heartexlabs/label-studio

Features

• Data Labeling Platforms - Functions as a comprehensive platform for preparing and annotating diverse datasets for machine learning training pipelines.
• Multi-Type Data Annotation - Supports labeling various data types using a standardized output format to ensure compatibility across training pipelines.
• Universal Annotation Schemas - Stores all labels in a universal JSON schema to ensure compatibility across different machine learning training pipelines.
• Data Annotation Workflows - Provides automated pipelines that import raw data, apply labels via a web interface, and export it into standardized formats.
• Annotation Format Converters - Converts labeled data into standardized formats compatible with various machine learning models for training.
• Annotation Dataset Creation - Creates high quality annotated datasets for training machine learning models across various data formats and types.
• Model Integrations - Provides a framework for connecting external machine learning model servers to perform pre-labeling and active learning.
• Annotation Project Management - Coordinates multi-user labeling tasks and manages large datasets within a shared workspace to ensure consistency.
• Cloud-Integrated Labeling Pipelines - Functions as a workflow tool that imports data from cloud storage and exports labels into various machine learning formats.
• Model-Assisted Labelers - Integrates machine learning models to automatically generate initial annotations and refine training data.
• ML Model Server Integrations - Enables the connection of external model servers to automate data pre-labeling via automated predictions.
• Active Learning - Connects machine learning models to the labeling process to automate pre-labeling and refine predictions via human feedback.
• Hybrid Cloud Storage Configuration - Saves application assets and uploaded files using local directories or compatible cloud storage for data persistence.
• Training Data Pipelines - Organizes and cleans raw data through labeling and formatting to make it compatible with model training pipelines.
• External Data Connectors - Links the labeling interface to external data sources through a modular system of import and export scripts.
• Storage Backend Adapters - Implements a driver layer that abstracts data persistence across local filesystems and cloud storage buckets.
• Model Prediction Interfaces - Communicates with external machine learning servers via standardized API calls to fetch predictions for pre-labeling.
• Role-Based Access Control - Controls data visibility and annotation assignments by mapping user accounts to specific project permissions.
• Pipeline Integration - Allows embedding the annotation process into larger automated data processing workflows via a standardized interface.
• Annotation - Multi-type data labeling and annotation tool.
• Image Annotation Tools - Multi-modal data labeling tool with standardized output formats.
• Multi-Domain Annotation - Configurable annotation tool supporting various data types.
• Multimodal Annotation Tools - Versatile labeling tool supporting multiple data types with standardized output.
• Developer Tools - Provides a multi-type interface for data annotation and labeling.

Star history