[krapht]

> The catch is that for image resolutions >=700x700pixels (most production usecases)

Citation needed? 2XL looks like you go up to 800x800 pixel inputs. This isn't the dealbreaker you say it is - all pipelines benefit from thoughtful crop and rescaling before going to inference.

[MrGLaDOS]

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.

Rescaling is fine for some purposes but but not for all. For many domain-specific (often less common and odd dimensioned) objects, downscaling will severely reduce recall. There is a reason that Roboflow slaps a license that is not open source on those specific architectures.

In some cases tiled inferencing (for example with https://github.com/obss/sahi ) might do the job.

[yeldarb]

> 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.)

[1] https://arxiv.org/abs/2511.09554 [2] https://arxiv.org/abs/2505.20612