Lannotex

Fast, offline-first image annotation for bounding boxes, polygons, and image classes.
Free and MIT-licensed. LabelMe-compatible. Your images never leave your machine.

Documentation · Quick start · Coming from LabelMe · Releases

Why Lannotex?

• Everything you need is free. YOLO, COCO, Labelme and CSV export, standalone desktop builds, and team review are all free and open source. No license keys, no Pro tier.
• Built for real datasets. A project is one SQLite file instead of thousands of JSON files. The image list is virtualized, so its memory use stays flat; it was benchmarked at 100,000 records. Your source images are never moved or modified.
• You can't lose work. Every edit is written to a recovery draft, saves are transactional, and broken geometry (self-intersecting polygons, zero-size boxes) is rejected before it reaches your dataset.
• Private by default. No account, no telemetry, no cloud. The app makes network requests only if you choose to host or join a team on your own network.

Install

uv tool install lannotex   # recommended, isolated install
lannotex

pipx install lannotex
# or, inside a virtual environment
python -m pip install lannotex

git clone https://github.com/Lourdhu02/Lannotex.git && cd Lannotex
uv venv && uv pip install -e ".[dev]"
lannotex

Lannotex supports Python 3.11–3.14 on Windows, macOS, and Linux. See the installation guide for details.

Your first dataset in 60 seconds

1. Create a project and pick a pipeline: Classification, Object detection, Segmentation, or Combined. Each one shows only the tools it needs.
2. Import a folder (Ctrl+I). JPEG, PNG and WebP files are checked and referenced in place, not copied.
3. Add labels, then draw: B for boxes, P for polygons (Enter finishes), V to select and edit.
4. Save & next with Ctrl+Enter. Autosave and recovery drafts run in the background anyway.
5. Export to the format your training code expects.

Every shortcut can be remapped. See the annotating guide for the full list.

Light theme · polygon vertex editing	Dark theme · object detection

Features

Lannotex vs. LabelMe

LabelMe is a great tool and Lannotex reads and writes its JSON format, so you can switch without converting anything.

Based on LabelMe's published features and pricing as of October 2026. The full comparison and migration guide covers which LabelMe shapes import today.

Command line

lannotex new ./dataset                                  # create a project
lannotex analyze ./dataset                              # JSON health report: counts, problems, drafts
lannotex export ./dataset ./out --format "COCO JSON"    # Labelme JSON | YOLO boxes | COCO JSON | Classification CSV | Native JSONL
lannotex invite ./dataset "Alice"                       # print a one-time reviewer token
lannotex host ./dataset --bind 0.0.0.0 --port 8765      # share a project on your LAN (TLS via --cert/--key)

analyze writes clean JSON to stdout, so it can gate a training pipeline. See the CLI reference.

Privacy

Solo projects make no network requests. Team mode connects only to the host address you enter, reviewer tokens are kept in memory on clients, and the host stores only token hashes. There is no telemetry, no external AI service, and no automatic upload. Read the team guide for LAN and TLS setup.

Roadmap

Next up: a ready-to-train YOLO layout (images/, labels/, data.yaml), more LabelMe shape types, VOC and YOLO-seg export, and number-key label selection. See the roadmap and the measured performance.

Contributing

Bug reports, ideas, and pull requests are welcome. Start with CONTRIBUTING.md, and please follow the Code of Conduct. Report security issues privately, as described in SECURITY.md.

If Lannotex saves you time, a ⭐ helps other people find it.

License

MIT © 2026 Lourdhu Raju. Third-party licenses are listed in THIRD_PARTY_NOTICES.md. Demo images are public-domain and CC0 samples from scikit-image.