ImageObjectDetection Class

Reference

Definition

Namespace:: Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401

Assembly:: Az.MachineLearningServices.private.dll

Important

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Image Object Detection. Object detection is used to identify objects in an image and locate each object with a bounding box e.g. locate all dogs and cats in an image and draw a bounding box around each.

[System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ImageObjectDetectionTypeConverter))]
public class ImageObjectDetection : Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IImageObjectDetection, Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Runtime.IValidates

[<System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ImageObjectDetectionTypeConverter))>]
type ImageObjectDetection = class
    interface IImageObjectDetection
    interface IJsonSerializable
    interface IImageObjectDetectionBase
    interface IImageVertical
    interface IAutoMlVertical
    interface IValidates

Public Class ImageObjectDetection
Implements IImageObjectDetection, IValidates

Inheritance: Object
ImageObjectDetection

Attributes: TypeConverterAttribute

Implements: IAutoMlVertical IImageObjectDetection IImageObjectDetectionBase IImageVertical IJsonSerializable IValidates

Constructors

ImageObjectDetection()

Creates an new ImageObjectDetection instance.

Properties

EarlyTerminationDelayEvaluation	Number of intervals by which to delay the first evaluation.
EarlyTerminationEvaluationInterval	Interval (number of runs) between policy evaluations.
EarlyTerminationPolicyType	[Required] Name of policy configuration
LimitSetting	[Required] Limit settings for the AutoML job.
LimitSettingMaxConcurrentTrial	Maximum number of concurrent AutoML iterations.
LimitSettingMaxTrial	Maximum number of AutoML iterations.
LimitSettingTimeout	AutoML job timeout.
LogVerbosity	Log verbosity for the job.
ModelSetting	Settings used for training the model.
ModelSettingAdvancedSetting	Settings for advanced scenarios.
ModelSettingAmsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.
ModelSettingAugmentation	Settings for using Augmentations.
ModelSettingBeta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
ModelSettingBeta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
ModelSettingBoxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
ModelSettingBoxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
ModelSettingCheckpointFrequency	Frequency to store model checkpoints. Must be a positive integer.
ModelSettingCheckpointModel	The pretrained checkpoint model for incremental training.
ModelSettingCheckpointModelDescription	Description for the input.
ModelSettingCheckpointModelJobInputType	[Required] Specifies the type of job.
ModelSettingCheckpointModelMode	Input Asset Delivery Mode.
ModelSettingCheckpointModelUri	[Required] Input Asset URI.
ModelSettingCheckpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.
ModelSettingDistributed	Whether to use distributed training.
ModelSettingEarlyStopping	Enable early stopping logic during training.
ModelSettingEarlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
ModelSettingEarlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
ModelSettingEnableOnnxNormalization	Enable normalization when exporting ONNX model.
ModelSettingEvaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
ModelSettingGradientAccumulationStep	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.
ModelSettingImageSize	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.
ModelSettingLayersToFreeze	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.
ModelSettingLearningRate	Initial learning rate. Must be a float in the range [0, 1].
ModelSettingLearningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
ModelSettingMaxSize	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.
ModelSettingMinSize	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.
ModelSettingModelName	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.
ModelSettingModelSize	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.
ModelSettingMomentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
ModelSettingMultiScale	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.
ModelSettingNesterov	Enable nesterov when optimizer is 'sgd'.
ModelSettingNmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
ModelSettingNumberOfEpoch	Number of training epochs. Must be a positive integer.
ModelSettingNumberOfWorker	Number of data loader workers. Must be a non-negative integer.
ModelSettingOptimizer	Type of optimizer.
ModelSettingRandomSeed	Random seed to be used when using deterministic training.
ModelSettingStepLrGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
ModelSettingStepLrStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
ModelSettingTileGridSize	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.
ModelSettingTileOverlapRatio	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.
ModelSettingTilePredictionsNmsThreshold	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.
ModelSettingTrainingBatchSize	Training batch size. Must be a positive integer.
ModelSettingValidationBatchSize	Validation batch size. Must be a positive integer.
ModelSettingValidationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
ModelSettingValidationMetricType	Metric computation method to use for validation metrics.
ModelSettingWarmupCosineLrCycle	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
ModelSettingWarmupCosineLrWarmupEpoch	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
ModelSettingWeightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
PrimaryMetric	Primary metric to optimize for this task.
SearchSpace	Search space for sampling different combinations of models and their hyperparameters.
SweepSetting	Model sweeping and hyperparameter sweeping related settings.
SweepSettingEarlyTermination	Type of early termination policy.
SweepSettingSamplingAlgorithm	[Required] Type of the hyperparameter sampling algorithms.
TargetColumnName	Target column name: This is prediction values column. Also known as label column name in context of classification tasks.
TaskType	[Required] Task type for AutoMLJob.
TrainingData	[Required] Training data input.
TrainingDataDescription	Description for the input.
TrainingDataJobInputType	[Required] Specifies the type of job.
TrainingDataMode	Input Asset Delivery Mode.
TrainingDataUri	[Required] Input Asset URI.
ValidationData	Validation data inputs.
ValidationDataDescription	Description for the input.
ValidationDataJobInputType	[Required] Specifies the type of job.
ValidationDataMode	Input Asset Delivery Mode.
ValidationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.
ValidationDataUri	[Required] Input Asset URI.

Methods

DeserializeFromDictionary(IDictionary)	Deserializes a IDictionary into an instance of ImageObjectDetection.
DeserializeFromPSObject(PSObject)	Deserializes a PSObject into an instance of ImageObjectDetection.
FromJson(JsonNode)	Deserializes a JsonNode into an instance of Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IImageObjectDetection.
FromJsonString(String)	Creates a new instance of ImageObjectDetection, deserializing the content from a json string.
ToJson(JsonObject, SerializationMode)	Serializes this instance of ImageObjectDetection into a JsonNode.
ToJsonString()	Serializes this instance to a json string.
ToString()
Validate(IEventListener)	Validates that this object meets the validation criteria.

Applies to

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