ImageModelSettingsObjectDetection Class

Model settings for AutoML Image Object Detection Task.

Defining the automl image object detection or instance segmentation model settings.

   from azure.ai.ml import automl

   object_detection_model_settings = automl.ImageModelSettingsObjectDetection(min_size=600, max_size=1333)

Inheritance

azure.ai.ml.entities._job.automl.image.image_model_settings.ImageModelDistributionSettings

ImageModelSettingsObjectDetection

Constructor

ImageModelSettingsObjectDetection(*, advanced_settings: str | None = None, ams_gradient: bool | None = None, beta1: float | None = None, beta2: float | None = None, checkpoint_frequency: int | None = None, checkpoint_run_id: str | None = None, distributed: bool | None = None, early_stopping: bool | None = None, early_stopping_delay: int | None = None, early_stopping_patience: int | None = None, enable_onnx_normalization: bool | None = None, evaluation_frequency: int | None = None, gradient_accumulation_step: int | None = None, layers_to_freeze: int | None = None, learning_rate: float | None = None, learning_rate_scheduler: LearningRateScheduler | None = None, model_name: str | None = None, momentum: float | None = None, nesterov: bool | None = None, number_of_epochs: int | None = None, number_of_workers: int | None = None, optimizer: StochasticOptimizer | None = None, random_seed: int | None = None, step_lr_gamma: float | None = None, step_lr_step_size: int | None = None, training_batch_size: int | None = None, validation_batch_size: int | None = None, warmup_cosine_lr_cycles: float | None = None, warmup_cosine_lr_warmup_epochs: int | None = None, weight_decay: float | None = None, box_detections_per_image: int | None = None, box_score_threshold: float | None = None, image_size: int | None = None, max_size: int | None = None, min_size: int | None = None, model_size: ModelSize | None = None, multi_scale: bool | None = None, nms_iou_threshold: float | None = None, tile_grid_size: str | None = None, tile_overlap_ratio: float | None = None, tile_predictions_nms_threshold: float | None = None, validation_iou_threshold: float | None = None, validation_metric_type: ValidationMetricType | None = None, log_training_metrics: LogTrainingMetrics | None = None, log_validation_loss: LogValidationLoss | None = None, **kwargs: Any)

Parameters

Name	Description
advanced_settings Required	str Settings for advanced scenarios.
ams_gradient Required	bool Enable AMSGrad when optimizer is 'adam' or 'adamw'.
beta1 Required	float Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
beta2 Required	float Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].
checkpoint_frequency Required	int Frequency to store model checkpoints. Must be a positive integer.
checkpoint_run_id Required	str The id of a previous run that has a pretrained checkpoint for incremental training.
distributed Required	bool Whether to use distributed training.
early_stopping Required	bool Enable early stopping logic during training.
early_stopping_delay Required	int Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.
early_stopping_patience Required	int Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.
enable_onnx_normalization Required	bool Enable normalization when exporting ONNX model.
evaluation_frequency Required	int Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.
gradient_accumulation_step Required	int 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.
layers_to_freeze Required	int 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.
learning_rate Required	float Initial learning rate. Must be a float in the range [0, 1].
learning_rate_scheduler Required	str or LearningRateScheduler Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. Possible values include: "None", "WarmupCosine", "Step".
model_name Required	str 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 Required	float Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
nesterov Required	bool Enable nesterov when optimizer is 'sgd'.
number_of_epochs Required	int Number of training epochs. Must be a positive integer.
number_of_workers Required	int Number of data loader workers. Must be a non-negative integer.
optimizer Required	str or StochasticOptimizer Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
random_seed Required	int Random seed to be used when using deterministic training.
step_lr_gamma Required	float Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].
step_lr_step_size Required	int Value of step size when learning rate scheduler is 'step'. Must be a positive integer.
training_batch_size Required	int Training batch size. Must be a positive integer.
validation_batch_size Required	int Validation batch size. Must be a positive integer.
warmup_cosine_lr_cycles Required	float Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].
warmup_cosine_lr_warmup_epochs Required	int Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.
weight_decay Required	float Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].
box_detections_per_image Required	int Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
box_score_threshold Required	float During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
image_size Required	int 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.
max_size Required	int 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.
min_size Required	int 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.
model_size Required	str or ModelSize Model size. Must be 'small', 'medium', 'large'. 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. Possible values include: "None", "Small", "Medium", "Large", "ExtraLarge".
multi_scale Required	bool 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.
nms_iou_threshold Required	float IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].
tile_grid_size Required	str 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.
tile_overlap_ratio Required	float 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.
tile_predictions_nms_threshold Required	float 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.
validation_iou_threshold Required	float IOU threshold to use when computing validation metric. Must be float in the range [0, 1].
validation_metric_type Required	str or ValidationMetricType Metric computation method to use for validation metrics. Possible values include: "None", "Coco", "Voc", "CocoVoc".
log_training_metrics Required	str or <xref:azure.mgmt.machinelearningservices.models.LogTrainingMetrics> indicates whether or not to log training metrics
log_validation_loss Required	str or <xref:azure.mgmt.machinelearningservices.models.LogValidationLoss> indicates whether or not to log validation loss

Keyword-Only Parameters

Name	Description
advanced_settings Required
ams_gradient Required
beta1 Required
beta2 Required
checkpoint_frequency Required
checkpoint_run_id Required
distributed Required
early_stopping Required
early_stopping_delay Required
early_stopping_patience Required
enable_onnx_normalization Required
evaluation_frequency Required
gradient_accumulation_step Required
layers_to_freeze Required
learning_rate Required
learning_rate_scheduler Required
model_name Required
momentum Required
nesterov Required
number_of_epochs Required
number_of_workers Required
optimizer Required
random_seed Required
step_lr_gamma Required
step_lr_step_size Required
training_batch_size Required
validation_batch_size Required
warmup_cosine_lr_cycles Required
warmup_cosine_lr_warmup_epochs Required
weight_decay Required
box_detections_per_image Required
box_score_threshold Required
image_size Required
max_size Required
min_size Required
model_size Required
multi_scale Required
nms_iou_threshold Required
tile_grid_size Required
tile_overlap_ratio Required
tile_predictions_nms_threshold Required
validation_iou_threshold Required
validation_metric_type Required
log_training_metrics Required
log_validation_loss Required