Create a Collection

Collections are created by tagging/labeling images and then building groups (Collections) based on the tagged images. Tagging is a versatile feature used in almost all Encord Active workflows, whether you are relabeling, augmenting, exporting, or deleting data.

Data Collections consist of groups of image/video/audio files. The Collection focus is on data. For example, you want to create a Collection of specific things (like blueberries or cherries).

In Encord Active, creating Collections provides several advantages:

• Organization: Allows you to organize your data effectively within the platform. By assigning Collection tags to your data points, you can group and categorize data based on common characteristics, making it easier to manage and navigate large subsets of the dataset.

• Enhanced search and filtering: Collections in Encord Active enables powerful search and filtering capabilities. You can search for specific data points or filter data based on tags, narrowing down your focus to the relevant information you need.

• Customizable metadata: Collection tags serve as customizable metadata that can provide additional context and information about your data. You can define and assign tags that align with your specific project requirements, providing meaningful insights and annotations for efficient data analysis.

• Collaboration and knowledge sharing: Tagging promotes collaboration and knowledge sharing among team members in Encord Active. With consistent tagging conventions, team members can easily understand and access tagged data, facilitating efficient collaboration and ensuring everyone is on the same page.

• Performing bulk actions: Tagging allows the performing of bulk actions on groups of data units or labels. For example, you could modify data units with incorrect labels at the push of a button.

These are just a few of the advantages of tagging in Encord Active, and there may be more benefits specific to your project and workflow.

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Next Steps

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Data Cleansing/Curation and Label Correction/Validation

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Model and Prediction Validation