Compute
Elastic Cloud Server
Huawei Cloud Flexus
Bare Metal Server
Auto Scaling
Image Management Service
Dedicated Host
FunctionGraph
Cloud Phone Host
Huawei Cloud EulerOS
Networking
Virtual Private Cloud
Elastic IP
Elastic Load Balance
NAT Gateway
Direct Connect
Virtual Private Network
VPC Endpoint
Cloud Connect
Enterprise Router
Enterprise Switch
Global Accelerator
Management & Governance
Cloud Eye
Identity and Access Management
Cloud Trace Service
Resource Formation Service
Tag Management Service
Log Tank Service
Config
OneAccess
Resource Access Manager
Simple Message Notification
Application Performance Management
Application Operations Management
Organizations
Optimization Advisor
IAM Identity Center
Cloud Operations Center
Resource Governance Center
Migration
Server Migration Service
Object Storage Migration Service
Cloud Data Migration
Migration Center
Cloud Ecosystem
KooGallery
Partner Center
User Support
My Account
Billing Center
Cost Center
Resource Center
Enterprise Management
Service Tickets
HUAWEI CLOUD (International) FAQs
ICP Filing
Support Plans
My Credentials
Customer Operation Capabilities
Partner Support Plans
Professional Services
Analytics
MapReduce Service
Data Lake Insight
CloudTable Service
Cloud Search Service
Data Lake Visualization
Data Ingestion Service
GaussDB(DWS)
DataArts Studio
Data Lake Factory
DataArts Lake Formation
IoT
IoT Device Access
Others
Product Pricing Details
System Permissions
Console Quick Start
Common FAQs
Instructions for Associating with a HUAWEI CLOUD Partner
Message Center
Security & Compliance
Security Technologies and Applications
Web Application Firewall
Host Security Service
Cloud Firewall
SecMaster
Anti-DDoS Service
Data Encryption Workshop
Database Security Service
Cloud Bastion Host
Data Security Center
Cloud Certificate Manager
Edge Security
Situation Awareness
Managed Threat Detection
Blockchain
Blockchain Service
Web3 Node Engine Service
Media Services
Media Processing Center
Video On Demand
Live
SparkRTC
MetaStudio
Storage
Object Storage Service
Elastic Volume Service
Cloud Backup and Recovery
Storage Disaster Recovery Service
Scalable File Service Turbo
Scalable File Service
Volume Backup Service
Cloud Server Backup Service
Data Express Service
Dedicated Distributed Storage Service
Containers
Cloud Container Engine
Software Repository for Container
Application Service Mesh
Ubiquitous Cloud Native Service
Cloud Container Instance
Databases
Relational Database Service
Document Database Service
Data Admin Service
Data Replication Service
GeminiDB
GaussDB
Distributed Database Middleware
Database and Application Migration UGO
TaurusDB
Middleware
Distributed Cache Service
API Gateway
Distributed Message Service for Kafka
Distributed Message Service for RabbitMQ
Distributed Message Service for RocketMQ
Cloud Service Engine
Multi-Site High Availability Service
EventGrid
Dedicated Cloud
Dedicated Computing Cluster
Business Applications
Workspace
ROMA Connect
Message & SMS
Domain Name Service
Edge Data Center Management
Meeting
AI
Face Recognition Service
Graph Engine Service
Content Moderation
Image Recognition
Optical Character Recognition
ModelArts
ImageSearch
Conversational Bot Service
Speech Interaction Service
Huawei HiLens
Video Intelligent Analysis Service
Developer Tools
SDK Developer Guide
API Request Signing Guide
Terraform
Koo Command Line Interface
Content Delivery & Edge Computing
Content Delivery Network
Intelligent EdgeFabric
CloudPond
Intelligent EdgeCloud
Solutions
SAP Cloud
High Performance Computing
Developer Services
ServiceStage
CodeArts
CodeArts PerfTest
CodeArts Req
CodeArts Pipeline
CodeArts Build
CodeArts Deploy
CodeArts Artifact
CodeArts TestPlan
CodeArts Check
CodeArts Repo
Cloud Application Engine
MacroVerse aPaaS
KooMessage
KooPhone
KooDrive

Creating an Auto Labeling Job

Updated on 2025-01-06 GMT+08:00

In addition to manual labeling, ModelArts also provides the auto labeling function to quickly label data, reducing the labeling time by more than 70%. Auto labeling means learning and training are performed based on the labeled images and an existing model is used to quickly label the remaining images.

Context

  • Only labeling jobs of image classification and object detection types support auto labeling.
  • There are at least two types of labels in the labeling job for auto labeling, and each label has been added to at least five images.
  • At least one unlabeled image must exist when you enable auto labeling.
  • Before starting an auto labeling job, ensure that no auto labeling job is in progress.
  • Before starting an auto labeling job, ensure that the image data does not contain any RGBA four-channel images. These images will cause the job to fail. Delete them from the dataset if you find any.

Starting an Auto Labeling Job

  1. Log in to the ModelArts management console. In the navigation pane on the left, choose Data Preparation > Label Data. The Data Labeling page is displayed.
    NOTE:

    Data labeling is supported only in the following regions: CN North-Beijing4, CN North-Beijing1, CN East-Shanghai1, CN South-Guangzhou, CN Southwest-Guiyang1, CN-Hong Kong, AP-Singapore, AP-Bangkok, AP-Jakarta, LA-Santiago, LA-Sao Paulo1, and LA-Mexico City2.

  2. In the labeling job list, locate the row containing a labeling job of the object detection or image classification type and click Auto Labeling in the Operation column.
  3. On the Enable Auto Labeling page, select Active learning or Pre-labeling. For details, see Table 1 and Table 2.
    Table 1 Active learning

    Parameter

    Description

    Auto Labeling Type

    Active learning: The system uses semi-supervised learning and hard example filtering to perform auto labeling, reducing manual labeling workload and helping you find hard examples.

    Algorithm Type

    For a dataset of the image classification type, set the following parameters:

    Fast: Use the labeled samples for training.

    Precise: Use labeled and unlabeled samples for semi-supervised training, which improves the model precision.

    Specifications

    Resource specifications used by an auto labeling job.

    NOTE:

    Creating an auto labeling job is free, but you will be billed for OBS storage based on usage. For details, see Product Pricing Details. To avoid wasting resources, clear your OBS bucket after labeling jobs and their subsequent tasks are complete.

    Compute Nodes

    The default value is 1, indicating the single-node system mode. Only this parameter value is supported.

    Table 2 Pre-labeling

    Parameter

    Description

    Auto Labeling Type

    Pre-labeling: Select a model in the My AI Applications tab. Ensure that the model type matches the dataset labeling type. After the pre-labeling is complete, if the labeling result complies with the standard labeling format defined by the platform, the system filters hard examples. This step does not affect the pre-labeling result.

    Model and Version

    • My AI Applications: Select a model as required. Click the drop-down arrow on the left of the target AI application and select a proper version. For details about how to import a model, see Creating a Model

    Specifications

    In the drop-down list, you can select the node specifications supported by ModelArts.

    Compute Nodes

    The default value is 1. You can select a value based on site requirements. The maximum value is 5.

    NOTE:

    For labeling jobs of the object detection type, only rectangular boxes can be recognized and labeled when Active learning is selected.

    Figure 1 Enabling auto labeling (image classification)
    Figure 2 Enabling auto labeling (object detection)
    Figure 3 Enabling auto labeling (pre-labeling)
  4. After setting the parameters, click Submit to enable auto labeling.
  5. In the labeling job list, click a labeling job name to go to the labeling job details page.
  6. Click the To Be Confirmed tab to view the auto labeling progress.

    You can also enable auto labeling or view the auto labeling history in this tab.

    Figure 4 Labeling progress
    NOTE:

    If there are too many auto labeling jobs, they may have to wait in a queue due to limited free resources. This means that they will stay in the labeling state until their turn comes. To ensure that your labeling job can run properly, you are advised to avoid peak hours.

  7. After auto labeling is complete, all the labeled images are displayed on the To Be Confirmed page.
    • Image classification labeling job

      On the To Be Confirmed page, check whether labels are correct, select the correctly labeled images, and click OK to confirm the auto labeling results. The confirmed image will be categorized to the Labeled page.

      You can modify the labels of the images that are marked as hard examples according to your needs. For details, see For labeling jobs of the image classification type.

    • Object detection labeling job

      On the To Be Confirmed page, click images to view their labeling details and check whether labels and target bounding boxes are correct. For the correctly labeled images, click Labeled to confirm the auto labeling results. The confirmed image will be categorized to the Labeled page.

      You can modify the labels or target bounding boxes of the images marked as hard examples according to your needs. For details, see For labeling jobs of the object detection type.

FAQs

  • What can I do if auto labeling fails?

    Auto labeling is free of charge. If there are too many auto labeling jobs, they may have to wait in a queue due to limited free resources. Create an auto labeling job again or avoid peak hours.

  • What can I do if auto labeling takes a long time?

    Auto labeling is free of charge. If there are too many auto labeling jobs, they may have to wait in a queue due to limited free resources. You are advised to avoid peak hours.

We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out more

Feedback

Feedback

Feedback

0/500

Selected Content

Submit selected content with the feedback