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How to integrate
How to manage and annotate
Configure the label editor
Once you have created a dataset, you can further configure the labeling interface under Settings -> Labeling -> Categories:
Here you can set the color, name, and description for each category. You can also add one or more object-level attributes to a category by expanding its row, or add image-level attributes by clicking the button "Edit image attributes".
Attributes come in 4 types: select box, text, number, and checkbox. You can optionally set a default value for each attribute or make them mandatory.
In the label editor, the object-level and image-level attributes will be shown in the sidebar on the right. The image-level attributes are always visible, while the object-level attributes are only shown when an object is selected and has a category with object-level attributes.
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Editing the configuration file directly
If you click on the "Raw" tab, you can see the configuration in JSON format. You can copy-paste this configuration from one dataset to another, or update it programmatically using the Python SDK. The configuration should adhere to the format defined here.
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