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Filters

You can refine searches by data quality metrics, Collections, custom metadata, folders, data titles, and data types. 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 data units. The custom metadata added to data units in Folders persists to Annotate Projects and Active.
For information on importing custom metadata, refer to Adding Metadata in the documentation.
Data title: The file name of the image or video. Data Types: Data, labels, and Predictions can be filtered by: images, image sequences, image groups, and videos. File ID: The unique hash assigned to the image or video when it imports into Encord. Folder: The Folder images or videos reside in. Integration: The integration (if any) your images/videos reside in. Keyframe: Frames of interest you specified on your videos. MIME type: Also known as media type. The file type of an image or video. For example, video/mp4. Storage location: The location where your images/videos reside. Examples include AWS, GCP, Local. Uploaded at: The date and time that the image/video uploaded. To filter data:
  1. Navigate to Data > Explore and select a folder.
  2. Click the Filter dropdown or press F. Index Filtering
  3. Add and configure the filters you need.
  4. Click Search to update the results.

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Preset Filters

Presets are only available to customers with the Curation package.
Preset filters let you save and reuse filtering criteria across your workspace. Since presets are available everywhere during data curation, some may return no results in certain contexts. For example, a preset scoped to Folder A will return nothing when browsing Folder B if Folder A isn’t a subfolder of it. To create a Preset filter:
  1. Navigate to Data > Explore and select a folder.
  2. Click the Filter dropdown or press F.
  3. Add and configure the filters you need. Index Filtering
  4. Click +Create preset and give the preset a name.
  5. Click Create to finish creating the preset. Create Presets
To use an existing Preset:
  1. Navigate to Data > Explore and select a folder.
  2. Click the Filter dropdown or press F.
  3. Select the Preset you want to use from the dropdown. All presets are listed at the top of the list oif filters.
  4. Click Search to update the results.
Global filters apply to any Folder, but Local filters only apply on the Folder where the Preset was created.

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Sorting

Sort your data, in ascending or descending order, using data quality metrics. To sort your data:
  1. Navigate to Data > Explore and select a folder.
  2. Select the metric to sort the data. Index Sort
  3. Specify ascending or descending order.
  • Filter and use the natural language searches to further help get the results you want.
  • After filtering, sorting, and searching, create a Collection.

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Quality Metrics

Quality Metrics are only available to customers with the Curation package.
Quality metrics are only calculated when you upgrade your folder. If you don’t see the quality metrics during curation, make sure to upgrade your folder.
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.
If you add a large amount of new data to a folder, existing uniqueness and diversity scores may no longer reflect the full dataset. You can recompute these metrics at any time to bring them up to date.
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.
Label Quality Metrics are used for sorting data, filtering data, and data analytics.
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.
Natural language search and similarity search can only be performed after you upgrade your folder.
To use natural language or image search:
  1. Navigate to Data > Explore and select a folder.
  2. Type a search query or upload an image.
Natural Language Search

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Collections

Collections are only available to customers with the Curation package.
Collections are saved groups of data units or labels that let you curate subsets of your data and perform bulk actions on them, such as sending items to annotation, running bulk classifications, or exporting a curated Dataset. In the Explorer filter toolbar, each collection has a … (three-dot) menu that displays the collection name and its unique collection ID. Click the copy icon next to the ID to copy it to your clipboard — useful for API calls, support requests, or cross-referencing collections. Actions in this menu are organized into logical groups (navigate, manage, data, workflow, and danger) separated by dividers, making it faster to locate the right action. Empty groups are hidden automatically so no stray dividers appear.

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Folder Upgrade

After you data is added into Encord you can upgrade the storage folder that your data resides in. Upgrading your folder calculates quality metrics, generates data embeddings, and enables features like natural language search and embedding plots to support your data curation. To upgrade your folder:
  1. Navigate to Data > Files & Folders.
  2. Click into the folder.
  3. Click the info icon next to the folder name.
  4. Click Folder upgrade.

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Delete Files

You can delete files from the Explore Grid View.
To delete files from the Explore Grid View:
  1. Navigate to Data.
  2. Click Explore. The Explore page appears displaying files from the currently selected Folder in the Grid view.
  3. Change the selected Folder to the Folder with the data units you want to delete.
  4. Select one or more data units.
    Select one data unit and then use SHIFT or CMD/CTRL to select multiple data units.
  5. Click Actions > Delete.

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Re-index

Re-index forces a full re-indexing of all images, videos, and audio files in Encord.
Re-indexing helps resolve issues displaying, sorting, or filtering images, videos, frames, or audio files in Encord. It’s a troubleshooting tool, not something to use routinely — only re-index if you’re actively experiencing one of these issues.
Encord visualizes the contents of your Folders and sub-folders. As data arrives, it is indexed automatically so content is available almost immediately. Re-indexing clears the existing index and rebuilds it from scratch, ensuring all Folder contents are accurately reflected in Encord. When to use Re-index:
Please provide a reason why you are re-indexing. The more information you provide, the more prepared we can be with a follow-up.
Click the Re-index button in Encord. Re-index Re-indexing duration depends on the volume of data and the number of Folders involved. Folders with large amounts of data may take several hours to complete.
We strongly recommend against re-indexing while data is actively being imported into the Folder you are viewing in Encord.

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Recompute Quality Metrics

Recomputing quality metrics can only be run manually. It is not automatically triggered by adding or removing data from a folder.
Recompute Values You can recompute advanced quality metrics (uniqueness and diversity) for a folder. These scores are calculated relative to the whole dataset, so adding or removing a significant amount of data makes the existing scores stale. Recompute whenever you’ve made a significant change to a folder’s contents and plan to rely on uniqueness or diversity scores, for example before filtering, sorting, or sampling data based on these metrics.
Recomputing only updates uniqueness and diversity scores. Your data, embeddings, and other metrics are untouched.