Label Studio is an open-source data labeling tool that helps annotate various data types such as images, text, audio, and video. It provides a flexible interface for creating and managing labeling projects to improve machine learning dataset quality.
# No code needed here, but Label Studio is installed and run as:
# pip install label-studio
# label-studio start
Label Studio is used in computer vision, NLP, audio processing, and video annotation projects. It accelerates dataset creation, supports collaboration, and integrates with ML pipelines to streamline training and evaluation.
# Example: Launch Label Studio server for team collaboration
# label-studio start --host 0.0.0.0 --port 8080
Label Studio supports annotating images, text documents, audio files, and videos, making it versatile for many AI workflows involving different modalities.
# Example data formats:
# images: JPG, PNG
# text: TXT, JSON
# audio: WAV, MP3
# video: MP4, AVI
The UI features project dashboards, labeling interfaces with customizable tools, task management panels, and export options. Users can create, assign, and review labeling tasks efficiently.
# UI is web-based and accessible via browser at localhost:8080 by default
Label Studio requires Python 3.6+ and sufficient system resources like CPU and RAM. Docker installation requires a compatible environment supporting containerization.
# Check Python version
!python --version
Install via pip using pip install label-studio or run via Docker container for isolated environments.
# Pip install command
!pip install label-studio
# Docker run command (pull and start Label Studio)
# docker run -it -p 8080:8080 heartexlabs/label-studio:latest
Start Label Studio server and access it via browser at http://localhost:8080 to begin creating annotation projects.
# Launch Label Studio
!label-studio start
# Access in browser:
# http://localhost:8080
Configure Label Studio via CLI options or config files to set storage paths, authentication, and project defaults.
# Example: Start Label Studio with custom port and data directory
!label-studio start --port 9090 --data-dir /path/to/data
Create a new project in Label Studio by specifying a project name and selecting the data labeling type, such as image classification or text annotation.
# From UI: Click "Create Project", enter name and description
# Or using API (Python example):
from label_studio_sdk import Client
client = Client(url="http://localhost:8080", api_key="YOUR_API_KEY")
project = client.start_project(
title="My First Annotation Project",
label_config="""
"""
)
print(f"Project created with id: {project.id}")
Import data files (images, text, audio, etc.) into the project via UI upload or API to prepare annotation tasks.
# Upload tasks via Python API
tasks = [{"data": {"text": "This is a positive example."}}, {"data": {"text": "This is negative."}}]
project.create_tasks(tasks)
Select or customize labeling templates to fit your annotation needs, defining how the data is presented and labeled.
# Label config XML defines UI elements and labels (see previous example)
# You can customize template via the Label Studio UI or XML config files.
Assign annotation tasks to team members and manage permissions to ensure efficient project collaboration and quality control.
# Assign users and tasks via Label Studio UI or use API to add collaborators
# Example API call (conceptual):
# project.add_user(user_id=123, role="annotator")
Label Studio supports labeling various data types including images, text, audio, and video. Each type has specialized tools optimized for accurate annotation, allowing versatile dataset creation.
# Image, Text, Audio, Video can be loaded for annotation
# No code needed here—handled via Label Studio UI and config
Annotate images with bounding boxes for objects, polygons for precise shapes, and keypoints for landmarks. These tools help capture spatial details in computer vision tasks.
# Label config example snippet for bounding box """""" # Define polygons similarly with tag
Use Label Studio’s transcription tools to convert audio to text and classify text snippets with customizable categories to support NLP projects.
# Text classification example label config """"""
Learn and use keyboard shortcuts to speed up annotation tasks. For example, ‘Ctrl + S’ saves annotations, and number keys select labels quickly, improving productivity.
# Common shortcuts (in Label Studio UI):
# - Ctrl + S : Save annotation
# - Number keys: Select label options
# - Spacebar: Play/Pause audio or video
Project settings allow customization of labeling parameters, data storage, and interface options to suit specific project requirements. Permissions control who can view, annotate, or manage projects, ensuring data security and appropriate access. Proper configuration streamlines workflows and maintains control over sensitive data within the team environment.
# Example: Change project settings via API (conceptual)
# project.set_settings({'label_config': '... '})
Label Studio supports various user roles such as Admin, Annotator, and Reviewer. Defining roles enables efficient collaboration by restricting or granting access to certain features. This structure promotes organized teamwork, accountability, and smooth project progression with clear responsibilities assigned to each user.
# Assign role to user example (conceptual)
# project.add_user(user_id=101, role='annotator')
Annotations can be reviewed by designated users to ensure quality and consistency. The review process includes comparing annotations, providing feedback, and approving final labels before export. This step is critical to maintaining high-quality datasets for reliable machine learning model training.
# Review workflow (conceptual)
# reviewer views tasks, accepts or requests changes
# project.mark_task_as_reviewed(task_id)
After annotations are completed and approved, data can be exported in multiple formats such as JSON, COCO, or CSV. Export options are customizable to fit downstream ML pipelines, enabling seamless integration of labeled datasets into training and evaluation workflows.
# Export labeled data via API
exported = project.export()
with open('labeled_data.json', 'w') as f:
f.write(exported)
Label Studio allows users to customize the labeling interface using XML or JSON configurations. These configurations define the tools, labels, and layout, enabling tailored workflows for different data types and annotation tasks. This flexibility ensures users have exactly the controls they need for precise and efficient labeling.
# Example of XML label config for image classification """"""
Complex workflows allow multiple annotation stages, such as initial labeling, review, and approval. Workflows can be customized to fit project needs, enforcing rules like sequential task assignments or conditional labels. This helps improve annotation quality and supports collaboration in larger teams.
# Conceptual example: Workflow steps in Label Studio project settings
# steps = ['annotation', 'review', 'approval']
# project.set_workflow(steps)
Pre-annotation leverages machine learning models to automatically generate initial labels, which annotators then review and correct. This model-assisted approach accelerates labeling, reduces manual work, and improves consistency by providing a smart starting point for human annotators.
# Example: Integrate model predictions for pre-annotation via API
# predictions = model.predict(data)
# project.create_tasks([{'data': d, 'predictions': p} for d, p in zip(data, predictions)])
Label Studio’s ML Backend allows seamless integration of machine learning models to provide predictions during annotation. This backend connects your models to Label Studio, enabling real-time model inference to assist annotators and improve labeling efficiency.
# Example: Start ML backend server and connect to Label Studio
# python label_studio_ml/server.py --root-dir ./my_model_backend
Configure Label Studio to use model predictions for pre-annotation, enabling active learning where the model improves iteratively based on human corrections. This reduces annotation effort and improves model accuracy over time.
# Enable active learning in project settings or via API
# project.enable_active_learning(True)
Use annotated datasets exported from Label Studio to train or fine-tune machine learning models. The integration facilitates a smooth workflow from data labeling to model development and evaluation within the same ecosystem.
# Load exported data and train a model (example with scikit-learn)
# from sklearn.model_selection import train_test_split
# X_train, X_test, y_train, y_test = train_test_split(data, labels)
# model.fit(X_train, y_train)
Automate repetitive annotation tasks by leveraging AI assistance, where models provide initial labels that humans can verify or adjust. This hybrid approach speeds up labeling while maintaining quality and consistency.
# Automated annotation loop (conceptual)
# predictions = model.predict(new_data)
# tasks = [{'data': d, 'predictions': p} for d, p in zip(new_data, predictions)]
# project.create_tasks(tasks)
Label Studio supports exporting labeled data in various popular formats such as CSV for tabular data, JSON for structured annotations, COCO for object detection, and VOC for image segmentation. These formats allow easy integration with different machine learning frameworks and tools.
# Export labeled data to JSON format via API
exported_json = project.export()
with open('annotations.json', 'w') as f:
f.write(exported_json)
Integrate Label Studio exports directly into machine learning pipelines by automating data ingestion. This connection helps streamline workflows from annotation to training, enabling continuous model improvement with fresh labeled data.
# Example: Load exported data into training pipeline
# data = load_annotations('annotations.json')
# train_model(data)
Label Studio’s RESTful APIs enable automation of tasks like creating projects, uploading data, fetching annotations, and managing users, making it ideal for scalable and automated labeling workflows.
# Example: Use Python requests to fetch project info
import requests
response = requests.get('http://localhost:8080/api/projects', headers={'Authorization': 'Token YOUR_API_KEY'})
projects = response.json()
print(projects)
Label Studio can sync data with cloud storage platforms like AWS S3, Google Cloud Storage, or Azure Blob. This ensures data persistence, easy sharing, and centralized storage accessible across teams and systems.
# Example: Upload labeled data file to AWS S3
import boto3
s3 = boto3.client('s3')
with open('annotations.json', 'rb') as f:
s3.upload_fileobj(f, 'my-bucket', 'annotations.json')
Managing large datasets in Label Studio requires optimizing storage and task loading times. Techniques include chunking data into smaller batches, using efficient file formats, and leveraging database indexing to ensure smooth annotation and faster data access even at scale.
# Conceptual example: Split large dataset into smaller chunks for upload
def chunk_data(data, chunk_size=1000):
for i in range(0, len(data), chunk_size):
yield data[i:i + chunk_size]
for chunk in chunk_data(large_dataset):
project.create_tasks(chunk)
Effective team collaboration in Label Studio involves assigning roles, monitoring task progress, and using shared project dashboards. Clear communication channels and regular reviews improve annotation quality and project efficiency when multiple users work simultaneously.
# Example: List project users and roles via API
users = project.get_users()
for user in users:
print(f"User: {user['username']}, Role: {user['role']}")
Label Studio can be deployed on cloud services (AWS, GCP, Azure) or on-premise servers. Cloud deployments enable scalability and remote access, while on-premise offers data privacy and control. Proper resource allocation and container orchestration (Kubernetes, Docker) ensure reliable operation.
# Example: Running Label Studio using Docker Compose for scalable deployment
# docker-compose.yml snippet:
# version: '3'
# services:
# label-studio:
# image: heartexlabs/label-studio:latest
# ports:
# - "8080:8080"
# volumes:
# - ./data:/data
Label Studio supports extending functionality through custom plugins and widgets. Developers can build interactive UI components tailored for specific annotation needs, enhancing user experience and productivity by integrating domain-specific tools into the labeling interface.
# Example: Plugin scaffold (conceptual)
# Create a React widget, register in Label Studio config
# import React from 'react';
# export default function CustomWidget(props) { return <div>Custom Widget</div>; }
Custom backend integrations allow Label Studio to connect with external services such as proprietary ML models, databases, or authentication systems. This flexibility enables organizations to embed Label Studio within existing infrastructures and automate complex workflows.
# Example: Build ML backend server (Python Flask)
# from flask import Flask, request, jsonify
# app = Flask(__name__)
# @app.route('/predict', methods=['POST'])
# def predict():
# data = request.json
# # Run model inference here
# return jsonify({'result': 'prediction'})
# app.run(host='0.0.0.0', port=9090)
Label Studio is open source, welcoming contributions ranging from bug fixes to new features. Developers can participate via GitHub by submitting pull requests, reporting issues, and engaging with the community to help improve the platform continuously.
# Contribution steps:
# 1. Fork the repo on GitHub
# 2. Create a feature branch
# 3. Implement your changes
# 4. Submit a pull request for review
Label Studio lets you create custom labeling templates using XML or JSON. These templates define the UI components and labeling options, allowing you to tailor the interface to specific data types and annotation requirements, improving accuracy and user experience.
# XML example for a custom dropdown label config """"""
Use advanced input controls like dropdown menus, sliders, and color pickers to enhance annotation precision. These controls allow annotators to select from predefined values, adjust numeric ranges, or pick colors, enabling richer, more nuanced data labeling.
# Slider example in XML config """"""
Conditional logic enables dynamic interfaces where label options or input fields appear based on previous selections. This simplifies complex annotation tasks by showing only relevant controls, reducing annotator errors and speeding up the labeling process.
# Conditional example (conceptual) ## # Show vehicle-specific labels only if "Vehicle" is selected
Multi-task and nested labeling allow annotators to label multiple aspects of data simultaneously or create hierarchical labels. This supports complex datasets requiring detailed annotations, such as nested entities in text or multiple objects in images.
# Example nested labeling config (conceptual) ## # # # # #
Pre-annotation uses machine learning models to generate initial labels automatically before human review. This speeds up the annotation process by reducing manual effort and providing a baseline that annotators can verify or correct, improving overall productivity and consistency.
# Example: Use ML model to pre-label images before manual annotation
# predictions = model.predict(images)
# tasks = [{'data': img, 'predictions': pred} for img, pred in zip(images, predictions)]
# project.create_tasks(tasks)
Label Studio integrates with ML backends to provide real-time predictions during annotation. Setting up model predictions involves connecting your model server, configuring Label Studio to request predictions, and displaying those predictions to assist annotators.
# Start ML backend server example
# python label_studio_ml/server.py --root-dir ./my_model_backend
# Connect to Label Studio in project settings
Active learning optimizes annotation by selecting the most uncertain or informative samples for human labeling, which in turn improves model training. This iterative feedback loop enhances model accuracy while minimizing labeling costs.
# Conceptual active learning loop
# while model_accuracy < threshold:
# uncertain_samples = select_uncertain(data, model)
# labels = human_annotate(uncertain_samples)
# model.train(labels)
Leveraging AI assistance through pre-annotations and active learning dramatically improves labeling speed and quality. By reducing repetitive tasks and focusing human effort where it matters most, projects achieve faster turnaround and higher data quality.
# Example: Auto-accept confident predictions, manual review for uncertain ones
# for task in tasks:
# if task.prediction_confidence > 0.9:
# accept_annotation(task)
# else:
# send_for_manual_review(task)
Audio annotation involves transcribing speech and segmenting audio streams into meaningful parts like speaker turns or sound events. Label Studio provides tools to label timestamps, transcribe dialogues, and categorize audio clips for tasks such as speech recognition or sound classification.
# Example: Audio transcription task configuration in XML """"""
Video annotation requires labeling objects or events frame-by-frame or within time intervals. Label Studio supports drawing bounding boxes, polygons, or keypoints on video frames, enabling detailed tracking and analysis for computer vision applications.
# Video bounding box config snippet (conceptual) """"""
Synchronization combines audio, video, and other data types for cohesive annotation. Label Studio supports multi-modal tasks where annotators view and label synchronized streams, improving context and accuracy in complex datasets.
# Example: Multi-modal labeling config (conceptual)
# Combine $audio and $video streams with synchronized timeline
Annotated audio and video data can be exported in formats compatible with machine learning workflows, such as JSON with timestamps, segmentation data, and labels. These exports facilitate training models for speech recognition, video analysis, or multimedia applications.
# Export example via API
exported_data = project.export()
with open('audio_video_annotations.json', 'w') as f:
f.write(exported_data)
Label Studio offers a comprehensive REST API enabling users to programmatically manage projects, tasks, annotations, and users. This API facilitates automation, integration with other systems, and scalability for large annotation workflows.
# Example: Retrieve list of projects using Python requests
import requests
response = requests.get('http://localhost:8080/api/projects', headers={'Authorization': 'Token YOUR_API_KEY'})
projects = response.json()
print(projects)
Automate uploading data and creating annotation tasks via the API. This enables bulk processing and integration with data ingestion pipelines, making it efficient to handle large datasets without manual intervention.
# Example: Create tasks via API
tasks = [
{'data': {'image': 'http://example.com/image1.jpg'}},
{'data': {'image': 'http://example.com/image2.jpg'}}
]
response = requests.post('http://localhost:8080/api/tasks', json=tasks, headers={'Authorization': 'Token YOUR_API_KEY'})
print(response.status_code)
Fetch annotations programmatically using the API for downstream analysis or model training. This enables integration of Label Studio annotations into machine learning pipelines seamlessly.
# Example: Get annotations from a project
project_id = 1
response = requests.get(f'http://localhost:8080/api/projects/{project_id}/annotations', headers={'Authorization': 'Token YOUR_API_KEY'})
annotations = response.json()
print(annotations)
Label Studio can be integrated into continuous integration and deployment pipelines, automating annotation updates, retraining models, and deployment. This integration ensures that labeling and model training keep pace with development cycles.
# Conceptual CI/CD script snippet (bash)
# curl -X POST -H "Authorization: Token YOUR_API_KEY" -d @new_tasks.json http://localhost:8080/api/tasks
# python train_model.py --data annotations.json
# ./deploy_model.sh
Label Studio exports labeled datasets in formats compatible with machine learning frameworks. These labeled data files are then ingested into training pipelines, facilitating the development of accurate models based on human-verified annotations.
# Example: Load exported JSON labels into training script
import json
with open('annotations.json', 'r') as f:
labeled_data = json.load(f)
# Process labeled_data for model training
As new annotations are collected, models can be incrementally retrained to improve performance. This allows continuous learning from fresh data without starting training from scratch, enhancing model accuracy over time.
# Conceptual incremental training loop
# while new_data_available:
# model.train(new_labeled_data)
# save_model(model)
Integrating Label Studio into a feedback loop enables model predictions to improve annotation quality. Annotators review model outputs, correcting errors, which are then used to further train the model, creating a cycle of continuous improvement.
# Example: Use model predictions as pre-annotations, then update model
# predictions = model.predict(unlabeled_data)
# corrected_labels = human_review(predictions)
# model.train(corrected_labels)
Label Studio fits into MLOps by providing annotated data management, integration with training pipelines, and automated retraining triggers. This ensures reproducible, scalable, and efficient machine learning operations within enterprise environments.
# Example MLOps integration (conceptual)
# Trigger labeling -> export data -> train model -> deploy -> monitor -> repeat
Label Studio can be deployed using Docker containers for easy setup, Kubernetes for scalable orchestration, or directly on cloud platforms like AWS, GCP, or Azure. These options offer flexibility in scaling and managing labeling infrastructure according to project needs.
# Docker run command example
# docker run -d -p 8080:8080 heartexlabs/label-studio:latest
Scaling involves configuring Label Studio for multiple concurrent users by deploying on scalable infrastructure, load balancing requests, and using shared storage. Proper resource allocation ensures smooth performance and collaboration across large annotation teams.
# Kubernetes deployment snippet (conceptual)
# apiVersion: apps/v1
# kind: Deployment
# metadata:
# name: label-studio
# spec:
# replicas: 3
# template:
# spec:
# containers:
# - name: label-studio
# image: heartexlabs/label-studio:latest
Secure deployments require using HTTPS, strong authentication, role-based access controls, and regularly updating software to patch vulnerabilities. Isolating sensitive data and auditing user activity help maintain compliance and protect data integrity.
# Example: Enable HTTPS with Nginx reverse proxy (conceptual)
# server {
# listen 443 ssl;
# ssl_certificate /etc/ssl/certs/cert.pem;
# ssl_certificate_key /etc/ssl/private/key.pem;
# location / {
# proxy_pass http://label-studio:8080;
# }
# }
Monitoring system health, user activity, and error logs is essential for maintaining reliable production environments. Integrate Label Studio with logging tools like ELK stack or Prometheus for real-time insights and proactive issue detection.
# Example: Configure logging to external system (conceptual)
# LABEL_STUDIO_LOG_LEVEL=INFO
# Use tools like Fluentd or Logstash to collect logs
Effective collaboration requires managing users by assigning roles such as annotator, reviewer, or admin. This controls access and responsibilities, ensuring data security and task distribution tailored to team members’ expertise.
# Example: Assign roles via API or UI
# Roles: 'annotator', 'reviewer', 'admin'
# user.assign_role('reviewer')
Quality control processes involve reviewing annotations, flagging inconsistencies, and approving finalized labels. Label Studio supports workflows where reviewers validate or correct annotations to maintain high data quality standards.
# Example: Review task status update
# task.status = 'reviewed'
# save(task)
When multiple annotators label the same data differently, conflict resolution techniques like majority voting or consensus meetings help finalize labels. This improves reliability and reduces bias in annotated datasets.
# Example: Aggregate annotations with majority vote (conceptual)
# final_label = majority_vote([label1, label2, label3])
Generate reports on annotation progress, accuracy, and team productivity. Analytics help identify bottlenecks, monitor quality, and inform project decisions for more efficient management.
# Example: Export annotation stats via API
# stats = project.get_annotation_stats()
# print(stats)
Custom frontend widgets allow you to tailor the annotation UI with new interactive components. Using React, you can build specialized widgets that fit unique labeling needs, enhancing user experience and expanding Label Studio’s functionality.
# Example: Basic React widget skeleton (conceptual)
# import React from 'react';
# export default function CustomWidget(props) {
# return <div>Custom Widget UI</div>;
# }
Backend extensions add new API endpoints or customize existing ones to support advanced workflows or integrations. This flexibility allows developers to embed Label Studio in broader systems seamlessly.
# Example: Flask API route addition (conceptual)
# @app.route('/custom-endpoint')
# def custom_endpoint():
# return jsonify({'status': 'success'})
Once developed, plugins can be shared with the community via repositories or package managers. Publishing promotes reuse, collaboration, and accelerates adoption of new features within the Label Studio ecosystem.
# Example: Publish widget as npm package
# npm publish
Many successful extensions solve domain-specific problems, such as medical image labeling or document annotation. Studying these cases provides insights into plugin design, best practices, and potential applications.
# Example: Medical image annotation plugin enhancing polygon labeling
# Adds support for DICOM format and custom tools
Managing sensitive data requires strict policies to protect user privacy. Label Studio supports encryption, anonymization, and controlled access to ensure that sensitive information is handled securely throughout the annotation lifecycle.
# Example: Encrypt sensitive fields before storage (conceptual)
# encrypted_data = encrypt(sensitive_data, encryption_key)
# save_to_database(encrypted_data)
Compliance with GDPR, CCPA, and other regulations involves data minimization, user consent, and the right to access or delete personal data. Label Studio workflows can be configured to meet these legal requirements.
# Example: Logging user consent and data processing activities
# log_consent(user_id, timestamp)
Secure storage uses encrypted databases or cloud storage with role-based access control (RBAC). Label Studio integrates with authentication providers and access policies to restrict data visibility and modification to authorized personnel only.
# Example: Define RBAC roles in configuration
# roles = {
# 'admin': ['read', 'write', 'delete'],
# 'annotator': ['read', 'write'],
# 'viewer': ['read']
# }
Audit trails record all user actions and system changes, supporting transparency and accountability. Compliance reports summarize data handling and user activities, essential for audits and regulatory reviews.
# Example: Log annotation edits with timestamps and user IDs
# log_event(user_id, task_id, action, timestamp)
Label Studio users may encounter installation errors, task upload failures, or interface glitches. Common fixes include verifying environment dependencies, checking API keys, and updating to the latest software versions to ensure compatibility and stability.
# Example: Check Label Studio version
# label-studio --version
# Upgrade with pip if outdated
# pip install --upgrade label-studio
Optimize performance by configuring caching, increasing server resources, and limiting simultaneous user connections. Use database indexing and optimize task batch sizes to maintain responsiveness in large projects.
# Example: Adjust server worker count
# label-studio start --workers 4
Users can access free support through community forums, GitHub issues, and documentation. Enterprise customers receive dedicated support, SLAs, and consulting services to meet business needs and ensure smooth operation.
# Example: Visit Label Studio community
# https://github.com/heartexlabs/label-studio/discussions
Contributions improve Label Studio’s features and reliability. Developers can submit pull requests, report bugs, and participate in discussions. Following contribution guidelines ensures quality and fosters community collaboration.
# Example: Basic git workflow
# git clone https://github.com/heartexlabs/label-studio.git
# git checkout -b feature-branch
# git commit -m "Add new feature"
# git push origin feature-branch
# Create pull request on GitHub