Annotate data (Human-in-the-Loop)
The Embodied Intelligence Platform integrates Label Studio for data annotation. You can create annotation tasks with built-in or custom templates, configure automated pre-annotation operators, and export results in multiple formats.
Annotation templates
The platform ships with seven built-in Label Studio templates for common annotation tasks:
Template | Data type |
Image classification | Image |
Image annotation | Image |
Object detection (bounding box) | Image |
General image template | Image |
Video classification | Video |
Video time segment annotation | Video |
General video template | Video |
Browse built-in templates
In the left navigation pane, choose Data Management > Labeling jobs.
Click the Labeling templates tab.
Click Details next to a template to inspect its XML configuration.
Create a custom template
When the built-in templates do not cover your use case, you can define a custom annotation interface.
On the Labeling templates tab, click Create custom template.
Author the annotation interface in XML using the Label Studio template editor.
Verify the annotation interactions in the preview area, then save.
Templates use Label Studio XML syntax. For the full tag reference, see the Label Studio documentation.
Create an annotation task
On the Labeling tasks tab, click Create labeling task.
Set the following parameters:
Parameter
Required
Description
Task name
Required
A descriptive name for the annotation task.
Labeling type
Required
Select Video or Image. The template list updates to match the selected type.
Labeling template
Required
Choose a template that matches the selected data type. Video offers 3 templates; Image offers 4.
Annotator
Required
Assign one or more annotators. You can reassign annotators after the task is created.
Automatic labeling
Optional
Enable this to have an operator pre-annotate the data before human review. You must configure the operator first. For details, see Automated pre-annotation.
Data source
Required
The dataset to annotate. For details on preparing datasets, see Collect and manage data.
Expand Advanced settings to adjust the frame extraction rate for video tasks. The default is 30 fps.
Click OK.
Templates by data type
Data type | Available templates |
Video | Video classification, Video time segment annotation, General video template |
Image | Image classification, Image annotation, Object detection (bounding box), General image template |
Manage annotation tasks
The Labeling tasks tab lists all tasks with the following information:
Column | Description |
Task name | The task identifier. Click the name to open task details and access the Label Studio annotation page. |
Labeling template | The annotation template bound to this task. |
Status | Current task status: pending, in progress, or completed. |
Labeling method | Manual or automated (with pre-annotation). |
Progress | The percentage of items annotated. |
You can perform the following actions on each task:
Run pre-labeling: Invoke the configured operator to pre-annotate all unlabeled items.
Mark as complete: Accept the pre-annotation results as final annotations.
View labeling results: Review completed annotations.
Open Label Studio: Launch the Label Studio interface to annotate or correct items manually.
Automated pre-annotation
The platform provides six built-in operators powered by the Model Studio OpenAI-compatible API. These operators pre-annotate data so that human annotators only need to review and correct the results.
Operator | Task type |
Object detection | Bounding box annotation for images and videos |
Segmentation | Semantic segmentation for images and videos |
Temporal segmentation | Time-segment annotation for videos |
Object tracking | Multi-object tracking across video frames |
Classification | Category labeling for images and videos |
Keypoint | Keypoint annotation for pose estimation |
Set up an operator
Each operator must be configured before you can use it in an annotation task.
On the Labeling operators tab, click Configure next to the target operator.
Enter your Model Studio API key and select a model.
Click OK.
Once configured, turn on Automatic labeling when creating a task and select the operator. The system pre-annotates the data, then annotators review and correct the results in Label Studio.
Export annotation results
Completed annotations appear on the Labeled Datasets tab of the Datasets page. You can export results in JSON format directly, or use the Label Studio export feature for additional formats such as COCO, Pascal VOC, and YOLO.