Just a reminder that RF-DETR is better than yolo26
An Introduction to YOLO26
21–30 of 38 posts
Re: An Introduction to YOLO26
#22Can it measure the speed of a car on a video ?
Re: An Introduction to YOLO26
#23I'm sure the model is capable, but I find it funny that the sample image that contains three bears gets detected as two elephants.
Re: An Introduction to YOLO26
#24Can it measure the speed of a car on a video ?
Same question, same answer: In pixels/second? Sure!
What are you trying to accomplish by those questions? Are you genuinely asking, or just baiting? If the former, didnt answers to your previous question make it clear that your question makes less sense than you might assume?
Re: An Introduction to YOLO26
#25Was 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 doe…
I would prefer GroundingDINo which is a sort of SAM and Dino combo which does open vocabulary.
Doesn't work for my use-case. GroundingDINO is a text to bounding box model. SAM2 supports coordinate based masks (user taps or clicks somewhere in an image), which is what my research app needs.
Re: An Introduction to YOLO26
#26My 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?
Re: An Introduction to YOLO26
#27I'm sure the model is capable, but I find it funny that the sample image that contains three bears gets detected as two elephants.
It’s an accurate representation of the model capabilities in my experience.
Re: An Introduction to YOLO26
#28Is the license for this AGPL? Can someone please confirm?
Yes AGPL-3.0
Re: An Introduction to YOLO26
#29It’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.)
Re: An Introduction to YOLO26
#30It’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 discl…
“RF-DETR is both faster and more accurate and truly open source with an Apache 2.0 license”
Misleading marketing statement.
The catch is that for image resolutions >=700x700pixels (most production usecases), the roboflow license is actually PML1.0 instead of Apache2.0 https://github.com/roboflow/rf-detr#license