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doc-exports/docs/modelarts/api-ref/UpdateWorkforceTaskSamples.html
Lai, Weijian 68e5cd0687 ModelArts API 23.3.0 Version. 
Reviewed-by: Pruthi, Vineet <vineet.pruthi@t-systems.com>
Co-authored-by: Lai, Weijian <laiweijian4@huawei.com>
Co-committed-by: Lai, Weijian <laiweijian4@huawei.com>
2024-06-18 11:02:37 +00:00

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<a name="EN-US_TOPIC_0000001910007888"></a><a name="EN-US_TOPIC_0000001910007888"></a>
<h1 class="topictitle1">Updating Labels of Team Labeling Samples in Batches</h1>
<div id="body0000001588993221"><div class="section"><h4 class="sectiontitle">Function</h4><p>This API is used to update labels of team labeling samples in batches.</p>
</div>
<div class="section" id="EN-US_TOPIC_0000001910007888__atuogenerate_1"><h4 class="sectiontitle">Debugging</h4><p>You can debug this API through automatic authentication in or use the SDK sample code generated by API Explorer.</p>
</div>
<div class="section" id="EN-US_TOPIC_0000001910007888__atuogenerate_2"><h4 class="sectiontitle">URI</h4><p>PUT /v2/{project_id}/datasets/{dataset_id}/workforce-tasks/{workforce_task_id}/data-annotations/samples</p>
<div class="tablenoborder"><table cellpadding="4" cellspacing="0" summary="" frame="border" border="1" rules="all"><caption><b>Table 1 </b>Path Parameters</caption><thead align="left"><tr><th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.3.3.2.5.1.1"><p>Parameter</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.3.3.2.5.1.2"><p>Mandatory</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.3.3.2.5.1.3"><p>Type</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="40%" id="mcps1.3.3.3.2.5.1.4"><p>Description</p>
</th>
</tr>
</thead>
<tbody><tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.3.3.2.5.1.1 "><p>dataset_id</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.3.3.2.5.1.2 "><p>Yes</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.3.3.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.3.3.2.5.1.4 "><p>Dataset ID.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.3.3.2.5.1.1 "><p>project_id</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.3.3.2.5.1.2 "><p>Yes</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.3.3.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.3.3.2.5.1.4 "><p>Project ID. For details about how to obtain a project ID, see <a href="modelarts_03_0147.html">Obtaining a Project ID and Name</a>.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.3.3.2.5.1.1 "><p>workforce_task_id</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.3.3.2.5.1.2 "><p>Yes</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.3.3.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.3.3.2.5.1.4 "><p>ID of a labeling task.</p>
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section"><h4 class="sectiontitle">Request Parameters</h4>
<div class="tablenoborder"><table cellpadding="4" cellspacing="0" summary="" id="EN-US_TOPIC_0000001910007888__request_UpdateSamplesReq" frame="border" border="1" rules="all"><caption><b>Table 2 </b>Request body parameters</caption><thead align="left"><tr><th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.2.2.5.1.1"><p>Parameter</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.2.2.5.1.2"><p>Mandatory</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.2.2.5.1.3"><p>Type</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="40%" id="mcps1.3.4.2.2.5.1.4"><p>Description</p>
</th>
</tr>
</thead>
<tbody><tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.2.2.5.1.1 "><p>email</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.2.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.2.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.2.2.5.1.4 "><p>Email address of a labeling team member.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.2.2.5.1.1 "><p>samples</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.2.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.2.2.5.1.3 "><p>Array of <a href="#EN-US_TOPIC_0000001910007888__request_SampleLabels">SampleLabels</a> objects</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.2.2.5.1.4 "><p>Updated sample list.</p>
</td>
</tr>
</tbody>
</table>
</div>
<div class="tablenoborder"><a name="EN-US_TOPIC_0000001910007888__request_SampleLabels"></a><a name="request_SampleLabels"></a><table cellpadding="4" cellspacing="0" summary="" id="EN-US_TOPIC_0000001910007888__request_SampleLabels" frame="border" border="1" rules="all"><caption><b>Table 3 </b>SampleLabels</caption><thead align="left"><tr><th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.3.2.5.1.1"><p>Parameter</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.3.2.5.1.2"><p>Mandatory</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.3.2.5.1.3"><p>Type</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="40%" id="mcps1.3.4.3.2.5.1.4"><p>Description</p>
</th>
</tr>
</thead>
<tbody><tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.1 "><p>labels</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.3 "><p>Array of <a href="#EN-US_TOPIC_0000001910007888__request_SampleLabel">SampleLabel</a> objects</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.3.2.5.1.4 "><p>Sample label list. If this parameter is left blank, all sample labels are deleted.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.1 "><p>metadata</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.3 "><p><a href="#EN-US_TOPIC_0000001910007888__request_SampleMetadata">SampleMetadata</a> object</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.3.2.5.1.4 "><p>Key-value pair of the sample <strong>metadata</strong> attribute.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.1 "><p>sample_id</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.3.2.5.1.4 "><p>Sample ID.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.1 "><p>sample_type</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.3 "><p>Integer</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.3.2.5.1.4 "><p>Sample type. Options:</p>
<ul><li><p><strong>0</strong>: image</p>
</li><li><p><strong>1</strong>: text</p>
</li><li><p><strong>2</strong>: speech</p>
</li><li><p><strong>4</strong>: table</p>
</li><li><p><strong>6</strong>: video</p>
</li><li><p><strong>9</strong>: custom format</p>
</li></ul>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.1 "><p>sample_usage</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.3.2.5.1.4 "><p>Sample usage. Options:</p>
<ul><li><p><strong>TRAIN</strong>: training</p>
</li><li><p><strong>EVAL</strong>: evaluation</p>
</li><li><p><strong>TEST</strong>: test</p>
</li><li><p><strong>INFERENCE</strong>: inference</p>
</li></ul>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.1 "><p>source</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.3.2.5.1.4 "><p>Source address of sample data.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.1 "><p>worker_id</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.3.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.3.2.5.1.4 "><p>ID of a labeling team member.</p>
</td>
</tr>
</tbody>
</table>
</div>
<div class="tablenoborder"><a name="EN-US_TOPIC_0000001910007888__request_SampleLabel"></a><a name="request_SampleLabel"></a><table cellpadding="4" cellspacing="0" summary="" id="EN-US_TOPIC_0000001910007888__request_SampleLabel" frame="border" border="1" rules="all"><caption><b>Table 4 </b>SampleLabel</caption><thead align="left"><tr><th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.4.2.5.1.1"><p>Parameter</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.4.2.5.1.2"><p>Mandatory</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.4.2.5.1.3"><p>Type</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="40%" id="mcps1.3.4.4.2.5.1.4"><p>Description</p>
</th>
</tr>
</thead>
<tbody><tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.1 "><p>annotated_by</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.4.2.5.1.4 "><p>Video labeling method, which is used to distinguish whether a video is labeled manually or automatically. Options:</p>
<ul><li><p><strong>human</strong>: manual labeling</p>
</li><li><p><strong>auto</strong>: automatic labeling</p>
</li></ul>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.1 "><p>id</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.4.2.5.1.4 "><p>Label ID.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.1 "><p>name</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.4.2.5.1.4 "><p>Label name.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.1 "><p>property</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.3 "><p><a href="#EN-US_TOPIC_0000001910007888__request_SampleLabelProperty">SampleLabelProperty</a> object</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.4.2.5.1.4 "><p>Attribute key-value pair of the sample label, such as the object shape and shape feature.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.1 "><p>score</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.3 "><p>Float</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.4.2.5.1.4 "><p>Confidence.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.1 "><p>type</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.4.2.5.1.3 "><p>Integer</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.4.2.5.1.4 "><p>Label type. Options:</p>
<ul><li><p><strong>0</strong>: image classification</p>
</li><li><p><strong>1</strong>: object detection</p>
</li><li><p><strong>3</strong>: image segmentation</p>
</li><li><p><strong>100</strong>: text classification</p>
</li><li><p><strong>101</strong>: named entity recognition</p>
</li><li><p><strong>102</strong>: text triplet relationship</p>
</li><li><p><strong>103</strong>: text triplet entity</p>
</li><li><p><strong>200</strong>: sound classification</p>
</li><li><p><strong>201</strong>: speech content</p>
</li><li><p><strong>202</strong>: speech paragraph labeling</p>
</li><li><p><strong>600</strong>: video labeling</p>
</li></ul>
</td>
</tr>
</tbody>
</table>
</div>
<div class="tablenoborder"><a name="EN-US_TOPIC_0000001910007888__request_SampleLabelProperty"></a><a name="request_SampleLabelProperty"></a><table cellpadding="4" cellspacing="0" summary="" id="EN-US_TOPIC_0000001910007888__request_SampleLabelProperty" frame="border" border="1" rules="all"><caption><b>Table 5 </b>SampleLabelProperty</caption><thead align="left"><tr><th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.5.2.5.1.1"><p>Parameter</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.5.2.5.1.2"><p>Mandatory</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.5.2.5.1.3"><p>Type</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="40%" id="mcps1.3.4.5.2.5.1.4"><p>Description</p>
</th>
</tr>
</thead>
<tbody><tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:content</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>Speech text content, which is a default attribute dedicated to the speech label (including the speech content and speech start and end points).</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:end_index</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>Integer</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>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 <strong>end_index</strong>. Example:</p>
<ul><li><p>If the text is "<strong>Barack Hussein Obama II (born August 4, 1961) is an attorney and politician.</strong>", <strong>start_index</strong> and <strong>end_index</strong> of <strong>Barack Hussein Obama II</strong> are <strong>0</strong> and <strong>23</strong>, respectively.</p>
</li><li><p>If the text is "<strong>Hope is the thing with feathers</strong>", <strong>start_index</strong> and <strong>end_index</strong> of <strong>Hope</strong> are <strong>0</strong> and <strong>4</strong>, respectively.</p>
</li></ul>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:end_time</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>Speech end time, which is a default attribute dedicated to the speech start/end point label, in the format of <strong>hh:mm:ss.SSS</strong>. (<strong>hh</strong> indicates hour; <strong>mm</strong> indicates minute; <strong>ss</strong> indicates second; and <strong>SSS</strong> indicates millisecond.)</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:feature</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>Object</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>Shape feature, which is a default attribute dedicated to the object detection label, with type of <strong>List</strong>. 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 &gt;=0). The format of each shape is as follows:</p>
<ul><li><p><strong>bndbox</strong> consists of two points, for example, <strong>[[0,10],[50,95]]</strong>. 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.</p>
</li><li><p><strong>polygon</strong>: consists of multiple points that are connected in sequence to form a polygon, for example, <strong>[[0,100],[50,95],[10,60],[500,400]]</strong>.</p>
</li><li><p><strong>circle</strong>: consists of the center and radius, for example, <strong>[[100,100],[50]]</strong>.</p>
</li><li><p><strong>line</strong>: consists of two points, for example, <strong>[[0,100],[50,95]]</strong>. The first point is the start point, and the second point is the end point.</p>
</li><li><p><strong>dashed</strong>: consists of two points, for example, <strong>[[0,100],[50,95]]</strong>. The first point is the start point, and the second point is the end point.</p>
</li><li><p><strong>point</strong>: consists of one point, for example, <strong>[[0,100]]</strong>.</p>
</li><li><p><strong>polyline</strong>: consists of multiple points, for example, <strong>[[0,100],[50,95],[10,60],[500,400]]</strong>.</p>
</li></ul>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:from</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>ID of the head entity in the triplet relationship label, which is a default attribute dedicated to the triplet relationship label.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:hard</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>Sample labeled as a hard sample or not, which is a default attribute. Options:</p>
<ul><li><p><strong>0/false</strong>: not a hard example</p>
</li><li><p><strong>1/true</strong>: hard example</p>
</li></ul>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:hard_coefficient</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>Coefficient of difficulty of each label level, which is a default attribute. The value range is <strong>[0,1]</strong>.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:hard_reasons</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>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, <strong>3-20-21-19</strong>. Options:</p>
<ul><li><p><strong>0</strong>: No target objects are identified.</p>
</li><li><p><strong>1</strong>: The confidence is low.</p>
</li><li><p><strong>2</strong>: The clustering result based on the training dataset is inconsistent with the prediction result.</p>
</li><li><p><strong>3</strong>: The prediction result is greatly different from the data of the same type in the training dataset.</p>
</li><li><p><strong>4</strong>: The prediction results of multiple consecutive similar images are inconsistent.</p>
</li><li><p><strong>5</strong>: There is a large offset between the image resolution and the feature distribution of the training dataset.</p>
</li><li><p><strong>6</strong>: There is a large offset between the aspect ratio of the image and the feature distribution of the training dataset.</p>
</li><li><p><strong>7</strong>: There is a large offset between the brightness of the image and the feature distribution of the training dataset.</p>
</li><li><p><strong>8</strong>: There is a large offset between the saturation of the image and the feature distribution of the training dataset.</p>
</li><li><p><strong>9</strong>: There is a large offset between the color richness of the image and the feature distribution of the training dataset.</p>
</li><li><p><strong>10</strong>: There is a large offset between the definition of the image and the feature distribution of the training dataset.</p>
</li><li><p><strong>11</strong>: There is a large offset between the number of frames of the image and the feature distribution of the training dataset.</p>
</li><li><p><strong>12</strong>: There is a large offset between the standard deviation of area of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>13</strong>: There is a large offset between the aspect ratio of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>14</strong>: There is a large offset between the area portion of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>15</strong>: There is a large offset between the edge of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>16</strong>: There is a large offset between the brightness of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>17</strong>: There is a large offset between the definition of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>18</strong>: There is a large offset between the stack of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>19</strong>: The data enhancement result based on GaussianBlur is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>20</strong>: The data enhancement result based on fliplr is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>21</strong>: The data enhancement result based on Crop is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>22</strong>: The data enhancement result based on flipud is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>23</strong>: The data enhancement result based on scale is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>24</strong>: The data enhancement result based on translate is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>25</strong>: The data enhancement result based on shear is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>26</strong>: The data enhancement result based on superpixels is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>27</strong>: The data enhancement result based on sharpen is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>28</strong>: The data enhancement result based on add is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>29</strong>: The data enhancement result based on invert is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>30</strong>: The data is predicted to be abnormal.</p>
</li></ul>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:shape</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>Object shape, which is a default attribute dedicated to the object detection label and is left empty by default. Options:</p>
<ul><li><p><strong>bndbox</strong>: rectangle</p>
</li><li><p><strong>polygon</strong>: polygon</p>
</li><li><p><strong>circle</strong>: circle</p>
</li><li><p><strong>line</strong>: straight line</p>
</li><li><p><strong>dashed</strong>: dotted line</p>
</li><li><p><strong>point</strong>: point</p>
</li><li><p><strong>polyline</strong>: polyline</p>
</li></ul>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:source</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>Speech source, which is a default attribute dedicated to the speech start/end point label and can be set to a speaker or narrator.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:start_index</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>Integer</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>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 <strong>start_index</strong>.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:start_time</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>Speech start time, which is a default attribute dedicated to the speech start/end point label, in the format of <strong>hh:mm:ss.SSS</strong>. (<strong>hh</strong> indicates hour; <strong>mm</strong> indicates minute; <strong>ss</strong> indicates second; and <strong>SSS</strong> indicates millisecond.)</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.1 "><p>@modelarts:to</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.5.2.5.1.3 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.5.2.5.1.4 "><p>ID of the tail entity in the triplet relationship label, which is a default attribute dedicated to the triplet relationship label.</p>
</td>
</tr>
</tbody>
</table>
</div>
<div class="tablenoborder"><a name="EN-US_TOPIC_0000001910007888__request_SampleMetadata"></a><a name="request_SampleMetadata"></a><table cellpadding="4" cellspacing="0" summary="" id="EN-US_TOPIC_0000001910007888__request_SampleMetadata" frame="border" border="1" rules="all"><caption><b>Table 6 </b>SampleMetadata</caption><thead align="left"><tr><th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.6.2.5.1.1"><p>Parameter</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.6.2.5.1.2"><p>Mandatory</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.4.6.2.5.1.3"><p>Type</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="40%" id="mcps1.3.4.6.2.5.1.4"><p>Description</p>
</th>
</tr>
</thead>
<tbody><tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.1 "><p>@modelarts:import_origin</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.3 "><p>Integer</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.6.2.5.1.4 "><p>Sample source, which is a built-in attribute.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.1 "><p>@modelarts:hard</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.3 "><p>Double</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.6.2.5.1.4 "><p>Whether the sample is labeled as a hard sample, which is a default attribute. Options:</p>
<ul><li><p><strong>0</strong>: non-hard sample</p>
</li><li><p><strong>1</strong>: hard sample</p>
</li></ul>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.1 "><p>@modelarts:hard_coefficient</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.3 "><p>Double</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.6.2.5.1.4 "><p>Coefficient of difficulty of each sample level, which is a default attribute. The value range is <strong>[0,1]</strong>.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.1 "><p>@modelarts:hard_reasons</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.3 "><p>Array of integers</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.6.2.5.1.4 "><p>ID of a hard sample reason, which is a default attribute. Options:</p>
<ul><li><p><strong>0</strong>: No object is identified.</p>
</li><li><p><strong>1</strong>: The confidence is low.</p>
</li><li><p><strong>2</strong>: The clustering result based on the training dataset is inconsistent with the prediction result.</p>
</li><li><p><strong>3</strong>: The prediction result is greatly different from the data of the same type in the training dataset.</p>
</li><li><p><strong>4</strong>: The prediction results of multiple consecutive similar images are inconsistent.</p>
</li><li><p><strong>5</strong>: There is a large offset between the image resolution and the feature distribution of the training dataset.</p>
</li><li><p><strong>6</strong>: There is a large offset between the aspect ratio of the image and the feature distribution of the training dataset.</p>
</li><li><p><strong>7</strong>: There is a large offset between the brightness of the image and the feature distribution of the training dataset.</p>
</li><li><p><strong>8</strong>: There is a large offset between the saturation of the image and the feature distribution of the training dataset.</p>
</li><li><p><strong>9</strong>: There is a large offset between the color richness of the image and the feature distribution of the training dataset.</p>
</li><li><p><strong>10</strong>: There is a large offset between the definition of the image and the feature distribution of the training dataset.</p>
</li><li><p><strong>11</strong>: There is a large offset between the number of frames of the image and the feature distribution of the training dataset.</p>
</li><li><p><strong>12</strong>: There is a large offset between the standard deviation of area of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>13</strong>: There is a large offset between the aspect ratio of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>14</strong>: There is a large offset between the area portion of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>15</strong>: There is a large offset between the edge of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>16</strong>: There is a large offset between the brightness of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>17</strong>: There is a large offset between the definition of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>18</strong>: There is a large offset between the stack of image frames and the feature distribution of the training dataset.</p>
</li><li><p><strong>19</strong>: The data enhancement result based on GaussianBlur is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>20</strong>: The data enhancement result based on fliplr is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>21</strong>: The data enhancement result based on Crop is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>22</strong>: The data enhancement result based on flipud is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>23</strong>: The data enhancement result based on scale is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>24</strong>: The data enhancement result based on translate is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>25</strong>: The data enhancement result based on shear is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>26</strong>: The data enhancement result based on superpixels is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>27</strong>: The data enhancement result based on sharpen is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>28</strong>: The data enhancement result based on add is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>29</strong>: The data enhancement result based on invert is inconsistent with the prediction result of the original image.</p>
</li><li><p><strong>30</strong>: The data is predicted to be abnormal.</p>
</li></ul>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.1 "><p>@modelarts:size</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.2 "><p>No</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.4.6.2.5.1.3 "><p>Array of objects</p>
</td>
<td class="cellrowborder" valign="top" width="40%" headers="mcps1.3.4.6.2.5.1.4 "><p>Image size (width, height, and depth of the image), which is a default attribute, with type of <strong>List&lt;Integer&gt;</strong>. 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 <strong>3</strong>). For example, <strong>[100,200,3]</strong> and <strong>[100,200]</strong> are both valid. Note: This parameter is mandatory only when the sample label list contains the object detection label.</p>
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section"><h4 class="sectiontitle">Response Parameters</h4><p><strong>Status code: 200</strong></p>
<div class="tablenoborder"><table cellpadding="4" cellspacing="0" summary="" id="EN-US_TOPIC_0000001910007888__response_UpdateSamplesResp" frame="border" border="1" rules="all"><caption><b>Table 7 </b>Response body parameters</caption><thead align="left"><tr><th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.5.3.2.4.1.1"><p>Parameter</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.5.3.2.4.1.2"><p>Type</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="60%" id="mcps1.3.5.3.2.4.1.3"><p>Description</p>
</th>
</tr>
</thead>
<tbody><tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.3.2.4.1.1 "><p>error_code</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.3.2.4.1.2 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="60%" headers="mcps1.3.5.3.2.4.1.3 "><p>Error code.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.3.2.4.1.1 "><p>error_msg</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.3.2.4.1.2 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="60%" headers="mcps1.3.5.3.2.4.1.3 "><p>Error message.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.3.2.4.1.1 "><p>results</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.3.2.4.1.2 "><p>Array of <a href="#EN-US_TOPIC_0000001910007888__response_BatchResponse">BatchResponse</a> objects</p>
</td>
<td class="cellrowborder" valign="top" width="60%" headers="mcps1.3.5.3.2.4.1.3 "><p>Response list for updating sample labels in batches.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.3.2.4.1.1 "><p>success</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.3.2.4.1.2 "><p>Boolean</p>
</td>
<td class="cellrowborder" valign="top" width="60%" headers="mcps1.3.5.3.2.4.1.3 "><p>Whether the operation is successful. Options:</p>
<ul><li><p><strong>true</strong>: successful</p>
</li><li><p><strong>false</strong>: failed</p>
</li></ul>
</td>
</tr>
</tbody>
</table>
</div>
<div class="tablenoborder"><a name="EN-US_TOPIC_0000001910007888__response_BatchResponse"></a><a name="response_BatchResponse"></a><table cellpadding="4" cellspacing="0" summary="" id="EN-US_TOPIC_0000001910007888__response_BatchResponse" frame="border" border="1" rules="all"><caption><b>Table 8 </b>BatchResponse</caption><thead align="left"><tr><th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.5.4.2.4.1.1"><p>Parameter</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="20%" id="mcps1.3.5.4.2.4.1.2"><p>Type</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="60%" id="mcps1.3.5.4.2.4.1.3"><p>Description</p>
</th>
</tr>
</thead>
<tbody><tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.4.2.4.1.1 "><p>error_code</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.4.2.4.1.2 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="60%" headers="mcps1.3.5.4.2.4.1.3 "><p>Error code.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.4.2.4.1.1 "><p>error_msg</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.4.2.4.1.2 "><p>String</p>
</td>
<td class="cellrowborder" valign="top" width="60%" headers="mcps1.3.5.4.2.4.1.3 "><p>Error message.</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.4.2.4.1.1 "><p>success</p>
</td>
<td class="cellrowborder" valign="top" width="20%" headers="mcps1.3.5.4.2.4.1.2 "><p>Boolean</p>
</td>
<td class="cellrowborder" valign="top" width="60%" headers="mcps1.3.5.4.2.4.1.3 "><p>Check whether the operation is successful. Options:</p>
<ul><li><p><strong>true</strong>: The operation is successful.</p>
</li><li><p><strong>false</strong>: The operation is failed.</p>
</li></ul>
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section"><h4 class="sectiontitle">Example Requests</h4><p>Updating Labels of Team Labeling Samples in Batches</p>
<pre class="screen">{
"samples" : [ {
"sample_id" : "0a0939d6d3c48a3d2a2619245943ac21",
"worker_id" : "8c15ad080d3eabad14037b4eb00d6a6f",
"labels" : [ {
"name" : "tulips"
} ]
}, {
"sample_id" : "0e1b5a16a5a577ee53aeb34278a4b3e7",
"worker_id" : "8c15ad080d3eabad14037b4eb00d6a6f",
"labels" : [ {
"name" : "tulips"
} ]
} ]
}</pre>
</div>
<div class="section"><h4 class="sectiontitle">Example Responses</h4><p><strong>Status code: 200</strong></p>
<p>OK</p>
<pre class="screen">{
"success" : true
}</pre>
</div>
<div class="section"><h4 class="sectiontitle">Status Codes</h4>
<div class="tablenoborder"><table cellpadding="4" cellspacing="0" summary="" id="EN-US_TOPIC_0000001910007888__status_code" frame="border" border="1" rules="all"><thead align="left"><tr><th align="left" class="cellrowborder" valign="top" width="15%" id="mcps1.3.8.2.1.3.1.1"><p>Status Code</p>
</th>
<th align="left" class="cellrowborder" valign="top" width="85%" id="mcps1.3.8.2.1.3.1.2"><p>Description</p>
</th>
</tr>
</thead>
<tbody><tr><td class="cellrowborder" valign="top" width="15%" headers="mcps1.3.8.2.1.3.1.1 "><p>200</p>
</td>
<td class="cellrowborder" valign="top" width="85%" headers="mcps1.3.8.2.1.3.1.2 "><p>OK</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="15%" headers="mcps1.3.8.2.1.3.1.1 "><p>401</p>
</td>
<td class="cellrowborder" valign="top" width="85%" headers="mcps1.3.8.2.1.3.1.2 "><p>Unauthorized</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="15%" headers="mcps1.3.8.2.1.3.1.1 "><p>403</p>
</td>
<td class="cellrowborder" valign="top" width="85%" headers="mcps1.3.8.2.1.3.1.2 "><p>Forbidden</p>
</td>
</tr>
<tr><td class="cellrowborder" valign="top" width="15%" headers="mcps1.3.8.2.1.3.1.1 "><p>404</p>
</td>
<td class="cellrowborder" valign="top" width="85%" headers="mcps1.3.8.2.1.3.1.2 "><p>Not Found</p>
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section"><h4 class="sectiontitle">Error Codes</h4><p>See <a href="modelarts_03_0095.html">Error Codes</a>.</p>
</div>
</div>
<div>
<div class="familylinks">
<div class="parentlink"><strong>Parent topic:</strong> <a href="dataset_management.html">Data Management</a></div>
</div>
</div>
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