Home Projects tfjs-yolo-tiny
tfjs-yolo-tiny
JavaScript

tfjs-yolo-tiny

In-Browser Object Detection using Tiny YOLO on Tensorflow.js

by ModelDepot · GitHub
Stars
Forks
License
Created
Last commit
Category
Language
browsercomputer-visiondeep-learningMITJavaScript
View on GitHub
In plain words

Detect objects in images directly from your web browser using a lightweight model for quick analysis.

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 →
tfjs-yolo-tiny — GitHub preview card
📈 Star history
534533
2026-07-202026-08-31
📈 Track tfjs-yolo-tiny

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

In-Browser Object Detection using Tiny YOLO on Tensorflow.js

tfjs-yolo-tiny has 533 stars on GitHub. It has been forked 88 times. tfjs-yolo-tiny is written mainly in JavaScript. It has been in active development since 2018. tfjs-yolo-tiny is available under the MIT license. Its main topics are browser, computer-vision, deep-learning, detection.

Frequently asked questions

What is tfjs-yolo-tiny?

In-Browser Object Detection using Tiny YOLO on Tensorflow.js

Is tfjs-yolo-tiny open source?

tfjs-yolo-tiny is an open-source project. It is released under the MIT license.

Is tfjs-yolo-tiny free?

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

What license does tfjs-yolo-tiny use?

tfjs-yolo-tiny is available under the MIT license.

What language is tfjs-yolo-tiny written in?

tfjs-yolo-tiny is written mainly in JavaScript.

🏅 Maintainer of this project?
olud.ai badge — tfjs-yolo-tiny

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

[![olud.ai](https://olud.ai/badge.php?tool=modeldepot-tfjs-yolo-tiny)](https://olud.ai/project/modeldepot-tfjs-yolo-tiny.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.