Open Source
Data Labeling Tool

The most flexible data annotation tool. Quickly installable.
Build custom UIs or use pre-built labeling templates.

New

Release 1.5

Dynamic & custom label sets

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Quick Start

# Install the package
# into python virtual environment

pip install -U label-studio

# Launch it!

label-studio

# Install the cask

brew install heartexlabs/tap/label-studio

# Launch it!

label-studio

# Clone repo

git clone https://github.com
/heartexlabs/label-studio.git

# Install dependencies

cd label-studio

pip install -e .

# Apply DB migrations

python label_studio/manage.py migrate

# Collect static files

python label_studio/manage.py collectstatic

# Launch

python label_studio/manage.py runserver

# Run latest Docker version

docker run -it -p 8080:8080 -v `pwd`/mydata:/label-studio/data heartexlabs/label-studio:latest

# Now visit http://localhost:8080/

Label Every Data Type

Computer Vision

Image Classification
Put images into categories
Object Detection
Detect objects on image, bboxes, polygons, circular, and keypoints supported
Semantic Segmentation
Partition image into multiple segments. Use ML models to pre-label and optimize the process

Audio & Speech Applications

Classification
Put audio into categories
Speaker Diarization
Partition an input audio stream into homogeneous segments according to the speaker identity
Emotion Recognition
Tag and identify emotion from the audio.
Audio Transcription
Write down verbal communication in text

NLP, Documents, Chatbots, Transcripts

Classification
Classify document into one or multiple categories. Use taxonomies of up to 10000 classes
Named Entity
Extract and put relevant bits of information into pre-defined categories
Question Answering
Answer questions based on context
Sentiment Analysis
Determine whether a document is positive, negative or neutral

Robots, Sensors, IoT Devices

Classification
Put time series into categories
Segmentation
Identify regions relevant to the activity type you're building your ML algorithm for
Event Recognition
Label single events on plots of time series data

Multi-Domain Applications

Dialogue Processing
Call center recording can be simultaneously transcribed and processed as text
Optical Character Recognition
Put an image and text right next to each other
Time Series with Reference
Use video or audio streams to easier segment time series data

Global Community

Join the largest community of Data Scientists working on enhancing their models
‍
Model frameworks integrations

Slack channel with more than 4500+ members, 15000+ threads, 50000+ answers, and plenty of GitHub stars

Millions of data items labeled

Thanks to Core Contributors

@sadnen

@deppp

Latest Blog Articles

Improving & evaluating model accuracy with labeling

Perform Interactive ML-Assisted Labeling with Label Studio 1.3.0

At Label Studio, we’re always looking for ways to help you accelerate your data annotation process. With the release of version 1.3.0, you can perform model-assisted labeling with any connected machine learning backend.

By interactively predicting annotations, expert human annotators can work alongside pretrained machine learning models or rule-based heuristics to more efficiently complete labeling tasks, helping you get more value from your annotation process and make progress in your machine learning workflow sooner.

TUTORIAL

From raw data to a trained model: Automate your ML pipeline with Label Studio & Amazon SageMaker

It can be difficult to get from raw data to a fully trained model, but the more you can do to automate your machine learning pipeline, the easier the process is. If you’re using Amazon SageMaker but have complex labeling scenarios and corner cases, add Label Studio to your Amazon SageMaker machine learning pipeline and simplify annotating your data.

If you have a machine learning pipeline, or retrain your models frequently based on newly-annotated data, you know that it can be challenging to automate that process. Now that Label Studio supports webhooks, you can automatically receive updates every time a new annotation is created or a project is updated to include different labels.

TITORIAL

Quickly Create Datasets for Training YOLO Object Detection with Label Studio

Object detection is an important task in machine learning, used to underpin facial recognition technologies, essential computer vision tasks for autonomous driving use cases, and more.

Like all machine learning tasks, creating datasets and training the machine learning models for your use case is a tedious and time-consuming requirement. With Label Studio you can collaborate with a team of annotators and quickly label a training dataset for a custom YOLO object detection model.

Open Source
Data Labeling Tool