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LibreYOLO mentions

Xuban

This is a living page. Every so often LibreYOLO shows up somewhere: a conference tutorial, a comparison blog, a Reddit thread. This page collects those mentions in one place. It gets updated as new ones come in, so check back.

If you have written about LibreYOLO, given a talk with it, or spotted it somewhere we missed, we would love to add it. Open an issue on GitHub or reach out.

Talks and conferences

  • CVPR 2026, "Edge AI in Action: Mastering On-Device Inference" (slides). A team from Jabra and the IT University of Copenhagen picked LibreYOLOXs as the example model for their edge-inference tutorial in Denver, running it on a Hailo-8L and on Snapdragon. We wrote up the full story here: LibreYOLO showed up at CVPR 2026.

In production

  • Morgan Rural Tech (site). This Queensland agtech firm, building AI-powered animal detection and other tools for rural operations, lists LibreYOLO under "Technologies We Work With" in their footer, alongside TensorRT for object detection.

Blogs and comparisons

  • Lightly, "Best Ultralytics Alternatives in 2026" (article). Lightly lists LibreYOLO among the top alternatives, highlighting the MIT license as "the most permissive option on this list" and the familiar train() / predict() / val() / export() API for easy migration.

On Hacker News

When Roboflow's "An Introduction to YOLO26" reached the Hacker News front page, someone recommended LibreYOLO in the comments as a license-clean alternative: "there are today many more alternatives with better license. Here is a good meta repo for object detection with different model variants." The community upvoted that comment straight to the top of the thread, and a wave of readers followed the link and starred the repo off the back of it. You can read it here: An Introduction to YOLO26 on Hacker News.

The community on Reddit

We post LibreYOLO releases on r/computervision, and the reception has been incredible. Across the three threads below: more than 90,000 combined views, over 500 upvotes, and over 110 comments from people happy to finally have a permissive, MIT-licensed option. The posts are ours, but the comments are all community, and they say it better than we could. Go read them:

Social

  • Hitesh Choudhary, developer educator and YouTuber, shared LibreYOLO with his audience: on LinkedIn and on X.
  • Katsuya Hyodo (PINTO0309), research engineer and maintainer of the widely used PINTO model zoo, mentioned LibreYOLO on X.

Try it

pip install libreyolo

from libreyolo import LibreYOLO

model = LibreYOLO("LibreYOLOXs.pt")
model.predict("image.jpg")

LibreYOLO is MIT-licensed, runs on Linux, Mac, and Windows, and works on GPU, Apple Silicon, and plain CPU with no code change. One API spans YOLOX, RF-DETR, D-FINE, DEIM, YOLO-NAS, segmentation, pose, depth, and more.

Star it on GitHub: github.com/LibreYOLO/libreyolo | Docs: libreyolo.com/docs