Overview

A Label is constructed from a Data object and its associated Annotations.

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Create Labels with Annotation Types (Recommended)

When you bulk upload Label (similar for Model-assisted Labels) via Annotation Type, you will create a Label list that contains a list of Labels, each of which is constructed by a Data (constructed from Data Row ids) and a list of Annotations.

Here are the Python annotation types that are supported for the Labels and Model-assisted Labels creation.

Python annotation type

Image

Video

Text

Tiled imagery

Bounding box

N/A

Polygon

N/A

Point

N/A

Polyline

N/A

Segmentation mask

N/A

Entity

N/A

N/A

N/A

Relationship

Radio

Checklist

Free-form text

Import relevant modules for your data type and annotation types

from labelbox import Client, LabelImport
from labelbox.data.serialization import NDJsonConverter
# For working with images, videos, text and documents
from labelbox.data.annotation_types import (
    Label, ImageData, MaskData, LabelList, TextData, VideoData,
    ObjectAnnotation, ClassificationAnnotation, Polygon, Rectangle, Line, Mask,
    Point, Checklist, Radio, Text, TextEntity, ClassificationAnswer)

## For working with geospatial data
from labelbox.data.annotation_types.data.tiled_image import TiledBounds, TiledImageData, TileLayer, EPSG, EPSGTransformer

Create a Label and upload it to project

Here is a simple example of creating a label object with an ImageData and an Annotation.

client = Client("<YOUR_API_KEY>)

# 1. Make sure the project has the right ontology for the Label's annotations.
# Here we will create a new project to show the ontology creation, you can also do it via the App.
project = client.create_project(name="test_label_import_project")
dataset = client.create_dataset(name="image_annotation_import_demo_dataset")
test_img_url = "https://raw.githubusercontent.com/Labelbox/labelbox-python/develop/examples/assets/2560px-Kitano_Street_Kobe01s5s4110.jpg"
data_row = dataset.create_data_row(row_data=test_img_url)
project.datasets.connect(dataset)
# Create ontology that matches the labels' annotation, in this example, we only need a bounding box.
ontology_builder = OntologyBuilder(tools=[
    Tool(tool=Tool.Type.BBOX, name="box")
])
ontology = client.create_ontology("bbox ontology", ontology_builder.asdict())
# Attach ontology to project
project.setup_editor(ontology)

# 2. Create annotation(s)
rectangle = Rectangle(start=Point(x=30,y=30), end=Point(x=200,y=200))
# Note this Annotation matches with the ontology's feature box by name
rectangle_annotation = ObjectAnnotation(value=rectangle, name="box")

# 3. Create a Label with a list of annotations associated with the data row.
annotations_list = [rectangle_annotation]
data = ImageData(uid = data_row.uid)
label = Label(data= data, annotations = annotations_list)

# 4. Upload the Label to project 
label_list = LabelList()
label_list.append(label)
labels_ndjson = list(NDJsonConverter.serialize(label_list))
upload_job = LabelImport.create_from_objects(
    client = client, 
    project_id = project.uid, 
    name="upload_label_import_job", 
    labels=labels_ndjson)
print("Errors:", upload_job.errors)

Bulk import Labels

This example creates a bounding box label on each of the queued Data Rows in your project.

Configure the ontology for your project

Each Annotation of your label must correspond to a Feature inside the ontology of your project. You can configure project ontology in the app, or via SDK.

Construct a LabelList

## Get a list of unlabeled Data Rows to import Labels
project = client.get_project("<YOUR_PROJECT_ID>")
queued_data_rows = project.export_queued_data_rows()

label_list = LabelList()

for datarow in queued_data_rows:
  annotations_list = []
  ## replace this with your own function
  ground_truth_label = get_ground_truth_function(datarow)
  
  for annotation in ground_truth_label:
    # Specify annotation class name. This should be exact match of a feature name in ontology
    class_name = annotation.class_name
    bbox = annotation.bbox

    # Create an annotation type
    annotations_list.append(ObjectAnnotation(
        name = class_name,
        value = Rectangle.from_xyhw(*bbox),
    ))
  
  # Create a label type with data type and annotation types
  data = ImageData(uid = datarow['id'])
  label_list.append(Label(data = data, annotations = annotations_list))

Convert label list to NDJSON for import

To import annotations in Labelbox, you will need to convert the python types to NDJSON format. The NDJSON format is used as a normalized interface to connect Python SDK or any other external method and Labelbox backend service.

labels_ndjson = list(NDJsonConverter.serialize(label_list))

upload_job = LabelImport.create_from_objects(
    client = client, 
    project_id = project.uid, 
    name="upload_label_import_job", 
    labels=labels_ndjson)

print("Errors:", upload_job.errors)

Option 2: Create Labels with NDJSON

Alternatively, you can create and upload Labels with NDJSON. Here are the NDJSON supported Annotation kinds for the Labels and Model-assisted Labels creation.

Annotation

Image

Video

Text

Audio

Document

Tiled imagery

Bounding box

N/A

N/A

Polygon

N/A

N/A

N/A

Point

N/A

N/A

N/A

Polyline

N/A

N/A

N/A

Segmentation mask

N/A

N/A

N/A

Entity

N/A

N/A

N/A

N/A

N/A

Relationship

N/A

N/A

Radio

Checklist

Free-form text

Check out this tutorial notebook for an example of video MAL import via NDJSON. Open In ColabOpen In Colab

Export Labels

Export Labels Annotation Type objects

label_generator = project.label_generator()

label = next(label_generator) #you can iterate thru the paginated labels from generator
# View some specific fields of the label instance
print("Label ID:", label.uid)
print("Created By:", label.extra['Created By'])
print("Created At:", label.extra['Created At'])
print("Media Type:", label.extra['media_type'])
print("Reviews:", label.extra['Reviews'])


# You can convert to a LabelList for small to medium-sized datasets.
# This is more convenient than the LabelGenerator, but less memory efficient. Read more about the differences here.
labels = labels.as_list()


# For videos, you will need to use video_label_generator
video_label_generator = project.video_label_generator()
# Same as above

Export Labels as a JSON file.

Fields in JSON export files

Parameter

Description

ID

Label ID.

DataRow ID

ID of the Data Row.

Labeled Data

URL to the Asset that was labeled.

Label.frames (video only)

URL to the video frame annotations.

Label.frameNumber (video only)

Frame number where the annotation is located.

Label.objects

An array of the Object-type annotations. If there are no Object-type annotations in the Label, the array will be empty. The Object-type annotations are Bounding box, Segmentation mask, Polygon, Point, Polyline, and Text entity. See Annotations section.

Label.classifications

An array of Classification-type annotations at the global level. Classification-type annotations are Radio, Checklist, Free-form text, and Dropdown. See Annotations section.

Created At

Timestamp indicating when the Label was created. Includes only labeling time. Does not include review time.

Updated At

Timestamp indicating when the Label was updated. If the Label has not been updated since it was created, the timestamp will match the Created At timestamp.

Seconds to Label

Number of seconds taken to create the Label. Includes review time.

External ID

User-generated filename or ID for the Data Row.

Agreement

Consensus agreement score.

Benchmark Agreement

Benchmark agreement score.

Benchmark ID

ID of the Benchmark if enabled for this project.

Dataset Name

Name of the Dataset containing the Data Row.

Reviews.score

1 indicates a Reviewer approved the Label. -1 indicates a Reviewer rejected the Label.

Reviews.id

ID of the Label review.

Reviews.createdAt

Timestamp indicating when the Label was reviewed.

Reviews.createdBy

Member that reviewed the Label.

View Label

Link to open the Asset (and Annotations) in the Editor.

Has Open Issues

Indicates whether the Label has any issues that need to be addressed.

Example export file formats

[
    {
        "ID": "ckn95nnx000073g68fufpnw7a",
        "DataRow ID": "ckn93aswv1nfw0rfz18rq00mt",
        "Labeled Data": "https://storage.labelbox.com/ck2yve0h0005b07274s4f0v02%2Fca0697fd-a922-a004-3dfd-4c572921ab2a-eva-waardenburg-v-3NQ3pmWkY-unsplash.jpg?Expires=1598717540394&KeyName=labelbox-assets-key-1&Signature=ozyDhfwJNhP_Yijj4rZIan05nS8",
        "Label": {
            "objects": [
                {
                    "featureId": "ckn94on8s00013g68cgjl632a",
                    "schemaId": "ckn94hhay0opt0y6993fb64bl",
                    "title": "Sample object 1",
                    "value": "sample_object_1",
                    "color": "#ffb31c",
                    "bbox": {
                        "top": 1099,
                        "left": 2010,
                        "height": 690,
                        "width": 591
                    },
                    "instanceURI": "https://api.labelbox.com/masks/feature/ckn94on8s00013g68cgjl632a?token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VySWQiOiJjazUycnZ4MWtxYXpiMDc3MDBtcTI3eDRsIiwib3JnYW5pemF0aW9uSWQiOiJjazUycnZ4MG1wdzRnMDc2NndncXZqdGw5IiwiaWF0IjoxNjE3OTAzMDU5LCJleHAiOjE2MjA0OTUwNTl9.r6Qw2Qx_wbtflEwnZm-7XzPP-AbZn5VuIQ4_ETjBqJc"
                },
                {
                    "featureId": "ckn94pfs700033g68hidmdqsg",
                    "schemaId": "ckn94hhay0opv0y69fbpg76bs",
                    "title": "Sample object 2",
                    "value": "sample_object_2",
                    "color": "#FF34FF",
                    "instanceURI": "https://api.labelbox.com/masks/feature/ckn94pfs700033g68hidmdqsg?token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VySWQiOiJjazUycnZ4MWtxYXpiMDc3MDBtcTI3eDRsIiwib3JnYW5pemF0aW9uSWQiOiJjazUycnZ4MG1wdzRnMDc2NndncXZqdGw5IiwiaWF0IjoxNjE3OTAzMDU5LCJleHAiOjE2MjA0OTUwNTl9.r6Qw2Qx_wbtflEwnZm-7XzPP-AbZn5VuIQ4_ETjBqJc"
                }
            ],
            "classifications": [
                {
                    "featureId": "ckn95n44p00063g682vrko4pp",
                    "schemaId": "ckn94hhax0opr0y6936u2db78",
                    "title": "Is it daytime?",
                    "value": "is_it_daytime?",
                    "answer": {
                        "featureId": "ckn95n44o00053g68a1dckji7",
                        "schemaId": "ckn94hhbn0opx0y69bwh6b3je",
                        "title": "yes",
                        "value": "yes"
                    }
                }
            ]
        },
        "Created By": "[email protected]",
        "Project Name": "Beginner tutorial project",
        "Created At": "2021-04-08T17:29:33.000Z",
        "Updated At": "2021-04-08T17:30:34.000Z",
        "Seconds to Label": 149.852,
        "External ID": "layers-testing",
        "Agreement": -1,
        "Benchmark Agreement": -1,
        "Benchmark ID": null,
        "Dataset Name": "image-layers-sample.json",
        "Reviews": [],
        "View Label": "https://editor.labelbox.com?project=ckn889kbvt35m0789527t5rr5&label=ckn95nnx000073g68fufpnw7a",
        "Has Open Issues": 0
    }
]
[
    {
        "ID": "cknp36mns00033g68sqqsq844",
        "DataRow ID": "cklifdz2e1yf10rco407yaita",
        "Labeled Data": "https://storage.labelbox.com/ck52rvx0mpw4g0766wgqvjtl9%2F98c62818-8aad-2da8-aa86-141fb32742f4-lioness_walking.mp4?Expires=1620075890645&KeyName=labelbox-assets-key-3&Signature=Es1jENXKqKPt4nShF89ASvVmLzE",
        "Label": {
            "frames": "https://api.labelbox.com/v1/frames/cknp36mns00033g68sqqsq844"
        },
        "Created By": "[email protected]",
        "Project Name": "Lioness walking",
        "Created At": "2021-04-19T21:04:38.000Z",
        "Updated At": "2021-04-19T21:04:45.000Z",
        "Seconds to Label": 20,
        "External ID": "lioness_walking.mp4",
        "Agreement": -1,
        "Benchmark Agreement": -1,
        "Benchmark ID": null,
        "Dataset Name": "lioness_walking",
        "Reviews": [],
        "View Label": "https://editor.labelbox.com?project=cklifeflvf7dx0795p3rzpa6g&label=cknp36mns00033g68sqqsq844",
        "Has Open Issues": 0
    }
]

// Access per-frame annotation info via Label.frames URL (see above)
{
    "frameNumber": 1,
    "classifications": [
        {
            "featureId": "ckmvaolhj2f2p0y8h7it0gpb2",
            "schemaId": "ckmvaijzd2elz0y8h06ribh9b",
            "title": "Is it daytime?",
            "value": "is_it_daytime?",
            "answer": {
                "featureId": "ckmvaq6p3000c3g68vlqkjo18",
                "schemaId": "ckmvaik0p2eml0y8h12z632kq",
                "title": "Yes",
                "value": "yes",
                "keyframe": true
            }
        }
    ],
    "objects": [
        {
            "featureId": "ckmvapiqw00083g688yjpvd34",
            "schemaId": "cklifp8jw0tn40y5p3xtr2kk2",
            "title": "right_ear",
            "value": "right_ear",
            "color": "#1CE6FF",
            "keyframe": true,
            "point": {
                "x": 1447,
                "y": 430
            },
            "classifications": [
                {
                    "featureId": "ckmvaplfj00093g68elakf0q2",
                    "schemaId": "cklifp8ly0tnu0y5pd2jrcams",
                    "title": "Visibility",
                    "value": "visibility",
                    "answer": {
                        "featureId": "ckmvaplfj000a3g68rax7qw9k",
                        "schemaId": "cklifp8n20to80y5p6pyo0jyu",
                        "title": "0",
                        "value": "0",
                        "keyframe": true
                    }
                }
            ]
        },
        {
            "featureId": "ckmvaqsco000g3g681txq5v8h",
            "schemaId": "ckmvaobyl20s70y8u13eu6nmc",
            "title": "Lion",
            "value": "lion",
            "color": "#997D87",
            "keyframe": true,
            "bbox": {
                "top": 366,
                "left": 557,
                "height": 571,
                "width": 977
            },
            "classifications": [
                {
                    "featureId": "ckmvaqvbc000h3g68i6d37w8y",
                    "schemaId": "ckmvaoc0120si0y8uang3hwza",
                    "title": "Is the lion walking?",
                    "value": "is_the_lion_walking?",
                    "answer": {
                        "featureId": "ckmvaqvbd000i3g68z4c3i78s",
                        "schemaId": "ckmvaoc1s20tb0y8ub1p79fl8",
                        "title": "No",
                        "value": "no",
                        "keyframe": true
                    }
                }
            ]
        }
    ]
}
[
    {
        "ID": "cknp3dwk4000b3g68ghqpti7y",
        "DataRow ID": "ckekrwg3zqjbo0bs3hl15ex1i",
        "Labeled Data": "https://storage.googleapis.com/labelbox-sample-datasets/nlp/lorem-ipsum.txt",
        "Label": {
            "objects": [
                {
                    "featureId": "cknp3dvhg000a3g68hujtu6l1",
                    "schemaId": "ckkd4yl0e0le30ycl7i4944l5",
                    "title": "A",
                    "value": "a",
                    "color": "#1CE6FF",
                    "version": 1,
                    "format": "text.location",
                    "data": {
                        "location": {
                            "start": 6,
                            "end": 145
                        }
                    },
                    "instanceURI": "https://api.labelbox.com/masks/feature/cknp3dvhg000a3g68hujtu6l1?token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VySWQiOiJjazUycnZ4MWtxYXpiMDc3MDBtcTI3eDRsIiwib3JnYW5pemF0aW9uSWQiOiJjazUycnZ4MG1wdzRnMDc2NndncXZqdGw5IiwiaWF0IjoxNjE4ODY2NjI4LCJleHAiOjE2MjE0NTg2Mjh9.tW78CH7C7Nb85w6epSULEpaY4sEg6UeWeSk0WmCP5jg"
                }
            ],
            "classifications": [
                {
                    "featureId": "cknp3d0hw00013g68wkjemi6o",
                    "schemaId": "cknp3ctst06nq0ycte8icc2l3",
                    "title": "Radio",
                    "value": "radio",
                    "answer": {
                        "featureId": "cknp3d0hw00003g685c0nj5c1",
                        "schemaId": "cknp3cttx06oe0yct3f0d6pmz",
                        "title": "Yes",
                        "value": "yes"
                    }
                },
                {
                    "featureId": "cknp3dk5r00013g68lyduuwvp",
                    "schemaId": "cknp3dfo51h900y9y5j10g7hg",
                    "title": "Checklist",
                    "value": "checklist",
                    "answers": [
                        {
                            "featureId": "cknp3dk5r00003g6864se47ef",
                            "schemaId": "cknp3dfv21h9a0y9yagz41wtq",
                            "title": "1",
                            "value": "1"
                        },
                        {
                            "featureId": "cknp3dko500033g685s10zw9u",
                            "schemaId": "cknp3dfv21h9b0y9yg34i8xru",
                            "title": "2",
                            "value": "2"
                        }
                    ]
                }
            ]
        },
        "Created By": "[email protected]",
        "Project Name": "NER",
        "Created At": "2021-04-19T21:10:17.000Z",
        "Updated At": "2021-04-19T21:10:18.000Z",
        "Seconds to Label": 28.293,
        "External ID": null,
        "Agreement": -1,
        "Benchmark Agreement": -1,
        "Benchmark ID": null,
        "Dataset Name": "Lorem ipsum.txt",
        "Reviews": [],
        "View Label": "https://editor.labelbox.com?project=ckkd4xstjqnsb0740zwio6grd&label=cknp3dwk4000b3g68ghqpti7y",
        "Has Open Issues": 0
    }
]
[
   {
      "ID":"cl2zjh5cy0jrk0896c8cibz7c",
      "DataRow ID":"cl1l52wx403vs0zy1g35hcj9h",
      "Labeled Data":"https://storage.googleapis.com/labelbox-developer-testing-assets/conversational_text/data.json",
      "Label":{
         "objects":[
            {
               "featureId":"cl2zji82m00043g6cunt8telp",
               "schemaId":"cl2zjh37t0jrd08965lr80xva",
               "color":"#1CE6FF",
               "title":"Person",
               "value":"person",
               "version":1,
               "format":"text.location",
               "data":{
                  "location":{
                     "messageId":"2", //This matches the messageID on the import file
                     "start":0,  
                     "end":22
                  }
               }
            },
            {
               "featureId":"cl2zjihh600053g6cztv2bblb",
               "schemaId":"cl2zjh37t0jrd08965lr80xva",
               "color":"#1CE6FF",
               "title":"Person",
               "value":"person",
               "version":1,
               "format":"text.location",
               "data":{
                  "location":{
                     "messageId":"4",
                     "start":0,
                     "end":13
                  }
               }
            }
         ],
         "classifications":[
            
         ],
         "relationships":[
            {
               "featureId":"cl2zjivad00203g6ck8nk7jrt",
               "schemaId":"ckrdp1ln400003h6jq23cbjfl",
               "data":{
                  "source":"cl2zji82m00043g6cunt8telp",
                  "target":"cl2zjihh600053g6cztv2bblb",
                  "label":"Person"
               },
               "relationshipType":"unidirectional",
               "version":1
            }
         ]
      },
      "Created By":"[email protected]",
      "Project Name":"Convo Docs",
      "Created At":"2022-05-10T02:35:24.000Z",
      "Updated At":"2022-05-10T02:35:24.834Z",
      "Seconds to Label":103.85499999999999,
      "External ID":"Longer_Convo_Example",
      "Agreement":-1,
      "Benchmark Agreement":-1,
      "Benchmark ID":null,
      "Dataset Name":"Conversation 1.json",
      "Reviews":[
         
      ],
      "View Label":"https://staging.labelbox.dev/editor?project=cl2zjfkt60jnv0896gb9367nd&label=cl2zjh5cy0jrk0896c8cibz7c",
      "Has Open Issues":0,
      "Skipped":false
   }
]
[
    {
        "ID":"ckuzw2q8j00om0z940h5mg598",
        "DataRow ID":"ckuyk41ex02gy0zvi2yg49ut5",
        "Labeled Data":"https://storage.labelbox.com/ck9lrkuahv4no0740xvh19b7p%2Fe731037a-e9de-2cc2-ffb6-43af679e3656-Taylor%20Swift%20-%20Bad%20Blood%20(feat.%20Kendrick%20Lamar).mp3?Expires=1635966660273&KeyName=labelbox-assets-key-3&Signature=JzpFYV_gIAu2SltCBXE0lY47AyY",
        "Label":{
            "objects":[],
            "classifications":[
                {
                    "featureId":"ckuzw35hi00013e66y3n488v2",
                    "schemaId":"ckuzw2j6600o90z8qawks1mcj",
                    "title":"Who sings this?",
                    "value":"who_sings_this?",
                    "uiMode":"hotkey",
                    "answer":{
                        "featureId":"ckuzw35hi00003e66biwp6hjw",
                        "schemaId":"ckuzw2j7e00oq0z8qftr9evz1",
                        "title":"Taylor Swift",
                        "value":"taylor_swift"
                    }
                },
                {
                    "featureId":"ckuzw36ys00033e66vozh0tua",
                    "schemaId":"ckuzw2j6700ob0z8q50glabn1",
                    "title":"What genre is this?",
                    "value":"what_genre_is_this?",
                    "answers":[
                        {
                            "featureId":"ckuzw36ys00023e66tuoo50xv",
                            "schemaId":"ckuzw2j7c00og0z8q9xkb6a2o",
                            "title":"Pop",
                            "value":"pop"
                        },
                        {
                            "featureId":"ckuzw37hl00053e66mt2d3zpv",
                            "schemaId":"ckuzw2j7c00oi0z8q25wo7ecz",
                            "title":"Country",
                            "value":"country"
                        }
                    ]
                }
            ],
            "Created By": "[email protected]",
            "Project Name": "Song Categorization",
            "Created At": "2021-10-20T19:09:41.000Z",
            "Updated At": "2021-10-20T19:09:41.359Z",
            "Seconds to Label": 3.202,
            "External ID": "Taylor Swift - Bad Blood (feat. Kendrick Lamar).mp3",
            "Agreement": -1,
            "Benchmark Agreement": -1,
            "Benchmark ID": null,
            "Dataset Name": "Taylor Swift",
            "Reviews": [],
            "View Label": "https://editor.labelbox.com?project=ckuzw15p700g70z8q0qjahpe7&label=ckuzw2q8j00om0z940h5mg598",
            "Has Open Issues": 0,
            "Skipped": false
        }
    }
]
[
    {
        "ID": "cky8xqftnocd50zbvh8h67z4x",
        "DataRow ID": "cky8wroom000v0zsqddjg2ges",
        "Labeled Data": "https://storage.labelbox.com/cjhfn5y6s0pk507024nz1ocys%2F5e697840-a142-d5c4-39cc-10a2efa070c8.pdf",
        "Label": {
            "objects": [
                {
                    "featureId": "cky8ybaew00013g67ywqyfboe",
                    "schemaId": "cky8xqathotfg10axdntqcja1",
                    "title": "Sample object 1",
                    "value": "sample_object_1",
                    "color": "#70d4e0",
                    "bbox":{
                        "top":107.442,
                        "left":102.326,
                        "height":181.116,
                        "width":274.232
                     },
                     "page":1,
                     "unit":"POINTS", // points represent 1/72 of an inch.
                    "instanceURI": "https://api.labelbox.com/masks/feature/cky8ybaew00013g67ywqyfboe"
                },
                {
                    "featureId": "ckn94pfs700033g68hidmdqsg",
                    "schemaId": "ckn94hhay0opv0y69fbpg76bs",
                    "title": "Sample object 2",
                    "value": "sample_object_2",
                    "color": "#FF34FF",
                    "instanceURI": "https://api.labelbox.com/masks/feature/ckn94pfs700033g68hidmdqsg?token=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VySWQiOiJjazUycnZ4MWtxYXpiMDc3MDBtcTI3eDRsIiwib3JnYW5pemF0aW9uSWQiOiJjazUycnZ4MG1wdzRnMDc2NndncXZqdGw5IiwiaWF0IjoxNjE3OTAzMDU5LCJleHAiOjE2MjA0OTUwNTl9.r6Qw2Qx_wbtflEwnZm-7XzPP-AbZn5VuIQ4_ETjBqJc"
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            ],
            "classifications": [
                {
                    "featureId": "cky8ybfqf00043g670ies4irl",
                    "schemaId": "cky8xqathotfh10axbjzofimy",
                    "title": "Role:",
                    "value": "role",
                    "answers": {
                        "featureId": "cky8ybfqf00033g67f1vrj24v",
                        "schemaId": "cky8xqathotfi10ax06z82rup",
                        "title": "Author",
                        "value": "author"
                    }
                }
            ]
        },
        "Created By": "[email protected]",
        "Project Name": "Document Labeling",
        "Created At": "2022-01-10T17:22:46.000Z",
        "Updated At": "2022-01-10T17:22:46.602Z",
        "Seconds to Label": 0,
        "External ID": "Navigating the New Landscape of AI Platforms.pdf",
        "Agreement": -1,
        "Benchmark Agreement": -1,
        "Benchmark ID": null,
        "Dataset Name": "Document Samples",
        "Reviews": [],
        "View Label": "https://editor.labelbox.com?project=cky8xlauaow3710bx1s2bev2l&label=cky8xqftnocd50zbvh8h67z4x",
        "Has Open Issues": 0,
        "Skipped": false}
    }
]
[   
    {
        "ID": "cknjenzdz000j3g68m4m0si3n",
        "DataRow ID": "cknje9wzy3zjo0ysiaxbqcpmn",
        "Labeled Data": "{\"tileLayerUrl\":\"https://s3-us-east-2.amazonaws.com/lb-ron/CACI/ron_mctiles/{z}/{x}/{y}.png\",\"bounds\":[[19.37468183118193,-99.21052827588443],[19.36951840079928,-99.20534818927473]],\"minZoom\":12,\"maxZoom\":20,\"epsg\":\"EPSG4326\",\"alternativeLayers\":[{\"tileLayerUrl\":\"https://api.mapbox.com/styles/v1/mapbox/satellite-streets-v11/tiles/{z}/{x}/{y}?access_token=pk.eyJ1IjoibWFwYm94IiwiYSI6ImNpejY4NXVycTA2emYycXBndHRqcmZ3N3gifQ.rJcFIG214AriISLbB6B5aw\",\"name\":\"Satellite\"},{\"tileLayerUrl\":\"https://api.mapbox.com/styles/v1/mapbox/navigation-guidance-night-v4/tiles/{z}/{x}/{y}?access_token=pk.eyJ1IjoibWFwYm94IiwiYSI6ImNpejY4NXVycTA2emYycXBndHRqcmZ3N3gifQ.rJcFIG214AriISLbB6B5aw\",\"name\":\"Guidance\"}]}",
        "Label": {
            "objects": [
                {
                    "featureId": "cknjen3yk000b3g68nkz6ykfs",
                    "schemaId": "cknjek2a80jia0y943yo56ken",
                    "title": "test1",
                    "value": "test1",
                    "color": "#1CE6FF",
                    "type": "polygon",
                    "geometry": {
                        "coordinates": [
                            [
                                [
                                    -99.20909603539366,
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                                ],
                                [
                                    -99.20786968446487,
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                                ],
                                [
                                    -99.20868616690551,
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                                ],
                                [
                                    -99.20909603539366,
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                            ]
                        ]
                    }
                },
                {
                    "featureId": "cknjenq3c000c3g68lpyqvwtk",
                    "schemaId": "cknjek2a80jic0y94dn8xgh5a",
                    "title": "test2",
                    "value": "test2",
                    "color": "#FF34FF",
                    "type": "polygon",
                    "geometry": {
                        "coordinates": [
                            [
                                [
                                    -99.20775055066333,
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                                ],
                                [
                                    -99.20764572089253,
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                                ],
                                [
                                    -99.20759511302744,
                                    19.37308946264962
                                ],
                                [
                                    -99.20766178156738,
                                    19.37316566511257
                                ],
                                [
                                    -99.20775055066333,
                                    19.37308760065266
                                ]
                            ]
                        ]
                    }
                },
                {
                    "featureId": "cknjenvly000e3g68u0b05595",
                    "schemaId": "cknjemcpt0l4n0y92d3oo7no3",
                    "title": "test3",
                    "value": "test3",
                    "color": "#FF4A46",
                    "type": "point",
                    "geometry": {
                        "coordinates": [
                            -99.20803986635566,
                            19.372954675921154
                        ]
                    }
                }
            ],
            "classifications": []
        },
        "Created By": "[email protected]",
        "Project Name": "Geospatial export test",
        "Created At": "2021-04-15T21:39:26.000Z",
        "Updated At": "2021-04-15T21:41:16.000Z",
        "Seconds to Label": 60.72,
        "External ID": "cklidjjbk0ngb0y5p3nbw2960",
        "Agreement": -1,
        "Benchmark Agreement": -1,
        "Benchmark ID": null,
        "Dataset Name": "geospatial.json",
        "Reviews": [],
        "View Label": "https://editor.labelbox.com?project=cknje90o908gu0807i5ukcwvr&label=cknjenzdz000j3g68m4m0si3n",
        "Has Open Issues": 0
    }
]
[
   {
      "ID":"cl2y5gyv5pl2s07749ceb5mr2",
      "DataRow ID":"ckzohk4gy07d60ztx8orb8nbi",
      "Labeled Data":"https://storage.googleapis.com/bentz-test/dicom-assets/cat-scan1.dcm",
      "Label":{
         "objects":[
            
         ],
         "classifications":[
            
         ],
         "relationships":[
            
         ],
         "groups":[
            {
               "name":"axial",
               "objects":[
                  
               ],
               "classifications":[
                  
               ],
               "relationships":[
                  
               ],
               "frames":"https://api.labelbox.com/v1/frames/cl2y5gyv5pl2s07749ceb5mr2?group=axial"
            }
         ]
      },
      "Created By":"[email protected]",
      "Project Name":"DICOM",
      "Created At":"2022-05-09T03:14:41.000Z",
      "Updated At":"2022-05-09T03:14:42.740Z",
      "Seconds to Label":1,
      "External ID":"2",
      "Agreement":-1,
      "Benchmark Agreement":-1,
      "Benchmark ID":null,
      "Dataset Name":"cat-scan.json",
      "Reviews":[
         
      ],
      "View Label":"https://editor.labelbox.com?project=cl2sl1qnnbfrl0767bv649iud&label=cl2y5gyv5pl2s07749ceb5mr2",
      "Has Open Issues":0,
      "Skipped":false
   }
  
  // Access per-frame annotation for each axis via Label.frames URL (see above)
  {
    "frameNumber": 172,
    "classifications": [],
    "objects": [
        {
            "featureId": "cl2y5hdq200053g6cklrdauj7",
            "schemaId": "ckzohkul206400z976bjrguy3",
            "title": "Tool 1",
            "value": "tool_1",
            "color": "#1CE6FF",
            "keyframe": true,
            "instanceURI": "https://api.labelbox.com/masks/feature/cl2y5hdq200053g6cklrdauj7/172",
            "classifications": []
        }
    ],
    "relationships": []
}
{
    "frameNumber": 185,
    "classifications": [],
    "objects": [
        {
            "featureId": "cl2y5hjr200073g6cidkf0pqj",
            "schemaId": "ckzohkul206420z974x2q4a9h",
            "title": "Tool 2",
            "value": "tool_2",
            "color": "#FF34FF",
            "keyframe": true,
            "line": [
                {
                    "x": 95.237,
                    "y": 117.232
                },
                {
                    "x": 223.735,
                    "y": 196
                },
                {
                    "x": 262.608,
                    "y": 124.785
                }
            ],
            "classifications": []
        }
    ],
    "relationships": []
}