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Autotag uses machine learning models to automatically generate suggested tags and labels for your data. These auto-generated tags help accelerate annotation by pre-labeling items that can then be reviewed and corrected by annotators.

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Concept

When autotag is enabled for a project, ML models analyze your data and produce confidence-scored predictions. These predictions appear as suggested annotations that annotators can accept, modify, or reject. Autotag works with both image and object-level predictions.

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Image Prefix Queries

Filter items by image-level autotag predictions using the image_prefix: syntax: 
image_prefix:weather_clear
image_prefix:scene_highway
image_prefix:time_of_day_night
Image prefix queries match items where the autotag model has predicted a specific image-level classification.

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Object Prefix Queries

Filter items by object-level autotag predictions using the object_prefix: syntax: 
object_prefix:car
object_prefix:pedestrian
object_prefix:traffic_sign
Object prefix queries match items that contain at least one predicted object of the specified type.

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Score Ranges

Autotag predictions include a confidence score ranging from -1 to 1:
Score RangeMeaning
0.8 to 1.0High confidence — model is very certain
0.5 to 0.8Medium confidence — likely correct but should be verified
0.0 to 0.5Low confidence — uncertain prediction
-1.0 to 0.0Negative confidence — model predicts the tag does not apply
Filter by confidence score to focus on predictions that need review: 
image_prefix:weather_clear AND score >= 0.8
object_prefix:car AND score < 0.5
Items with low confidence scores are good candidates for manual review, as they represent cases where the model is uncertain.

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Training Set Queries

Filter items by the training set used to generate autotag predictions: 
training_set = "model_v2"
training_set = "detector_2025_01"
This is useful when multiple autotag models have been run on the same dataset and you want to compare or filter by a specific model version.

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Usage in the Annotation Workflow

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Enabling Autotag

  1. Navigate to your project in Mission Control.
  2. Go to Settings → Autotag.
  3. Select the ML model to use for predictions.
  4. Choose the data to run autotag on (full dataset or specific slices).
  5. Click Run Autotag to start the prediction job.

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Reviewing Autotag Predictions

  1. Open the annotation editor for an item with autotag predictions.
  2. Suggested annotations appear with a distinct visual indicator.
  3. For each suggestion:
    • Accept — Confirm the prediction as correct.
    • Modify — Adjust the annotation (resize, relabel, etc.).
    • Reject — Remove the incorrect prediction.
  4. Save your review to finalize the annotations.

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Filtering by Autotag Status

Use the query language to find items based on their autotag review status: 
autotag_status = "pending"
autotag_status = "reviewed"
autotag_status = "accepted"

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Best Practices

  • Start with high-confidence predictions — Review items with scores above 0.8 first for quick wins.
  • Use low-confidence items for model improvement — These edge cases are valuable for retraining.
  • Run autotag on new data incrementally — Process new uploads as they arrive rather than waiting for large batches.
  • Compare model versions — Use training set queries to evaluate whether a newer model performs better.