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)
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
|
Settings for advanced scenarios. |
|
ams_gradient
Required
|
Enable AMSGrad when optimizer is 'adam' or 'adamw'. |
|
beta1
Required
|
Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. |
|
beta2
Required
|
Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. |
|
checkpoint_frequency
Required
|
Frequency to store model checkpoints. Must be a positive integer. |
|
checkpoint_run_id
Required
|
The id of a previous run that has a pretrained checkpoint for incremental training. |
|
distributed
Required
|
Whether to use distributed training. |
|
early_stopping
Required
|
Enable early stopping logic during training. |
|
early_stopping_delay
Required
|
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
|
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
|
Enable normalization when exporting ONNX model. |
|
evaluation_frequency
Required
|
Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. |
|
gradient_accumulation_step
Required
|
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
|
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://free.blessedness.top/azure/machine-learning/how-to-auto-train-image-models. |
|
learning_rate
Required
|
Initial learning rate. Must be a float in the range [0, 1]. |
|
learning_rate_scheduler
Required
|
Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. Possible values include: "None", "WarmupCosine", "Step". |
|
model_name
Required
|
Name of the model to use for training. For more information on the available models please visit the official documentation: https://free.blessedness.top/azure/machine-learning/how-to-auto-train-image-models. |
|
momentum
Required
|
Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. |
|
nesterov
Required
|
Enable nesterov when optimizer is 'sgd'. |
|
number_of_epochs
Required
|
Number of training epochs. Must be a positive integer. |
|
number_of_workers
Required
|
Number of data loader workers. Must be a non-negative integer. |
|
optimizer
Required
|
Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw". |
|
random_seed
Required
|
Random seed to be used when using deterministic training. |
|
step_lr_gamma
Required
|
Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. |
|
step_lr_step_size
Required
|
Value of step size when learning rate scheduler is 'step'. Must be a positive integer. |
|
training_batch_size
Required
|
Training batch size. Must be a positive integer. |
|
validation_batch_size
Required
|
Validation batch size. Must be a positive integer. |
|
warmup_cosine_lr_cycles
Required
|
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
|
Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. |
|
weight_decay
Required
|
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
|
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
|
During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
|
image_size
Required
|
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
|
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
|
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
|
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
|
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
|
IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1]. |
|
tile_grid_size
Required
|
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
|
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
|
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
|
IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. |
|
validation_metric_type
Required
|
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
|
Default value: None
|
|
ams_gradient
|
Default value: None
|
|
beta1
|
Default value: None
|
|
beta2
|
Default value: None
|
|
checkpoint_frequency
|
Default value: None
|
|
checkpoint_run_id
|
Default value: None
|
|
distributed
|
Default value: None
|
|
early_stopping
|
Default value: None
|
|
early_stopping_delay
|
Default value: None
|
|
early_stopping_patience
|
Default value: None
|
|
enable_onnx_normalization
|
Default value: None
|
|
evaluation_frequency
|
Default value: None
|
|
gradient_accumulation_step
|
Default value: None
|
|
layers_to_freeze
|
Default value: None
|
|
learning_rate
|
Default value: None
|
|
learning_rate_scheduler
|
Default value: None
|
|
model_name
|
Default value: None
|
|
momentum
|
Default value: None
|
|
nesterov
|
Default value: None
|
|
number_of_epochs
|
Default value: None
|
|
number_of_workers
|
Default value: None
|
|
optimizer
|
Default value: None
|
|
random_seed
|
Default value: None
|
|
step_lr_gamma
|
Default value: None
|
|
step_lr_step_size
|
Default value: None
|
|
training_batch_size
|
Default value: None
|
|
validation_batch_size
|
Default value: None
|
|
warmup_cosine_lr_cycles
|
Default value: None
|
|
warmup_cosine_lr_warmup_epochs
|
Default value: None
|
|
weight_decay
|
Default value: None
|
|
box_detections_per_image
|
Default value: None
|
|
box_score_threshold
|
Default value: None
|
|
image_size
|
Default value: None
|
|
max_size
|
Default value: None
|
|
min_size
|
Default value: None
|
|
model_size
|
Default value: None
|
|
multi_scale
|
Default value: None
|
|
nms_iou_threshold
|
Default value: None
|
|
tile_grid_size
|
Default value: None
|
|
tile_overlap_ratio
|
Default value: None
|
|
tile_predictions_nms_threshold
|
Default value: None
|
|
validation_iou_threshold
|
Default value: None
|
|
validation_metric_type
|
Default value: None
|
|
log_training_metrics
|
Default value: None
|
|
log_validation_loss
|
Default value: None
|
