
    ViTForImageClassification
  139. 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
  140. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  141. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  142. 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
  143. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  144. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  145. 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
  146. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  147. val uid: String
    Definition Classes
    SwinForImageClassification → ViTForImageClassification → Identifiable
  148. 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
  149. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  150. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  151. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  152. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  153. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  154. def writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: OnnxWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOnnxModel
  155. def writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String): Unit
    Definition Classes
    WriteOnnxModel
  156. def writeTensorflowHub(path: String, tfPath: String, spark: SparkSession, suffix: String = "_use"): Unit
    Definition Classes
    WriteTensorflowModel
  157. def writeTensorflowModel(path: String, spark: SparkSession, tensorflow: TensorflowWrapper, suffix: String, filename: String, configProtoBytes: Option[Array[Byte]] = None): Unit
    Definition Classes
    WriteTensorflowModel
  158. 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 HasRescaleFactor

Inherited from ViTForImageClassification

Inherited from HasEngine

Inherited from WriteOnnxModel

Inherited from WriteTensorflowModel

Inherited from HasImageFeatureProperties

Inherited from HasBatchedAnnotateImage[ViTForImageClassification]

Inherited from AnnotatorModel[ViTForImageClassification]

Inherited from CanBeLazy

Inherited from RawAnnotator[ViTForImageClassification]

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