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Documentation Index

Fetch the complete documentation index at: https://docs.encord.com/llms.txt

Use this file to discover all available pages before exploring further.

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Compute Metrics and Embeddings

Computing metrics and embeddings is required to use Similarity Search, Natural Language Search, Quality Metric filters, and Embeddings Analysis.
  1. Navigate to Projects > Explore.
  2. Hover over Metrics & Embeddings
  3. Click Compute.
  1. Configure the computation:
    • Select whether you want Similarity & Natural language search and quality metrics
    • Select which embeddings to use.
    • Select whether you want the embeddings plot, and Diversity and Uniqueness metrics.
  2. Click Start computation.

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Quality Metrics

Quality metrics are only calculated when you compute metrics and embeddings.
Quality metrics evaluate your data, labels, and model predictions, forming the foundation of effective data curation. They provide meaningful ways to surface, rank, and explore your data — helping you identify issues, spot patterns, and make informed decisions about what to curate, fix, or prioritize. Video Quality Metrics: Video quality metrics must be calculated by upgrading your folder. Examples include Area, Clip duration, Frames per second, Number of frames. Data Quality Metrics: Data quality metrics must be calculated by upgrading your folder. Examples include Area, Frame number, Random value.
For more detailed information on Data Quality Metrics, refer to the Data Quality Metrics documentation.
TitleMetric TypeOntology Type
Area - Ranks images by their area (width/height).image
Aspect Ratio - Ranks images by their aspect ratio (width/height).image
Blue Value - Ranks images by how blue the average value of the image is.image
Brightness - Ranks images by their brightness.image
Contrast - Ranks images by their contrast.image
Diversity - Forms clusters based on the Ontology and ranks images from easy samples to annotate to hard samples to annotate.image
Frame Number - Selects images based on a specified range.image
Green Value - Ranks images by how green the average value of the image is.image
Height - Ranks images by the height of the image.image
Object Count - Counts number of objects in the image.imagebounding box, checklist, point, polygon, polyline, radio, rotatable bounding box, skeleton, text
Object Density - Computes the percentage of image area that is occupied by objects.imagebounding box, polygon, rotatable bounding box
Randomize Images - Assigns a random value between 0 and 1 to images.image
Red Value - Ranks images by how red the average value of the image is.image
Sharpness - Ranks images by their sharpness.image
Uniqueness - Finds duplicate and near-duplicate images.image
Width - Ranks images by the width of the image.image
Label Quality Metrics are used for sorting data, filtering data, and data analytics.
TitleMetric TypeOntology Type
Absolute Area - Computes object size in amount of pixels.imagebounding box, polygon, rotatable bounding box
Aspect Ratio - Computes aspect ratios of objects.imagebounding box, polygon, rotatable bounding box
Blue Value - Ranks annotated objects by how blue the average value of the object is.imagebounding box, polygon, rotatable bounding box
Border Proximity - Ranks annotations by how close they are to image borders.imagebounding box, point, polygon, polyline, rotatable bounding box, skeleton
Brightness - Ranks annotated objects by their brightness.imagebounding box, polygon, rotatable bounding box
Broken Object Tracks - Identifies broken object tracks based on object overlaps.sequence, videobounding box, polygon, rotatable bounding box
Classification Quality - Compares image classifications against similar images.imageradio
Confidence - The confidence that an object was annotated correctly.imagebounding box, polygon, rotatable bounding box
Contrast - Ranks annotated objects by their contrast.imagebounding box, polygon, rotatable bounding box
Green Value - Ranks annotated objects by how green the average value of the object is.imagebounding box, polygon, rotatable bounding box
Height - Ranks annotated objects by the height of the object.imagebounding box, polygon, rotatable bounding box
Inconsistent Object Class - Looks for overlapping objects with different classes across frames.sequence, videobounding box, polygon, rotatable bounding box
Inconsistent Track ID - Looks for overlapping objects with different track IDs across frames.sequence, videobounding box, polygon, rotatable bounding box
Label Duplicates - Ranks labels by how likely they are to represent the same object.imagebounding box, polygon, rotatable bounding box
Missing Objects - Identifies missing objects based on object overlaps.sequence, videobounding box, polygon, rotatable bounding box
Object Classification Quality - Compares object annotations against similar image crops.imagebounding box, polygon, rotatable bounding box
Occlusion Risk - Tracks objects and detects outliers in videos.sequence, videobounding box, rotatable bounding box
Polygon Shape Anomaly - Calculates potential outliers by polygon shape.imagepolygon
Randomize Objects - Assigns a random value between 0 and 1 to objects.imagebounding box, polygon, rotatable bounding box
Red Value - Ranks annotated objects by how red the average value of the object is.imagebounding box, polygon, rotatable bounding box
Relative Area - Computes object size as a percentage of total image size.imagebounding box, polygon, rotatable bounding box
Sharpness - Ranks annotated objects by their sharpness.imagebounding box, polygon, rotatable bounding box
Width - Ranks annotated objects by the width of the object.imagebounding box, polygon, rotatable bounding box
TitleMetric TypeOntology Type
Area - Ranks videos by their area (width/height).video
Aspect Ratio - Ranks videos by their aspect ratio (width/height).video
Blue Value - Ranks videos by how blue the average value of the video is.video
Brightness - Ranks videos by their brightness.video
Clip Duration - Ranks videos based on the video’s duration.video
Contrast - Ranks videos by their contrast.video
Diversity - Forms clusters based on the ontology and ranks videos from easy samples to annotate to hard samples to annotate.video
Frame Number - Selects videos based on a specified range.video
Frame Label Countvideo
Frames Per Secondvideo
Green Value - Ranks videos by how green the average value of the video is.video
Height - Ranks videos by the height of the video.video
Instance Label Count - Ranks videos by the number of unique objects in the video.videobounding box, checklist, point, polygon, polyline, radio, rotatable bounding box, skeleton, text
Red Value - Ranks videos by how red the average value of the video is.video
Sharpness - Ranks videos by their sharpness.video
Uniqueness - Finds duplicate and near-duplicate videos.video
Unlabelled Frames (%) - Ranks videos based on the percentage of unlabelled frames in the video.video
Unlabelled Frames (#) - Ranks videos based on the number of unlabelled frames in the video.video
Width - Ranks videos by the width of the video.video
Model quality metrics help you evaluate your data and labels based on a trained model and imported model predictions.Acquisition FunctionsAcquisition functions are a special type of model quality metric, primarily used in active learning to score data samples according to how informative they are for the model, enabling smart labeling of unannotated data.
TitleMetric TypeData Type
Entropy - Ranks images by their entropy.image
Least Confidence - Ranks images by their least confidence score.image
Margin - Ranks images by their margin score.image
Variance - Ranks images by their variance.image
Mean Object Score - Ranks images by their average object score.imageobject

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Collections

Collections provide a way to save interesting groups of data units and labels, to support and guide your downstream workflow. By grouping your data into Collections you are able to:
  • Save data units or labels, individually or in bulk, into new or existing collections.
  • Curate higher-quality data collections for annotation, creating and grouping a range of different data units or labels into collections for better overview, data management, and structure.
To create or edit a Collection:
  1. Select the data you want to add to a collection by clicking the checkbox on the data card.
  2. Click Actions or press A.
  3. Select Add to collection.
  4. Select +New Collection to create a new collection, or Existing Collection to add the data to an existing collection.
  5. Click Submit.
To export a Collection as a CSV file:
  1. Click Filter or press F.
  2. Click the ellipsis icon next to the collection you want to export.
  3. Click Download CSV.

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Analytics View

Use the Analytics view to display Metric Correlation and prediction distribution for your ML model. Prediction distribution provides class and underrepresented class data. You can adjust the X and Y values on the Metric Correlation across a number of data and label metrics. You can create custom analytics dashboards from the Analytics View using Distribution and Correlation charts.

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Distribution charts

Distribution charts display distributions and summaries of the selected metrics and custom metadata. Here are some examples:
  • Data unit: Frame number, random value, area
  • Metadata: Enum with their enum options, numeric, date time, and boolean
varchar (previously string), text (previously long_string), and uuid are NOT SUPPORTED for use in Distribution charts.
Add Distribution chart

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Correlation charts

Correlation charts display a scatter plot of two attributes to show correlation within your current filtered view. Correlation charts require numeric data.
Distribution charts support a number of custom metadata types, however Correlation charts ONLY SUPPORT numeric custom metadata.
Add Correlation chart

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Custom Analytics Dashboard

Before creating a custom analytics dashboard, we recommend having custom metadata available in your data. Custom metadata can make the insights you get from the dashboard much more useful.
  1. Navigate to Project > Explore and select a folder.
  2. Click Analytics view.
  3. Specify the display criteria for the Distribution and Correlation cards that display by default.
  4. Click Add chart to add additional Distribution and Correlation cards.
  5. Specify the display criteria for the added Distribution and Correlation.

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Crop View

Crop View is only available from the Labels page of the Explore tab in your Project.
The Labels page displays all object and classification annotations on your images and video frames. Crop View zooms in on each annotated object, making it easier to inspect small or densely annotated regions. For example, if a blueberry is annotated in an HD video frame containing many blueberries, Crop View off shows the full frame with the annotation highlighted but hard to see. Crop View on zooms directly into the annotated object. To turn ON Crop View for all labels:
  1. Navigate to your Project and click Explore.
  2. Click Labels. The Labels page appears.
  3. Click Display. The Display tab appears. Crop View
  4. Toggle the Crop View switch. Object labels immediately are zoomed in on. Images/video frames with Classifications remain unchanged.
    Classifications on images/video frames are not affected by the Crop View feature. This is because Classifications apply to the entire image/video frame, while object annotations apply to specific areas/regions of an image/video frame. The following image has a Classification label/annotation Blueberry or Cherry? Blueberry and a bitmask object label/annotation Blueberry. The bitmask object annotation zooms in, while the classification does not.
  5. Adjust the Crop View Zoom as required.