Skip to main content

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.

​
Calculate Metrics & Embeddings

  1. Navigate to Projects > Explore.
  2. Click Metrics & Embeddings.
  3. Click Compute for either option.
  4. Specify the following:
    • Similarity & Natural language search and quality metrics: Enable to compute embeddings and quality metrics. Access quality metrics for filtering and sorting.
    • Select embeddings: Default embeddings are computed by Encord. Alternatively, import and select your own custom embeddings.
    • Embeddings plot, Diversity and Uniqueness metrics: Enable to compute UMAP reduction to generate 2D embeddings plots to visualize your data. Also access diversity and uniqueness metrics for curation.
  5. Click Start computation.

​
Filters

In the Active Explorer for a Project you can refine searches by data quality metrics, label quality metrics, Collections, data types, annotation types, annotation classes, and by annotator from Annotate.
Filters provide the ability to include or exclude images based on your filtering criteria.
For more detailed information on Data Quality Metrics go here.
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
For more detailed information on Label Quality Metrics go here.
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
Brightness - Ranks annotated objects by their brightness.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
Broken Object Tracks - Identifies broken object tracks based on object overlaps.sequence, videobounding box, polygon, rotatable bounding box
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
Classification Quality - Compares image classifications against similar images.imageradio
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 detect 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
Collections: Collections are a way to save interesting groups of data units and labels, to support and guide your downstream workflow. Custom Metadata: Custom metadata added to Annotate Projects. When an Annotate Project imports to Active, if that Annotate Project has custom metadata, the custom metadata is available to filter your data in Active.
For information on importing custom metadata to an Annotate project, refer to Adding Metadata in the documentation.
Custom embeddings: Data, labels, and Predictions can be filtered by custom embeddings. Annotation invalid: Data, labels, and Predictions can be filtered by: Well-formed Annotation and Invalid Annotation. Annotation Type: Data, labels, and Predictions can be filtered by: classification, bounding box, rotatable bounding box, point, polyline, polygon, skeleton, and bitmask. Annotator: Data, labels, and Predictions can be filtered by the person who annotated the images/videos. Class: Data, labels, and Predictions can be filtered by annotation class. Data title: Data, labels, and Predictions can be filtered by the name of the data unit. Data Types: Data, labels, and Predictions can be filtered by: images, image sequences, image groups, DICOM series, audio files, and videos. Dataset: Datasets included in an Annotate Project. Manual Annotation: Data, labels, and Predictions can be filtered by: Manual Annotation and Automated Annotation. Prediction IOU: Data, labels, and Predictions can be filtered by Prediction IOU. Priorities: Data, labels, and Predictions can be filtered by the priority specified in Annotate. Workflow stage: Data, labels, and Predictions can filter by non-Consensus stages (data, labels, and annotators) and Consensus stages (data only). To filter data, labels, or predictions in Active:
  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. Filter with example
  4. Select Data, Labels, or Predictions.
  5. Click Filters. A menu appears.
  6. Add and configure the filters you need. Images/video/audio files filter in the Explorer workspace.
  • Sort and use the NLP or image searches to further help get the results you want.
  • After filtering, sorting, and searching, create a Collection.

​
Preset Filters

Preset filters provide a way to save your filtering criteria for use and reuse on other Projects. Preset filters are made up of global and local filter criteria. Global filter criteria are filters, and their settings, that can apply to any Project. For example, Data Quality Metrics like Area, Blue Value, and Sharpness or Label Quality Metrics like Annotation Quality, Confidence, or Label Duplicates. Local filter criteria are filters that can only be applied to a specific Project. For example, the following filters and their settings are likely only applicable to a specific Project: Class, Dataset, or Collection.
  1. Log in to the Encord platform.
  2. Click Active in the main menu.
  3. Click the Project.
  4. Select Data, Labels, or Predictions. Create Preset
  5. Click Filters. A menu appears.
  6. Add and configure the filters you need.
  7. Click Create preset once you have added all the filters you need and specified each filter’s settings. After creating the Preset you can use the Preset in this or any other Project.
  1. Log in to the Encord platform.
  2. Click Active in the main menu.
  3. Click the Project.
  4. Select Data, Labels, or Predictions. Use a Preset
  5. Click Filters. A menu appears.
  6. Select the Preset you want to use from the dropdown. Images/video/audio files filter in the Explorer workspace based on the Preset Filters and their settings.
Global filters apply to any Project, but Local filters only apply on the Project where the Preset was created.

​
Sorting

Sort your data, labels, or predictions in ascending or descending order using data or label quality metrics. To sort data, labels, or predictions in Active:
  1. Log in to the Encord platform.
  2. Click Active in the main menu.
  3. Click the Project.
  4. Select Data, Labels, or Predictions. Sort
  5. Select the metric to sort the data.
  6. Specify ascending or descending order.
  • Filter and use the NLP or image searches to further help get the results you want.
  • After filtering, sorting, and searching, create a Collection.