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
On this page

Auto Labeling

Updated on 2023-09-06 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 selected labels and images and an existing model is selected to quickly label the remaining images.

Background

  • Only datasets of image classification and object detection types support the auto labeling function.
  • To enable Auto Labeling, add at least two types of labels to the dataset and add each type of the label to at least 5 objects.
  • At least one unlabeled image must exist when you enable Auto Labeling.
  • Before enabling Auto Labeling, ensure that no auto labeling task is in progress in the system.
  • Check the image data used for labeling and ensure that no RGBA four-channel image exists in the image data. If four-channel images exist, the auto labeling task will fail. Therefore, delete the four-channel images from the dataset and then start the auto labeling task.

Auto Labeling

  1. Log in to the ModelArts management console. In the left navigation pane, choose Data Management > Datasets. The Datasets page is displayed.
  2. In the dataset list, select a dataset of the object detection or image classification type and click Auto Labeling in the Operation column to start an intelligent labeling job.
  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, you need to 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.

    Table 2 Pre-labeling

    Parameter

    Description

    Auto Labeling Type

    Pre-labeling: Select an AI application created on the AI Applications page. 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: You can select a model based on site requirements. Click the drop-down arrow on the left of the target AI application and select a proper version. For details about how to create an AI application, see Creating an AI Application.

    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 datasets of the object detection type, only rectangular boxes can be recognized and labeled when Active Learning is selected.
    • If there are too many auto labeling jobs in the system, the jobs may be queued. As a result, the jobs are always in the labeling state. The system will complete labeling jobs in sequence.
    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 dataset list, click a dataset name to go to the Dashboard page.
  6. On the Dashboard page of the dataset, click Label in the upper right corner. The dataset details page is displayed.
  7. On the dataset details page, click the To be Confirmed tab to view the auto labeling progress.
    You can also enable auto labeling or view the auto labeling history on this tab page.
    Figure 4 Labeling progress
  8. After auto labeling is complete, all the labeled images are displayed on the To Be Confirmed page.
    • Datasets of the image classification type

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

      You can manually modify the labels of the images marked as hard examples based on site requirements. For details, see For datasets of the image classification type.

    • Datasets of the object detection type

      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 manually modify the labels or target bounding boxes of the images marked as hard examples based on site requirements. For details, see For datasets of the object detection type.

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