ImageModelDistributionSettingsObjectDetection Class

Definition

Namespace:

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

Assembly:

Az.MachineLearningServices.private.dll

Distribution expressions to sweep over values of model settings. 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.

[System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ImageModelDistributionSettingsObjectDetectionTypeConverter))]
public class ImageModelDistributionSettingsObjectDetection : Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IImageModelDistributionSettingsObjectDetection, Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Runtime.IValidates

[<System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ImageModelDistributionSettingsObjectDetectionTypeConverter))>]
type ImageModelDistributionSettingsObjectDetection = class
    interface IImageModelDistributionSettingsObjectDetection
    interface IJsonSerializable
    interface IImageModelDistributionSettings
    interface IValidates

Public Class ImageModelDistributionSettingsObjectDetection
Implements IImageModelDistributionSettingsObjectDetection, IValidates

Inheritance

Object

ImageModelDistributionSettingsObjectDetection

Attributes

TypeConverterAttribute

Implements

IImageModelDistributionSettings IImageModelDistributionSettingsObjectDetection IJsonSerializable IValidates

Constructors

ImageModelDistributionSettingsObjectDetection()

Creates an new ImageModelDistributionSettingsObjectDetection instance.

Properties

AmsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.
Augmentation	Settings for using Augmentations.
Beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
Beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
BoxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
BoxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
Distributed	Whether to use distributer training.
EarlyStopping	Enable early stopping logic during training.
EarlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
EarlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
EnableOnnxNormalization	Enable normalization when exporting ONNX model.
EvaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
GradientAccumulationStep	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.
ImageSize	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.
LayersToFreeze	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.
LearningRate	Initial learning rate. Must be a float in the range [0, 1].
LearningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.
MaxSize	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.
MinSize	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.
ModelName	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.
ModelSize	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.
Momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
MultiScale	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.
Nesterov	Enable nesterov when optimizer is 'sgd'.
NmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].
NumberOfEpoch	Number of training epochs. Must be a positive integer.
NumberOfWorker	Number of data loader workers. Must be a non-negative integer.
Optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
RandomSeed	Random seed to be used when using deterministic training.
StepLrGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
StepLrStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
TileGridSize	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.
TileOverlapRatio	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.
TilePredictionsNmsThreshold	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
TrainingBatchSize	Training batch size. Must be a positive integer.
ValidationBatchSize	Validation batch size. Must be a positive integer.
ValidationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
ValidationMetricType	Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.
WarmupCosineLrCycle	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
WarmupCosineLrWarmupEpoch	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
WeightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].

Methods

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