Open Source Data Labeling | Label Studio

Label Studio 1.12.0 🚀Automate & Evaluate Labeling Predictions Using LLMs & ML Models

Open Source
Data Labeling Platform

The most flexible data labeling platform to fine-tune LLMs, prepare training data or validate AI models.

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Quick Start

PIP
Brew
Git
Docker


                    # Install the package
# into python virtual environment
                  pip install -U label-studio# Launch it!label-studio

# Install the caskbrew install humansignal/tap/label-studio# Launch it!label-studio

# clone repo
                    git clone https://github.com/HumanSignal/label-studio.git
                  # install dependencies
                    cd label-studio
pip install poetry
poetry install
                  # apply db migrationspoetry run python label_studio/manage.py migrate# collect static filespoetry run python label_studio/manage.py collectstatic
                  # launchpoetry run 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/

Images
Audio
Text
Time Series
Multi-Domain
Video

Computer Vision

Image Classification

Put images into categories

Object Detection

Detect objects on image, boxes, 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

Video

Classification

Put videos into categories

Object Tracking

Label and track multiple objects frame-by-frame

Assisted Labeling

Add keyframes and automatically interpolate bounding boxes between keyframes

Flexible and configurable

Configurable layouts and templates adapt to your dataset and workflow.

Integrate with your ML/AI pipeline

Webhooks, Python SDK and API allow you to authenticate, create projects, import tasks, manage model predictions, and more.

ML-assisted labeling

Save time by using predictions to assist your labeling process with ML backend integration.

Connect your cloud storage

Connect to cloud object storage and label data there directly with S3 and GCP.

Explore & understand your data

Prepare and manage your dataset in our Data Manager using advanced filters.

Multiple projects and users

Support multiple projects, use cases and data types in one platform.

Trusted by companies large and small

Millions of data items labeled

11,000+ Slack members

GitHub stars