Package com.azure.resourcemanager.machinelearning.models

Class ImageModelDistributionSettingsObjectDetection

java.lang.Object
com.azure.resourcemanager.machinelearning.models.ImageModelDistributionSettings
com.azure.resourcemanager.machinelearning.models.ImageModelDistributionSettingsObjectDetection
All Implemented Interfaces:
com.azure.json.JsonSerializable<ImageModelDistributionSettings>
public final class ImageModelDistributionSettingsObjectDetection extends ImageModelDistributionSettings
Distribution expressions to sweep over values of model settings. <example> Some examples are: ``` ModelName = "choice('seresnext', 'resnest50')"; LearningRate = "uniform(0.001, 0.01)"; LayersToFreeze = "choice(0, 2)"; ```</example> For more details on how to compose distribution expressions please check the documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters For more information on the available settings please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.

  • Constructor Details

    • ImageModelDistributionSettingsObjectDetection

      public ImageModelDistributionSettingsObjectDetection()
      Creates an instance of ImageModelDistributionSettingsObjectDetection class.

  • Method Details

    • boxDetectionsPerImage

      public String boxDetectionsPerImage()
      Get the boxDetectionsPerImage property: Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
      Returns:
      the boxDetectionsPerImage value.

    • withBoxDetectionsPerImage

      public ImageModelDistributionSettingsObjectDetection withBoxDetectionsPerImage(String boxDetectionsPerImage)
      Set the boxDetectionsPerImage property: Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
      Parameters:
      boxDetectionsPerImage - the boxDetectionsPerImage value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • boxScoreThreshold

      public String boxScoreThreshold()
      Get the boxScoreThreshold property: During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
      Returns:
      the boxScoreThreshold value.

    • withBoxScoreThreshold

      public ImageModelDistributionSettingsObjectDetection withBoxScoreThreshold(String boxScoreThreshold)
      Set the boxScoreThreshold property: During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
      Parameters:
      boxScoreThreshold - the boxScoreThreshold value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • imageSize

      public String imageSize()
      Get the imageSize property: Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
      Returns:
      the imageSize value.

    • withImageSize

      public ImageModelDistributionSettingsObjectDetection withImageSize(String imageSize)
      Set the imageSize property: Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
      Parameters:
      imageSize - the imageSize value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • maxSize

      public String maxSize()
      Get the maxSize property: Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
      Returns:
      the maxSize value.

    • withMaxSize

      public ImageModelDistributionSettingsObjectDetection withMaxSize(String maxSize)
      Set the maxSize property: Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
      Parameters:
      maxSize - the maxSize value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • minSize

      public String minSize()
      Get the minSize property: Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
      Returns:
      the minSize value.

    • withMinSize

      public ImageModelDistributionSettingsObjectDetection withMinSize(String minSize)
      Set the minSize property: Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.
      Parameters:
      minSize - the minSize value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • modelSize

      public String modelSize()
      Get the modelSize property: Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
      Returns:
      the modelSize value.

    • withModelSize

      public ImageModelDistributionSettingsObjectDetection withModelSize(String modelSize)
      Set the modelSize property: Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.
      Parameters:
      modelSize - the modelSize value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • multiScale

      public String multiScale()
      Get the multiScale property: Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
      Returns:
      the multiScale value.

    • withMultiScale

      public ImageModelDistributionSettingsObjectDetection withMultiScale(String multiScale)
      Set the multiScale property: Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.
      Parameters:
      multiScale - the multiScale value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • nmsIouThreshold

      public String nmsIouThreshold()
      Get the nmsIouThreshold property: IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
      Returns:
      the nmsIouThreshold value.

    • withNmsIouThreshold

      public ImageModelDistributionSettingsObjectDetection withNmsIouThreshold(String nmsIouThreshold)
      Set the nmsIouThreshold property: IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
      Parameters:
      nmsIouThreshold - the nmsIouThreshold value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • tileGridSize

      public String tileGridSize()
      Get the tileGridSize property: The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
      Returns:
      the tileGridSize value.

    • withTileGridSize

      public ImageModelDistributionSettingsObjectDetection withTileGridSize(String tileGridSize)
      Set the tileGridSize property: The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.
      Parameters:
      tileGridSize - the tileGridSize value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • tileOverlapRatio

      public String tileOverlapRatio()
      Get the tileOverlapRatio property: Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
      Returns:
      the tileOverlapRatio value.

    • withTileOverlapRatio

      public ImageModelDistributionSettingsObjectDetection withTileOverlapRatio(String tileOverlapRatio)
      Set the tileOverlapRatio property: Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.
      Parameters:
      tileOverlapRatio - the tileOverlapRatio value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • tilePredictionsNmsThreshold

      public String tilePredictionsNmsThreshold()
      Get the tilePredictionsNmsThreshold property: The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression.
      Returns:
      the tilePredictionsNmsThreshold value.

    • withTilePredictionsNmsThreshold

      public ImageModelDistributionSettingsObjectDetection withTilePredictionsNmsThreshold(String tilePredictionsNmsThreshold)
      Set the tilePredictionsNmsThreshold property: The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression.
      Parameters:
      tilePredictionsNmsThreshold - the tilePredictionsNmsThreshold value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • validationIouThreshold

      public String validationIouThreshold()
      Get the validationIouThreshold property: IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
      Returns:
      the validationIouThreshold value.

    • withValidationIouThreshold

      public ImageModelDistributionSettingsObjectDetection withValidationIouThreshold(String validationIouThreshold)
      Set the validationIouThreshold property: IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
      Parameters:
      validationIouThreshold - the validationIouThreshold value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • validationMetricType

      public String validationMetricType()
      Get the validationMetricType property: Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
      Returns:
      the validationMetricType value.

    • withValidationMetricType

      public ImageModelDistributionSettingsObjectDetection withValidationMetricType(String validationMetricType)
      Set the validationMetricType property: Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
      Parameters:
      validationMetricType - the validationMetricType value to set.
      Returns:
      the ImageModelDistributionSettingsObjectDetection object itself.

    • withAmsGradient

      public ImageModelDistributionSettingsObjectDetection withAmsGradient(String amsGradient)
      Set the amsGradient property: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
      Overrides:
      withAmsGradient in class ImageModelDistributionSettings
      Parameters:
      amsGradient - the amsGradient value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withAugmentations

      public ImageModelDistributionSettingsObjectDetection withAugmentations(String augmentations)
      Set the augmentations property: Settings for using Augmentations.
      Overrides:
      withAugmentations in class ImageModelDistributionSettings
      Parameters:
      augmentations - the augmentations value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withBeta1

      public ImageModelDistributionSettingsObjectDetection withBeta1(String beta1)
      Set the beta1 property: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
      Overrides:
      withBeta1 in class ImageModelDistributionSettings
      Parameters:
      beta1 - the beta1 value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withBeta2

      public ImageModelDistributionSettingsObjectDetection withBeta2(String beta2)
      Set the beta2 property: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
      Overrides:
      withBeta2 in class ImageModelDistributionSettings
      Parameters:
      beta2 - the beta2 value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withDistributed

      public ImageModelDistributionSettingsObjectDetection withDistributed(String distributed)
      Set the distributed property: Whether to use distributer training.
      Overrides:
      withDistributed in class ImageModelDistributionSettings
      Parameters:
      distributed - the distributed value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withEarlyStopping

      public ImageModelDistributionSettingsObjectDetection withEarlyStopping(String earlyStopping)
      Set the earlyStopping property: Enable early stopping logic during training.
      Overrides:
      withEarlyStopping in class ImageModelDistributionSettings
      Parameters:
      earlyStopping - the earlyStopping value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withEarlyStoppingDelay

      public ImageModelDistributionSettingsObjectDetection withEarlyStoppingDelay(String earlyStoppingDelay)
      Set the earlyStoppingDelay property: Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
      Overrides:
      withEarlyStoppingDelay in class ImageModelDistributionSettings
      Parameters:
      earlyStoppingDelay - the earlyStoppingDelay value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withEarlyStoppingPatience

      public ImageModelDistributionSettingsObjectDetection withEarlyStoppingPatience(String earlyStoppingPatience)
      Set the earlyStoppingPatience property: Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
      Overrides:
      withEarlyStoppingPatience in class ImageModelDistributionSettings
      Parameters:
      earlyStoppingPatience - the earlyStoppingPatience value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withEvaluationFrequency

      public ImageModelDistributionSettingsObjectDetection withEvaluationFrequency(String evaluationFrequency)
      Set the evaluationFrequency property: Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
      Overrides:
      withEvaluationFrequency in class ImageModelDistributionSettings
      Parameters:
      evaluationFrequency - the evaluationFrequency value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withEnableOnnxNormalization

      public ImageModelDistributionSettingsObjectDetection withEnableOnnxNormalization(String enableOnnxNormalization)
      Set the enableOnnxNormalization property: Enable normalization when exporting ONNX model.
      Overrides:
      withEnableOnnxNormalization in class ImageModelDistributionSettings
      Parameters:
      enableOnnxNormalization - the enableOnnxNormalization value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withGradientAccumulationStep

      public ImageModelDistributionSettingsObjectDetection withGradientAccumulationStep(String gradientAccumulationStep)
      Set the gradientAccumulationStep property: Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
      Overrides:
      withGradientAccumulationStep in class ImageModelDistributionSettings
      Parameters:
      gradientAccumulationStep - the gradientAccumulationStep value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withLayersToFreeze

      public ImageModelDistributionSettingsObjectDetection withLayersToFreeze(String layersToFreeze)
      Set the layersToFreeze property: Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
      Overrides:
      withLayersToFreeze in class ImageModelDistributionSettings
      Parameters:
      layersToFreeze - the layersToFreeze value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withLearningRate

      public ImageModelDistributionSettingsObjectDetection withLearningRate(String learningRate)
      Set the learningRate property: Initial learning rate. Must be a float in the range [0, 1].
      Overrides:
      withLearningRate in class ImageModelDistributionSettings
      Parameters:
      learningRate - the learningRate value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withLearningRateScheduler

      public ImageModelDistributionSettingsObjectDetection withLearningRateScheduler(String learningRateScheduler)
      Set the learningRateScheduler property: Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
      Overrides:
      withLearningRateScheduler in class ImageModelDistributionSettings
      Parameters:
      learningRateScheduler - the learningRateScheduler value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withModelName

      public ImageModelDistributionSettingsObjectDetection withModelName(String modelName)
      Set the modelName property: Name of the model to use for training. For more information on the available models please visit the official documentation: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-auto-train-image-models.
      Overrides:
      withModelName in class ImageModelDistributionSettings
      Parameters:
      modelName - the modelName value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withMomentum

      public ImageModelDistributionSettingsObjectDetection withMomentum(String momentum)
      Set the momentum property: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
      Overrides:
      withMomentum in class ImageModelDistributionSettings
      Parameters:
      momentum - the momentum value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withNesterov

      public ImageModelDistributionSettingsObjectDetection withNesterov(String nesterov)
      Set the nesterov property: Enable nesterov when optimizer is 'sgd'.
      Overrides:
      withNesterov in class ImageModelDistributionSettings
      Parameters:
      nesterov - the nesterov value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withNumberOfEpochs

      public ImageModelDistributionSettingsObjectDetection withNumberOfEpochs(String numberOfEpochs)
      Set the numberOfEpochs property: Number of training epochs. Must be a positive integer.
      Overrides:
      withNumberOfEpochs in class ImageModelDistributionSettings
      Parameters:
      numberOfEpochs - the numberOfEpochs value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withNumberOfWorkers

      public ImageModelDistributionSettingsObjectDetection withNumberOfWorkers(String numberOfWorkers)
      Set the numberOfWorkers property: Number of data loader workers. Must be a non-negative integer.
      Overrides:
      withNumberOfWorkers in class ImageModelDistributionSettings
      Parameters:
      numberOfWorkers - the numberOfWorkers value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withOptimizer

      public ImageModelDistributionSettingsObjectDetection withOptimizer(String optimizer)
      Set the optimizer property: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
      Overrides:
      withOptimizer in class ImageModelDistributionSettings
      Parameters:
      optimizer - the optimizer value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withRandomSeed

      public ImageModelDistributionSettingsObjectDetection withRandomSeed(String randomSeed)
      Set the randomSeed property: Random seed to be used when using deterministic training.
      Overrides:
      withRandomSeed in class ImageModelDistributionSettings
      Parameters:
      randomSeed - the randomSeed value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withStepLRGamma

      public ImageModelDistributionSettingsObjectDetection withStepLRGamma(String stepLRGamma)
      Set the stepLRGamma property: Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
      Overrides:
      withStepLRGamma in class ImageModelDistributionSettings
      Parameters:
      stepLRGamma - the stepLRGamma value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withStepLRStepSize

      public ImageModelDistributionSettingsObjectDetection withStepLRStepSize(String stepLRStepSize)
      Set the stepLRStepSize property: Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
      Overrides:
      withStepLRStepSize in class ImageModelDistributionSettings
      Parameters:
      stepLRStepSize - the stepLRStepSize value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withTrainingBatchSize

      public ImageModelDistributionSettingsObjectDetection withTrainingBatchSize(String trainingBatchSize)
      Set the trainingBatchSize property: Training batch size. Must be a positive integer.
      Overrides:
      withTrainingBatchSize in class ImageModelDistributionSettings
      Parameters:
      trainingBatchSize - the trainingBatchSize value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withValidationBatchSize

      public ImageModelDistributionSettingsObjectDetection withValidationBatchSize(String validationBatchSize)
      Set the validationBatchSize property: Validation batch size. Must be a positive integer.
      Overrides:
      withValidationBatchSize in class ImageModelDistributionSettings
      Parameters:
      validationBatchSize - the validationBatchSize value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withWarmupCosineLRCycles

      public ImageModelDistributionSettingsObjectDetection withWarmupCosineLRCycles(String warmupCosineLRCycles)
      Set the warmupCosineLRCycles property: Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
      Overrides:
      withWarmupCosineLRCycles in class ImageModelDistributionSettings
      Parameters:
      warmupCosineLRCycles - the warmupCosineLRCycles value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withWarmupCosineLRWarmupEpochs

      public ImageModelDistributionSettingsObjectDetection withWarmupCosineLRWarmupEpochs(String warmupCosineLRWarmupEpochs)
      Set the warmupCosineLRWarmupEpochs property: Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
      Overrides:
      withWarmupCosineLRWarmupEpochs in class ImageModelDistributionSettings
      Parameters:
      warmupCosineLRWarmupEpochs - the warmupCosineLRWarmupEpochs value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • withWeightDecay

      public ImageModelDistributionSettingsObjectDetection withWeightDecay(String weightDecay)
      Set the weightDecay property: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
      Overrides:
      withWeightDecay in class ImageModelDistributionSettings
      Parameters:
      weightDecay - the weightDecay value to set.
      Returns:
      the ImageModelDistributionSettings object itself.

    • validate

      public void validate()
      Validates the instance.
      Overrides:
      validate in class ImageModelDistributionSettings
      Throws:
      IllegalArgumentException - thrown if the instance is not valid.

    • toJson

      public com.azure.json.JsonWriter toJson(com.azure.json.JsonWriter jsonWriter) throws IOException
      Specified by:
      toJson in interface com.azure.json.JsonSerializable<ImageModelDistributionSettings>
      Overrides:
      toJson in class ImageModelDistributionSettings
      Throws:
      IOException

    • fromJson

      public static ImageModelDistributionSettingsObjectDetection fromJson(com.azure.json.JsonReader jsonReader) throws IOException
      Reads an instance of ImageModelDistributionSettingsObjectDetection from the JsonReader.
      Parameters:
      jsonReader - The JsonReader being read.
      Returns:
      An instance of ImageModelDistributionSettingsObjectDetection if the JsonReader was pointing to an instance of it, or null if it was pointing to JSON null.
      Throws:
      IOException - If an error occurs while reading the ImageModelDistributionSettingsObjectDetection.