ArmMachineLearningModelFactory.ImageModelSettingsObjectDetection Method

Definition

Namespace:: Azure.ResourceManager.MachineLearning.Models

Assembly:: Azure.ResourceManager.MachineLearning.dll

Package:: Azure.ResourceManager.MachineLearning v1.3.0

Source:: ArmMachineLearningModelFactory.cs

Important

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Settings used for training the model. 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.

public static Azure.ResourceManager.MachineLearning.Models.ImageModelSettingsObjectDetection ImageModelSettingsObjectDetection(string advancedSettings = default, bool? amsGradient = default, string augmentations = default, float? beta1 = default, float? beta2 = default, int? checkpointFrequency = default, Azure.ResourceManager.MachineLearning.Models.MachineLearningFlowModelJobInput checkpointModel = default, string checkpointRunId = default, bool? distributed = default, bool? earlyStopping = default, int? earlyStoppingDelay = default, int? earlyStoppingPatience = default, bool? enableOnnxNormalization = default, int? evaluationFrequency = default, int? gradientAccumulationStep = default, int? layersToFreeze = default, float? learningRate = default, Azure.ResourceManager.MachineLearning.Models.LearningRateScheduler? learningRateScheduler = default, string modelName = default, float? momentum = default, bool? nesterov = default, int? numberOfEpochs = default, int? numberOfWorkers = default, Azure.ResourceManager.MachineLearning.Models.StochasticOptimizer? optimizer = default, int? randomSeed = default, float? stepLRGamma = default, int? stepLRStepSize = default, int? trainingBatchSize = default, int? validationBatchSize = default, float? warmupCosineLRCycles = default, int? warmupCosineLRWarmupEpochs = default, float? weightDecay = default, int? boxDetectionsPerImage = default, float? boxScoreThreshold = default, int? imageSize = default, int? maxSize = default, int? minSize = default, Azure.ResourceManager.MachineLearning.Models.MachineLearningModelSize? modelSize = default, bool? multiScale = default, float? nmsIouThreshold = default, string tileGridSize = default, float? tileOverlapRatio = default, float? tilePredictionsNmsThreshold = default, float? validationIouThreshold = default, Azure.ResourceManager.MachineLearning.Models.ValidationMetricType? validationMetricType = default);

static member ImageModelSettingsObjectDetection : string * Nullable<bool> * string * Nullable<single> * Nullable<single> * Nullable<int> * Azure.ResourceManager.MachineLearning.Models.MachineLearningFlowModelJobInput * string * Nullable<bool> * Nullable<bool> * Nullable<int> * Nullable<int> * Nullable<bool> * Nullable<int> * Nullable<int> * Nullable<int> * Nullable<single> * Nullable<Azure.ResourceManager.MachineLearning.Models.LearningRateScheduler> * string * Nullable<single> * Nullable<bool> * Nullable<int> * Nullable<int> * Nullable<Azure.ResourceManager.MachineLearning.Models.StochasticOptimizer> * Nullable<int> * Nullable<single> * Nullable<int> * Nullable<int> * Nullable<int> * Nullable<single> * Nullable<int> * Nullable<single> * Nullable<int> * Nullable<single> * Nullable<int> * Nullable<int> * Nullable<int> * Nullable<Azure.ResourceManager.MachineLearning.Models.MachineLearningModelSize> * Nullable<bool> * Nullable<single> * string * Nullable<single> * Nullable<single> * Nullable<single> * Nullable<Azure.ResourceManager.MachineLearning.Models.ValidationMetricType> -> Azure.ResourceManager.MachineLearning.Models.ImageModelSettingsObjectDetection

Public Shared Function ImageModelSettingsObjectDetection (Optional advancedSettings As String = Nothing, Optional amsGradient As Nullable(Of Boolean) = Nothing, Optional augmentations As String = Nothing, Optional beta1 As Nullable(Of Single) = Nothing, Optional beta2 As Nullable(Of Single) = Nothing, Optional checkpointFrequency As Nullable(Of Integer) = Nothing, Optional checkpointModel As MachineLearningFlowModelJobInput = Nothing, Optional checkpointRunId As String = Nothing, Optional distributed As Nullable(Of Boolean) = Nothing, Optional earlyStopping As Nullable(Of Boolean) = Nothing, Optional earlyStoppingDelay As Nullable(Of Integer) = Nothing, Optional earlyStoppingPatience As Nullable(Of Integer) = Nothing, Optional enableOnnxNormalization As Nullable(Of Boolean) = Nothing, Optional evaluationFrequency As Nullable(Of Integer) = Nothing, Optional gradientAccumulationStep As Nullable(Of Integer) = Nothing, Optional layersToFreeze As Nullable(Of Integer) = Nothing, Optional learningRate As Nullable(Of Single) = Nothing, Optional learningRateScheduler As Nullable(Of LearningRateScheduler) = Nothing, Optional modelName As String = Nothing, Optional momentum As Nullable(Of Single) = Nothing, Optional nesterov As Nullable(Of Boolean) = Nothing, Optional numberOfEpochs As Nullable(Of Integer) = Nothing, Optional numberOfWorkers As Nullable(Of Integer) = Nothing, Optional optimizer As Nullable(Of StochasticOptimizer) = Nothing, Optional randomSeed As Nullable(Of Integer) = Nothing, Optional stepLRGamma As Nullable(Of Single) = Nothing, Optional stepLRStepSize As Nullable(Of Integer) = Nothing, Optional trainingBatchSize As Nullable(Of Integer) = Nothing, Optional validationBatchSize As Nullable(Of Integer) = Nothing, Optional warmupCosineLRCycles As Nullable(Of Single) = Nothing, Optional warmupCosineLRWarmupEpochs As Nullable(Of Integer) = Nothing, Optional weightDecay As Nullable(Of Single) = Nothing, Optional boxDetectionsPerImage As Nullable(Of Integer) = Nothing, Optional boxScoreThreshold As Nullable(Of Single) = Nothing, Optional imageSize As Nullable(Of Integer) = Nothing, Optional maxSize As Nullable(Of Integer) = Nothing, Optional minSize As Nullable(Of Integer) = Nothing, Optional modelSize As Nullable(Of MachineLearningModelSize) = Nothing, Optional multiScale As Nullable(Of Boolean) = Nothing, Optional nmsIouThreshold As Nullable(Of Single) = Nothing, Optional tileGridSize As String = Nothing, Optional tileOverlapRatio As Nullable(Of Single) = Nothing, Optional tilePredictionsNmsThreshold As Nullable(Of Single) = Nothing, Optional validationIouThreshold As Nullable(Of Single) = Nothing, Optional validationMetricType As Nullable(Of ValidationMetricType) = Nothing) As ImageModelSettingsObjectDetection

Parameters

advancedSettings: String

Settings for advanced scenarios.

amsGradient: Nullable<Boolean>

Enable AMSGrad when optimizer is 'adam' or 'adamw'.

augmentations: String

Settings for using Augmentations.

beta1: Nullable<Single>

Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].

beta2: Nullable<Single>

Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].

checkpointFrequency: Nullable<Int32>

Frequency to store model checkpoints. Must be a positive integer.

checkpointModel: MachineLearningFlowModelJobInput

The pretrained checkpoint model for incremental training.

checkpointRunId: String

The id of a previous run that has a pretrained checkpoint for incremental training.

distributed: Nullable<Boolean>

Whether to use distributed training.

earlyStopping: Nullable<Boolean>

Enable early stopping logic during training.

earlyStoppingDelay: Nullable<Int32>

Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.

earlyStoppingPatience: Nullable<Int32>

Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.

enableOnnxNormalization: Nullable<Boolean>

Enable normalization when exporting ONNX model.

evaluationFrequency: Nullable<Int32>

Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.

gradientAccumulationStep: Nullable<Int32>

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.

layersToFreeze: Nullable<Int32>

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: Nullable<Single>

Initial learning rate. Must be a float in the range [0, 1].

learningRateScheduler: Nullable<LearningRateScheduler>

Learning rate scheduler enum.

modelName: String

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.

momentum: Nullable<Single>

Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].

nesterov: Nullable<Boolean>

Enable nesterov when optimizer is 'sgd'.

numberOfEpochs: Nullable<Int32>

Number of training epochs. Must be a positive integer.

numberOfWorkers: Nullable<Int32>

Number of data loader workers. Must be a non-negative integer.

optimizer: Nullable<StochasticOptimizer>

Stochastic optimizer for image models.

randomSeed: Nullable<Int32>

Random seed to be used when using deterministic training.

stepLRGamma: Nullable<Single>

Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].

stepLRStepSize: Nullable<Int32>

Value of step size when learning rate scheduler is 'step'. Must be a positive integer.

trainingBatchSize: Nullable<Int32>

Training batch size. Must be a positive integer.

validationBatchSize: Nullable<Int32>

Validation batch size. Must be a positive integer.

warmupCosineLRCycles: Nullable<Single>

Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].

warmupCosineLRWarmupEpochs: Nullable<Int32>

Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.

weightDecay: Nullable<Single>

Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].

boxDetectionsPerImage: Nullable<Int32>

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: Nullable<Single>

During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].

imageSize: Nullable<Int32>

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.

maxSize: Nullable<Int32>

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: Nullable<Int32>

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.

modelSize: Nullable<MachineLearningModelSize>

Image model size.

multiScale: Nullable<Boolean>

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.

nmsIouThreshold: Nullable<Single>

IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].

tileGridSize: String

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: Nullable<Single>

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: Nullable<Single>

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.

validationIouThreshold: Nullable<Single>

IOU threshold to use when computing validation metric. Must be float in the range [0, 1].

validationMetricType: Nullable<ValidationMetricType>

Metric computation method to use for validation metrics in image tasks.

Returns

ImageModelSettingsObjectDetection

A new ImageModelSettingsObjectDetection instance for mocking.

Applies to

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