Overview Shortcuts and Prediction Types

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Overview shortcuts for Data, Labels, and Predictions and Prediction Types for Predictions, give you a quick method to filter your unwanted or problematic images. They are shortcuts to improving datasets and model performance. They give you a quick launch pad to improve your data and your model performance.

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Note

The overview shortcuts for Data, Labels, and Predictions in the Overview tab are generalized. Contact us if you want personalized shortcuts populating the Overview tab.

Data Issue Shortcuts

Data Issues Overview

TitleMetric TypeOntology Type
Duplicates - Duplicate and near-duplicate images. Images with a Uniqueness score of 0.0 to 0.00001 are flagged as duplicates.image
Blur - Images that are too blurry. Images with a Sharpness score of 0.0 to 0.005 are flagged as blurry.image
Dark - Images that are too dark. Images with a Brightness score of 0.0 to 0.1 are flagged as too dark.image
Bright - Images that are too bright. Images with a Brightness score of 0.7 to 1.0 are flagged as too bright.image

Label Issue Shortcuts

Label Issues Overview

TitleMetric TypeOntology Type
Aspect Ratio - Identifies objects with a low aspect ratio value. Images with an Aspect Ratio score of 0.0 to 0.1 are flagged as having an issue.imagebounding box, polygon, rotatable bounding box
Border Proximity - Identifies annotations that are too close to image borders. Images with a Border Proximity score of 1 are flagged as being too close to the border.imagebounding box, point, polygon, polyline, rotatable bounding box, skeleton
Low Annotation Quality - Compares image classifications against the 60 most similar images. Images with a Classification Quality score of 0.0 to 0.02 are flagged as having an issue.imageradio
Relative Area - Identifies annotations that are relatively too small compared to the size of the image. Images with a Relative Area score of 0.0 to 0.003 are flagged being too small.imagebounding box, polygon, rotatable bounding box

Prediction Issues and Types

Prediction Issues Overview

TitleMetric TypeOntology Type
Shape Outliers - Duplicate and near-duplicate images. Images with a Polygon Shape Anomaly score of 0.0 to 0.02 are flagged as duplicates.image
Border Proximity - Identifies annotations that are too close to image borders. Images with a Border Proximity score of 1 are flagged as being too close to the border.imagebounding box, point, polygon, polyline, rotatable bounding box, skeleton
Aspect Ratio - Identifies objects with a low aspect ratio value. Images with an Aspect Ratio score of 0.0 to 0.1 are flagged as having an issue.imagebounding box, polygon, rotatable bounding box

Prediction Types

TitleMetric TypeOntology Type
All - All model outcomes.image
All Positives - All model outcomes that are True Positive and False Positive.image
True Positives - All model outcomes where the model correctly identified objects.image
False Positives - All model outcomes where the model incorrectly identified objects as the correct object.image
False Negatives - All model outcomes where the model incorrectly identified objects as the wrong object.image

Use Issue and Prediction Type shortcuts

This process assumes that there is already one or more Projects in Active.

To use Issue and Prediction Type shortcuts:

  1. Log in to the Encord platform.
    The landing page for the Encord platform appears.

  2. Click Active in the main menu.
    The landing page for Active appears.

  3. Click the Project.
    The landing page for the Project appears with the Explorer tab selected.

  4. Click Data, Labels, or Predictions.
    The Explorer workspace changes based on what you clicked. The Overview tab displays with the shortcuts.

  5. Click a shortcut.
    A filter is applied to the images. The images appearing in the Explorer workspace changes depending on which shortcut you click.

  6. Click Filter if you want to modify the filter settings.

  7. Further search, sort, and filter the data.

  8. Create a Collection based on the results.

  9. Create a Dataset (and Project) and send that Dataset to Annotate.

  10. Further annotate your data.

  11. Rinse and repeat until you have the dataset you need for optimal model performance.


What’s Next