[kouteiheika]

They probably mean the algorithm, but nevertheless the YOLO models are relatively simple so if you know what you're doing it's pretty easy to reimplement them from scratch and avoid the AGPL license for code. I did so once for the YOLOv11 model myself, so I assume any researcher worth their salt would also be able to do so too if they wanted to commercialize a similar architecture.

[sigmoid10]

You don't just need to reimplement the architecture (which is trivial even for non-researcher level devs), you need to re-train the weights from scratch. According to the legal team behind Yolo, weights (including modifications via fine tuning) fall under the AGPL as well and you need to contact their sales team for a custom license if you want to deviate from AGPL.

[kouteiheika]

At least for the Ultralytics YOLO models this is also relatively easy (I've done it too). These models are tiny by today's standards, so training them from scratch even on consumer hardware is doable in reasonable time. The only tricky part is writing the training code which is a little more complicated than just reimplementing the architecture itself, but, again, if a random scrub like me can do it then any researcher worth their salt will be able to do it too.

[sigmoid10]

You don't just need the training algorithm, but also the training data. Which in turn might have additional license requirements.

[kouteiheika]

AFAIK their pretrained models just use publicly available datasets. From their README:

> YOLO11 Detect, Segment and Pose models pretrained on the COCO dataset are available here, as well as YOLO11 Classify models pretrained on the ImageNet dataset.