Updating Labels of Team Labeling Samples in Batches

dataset_id

Yes

String

Dataset ID.

project_id

Yes

String

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

workforce_task_id

Yes

String

ID of a labeling task.

email

No

String

Email address of a labeling team member.

samples

No

Array of SampleLabels objects

Updated sample list.

labels

No

Array of SampleLabel objects

Sample label list. If this parameter is left blank, all sample labels are deleted.

metadata

No

SampleMetadata object

Key-value pair of the sample metadata attribute.

sample_id

No

String

Sample ID.

sample_type

No

Integer

Sample type. The options are as follows: - 0: image - 1: text - 2: speech - 4: table - 6: video - 9: custom format

sample_usage

No

String

Sample usage. The options are as follows: - TRAIN: training - EVAL: evaluation - TEST: test - INFERENCE: inference

source

No

String

Source address of sample data.

worker_id

No

String

ID of a labeling team member.

annotated_by

No

String

Video labeling method, which is used to distinguish whether a video is labeled manually or automatically. The options are as follows: - human: manual labeling - auto: automatic labeling

id

No

String

Label ID.

name

No

String

Label name.

property

No

SampleLabelProperty object

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

score

No

Float

Confidence.

type

No

Integer

Label type. The options are as follows: - 0: image classification - 1: object detection - 100: text classification - 101: named entity recognition - 102: text triplet relationship - 103: text triplet entity - 200: speech classification - 201: speech content - 202: speech paragraph labeling - 600: video classification

@modelarts:content

No

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

No

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. Examples are as follows. - If the text content is "Barack Hussein Obama II (born August 4, 1961) is an American attorney and politician.", the start_index and end_index values of "Barack Hussein Obama II" are 0 and 23, respectively. - If the text content is "By the end of 2018, the company has more than 100 employees.", the start_index and end_index values of "By the end of 2018" are 0 and 18, respectively.

@modelarts:end_time

No

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

No

Object

Shape feature, which is a default attribute dedicated to the object detection label, with type of List. The upper left corner of an image is used as the coordinate origin [0,0]. Each coordinate point is represented by [x, y]. x indicates the horizontal coordinate, and y indicates the vertical coordinate (both x and y are greater than or equal to 0). The format of each shape is as follows: - bndbox: consists of two points, for example, [[0,10],[50,95]]. The first point is located at the upper left corner of the rectangle and the second point is located at the lower right corner of the rectangle. That is, the X coordinate of the first point must be smaller than that of the second point, and the Y coordinate of the second point must be smaller than that of the first 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 point 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

No

String

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

@modelarts:hard

No

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

No

String

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

@modelarts:hard_reasons

No

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. The options are as follows: - 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

No

String

Object shape, which is a default attribute dedicated to the object detection label and is left empty by default. The options are as follows: - bndbox: rectangle - polygon: polygon - circle: circle - line: straight line - dashed: dotted line - point: point - polyline: polyline

@modelarts:source

No

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

No

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

No

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

No

String

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

@modelarts:hard

No

Double

Whether the sample is labeled as a hard sample, which is a default attribute. The options are as follows: - 0: non-hard sample - 1: hard sample

@modelarts:hard_coefficient

No

Double

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

@modelarts:hard_reasons

No

Array of integers

ID of a hard sample reason, which is a default attribute. The options are as follows: - 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:size

No

Array of objects

Image size (width, height, and depth of the image), which is a default attribute, with type of List. 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.

error_code

String

Error code.

error_msg

String

Error message.

results

Array of BatchResponse objects

Response list for updating sample labels in batches.

success

Boolean

Whether the operation is successful. The options are as follows: - true: successful - false: failed

error_code

String

Error code.

error_msg

String

Error message.

success

Boolean

Check whether the operation is successful. The options are as follows: - true: The operation is successful. - false: The operation is failed.

200

OK

401

Unauthorized

403

Forbidden

404

Not Found