An Introduction to YOLO26 (opens in new tab)

It’s a big improvement if you’re already paying them but, given their aggressive approach to licensing, I can’t imagine why anyone would choose to use an Ultralytics model on a new project in 2026. You’re just asking to be shaken down and have to pay off a large bill down the line.

RF-DETR is both faster and more accurate and truly open source with an Apache 2.0 license: https://github.com/roboflow/rf-detr

Full disclosure: I’m one of the co-founders of Roboflow (we made RF-DETR, wrote this blog post, and are a sub-licensor of Ultralytics’ models.)

FWIW there are today many more alternatives with better license. Here is a good meta repo for object detection with different model variants:

https://github.com/LibreYOLO/libreyolo

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.

My buddy has some vision impairments, and I remember training a much older of YOLO's models to detect objects/enemies in Terraria for him. It worked very well.

I then tried trained it on a lot of sample images from a 3D point & shoot game, and was quite disappointed in how it performed.

Has anyone else experimented with it recently? How does this suit as a base-model for training custom classifiers? And with hardware growth in the last ~5 years, is it suitable to run in parallel with games which are graphically intensive?

Was evaluating YOLO26 within the last month for its on-device (iPhone 16 Pro) segmentation capabilities. Its decent, but its biggest limitation is that its only trained on 80 COCO classes (meaning pre-labeled images). If whatever is in your images isn't in the 80 classes, its invisible to YOLO26. Conversely I have SAM2 running on-device and its my current workhorse. The biggest benefit with SAM2 for me is that it does fine-grained segmentation masks but isn't trained on labeled images. This was a specific requirement for the app I'm building. SAM2 isn't anywhere as speedy as the native Vision framework apis, but it is more capable across a vastly wider array of potential image targets.

Is the license for this AGPL? Can someone please confirm?

I found that while CLIPSeg is slower than YOLOn, it is still pretty fast and if gave me much much better results without training.

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.

Wow I'm old, I still remember working with YOLOv2.

Reminder that Ultralytics is pushing AGPL in a very overreaching way with their models that's why they are not available in Frigate

https://github.com/blakeblackshear/frigate/pull/10717

I'm sure the model is capable, but I find it funny that the sample image that contains three bears gets detected as two elephants.

One thing I don’t get I why the article is credited to ‘Contributing Author’.

Meanwhile their very own Peter Skalski already does super job with host write ups and examples of all YOLO sorts and is well respected.

With some previous versions of YOLO I‘ve found pages that run it in real-time locally on your browser, analyzing the webcam.

Is there a demo like that available for YOLO26?

Just a reminder that RF-DETR is better than yolo26

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.

Can it measure the speed of a car on a video ?

I am curious why there is no desire to produce a paper showcasing key details.