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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-annotations that you import on the Labeling page for each task.
To generate interactive pre-annotations with a machine learning model while labeling, see Set up machine learning with Label Studio.
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.
For image pre-annotations, Label Studio expects the x, y, width, and height of image annotations to be provided in percentages of overall image dimension. See Units for image annotations on this page for more about how to convert formats.
Import pre-annotated tasks into Label Studio using the UI or using the API.
Import pre-annotations for images
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="$image"/>
</View>After you set up an example project, create example tasks that match the following format.
Click to expand the example image JSON
Save this example JSON as a file to import it into Label Studio, for example, example_prediction_task.json.
[{
"data": {
"image": "http://localhost:8080/static/samples/sample.jpg"
},
"predictions": [{
"result": [
{
"id": "result1",
"type": "rectanglelabels",
"from_name": "label", "to_name": "image",
"original_width": 600, "original_height": 403,
"image_rotation": 0,
"value": {
"rotation": 0,
"x": 4.98, "y": 12.82,
"width": 32.52, "height": 44.91,
"rectanglelabels": ["Airplane"]
}
},
{
"id": "result2",
"type": "rectanglelabels",
"from_name": "label", "to_name": "image",
"original_width": 600, "original_height": 403,
"image_rotation": 0,
"value": {
"rotation": 0,
"x": 75.47, "y": 82.33,
"width": 5.74, "height": 7.40,
"rectanglelabels": ["Car"]
}
},
{
"id": "result3",
"type": "choices",
"from_name": "choice", "to_name": "image",
"value": {
"choices": ["Airbus"]
}
}],
"score": 0.95
}]
}]In this example there are 3 results inside 1 prediction, or pre-annotation:
result1- the first bounding boxresult2- the second bounding boxresult3- choice selection
The prediction score applies to the entire prediction.
Import pre-annotated tasks into Label Studio using the UI or using the API.
In the Label Studio UI, the imported prediction for this task looks like the following:
Import pre-annotated regions for images
If you want to import images with pre-annotated regions without labels assigned to them, follow this example.
Use the following labeling configuration:
<View>
<View style="display:flex;align-items:start;gap:8px;flex-direction:row">
<Image name="image" value="$image" zoom="true" zoomControl="true" rotateControl="false"/>
<Rectangle name="rect" toName="image" showInline="false"/>
</View>
<Ellipse name="ellipse" toName="image"/>
<KeyPoint name="kp" toName="image"/>
<Polygon name="polygon" toName="image"/>
<Brush name="brush" toName="image"/>
<Labels name="labels" toName="image" fillOpacity="0.5" strokeWidth="5">
<Label value="Vehicle" background="green"/>
<Label value="Building" background="blue"/>
<Label value="Pavement" background="red"/>
</Labels>
</View>After you set up an example project, create example tasks that match the following format.
Click to expand the example image region JSON
Save this example JSON as a file to import it into Label Studio, for example, example_prediction_task.json.
[{
"id":8,
"predictions":[
{
"id":10,
"result":[
{
"original_width":800,
"original_height":450,
"image_rotation":0,
"value":{
"x":55.46666666666667,
"y":2.3696682464454977,
"width":35.86666666666667,
"height":46.91943127962085,
"rotation":0
},
"id":"ABC",
"from_name":"rect",
"to_name":"image",
"type":"rectangle"
},
{
"original_width":800,
"original_height":450,
"image_rotation":0,
"value":{
"x":58.4,
"y":64.21800947867298,
"width":30.533333333333335,
"height":19.90521327014218,
"rotation":0
},
"id":"DEF",
"from_name":"rect",
"to_name":"image",
"type":"rectangle"
},
{
"original_width":800,
"original_height":450,
"image_rotation":0,
"value":{
"points":[
[
20.933333333333334,
28.90995260663507
],
[
25.866666666666667,
64.69194312796209
],
[
38.4,
62.796208530805686
],
[
34.13333333333333,
27.488151658767773
]
]
},
"id":"GHI",
"from_name":"polygon",
"to_name":"image",
"type":"polygon"
},
{
"original_width":800,
"original_height":450,
"image_rotation":0,
"value":{
"x":8.4,
"y":20.14218009478673,
"radiusX":4,
"radiusY":7.109004739336493,
"rotation":0
},
"id":"JKL",
"from_name":"ellipse",
"to_name":"image",
"type":"ellipse"
}
],
"task":8
}
],
"data":{
"image":"/data/upload/31159626248_d0362d027c_c.jpg"
},
"project":4
}]In this example there are 3 regions inside 1 result field for a prediction, or pre-annotation:
- region
ABC- a rectangle bounding box - region
DEF- a second rectangle bounding box - region
GHI- a polygonal segmentation - region
JKL- an ellipse
None of the regions have labels applied. The labeling configuration must use theRectangletag instead of theRectangleLabelstag to support this type of prediction. Even though the labeling configuration for this example has aLabelstag, the predictions do not need to specify labels for the regions.
Units of image annotations
The units the x, y, width and height of image annotations are provided in percentages of overall image dimension.
Use the following conversion formulas for x, y, width, height:
pixel_x = x / 100.0 * original_width
pixel_y = y / 100.0 * original_height
pixel_width = width / 100.0 * original_width
pixel_height = height / 100.0 * original_height
For example:
task = {
"annotations": [{
"result": [
{
"...": "...",
"original_width": 600,
"original_height": 403,
"image_rotation": 0,
"value": {
"x": 5.33,
"y": 23.57,
"width": 29.16,
"height": 31.26,
"rotation": 0,
"rectanglelabels": [
"Airplane"
]
}
}
]
}]
}
# convert from LS percent units to pixels
def convert_from_ls(result):
if 'original_width' not in result or 'original_height' not in result:
return None
value = result['value']
w, h = result['original_width'], result['original_height']
if all([key in value for key in ['x', 'y', 'width', 'height']]):
return w * value['x'] / 100.0, \
h * value['y'] / 100.0, \
w * value['width'] / 100.0, \
h * value['height'] / 100.0
# convert from pixels to LS percent units
def convert_to_ls(x, y, width, height, original_width, original_height):
return x / original_width * 100.0, y / original_height * 100.0, \
width / original_width * 100.0, height / original_height * 100
# convert from LS
output = convert_from_ls(task['completions'][0]['result'][0])
if output is None:
raise Exception('Wrong convert')
pixel_x, pixel_y, pixel_width, pixel_height = output
print(pixel_x, pixel_y, pixel_width, pixel_height)
# convert back to LS
x, y, width, height = convert_to_ls(pixel_x, pixel_y, pixel_width, pixel_height, 600, 403)
print(x, y, width, height)
Import pre-annotations for text
In this example, import pre-annotations for text using the named entity recognition template:
<View>
<Labels name="label" toName="text">
<Label value="Person"></Label>
<Label value="Organization"></Label>
<Label value="Fact"></Label>
<Label value="Money"></Label>
<Label value="Date"></Label>
<Label value="Time"></Label>
<Label value="Ordinal"></Label>
<Label value="Percent"></Label>
<Label value="Product"></Label>
<Label value="Language"></Label>
<Label value="Location"></Label>
</Labels>
<Text name="text" value="$text"></Text>
</View>Example JSON
This example JSON file contains two tasks, each with two sets of pre-annotations from different models. The first task also contains prediction scores for each NER span.
Click to expand the example NER JSON
Save this example JSON as a file, for example: example_preannotated_ner_tasks.json.
[
{
"data": {
"text": "All that changed when he was 27 and he came to Jerusalem. It was the weekend of both Easter and Passover, and the city was flooded with tourists."
},
"predictions": [
{
"model_version": "one",
"result": [
{
"id": "abc",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 29,
"end": 31,
"score": 0.70,
"text": "27",
"labels": [
"Date"
]
}
},
{
"id": "def",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 47,
"end": 56,
"score": 0.65,
"text": "Jerusalem",
"labels": [
"Location"
]
}
},
{
"id": "ghi",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 65,
"end": 76,
"score": 0.95,
"text": "the weekend",
"labels": [
"Date"
]
}
},
{
"id": "jkl",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 85,
"end": 91,
"score": 0.50,
"text": "Easter",
"labels": [
"Date"
]
}
}
]
},
{
"model_version": "two",
"result": [
{
"id": "mno",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 29,
"end": 31,
"score": 0.55,
"text": "27",
"labels": [
"Date"
]
}
},
{
"id": "pqr",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 47,
"end": 56,
"score": 0.40,
"text": "Jerusalem",
"labels": [
"Location"
]
}
},
{
"id": "stu",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 65,
"end": 76,
"score": 0.32,
"text": "the weekend",
"labels": [
"Time"
]
}
},
{
"id": "vwx",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 85,
"end": 91,
"score": 0.22,
"text": "Easter",
"labels": [
"Location"
]
}
},
{
"id": "yz1",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 96,
"end": 104,
"score": 0.96,
"text": "Passover",
"labels": [
"Date"
]
}
}
]
}
]
},
{
"data": {
"text": " Each journal was several inches thick and bound in leather. On one page are drawn portraits of Sunny in a flowery, Easter dress and sun hat. On another page are hundreds of sketches of leaves that Niyati saw in her yard."
},
"predictions": [
{
"model_version": "one",
"result": [
{
"id": "ab2",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 18,
"end": 32,
"text": "several inches",
"labels": [
"Product"
]
}
},
{
"id": "cd3",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 64,
"end": 67,
"text": "one",
"labels": [
"Percent"
]
}
},
{
"id": "ef4",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 96,
"end": 101,
"text": "Sunny",
"labels": [
"Person"
]
}
},
{
"id": "gh5",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 162,
"end": 170,
"text": "hundreds",
"labels": [
"Percent"
]
}
},
{
"id": "ij6",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 198,
"end": 204,
"text": "Niyati",
"labels": [
"Person"
]
}
}
]
},
{
"model_version": "two",
"result": [
{
"id": "kl7",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 18,
"end": 32,
"text": "several inches",
"labels": [
"Fact"
]
}
},
{
"id": "mn8",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 64,
"end": 67,
"text": "one",
"labels": [
"Percent"
]
}
},
{
"id": "op9",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 96,
"end": 101,
"text": "Sunny",
"labels": [
"Time"
]
}
},
{
"id": "qr10",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 116,
"end": 122,
"text": "Easter",
"labels": [
"Location"
]
}
},
{
"id": "st11",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 162,
"end": 170,
"text": "hundreds",
"labels": [
"Money"
]
}
},
{
"id": "uv12",
"from_name": "label",
"to_name": "text",
"type": "labels",
"value": {
"start": 198,
"end": 204,
"text": "Niyati",
"labels": [
"Person"
]
}
}
]
}
]
}
]Import pre-annotated tasks into Label Studio using the UI or using the API.
In the Label Studio UI, the imported prediction for the first task looks like the following:
You can sort the prediction scores for each labeled region using the Regions pane options.
Troubleshoot pre-annotations
If you encounter unexpected behavior after you import pre-annotations into Label Studio, review this guidance to resolve the issues.
Check the configuration values of your labeling configuration and tasks
The from_name of the pre-annotation task JSON must match the value of the name in the <Labels name="label" toName="text"> portion of the labeling configuration. The to_name must match the toName value.
In the text example on this page, the JSON includes "from_name": "label" to correspond with the <Labels name="label" and "to_name": text to correspond with the toName="text of the labeling configuration. The default template might contain <Labels name="ner" toName="text">. To work with this example JSON, you need to update the values to match.
In the image example on this page, the XML includes
...
<Choices name="choice" toName="image" showInLine="true">`
...
<RectangleLabels name="label" toName="image">
...To correspond with the following portions of the example JSON:
...
"type": "rectanglelabels",
"from_name": "label", "to_name": "image",
...
type": "choices",
"from_name": "choice", "to_name": "image",
...Check the labels in your configuration and your tasks
Make sure that you have a labeling configuration set up for the labeling interface, and that the labels in your JSON file exactly match the labels in your configuration. If you’re using a tool to transform your model output, make sure that the labels aren’t altered by the tool.
Check the IDs and toName values
If you’re performing nested labeling, such as displaying a TextArea tag for specific Label or Choice values, the IDs for those results must match.
For example, if you want to transcribe text alongside a named entity resolution task, you might have the following labeling configuration:
<View>
<Labels name="label" toName="text">
<Label value="PER" background="red"/>
<Label value="ORG" background="darkorange"/>
<Label value="LOC" background="orange"/>
<Label value="MISC" background="green"/>
</Labels>
<Text name="text" value="$text"/>
<TextArea name="entity" toName="text" perRegion="true"/>
</View>If you wanted to add predicted text and suggested transcriptions for this labeling configuration, you might use the following example JSON.
{
"data":{
"text":"The world that we live in is a broad expanse of nothingness, said the existential philosopher, before he rode away with his cat on his motorbike. "
},
"predictions":[
{
"result":[
{
"value":{
"start":135,
"end":144,
"text":"motorbike",
"labels":[
"ORG"
]
},
"id":"def",
"from_name":"ner",
"to_name":"text",
"type":"labels"
},
{
"value":{
"start":135,
"end":144,
"text":[
"yay"
]
},
"id":"def",
"from_name":"entity",
"to_name":"text",
"type":"textarea"
}
]
}
]
}Because the TextArea tag applies to each labeled region, the IDs for the label results and the textarea results must match.
If you found an error, you can file an issue on GitHub!