Home Projects autodistill
autodistill
Python

autodistill

Images to inference with no labeling (use foundation models to train supervised models).

by autodistill · GitHub
Stars
Forks
License
Created
Last commit
Language
auto-labelingcomputer-visiondeep-learningApache-2.0Python
View on GitHub
In plain words

Automatically label images and create custom models without any manual work.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
autodistill — GitHub preview card
📈 Star history
2 7422 736
2026-07-072026-08-31
📈 Track autodistill

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

Images to inference with no labeling (use foundation models to train supervised models).

autodistill has 2.7k stars on GitHub. It has been forked 219 times. autodistill is written mainly in Python. It has been in active development since 2023. autodistill is available under the Apache-2.0 license. Its main topics are auto-labeling, computer-vision, deep-learning, foundation-models.

Frequently asked questions

What is autodistill?

Images to inference with no labeling (use foundation models to train supervised models).

Is autodistill open source?

autodistill is an open-source project. It is released under the Apache-2.0 license.

Is autodistill free?

Yes. autodistill is free and open source — you can use, modify and self-host it.

What license does autodistill use?

autodistill is available under the Apache-2.0 license.

What language is autodistill written in?

autodistill is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — autodistill

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=autodistill-autodistill)](https://olud.ai/project/autodistill-autodistill.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.