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Export results from Label Studio
Export data from Label Studio
Export your completed annotations from Label Studio. Label Studio stores your annotations in a raw JSON format in the SQLite database backend or whichever cloud or database storage you specify as target storage. Cloud storage buckets contain one file per labeled task named as task_id.json.
You can convert the raw JSON completed annotations stored by Label Studio into a more common format and export that data in several different ways:
- Export from the Label Studio UI on the /export page.
- Call the API to export data. See the Label Studio API documentation.
- For versions of Label Studio earlier than 1.0.0, run the relevant converter tool on the directory of completed annotations using the command line or Python. You can also run the relevant converter tool on exported JSON from version 1.0.0.
Export formats supported by Label Studio
Label Studio supports many common and standard formats for exporting completed labeling tasks. If you don’t see a format that works for you, you can contribute one. See the GitHub repository for the Label Studio Converter tool.
JSON
List of items in raw JSON format stored in one JSON file. Use to export both the data and the annotations for a dataset.
JSON_MIN
List of items where only "from_name", "to_name" values from the raw JSON format are exported. Use to export only the annotations and the data for a dataset, and no Label-Studio-specific fields.
{
"image": "https://htx-misc.s3.amazonaws.com/opensource/label-studio/examples/images/nick-owuor-astro-nic-visuals-wDifg5xc9Z4-unsplash.jpg",
"tag": [{
"height": 10.458911419423693,
"rectanglelabels": [
"Moonwalker"
],
"rotation": 0,
"width": 12.4,
"x": 50.8,
"y": 5.869797225186766
}]
}CSV
Results are stored as comma-separated values with the column names specified by the values of the "from_name" and "to_name" fields.
TSV
Results are stored in tab-separated tabular file with column names specified by "from_name" "to_name" values
CONLL2003
Popular format used for the CoNLL-2003 named entity recognition challenge.
COCO
Popular machine learning format used by the COCO dataset for object detection and image segmentation tasks.
Pascal VOC XML
Popular XML-formatted task data used for object detection and image segmentation tasks.
Brush labels to NumPy & PNG
Export your brush labels as NumPy 2d arrays and PNG images. Each label outputs as one image.
ASR_MANIFEST
Export audio transcription labels for automatic speech recognition as the JSON manifest format expected by NVIDIA NeMo models.
{“audio_filepath”: “/path/to/audio.wav”, “text”: “the transcription”, “offset”: 301.75, “duration”: 0.82, “utt”: “utterance_id”, “ctm_utt”: “en_4156”, “side”: “A”}Label Studio JSON format of annotated tasks
When you annotate data, Label Studio stores the output in JSON format. The raw JSON structure of each completed task follows this example:
{
"id": 1,
"data": {
"image": "https://example.com/opensource/label-studio/examples/images/nick-owuor-astro-nic-visuals-wDifg5xc9Z4-unsplash.jpg"
},
"created_at":"2021-03-09T21:52:49.513742Z",
"updated_at":"2021-03-09T22:16:08.746926Z",
"project":83,
"annotations": [
{
"id": "1001",
"result": [
{
"from_name": "tag",
"id": "Dx_aB91ISN",
"source": "$image",
"to_name": "img",
"type": "rectanglelabels",
"value": {
"height": 10.458911419423693,
"rectanglelabels": [
"Moonwalker"
],
"rotation": 0,
"width": 12.4,
"x": 50.8,
"y": 5.869797225186766
}
}
]
"was_cancelled":false,
"ground_truth":false,
"created_at":"2021-03-09T22:16:08.728353Z",
"updated_at":"2021-03-09T22:16:08.728378Z",
"lead_time":4.288,
"result_count":0,
"task":1,
"completed_by":10
}
],
"predictions": [
{
"created_ago": "3 hours",
"model_version": "model 1",
"result": [
{
"from_name": "tag",
"id": "t5sp3TyXPo",
"source": "$image",
"to_name": "img",
"type": "rectanglelabels",
"value": {
"height": 11.612284069097889,
"rectanglelabels": [
"Moonwalker"
],
"rotation": 0,
"width": 39.6,
"x": 13.2,
"y": 34.702495201535505
}
}
]
},
{
"created_ago": "4 hours",
"model_version": "model 2",
"result": [
{
"from_name": "tag",
"id": "t5sp3TyXPo",
"source": "$image",
"to_name": "img",
"type": "rectanglelabels",
"value": {
"height": 33.61228406909789,
"rectanglelabels": [
"Moonwalker"
],
"rotation": 0,
"width": 39.6,
"x": 13.2,
"y": 54.702495201535505
}
}
]
}
]
}Relevant JSON property descriptions
Review the full list of JSON properties in the API documentation.
| JSON property name | Description |
|---|---|
| id | Identifier for the labeling task from the dataset. |
| data | Data copied from the input data task format. See the documentation for Task Format. |
| project | Identifier for a specific project in Label Studio. |
| annotations | Array containing the labeling results for the task. |
| annotations.id | Identifier for the completed task. |
| annotations.lead_time | Time in seconds to label the task. |
| annotations.result | Array containing the results of the labeling or annotation task. |
| annotations.completed_by | User ID of the user that created the annotation. Matches the list order of users on the People page on the Label Studio UI. |
| result.id | Identifier for the specific annotation result for this task. |
| result.from_name | Name of the tag used to label the region. See control tags. |
| result.to_name | Name of the object tag that provided the region to be labeled. See object tags. |
| result.type | Type of tag used to annotate the task. |
| result.value | Tag-specific value that includes details of the result of labeling the task. The value structure depends on the tag for the label. Explore each tag for more details. |
| predictions | Array of machine learning predictions. Follows the same format as the completions array, with one additional parameter. |
| predictions.score | The overall score of the result, based on the probabilistic output, confidence level, or other. |
Units of image annotations
The units the x, y, width and height of image annotations are provided in percentages of overall image dimension. To convert those percentages to pixels, multiply the values by 100.
You can 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_heightFor 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)