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Export annotations and data from Label Studio
At any point in your labeling project, you can export the annotations from Label Studio.
Label Studio stores your annotations in a raw JSON format in the SQLite database backend, PostGreSQL 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. See Cloud storage setup for more details about syncing target storage.
Image annotations exported in JSON format use percentages of overall image size, not pixels, to describe the size and location of the bounding boxes. See how to convert the image annotation units.
Export data from Label Studio
Export your completed annotations from Label Studio.
Some export formats export only the annotations and not the data from the task. See the export formats supported by Label Studio.
Export using the UI in Community Edition of Label Studio
You can export data and annotations from the Label Studio UI.
- For a project, click Export.
- Select an available export format.
- Click Export to export your data.
Notes
- Export will always include the annotated tasks, regardless of filters set on the tab.
- Cancelled annotated tasks will be included in the exported result too.
- If you want to apply tab filters to the export, try to use export snapshots using the SDK or API.
- If the export times out, see how to export snapshots using the SDK or API.
Export snapshots using the UI
In Label Studio Enterprise, create a snapshot of your data and annotations. Create a snapshot to export exactly what you want from your data labeling project. This delayed export method makes it easier to export large labeling projects from the Label Studio UI.
- Within a project in the Label Studio UI, click Export.
- Click Create New Snapshot.
- For Export from…, select the option for All Tasks. The default option is THIS.
- For Include in the Snapshot…, choose which type of data you want to include in the snapshot. Select All tasks, Only annotated tasks, or Only reviewed tasks.
- For Annotations, enable the types of annotations that you want to export. You can specify Annotations, Ground Truth annotations, and Skipped annotations. By default, only annotations are exported.
- (Optional) Add a Snapshot Name to make it easier to find in the future. By default, export snapshots are named
PROJECT-NAME-at-YEAR-MM-DD-HH-MM, where the time is in UTC. - For Drafts, choose whether to export the complete draft annotations for tasks, or only the IDs of draft annotations, to indicate that drafts exist.
- For Predictions, choose whether to export the complete predictions for tasks, or only the IDs of predictions to indicate that the tasks had predictions.
- (Optional) Enable the option to remove annotator emails to anonymize your result dataset.
- Click Create a Snapshot to start the export process.
- You see the list of snapshots available to download, with details about what is included in the snapshot and when and by whom it was created.
- Click Download and select the export format that you want to use. The snapshot file downloads to your computer.
Export using the API
You can call the Label Studio API to export annotations. For a small labeling project, call the export endpoint to export annotations.
Export snapshots using the API
For a large labeling project with hundreds of thousands of tasks, do the following:
- Make a POST request to create a new export file or snapshot. The response includes an
idfor the created file. - Check the status of the export file creation using the
idas theexport_pk. - Using the
idfrom the created snapshot as the export primary key, orexport_pk, make a GET request to download the export file.
Manually convert JSON annotations to another format
You can run the Label Studio converter tool on a directory or file of completed JSON annotations using the command line or Python to convert the completed annotations from Label Studio JSON format into another format. If you use versions of Label Studio earlier than 1.0.0, this is the only way to convert your Label Studio JSON format annotations into another labeling format.
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 this format to export both the data and the annotations for a dataset. Supports all project types.
JSON_MIN
List of items where only "from_name", "to_name" values from the raw JSON format are exported. Use this format to export the annotations and the data for a dataset, and no Label-Studio-specific fields. Supports all project types.
For example:
{
"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 in the labeling configuration. Supports all project types.
TSV
Results are stored in tab-separated tabular file with column names specified by "from_name" and "to_name" values in the labeling configuration. Supports all project types.
CONLL2003
Popular format used for the CoNLL-2003 named entity recognition challenge. Supports text labeling projects that use the Text and Labels tags.
COCO
Popular machine learning format used by the COCO dataset for object detection and image segmentation tasks. Supports bounding box and polygon image labeling projects that use the RectangleLabels or PolygonLabels tags.
Pascal VOC XML
Popular XML-formatted task data used for object detection and image segmentation tasks. Supports bounding box image labeling projects that use the RectangleLabels tag..
Brush labels to NumPy & PNG
Export your brush mask labels as NumPy 2d arrays and PNG images. Each label outputs as one image. Supports brush labeling image projects that use the BrushLabels tag.
ASR_MANIFEST
Export audio transcription labels for automatic speech recognition as the JSON manifest format expected by NVIDIA NeMo models. Supports audio transcription labeling projects that use the Audio or AudioPlus tags with the TextArea tag.
{“audio_filepath”: “/path/to/audio.wav”, “text”: “the transcription”, “offset”: 301.75, “duration”: 0.82, “utt”: “utterance_id”, “ctm_utt”: “en_4156”, “side”: “A”}YOLO
Export object detection annotations in the YOLOv3 and YOLOv4 format. Supports object detection labeling projects that use the RectangleLabels tag.
spaCy
Label Studio doesn’t support exporting directly to spaCy binary format, but you can convert annotations exported from Label Studio to a format compatible with spaCy. You must have the spacy python package installed to perform this conversion.
To transform Label Studio annotations into spaCy binary format, do the following:
- Export your annotations to CONLL2003 format.
- Open the downloaded file and update the first line of the exported file to add a O on the first line:
-DOCSTART- -X- O O - From the command line, run spacy convert to convert the CONLL-formatted annotations to spaCy binary format, replacing
/path/to/<filename>with the path and file name of your annotations:spacy convert /path/to/<filename>.conll -c conll .
See the spaCy documentation on Converting existing corpora and annotations for more details on running spacy convert.
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. |
| 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. |
| 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. |
| annotations.was_cancelled | Boolean. Details about whether or not the annotation was skipped, or cancelled. |
| annotations.reviews | Enterprise only. Array containing the details of reviews for this annotation. |
| reviews.id | Enterprise only. ID of the specific annotation review. |
| reviews.created_by | Enterprise only. Dictionary containing user ID, email, first name and last name of the user performing the review. |
| reviews.accepted | Enterprise only. Boolean. Whether the reviewer accepted the annotation as part of their review. |
| drafts | Array of draft annotations. Follows similar format as the annotations array. Included only for tasks exported as a snapshot from the UI or using the API. |
| predictions | Array of machine learning predictions. Follows the same format as the annotations 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.
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)
How Label Studio saves results in annotations
Each annotation that you create when you label a task contains regions and results.
- Regions refer to the selected area of the data, whether a text span, image area, audio segment, or something else.
- Results refer to the labels assigned to the region.
Each region has a unique ID for each annotation, formed as a string with the characters A-Za-z0-9_-. Each result ID is the same as the region ID that it applies to.
When a prediction is used to create an annotation, the result IDs stay the same in the annotation field. This lets you track the regions generated by your machine learning model and compare them directly to the human-created and reviewed annotations.
If you found an error, you can file an issue on GitHub!