Obtaining an Auto Labeling Sample List_ModelArts

Function

Obtain a list of auto labeling samples in a dataset.

Debugging

You can debug this API in API Explorer which supports automatic authentication. API Explorer can automatically generate SDK code examples and provide the SDK code example debugging.

URI

GET /v2/{project_id}/datasets/{dataset_id}/auto-annotations/samples

**Table 1** URI parameters
Parameter	Mandatory	Type	Description
dataset_id	Yes	String	Dataset ID
project_id	Yes	String	Project ID. For details, see Obtaining a Project ID and Name.

**Table 2** Query parameters
Parameter	Mandatory	Type	Description
high_score	No	String	Upper limit of the confidence score. The default value is 1.
label_name	No	String	Label name
label_type	No	Integer	Labeling type. Options: 0: image classification 1: object detection 3: image segmentation 100: text classification 101: named entity recognition 102: text triplet 200: sound classification 201: speech content 202: speech paragraph labeling 400: table dataset 600: video labeling 900: free format
limit	No	Integer	Maximum number of records returned on each page. The value ranges from 1 to 100. The default value is 10.
low_score	No	String	Lower limit of the confidence score. The default value is 0.
offset	No	Integer	Start page for pagination display. The default value is 0.
order	No	String	Sorting sequence of the query. Options: asc: ascending order desc: descending order (default value)
process_parameter	No	String	Image resize configuration, which is the same as OBS settings. For details, see Resizing Images. For example, image/resize,m_lfit,h_200 indicates that the target image is resized proportionally and the height is set to 200 pixels.
search_conditions	No	String	Multi-dimensional search criteria after URL encoding. The relationship between multiple search conditions is AND.

Request Parameters

None

Response Parameters

Status code: 200

**Table 3** Response body parameters
Parameter	Type	Description
sample_count	Integer	Number of samples
samples	Array of DescribeSampleResp objects	List of samples

**Table 4** DescribeSampleResp
Parameter	Type	Description
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>	Difficult problem details, including description, causes, and suggestions of difficult problems.
labelers	Array of Worker objects	Labeling personnel list of sample allocation. The list records the team members receiving the samples, which is used for team labeling.
labels	Array of SampleLabel objects	List of sample labels
metadata	SampleMetadata object	Attribute key-value pair of the sample metadata
review_accept	Boolean	Whether to accept the review, which is used for team labeling. Options: true: The review is accepted. false: The review is 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	List of sample data
sample_dir	String	Path for storing a sample
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. Options: __ALL__: labeled __NONE__: unlabeled __UNCHECK__: to be accepted __ACCEPTED__: accepted __REJECTED__: rejected __UNREVIEWED__: to be reviewed __REVIEWED__: reviewed __WORKFORCE_SAMPLED__: sampled __WORKFORCE_SAMPLED_UNCHECK__: sampling pending check __WORKFORCE_SAMPLED_CHECKED__: sampling checked __WORKFORCE_SAMPLED_ACCEPTED__: sampling accepted __WORKFORCE_SAMPLED_REJECTED__: sampling 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: audio 4: table 6: video 9: free 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.

**Table 5** HardDetail
Parameter	Type	Description
alo_name	String	Alias
id	Integer	Reason ID
reason	String	Reason description
suggestion	String	Handling suggestion

**Table 6** Worker
Parameter	Type	Description
create_time	Long	Worker creation time
description	String	Labeling team member description. The value contains 0 to 256 characters. Special characters (^!<>=&"') are not allowed.
email	String	Email address of a labeling team member
role	Integer	Role. Options: 0: marker 1: reviewer 2: team manager 3: dataset owner
status	Integer	Current login status of a labeled member. Options: 0: No invitation email is sent. 1: The invitation email is sent but the member has not logged in. 2: The member has logged in. 3: The member has been deleted.
update_time	Long	Worker update time
worker_id	String	ID of a labeling team member
workforce_id	String	ID of a labeling team

**Table 7** SampleLabel
Parameter	Type	Description
annotated_by	String	Video labeling method, which is used to determine whether a video is labeled manually or automatically. Options: human: manual labeling auto: auto 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 ranges from 0 to 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

**Table 8** SampleLabelProperty
Parameter	Type	Description
@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. Examples: If the text is "Barack Hussein Obama II (born on August 4, 1961) is an attorney and politician.", the 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 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 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	Start entity ID of the triplet relationship label, which is a default attribute dedicated to the triplet relationship label
@modelarts:hard	String	Whether the sample is labeled as a hard example, which is a default attribute. Options: 0/false: The label is not a hard example. 1/true: The label is a hard example.
@modelarts:hard_coefficient	String	Coefficient of difficulty of each label level, which is a default attribute. The value ranges from 0 to 1.
@modelarts:hard_reasons	String	Reasons why the sample is a hard example, which is a default attribute. Use a hyphen (-) to separate every two hard example reason IDs, for example, 3-20-21-19. 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 augmentation result based on GaussianBlur is inconsistent with the prediction result of the original image. 20: The data augmentation result based on fliplr is inconsistent with the prediction result of the original image. 21: The data augmentation result based on Crop is inconsistent with the prediction result of the original image. 22: The data augmentation result based on flipud is inconsistent with the prediction result of the original image. 23: The data augmentation result based on scale is inconsistent with the prediction result of the original image. 24: The data augmentation result based on translate is inconsistent with the prediction result of the original image. 25: The data augmentation result based on shear is inconsistent with the prediction result of the original image. 26: The data augmentation result based on superpixels is inconsistent with the prediction result of the original image. 27: The data augmentation result based on sharpen is inconsistent with the prediction result of the original image. 28: The data augmentation result based on add is inconsistent with the prediction result of the original image. 29: The data augmentation 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: dashed 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	Direction entity ID of the triplet relationship label, which is a default attribute dedicated to the triplet relationship label

**Table 9** SampleMetadata
Parameter	Type	Description
@modelarts:import_origin	Integer	Sample source, which is a default attribute.
@modelarts:hard	Double	Whether the sample is labeled as a hard example, which is a default attribute. Options: 0: The label is not a hard example. 1: The label is a hard example.
@modelarts:hard_coefficient	Double	Coefficient of difficulty of each sample level, which is a default attribute. The value ranges from 0 to 1.
@modelarts:hard_reasons	Array of integers	ID of a hard example 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 augmentation result based on GaussianBlur is inconsistent with the prediction result of the original image. 20: The data augmentation result based on fliplr is inconsistent with the prediction result of the original image. 21: The data augmentation result based on Crop is inconsistent with the prediction result of the original image. 22: The data augmentation result based on flipud is inconsistent with the prediction result of the original image. 23: The data augmentation result based on scale is inconsistent with the prediction result of the original image. 24: The data augmentation result based on translate is inconsistent with the prediction result of the original image. 25: The data augmentation result based on shear is inconsistent with the prediction result of the original image. 26: The data augmentation result based on superpixels is inconsistent with the prediction result of the original image. 27: The data augmentation result based on sharpen is inconsistent with the prediction result of the original image. 28: The data augmentation result based on add is inconsistent with the prediction result of the original image. 29: The data augmentation 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, including width, height, and depth. The type is List[/topic/body/section/table/tgroup/tbody/row/entry/p/br. {""}) (br]. 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.

Request Example

Run the following command to obtain an auto labeling sample list:

GET https://{endpoint}/v2/{project_id}/datasets/{dataset_id}/auto-annotations/samples

Response Example

Status code: 200

OK

{
  "sample_count" : 1,
  "samples" : [ {
    "sample_id" : "10de574cbf0f09d4798b87ba0eb34e37",
    "sample_type" : 0,
    "labels" : [ {
      "name" : "sunflowers",
      "type" : 0,
      "id" : "1",
      "property" : {
        "@modelarts:hard_coefficient" : "0.0",
        "@modelarts:hard" : "false"
      },
      "score" : 1.0
    } ],
    "source" : "https://test-obs.obs.xxx.com:443/animals/8_1597649054631.jpeg?AccessKeyId=alRn0xskf5luJaG2jBJe&Expires=1606299230&x-image-process=image%2Fresize%2Cm_lfit%2Ch_200&Signature=MNAAjXz%2Fmwn%2BSabSK9wkaG6b6bU%3D",
    "metadata" : {
      "@modelarts:hard_coefficient" : 1.0,
      "@modelarts:hard" : true,
      "@modelarts:import_origin" : 0,
      "@modelarts:hard_reasons" : [ 8, 6, 5, 3 ]
    },
    "sample_time" : 1601432758000,
    "sample_status" : "UN_ANNOTATION"
  } ]
}

Status Code

Status Code	Description
200	OK
401	Unauthorized
403	Forbidden
404	Not Found

Error Code

For details, see Error Codes.

Obtaining an Auto Labeling Sample List