class ConvNextForImageClassification extends SwinForImageClassification

ConvNextForImageClassification is an image classifier based on ConvNet models.

The ConvNeXT model was proposed in A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie. ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them.

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

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

The default model is "image_classifier_convnext_tiny_224_local", 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 ConvNextForImageClassificationTestSpec.

References:

A ConvNet for the 2020s

Paper Abstract:

The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.

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 = ConvNextForImageClassification
  .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
SwinForImageClassification, HasRescaleFactor, ViTForImageClassification, HasEngine, 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
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Inherited
  1. ConvNextForImageClassification
  2. SwinForImageClassification
  3. HasRescaleFactor
  4. ViTForImageClassification
  5. HasEngine
  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
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Instance Constructors

  1. new ConvNextForImageClassification()

    Annotator reference id.

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

  2. new ConvNextForImageClassification(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
    ConvNextForImageClassification → 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[_]): ConvNextForImageClassification.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. val cropPct: DoubleParam

    Determines rescale and crop percentage for images smaller than the configured size (Default: 224 / 256d).

    Determines rescale and crop percentage for images smaller than the configured size (Default: 224 / 256d).

    If the image size is smaller than the specified size, the smaller edge of the image will be matched to int(size / cropPct). Afterwards the image is cropped to (size, size).

  23. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  24. 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
  25. val doRescale: BooleanParam

    Whether to rescale the image values by rescaleFactor.

    Whether to rescale the image values by rescaleFactor.

    Definition Classes
    HasRescaleFactor
  26. val doResize: BooleanParam

    Whether to resize the input to a certain size

    Whether to resize the input to a certain size

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

    Override for additional custom schema checks

    Override for additional custom schema checks

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

    Name of model's architecture for feature extraction

    Name of model's architecture for feature extraction

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

    Size of every batch.

    Size of every batch.

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

    Returns labels used to train this model

    Returns labels used to train this model

    Definition Classes
    ViTForImageClassification
  47. 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
  48. def getCropPct: Double

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

    Definition Classes
    HasImageFeatureProperties
  51. def getDoRescale: Boolean

    Definition Classes
    HasRescaleFactor
  52. def getDoResize: Boolean

    Definition Classes
    HasImageFeatureProperties
  53. def getEngine: String

    Definition Classes
    HasEngine
  54. def getFeatureExtractorType: String

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

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

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

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  58. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  59. def getModelIfNotSet: ConvNextClassifier

    Definition Classes
    ConvNextForImageClassification → ViTForImageClassification
  60. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  61. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

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

    Definition Classes
    HasImageFeatureProperties
  64. def getRescaleFactor: Double

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

    Definition Classes
    ViTForImageClassification
  66. def getSize: Int

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

    Input annotator type : IMAGE

    Input annotator type : IMAGE

    Definition Classes
    ViTForImageClassification → HasInputAnnotationCols
  76. 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
  77. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  78. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  79. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  80. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  81. 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
  82. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  83. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  84. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  86. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  88. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  89. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  90. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  91. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  92. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  93. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  94. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  95. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  96. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  97. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  98. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  99. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    ConvNextForImageClassification → SwinForImageClassification → ViTForImageClassification → ParamsAndFeaturesWritable
  100. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  101. val outputAnnotatorType: AnnotatorType

    Output annotator type : CATEGORY

    Output annotator type : CATEGORY

    Definition Classes
    ViTForImageClassification → HasOutputAnnotatorType
  102. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  103. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  104. var parent: Estimator[ViTForImageClassification]
    Definition Classes
    Model
  105. 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
  106. 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
  107. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  108. def set[T](feature: StructFeature[T], value: T): ConvNextForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  109. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): ConvNextForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  110. def set[T](feature: SetFeature[T], value: Set[T]): ConvNextForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  111. def set[T](feature: ArrayFeature[T], value: Array[T]): ConvNextForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  112. final def set(paramPair: ParamPair[_]): ConvNextForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  113. final def set(param: String, value: Any): ConvNextForImageClassification.this.type
    Attributes
    protected
    Definition Classes
    Params
  114. final def set[T](param: Param[T], value: T): ConvNextForImageClassification.this.type
    Definition Classes
    Params
  115. def setBatchSize(size: Int): ConvNextForImageClassification.this.type

    Size of every batch.

    Size of every batch.

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

    ConfigProto from tensorflow, serialized into byte array.

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

    Definition Classes
    ViTForImageClassification
  117. def setCropPct(value: Double): ConvNextForImageClassification.this.type

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

    Definition Classes
    HasImageFeatureProperties
  125. def setDoRescale(value: Boolean): ConvNextForImageClassification.this.type

    Definition Classes
    HasRescaleFactor
  126. def setDoResize(value: Boolean): ConvNextForImageClassification.this.type

    Definition Classes
    HasImageFeatureProperties
  127. def setFeatureExtractorType(value: String): ConvNextForImageClassification.this.type

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

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

    Definition Classes
    HasImageFeatureProperties
  130. final def setInputCols(value: String*): ConvNextForImageClassification.this.type
    Definition Classes
    HasInputAnnotationCols
  131. def setInputCols(value: Array[String]): ConvNextForImageClassification.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

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

    Definition Classes
    ViTForImageClassification
  133. def setLazyAnnotator(value: Boolean): ConvNextForImageClassification.this.type
    Definition Classes
    CanBeLazy
  134. def setModelIfNotSet(spark: SparkSession, tensorflow: TensorflowWrapper, preprocessor: Preprocessor): ConvNextForImageClassification.this.type

    Definition Classes
    ConvNextForImageClassification → ViTForImageClassification
  135. final def setOutputCol(value: String): ConvNextForImageClassification.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

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

    Definition Classes
    HasImageFeatureProperties
  138. def setRescaleFactor(value: Double): ConvNextForImageClassification.this.type

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

    Definition Classes
    ViTForImageClassification
  140. def setSize(value: Int): ConvNextForImageClassification.this.type

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

Inherited from HasRescaleFactor

Inherited from ViTForImageClassification

Inherited from HasEngine

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