Label Studio

A flexible data labeling tool for all data types. Prepare training data for computer vision, natural language processing, speech, voice, and video mod

The most flexible data labeling platform to fine-tune LLMs, prepare training data, or evaluate AI systems. Label data for supervised fine-tuning or refine models using RLHF Response moderation, grading, and side-by-side comparison Use Ragas scores and human feedback Detect objects on image, boxes, polygons, circular, and keypoints supported Partition image into multiple segments. Use ML models to pre-label and optimize the process Partition an input audio stream into homogeneous segments according to the speaker identity Tag and identify emotion from the audio Write down verbal communication in text Classify document into one or multiple categories. Use taxonomies of up to 10000 classes Extract and put relevant bits of information into pre-defined categories Answer questions based on context Determine whether a document is positive, negative or neutral Put time series into categories Identify regions relevant to the activity type you're building your ML algorithm for Label single events on plots of time series data Call center recording can be simultaneously transcribed and processed as text Put an image and text right next to each other Use video or audio streams to easier segment time series data Label and track multiple objects frame-by-frame Add keyframes and automatically interpolate bounding boxes between keyframes Configurable layouts and templates adapt to your dataset and workflow. Webhooks, Python SDK and API allow you to authenticate, create projects, import tasks, manage model predictions, and more. Save time by using predictions to assist your labeling process with ML backend integration. Connect to cloud object storage and label data there directly with S3 and GCP. Prepare and manage your dataset in our Data Manager using advanced filters. Support multiple projects, use cases and data types in one platform. Vector annotation, an interactive task source viewer, and workflow improvements across the Data Manager and Template Builder. A practical workflow for onboarding and evaluating annotators with clear instructions, calibration, quality gates, reviewer feedback, and dashboards that keep labeling consistent as volume grows. How do you build the right labeling interface in Label Studio? This video walks through a practical progression: start with templates, customize with XML tags, extend with React Code, and standardize workflows with plugins.