Label Studio · ·

Website • Docs • Twitter • Join Slack Community

Label Studio is a swiss army knife of data labeling and annotation tools ✌️

Try it now in a running app and check out the introductory post.

Its purpose is to help you label different types of data using a simple interface with a standardized output format. You're dealing with the custom dataset and thinking about creating your tool? Don't - using Label Studio, you can save time and create a custom tool and interface in minutes.

Summary

• Quick Start
• One Click Deploy
• Features 🌟
• Use Cases
• Machine Learning Integration
• For Teams and Enterprises 🏢
• Ecosystem
• License

Quick Start

# Requires >=Python3.5
pip install label-studio

# Initialize the project in labeling_project path
label-studio init labeling_project

# Start the server at http://localhost:8080
label-studio start labeling_project

Install on Windows

For running on Windows, the following wheel packages are needed to be manually downloaded from Gohlke builds, by ensuring the right python version:

• lxml

Install Label Studio:

# Upgrade pip 
pip install -U pip

# Assuming you are running Win64 with Python 3.8, install packages downloaded form Gohlke:
pip install lxml‑4.5.0‑cp38‑cp38‑win_amd64.whl

# Install label studio
pip install label-studio

Install from Anaconda

conda create --name label-studio python=3.8
conda activate label-studio
pip install label-studio

If you see any errors during installation, try to rerun installation

pip install --ignore-installed label-studio

Local development

Running the latest Label Studio version locally without installing package from pip could be done by:

# Install all package dependencies
pip install -e .

# Start the server at http://localhost:8080
python label_studio/server.py start labeling_project --init

Run docker

You can also start serving at http://localhost:8080 by using docker:

docker run --rm -p 8080:8080 -v `pwd`/my_project:/label-studio/my_project --name label-studio heartexlabs/label-studio:latest label-studio start my_project --init

By default, it starts blank project in ./my_project directory.

Note: if ./my_project folder exists, an exception will be thrown. Please delete this folder or use --force option.

You can override the default startup command by appending:

docker run -p 8080:8080 -v `pwd`/my_project:/label-studio/my_project --name label-studio heartexlabs/label-studio:latest label-studio start my_project --init --force --template text_classification

If you want to build a local image, run:

docker build -t heartexlabs/label-studio:latest .

Run docker-compose

You can also start serving at http://localhost:8080 using docker-compose.

First time to run the app

INIT_COMMAND='--init' docker-compose up -d

Run the app with existing project data

docker-compose up -d

Run the app reseting project data

INIT_COMMAND='--init --force' docker-compose up -d

Or you can just use .env file instead of INIT_COMMAND='...' adding this line:

INIT_COMMAND=--init --force

One Click Deploy

You can deploy LS right now in one click on any of these clouds:

Features 🌟

• Simple: Crafted with minimal UI design. A simple design is the best design.
• Configurable: Using high-level jsx tags config, you can fully customize the visual interface for your data. It feels like building a custom labeling tool for your specific needs. And it's fast to do.
• Collaborative Annotations: Label the same task by two or more people and compare the results.
• Multiple Data Types: Label Images, Audios, Texts, HTMLs, Pairwise types with different labeling scenarios that you define yourself.
• Import Formats: JSON, CSV, TSV, RAR and ZIP archives
• Mobile-Friendly: Works on devices of different sizes.
• Embeddable: It's an NPM package too. You can include it in your projects.
• Machine Learning: Integration support for machine learning. Visualize and compare predictions from different models. Use the best ones for pre-labeling.
• Stylable: Configure the visual appearance to match your company brand, distribute the labeling tasks as a part of your product.
• Amazon S3 and Google GCS: Read more about Cloud Storages Support and release 0.7.0.

Use Cases

The list of supported use cases for data annotation. Please contribute your own configs and feel free to extend the base types to support more scenarios. Note that it's not an extensive list and has only major scenarios.

Task	Description
Image
Classification	Put images into categories
Object Detection	Detect objects in an image using a bounding box or polygons
Semantic Segmentation	Detect for each pixel the object category it belongs to
Pose Estimation	Mark positions of a person’s joints
Text
Classification	Put texts into categories
Summarization	Create a summary that represents the most relevant information within the original content
HTML Tagging	Annotate things like resumes, research, legal papers and excel sheet converted to HTML
Audio
Classification	Put audios into categories
Speaker Diarisation	partitioning an input audio stream into homogeneous segments according to the speaker identity
Emotion Recognition	Tag and identifying emotion from the audio
Transcription	Write down verbal communication in text
Video
Classification	Put videos into categories
Comparison
Pairwise	Comparing entities in pairs to judge which of each entity is preferred
Ranking	Sort items in the list according to some property
Time Series
Classification
Segmentation

Machine Learning Integration

You can easily connect your favorite machine learning framework with Label Studio Machine Learning SDK. It's done in the simple 2 steps:

1. Start your own ML backend server (check here for detailed instructions),
2. Connect Label Studio to the running ML backend on /model page

That gives you the opportunities to use:

• Pre-labeling: Use model predictions for pre-labeling (e.g. make use on-the-fly model predictions for creating rough image segmentations for further manual refinements)
• Autolabeling: Create automatic annotations
• Online Learning: Simultaneously update (retrain) your model while new annotations are coming
• Active Learning: Perform labeling in active learning mode - select only most complex examples
• Prediction Service: Instantly create running production-ready prediction service

Label Studio Integration to your services

• You can use Label Studio Frontend as a separated React library, read more here.
• Label Studio Backend (this repo) can be integrated to your app via Flask Blueprints. See example of integration here.

Label Studio for Teams, Startups, and Enterprises 🏢

Label Studio for Teams is our enterprise edition (cloud & on-prem), that includes a data manager, high-quality baseline models, active learning, collaborators support, and more. Please visit the website to learn more.

Ecosystem

Project	Description
label-studio	Server part, distributed as a pip package
label-studio-frontend	Frontend part, written in JavaScript and React, can be embedded into your application
label-studio-converter	Encode labels into the format of your favorite machine learning library
label-studio-transformers	Transformers library connected and configured for use with label studio

Citation

@misc{Label Studio,
  title={{Label Studio}: Data labeling software},
  url={https://github.com/heartexlabs/label-studio},
  note={Open source software available from https://github.com/heartexlabs/label-studio},
  author={
    Maxim Tkachenko and
    Mikhail Malyuk and
    Nikita Shevchenko and
    Andrey Holmanyuk and
    Nikolai Liubimov},
  year={2020},
}

License

This software is licensed under the Apache 2.0 LICENSE © Heartex. 2020