Training a model uses a large number of labeled images. Therefore, label images before the model training. You can add labels to images by manual labeling or auto labeling. In addition, you can modify the labels of images, or remove their labels and label the images again.
Before labeling an image in image classification scenarios, pay attention to the following:
ModelArts automatically synchronizes data and labeling information from datasets to the labeling job.
To quickly obtain the latest data in a dataset, in the All statuses, Unlabeled, or Labeled tab of the labeling job details page, click Synchronize New Data.
In the All statuses, Unlabeled, or tab, click in the filter criteria area and add filter criteria to quickly filter the data you want to view.
The following filter criteria are available. You can set one or more filter criteria.
The labeling job details page displays the All statuses, Unlabeled, and Labeled tabs. The Unlabeled tab is displayed by default. Click an image to preview it. For the images that have been labeled, the label information is displayed at the bottom of the preview page.
Click the Label text box and select an existing label from the drop-down list. If the existing labels cannot meet the requirements, input a label in the text box.
For details about how to label data, see Labeling Description on the dataset details page.
On the labeling job details page, click the Labeled tab to view the list of labeled images. By default, the corresponding labels are displayed under the image thumbnails. You can also select an image and view the label information of the image in the Labels of Selected Images area on the right.
On the labeling job details page, click the Labeled tab, and select one or more images to be modified from the image list. Modify the image information in the label information area on the right.
Modifying a label: In the Labels of Selected Images area, click the edit icon in the Operation column, enter the correct label name in the text box, and click the check mark to complete the modification.
Deleting a label: In the Labels of Selected Images area, click the delete icon in the Operation column to delete the label. This operation deletes only the labels added to the selected image.
In addition to the data automatically synchronized from datasets, you can directly add images to labeling jobs for labeling. The added data is first imported to the dataset associated with the labeling job. Then, the labeling job automatically synchronizes the latest data from the dataset.
The images you have added will be automatically displayed in the image list in the All statuses tab. You can choose Add data > View historical records to view task history.
You can quickly delete the images you want to discard.
In the All statuses, Unlabeled, or Labeled tab, select the images to be deleted or click Select Images on Current Page, and click Delete. In the displayed dialog box, select or deselect Delete the source files from OBS as required. After confirmation, click Yes to delete the images.
If a tick is displayed in the upper left corner of an image, the image is selected. If no image is selected on the page, the Delete button is unavailable.
If you select Delete the source files from OBS, images stored in the OBS directory will be deleted accordingly. This operation may affect other dataset versions or datasets using those files, for example, leading to an error in page display, training, or inference. Deleted data cannot be recovered. Exercise caution when performing this operation.
If team labeling is enabled for a labeling job, view its labeling details in the Annotator Management tab. Additionally, you can add, modify, or delete annotators.
Click Add Member, select a member name, and click OK.
Click Send Email in the Operation column to send the labeling job to the annotator by email.
Click Modify in the Operation column to modify the role of the annotator.
Click Delete in the Operation column to delete the annotator.