ImageModelDistributionSettingsObjectDetection Class

Definition

Namespace:

Azure.ResourceManager.MachineLearning.Models

Assembly:

Azure.ResourceManager.MachineLearning.dll

Package:

Azure.ResourceManager.MachineLearning v1.1.1

Source:

ImageModelDistributionSettingsObjectDetection.cs

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.

public class ImageModelDistributionSettingsObjectDetection : Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettings

type ImageModelDistributionSettingsObjectDetection = class
    inherit ImageModelDistributionSettings

Public Class ImageModelDistributionSettingsObjectDetection
Inherits ImageModelDistributionSettings

Inheritance

Object

ImageModelDistributionSettings

ImageModelDistributionSettingsObjectDetection

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)