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ImageModelDistributionSettingsObjectDetection Class

Definition

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)";
```&lt;/example&gt;
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.
public class ImageModelDistributionSettingsObjectDetection : Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettings, System.ClientModel.Primitives.IJsonModel<Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettingsObjectDetection>, System.ClientModel.Primitives.IPersistableModel<Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettingsObjectDetection>
public class ImageModelDistributionSettingsObjectDetection : Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettings
type ImageModelDistributionSettingsObjectDetection = class
    inherit ImageModelDistributionSettings
    interface IJsonModel<ImageModelDistributionSettingsObjectDetection>
    interface IPersistableModel<ImageModelDistributionSettingsObjectDetection>
type ImageModelDistributionSettingsObjectDetection = class
    inherit ImageModelDistributionSettings
Public Class ImageModelDistributionSettingsObjectDetection
Inherits ImageModelDistributionSettings
Implements IJsonModel(Of ImageModelDistributionSettingsObjectDetection), IPersistableModel(Of ImageModelDistributionSettingsObjectDetection)
Public Class ImageModelDistributionSettingsObjectDetection
Inherits ImageModelDistributionSettings
Inheritance
ImageModelDistributionSettingsObjectDetection
Implements

Constructors

ImageModelDistributionSettingsObjectDetection()

Initializes a new instance of ImageModelDistributionSettingsObjectDetection.

Properties

AmsGradient

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

(Inherited from ImageModelDistributionSettings)
Augmentations

Settings for using Augmentations.

(Inherited from ImageModelDistributionSettings)
Beta1

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

(Inherited from ImageModelDistributionSettings)
Beta2

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

(Inherited from ImageModelDistributionSettings)
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.

(Inherited from ImageModelDistributionSettings)
EarlyStopping

Enable early stopping logic during training.

(Inherited from ImageModelDistributionSettings)
EarlyStoppingDelay

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

(Inherited from ImageModelDistributionSettings)
EarlyStoppingPatience

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

(Inherited from ImageModelDistributionSettings)
EnableOnnxNormalization

Enable normalization when exporting ONNX model.

(Inherited from ImageModelDistributionSettings)
EvaluationFrequency

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

(Inherited from ImageModelDistributionSettings)
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.

(Inherited from ImageModelDistributionSettings)
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.

(Inherited from ImageModelDistributionSettings)
LearningRate

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

(Inherited from ImageModelDistributionSettings)
LearningRateScheduler

Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.

(Inherited from ImageModelDistributionSettings)
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.

(Inherited from ImageModelDistributionSettings)
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].

(Inherited from ImageModelDistributionSettings)
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'.

(Inherited from ImageModelDistributionSettings)
NmsIouThreshold

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

NumberOfEpochs

Number of training epochs. Must be a positive integer.

(Inherited from ImageModelDistributionSettings)
NumberOfWorkers

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

(Inherited from ImageModelDistributionSettings)
Optimizer

Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.

(Inherited from ImageModelDistributionSettings)
RandomSeed

Random seed to be used when using deterministic training.

(Inherited from ImageModelDistributionSettings)
StepLRGamma

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

(Inherited from ImageModelDistributionSettings)
StepLRStepSize

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

(Inherited from ImageModelDistributionSettings)
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.

(Inherited from ImageModelDistributionSettings)
ValidationBatchSize

Validation batch size. Must be a positive integer.

(Inherited from ImageModelDistributionSettings)
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'.

WarmupCosineLRCycles

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

(Inherited from ImageModelDistributionSettings)
WarmupCosineLRWarmupEpochs

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

(Inherited from ImageModelDistributionSettings)
WeightDecay

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

(Inherited from ImageModelDistributionSettings)

Explicit Interface Implementations

IJsonModel<ImageModelDistributionSettings>.Create(Utf8JsonReader, ModelReaderWriterOptions)

Reads one JSON value (including objects or arrays) from the provided reader and converts it to a model.

(Inherited from ImageModelDistributionSettings)
IJsonModel<ImageModelDistributionSettings>.Write(Utf8JsonWriter, ModelReaderWriterOptions)

Writes the model to the provided Utf8JsonWriter.

(Inherited from ImageModelDistributionSettings)
IJsonModel<ImageModelDistributionSettingsObjectDetection>.Create(Utf8JsonReader, ModelReaderWriterOptions)

Reads one JSON value (including objects or arrays) from the provided reader and converts it to a model.

IJsonModel<ImageModelDistributionSettingsObjectDetection>.Write(Utf8JsonWriter, ModelReaderWriterOptions)

Writes the model to the provided Utf8JsonWriter.

IPersistableModel<ImageModelDistributionSettings>.Create(BinaryData, ModelReaderWriterOptions)

Converts the provided BinaryData into a model.

(Inherited from ImageModelDistributionSettings)
IPersistableModel<ImageModelDistributionSettings>.GetFormatFromOptions(ModelReaderWriterOptions)

Gets the data interchange format (JSON, Xml, etc) that the model uses when communicating with the service.

(Inherited from ImageModelDistributionSettings)
IPersistableModel<ImageModelDistributionSettings>.Write(ModelReaderWriterOptions)

Writes the model into a BinaryData.

(Inherited from ImageModelDistributionSettings)
IPersistableModel<ImageModelDistributionSettingsObjectDetection>.Create(BinaryData, ModelReaderWriterOptions)

Converts the provided BinaryData into a model.

IPersistableModel<ImageModelDistributionSettingsObjectDetection>.GetFormatFromOptions(ModelReaderWriterOptions)

Gets the data interchange format (JSON, Xml, etc) that the model uses when communicating with the service.

IPersistableModel<ImageModelDistributionSettingsObjectDetection>.Write(ModelReaderWriterOptions)

Writes the model into a BinaryData.

Applies to