workflowstacks
design

Labelme: Fast Image Annotation

Get accurate image annotations with Labelme, a Python tool for founders, with 16k+ GitHub stars.
16,098 stars3,696 forksPythonGuide quality 8/10Updated 8/8/2026100% free · open source
What it does

Labelme is a Python tool for accurately annotating images with various shapes and AI-assisted features to support machine learning model training.

When to use it
  • •When training a machine learning model that requires annotated images
  • •When creating a dataset for object detection or image classification tasks
  • •When needing to annotate images with specific shapes like polygons, rectangles, or circles
Ready-to-paste prompt
labelme --labels labels.txt image1.jpg
Heads up: The AI-assisted annotation feature requires a pre-trained model, which can be downloaded using the `labelme --download_model` command, but this may require additional dependencies or setup
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

✅ Light setup

Installs with a command or two; your AI agent can do it for you.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/labelme && curl -fsSL https://workflowstacks.com/api/skills/labelme/claude-skill -o ~/.claude/skills/labelme/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How Labelme: Fast Image Annotation works
Codeflow
Free to inspect

Labelme: Fast Image Annotation is a large Python project (~35k lines across 76 code files, plus 91 test files). Setup is light: installs like a normal app. Reading the code is optional. Last commit this month, GPL-3.0 license, has a test suite.

Size
Large codebase
~35k lines · 76 code files · ~5 h to skim
Setup
One-command install
Installs like a normal app. Reading the code is optional.
Runs on
Python
No API keys detected
Python 100%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    AGENTS.md
    The instructions the AI actually follows
  3. 3
    labelme/__main__.py
    Where the program starts running
  4. 4
    pyproject.toml
    Dependencies and the commands it exposes
  5. 5
    labelme/__init__.py
    Inside labelme/ — the main logic begins here
What's in each folder
labelme/Core code — the actual logic130 files
examples/Examples you can copy148 files
docs/Documentation10 files
tools/Helper scripts2 files
changelog.d/Folder1 files
tests/Tests — proof it works107 files
.github/CI / automation (GitHub Actions)8 files
.out-of-scope/Tool / agent settings3 files
READMEHas testsDocumentedCI checksExamples includedGPL-3.0 licenseUpdated this month
Quick Actions
Details
Creator
wkentaro
Language
Python
Category
design
Published
5/9/2016

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