Detection Number

Choose specific detections based on count and detection classes. Collect and save images for use in improving your model.

Tip

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

This strategy is available for the following model types:

object-detection
instance-segmentation
keypoints-detection

Configuration¶

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 detections_number_based is used to identify close to threshold sampling strategy (required)
selected_class_names: list of class names to consider during sampling; if not provided, all classes can be sampled. (Optional)
probability: fraction of datapoints that matches sampling criteria that will be persisted. It is meant to be float value in range [0.0, 1.0] (required)
more_than: minimal number of detected objects - if given it is meant to be integer >= 0 (optional - if not given - lower limit is not applied)
less_than: maximum number of detected objects - if given it is meant to be integer >= 0 (optional - if not given - upper limit is not applied)
NOTE: if both more_than and less_than is not given - any number of matching detections will match the sampling condition
tags: list of tags (each contains 1-64 characters from range a-z, A-Z, 0-9, and -_:/.[]<>{}@) (optional)

Example¶

{
  "name": "multiple_detections",
  "type": "detections_number_based",
  "probability": 0.2,
  "more_than": 3,
  "tags": ["crowded"],
  "limits": [
    { "type": "minutely", "value": 10 },
    { "type": "hourly", "value": 100 },
    { "type": "daily", "value": 1000 }
  ]
}

Learn how to configure active learning for your model.