Home Projects glue-factory
glue-factory
Python

glue-factory

Training library for local feature detection and matching

by cvg · GitHub
Stars
Forks
License
Created
Last commit
Language
Likely
Self-hostable
computer-visiondeep-learningiccv2023Apache-2.0PythonSelf-hostable
View on GitHub
In plain words

Train and assess models that identify and match visual features in images.

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 →
glue-factory — GitHub preview card
📈 Star history
1.13k1.12k
2026-07-202026-08-31
📈 Track glue-factory

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

Training library for local feature detection and matching

glue-factory has 1.1k stars on GitHub. It has been forked 153 times. glue-factory is written mainly in Python. It has been in active development since 2023. glue-factory is available under the Apache-2.0 license. Its main topics are computer-vision, deep-learning, iccv2023, image-matching.

Frequently asked questions

What is glue-factory?

Training library for local feature detection and matching

Is glue-factory open source?

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

Is glue-factory free?

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

What license does glue-factory use?

glue-factory is available under the Apache-2.0 license.

What language is glue-factory written in?

glue-factory is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — glue-factory

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

[![olud.ai](https://olud.ai/badge.php?tool=cvg-glue-factory)](https://olud.ai/project/cvg-glue-factory.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.