ImageModelSettingsObjectDetection Class

Initializes a new instance of ImageModelSettingsObjectDetection.

Settings for advanced scenarios.

Enable AMSGrad when optimizer is 'adam' or 'adamw'.

Settings for using Augmentations.

Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].

Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].

Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.

During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].

Frequency to store model checkpoints. Must be a positive integer.

The pretrained checkpoint model for incremental training.

The id of a previous run that has a pretrained checkpoint for incremental training.

Whether to use distributed training.

Enable early stopping logic during training.

Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.

Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.

Enable normalization when exporting ONNX model.

Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.

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.

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.

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.

Initial learning rate. Must be a float in the range [0, 1].

Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.

Enable computing and logging training metrics.

Enable computing and logging validation loss.

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.

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.

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.

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.

Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].

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.

Enable nesterov when optimizer is 'sgd'.

IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].

Number of training epochs. Must be a positive integer.

Number of data loader workers. Must be a non-negative integer.

Type of optimizer.

Random seed to be used when using deterministic training.

Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].

Value of step size when learning rate scheduler is 'step'. Must be a positive integer.

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.

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.

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.

Training batch size. Must be a positive integer.

Validation batch size. Must be a positive integer.

IOU threshold to use when computing validation metric. Must be float in the range [0, 1].

Metric computation method to use for validation metrics.

Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].

Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.

Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].

Reads one JSON value (including objects or arrays) from the provided reader and converts it to a model.

Writes the model to the provided Utf8JsonWriter.

Reads one JSON value (including objects or arrays) from the provided reader and converts it to a model.

Writes the model to the provided Utf8JsonWriter.

Converts the provided BinaryData into a model.

Gets the data interchange format (JSON, Xml, etc) that the model uses when communicating with the service.

Writes the model into a BinaryData.

Converts the provided BinaryData into a model.

Gets the data interchange format (JSON, Xml, etc) that the model uses when communicating with the service.

Writes the model into a BinaryData.