trait HasImageFeatureProperties extends ParamsAndFeaturesWritable
example of required parameters
{ "do_normalize": true, "do_resize": true, "feature_extractor_type": "ViTFeatureExtractor", "image_mean": [ 0.5, 0.5, 0.5 ], "image_std": [ 0.5, 0.5, 0.5 ], "resample": 2, "size": 224 }
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- HasImageFeatureProperties
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$[T](param: Param[T]): T
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def
$$[T](feature: StructFeature[T]): T
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def
$$[K, V](feature: MapFeature[K, V]): Map[K, V]
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def
$$[T](feature: SetFeature[T]): Set[T]
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def
$$[T](feature: ArrayFeature[T]): Array[T]
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def
clear(param: Param[_]): HasImageFeatureProperties.this.type
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copyValues[T <: Params](to: T, extra: ParamMap): T
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def
defaultCopy[T <: Params](extra: ParamMap): T
- Attributes
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val
doNormalize: BooleanParam
Whether or not to normalize the input with mean and standard deviation
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val
doResize: BooleanParam
Whether to resize the input to a certain size
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def
eq(arg0: AnyRef): Boolean
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explainParam(param: Param[_]): String
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def
extractParamMap(): ParamMap
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final
def
extractParamMap(extra: ParamMap): ParamMap
- Definition Classes
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val
featureExtractorType: Param[String]
Name of model's architecture for feature extraction
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val
features: ArrayBuffer[Feature[_, _, _]]
- Definition Classes
- HasFeatures
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def
finalize(): Unit
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def
get[T](feature: StructFeature[T]): Option[T]
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def
get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
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def
get[T](feature: SetFeature[T]): Option[Set[T]]
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def
get[T](feature: ArrayFeature[T]): Option[Array[T]]
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def
get[T](param: Param[T]): Option[T]
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def
getClass(): Class[_]
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final
def
getDefault[T](param: Param[T]): Option[T]
- Definition Classes
- Params
- def getDoNormalize: Boolean
- def getDoResize: Boolean
- def getFeatureExtractorType: String
- def getImageMean: Array[Double]
- def getImageStd: Array[Double]
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final
def
getOrDefault[T](param: Param[T]): T
- Definition Classes
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def
getParam(paramName: String): Param[Any]
- Definition Classes
- Params
- def getResample: Int
- def getSize: Int
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def
hasDefault[T](param: Param[T]): Boolean
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def
hasParam(paramName: String): Boolean
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def
hashCode(): Int
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val
imageMean: DoubleArrayParam
The sequence of means for each channel, to be used when normalizing images
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val
imageStd: DoubleArrayParam
The sequence of standard deviations for each channel, to be used when normalizing images
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isDefined(param: Param[_]): Boolean
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notifyAll(): Unit
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def
onWrite(path: String, spark: SparkSession): Unit
- Attributes
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- Definition Classes
- ParamsAndFeaturesWritable
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lazy val
params: Array[Param[_]]
- Definition Classes
- Params
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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
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def
save(path: String): Unit
- Definition Classes
- MLWritable
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- @Since( "1.6.0" ) @throws( ... )
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def
set[T](feature: StructFeature[T], value: T): HasImageFeatureProperties.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
set[K, V](feature: MapFeature[K, V], value: Map[K, V]): HasImageFeatureProperties.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
set[T](feature: SetFeature[T], value: Set[T]): HasImageFeatureProperties.this.type
- Attributes
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- Definition Classes
- HasFeatures
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def
set[T](feature: ArrayFeature[T], value: Array[T]): HasImageFeatureProperties.this.type
- Attributes
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- Definition Classes
- HasFeatures
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final
def
set(paramPair: ParamPair[_]): HasImageFeatureProperties.this.type
- Attributes
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final
def
set(param: String, value: Any): HasImageFeatureProperties.this.type
- Attributes
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final
def
set[T](param: Param[T], value: T): HasImageFeatureProperties.this.type
- Definition Classes
- Params
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def
setDefault[T](feature: StructFeature[T], value: () ⇒ T): HasImageFeatureProperties.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): HasImageFeatureProperties.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): HasImageFeatureProperties.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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def
setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): HasImageFeatureProperties.this.type
- Attributes
- protected
- Definition Classes
- HasFeatures
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final
def
setDefault(paramPairs: ParamPair[_]*): HasImageFeatureProperties.this.type
- Attributes
- protected
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- Params
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final
def
setDefault[T](param: Param[T], value: T): HasImageFeatureProperties.this.type
- Attributes
- protected[org.apache.spark.ml]
- Definition Classes
- Params
- def setDoNormalize(value: Boolean): HasImageFeatureProperties.this.type
- def setDoResize(value: Boolean): HasImageFeatureProperties.this.type
- def setFeatureExtractorType(value: String): HasImageFeatureProperties.this.type
- def setImageMean(value: Array[Double]): HasImageFeatureProperties.this.type
- def setImageStd(value: Array[Double]): HasImageFeatureProperties.this.type
- def setResample(value: Int): HasImageFeatureProperties.this.type
- def setSize(value: Int): HasImageFeatureProperties.this.type
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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.
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def
synchronized[T0](arg0: ⇒ T0): T0
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def
toString(): String
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def
wait(arg0: Long): Unit
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def
write: MLWriter
- Definition Classes
- ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable