CLI (v2) Automated ML image object detection job YAML schema

Article
08/28/2024

APPLIES TO: Azure CLI ml extension v2 (current)

The source JSON schema can be found at https://azuremlsdk2.blob.core.windows.net/preview/0.0.1/autoMLImageObjectDetectionJob.schema.json.

Note

The YAML syntax detailed in this document is based on the JSON schema for the latest version of the ML CLI v2 extension. This syntax is guaranteed only to work with the latest version of the ML CLI v2 extension. You can find the schemas for older extension versions at https://azuremlschemasprod.azureedge.net/.

YAML syntax

For information on all the keys in Yaml syntax, see Yaml syntax of image classification task. Here we only describe the keys that have different values as compared to what's specified for image classification task.

Key	Type	Description	Allowed values	Default value
`task`	const	Required. The type of AutoML task.	`image_object_detection`	`image_object_detection`
`primary_metric`	string	The metric that AutoML will optimize for model selection.	`mean_average_precision`	`mean_average_precision`
`training_parameters`	object	Dictionary containing training parameters for the job. Provide an object that has keys as listed in following sections. - Model Specific Hyperparameters for yolov5 (if you're using yolov5 for object detection) - Model agnostic hyperparameters - Object detection and instance segmentation task specific hyperparameters. For an example, see Supported model architectures section.

Remarks

The az ml job command can be used for managing Azure Machine Learning jobs.

Examples

Examples are available in the examples GitHub repository. Examples relevant to image object detection job are shown below.

YAML: AutoML image object detection job

$schema: https://azuremlsdk2.blob.core.windows.net/preview/0.0.1/autoMLJob.schema.json
type: automl

# <experiment_name>
experiment_name: dpv2-cli-automl-image-object-detection-experiment
# </experiment_name>
description: An Image Object Detection job using fridge items dataset

# <compute_settings>
compute: azureml:gpu-cluster
# </compute_settings>

# <task_settings>
task: image_object_detection
log_verbosity: debug
primary_metric: mean_average_precision
# </task_settings>

# <mltable_settings>
target_column_name: label
training_data:
  # Update the path, if prepare_data.py is using data_path other than "./data"
  path: data/training-mltable-folder
  type: mltable
validation_data:
  # Update the path, if prepare_data.py is using data_path other than "./data"
  path: data/validation-mltable-folder
  type: mltable
# </mltable_settings>

# <limit_settings>
limits:
  timeout_minutes: 60
  max_trials: 10
  max_concurrent_trials: 2
# </limit_settings>

# <fixed_settings>
training_parameters:
  early_stopping: True
  evaluation_frequency: 1
# </fixed_settings>

# <sweep_settings>
sweep:
  sampling_algorithm: random
  early_termination:
    type: bandit
    evaluation_interval: 2
    slack_factor: 0.2
    delay_evaluation: 6
# </sweep_settings>

# <search_space_settings>
search_space:
  - model_name:
      type: choice
      values: [yolov5]
    learning_rate:
      type: uniform
      min_value: 0.0001
      max_value: 0.01
    model_size:
      type: choice
      values: ['small', 'medium']
  - model_name:
      type: choice
      values: [fasterrcnn_resnet50_fpn]
    learning_rate:
      type: uniform
      min_value: 0.0001
      max_value: 0.001
    optimizer:
      type: choice
      values: ['sgd', 'adam', 'adamw']
    min_size:
      type: choice
      values: [600, 800]
# </search_space_settings>

YAML: AutoML image object detection pipeline job

$schema: https://azuremlschemas.azureedge.net/latest/pipelineJob.schema.json
type: pipeline

description: Pipeline using AutoML Image Object Detection task

display_name: pipeline-with-image-object-detection
experiment_name: pipeline-with-automl

settings:
  default_compute: azureml:gpu-cluster

inputs:
  image_object_detection_training_data:
    type: mltable
    # Update the path, if prepare_data.py is using data_path other than "./data"
    path: data/training-mltable-folder
  image_object_detection_validation_data:
    type: mltable
    # Update the path, if prepare_data.py is using data_path other than "./data"
    path: data/validation-mltable-folder

jobs:
  image_object_detection_node:
    type: automl
    task: image_object_detection
    log_verbosity: info
    primary_metric: mean_average_precision
    limits:
      timeout_minutes: 180
      max_trials: 10
      max_concurrent_trials: 2
    target_column_name: label
    training_data: ${{parent.inputs.image_object_detection_training_data}}
    validation_data: ${{parent.inputs.image_object_detection_validation_data}}
    training_parameters:
      early_stopping: True
      evaluation_frequency: 1
    sweep:
      sampling_algorithm: random
      early_termination:
        type: bandit
        evaluation_interval: 2
        slack_factor: 0.2
        delay_evaluation: 6
    search_space:
      - model_name:
          type: choice
          values: [yolov5]
        learning_rate:
          type: uniform
          min_value: 0.0001
          max_value: 0.001
        model_size:
          type: choice
          values: ['small', 'medium']

      - model_name:
          type: choice
          values: [fasterrcnn_resnet50_fpn]
        learning_rate:
          type: uniform
          min_value: 0.0001
          max_value: 0.001
        optimizer:
          type: choice
          values: ['sgd', 'adam', 'adamw']
        min_size:
          type: choice
          values: [600, 800]
    # currently need to specify outputs "mlflow_model" explicitly to reference it in following nodes
    outputs:
      best_model:
        type: mlflow_model
  register_model_node:
    type: command
    component: file:./components/component_register_model.yaml
    inputs:
      model_input_path: ${{parent.jobs.image_object_detection_node.outputs.best_model}}
      model_base_name: fridge_items_object_detection_model

Next steps

Install and use the CLI (v2)

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