> See the url in my comment (search for the term rfdetr-2xlarge). 2XL does indeed go up to 800x800 and has PML1.0 license instead of apache 2.0.
All of the models, including the Apache 2.0 ones, can be configured to go higher than 800x800. The difference between the ones with the PML license and the Apache 2.0 ones is the backbone, not the resolution.
I'd suggest you read the ICLR paper[1] which shows clearly the difference between the backbones at various latencies in Figure 1.
> For many domain-specific (often less common and odd dimensioned) objects, downscaling will severely reduce recall.
We released an entire paper[2] at Neurips about the long-tail transferability of models across a multitude of domains and benchmarked RF-DETR against that benchmark. The Apache 2.0 model is pareto optimal over the larger PML model at latencies less than the XL size.
(I'm one of the co-founders of Roboflow and worked on RF-DETR and RF100-VL.)
That may be true for legacy CNNs but very few production use-cases require such a large resolution with DETRs. The latency scales quadratically with the resolution.
Regardless, you can do whatever resolution you want with the Apache 2.0 model. Just change the config at runtime; it was trained to be resolution agnostic.
You are correct that we also released larger models with a larger backbone under a different, non open-source license.
Suspect choice for the paper to only include a single DETR from 2022 in the headline pareto chart and claim to have "the strongest AP–latency trade-off"... Clearly the authors were aware of models that exceed theirs given they even mentioned some of them in the introduction.
> In parallel, DETR [5] cast detection as end-to-end set prediction, and its real-time descendants (RT-DETR [98], D-FINE [55], DEIM [21], RF-DETR [62]) have narrowed the accuracy gap with CNN based detectors on standard benchmarks.
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.
We (Roboflow) have had early access to this model for the past few weeks. It's really, really good. This feels like a seminal moment for computer vision. I think there's a real possibility this launch goes down in history as "the GPT Moment" for vision.
The two areas I think this model is going to be transformative in the immediate term are for rapid prototyping and distillation.
Two years ago we released autodistill[1], an open source framework that uses large foundation models to create training data for training small realtime models. I'm convinced the idea was right, but too early; there wasn't a big model good enough to be worth distilling from back then. SAM3 is finally that model (and will be available in Autodistill today).
We are also taking a big bet on SAM3 and have built it into Roboflow as an integral part of the entire build and deploy pipeline[2], including a brand new product called Rapid[3], which reimagines the computer vision pipeline in a SAM3 world. It feels really magical to go from an unlabeled video to a fine-tuned realtime segmentation model with minimal human intervention in just a few minutes (and we rushed the release of our new SOTA realtime segmentation model[4] last week because it's the perfect lightweight complement to the large & powerful SAM3).
We also have a playground[5] up where you can play with the model and compare it to other VLMs.
We (Roboflow) have had early access to this model for the past few weeks. It's really, really good. This feels like a seminal moment for computer vision. I think there's a real possibility this launch goes down in history as "the GPT Moment" for vision.
The two areas I think this model is going to be transformative in the immediate term are for rapid prototyping and distillation.
Two years ago we released autodistill[1], an open source framework that uses large foundation models to create training data for training small realtime models. I'm convinced the idea was right, but too early; there wasn't a big model good enough to be worth distilling from back then. SAM3 is finally that model (and will be available in Autodistill today).
We are also taking a big bet on SAM3 and have built it into Roboflow as an integral part of the entire build and deploy pipeline[2], including a brand new product called Rapid[3], which reimagines the computer vision pipeline in a SAM3 world. It feels really magical to go from an unlabeled video to a fine-tuned realtime segmentation model with minimal human intervention in just a few minutes (and we rushed the release of our new SOTA realtime segmentation model[4] last week because it's the perfect lightweight complement to the large & powerful SAM3).
We also have a playground[5] up where you can play with the model and compare it to other VLMs.
How can a project with 260 contributors and no CLA change their license? Wouldn’t they have to get approval from all of those contributors or remove all that code?
All of the models, including the Apache 2.0 ones, can be configured to go higher than 800x800. The difference between the ones with the PML license and the Apache 2.0 ones is the backbone, not the resolution.
I'd suggest you read the ICLR paper[1] which shows clearly the difference between the backbones at various latencies in Figure 1.
> For many domain-specific (often less common and odd dimensioned) objects, downscaling will severely reduce recall.
We released an entire paper[2] at Neurips about the long-tail transferability of models across a multitude of domains and benchmarked RF-DETR against that benchmark. The Apache 2.0 model is pareto optimal over the larger PML model at latencies less than the XL size.
(I'm one of the co-founders of Roboflow and worked on RF-DETR and RF100-VL.)
[1] https://arxiv.org/abs/2511.09554 [2] https://arxiv.org/abs/2505.20612