Annotix

Open-source desktop platform for ML dataset annotation, training, and collaboration
Images · Video · Audio · Time Series · Tabular Data

Download • Read the Paper • System Overview • Build from Source • Cite

Paper

Annotix: An Open-Source Desktop Platform for Comprehensive Machine Learning Dataset Annotation

Published on Preprints.org (April 2026)

Universidad Austral de Chile, Campus Puerto Montt — TecMedHub

If you use Annotix in your research, please cite the paper.

Why Annotix?

Most annotation tools focus on a single data type or require cloud accounts. Annotix is different:

	Annotix	Cloud tools (CVAT, Label Studio)	Desktop tools (labelImg, LabelMe)
Runs fully offline	Yes	No	Yes
Images + Video + Audio + Time Series + Tabular	Yes	Partial	No
SAM-assisted segmentation (local ONNX)	Yes	Partial	No
Integrated ML training (19 backends)	Yes	No	No
P2P collaboration (no server)	Yes	Server required	No
LAN sharing via browser (no install)	Yes	Server required	No
Free GPU training (Colab automation)	Yes	No	No
Export to 17 formats	Yes	Yes	Limited
Cross-platform native app	Yes	Browser	Partial

Status

Last updated: June 2026 — v2.9.10

Feature	Status	Notes
Image annotation (7 tools)	✅ Stable	BBox, OBB, Mask, Polygon, Keypoints, Landmarks, Pan
SAM assist	✅ Stable	Local ONNX encoder/decoder, AMG candidates, click-refine, app-level model store
Video annotation	✅ Stable	Tracks, keyframes, interpolation, bake
Audio annotation	✅ Stable	Waveform UI, classification, ASR transcription, sound events, TTS recording
Time series annotation	✅ Stable	5 annotation types, Chart.js canvas
Tabular ML	✅ Stable	scikit-learn integration, column selector
ONNX inference	✅ Stable	Auto-detects YOLOv5-v12, DETR, SSD, classification; opt-in TensorRT/CUDA/DirectML/CoreML EPs
Export (17 formats)	✅ Stable	YOLO, COCO, VOC, CSV, U-Net, TIX, rasterized previews, audio formats
Import (8 formats)	✅ Stable	Auto-detection with confidence scoring; PDF pages as images
Local ML training (19 backends)	✅ Stable	Isolated Python env, GPU auto-detection, live metrics, PDF report
Cloud training (7 providers)	✅ Stable	Vertex AI, Kaggle, Lightning AI, HuggingFace, Saturn Cloud, Colab Enterprise
Browser automation (Colab free)	✅ Stable	T4 GPU, real-time progress
Network sharing (serve)	✅ Stable	HTTP server on LAN with bearer-token auth, browser annotation UI
P2P collaboration	✅ Stable	Iroh QUIC + N0 discovery, live image/mark sync, work distribution, data approval, roles; syncs images/annotations/classes (not video, audio or time series)
Annotation inspector & filters	✅ Stable	Per-class/per-image filters, cross-project comparison
Keyboard shortcuts	✅ Stable	Fully customizable, conflict detection
i18n (10 languages)	✅ Stable	47 namespaces, lazy loading, English fallback
Audio import	🚧 In progress	Export implemented (HF ASR, LJSpeech, CSV); import not yet available
Audio training backends	🚧 Not implemented	Audio projects annotate & export only; train externally
SAM model auto-download	🚧 In progress	Manual upload works; HuggingFace presets (MobileSAM / ViT-B / SAM2) pending
LLM chat via browser	🚧 Beta	Kimi, Qwen, DeepSeek, HuggingChat; generic runner works, provider modules partially wired
macOS build	⚠️ Not tested	No CI for macOS; should build from source but untested

✅ = production-ready · 🚧 = usable but incomplete · ⚠️ = known limitation

Download

Pre-built binaries for the latest release:

Platform	Download
Windows (x64)	`.exe` installer \| `.msi`
Linux (x64)	`.tar.gz` (portable binary + libs + installer script)
macOS	Build from source (see Getting Started)

Linux packaging is a self-contained tarball (annotix-v<version>-linux-x86_64.tar.gz) with the binary, libpdfium.so and a run.sh launcher — no .deb/.rpm/AppImage. It needs libwebkit2gtk-4.1-0, libgtk-3-0 and librsvg2-2 on the system, and can register itself as a desktop entry. Windows installers bundle FFmpeg DLLs and pdfium.

All releases: github.com/Debaq/Annotix/releases

The app checks GitHub for new releases and shows an in-app update banner with a dynamic changelog.

Features at a Glance

Annotation Tools

7 tools on a high-performance Konva canvas:

Tool	Key	Description
BBox	`B`	Rectangular bounding box with drag & resize
OBB	`O`	Oriented bounding box with free rotation
Mask	`M`	Freehand painting with configurable brush and eraser
Polygon	`P`	Point-by-point polygon with auto-close
Keypoints	`K`	Skeleton presets (COCO, face, hand, MediaPipe)
Landmarks	`L`	Named reference points with labels
Pan	`H`	Canvas navigation

Plus: mouse wheel zoom, image rotation, label/grid toggles, per-annotation visibility, draggable floating panels persisted per project, quick class selection (1-0, Q-P for up to 20 classes), and undo/redo with 100-step history.

SAM-Assisted Segmentation

Segment Anything runs locally via ONNX — no cloud, no API key.

AMG mode: a 16×16 point grid (32×32 in HQ mode) feeds the decoder in batch; click a candidate mask + press a class key to convert it into the active tool's format (BBox / OBB / Mask / Polygon)
Refine mode: click-by-click positive/negative prompts on a cached image embedding
Each candidate keeps all 3 multimask outputs as uint8 logits at 256px, so the granularity slider switches between them without re-running the decoder
Frontend sliders (granularity, score, NMS, overlap) re-filter candidates without re-running AMG
Encoder/decoder models are stored app-level ({data_dir}/sam_models/), shared across projects
Candidates are ephemeral — never written to project.json

AMG default	Value	Role
`points_per_side`	16	prompt grid (32 in HQ mode)
`pred_iou_thresh`	0.70	drops low-confidence decoder masks
`stability_score_thresh`	0.85	stability under logit-threshold shift
`box_nms_thresh`	0.70	non-max suppression over boxes
`min_mask_region_area`	100 px	smallest mask kept
`overlap_with_existing_thresh`	0.50	max overlap with existing annotations

Project Types

23 project types across four families, each with its own editor and export path:

Images (9) — detection, oriented detection, semantic segmentation, instance segmentation, polygon, keypoints, landmarks, single- and multi-label classification
Time Series (9) — classification, forecasting, anomaly detection, segmentation, pattern recognition, event detection, regression, clustering, imputation
Audio (4) — classification, speech recognition, sound event detection, TTS recording
Tabular (1) — built-in editor with column selection and scikit-learn training

Video is a capture path rather than a project type: FFmpeg extracts frames at a configurable rate into the project's images, annotated as tracks with keyframes and linear interpolation, then baked to per-frame annotations.

Images can also be ingested from PDF documents (pages rasterized natively with pdfium) and stored as WebP per project.

Integrated ML Training (19 Backends)

Train models directly from the app with real-time metrics charts, a training monitor with suggestions ("coach"), free-text observations, and an exportable PDF training report.

Full backend list

Object Detection

Backend	Models
YOLO (Ultralytics)	YOLO26, YOLOv8–v12
RT-DETR (Ultralytics)	RT-DETR-l, RT-DETR-x
RF-DETR (Roboflow)	RF-DETR-base, RF-DETR-large
MMDetection (OpenMMLab)	30+ architectures (Faster R-CNN, DINO, Co-DETR, etc.)

Semantic Segmentation

Backend	Models
SMP	U-Net, DeepLabV3+, FPN, PSPNet, etc.
HuggingFace Segmentation	SegFormer, Mask2Former, etc.
MMSegmentation	Full OpenMMLab catalog

Instance Segmentation

Backend	Models
Detectron2 (Meta)	Mask R-CNN, Cascade R-CNN, etc.

Keypoints & Pose

Backend	Models
MMPose	HRNet, ViTPose, RTMPose, etc.

Oriented Object Detection (OBB)

Backend	Models
MMRotate	Oriented R-CNN, RoI Transformer, etc.

Image Classification

Backend	Models
timm	700+ models (ResNet, EfficientNet, ViT, ConvNeXt, etc.)
HuggingFace Classification	ViT, BEiT, DeiT, Swin, etc.

Time Series

Backend	Task
tsai	Classification, regression, forecasting
PyTorch Forecasting	TFT, N-BEATS, etc.
PyOD	Anomaly detection
tslearn	Temporal clustering
PyPOTS	Missing value imputation
STUMPY	Matrix Profile (motif/pattern discovery)

Tabular

Backend	Task
scikit-learn	RandomForest, SVM, kNN, GradientBoosting, etc.

4 execution modes:

Mode	Description
Local	Isolated Python env via micromamba, GPU auto-detection (CUDA / MPS)
Download Package	ZIP with script + data for external execution
Cloud	Vertex AI, Kaggle, Lightning AI, HuggingFace, Saturn Cloud
Browser Automation	Free T4 GPU on Google Colab via CDP automation

6 training presets: small_objects, industrial, traffic, edge_mobile, medical, aerial.

Model export: PyTorch .pt, ONNX, TorchScript, TFLite, CoreML, TensorRT.

Inference

Run trained or third-party ONNX models over a whole project:

Auto-detection of architecture and metadata (classes, input size, nc/names) across five output families: YOLOv8+, YOLOv5–v7 (objectness), YOLOv10/YOLO26 (end-to-end, no NMS), multi-output (SSD, EfficientDet, Faster R-CNN, DETR) and classification
Batch inference with cancel, per-prediction accept/reject, conversion to annotations
Model archives can be dropped in directly (ZIP extraction + drag & drop import)
Execution providers are opt-in (TensorRT, CUDA, DirectML, CoreML); CPU path uses SIMD preprocessing and a parallel pipeline

P2P Collaboration

Real-time collaborative annotation powered by Iroh (QUIC). No central server, no port forwarding: N0 discovery (pkarr + DNS + relays) keeps peers reachable across networks and re-syncs automatically when a peer comes back online.

Host or join with a session code; the host secret is encrypted at rest
Roles: LeadResearcher (full control) / Annotator / DataCurator, with per-session rule overrides (upload, export, edit classes, delete) and live role changes
Live sync of images, annotations and classes with author attribution; blob transfer with retries and resumable downloads
Image presence locks (3-min TTL, auto-renewed) so you can see who is on which image
Work distribution: auto-assign batches across peers, adjust assignments, per-peer progress stats
Data approval queue: submissions from collaborators can require host approval
Pause / resume a session; peer list with heartbeat-based online status

Conflict resolution is per-key last-write-wins (iroh-docs). Video, audio and time-series assets are not part of the synced document — share those via TIX export.

Network Sharing (Serve)

Publish a project over the LAN as an HTTP server with a bearer-token protected web UI — collaborators annotate from a browser with no install. It picks a free port, enumerates local addresses and self-checks reachability before announcing the URL. Optional auto-save.

REST endpoints: /api/projects, /api/projects/{id}, /api/projects/{id}/images, and per-image …/{image_id}, …/file, …/thumbnail, …/annotations (GET and POST).

Browser Automation

Train on Google Colab for free (T4 GPU) via Chrome DevTools Protocol:

Auto-detects Chromium browsers, uploads dataset, runs training
Real-time progress with pause / resume / cancel

Query LLMs without API keys through the user's browser: Kimi, Qwen, DeepSeek, HuggingChat.

Export & Import

17 export formats: YOLO Detection, YOLO Segmentation, COCO JSON, Pascal VOC, CSV (Detection / Classification / Keypoints / Landmarks), Folders by Class, U-Net Masks, TIX (native), rasterized preview (with and without labels), HuggingFace ASR, LJSpeech, CSV Audio Classification, CSV Sound Events.

8 import formats with automatic detection: YOLO (detection & segmentation), COCO, Pascal VOC, CSV (4 variants), U-Net Masks, Folders by Class, TIX. Multiple .tix files can be merged, homogenizing class sets.

Inspector & Filters

Annotation inspector reachable from the project gallery, with debug/observation filters over marks, classes and gallery, per-class counters, and multi-project comparison.

Keyboard Shortcuts

All shortcuts are fully customizable from Settings with per-context conflict detection.

Default shortcuts

Image Tools

Shortcut	Action
`B`	Bounding Box
`O`	OBB
`M`	Mask
`P`	Polygon
`K`	Keypoints
`L`	Landmarks
`V`	Select
`H`	Pan
`[` / `]`	Decrease / Increase brush size
`E`	Toggle eraser
`A` / `D`	Rotate image
`Enter`	Confirm drawing
`Esc`	Cancel drawing

SAM assist is toggled from the canvas toolbar; while active, Tab cycles candidates, Esc exits refine mode and the class keys accept the hovered mask.

Navigation

Shortcut	Action
`PageUp` / `PageDown`	Previous / Next sample
`Ctrl++` / `Ctrl+-`	Zoom in / out
`Ctrl+0`	Zoom to fit

General

Shortcut	Action
`Ctrl+S`	Save
`Ctrl+Z` / `Ctrl+Y`	Undo / Redo
`Del` / `Backspace`	Delete selection
`Esc`	Deselect

Quick Class Selection

Keys	Classes
`1` - `0`	Classes 1 to 10
`Q` - `P`	Classes 11 to 20

Video

Shortcut	Action
`T`	New track

Time Series

Shortcut	Action
`V`	Select
`P`	Point
`R`	Range
`E`	Event
`A`	Anomaly

Audio

Shortcut	Action
`F2`	Play / Pause
`F3` / `F4`	Replay / Rewind
`Left` / `Right`	Scrub
`Enter`	Split
`Tab`	Save & next

TTS Recording

Shortcut	Action
`Space`	Record
`Enter`	Accept take
`R`	Repeat
`S`	Skip sentence

Languages

10 languages across 47 namespaces, with lazy loading and English fallback:

de Deutsch · en English · es Espanol · fr Francais · it Italiano · ja Japanese · ko Korean · pt Portugues · ru Russian · zh Chinese

Architecture

+-----------------------------------------------------+
|                    Frontend                           |
|   React 19 + TypeScript + Tailwind + shadcn/ui       |
|   Konva (canvas) . Chart.js (metrics) . i18next      |
|   Zustand (state) . React Router 7                   |
+-----------------------------------------------------+
|                  Tauri 2 IPC                          |
|             194 registered commands                   |
+-----------------------------------------------------+
|                  Backend (Rust)                       |
|   +------------+ +-----------+ +-----------------+   |
|   |   Store    | | Commands  | | Export/Import   |   |
|   | (JSON+RAM) | | (24 mod)  | | (17+8 formats)  |   |
|   +------------+ +-----------+ +-----------------+   |
|   +------------+ +-----------+ +-----------------+   |
|   |  Training  | | Browser   | | P2P (Iroh)      |   |
|   | (19 backs) | | Automat.  | | QUIC mesh       |   |
|   +------------+ +-----------+ +-----------------+   |
|   +------------+ +-----------+ +-----------------+   |
|   | Inference  | |    SAM    | | Serve (axum)    |   |
|   | (ONNX/ort) | | enc/dec   | | LAN web UI      |   |
|   +------------+ +-----------+ +-----------------+   |
+-----------------------------------------------------+
|               External Integrations                   |
|   Python (micromamba) . FFmpeg . pdfium . Chromium    |
|   Cloud APIs . Iroh P2P network                      |
+-----------------------------------------------------+

Storage

All data stored as JSON + raw assets on disk. No database.

~/.local/share/annotix/config.json        -> global configuration (projects_dir)
~/.local/share/annotix/sam_models/        -> SAM encoder/decoder ONNX (app-level)
~/.local/share/annotix/p2p/iroh/          -> P2P blobs + replicated docs
{projects_dir}/{uuid}/project.json        -> the whole project
{projects_dir}/{uuid}/images/             -> original images
{projects_dir}/{uuid}/thumbnails/         -> generated thumbnails
{projects_dir}/{uuid}/videos/             -> video files
{projects_dir}/{uuid}/audio/              -> audio files
{projects_dir}/{uuid}/models/             -> registered inference models
{projects_dir}/{uuid}/training/           -> per-job datasets and results

project.json is a single document holding metadata, classes, images with their annotations, time series, videos with their tracks, audio, tabular data, training jobs with per-epoch metric history, inference models, TTS sentences and the collaboration config. An in-memory cache with dirty-flag tracking sits in front of it; writes go to a .tmp file and are then renamed.

IDs are UUID v4 end to end. Class IDs are reindexed to their position on save, and the remap propagates to image annotations, video tracks, audio and time series.

Tech Stack

Frontend

Technology	Version	Purpose
React	19	UI framework
TypeScript	5.7	Static typing
Vite	6	Bundler and dev server
Tailwind CSS	3.4	Utility-first styling
shadcn/ui	—	Component library (Radix UI)
Zustand	5	Global state with persistence
React Router	7	SPA routing
Konva	10	2D annotation canvas
Chart.js	4	Metrics visualization
jsPDF / html2canvas	4 / 1.4	Training report PDF
TanStack Virtual	3	Virtualized gallery
i18next	24	Internationalization

Backend (Rust)

Crate	Version	Purpose
tauri	2	Desktop application framework
serde / serde_json	1	JSON serialization
image / imageproc	0.25	Image processing
fast_image_resize	5	SIMD resizing
rayon	1.10	Data parallelism
geo	0.29	Polygon simplification
webp	0.3	WebP encoding
ort	2.0-rc	ONNX Runtime (inference + SAM)
ffmpeg-the-third	4.1	Video frame extraction
pdfium-render	0.8	PDF page rasterization
zip	2	Export/import packaging
quick-xml	0.37	Pascal VOC XML
csv	1.3	CSV import/export
reqwest	0.12	HTTP client (cloud providers)
jsonwebtoken	9	GCP service-account auth
axum	0.8	LAN serve HTTP server
chacha20poly1305 / subtle	0.10 / 2	Session secret encryption
headless_chrome	1.0	Browser automation (CDP)
iroh + blobs/gossip/docs	0.96–0.98	P2P networking (QUIC)
tokio	1	Async runtime
blake3	1	Hashing

Python (via micromamba)

Package	Purpose
ultralytics	YOLO, RT-DETR
rfdetr	RF-DETR
mmdet, mmseg, mmpose, mmrotate	OpenMMLab suite
segmentation-models-pytorch	Semantic segmentation
timm	Classification (700+ models)
detectron2	Instance segmentation
tsai, pytorch-forecasting	Time series deep learning
pyod, tslearn, pypots, stumpy	Time series classical ML
scikit-learn	Tabular ML

System Requirements

OS: Windows 10+, macOS 12+, Linux (glibc 2.31+)
RAM: 4 GB minimum, 8 GB recommended
Disk: ~500 MB for the app + space for datasets
GPU (optional): NVIDIA with CUDA or Apple Silicon with MPS for accelerated training
FFmpeg: required for video annotation (bundled in release builds)
Chromium browser (optional): for browser automation (Chrome, Brave, Edge)

Getting Started

Prerequisites

Node.js >= 18 (CI uses Node 24)
pnpm (the repo ships pnpm-lock.yaml)
Rust >= 1.89
Tauri 2 prerequisites for your platform

Build & Run

git clone https://github.com/Debaq/Annotix.git
cd Annotix
pnpm install
pnpm tauri:dev       # development (hot-reload)
pnpm tauri:build     # production build

Scripts

Script	Description
`pnpm dev`	Frontend only (Vite dev server)
`pnpm build`	Build frontend (TypeScript check + Vite)
`pnpm tauri:dev`	Full dev (frontend + Rust backend)
`pnpm tauri:build`	Production build with installers
`pnpm lint`	ESLint (flat config)

CI runs cargo fmt/clippy, cargo nextest and the frontend build on every push.

Project Structure

annotix/
├── src/                         # React frontend (~46k LOC)
│   ├── App.tsx                  # Router and providers
│   ├── lib/
│   │   ├── db.ts                # Type definitions (mirrors Rust structs)
│   │   ├── tauriDb.ts           # Centralized Tauri invoke bridge
│   │   └── i18n.ts              # i18next configuration
│   ├── components/ui/           # shadcn/ui components
│   └── features/
│       ├── canvas/              # Annotation canvas (7 tools)
│       ├── sam/                 # SAM assist (overlay, panel, store)
│       ├── gallery/             # Virtualized gallery + filters
│       ├── video/               # Video annotation
│       ├── audio/               # Audio annotation + TTS recording
│       ├── timeseries/          # Time series annotation
│       ├── tabular/             # Tabular data editor
│       ├── classification/      # Classification workflow
│       ├── training/            # ML training panel + monitor + PDF report
│       ├── export/              # 17 export formats
│       ├── import/              # 8 import formats
│       ├── inference/           # Model inference
│       ├── p2p/                 # P2P collaboration
│       ├── serve/               # LAN sharing dialog
│       ├── browser-automation/  # Chrome automation
│       ├── setup/               # First-run setup
│       └── settings/            # App settings
├── src-tauri/                   # Rust backend (~41k LOC)
│   └── src/
│       ├── lib.rs               # 194 Tauri command registrations
│       ├── store/               # Storage layer (state, IO, cache)
│       ├── commands/            # 24 command modules
│       ├── export/              # Export format modules
│       ├── import/              # Import + auto-detector + merge
│       ├── training/            # Multi-backend ML pipeline + cloud providers
│       ├── browser_automation/  # Headless Chrome (Colab, LLM chat)
│       ├── p2p/                 # Iroh P2P networking
│       ├── serve/               # axum LAN server + web UI
│       └── inference/           # ONNX inference + SAM (encoder/decoder/AMG)
├── docs/                        # Roadmaps and backend references
├── wiki/                        # User documentation
└── public/locales/              # 10 languages x 47 namespaces

Citation

If you use Annotix in your research, please cite:

@article{annotix2026,
  title     = {Annotix: An Open-Source Desktop Platform for Comprehensive Machine Learning Dataset Annotation},
  year      = {2026},
  publisher = {Preprints.org},
  url       = {https://www.preprints.org/manuscript/202604.0919}
}

Full paper: https://www.preprints.org/manuscript/202604.0919

Contributing

Contributions are welcome. Please open an issue first to discuss what you'd like to change.

License

MIT License — TecMedHub, Universidad Austral de Chile, Campus Puerto Montt.

Name		Name	Last commit message	Last commit date
.github/workflows		.github/workflows
docs		docs
public		public
scripts		scripts
src-tauri		src-tauri
src		src
tools/study_analysis		tools/study_analysis
wiki		wiki
.env.example		.env.example
.gitignore		.gitignore
.zenodo.json		.zenodo.json
ARCHITECTURE.md		ARCHITECTURE.md
README.md		README.md
VERSION		VERSION
annotix.sh		annotix.sh
build.json		build.json
check-translations.ts		check-translations.ts
components.json		components.json
eslint.config.js		eslint.config.js
index.html		index.html
package.json		package.json
pnpm-lock.yaml		pnpm-lock.yaml
postcss.config.js		postcss.config.js
releases.json		releases.json
tailwind.config.js		tailwind.config.js
tsconfig.json		tsconfig.json
tsconfig.node.json		tsconfig.node.json
vite.config.ts		vite.config.ts

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Annotix

Paper

Why Annotix?

Status

Download

Features at a Glance

Annotation Tools

SAM-Assisted Segmentation

Project Types

Integrated ML Training (19 Backends)

Object Detection

Semantic Segmentation

Instance Segmentation

Keypoints & Pose

Oriented Object Detection (OBB)

Image Classification

Time Series

Tabular

Inference

P2P Collaboration

Network Sharing (Serve)

Browser Automation

Export & Import

Inspector & Filters

Keyboard Shortcuts

Image Tools

Navigation

General

Quick Class Selection

Video

Time Series

Audio

TTS Recording

Languages

Architecture

Storage

Tech Stack

System Requirements

Getting Started

Prerequisites

Build & Run

Scripts

Project Structure

Citation

Contributing

License

About

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