Complete Label Studio Tutorial: Data Labeling for Machine Learning

Label Studio is an open-source data labeling platform for machine learning. It supports labeling various data types including text, images, audio, video, and time series, making it essential for building high-quality ML training datasets.

Why Label Studio?

• Multi-modal: Label text, images, audio, video, HTML
• Flexible: Customizable labeling interfaces
• Collaborative: Team-based annotation workflows
• Integrations: ML backends, cloud storage, webhooks
• Open source: Self-hosted with enterprise options

• NLP annotation (NER, sentiment, classification)
• Computer vision labeling (bounding boxes, segmentation)
• Audio transcription and classification
• Multi-modal data annotation
• Active learning workflows

Installation

# Install with pip
pip install label-studio

Start Label Studio
label-studio start

Or with Docker
docker run -it -p 8080:8080 \
    -v $(pwd)/mydata:/label-studio/data \
    heartexlabs/label-studio:latest

Access at http://localhost:8080

Quick Start

1. Create Project

from labelstudiosdk import Client

Connect to Label Studio
ls = Client(url='http://localhost:8080', apikey='your-api-key')


Create project
project = ls.startproject(
    title='Sentiment Analysis',
    labelconfig='''

    
      
      
        
        
        
      
    
    '''
)

print(f"Project created: {project.id}")

# Import from list
tasks = [
    {"text": "I love this product!"},
    {"text": "This is terrible."},
    {"text": "It's okay, nothing special."}
]

project.importtasks(tasks)

Import from file
project.importtasks('data.json')


Import from URL
project.importtasks([
    {"image": "https://example.com/image1.jpg"},
    {"image": "https://example.com/image2.jpg"}
])

3. Export Annotations

# Export all annotations
annotations = project.exporttasks()


Export in specific format
annotations = project.exporttasks(exporttype='JSON')

annotations = project.exporttasks(exporttype='CSV')

annotations = project.exporttasks(exporttype='COCO')

annotations = project.exporttasks(exporttype='YOLO')


Save to file
import json
with open('annotations.json', 'w') as f:
    json.dump(annotations, f)