NEWDark Mode is Here 🌓 Label Studio 1.18.0 Release

Label Studio: Open Source
Data Labeling Platform

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

Last Commit:

Latest version:


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

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Label every data type with Label Studio.

GenAI

LLM Fine-Tuning

Label data for supervised fine-tuning or refine models using RLHF

LLM Evaluations

Response moderation, grading, and side-by-side comparison

RAG Evaluation

Use Ragas scores and human feedback

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.

Data scientists love our open source data labeling tool. See why!

“At the end of the day, the value we're providing is novel machine learning algorithms that can solve problems for patients. None of that is possible without being able to create proprietary datasets that are highly accurate and labeled in an efficient, compliant, and streamlined way.”

Robhy Bustami

CEO, BioticsAI

From the Blog

View All Articles
  • Dark Mode is Here in Label Studio 1.18

    The 1.18.0 release includes the much anticipated Dark Mode setting, the ability to export polygons in COCO format, and a bunch of usability improvements for annotators.

    Label Studio Team

    May 14, 2025

  • Everybody Is (Unintentionally) Cheating

    AI benchmarks are quietly failing us. Studies reveal that data leakage, leaderboard manipulation, and misaligned incentives are inflating model performance. This blog explores four pillars of reform, governance, transparency, broad-spectrum metrics, and oversight, and outlines how enterprises can build trust through a centralized benchmark management platform.

    Nikolai Liubimov

    May 13, 2025

  • How Model Context Protocol Connects LLMs to the Real World

    The latest In the Loop episode breaks down the Model Context Protocol (MCP)—a new standard for connecting LLMs to tools, data, and real-world actions. Learn how MCP enables practical, production-ready AI.

    Micaela Kaplan

    May 8, 2025

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Join the largest community of Data Scientists working on enhancing their models with our open source data labeling platform.

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