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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.
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”
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
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.
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.
Same tradeoff we had with Haar cascades vs the early DPM detectors circa 2008. Open vocab is slower and needs a text encoder in the loop, closed set is fast and dumb. Pick one depending on whether you control the deployment box.
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?
Yep, and the network-use clause is what actually trips people up, not modification. Serve inference over an API and you're on the hook same as if you'd shipped source. Ultralytics has a paid tier to dodge this, curious if Roboflow offers similar.
AGPL only bites you if you modify the code and offer it as a network service. Just running inference with the pretrained weights in your own product doesn't trigger the copyleft clause people panic about.
Ran into this constantly with a wildlife cam project. Turned out our training set was heavy on African animals so anything furry and mid-sized defaulted to elephant/rhino. Fixed it by adding way more negative examples, not just more bear photos.
Global-shutter cameras are fast and expensive, while Doppler radar modules are robust and under $30 these days.
Running machine-vision outside in the Sun or Weather can get tricky. There is also a limited supply of BS a firm can shovel before some bystander ends up dead. =3
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?
Retraining pipeline every time a new YOLO drops is its own maintenance burden. Model gets 2% better, your Docker image breaks, CUDA version mismatch at 3am. Pin your version and don't touch it.
https://github.com/LibreYOLO/libreyolo