We've been running YOLO for a number of years (since v5) on soccer videos. None of the recent iterations have been significantly better, with v26 scoring worse then v9 and v11 on our tasks. Makes me wonder why this version is being pushed by roboflow and ultralytics.
When I was working with YOLO models it did seem like any practical improvements were between all of the spinoff models. It seemed people were pushing new models for personal recognition since the original creator stopped working on it.
That said, many of the claimed improvements in this model were are efficiency related.
Ive used YOLO26 in one of my projects, It was very easy to train on our custom dataset and also very easy to deploy even on rust with AVX2 support. This model is indeed fast and can be used for almost real time inference.
That said, many of the claimed improvements in this model were are efficiency related.
If you want to detect objects and speed is important so you can’t use a LLM architecture, you can give it a try too.
Is there a demo like that available for YOLO26?
https://arxiv.org/abs/2606.03748