Get Started
- Get started
- Create user accounts
Install and Upgrade
- Install and upgrade
- Start
- Troubleshoot
- Set up database storage
Import Data
- Get data
- Import pre-annotated data
- Sync data from cloud or Redis storage
Manage Data & Projects
- Set up your labeling project
- Label and annotate data
- Export results
Machine Learning Setup
- Set up machine learning with your labeling process
Advanced Development
- Frontend library
- Frontend reference
- Backend API
Import pre-annotated data into Label Studio
If you have predictions generated for your dataset from a model, either as pre-annotated tasks or pre-labeled tasks, you can import the predictions with your dataset into Label Studio for review and correction. Label Studio automatically displays the pre-labels that you import on the Labeling page for each task.
To import predicted labels into Label Studio, you must use the Basic Label Studio JSON format and set up your tasks with the predictions
JSON key. The Label Studio ML backend also outputs tasks in this format.
You must use different IDs for each task elements, annotations, predictions and their
result
items.
Example of importing predicted labels
For example, import predicted labels for tasks to determine whether an item in an image is an airplane or a car.
Use the following labeling configuration:
<View>
<Choices name="choice" toName="image" showInLine="true">
<Choice value="Boeing" background="blue"/>
<Choice value="Airbus" background="green" />
</Choices>
<RectangleLabels name="label" toName="image">
<Label value="Airplane" background="green"/>
<Label value="Car" background="blue"/>
</RectangleLabels>
<Image name="image" value="$image"/>
</View>
After you set up an example project, import this task into Label Studio. Save it as a file first, for example, example_prediction_task.json
.
{
"data": {
"image": "http://localhost:8080/static/samples/sample.jpg"
},
"predictions": [{
"result": [
{
"id": "result1",
"type": "rectanglelabels",
"from_name": "label", "to_name": "image",
"original_width": 600, "original_height": 403,
"image_rotation": 0,
"value": {
"rotation": 0,
"x": 4.98, "y": 12.82,
"width": 32.52, "height": 44.91,
"rectanglelabels": ["Airplane"]
}
},
{
"id": "result2",
"type": "rectanglelabels",
"from_name": "label", "to_name": "image",
"original_width": 600, "original_height": 403,
"image_rotation": 0,
"value": {
"rotation": 0,
"x": 75.47, "y": 82.33,
"width": 5.74, "height": 7.40,
"rectanglelabels": ["Car"]
}
},
{
"id": "result3",
"type": "choices",
"from_name": "choice", "to_name": "image",
"value": {
"choices": ["Airbus"]
}
}
]
}]
}
In this example there are 3 results inside of 1 prediction:
result1
- the first bounding boxresult2
- the second bounding boxresult3
- choice selection
In the Label Studio UI, the imported prediction for this task looks like the following:
