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Nicolás Baier Quezada edited this page Mar 27, 2026 · 6 revisions

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Annotix Wiki

Desktop annotation platform for Machine Learning datasets
Images · Video · Time Series · Tabular Data


Welcome to the Annotix documentation. Annotix is a cross-platform desktop application for creating, managing, and exporting annotated ML datasets. It pairs a React 19 frontend with a high-performance Rust backend through Tauri 2.

Quick Navigation

Getting Started

  • Getting Started — Installation, prerequisites, first run, and development setup.

Annotation

  • Annotation Tools — The 7 canvas tools (BBox, OBB, Polygon, Mask, Keypoints, Landmarks, Select), zoom, pan, class selection, undo/redo, and image adjustments.
  • Video Annotation — Frame extraction, tracks, keyframes, interpolation, bake, and timeline controls.
  • Time Series — CSV import, visualization, and the 5 annotation types (point, range, classification, event, anomaly).
  • Tabular Data — Data editor, column selection, and classical ML training.

ML Pipeline

  • Integrated ML Training — 19 ML backends, 4 execution modes, presets, real-time metrics, cloud providers, and model export.
  • Inference — ONNX and PyTorch model inference, batch processing, NMS, and confidence thresholds.
  • Export and Import — 11 export formats, 8 import formats with auto-detection, coordinate systems, and file structures.

Collaboration & Automation

  • P2P Collaboration — Real-time annotation with Iroh (QUIC), roles, permissions, image locking, work distribution, and CRDT sync.
  • Browser Automation — Free Google Colab training and LLM queries without API keys via Chrome DevTools Protocol.

Reference

  • Keyboard Shortcuts — Full shortcut reference, customization system, and conflict detection.
  • Languages — 10 supported languages with lazy loading.
  • Architecture — Storage model, IPC layer, state management, handler pattern, and project structure.

At a Glance

Feature Details
Annotation Tools 7 tools on high-performance Konva canvas
Data Types Images, Video, Time Series, Tabular
ML Backends 19 (YOLO, RT-DETR, MMDetection, Detectron2, timm, SMP, etc.)
Execution Modes Local, Download Package, Cloud, Browser Automation
Export Formats 11 with real-time progress
Import Formats 8 with automatic format detection
P2P Collaboration Iroh QUIC, no central server
Languages 10 (de, en, es, fr, it, ja, ko, pt, ru, zh)
Platforms Windows, macOS, Linux
Tech Stack React 19 + Rust + Tauri 2
Version 2.3.1

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