Home Projects yolov3
yolov3
Python

yolov3

Ultralytics YOLOv3 in PyTorch > ONNX > CoreML > TFLite

by ultralytics · GitHub
Stars
Forks
License
Created
Last commit
Language
deep-learningmachine-learningobject-detectionAGPL-3.0Python
View on GitHub
In plain words

Detect objects in images using a fast and efficient model that you can train and deploy.

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 →
yolov3 — GitHub preview card
📈 Star history
10 58310 575
2026-07-042026-08-31
📈 Track yolov3

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

Ultralytics YOLOv3 in PyTorch > ONNX > CoreML > TFLite

yolov3 has 10.6k stars on GitHub. It has been forked 3.4k times. yolov3 is written mainly in Python. It has been in active development since 2018. yolov3 is available under the AGPL-3.0 license. Its main topics are deep-learning, machine-learning, object-detection, ultralytics.

Frequently asked questions

What is yolov3?

Ultralytics YOLOv3 in PyTorch > ONNX > CoreML > TFLite

Is yolov3 open source?

yolov3 is an open-source project. It is released under the AGPL-3.0 license.

Is yolov3 free?

Yes. yolov3 is free and open source — you can use, modify and self-host it. Its AGPL-3.0 license is a strong copyleft: if you distribute a modified version — including offering it as a network service — your changes must be released under the same license.

What license does yolov3 use?

yolov3 is available under the AGPL-3.0 license.

What language is yolov3 written in?

yolov3 is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — yolov3

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

[![olud.ai](https://olud.ai/badge.php?tool=ultralytics-yolov3)](https://olud.ai/project/ultralytics-yolov3.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.