ImageModelSettingsObjectDetection Class

Definition

Namespace:

Azure.ResourceManager.MachineLearning.Models

Assembly:

Azure.ResourceManager.MachineLearning.dll

Package:

Azure.ResourceManager.MachineLearning v1.1.1

Source:

ImageModelSettingsObjectDetection.cs

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 class ImageModelSettingsObjectDetection : Azure.ResourceManager.MachineLearning.Models.ImageModelSettings

type ImageModelSettingsObjectDetection = class
    inherit ImageModelSettings

Public Class ImageModelSettingsObjectDetection
Inherits ImageModelSettings

Inheritance

Object

ImageModelSettings

ImageModelSettingsObjectDetection

Constructors

ImageModelSettingsObjectDetection()

Initializes a new instance of ImageModelSettingsObjectDetection.

Properties

AdvancedSettings	Settings for advanced scenarios. (Inherited from ImageModelSettings)
AmsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'. (Inherited from ImageModelSettings)
Augmentations	Settings for using Augmentations. (Inherited from ImageModelSettings)
Beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. (Inherited from ImageModelSettings)
Beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. (Inherited from ImageModelSettings)
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].
CheckpointFrequency	Frequency to store model checkpoints. Must be a positive integer. (Inherited from ImageModelSettings)
CheckpointModel	The pretrained checkpoint model for incremental training. (Inherited from ImageModelSettings)
CheckpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training. (Inherited from ImageModelSettings)
Distributed	Whether to use distributed training. (Inherited from ImageModelSettings)
EarlyStopping	Enable early stopping logic during training. (Inherited from ImageModelSettings)
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 ImageModelSettings)
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 ImageModelSettings)
EnableOnnxNormalization	Enable normalization when exporting ONNX model. (Inherited from ImageModelSettings)
EvaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. (Inherited from ImageModelSettings)
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 ImageModelSettings)
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 ImageModelSettings)
LearningRate	Initial learning rate. Must be a float in the range [0, 1]. (Inherited from ImageModelSettings)
LearningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. (Inherited from ImageModelSettings)
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 ImageModelSettings)
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 ImageModelSettings)
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 ImageModelSettings)
NmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
NumberOfEpochs	Number of training epochs. Must be a positive integer. (Inherited from ImageModelSettings)
NumberOfWorkers	Number of data loader workers. Must be a non-negative integer. (Inherited from ImageModelSettings)
Optimizer	Type of optimizer. (Inherited from ImageModelSettings)
RandomSeed	Random seed to be used when using deterministic training. (Inherited from ImageModelSettings)
StepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. (Inherited from ImageModelSettings)
StepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer. (Inherited from ImageModelSettings)
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.
TrainingBatchSize	Training batch size. Must be a positive integer. (Inherited from ImageModelSettings)
ValidationBatchSize	Validation batch size. Must be a positive integer. (Inherited from ImageModelSettings)
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.
WarmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. (Inherited from ImageModelSettings)
WarmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. (Inherited from ImageModelSettings)
WeightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. (Inherited from ImageModelSettings)