Querying Details About an Auto Labeling Sample

dataset_id

Yes

String

Dataset ID.

project_id

Yes

String

Project ID. For details about how to obtain a project ID, see Obtaining a Project ID and Name.

sample_id

Yes

String

Sample ID.

check_accept

Boolean

Whether the acceptance is passed, which is used for team labeling. Options:

• true: The acceptance is passed.

• false: The acceptance is not passed.

check_comment

String

Acceptance comment, which is used for team labeling.

check_score

String

Acceptance score, which is used for team labeling.

deletion_reasons

Array of strings

Reason for deleting a sample, which is used for healthcare.

hard_details

Map<String,HardDetail>

Details about difficulties, including description, causes, and suggestions of difficult problems.

labelers

Array of Worker objects

Labeling personnel list of sample assignment. The labelers record the team members to which the sample is allocated for team labeling.

labels

Array of SampleLabel objects

Sample label list.

metadata

SampleMetadata object

Key-value pair of the sample metadata attribute.

review_accept

Boolean

Whether to accept the review, which is used for team labeling. Options:

• true: accepted

• false: rejected

review_comment

String

Review comment, which is used for team labeling.

review_score

String

Review score, which is used for team labeling.

sample_data

Array of strings

Sample data list.

sample_dir

String

Sample path.

sample_id

String

Sample ID.

sample_name

String

Sample name.

sample_size

Long

Sample size or text length, in bytes.

sample_status

String

Sample status. The options are as follows:

• __ALL__: labeled

• __NONE__: not marked

• __UNCHECK__: to be accepted

• __ACCEPTED__: The acceptance is passed.

• __REJECTED__: rejected

• __UNREVIEWED__: to be reviewed

• __REVIEWED__: approved

• __WORKFORCE_SAMPLED__: sampled

• __WORKFORCE_SAMPLED_UNCHECK__: Sampling is to be accepted.

• __WORKFORCE_SAMPLED_CHECKED__: Sampling has been accepted.

• __WORKFORCE_SAMPLED_ACCEPTED__: The sampling is passed.

• __WORKFORCE_SAMPLED_REJECTED__: The sampling has been rejected.

• __AUTO_ANNOTATION__: to be confirmed

sample_time

Long

Sample time, when OBS is last modified.

sample_type

Integer

Sample type. Options:

• 0: image

• 1: text

• 2: speech

• 4: table

• 6: video

• 9: custom format

score

String

Comprehensive score, which is used for team labeling.

source

String

Source address of sample data.

sub_sample_url

String

Subsample URL, which is used for healthcare.

worker_id

String

ID of a labeling team member, which is used for team labeling.

alo_name

String

Alias.

id

Integer

Reason ID.

reason

String

Reason description.

suggestion

String

Handling suggestion.

create_time

Long

Creation time.

description

String

Labeling team member description. The value contains 0 to 256 characters and does not support the following special characters: ^!<>=&"'

email

String

Email address of a labeling team member.

role

Integer

Role. Options:

• 0: labeling personnel

• 1: reviewer

• 2: team administrator

• 3: dataset owner

status

Integer

Current login status of a labeling team member. Options:

• 0: The invitation email has not been sent.

• 1: The invitation email has been sent but the user has not logged in.

• 2: The user has logged in.

• 3: The labeling team member has been deleted.

update_time

Long

Update time.

worker_id

String

ID of a labeling team member.

workforce_id

String

ID of a labeling team.

annotated_by

String

Video labeling method, which is used to distinguish whether a video is labeled manually or automatically. Options:

• human: manual labeling

• auto: automatic labeling

id

String

Label ID.

name

String

Label name.

property

SampleLabelProperty object

Attribute key-value pair of the sample label, such as the object shape and shape feature.

score

Float

Confidence. The value range is [0,1].

type

Integer

Label type. Options:

• 0: image classification

• 1: object detection

• 3: image segmentation

• 100: text classification

• 101: named entity recognition

• 102: text triplet relationship

• 103: text triplet entity

• 200: sound classification

• 201: speech content

• 202: speech paragraph labeling

• 600: video labeling

@modelarts:content

String

Speech text content, which is a default attribute dedicated to the speech label (including the speech content and speech start and end points).

@modelarts:end_index

Integer

End position of the text, which is a default attribute dedicated to the named entity label. The end position does not include the character corresponding to the value of end_index. Example:

• If the text is "Barack Hussein Obama II (born August 4, 1961) is an attorney and politician.", start_index and end_index of Barack Hussein Obama II are 0 and 23, respectively.

• If the text is "Hope is the thing with feathers", start_index and end_index of Hope are 0 and 4, respectively.

@modelarts:end_time

String

Speech end time, which is a default attribute dedicated to the speech start/end point label, in the format of hh:mm:ss.SSS. (hh indicates hour; mm indicates minute; ss indicates second; and SSS indicates millisecond.)

@modelarts:feature

Object

Shape feature, which is a default attribute dedicated to the object detection label, with type of List. The upper left corner of the image is used as the coordinate origin [0, 0]. Each coordinate point is represented by [x, y], where x indicates the horizontal coordinate and y indicates the vertical coordinate (both x and y are >=0). The format of each shape is as follows:

• bndbox consists of two points, for example, [[0,10],[50,95]]. The upper left vertex of the rectangle is the first point, and the lower right vertex is the second point. That is, the x-coordinate of the first point must be less than the x-coordinate of the second point, and the y-coordinate of the first point must be less than the y-coordinate of the second point.

• polygon: consists of multiple points that are connected in sequence to form a polygon, for example, [[0,100],[50,95],[10,60],[500,400]].

• circle: consists of the center and radius, for example, [[100,100],[50]].

• line: consists of two points, for example, [[0,100],[50,95]]. The first point is the start point, and the second point is the end point.

• dashed: consists of two points, for example, [[0,100],[50,95]]. The first point is the start point, and the second point is the end point.

• point: consists of one point, for example, [[0,100]].

• polyline: consists of multiple points, for example, [[0,100],[50,95],[10,60],[500,400]].

@modelarts:from

String

ID of the head entity in the triplet relationship label, which is a default attribute dedicated to the triplet relationship label.

@modelarts:hard

String

Sample labeled as a hard sample or not, which is a default attribute. Options:

• 0/false: not a hard example

• 1/true: hard example

@modelarts:hard_coefficient

String

Coefficient of difficulty of each label level, which is a default attribute. The value range is [0,1].

@modelarts:hard_reasons

String

Reasons that the sample is a hard sample, which is a default attribute. Use a hyphen (-) to separate every two hard sample reason IDs, for example, 3-20-21-19. Options:

• 0: No target objects are identified.

• 1: The confidence is low.

• 2: The clustering result based on the training dataset is inconsistent with the prediction result.

• 3: The prediction result is greatly different from the data of the same type in the training dataset.

• 4: The prediction results of multiple consecutive similar images are inconsistent.

• 5: There is a large offset between the image resolution and the feature distribution of the training dataset.

• 6: There is a large offset between the aspect ratio of the image and the feature distribution of the training dataset.

• 7: There is a large offset between the brightness of the image and the feature distribution of the training dataset.

• 8: There is a large offset between the saturation of the image and the feature distribution of the training dataset.

• 9: There is a large offset between the color richness of the image and the feature distribution of the training dataset.

• 10: There is a large offset between the definition of the image and the feature distribution of the training dataset.

• 11: There is a large offset between the number of frames of the image and the feature distribution of the training dataset.

• 12: There is a large offset between the standard deviation of area of image frames and the feature distribution of the training dataset.

• 13: There is a large offset between the aspect ratio of image frames and the feature distribution of the training dataset.

• 14: There is a large offset between the area portion of image frames and the feature distribution of the training dataset.

• 15: There is a large offset between the edge of image frames and the feature distribution of the training dataset.

• 16: There is a large offset between the brightness of image frames and the feature distribution of the training dataset.

• 17: There is a large offset between the definition of image frames and the feature distribution of the training dataset.

• 18: There is a large offset between the stack of image frames and the feature distribution of the training dataset.

• 19: The data enhancement result based on GaussianBlur is inconsistent with the prediction result of the original image.

• 20: The data enhancement result based on fliplr is inconsistent with the prediction result of the original image.

• 21: The data enhancement result based on Crop is inconsistent with the prediction result of the original image.

• 22: The data enhancement result based on flipud is inconsistent with the prediction result of the original image.

• 23: The data enhancement result based on scale is inconsistent with the prediction result of the original image.

• 24: The data enhancement result based on translate is inconsistent with the prediction result of the original image.

• 25: The data enhancement result based on shear is inconsistent with the prediction result of the original image.

• 26: The data enhancement result based on superpixels is inconsistent with the prediction result of the original image.

• 27: The data enhancement result based on sharpen is inconsistent with the prediction result of the original image.

• 28: The data enhancement result based on add is inconsistent with the prediction result of the original image.

• 29: The data enhancement result based on invert is inconsistent with the prediction result of the original image.

• 30: The data is predicted to be abnormal.

@modelarts:shape

String

Object shape, which is a default attribute dedicated to the object detection label and is left empty by default. Options:

• bndbox: rectangle

• polygon: polygon

• circle: circle

• line: straight line

• dashed: dotted line

• point: point

• polyline: polyline

@modelarts:source

String

Speech source, which is a default attribute dedicated to the speech start/end point label and can be set to a speaker or narrator.

@modelarts:start_index

Integer

Start position of the text, which is a default attribute dedicated to the named entity label. The start value begins from 0, including the character corresponding to the value of start_index.

@modelarts:start_time

String

Speech start time, which is a default attribute dedicated to the speech start/end point label, in the format of hh:mm:ss.SSS. (hh indicates hour; mm indicates minute; ss indicates second; and SSS indicates millisecond.)

@modelarts:to

String

ID of the tail entity in the triplet relationship label, which is a default attribute dedicated to the triplet relationship label.

@modelarts:import_origin

Integer

Sample source, which is a built-in attribute.

@modelarts:hard

Double

Whether the sample is labeled as a hard sample, which is a default attribute. Options:

• 0: non-hard sample

• 1: hard sample

@modelarts:hard_coefficient

Double

Coefficient of difficulty of each sample level, which is a default attribute. The value range is [0,1].

@modelarts:hard_reasons

Array of integers

ID of a hard sample reason, which is a default attribute. Options:

• 0: No object is identified.

• 1: The confidence is low.

• 2: The clustering result based on the training dataset is inconsistent with the prediction result.

• 3: The prediction result is greatly different from the data of the same type in the training dataset.

• 4: The prediction results of multiple consecutive similar images are inconsistent.

• 5: There is a large offset between the image resolution and the feature distribution of the training dataset.

• 6: There is a large offset between the aspect ratio of the image and the feature distribution of the training dataset.

• 7: There is a large offset between the brightness of the image and the feature distribution of the training dataset.

• 8: There is a large offset between the saturation of the image and the feature distribution of the training dataset.

• 9: There is a large offset between the color richness of the image and the feature distribution of the training dataset.

• 10: There is a large offset between the definition of the image and the feature distribution of the training dataset.

• 11: There is a large offset between the number of frames of the image and the feature distribution of the training dataset.

• 12: There is a large offset between the standard deviation of area of image frames and the feature distribution of the training dataset.

• 13: There is a large offset between the aspect ratio of image frames and the feature distribution of the training dataset.

• 14: There is a large offset between the area portion of image frames and the feature distribution of the training dataset.

• 15: There is a large offset between the edge of image frames and the feature distribution of the training dataset.

• 16: There is a large offset between the brightness of image frames and the feature distribution of the training dataset.

• 17: There is a large offset between the definition of image frames and the feature distribution of the training dataset.

• 18: There is a large offset between the stack of image frames and the feature distribution of the training dataset.

• 19: The data enhancement result based on GaussianBlur is inconsistent with the prediction result of the original image.

• 20: The data enhancement result based on fliplr is inconsistent with the prediction result of the original image.

• 21: The data enhancement result based on Crop is inconsistent with the prediction result of the original image.

• 22: The data enhancement result based on flipud is inconsistent with the prediction result of the original image.

• 23: The data enhancement result based on scale is inconsistent with the prediction result of the original image.

• 24: The data enhancement result based on translate is inconsistent with the prediction result of the original image.

• 25: The data enhancement result based on shear is inconsistent with the prediction result of the original image.

• 26: The data enhancement result based on superpixels is inconsistent with the prediction result of the original image.

• 27: The data enhancement result based on sharpen is inconsistent with the prediction result of the original image.

• 28: The data enhancement result based on add is inconsistent with the prediction result of the original image.

• 29: The data enhancement result based on invert is inconsistent with the prediction result of the original image.

• 30: The data is predicted to be abnormal.

@modelarts:size

Array of objects

Image size (width, height, and depth of the image), which is a default attribute, with type of List<Integer>. In the list, the first number indicates the width (pixels), the second number indicates the height (pixels), and the third number indicates the depth (the depth can be left blank and the default value is 3). For example, [100,200,3] and [100,200] are both valid. Note: This parameter is mandatory only when the sample label list contains the object detection label.

200

OK

401

Unauthorized

403

Forbidden

404

Not Found