cli-automl-image-object-detection-task-fridge-items.yml

$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>