IImageObjectDetectionBase Interface
Definition
Important
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[System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ImageObjectDetectionBaseTypeConverter))]
public interface IImageObjectDetectionBase : Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.IImageVertical[<System.ComponentModel.TypeConverter(typeof(Microsoft.Azure.PowerShell.Cmdlets.MachineLearningServices.Models.Api20240401.ImageObjectDetectionBaseTypeConverter))>]
type IImageObjectDetectionBase = interface
interface IJsonSerializable
interface IImageVerticalPublic Interface IImageObjectDetectionBase
Implements IImageVertical- Derived
- Attributes
- Implements
Properties
| EarlyTerminationDelayEvaluation |
Number of intervals by which to delay the first evaluation. (Inherited from IImageVertical) |
| EarlyTerminationEvaluationInterval |
Interval (number of runs) between policy evaluations. (Inherited from IImageVertical) |
| EarlyTerminationPolicyType |
[Required] Name of policy configuration (Inherited from IImageVertical) |
| LimitSettingMaxConcurrentTrial |
Maximum number of concurrent AutoML iterations. (Inherited from IImageVertical) |
| LimitSettingMaxTrial |
Maximum number of AutoML iterations. (Inherited from IImageVertical) |
| LimitSettingTimeout |
AutoML job timeout. (Inherited from IImageVertical) |
| ModelSettingAdvancedSetting |
Settings for advanced scenarios. |
| ModelSettingAmsGradient |
Enable AMSGrad when optimizer is 'adam' or 'adamw'. |
| ModelSettingAugmentation |
Settings for using Augmentations. |
| ModelSettingBeta1 |
Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. |
| ModelSettingBeta2 |
Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. |
| ModelSettingBoxDetectionsPerImage |
Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
| ModelSettingBoxScoreThreshold |
During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
| ModelSettingCheckpointFrequency |
Frequency to store model checkpoints. Must be a positive integer. |
| ModelSettingCheckpointModelDescription |
Description for the input. |
| ModelSettingCheckpointModelJobInputType |
[Required] Specifies the type of job. |
| ModelSettingCheckpointModelMode |
Input Asset Delivery Mode. |
| ModelSettingCheckpointModelUri |
[Required] Input Asset URI. |
| ModelSettingCheckpointRunId |
The id of a previous run that has a pretrained checkpoint for incremental training. |
| ModelSettingDistributed |
Whether to use distributed training. |
| ModelSettingEarlyStopping |
Enable early stopping logic during training. |
| ModelSettingEarlyStoppingDelay |
Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
| ModelSettingEarlyStoppingPatience |
Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
| ModelSettingEnableOnnxNormalization |
Enable normalization when exporting ONNX model. |
| ModelSettingEvaluationFrequency |
Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. |
| ModelSettingGradientAccumulationStep |
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. |
| ModelSettingImageSize |
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. |
| ModelSettingLayersToFreeze |
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. |
| ModelSettingLearningRate |
Initial learning rate. Must be a float in the range [0, 1]. |
| ModelSettingLearningRateScheduler |
Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. |
| ModelSettingMaxSize |
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. |
| ModelSettingMinSize |
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. |
| ModelSettingModelName |
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. |
| ModelSettingModelSize |
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. |
| ModelSettingMomentum |
Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. |
| ModelSettingMultiScale |
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. |
| ModelSettingNesterov |
Enable nesterov when optimizer is 'sgd'. |
| ModelSettingNmsIouThreshold |
IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1]. |
| ModelSettingNumberOfEpoch |
Number of training epochs. Must be a positive integer. |
| ModelSettingNumberOfWorker |
Number of data loader workers. Must be a non-negative integer. |
| ModelSettingOptimizer |
Type of optimizer. |
| ModelSettingRandomSeed |
Random seed to be used when using deterministic training. |
| ModelSettingStepLrGamma |
Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. |
| ModelSettingStepLrStepSize |
Value of step size when learning rate scheduler is 'step'. Must be a positive integer. |
| ModelSettingTileGridSize |
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. |
| ModelSettingTileOverlapRatio |
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. |
| ModelSettingTilePredictionsNmsThreshold |
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. |
| ModelSettingTrainingBatchSize |
Training batch size. Must be a positive integer. |
| ModelSettingValidationBatchSize |
Validation batch size. Must be a positive integer. |
| ModelSettingValidationIouThreshold |
IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. |
| ModelSettingValidationMetricType |
Metric computation method to use for validation metrics. |
| ModelSettingWarmupCosineLrCycle |
Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. |
| ModelSettingWarmupCosineLrWarmupEpoch |
Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. |
| ModelSettingWeightDecay |
Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. |
| SearchSpace |
Search space for sampling different combinations of models and their hyperparameters. |
| SweepSettingSamplingAlgorithm |
[Required] Type of the hyperparameter sampling algorithms. (Inherited from IImageVertical) |
| ValidationDataDescription |
Description for the input. (Inherited from IImageVertical) |
| ValidationDataJobInputType |
[Required] Specifies the type of job. (Inherited from IImageVertical) |
| ValidationDataMode |
Input Asset Delivery Mode. (Inherited from IImageVertical) |
| ValidationDataSize |
The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. (Inherited from IImageVertical) |
| ValidationDataUri |
[Required] Input Asset URI. (Inherited from IImageVertical) |
Methods
| ToJson(JsonObject, SerializationMode) | (Inherited from IJsonSerializable) |