ImageModelDistributionSettingsObjectDetection Class
Definition
Important
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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, System.ClientModel.Primitives.IJsonModel<Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettingsObjectDetection>, System.ClientModel.Primitives.IPersistableModel<Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettingsObjectDetection>public class ImageModelDistributionSettingsObjectDetection : Azure.ResourceManager.MachineLearning.Models.ImageModelDistributionSettingstype ImageModelDistributionSettingsObjectDetection = class
inherit ImageModelDistributionSettings
interface IJsonModel<ImageModelDistributionSettingsObjectDetection>
interface IPersistableModel<ImageModelDistributionSettingsObjectDetection>type ImageModelDistributionSettingsObjectDetection = class
inherit ImageModelDistributionSettingsPublic Class ImageModelDistributionSettingsObjectDetection
Inherits ImageModelDistributionSettings
Implements IJsonModel(Of ImageModelDistributionSettingsObjectDetection), IPersistableModel(Of ImageModelDistributionSettingsObjectDetection)Public Class ImageModelDistributionSettingsObjectDetection
Inherits ImageModelDistributionSettings- Inheritance
- Implements
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) |
Explicit Interface Implementations
Applies to
Azure SDK for .NET