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components train_image_classification_model
github-actions[bot] edited this page Dec 24, 2024
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Component to finetune AutoML legacy models for image classification.
Version: 0.0.13
View in Studio: https://ml.azure.com/registries/azureml/components/train_image_classification_model/version/0.0.13
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 | ||
task_type | Whether a single image can have multiple labels. | string | ['image-classification', 'image-classification-multilabel'] | ||
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 | ||
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. Must be warmup_cosine or step. | string | warmup_cosine | True | ['warmup_cosine', 'step'] |
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 | ||
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 | ||
training_batch_size | Training batch size. | integer | True | ||
training_crop_size | Image crop size that's input to your neural network for training dataset. Notes - seresnext doesn't take an arbitrary size. ViT-variants should have the same validation_crop_size and training_crop_size. | integer | True | ||
validation_batch_size | Validation batch size. | integer | True | ||
validation_crop_size | Image crop size that's input to your neural network for validation dataset. Note - seresnext doesn't take an arbitrary size. ViT-variants should have the same validation_crop_size and training_crop_size. | integer | True | ||
validation_resize_size | Image size to which to resize before cropping for validation dataset. Note - seresnext doesn't take an arbitrary size. | integer | True | ||
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 | ||
weighted_loss | 0 for no weighted loss, 1 for weighted loss with sqrt (class_weights), 2 for weighted loss with class_weights. | integer | True |
Name | Description | Type |
---|---|---|
mlflow_model_folder | Trained MLFlow model. | mlflow_model |
pytorch_model_folder | Trained Pytorch model. | custom_model |
azureml://registries/azureml/environments/automl-dnn-vision-gpu/labels/latest