How to export image annotations and sample export formats.
Open this Colab for an interactive tutorial on exporting annotations.
As of April 2024, Export v1 is deprecated in favor of Export v2. You should migrate remaining Export v1 tasks to Export v2. To learn more, see the Export migration guide. For best results, we recommend using Export v2 streamable alternatives when available.
Export as JSON
# Set the export params to include/exclude certain fields. Make sure each of these fields are correctly grabbed
export_params= {
"attachments": True,
"metadata_fields": True,
"data_row_details": True,
"project_details": True,
"label_details": True,
"performance_details": True
}
# You can set the range for last_activity_at and label_created_at.
# For context, last_activity_at captures the creation and modification of labels, metadata, status, comments and reviews.
# Note: This is an AND logic between the filters, so usually 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_v2(params=export_params, filters=filters)
export_task.wait_till_done()
if export_task.errors:
print(export_task.errors)
export_json = export_task.result
print("results: ", export_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>"
}
client.enable_experimental = True
export_task = project.export(params=export_params, filters=filters)
export_task.wait_till_done()
# Return JSON output strings from export task results/errors, one by one:
# Callback used for JSON Converter
def json_stream_handler(output: lb.JsonConverterOutput):
print(output.json_str)
if export_task.has_errors():
export_task.get_stream(
converter=lb.JsonConverter(),
stream_type=lb.StreamType.ERRORS
).start(stream_handler=lambda error: print(error))
if export_task.has_result():
export_json = export_task.get_stream(
converter=lb.JsonConverter(),
stream_type=lb.StreamType.RESULT
).start(stream_handler=json_stream_handler)
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": "clrf5csvi6ofm07lsf9pygwvi",
"feature_schema_id": "clrf5ck4a0b9b071paa9ncu15",
"name": "bounding_box",
"annotation_kind": "ImageBoundingBox",
"classifications": [],
"bounding_box": {
"top": 1264.0,
"left": 2096.0,
"height": 425.0,
"width": 144.0
}
}
Polygon
{
"feature_id": "clr0x0ol83tvt07ea4has2a4l",
"feature_schema_id": "clpk2me9t02v707xn08cv4ve5",
"name": "polygon",
"annotation_kind": "ImagePolygon",
"classifications": [],
"polygon": [
{
"x": 1489.581,
"y": 183.934
},
{
"x": 1489.581,
"y": 183.934
}
]
}
Polyline
{
"feature_id": "clr0x0ol83tvk07eagehegjde",
"feature_schema_id": "clpk2me9u02vh07xnfcmtbqvj",
"name": "polyline",
"annotation_kind": "ImagePolyline",
"classifications": [],
"line": [
{
"x": 2534.353,
"y": 249.471
},
{
"x": 90.929,
"y": 326.412
}
]
}
Point
{
"feature_id": "clr0x0ol83tvr07ea2fyl69g9",
"feature_schema_id": "clpk2me9u02vf07xnbpk45wxa",
"name": "point",
"annotation_kind": "ImagePoint",
"classifications": [],
"point": {
"x": 1166.606,
"y": 1441.768
}
}
Mask
Regardless of which tool you use 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 will take the same format in exports.
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).
The first screenshot below is an example of a composite mask in PNG format. In this example, 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".
When you export data rows that contain segmentation masks, you can use "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" represents the color of a single annotation or instance mask in a label.
As part of the Export v1 migration, individual raster mask URLs will no longer be exported. Instead, only composite_mask.url and color_rgb will appear in export results.
The green arrow indicates how to identify a single mask instance (e.g., "Human 1") and the yellow arrow indicates how to access the composite mask (all mask instances) for a label.
Open this Colab for an interactive tutorial on exporting composite masks.
{
"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
]
}
}
How to access mask data
To access the mask data, you must pass your Labelbox API key stored in client.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
Relationship
{
"feature_id": "clur69pfa00023b6rkftlkucz",
"feature_schema_id": "cltd3vkc201ab07ts1f9dhpkt",
"name": "relationship",
"value": "relationship",
"annotation_kind": "ImageUnidirectionalRelationship",
"classifications": [],
"unidirectional_relationship": {
"source": "clur5tlyq1n2d07dcg9xldg90",
"target": "clur5tlyr1n2l07dc1jsf15qo"
}
}
Classification - Radio
{
"feature_id": "clur5tlyr1n2k07dcezk08npu",
"feature_schema_id": "cltd3vkc201ad07ts3t78873p",
"name": "radio_question",
"value": "radio_question",
"radio_answer": {
"feature_id": "clur5tlyr1n2y07dcajpyg0d4",
"feature_schema_id": "cltd3vkc201ag07tsfoc2bdpi",
"name": "second_radio_answer",
"value": "second_radio_answer",
"classifications": []
}
}
Classification - Checklist
{
"feature_id": "clur5tlyr1n2p07dchs791672",
"feature_schema_id": "cltd3vkc201aj07tsait4cbzh",
"name": "checklist_question",
"value": "checklist_question",
"checklist_answers": [
{
"feature_id": "clur5tlyr1n2z07dc3abv19e8",
"feature_schema_id": "cltd3vkc201ak07tsfqxc740z",
"name": "first_checklist_answer",
"value": "first_checklist_answer",
"classifications": []
},
{
"feature_id": "clur5tlyr1n3007dc4vbr0628",
"feature_schema_id": "cltd3vkc201am07ts7s9e7x7k",
"name": "second_checklist_answer",
"value": "second_checklist_answer",
"classifications": []
}
]
}
Classification - Free-form text
{
"feature_id": "clur5tlyr1n2g07dc7wdv7p5z",
"feature_schema_id": "cltd3vkc201ap07ts58gma0bb",
"name": "free_text",
"value": "free_text",
"text_answer": {
"content": "sample text"
}
}
Sample project export
{
"data_row": {
"id": "clf9spzxq029m071nehy60a6v",
"global_key": "2560px-Kitano_Street_Kobe01s5s4110.jpeg",
"row_data": "https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg",
"details": {
"dataset_id": "clf9spwgw04w207ymfwy826qv",
"dataset_name": "demo_dataset_img",
"created_at": "2023-03-15T14:46:58.000+00:00",
"updated_at": "2023-03-15T14:46:58.000+00:00",
"last_activity_at": "2024-04-08T16:34:01.000+00:00",
"created_by": "[email protected]"
}
},
"media_attributes": {
"height": 1707,
"width": 2560,
"mime_type": "image/jpeg"
},
"attachments": [],
"metadata_fields": [],
"projects": {
"cltd3vkju021907u63w2odj69": {
"name": "Image Annotation Import Demo",
"labels": [
{
"label_kind": "Default",
"version": "1.0.0",
"id": "clur5tlda1n2a07dc0i0zdd4k",
"label_details": {
"created_at": "2024-04-08T16:21:26.000+00:00",
"updated_at": "2024-04-08T16:34:02.000+00:00",
"created_by": "[email protected]",
"content_last_updated_at": "2024-04-08T16:34:00.513+00:00",
"reviews": []
},
"performance_details": {
"seconds_to_create": 18,
"seconds_to_review": 6,
"skipped": false
},
"annotations": {
"objects": [
{
"feature_id": "clur5tlyq1n2d07dcg9xldg90",
"feature_schema_id": "cltd3vkc1019r07ts2lru4hi4",
"name": "bounding_box",
"value": "bounding_box",
"annotation_kind": "ImageBoundingBox",
"classifications": [],
"bounding_box": {
"top": 1264.0,
"left": 2096.0,
"height": 425.0,
"width": 144.0
}
},
{
"feature_id": "clur5tlyr1n2f07dcf78sa4y5",
"feature_schema_id": "cltd3vkc201a707ts9ullbhfo",
"name": "point",
"value": "point",
"annotation_kind": "ImagePoint",
"classifications": [],
"point": {
"x": 1166.606,
"y": 1441.768
}
},
{
"feature_id": "clur5tlyr1n2h07dc1pxnhoj3",
"feature_schema_id": "cltd3vkc1019t07ts2bk6hjjj",
"name": "bbox_with_radio_subclass",
"value": "bbox_with_radio_subclass",
"annotation_kind": "ImageBoundingBox",
"classifications": [
{
"feature_id": "clur5tlyr1n2v07dcbnnfh1ah",
"feature_schema_id": "cltd3vkc1019u07tsaari5i2k",
"name": "sub_radio_question",
"value": "sub_radio_question",
"radio_answer": {
"feature_id": "clur5tlyr1n3607dc98qpagxb",
"feature_schema_id": "cltd3vkc1019v07tsbow08f9u",
"name": "first_sub_radio_answer",
"value": "first_sub_radio_answer",
"classifications": []
}
}
],
"bounding_box": {
"top": 933.0,
"left": 541.0,
"height": 191.0,
"width": 330.0
}
},
{
"feature_id": "clur5tlyr1n2j07dc50cs4e11",
"feature_schema_id": "cltd3vkc201a907ts69dr5m86",
"name": "polyline",
"value": "polyline",
"annotation_kind": "ImagePolyline",
"classifications": [],
"line": [
{
"x": 2534.353,
"y": 249.471
},
{
"x": 2429.492,
"y": 182.092
},
{
"x": 2294.322,
"y": 221.962
},
{
"x": 2224.491,
"y": 180.463
},
{
"x": 2136.123,
"y": 204.716
},
{
"x": 1712.247,
"y": 173.949
},
{
"x": 1703.838,
"y": 84.438
},
{
"x": 1579.772,
"y": 82.61
},
{
"x": 1583.442,
"y": 167.552
},
{
"x": 1478.869,
"y": 164.903
},
{
"x": 1418.941,
"y": 318.149
},
{
"x": 1243.128,
"y": 400.815
},
{
"x": 1022.067,
"y": 319.007
},
{
"x": 892.367,
"y": 379.216
},
{
"x": 670.273,
"y": 364.408
},
{
"x": 613.114,
"y": 288.16
},
{
"x": 377.559,
"y": 238.251
},
{
"x": 368.087,
"y": 185.064
},
{
"x": 246.557,
"y": 167.286
},
{
"x": 236.648,
"y": 285.61
},
{
"x": 90.929,
"y": 326.412
}
]
},
{
"feature_id": "clur5tlyr1n2l07dc1jsf15qo",
"feature_schema_id": "cltd3vkc1019r07ts2lru4hi4",
"name": "bounding_box",
"value": "bounding_box",
"annotation_kind": "ImageBoundingBox",
"classifications": [],
"bounding_box": {
"top": 1346.0,
"left": 2272.0,
"height": 358.0,
"width": 144.0
}
},
{
"feature_id": "clur5tlyr1n2m07dc4x6xhzep",
"feature_schema_id": "cltd3vkc1019z07ts7g1n3o5m",
"name": "polygon",
"value": "polygon",
"annotation_kind": "ImagePolygon",
"classifications": [],
"polygon": [
{
"x": 1489.581,
"y": 183.934
},
{
"x": 2278.306,
"y": 256.885
},
{
"x": 2428.197,
"y": 200.437
},
{
"x": 2560.0,
"y": 335.419
},
{
"x": 2557.386,
"y": 503.165
},
{
"x": 2320.596,
"y": 503.103
},
{
"x": 2156.083,
"y": 628.943
},
{
"x": 2161.111,
"y": 785.519
},
{
"x": 2002.115,
"y": 894.647
},
{
"x": 1838.456,
"y": 877.874
},
{
"x": 1436.53,
"y": 874.636
},
{
"x": 1411.403,
"y": 758.579
},
{
"x": 1353.853,
"y": 751.74
},
{
"x": 1345.264,
"y": 453.461
},
{
"x": 1426.011,
"y": 421.129
},
{
"x": 1489.581,
"y": 183.934
}
]
},
{
"feature_id": "clur5tlyr1n2n07dc4qot5gbx",
"feature_schema_id": "cltd3vkc1019r07ts2lru4hi4",
"name": "bounding_box",
"value": "bounding_box",
"annotation_kind": "ImageBoundingBox",
"classifications": [],
"bounding_box": {
"top": 977.0,
"left": 1690.0,
"height": 330.0,
"width": 225.0
}
},
{
"feature_id": "clur5tlyr1n2q07dc5joc4k7u",
"feature_schema_id": "cltd3vkc101a107ts95sk63u0",
"name": "mask",
"value": "mask",
"annotation_kind": "ImageSegmentationMask",
"classifications": [],
"mask": {
"url": "https://api.labelbox.com/api/v1/projects/cltd3vkju021907u63w2odj69/annotations/clur5tlyr1n2q07dc5joc4k7u/index/1/mask"
},
"composite_mask": {
"url": "https://api.labelbox.com/api/v1/tasks/clur6dp8r04ul07wrgp9q4pla/masks/clur5tlyr1n2o07dc99lw3r8p/index/1",
"color_rgb": [
10,
10,
125
]
}
},
{
"feature_id": "clur5tlyr1n2r07dc6ym4d4mc",
"feature_schema_id": "cltd3vkc201a307tsforlev07",
"name": "mask_with_text_subclass",
"value": "mask_with_text_subclass",
"annotation_kind": "ImageSegmentationMask",
"classifications": [
{
"feature_id": "clur5tlyr1n2x07dc84a12l8i",
"feature_schema_id": "cltd3vkc201a407tsfo3agh7x",
"name": "sub_free_text",
"value": "sub_free_text",
"text_answer": {
"content": "free text answer sample"
}
}
],
"mask": {
"url": "https://api.labelbox.com/api/v1/projects/cltd3vkju021907u63w2odj69/annotations/clur5tlyr1n2r07dc6ym4d4mc/index/1/mask"
},
"composite_mask": {
"url": "https://api.labelbox.com/api/v1/tasks/clur6dp8r04ul07wrgp9q4pla/masks/clur5tlyr1n2o07dc99lw3r8p/index/1",
"color_rgb": [
105,
136,
97
]
}
},
{
"feature_id": "clur5tlyr1n2s07dc739a6kqs",
"feature_schema_id": "cltd3vkc201a307tsforlev07",
"name": "mask_with_text_subclass",
"value": "mask_with_text_subclass",
"annotation_kind": "ImageSegmentationMask",
"classifications": [
{
"feature_id": "clur5tlyr1n2w07dccdv0g7gg",
"feature_schema_id": "cltd3vkc201a407tsfo3agh7x",
"name": "sub_free_text",
"value": "sub_free_text",
"text_answer": {
"content": "free text answer sample"
}
}
],
"mask": {
"url": "https://api.labelbox.com/api/v1/projects/cltd3vkju021907u63w2odj69/annotations/clur5tlyr1n2s07dc739a6kqs/index/1/mask"
},
"composite_mask": {
"url": "https://api.labelbox.com/api/v1/tasks/clur6dp8r04ul07wrgp9q4pla/masks/clur5tlyr1n2o07dc99lw3r8p/index/1",
"color_rgb": [
7,
85,
11
]
}
},
{
"feature_id": "clur5tlyr1n2t07dc0oxlgoc8",
"feature_schema_id": "cltd3vkc201a307tsforlev07",
"name": "mask_with_text_subclass",
"value": "mask_with_text_subclass",
"annotation_kind": "ImageSegmentationMask",
"classifications": [
{
"feature_id": "clur5tlyr1n3107dccy5pbudr",
"feature_schema_id": "cltd3vkc201a407tsfo3agh7x",
"name": "sub_free_text",
"value": "sub_free_text",
"text_answer": {
"content": "free text answer sample"
}
}
],
"mask": {
"url": "https://api.labelbox.com/api/v1/projects/cltd3vkju021907u63w2odj69/annotations/clur5tlyr1n2t07dc0oxlgoc8/index/1/mask"
},
"composite_mask": {
"url": "https://api.labelbox.com/api/v1/tasks/clur6dp8r04ul07wrgp9q4pla/masks/clur5tlyr1n2o07dc99lw3r8p/index/1",
"color_rgb": [
35,
34,
212
]
}
},
{
"feature_id": "clur5tlyr1n2u07dc5wjmdm3v",
"feature_schema_id": "cltd3vkc101a107ts95sk63u0",
"name": "mask",
"value": "mask",
"annotation_kind": "ImageSegmentationMask",
"classifications": [],
"mask": {
"url": "https://api.labelbox.com/api/v1/projects/cltd3vkju021907u63w2odj69/annotations/clur5tlyr1n2u07dc5wjmdm3v/index/1/mask"
},
"composite_mask": {
"url": "https://api.labelbox.com/api/v1/tasks/clur6dp8r04ul07wrgp9q4pla/masks/clur5tlyr1n2o07dc99lw3r8p/index/1",
"color_rgb": [
232,
194,
86
]
}
}
],
"classifications": [
{
"feature_id": "clur5tlyr1n2e07dc2bez8p1u",
"feature_schema_id": "cltd3vkc201ar07ts3gq6a6sn",
"name": "nested_radio_question",
"value": "nested_radio_question",
"radio_answer": {
"feature_id": "clur5tlyr1n3307dcexth5ff7",
"feature_schema_id": "cltd3vkc201as07tsh2prgosu",
"name": "first_radio_answer",
"value": "first_radio_answer",
"classifications": [
{
"feature_id": "clur5tlyr1n3407dc306b5xib",
"feature_schema_id": "cltd3vkc201at07tsdjx9dlbw",
"name": "sub_radio_question",
"value": "sub_radio_question",
"radio_answer": {
"feature_id": "clur5tlyr1n3507dcef304jmb",
"feature_schema_id": "cltd3vkc201au07ts7nk47qny",
"name": "first_sub_radio_answer",
"value": "first_sub_radio_answer",
"classifications": []
}
}
]
}
},
{
"feature_id": "clur5tlyr1n2g07dc7wdv7p5z",
"feature_schema_id": "cltd3vkc201ap07ts58gma0bb",
"name": "free_text",
"value": "free_text",
"text_answer": {
"content": "sample text"
}
},
{
"feature_id": "clur5tlyr1n2i07dcd8lx87be",
"feature_schema_id": "cltd3vkc201az07ts7st3fn79",
"name": "nested_checklist_question",
"value": "nested_checklist_question",
"checklist_answers": [
{
"feature_id": "clur5tlyr1n3207dc4u4nfg08",
"feature_schema_id": "cltd3vkc201b007tsdhs07baf",
"name": "first_checklist_answer",
"value": "first_checklist_answer",
"classifications": [
{
"feature_id": "clur5tlyr1n3707dc2cy805ac",
"feature_schema_id": "cltd3vkc201b107ts12b1al1j",
"name": "sub_checklist_question",
"value": "sub_checklist_question",
"checklist_answers": [
{
"feature_id": "clur5tlyr1n3807dcgpgih5qj",
"feature_schema_id": "cltd3vkc201b207ts434b9iyx",
"name": "first_sub_checklist_answer",
"value": "first_sub_checklist_answer",
"classifications": []
}
]
}
]
}
]
},
{
"feature_id": "clur5tlyr1n2k07dcezk08npu",
"feature_schema_id": "cltd3vkc201ad07ts3t78873p",
"name": "radio_question",
"value": "radio_question",
"radio_answer": {
"feature_id": "clur5tlyr1n2y07dcajpyg0d4",
"feature_schema_id": "cltd3vkc201ag07tsfoc2bdpi",
"name": "second_radio_answer",
"value": "second_radio_answer",
"classifications": []
}
},
{
"feature_id": "clur5tlyr1n2p07dchs791672",
"feature_schema_id": "cltd3vkc201aj07tsait4cbzh",
"name": "checklist_question",
"value": "checklist_question",
"checklist_answers": [
{
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"feature_schema_id": "cltd3vkc201ak07tsfqxc740z",
"name": "first_checklist_answer",
"value": "first_checklist_answer",
"classifications": []
},
{
"feature_id": "clur5tlyr1n3007dc4vbr0628",
"feature_schema_id": "cltd3vkc201am07ts7s9e7x7k",
"name": "second_checklist_answer",
"value": "second_checklist_answer",
"classifications": []
}
]
}
],
"relationships": [
{
"feature_id": "clur69pfa00023b6rkftlkucz",
"feature_schema_id": "cltd3vkc201ab07ts1f9dhpkt",
"name": "relationship",
"value": "relationship",
"annotation_kind": "ImageUnidirectionalRelationship",
"classifications": [],
"unidirectional_relationship": {
"source": "clur5tlyq1n2d07dcg9xldg90",
"target": "clur5tlyr1n2l07dc1jsf15qo"
}
}
]
}
}
],
"project_details": {
"ontology_id": "cltd3vkbb019q07ts23eedy4o",
"task_id": "a384279a-45c0-4200-b85a-3c18469b3809",
"task_name": "Initial review task",
"batch_id": "3f724e20-da3d-11ee-86a4-0145011952fd",
"batch_name": "image-demo-batch",
"workflow_status": "IN_REVIEW",
"priority": 1,
"consensus_expected_label_count": 1,
"workflow_history": [
{
"action": "Move",
"created_at": "2024-04-08T16:21:26.492+00:00",
"created_by": "[email protected]",
"previous_task_name": "Initial labeling task",
"previous_task_id": "22ee463b-fbe3-0791-8159-94083be9dee3",
"next_task_name": "Initial review task",
"next_task_id": "a384279a-45c0-4200-b85a-3c18469b3809"
},
{
"action": "Move",
"created_at": "2024-04-08T16:21:26.481+00:00",
"created_by": "[email protected]",
"next_task_name": "Initial labeling task",
"next_task_id": "22ee463b-fbe3-0791-8159-94083be9dee3"
}
]
}
}
}
}
Sample model run export
{
"data_row": {
"id": "clo78lhhn3dxb07322mnn8plu",
"global_key": "2560px-Kitano_Street_Kobe01s.jpeg",
"row_data": "https://storage.googleapis.com/labelbox-datasets/image_sample_data/2560px-Kitano_Street_Kobe01s5s4110.jpeg",
"details": {
"dataset_id": "clo78lg8f003007321fe52lje",
"dataset_name": "image_prediction_demo",
"created_at": "2023-10-26T13:45:24.929+00:00",
"updated_at": "2023-10-26T13:45:25.965+00:00",
"created_by": "[email protected]"
}
},
"media_attributes": {
"height": 1707,
"width": 2560,
"mime_type": "image/jpeg",
"exif_rotation": "1"
},
"attachments": [],
"metadata_fields": [],
"experiments": {
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"name": "image_model_run_3e5bf56c-31a4-4eed-a731-9ca85e783225",
"runs": {
"a2fb1fec-b8a5-0f96-b61e-0d5643fb8dec": {
"name": "iteration 1",
"run_data_row_id": "42630da9-0dc4-462b-9aa0-5a17e85ff46a",
"labels": [
{
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"version": "1.0.0",
"id": "clpk3h87e005r14vsp4c33sfq",
"annotations": {
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"value": "polygon",
"annotation_kind": "ImagePolygon",
"classifications": [],
"polygon": [
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},
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"value": "mask_with_text_subclass",
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"name": "sub_free_text",
"value": "sub_free_text",
"text_answer": {
"content": "free text answer"
}
}
],
"mask": {
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},
"composite_mask": {
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{
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{
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"value": "bbox_with_radio_subclass",
"annotation_kind": "ImageBoundingBox",
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"classifications": [],
"mask": {
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},
"composite_mask": {
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],
"classifications": [
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"value": "checklist_question",
"checklist_answers": [
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},
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{
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],
"relationships": []
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}
],
"predictions": [
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"classifications": [
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"name": "free_text",
"value": "free_text",
"text_answer": {
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}
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{
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}
},
"models": {}
}