Open Source
Data Labeling Tool

The most flexible data annotation tool. Quickly installable.
Build custom UIs or use pre-built labeling templates.
Label Studio Python SDK
Last commit: November 22nd, 2021
Quick Start
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# Install the package
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pip install -U label-studio
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# Launch it!
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label-studio
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# Run latest Docker version
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docker run -it -p 8080:8080 -v `pwd`/mydata:/label-studio/data heartexlabs/label-studio:latest
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# Now visit http://localhost:8080/
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# Clone repo
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git clone https://github.com
/heartexlabs/label-studio.git
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# Install dependencies
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cd label-studio
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pip install -e .
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# Launch
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python label_studio/manage.py runserver

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

Already Using Label Studio

Global Community

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

Model frameworks integrations
Slack channel with more than 2600+ members, 8000+ threads, 30000+ answers, and 6600+ GitHub stars.

Millions of data items labeled

Thanks to Core Contributors

@secsilm
@sadnen
@farioas
@haroenv
@lordzuko
@hlomzik
@shevchenkonik
@triklozoid
@deppp
@smoreface

Latest Blog Articles

Improving & evaluating model accuracy with labeling

Perform Interactive ML-Assisted Labeling with Label Studio 1.3.0

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.

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

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.

Read More →
Quickly Create Datasets for Training YOLO Object Detection with Label Studio

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.

Read More →