Tator: Fast Image Data Annotation
From the repo · “a *fast* image-data annotator leveraging foundation models for automation”
Tator automates image data annotation by leveraging foundation models for fast and efficient annotation
- •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
python tator/annotate.py --image-path /path/to/image/directory --model_name 'resnet50' --output /path/to/output/directory
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Works after you add credentials — the setup agent will ask you for them.
mkdir -p ~/.claude/skills/tator && curl -fsSL https://workflowstacks.com/api/skills/tator/claude-skill -o ~/.claude/skills/tator/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
Tator: Fast Image Data Annotation is a very large Python project (~168k lines across 339 code files, plus 216 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.
- 1readme.mdStart here — what it does and how to install it
- 2pyproject.tomlDependencies and the commands it exposes
- 3.env.exampleThe API keys and settings you must provide
- 4app/__init__.pyInside app/ — the main logic begins here
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Works after you add credentials — the setup agent will ask you for them.
mkdir -p ~/.claude/skills/tator && curl -fsSL https://workflowstacks.com/api/skills/tator/claude-skill -o ~/.claude/skills/tator/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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