components train_instance_segmentation_model

Image Instance Segmentation AutoML Legacy Model Finetune

train_instance_segmentation_model

Overview

Component to finetune AutoML legacy models for instance segmentation.

Version: 0.0.12

View in Studio: https://ml.azure.com/registries/azureml/components/train_instance_segmentation_model/version/0.0.12

Inputs

Name	Description	Type	Default	Optional	Enum
training_data	Path to MLTable for training data.	mltable
validation_data	Path to MLTable for validation data.	mltable		True
ams_gradient	Enable ams_gradient when optimizer is adam or adamw.	boolean		True
beta1	Value of beta1 when optimizer is adam or adamw. Must be a float in the range [0, 1].	number		True
beta2	Value of beta2 when optimizer is adam or adamw. Must be a float in the range [0, 1].	number		True
box_detections_per_image	Maximum number of detections per image, for all classes. Must be a positive integer.	integer		True
box_score_threshold	During inference, only return proposals with a classification score greater than box_score_threshold. Must be a float in the range [0, 1].	number		True
checkpoint_frequency	Frequency to store model checkpoints. Must be a positive integer.	integer		True
checkpoint_run_id	The run ID of the experiment that has a pretrained checkpoint for incremental training.	string		True
early_stopping	Enable early stopping logic during training.	boolean		True
early_stopping_patience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	integer		True
early_stopping_delay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	integer		True
evaluation_frequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	integer		True
gradient_accumulation_step	Number of forward passes 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.	integer		True
layers_to_freeze	How many layers to freeze for your model. For instance, passing 2 as value for seresnext means freezing layer0 and layer1 referring to the below supported model layer info. Must be a positive integer.	integer		True
learning_rate	Initial learning rate	number		True
learning_rate_scheduler	Type of learning rate scheduler. Please check for https://learn.microsoft.com/azure/machine-learning/reference-automl-images-hyperparameters more information.	string	warmup_cosine	True	['warmup_cosine', 'step']
max_size	Maximum size of the image to be rescaled before feeding it to the backbone.	integer		True
min_size	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer.	integer		True
model_name	Model name	string		True
momentum	Value of momentum when optimizer is sgd. Must be a float in the range [0, 1].	number		True
nesterov	Enable nesterov when optimizer is sgd.	boolean		True
nms_iou_threshold	IOU threshold used during inference in non-maximum suppression post processing. Must be a float in the range [0, 1].	number		True
number_of_epochs	Number of training epochs	integer		True
number_of_workers	Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process.	integer		True
optimizer	Type of optimizer	string	sgd	True	['sgd', 'adam', 'adamw']
random_seed	Random seed that will be set at the beginning of training.	integer		True
step_lr_gamma	Value of gamma when learning rate scheduler is step. Please check for https://learn.microsoft.com/azure/machine-learning/reference-automl-images-hyperparameters more information.	number		True
step_lr_step_size	Value of step size when learning rate scheduler is step. Please check for https://learn.microsoft.com/azure/machine-learning/reference-automl-images-hyperparameters more information.	integer		True
tile_grid_size	The grid size to use for tiling each image. Should be passed as a string in '3x2' format. Example --tile_grid_size '3x2'	string		True
tile_overlap_ratio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range of [0, 1).	number		True
tile_predictions_nms_threshold	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 of [0, 1].	number		True
training_batch_size	Training batch size.	integer		True
validation_batch_size	Validation batch size.	integer		True
validation_iou_threshold	IOU threshold for box matching when computing validation metrics. Must be a float in the range [0.1, 1].	number		True
validation_metric_type	Metric computation method to use for validation metrics. Must be none, coco, voc, or coco_voc.	string	voc	True	['none', 'coco', 'voc', 'coco_voc']
warmup_cosine_lr_cycles	Value of cosine cycle when learning rate scheduler is warmup_cosine. Please check for https://learn.microsoft.com/azure/machine-learning/reference-automl-images-hyperparameters more information.	number		True
warmup_cosine_lr_warmup_epochs	Value of warmup epochs when learning rate scheduler is warmup_cosine. Please check for https://learn.microsoft.com/azure/machine-learning/reference-automl-images-hyperparameters more information.	integer		True
weight_decay	Value of weight decay used by the optimizer.	number		True

Outputs

Name	Description	Type
mlflow_model_folder	Trained MLFlow model.	mlflow_model
pytorch_model_folder	Trained Pytorch model.	custom_model

Environment

azureml://registries/azureml/environments/automl-dnn-vision-gpu/versions/34