Hyperparameters for computer vision tasks in automated machine learning

APPLIES TO: Azure CLI ml extension v2 (current) Python SDK azure-ai-ml v2 (current)

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 architecture and sweep hyperparameters. These model architectures 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 architecture.

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
image_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 extra_large. 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_threshold	During inference, only return proposals with a score greater than box_score_threshold. 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_threshold	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 Should be passed as a string in '3x2' format. Example: --tile_grid_size '3x2'	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_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]	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.
gradient_accumulation_step	Gradient accumulation means running a configured number of gradient_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
learning_rate_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
ams_gradient	Enable ams_gradient 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
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
model_name	Model name to be used for image classification task at hand. Must be one of mobilenetv2, resnet18, resnet34, resnet50, resnet101, resnet152, resnest50, resnest101, seresnext, vits16r224, vitb16r224, vitl16r224.	seresnext
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
validation_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
validation_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 validation_crop_size and training_crop_size. - Training run may get into CUDA OOM if the size is too big.	224
training_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 validation_crop_size and training_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 architecture. See the model specific hyperparameters section for yolov5 supported hyperparmeters.

Parameter name	Description	Default
model_name	Model name to be used for image classification task at hand. - For object detection task, must be one of yolov5, fasterrcnn_resnet18_fpn, fasterrcnn_resnet34_fpn, fasterrcnn_resnet50_fpn, fasterrcnn_resnet101_fpn, fasterrcnn_resnet152_fpn, retinanet_resnet50_fpn. - For instance segmentation task, must be one of maskrcnn_resnet18_fpn, maskrcnn_resnet34_fpn, maskrcnn_resnet50_fpn, maskrcnn_resnet101_fpn, maskrcnn_resnet152_fpn	- For object detection task, fasterrcnn_resnet50_fpn - For instance segmentation task, maskrcnn_resnet50_fpn
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_threshold	During inference, only return proposals with a classification score greater than box_score_threshold. Must be a float in the range [0, 1].	0.3
nms_iou_threshold	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_image	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. - tile_grid_size must not be None to enable small object detection logic. - tile_grid_size is not supported for instance segmentation tasks. Should be passed as a string in '3x2' format. Example: --tile_grid_size '3x2'	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_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]	0.25

Next steps

• Learn how to Set up AutoML to train computer vision models with Python.

• Tutorial: Train an object detection model with AutoML and Python.