automation

Tator: Fast Image Data Annotation

Automate image data annotation with Tator, a tool. Get fast annotation with foundation models.
93 stars13 forksPythonGuide quality 8/10Updated 8/8/2026100% free · open source
What it does

Tator automates image data annotation by leveraging foundation models for fast and efficient annotation

When to use it
  • When you have a large dataset of images that require annotation for machine learning model training
  • When you need to accelerate your image data annotation process without sacrificing accuracy
  • When you want to reduce the time and cost associated with manual image data annotation
Ready-to-paste prompt
python tator/annotate.py --image-path /path/to/image/directory --model_name 'resnet50' --output /path/to/output/directory
Heads up: Make sure you have the necessary GPU resources and a compatible CUDA version installed to run the foundation models, as Tator relies on GPU acceleration for efficient annotation
Saves to your device
Use with Claude
New

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

🔑 Needs an API key

Works after you add credentials — the setup agent will ask you for them.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/tator && curl -fsSL https://workflowstacks.com/api/skills/tator/claude-skill -o ~/.claude/skills/tator/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 Tator: Fast Image Data Annotation works
Codeflow
Free to inspect

Tator: Fast Image Data Annotation is a very large Python project (~105k lines across 235 code files, plus 185 test files). Setup is light: installs like a normal app — add your API keys. Reading the code is optional. Last commit this month, no license file, has a test suite.

Size
Very large codebase
~105k lines · 235 code files · days to read — use, don't read
Setup
One-command install
Installs like a normal app — add your API keys. Reading the code is optional.
Runs on
Python
Needs API keys (.env)
Python 75%JavaScript 21%HTML 3%CSS 1%
Where to start reading
  1. 1
    readme.md
    Start here — what it does and how to install it
  2. 2
    pyproject.toml
    Dependencies and the commands it exposes
  3. 3
    .env.example
    The API keys and settings you must provide
  4. 4
    app/__init__.py
    Inside app/ — the main logic begins here
What's in each folder
app/Core code — the actual logic1 files
services/Backend / API66 files
api/Backend / API39 files
docs/Documentation10 files
tools/Helper scripts93 files
utils/Helper scripts25 files
ybat-master/Folder16 files
models/Data models & types4 files
READMEHas testsDocumentedNo CINo licenseUpdated this month
Quick Actions
Details
Creator
stephansturges
Language
Python
Category
automation
Published
3/3/2025

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