Data Annotation

Features Overview

Data annotation module provides:

• Multiple Annotation Types: Image, text, audio, etc.
• Annotation Templates: Rich annotation templates and configurations
• Quality Control: Annotation review and consistency checks
• Team Collaboration: Multi-person collaborative annotation
• Annotation Export: Export annotation results

Annotation Types

Image Annotation

Text Annotation

Quick Start

1. Deploy Label Studio

make install-label-studio

Access: http://localhost:30001

Default credentials:

• Username: admin@demo.com
• Password: demoadmin

2. Create Annotation Task

Step 1: Enter Data Annotation Page

Select Data Annotation in the left navigation.

Step 2: Create Task

Click Create Task.

Step 3: Configure Basic Information

• Task name: e.g., image_classification_task
• Source dataset: Select dataset to annotate
• Annotation type: Select type

Step 4: Configure Annotation Template

Image Classification Template:

<View>
  <Image name="image" value="$image"/>
  <Choices name="choice" toName="image">
    <Choice value="cat"/>
    <Choice value="dog"/>
    <Choice value="bird"/>
  </Choices>
</View>

Step 5: Configure Annotation Rules

• Annotation method: Single label / Multi label
• Minimum annotations: Per sample (for consistency)
• Review mechanism: Enable/disable review

3. Start Annotation

1. Enter annotation interface
2. View sample to annotate
3. Perform annotation
4. Click Submit
5. Auto-load next sample

Advanced Features

Quality Control

Annotation Consistency

Check consistency between annotators:

• Cohen’s Kappa: Evaluate consistency
• Majority vote: Use majority annotation results
• Expert review: Expert reviews disputed annotations

Pre-annotation

Use models for pre-annotation:

1. Train or use existing model
2. Pre-annotate dataset
3. Annotators correct pre-annotations

Best Practices

1. Annotation Guidelines

Create clear guidelines:

• Define standards: Clear annotation standards
• Provide examples: Positive and negative examples
• Edge cases: Handle edge cases
• Train annotators: Ensure understanding

Common Questions

Q: Poor annotation quality?

A: Improve:

1. Refine guidelines
2. Strengthen training
3. Increase reviews
4. Use pre-annotation

Related Documentation

• Data Management - Manage data to annotate
• Data Evaluation - Evaluate annotation quality