Open In Colab
GitHub
Export as JSON
# Set the export params to include/exclude certain fields.
export_params= {
"attachments": True,
"metadata_fields": True,
"data_row_details": True,
"project_details": True,
"label_details": True,
"performance_details": True,
"interpolated_frames": True
}
# Note: Filters follow AND logic, so typically using one filter is sufficient.
filters= {
"last_activity_at": ["2000-01-01 00:00:00", "2050-01-01 00:00:00"],
"workflow_status": "<wkf-status>"
}
export_task = project.export(params=export_params, filters=filters)
export_task.wait_till_done()
# Stream the export using a callback function
def json_stream_handler(output: labelbox.BufferedJsonConverterOutput):
print(output.json)
export_task.get_buffered_stream(stream_type=labelbox.StreamType.RESULT).start(stream_handler=json_stream_handler)
# Collect all exported data into a list
export_json = [data_row.json for data_row in export_task.get_buffered_stream()]
print("file size: ", export_task.get_total_file_size(stream_type=lb.StreamType.RESULT))
print("line count: ", export_task.get_total_lines(stream_type=lb.StreamType.RESULT))
Annotation export formats
Bounding box
{
"feature_id": "cltd97cag01th14ys6fyyxyk9",
"feature_schema_id": "cltd977no037j07x5d82dacbc",
"name": "bbox_video",
"value": "bbox_video",
"annotation_kind": "VideoBoundingBox",
"classifications": [],
"bounding_box": {
"top": 617.0,
"left": 1371.0,
"height": 419.0,
"width": 505.0
}
}
Point
{
"feature_id": "cltd97cag01to14yslakirofl",
"feature_schema_id": "cltd977no037l07x56wfu4536",
"name": "point_video",
"value": "point_video",
"annotation_kind": "VideoPoint",
"classifications": [],
"point": {
"x": 660.134,
"y": 407.926
}
}
Polyline
{
"feature_id": "cltd97cag01tp14ysz3my848t",
"feature_schema_id": "cltd977np037n07x5d1buckz9",
"name": "line_video_frame",
"value": "line_video_frame",
"annotation_kind": "VideoPolyline",
"classifications": [],
"line": [
{
"x": 680.0,
"y": 100.0
},
{
"x": 100.0,
"y": 190.0
}
]
}
Mask
Regardless of which tool is used to create a segmentation mask, the annotation will appear in the following form in the export. Whether you use AutoSegment, the brush tool, the pen tool, or the fill tool, all masks take the same format in exports.Composite masks
Open this Colab for an interactive tutorial on exporting composite masks. A composite mask is a cloud-hosted PNG file that contains all raster segmentation mask instances on a labeled data row (one composite mask per label). This screenshot shows an example composite mask in PNG format. Here, the image has eight segmentation mask instances, each using the schema named “Human”.
A labeled data row containing 8 instances of a segmentation mask named "Human".
composite_mask.url to access all segmentation masks on that data row. To access a single instance of a segmentation mask, use the color_rgb values. The color_rgb value represents the color of a single annotation or instance mask in a label.
Sample composite masks export
{
"feature_id": "clsivokxg00043b70xuf7x5tz",
"feature_schema_id": "clsgbcaqw000j071t2ga23jnc",
"name": "car",
"value": "car",
"annotation_kind": "ImageSegmentationMask",
"classifications": [],
"mask": {
"url": "https://api.labelbox.com/api/v1/projects/clsivnieg050k074i2jqaa0c3/annotations/clsivokxg00043b70xuf7x5tz/index/1/mask"
},
"composite_mask": {
// This URL contains all mask instances on the data row
"url": "https://api.labelbox.com/api/v1/tasks/clsiw88n205mw072ke4n78y5o/masks/clsivok5f00023b70r1a4fgzy",
// This RGB reflects a single mask instance
"color_rgb": [
31,
185,
101
]
}
}
Access mask data
To access the mask data, you must pass your Labelbox API key stored inclient.headers in an API request.
When you grab a URL from the mask annotation in the export, the project_id and feature_id will already be in place, as shown above. Here, we provide the framework for structuring a URL with any project ID and feature ID.
import urllib.request
from PIL import Image
# Provide a project ID and feature ID if using instance masks. Alternatively, replace the entire mask_url with a URL grabbed from your export.
project_id = ""
feature_id = ""
mask_url = f"https://api.labelbox.com/api/v1/projects/{project_id}/annotations/{feature_id}/index/1/mask"
# Instead of providing the individual variables, we recommend utilizing the URL from export for composite mask
composite_mask_url = f"https://api.labelbox.com/api/v1/tasks/{task_id}/masks/{composite_mask_id}/index/1"
# Make the API request
req = urllib.request.Request(mask_url, headers=client.headers)
# Optionally, print the image of the mask
image = Image.open(urllib.request.urlopen(req))
image.show()
Classification - Radio
{
"feature_id": "cltd97cag01tf14ysho6tfl7j",
"feature_schema_id": "cltd977np038907x5ah8l9ha7",
"name": "radio_class",
"value": "radio_class",
"radio_answer": {
"feature_id": "cltd97cag01tg14ys171a3qqr",
"feature_schema_id": "cltd977np038a07x515axagkl",
"name": "first_radio_answer",
"value": "first_radio_answer",
"classifications": []
}
}
Classification - Checklist
{
"feature_id": "cltd97cag01tq14ysb4m3nyjo",
"feature_schema_id": "cltd977nq039107x5dsml2knm",
"name": "checklist_class_global",
"value": "checklist_class_global",
"checklist_answers": [
{
"feature_id": "cltd97cag01tr14ysozjbx0c6",
"feature_schema_id": "cltd977nq039207x5ei1605s1",
"name": "first_checklist_answer",
"value": "first_checklist_answer",
"classifications": []
},
{
"feature_id": "cltd97cag01ts14ys4vvxihnw",
"feature_schema_id": "cltd977nq039407x50qs3g61l",
"name": "second_checklist_answer",
"value": "second_checklist_answer",
"classifications": []
}
]
}
Classification - Free-form text
{
"feature_id": "cltd97cag01u314ysx1h72f0h",
"feature_schema_id": "cltd977nq039707x535ufeo39",
"name": "free_text",
"value": "free_text",
"text_answer": {
"content": "sample text"
}
}
Sample project export
{
"data_row": {
"id": "clp1jolki07uy0725flku4bmv",
"global_key": "sample-video-jellyfish.mp4",
"row_data": "https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4",
"details": {
"dataset_id": "clp1jokqp000p07307gtfy1w3",
"dataset_name": "video_demo_dataset",
"created_at": "2023-11-16T18:48:51.208+00:00",
"updated_at": "2023-11-16T18:48:51.801+00:00",
"last_activity_at": "2024-04-08T17:09:54.000+00:00",
"created_by": "[email protected]"
}
},
"media_attributes": {
"height": 1080,
"width": 1920,
"mime_type": "video/mp4",
"frame_rate": 10,
"frame_count": 100
},
"attachments": [],
"metadata_fields": [],
"embeddings": [
{
"id": "c400000000000000000000000",
"name": "Video Embedding V2 (Google MultiModal)",
"dimensions": 1408,
"is_custom": false,
"values": [
{
"value": [
-0.00489852577,
0.0287129097 ...
]
}
]
}
],
"projects": {
"cltd977uu016y08xyggep8aiv": {
"name": "Video Annotation Import Demo",
"labels": [
{
"label_kind": "Video",
"version": "1.0.0",
"id": "cltd97caz01ub14ysq8rf4x8w",
"label_details": {
"created_at": "2024-03-04T18:07:33.000+00:00",
"updated_at": "2024-03-04T18:07:33.000+00:00",
"created_by": "[email protected]",
"content_last_updated_at": "2024-03-04T18:07:33.369+00:00",
"reviews": []
},
"performance_details": {
"seconds_to_create": 0,
"seconds_to_review": 0,
"skipped": false
},
"annotations": {
"frames": {
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"objects": {
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"feature_id": "cltd97cag01tp14ysz3my848t",
"feature_schema_id": "cltd977np037n07x5d1buckz9",
"name": "line_video_frame",
"value": "line_video_frame",
"annotation_kind": "VideoPolyline",
"classifications": [],
"line": [
{
"x": 680.0,
"y": 100.0
},
{
"x": 100.0,
"y": 190.0
}
]
}
},
"classifications": [
{
"feature_id": "cltd97cag01tc14yspm0lev2d",
"feature_schema_id": "cltd977np038307x55dlf3rgs",
"name": "checklist_class",
"value": "checklist_class",
"checklist_answers": [
{
"feature_id": "cltd97cag01td14ysxw2n8xkz",
"feature_schema_id": "cltd977np038407x51hcvdad7",
"name": "first_checklist_answer",
"value": "first_checklist_answer",
"classifications": []
},
{
"feature_id": "cltd97cag01te14ys1jdf1fku",
"feature_schema_id": "cltd977np038607x58epu4nb5",
"name": "second_checklist_answer",
"value": "second_checklist_answer",
"classifications": []
}
]
}
]
},
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"feature_schema_id": "cltd977np037n07x5d1buckz9",
"name": "line_video_frame",
"value": "line_video_frame",
"annotation_kind": "VideoPolyline",
"classifications": [],
"line": [
{
"x": 680.0,
"y": 100.0
},
{
"x": 100.0,
"y": 190.0
}
]
}
},
"classifications": [
{
"feature_id": "cltd97cag01tc14yspm0lev2d",
"feature_schema_id": "cltd977np038307x55dlf3rgs",
"name": "checklist_class",
"value": "checklist_class",
"checklist_answers": [
{
"feature_id": "cltd97cag01td14ysxw2n8xkz",
"feature_schema_id": "cltd977np038407x51hcvdad7",
"name": "first_checklist_answer",
"value": "first_checklist_answer",
"classifications": []
},
{
"feature_id": "cltd97cag01te14ys1jdf1fku",
"feature_schema_id": "cltd977np038607x58epu4nb5",
"name": "second_checklist_answer",
"value": "second_checklist_answer",
"classifications": []
}
]
}
]
},
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"name": "line_video_frame",
"value": "line_video_frame",
"annotation_kind": "VideoPolyline",
"classifications": [],
"line": [
{
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"y": 100.00000000000001
},
{
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"y": 190.0
}
]
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"classifications": [
{
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"feature_schema_id": "cltd977np038907x5ah8l9ha7",
"name": "radio_class",
"value": "radio_class",
"radio_answer": {
"feature_id": "cltd97cag01tg14ys171a3qqr",
"feature_schema_id": "cltd977np038a07x515axagkl",
"name": "first_radio_answer",
"value": "first_radio_answer",
"classifications": []
}
}
]
},
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"feature_schema_id": "cltd977no037j07x5d82dacbc",
"name": "bbox_video",
"value": "bbox_video",
"annotation_kind": "VideoBoundingBox",
"classifications": [],
"bounding_box": {
"top": 617.0,
"left": 1371.0,
"height": 419.0,
"width": 505.0
}
},
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"feature_id": "cltd97cag01tp14ysz3my848t",
"feature_schema_id": "cltd977np037n07x5d1buckz9",
"name": "line_video_frame",
"value": "line_video_frame",
"annotation_kind": "VideoPolyline",
"classifications": [],
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"feature_schema_id": "cltd977np038907x5ah8l9ha7",
"name": "radio_class",
"value": "radio_class",
"radio_answer": {
"feature_id": "cltd97cag01tg14ys171a3qqr",
"feature_schema_id": "cltd977np038a07x515axagkl",
"name": "first_radio_answer",
"value": "first_radio_answer",
"classifications": []
}
}
]
},
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"value": "bbox_video",
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},
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"name": "bbox_class",
"value": "bbox_class",
"annotation_kind": "VideoBoundingBox",
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"feature_schema_id": "cltd977np037s07x5csbodf5f",
"name": "checklist_class",
"value": "checklist_class",
"radio_answer": {
"feature_id": "cltd97cag01tl14ysnbe8mw75",
"feature_schema_id": "cltd977np037v07x54esmdmws",
"name": "second_checklist_answer",
"value": "second_checklist_answer",
"classifications": []
}
}
],
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}
},
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"name": "line_video_frame",
"value": "line_video_frame",
"annotation_kind": "VideoPolyline",
"classifications": [],
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"name": "bbox_video",
"value": "bbox_video",
"annotation_kind": "VideoBoundingBox",
"classifications": [],
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},
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"value": "line_video_frame",
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"classifications": [],
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"value": "bbox_video",
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"classifications": [],
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"annotation_kind": "VideoPoint",
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"value": "bbox_class",
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},
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}
},
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{
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},
"classifications": []
},
"45": {
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"value": "line_video_frame",
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},
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"name": "video_mask",
"value": "video_mask",
"annotation_kind": "VideoSegmentationMask",
"classifications": [],
"mask": {
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},
"composite_mask": {
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"color_rgb": [
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}
},
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"value": "video_mask",
"annotation_kind": "VideoSegmentationMask",
"classifications": [],
"mask": {
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},
"composite_mask": {
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}
}
},
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}
},
"segments": {
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29,
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],
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29,
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]
],
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[
9,
15
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],
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[
13,
19
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],
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[
10,
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],
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[
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],
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[
22,
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],
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[
17,
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]
],
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],
[
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]
},
"key_frame_feature_map": {
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20,
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],
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],
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13,
19
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13,
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26
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1
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},
"classifications": [
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"value": "checklist_class_global",
"checklist_answers": [
{
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"value": "first_checklist_answer",
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},
{
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"name": "second_checklist_answer",
"value": "second_checklist_answer",
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]
},
{
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"feature_schema_id": "cltd977nq038v07x57qz18lnj",
"name": "radio_class_global",
"value": "radio_class_global",
"radio_answer": {
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"feature_schema_id": "cltd977nq038w07x5bxw3exp9",
"name": "first_radio_answer",
"value": "first_radio_answer",
"classifications": []
}
},
{
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"feature_schema_id": "cltd977nq038n07x52t8s0j1f",
"name": "nested_checklist_question",
"value": "nested_checklist_question",
"checklist_answers": [
{
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"feature_schema_id": "cltd977nq038o07x528j39vc4",
"name": "first_checklist_answer",
"value": "first_checklist_answer",
"classifications": [
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"value": "sub_checklist_question",
"checklist_answers": [
{
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"name": "first_sub_checklist_answer",
"value": "first_sub_checklist_answer",
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}
]
}
]
}
]
},
{
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"name": "nested_radio_question",
"value": "nested_radio_question",
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"value": "sub_radio_question",
"radio_answer": {
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"value": "first_sub_radio_answer",
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}
},
{
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"feature_schema_id": "cltd977nq039707x535ufeo39",
"name": "free_text",
"value": "free_text",
"text_answer": {
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}
}
]
}
}
],
"project_details": {
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}
}
Sample model run export
{
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"id": "cldtd2nai107407y5979xbu97",
"global_key": "3548cfa1-1a42-40f9-a246-2aa7c696dcbe",
"row_data": "https://storage.googleapis.com/labelbox-datasets/video-sample-data/sample-video-2.mp4",
"details": {
"dataset_id": "cldtd2mox11dz07wh3gkk9awx",
"dataset_name": "video_prediction_demo",
"created_at": "2023-02-06T22:04:54.000+00:00",
"updated_at": "2023-02-06T22:04:54.000+00:00",
"created_by": "[email protected]"
}
},
"media_attributes": {
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"width": 1920,
"mime_type": "video/mp4",
"frame_rate": 10,
"frame_count": 100
},
"attachments": [],
"metadata_fields": [],
"experiments": {
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"runs": {
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"name": "iteration 1",
"run_data_row_id": "999a247e-0b57-415e-af7e-d3a87cc6559b",
"labels": [
{
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"version": "1.0.0",
"id": "cldteelzh08us1331q4pzj2ke",
"annotations": {
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"name": "point_video",
"value": "point_video",
"annotation_kind": "VideoPoint",
"classifications": [],
"point": {
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}
}
},
"classifications": []
},
"5": {
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"name": "line_video_frame",
"value": "line_video_frame",
"annotation_kind": "VideoPolyline",
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"line": [
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]
},
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}
},
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},
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"20": {
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},
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"24": {
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},
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"45": {
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{
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}
},
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},
"key_frame_feature_map": {
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"value": "checklist_question",
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},
{
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},
{
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"name": "radio_question",
"value": "radio_question",
"radio_answer": {
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"name": "second_radio_answer",
"value": "second_radio_answer",
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},
{
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"name": "nested_checklist_question",
"value": "nested_checklist_question",
"checklist_answers": [
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},
{
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"name": "nested_radio_question",
"value": "nested_radio_question",
"radio_answer": {
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"value": "first_radio_answer",
"classifications": [
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"radio_answer": {
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"name": "first_sub_radio_answer",
"value": "first_sub_radio_answer",
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}
}
]
}
}
]
}
}
],
"predictions": [
{
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"version": "1.0.0",
"id": "cldteelzh08us1331q4pzj2ke",
"annotations": {
"frames": {
"5": {
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"name": "line_video_frame",
"value": "line_video_frame",
"annotation_kind": "VideoPolyline",
"classifications": [],
"line": [
{
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"y": 100.0
},
{
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