alt Hacker NewsAn Introduction to YOLO26
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FWIW there are today many more alternatives with better license. Here is a good meta repo for object detection with different model variants:
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.
Just a reminder that RF-DETR is better than yolo26
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.
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.
Is the license for this AGPL? Can someone please confirm?
Wow I'm old, I still remember working with YOLOv2.
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?
Reminder that Ultralytics is pushing AGPL in a very overreaching way with their models that's why they are not available in Frigate
I am curious why there is no desire to produce a paper showcasing key details.
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.
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.