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Hyperparameters for computer vision tasks in automated machine learning (v1)

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APPLIES TO: Python SDK azureml v1

Learn which hyperparameters are available specifically for computer vision tasks in automated ML experiments.

With support for computer vision tasks, you can control the model algorithm and sweep hyperparameters. These model algorithms and hyperparameters are passed in as the parameter space for the sweep. While many of the hyperparameters exposed are model-agnostic, there are instances where hyperparameters are model-specific or task-specific.

Model-specific hyperparameters

This table summarizes hyperparameters specific to the yolov5 algorithm.

Parameter name Description Default
validation_metric_type Metric computation method to use for validation metrics.
Must be none, coco, voc, or coco_voc.		 voc
validation_iou_threshold IOU threshold for box matching when computing validation metrics.
Must be a float in the range [0.1, 1].		 0.5
img_size Image size for train and validation.
Must be a positive integer.

Note: training run may get into CUDA OOM if the size is too big.		 640
model_size Model size.
Must be small, medium, large, or xlarge.

Note: training run may get into CUDA OOM if the model size is too big.		 medium
multi_scale Enable multi-scale image by varying image size by +/- 50%
Must be 0 or 1.

Note: training run may get into CUDA OOM if no sufficient GPU memory.		 0
box_score_thresh During inference, only return proposals with a score greater than box_score_thresh. The score is the multiplication of the objectness score and classification probability.
Must be a float in the range [0, 1].		 0.1
nms_iou_thresh IOU threshold used during inference in non-maximum suppression post processing.
Must be a float in the range [0, 1].		 0.5
tile_grid_size The grid size to use for tiling each image.
Note: tile_grid_size must not be None to enable small object detection logic
A tuple of two integers passed as a string. Example: --tile_grid_size "(3, 2)"		 No Default
tile_overlap_ratio Overlap ratio between adjacent tiles in each dimension.
Must be float in the range of [0, 1)		 0.25
tile_predictions_nms_thresh 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]		 0.25

This table summarizes hyperparameters specific to the maskrcnn_* for instance segmentation during inference.

Parameter name Description Default
mask_pixel_score_threshold Score cutoff for considering a pixel as part of the mask of an object. 0.5
max_number_of_polygon_points Maximum number of (x, y) coordinate pairs in polygon after converting from a mask. 100
export_as_image Export masks as images. False
image_type Type of image to export mask as (options are jpg, png, bmp). JPG

Model agnostic hyperparameters

The following table describes the hyperparameters that are model agnostic.

Parameter name Description Default
number_of_epochs Number of training epochs.
Must be a positive integer.		 15
(except yolov5: 30)
training_batch_size Training batch size.
Must be a positive integer.		 Multi-class/multi-label: 78
(except vit-variants:
vits16r224: 128
vitb16r224: 48
vitl16r224:10)

Object detection: 2
(except yolov5: 16)

Instance segmentation: 2

Note: The defaults are largest batch size that can be used on 12 GiB GPU memory.
validation_batch_size Validation batch size.
Must be a positive integer.		 Multi-class/multi-label: 78
(except vit-variants:
vits16r224: 128
vitb16r224: 48
vitl16r224:10)

Object detection: 1
(except yolov5: 16)

Instance segmentation: 1

Note: The defaults are largest batch size that can be used on 12 GiB GPU memory.
grad_accumulation_step Gradient accumulation means running a configured number of grad_accumulation_step 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.		 1
early_stopping Enable early stopping logic during training.
Must be 0 or 1.		 1
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.		 5
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.		 5
learning_rate Initial learning rate.
Must be a float in the range [0, 1].		 Multi-class: 0.01
(except vit-variants:
vits16r224: 0.0125
vitb16r224: 0.0125
vitl16r224: 0.001)

Multi-label: 0.035
(except vit-variants:
vits16r224: 0.025
vitb16r224: 0.025
vitl16r224: 0.002)

Object detection: 0.005
(except yolov5: 0.01)

Instance segmentation: 0.005
lr_scheduler Type of learning rate scheduler.
Must be warmup_cosine or step.		 warmup_cosine
step_lr_gamma Value of gamma when learning rate scheduler is step.
Must be a float in the range [0, 1].		 0.5
step_lr_step_size Value of step size when learning rate scheduler is step.
Must be a positive integer.		 5
warmup_cosine_lr_cycles Value of cosine cycle when learning rate scheduler is warmup_cosine.
Must be a float in the range [0, 1].		 0.45
warmup_cosine_lr_warmup_epochs Value of warmup epochs when learning rate scheduler is warmup_cosine.
Must be a positive integer.		 2
optimizer Type of optimizer.
Must be either sgd, adam, adamw.		 sgd
momentum Value of momentum when optimizer is sgd.
Must be a float in the range [0, 1].		 0.9
weight_decay Value of weight decay when optimizer is sgd, adam, or adamw.
Must be a float in the range [0, 1].		 1e-4
nesterov Enable nesterov when optimizer is sgd.
Must be 0 or 1.		 1
beta1 Value of beta1 when optimizer is adam or adamw.
Must be a float in the range [0, 1].		 0.9
beta2 Value of beta2 when optimizer is adam or adamw.
Must be a float in the range [0, 1].		 0.999
amsgrad Enable amsgrad when optimizer is adam or adamw.
Must be 0 or 1.		 0
evaluation_frequency Frequency to evaluate validation dataset to get metric scores.
Must be a positive integer.		 1
checkpoint_frequency Frequency to store model checkpoints.
Must be a positive integer.		 Checkpoint at epoch with best primary metric on validation.
checkpoint_run_id The run ID of the experiment that has a pretrained checkpoint for incremental training. no default
checkpoint_dataset_id FileDataset ID containing pretrained checkpoint(s) for incremental training. Make sure to pass checkpoint_filename along with checkpoint_dataset_id. no default
checkpoint_filename The pretrained checkpoint filename in FileDataset for incremental training. Make sure to pass checkpoint_dataset_id along with checkpoint_filename. no default
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.

'resnet': [('conv1.', 'bn1.'), 'layer1.', 'layer2.', 'layer3.', 'layer4.'],
'mobilenetv2': ['features.0.', 'features.1.', 'features.2.', 'features.3.', 'features.4.', 'features.5.', 'features.6.', 'features.7.', 'features.8.', 'features.9.', 'features.10.', 'features.11.', 'features.12.', 'features.13.', 'features.14.', 'features.15.', 'features.16.', 'features.17.', 'features.18.'],
'seresnext': ['layer0.', 'layer1.', 'layer2.', 'layer3.', 'layer4.'],
'vit': ['patch_embed', 'blocks.0.', 'blocks.1.', 'blocks.2.', 'blocks.3.', 'blocks.4.', 'blocks.5.', 'blocks.6.','blocks.7.', 'blocks.8.', 'blocks.9.', 'blocks.10.', 'blocks.11.'],
'yolov5_backbone': ['model.0.', 'model.1.', 'model.2.', 'model.3.', 'model.4.','model.5.', 'model.6.', 'model.7.', 'model.8.', 'model.9.'],
'resnet_backbone': ['backbone.body.conv1.', 'backbone.body.layer1.', 'backbone.body.layer2.','backbone.body.layer3.', 'backbone.body.layer4.']		 no default

Image classification (multi-class and multi-label) specific hyperparameters

The following table summarizes hyperparmeters for image classification (multi-class and multi-label) tasks.

Parameter name Description Default
weighted_loss 0 for no weighted loss.
1 for weighted loss with sqrt.(class_weights)
2 for weighted loss with class_weights.
Must be 0 or 1 or 2.		 0
valid_resize_size
  • Image size to which to resize before cropping for validation dataset.
  • Must be a positive integer.

    Notes:
  • seresnext doesn't take an arbitrary size.
  • Training run may get into CUDA OOM if the size is too big.
    		•  256  
    valid_crop_size
  • Image crop size that's input to your neural network for validation dataset.
  • Must be a positive integer.

    Notes:
  • seresnext doesn't take an arbitrary size.
  • ViT-variants should have the same valid_crop_size and train_crop_size.
  • Training run may get into CUDA OOM if the size is too big.
    		•  224 
    train_crop_size
  • Image crop size that's input to your neural network for train dataset.
  • Must be a positive integer.

    Notes:
  • seresnext doesn't take an arbitrary size.
  • ViT-variants should have the same valid_crop_size and train_crop_size.
  • Training run may get into CUDA OOM if the size is too big.
    		•  224 

    Object detection and instance segmentation task specific hyperparameters

    The following hyperparameters are for object detection and instance segmentation tasks.

    Warning

    These parameters are not supported with the yolov5 algorithm. See the model specific hyperparameters section for yolov5 supported hyperparmeters.

    Parameter name Description Default
    validation_metric_type Metric computation method to use for validation metrics.
    Must be none, coco, voc, or coco_voc.    		 voc
    validation_iou_threshold IOU threshold for box matching when computing validation metrics.
    Must be a float in the range [0.1, 1].    		 0.5
    min_size 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.    		 600
    max_size 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.    		 1333
    box_score_thresh During inference, only return proposals with a classification score greater than box_score_thresh.
    Must be a float in the range [0, 1].    		 0.3
    nms_iou_thresh IOU (intersection over union) threshold used in non-maximum suppression (NMS) for the prediction head. Used during inference.
    Must be a float in the range [0, 1].    		 0.5
    box_detections_per_img Maximum number of detections per image, for all classes.
    Must be a positive integer.    		 100
    tile_grid_size The grid size to use for tiling each image.
    Note: tile_grid_size must not be None to enable small object detection logic
    A tuple of two integers passed as a string. Example: --tile_grid_size "(3, 2)"    		 No Default
    tile_overlap_ratio Overlap ratio between adjacent tiles in each dimension.
    Must be float in the range of [0, 1)    		 0.25
    tile_predictions_nms_thresh 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]    		 0.25

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