Querying the Sample List

Function

This API is used to query the sample list by page.

Debugging

You can debug this API through automatic authentication in or use the SDK sample code generated by API Explorer.

URI

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

Table 1 Path Parameters

Parameter	Mandatory	Type	Description
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.

Table 2 Query Parameters

Parameter	Mandatory	Type	Description
email	No	String	Email address of a labeling team member.
high_score	No	String	Upper confidence limit. 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: custom format
limit	No	Integer	Maximum number of records returned on each page. The value ranges from 1 to 100. The default value is 10.
locale	No	String	Language.
low_score	No	String	Lower confidence limit. The default value is 0.
offset	No	Integer	Start page of the paging list. The default value is 0.
order	No	String	Sorting sequence of the query. Options: asc: ascending order desc: descending order (default value)
preview	No	Boolean	Whether to support preview. Options: true: Preview is supported. false: Preview is not supported.
process_parameter	No	String	Image resizing setting, which is the same as the OBS resizing setting. For details, see . For example, image/resize,m_lfit,h_200 indicates that the target image is resized proportionally and the height is set to 200 pixels.
sample_state	No	String	Sample status. Options: __ALL__: labeled __NONE__: unlabeled __UNCHECK__: to be checked __ACCEPTED__: accepted __REJECTED__: rejected __UNREVIEWED__: to be reviewed __REVIEWED__: reviewed __WORKFORCE_SAMPLED__: reviewed data sampled __WORKFORCE_SAMPLED_UNCHECK__: samples to be checked __WORKFORCE_SAMPLED_CHECKED__: samples checked __WORKFORCE_SAMPLED_ACCEPTED__: samples accepted __WORKFORCE_SAMPLED_REJECTED__: samples rejected __AUTO_ANNOTATION__: to be checked
sample_type	No	Integer	Sample file type. Options: 0: image 1: text 2: audio 4: table 6: video 9: custom format (default value)
search_conditions	No	String	Multi-dimensional search condition after URL encoding. The relationship between multiple search conditions is AND.
version_id	No	String	Dataset version ID.

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	Sample list.

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>	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. Options: __ALL__: labeled __NONE__: unlabeled __UNCHECK__: to be checked __ACCEPTED__: accepted __REJECTED__: rejected __UNREVIEWED__: to be reviewed __REVIEWED__: reviewed __WORKFORCE_SAMPLED__: reviewed data sampled __WORKFORCE_SAMPLED_UNCHECK__: samples to be checked __WORKFORCE_SAMPLED_CHECKED__: samples checked __WORKFORCE_SAMPLED_ACCEPTED__: samples accepted __WORKFORCE_SAMPLED_REJECTED__: samples rejected __AUTO_ANNOTATION__: to be checked
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.

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	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.

Table 7 SampleLabel

Parameter	Type	Description
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.
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. 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.

Table 9 SampleMetadata

Parameter	Type	Description
@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.

Example Requests

Querying the Sample List by Page

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

Example Responses

Status code: 200

OK

{
  "sample_count" : 2,
  "samples" : [ {
    "sample_id" : "012f99f3cf405860130b6ed2350c2228",
    "sample_type" : 0,
    "labels" : [ {
      "name" : "car",
      "type" : 0,
      "property" : { }
    } ],
    "source" : "https://test-obs.obs.xxx.com:443/image/aifood/%E5%86%B0%E6%BF%80%E5%87%8C/36502.jpg?AccessKeyId=RciyO7RHmhNTfOZVryUH&Expires=1606296688&x-image-process=image%2Fresize%2Cm_lfit%2Ch_200&Signature=icyvHhFew9vnmy3zh1uZMP15Mbg%3D",
    "metadata" : {
      "@modelarts:import_origin" : 0
    },
    "sample_time" : 1589190552106,
    "sample_status" : "MANUAL_ANNOTATION",
    "labelers" : [ {
      "email" : "xxx@xxx.com",
      "worker_id" : "5d8d4033b428fed5ac158942c33940a2",
      "role" : 0
    } ]
  }, {
    "sample_id" : "0192f3acfb000666033a0f85c21577c7",
    "sample_type" : 0,
    "labels" : [ {
      "name" : "car",
      "type" : 0,
      "property" : { }
    } ],
    "source" : "https://test-obs.obs.xxx.com:443/image/aifood/%E5%86%B0%E6%BF%80%E5%87%8C/36139.jpg?AccessKeyId=RciyO7RHmhNTfOZVryUH&Expires=1606296688&x-image-process=image%2Fresize%2Cm_lfit%2Ch_200&Signature=RRr9r2cghLCXk%2B0%2BfHtYJi8eZ4k%3D",
    "metadata" : {
      "@modelarts:import_origin" : 0
    },
    "sample_time" : 1589190543327,
    "sample_status" : "MANUAL_ANNOTATION",
    "labelers" : [ {
      "email" : "xxx@xxx.com",
      "worker_id" : "a2abd3f27b4e92c593c15282f8b6bd29",
      "role" : 0
    } ]
  } ]
}

Status Codes

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

Error Codes

See Error Codes.

last updated: 2024-11-28 19:02 UTC - commit: 7e602c6c50500da4fb13dea67589040fd3f7deb8