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Random Sampling

Randomly select data to be saved for future labeling.


Review the Active Learning page for more information about how to use active learning.

This strategy is available for the following model types:

  • stub
  • classification
  • object-detection
  • instance-segmentation
  • keypoints-detection


  • name: user-defined name of the strategy - must be non-empty and unique within all strategies defined in a single configuration (required)
  • type: with value random is used to identify random sampling strategy (required)
  • traffic_percentage: float value in range [0.0, 1.0] defining the percentage of traffic to be persisted (required)
  • tags: list of tags (each contains 1-64 characters from range a-z, A-Z, 0-9, and -_:/.[]<>{}@) (optional)
  • limits: definition of limits for data collection within a specific strategy


Here is an example of a configuration manifest for random sampling strategy:

  "name": "my_random_sampling",
  "type": "random",
  "traffic_percentage": 0.01,
  "tags": ["my_tag_1", "my_tag_2"],
  "limits": [
    { "type": "minutely", "value": 10 },
    { "type": "hourly", "value": 100 },
    { "type": "daily", "value": 1000 }

Learn how to configure active learning for your model.