The offline image annotation for vision AI
Build training datasets faster, privately. No cloud uploads, ever.
- Simple UI for polygons, rectangles, and any shape you need
- Auto-label with SAM3, YOLO-World, and smart suggestions
- 100% offline. Your data never leaves your machine
How it works
1. Annotate images with polygon, rectangle, or any shapes with Annotation app
2. Validate & export annotations as a dataset with Toolkit
3. Train your AI model
Annotation app
Desktop app to annotate images with polygons, rectangles, and more.
Toolkit
A set of command-line tools to streamline dataset creation.
Starter guide
Guide to get you started with annotating the first image fast.
Dataset guide
Guide to help you create, validate, and export a high-quality dataset fast.
Annotation app
Annotation app
Offline-first app you download and run on your computer.
Easy-to-use UI
Everything you need in a single window.
Offline & private
Keep data private & work confidently.
Any shapes
Polygon, rectangle, circle, line, & more.
Rich attributes
Add details (flags, texts) to annotations.
AI assisted
Real-time feedback & auto-annotation with AI. See more.
Multi-language
14 languages: English, 中文 (简体/繁體), Français, 日本語, Deutsch, Magyar, 한국어, Español, فارسی, Nederlands, Português, Italiano, Tiếng Việt
Automation toolkit
Toolkit
Command-line tools to streamline dataset creation.
Batch process
Annotate, edit, validate, and export many at once.
Automation ready
Integrate with your workflow and other tools. Accessible from CLI, easily connectable.
AI assisted annotation
Prompt to annotations
Type what to annotate for the AI to get it annotated.
Models: YOLO-World, SAM3
Shapes: rectangles, polygons, mask
A click to annotation
Instant object annotation by a few clicks with AI.
Models: EfficientSAM, SAM1, SAM2
Shapes: polygons, mask
Rectangle to polygon
Convert rough rectangles to accurate polygons with AI.
Perfect for objects with complex shapes (e.g., irregular, curved).
Examples
Make image annotations for any applications with any shapes & AI-assist.
Story
Kentaro Wada
Founder
Automation obsessor. Solo-built Labelme (15K+ stars), Gdown (4.7K+), and 9+ times top trending developers on GitHub.
Lead computer vision at Mujin (robotics startup), PhD at Imperial (early graduate, full-funded), UTokyo JSK robotics lab (first who went abroad in its 50-year history).
Hi, I'm Kentaro!
Here is the story behind Labelme.
I built it when I was at the end of my bachelor's degree / beginning of my master's. I was working in a robotics lab and created it to teach my robot to see the world.
Around 2015-2016, Amazon Robotics held the Amazon Picking Challenge (APC) shortly after acquiring Kiva Robotics, the company who developed the AGVs used at Amazon. They had automated material delivery in warehouses but were looking for an automation solution for material picking.
We participated as a team from the lab (Can you spot me in the picture?), and I ended up building Labelme for the challenge.
For the full story, check out my tweet.
Explore more
How it works
Annotate images with polygons, rectangles, and more. Validate & export annotations as a dataset.
Key features
AI-powered annotation app, automation-ready tools & guides to build high-quality datasets.
Examples
Self-driving, retail, agriculture, medical, and more. Build datasets for various computer vision tasks.
Story
Automation obsessor. Solo-built Labelme (15K+ stars), GH 9+ Top trending. PhD, Lead CV at Mujin.
Start building your dataset today
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