Managing Team Labeling Tasks

For datasets with team labeling enabled, you can create team labeling tasks and assign the labeling tasks to different teams so that team members can complete the labeling tasks together. During data labeling, members can initiate acceptance, continue acceptance, and view acceptance reports.

Creating Team Labeling Tasks

If you enable team labeling when creating a dataset and assign a team to label the dataset, the system creates a labeling task based on the team by default. After the dataset is created, you can view the labeling task on the Labeling Progress tab page of the dataset.

You can also create a team marking task and assign it to different members in the same team or to other labeling teams.

  1. Log in to the ModelArts management console. In the left navigation pane, choose Data Management > Datasets. A dataset list is displayed.
  2. In the dataset list, select a dataset that supports team labeling, and click the dataset name to go to the Dashboard tab page of the dataset.
  3. Click the Labeling Progress tab to view existing labeling tasks of the dataset. Click Create Team Labeling Task in the upper right corner to create a task.
  4. In the displayed Create Team Labeling Task dialog box, set related parameters and click OK.
    • Name: Enter a task name.
    • Type: Select a task type, Team or Task Manager.
    • Select Team: If Type is set to Team, you need to select a team and members for labeling. The Select Team drop-down list lists the labeling teams and members created by the current account. For details about team management, see Introduction to Team Labeling.
    • Select Task Manager: If Type is set to Task Manager, you need to select one Team Manager member from all teams as the task manager.
    • Label Set: All existing labels and label attributes of the dataset are displayed. You can also select Automatically synchronize new images to the team labeling task or Automatically load the intelligent labeling results to images that need to be labeled under Label Set.

      The process of loading auto labeling results to a team labeling task is as follows:

      • If you set Type to Team, you are required to create a team labeling task before executing the task.
      • If you set Type to Task Manager, you are required to log in to the data labeling console and assign a labeling task before executing the task.

      After the task is created, you can view the new task on the Labeling Progress tab page.

Labeling (Team Member)

After a labeling task is created, the team member to which the task is assigned receives a labeling notification email.

In the email details, click the labeling task link and use your email address and initial password to log in to the labeling platform. After login, change the password. After logging in to the labeling platform, you can view the assigned labeling task and click the task name to go to the labeling page. The labeling method varies depending on the dataset type. For details, see the following:

On the labeling platform, each member can view the images that are not labeled, to be corrected, rejected, to be reviewed, approved, and accepted. Pay attention to the images rejected by the administrator and the images to be corrected.

If the Reviewer role is assigned for a team labeling task, the labeling result needs to be reviewed. After the labeling result is reviewed, it is submitted to the administrator for acceptance.

Task Acceptance (Administrator)

Viewing an Acceptance Report

You can view the acceptance report of an ongoing or finished labeling task. On the Labeling Progress tab page, click Acceptance Report. In the displayed Acceptance Report dialog box, view report details.

Deleting a Labeling Task

On the Labeling Progress tab page, click Delete in the row where a labeling task to be deleted. After a task is deleted, the labeling details that are not accepted will be lost. Exercise caution when performing this operation. However, the original data in the dataset and the labeled data that has been accepted are still stored in the corresponding OBS bucket.