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cli-automl-img-od-task-fridge-items.yml
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cli-automl-img-od-task-fridge-items.yml
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$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>