RF-DETR+: Large-Scale Detection Models for RF-DETR

RF-DETR is the core package in the ecosystem. It provides the full training and inference stack, the {Nano, Small, Medium, Large} model lineup, and the APIs most users build on. RF-DETR+ is an extension package for RF-DETR that adds the XLarge and 2XLarge detection models for maximum accuracy.

RF-DETR+ models use a DINOv2 vision transformer backbone at higher resolutions and larger feature dimensions than the core RF-DETR lineup, pushing state-of-the-art accuracy on Microsoft COCO and RF100-VL while retaining real-time inference speeds. Use RF-DETR for the standard model set and RF-DETR+ when you need the highest-accuracy variants.

Install

Install RF-DETR+ in a Python>=3.10 environment with pip. This will also install rfdetr as a dependency, which provides the core APIs and model definitions.

pip install rfdetr-plus

Install from source

pip install git+https://github.com/roboflow/rf-detr-plus.git

Benchmarks

RF-DETR+ XLarge and 2XLarge sit at the top of the RF-DETR accuracy/latency curve, delivering the highest COCO AP scores in the family. All latency numbers were measured on an NVIDIA T4 using TensorRT, FP16, and batch size 1.

Size	Class	COCO AP50	COCO AP50:95	RF100VL AP50	RF100VL AP50:95	Latency (ms)	Params (M)	Resolution	Package / License
N	RFDETRNano	67.6	48.4	85.0	57.7	2.3	30.5	384x384	rfdetr / Apache 2.0
S	RFDETRSmall	72.1	53.0	86.7	60.2	3.5	32.1	512x512	rfdetr / Apache 2.0
M	RFDETRMedium	73.6	54.7	87.4	61.2	4.4	33.7	576x576	rfdetr / Apache 2.0
L	RFDETRLarge	75.1	56.5	88.2	62.2	6.8	33.9	704x704	rfdetr / Apache 2.0
⭐ XL	RFDETRXLarge	77.4	58.6	88.5	62.9	11.5	126.4	700x700	rfdetr_plus / PML 1.0
⭐ 2XL	RFDETR2XLarge	78.5	60.1	89.0	63.2	17.2	126.9	880x880	rfdetr_plus / PML 1.0

Run Models

Install RF-DETR+ to use XL and 2XL models alongside the core RF-DETR lineup:

pip install rfdetr_plus

RF-DETR+ models require you to accept the Platform Model License before use. Once accepted, usage mirrors the standard RF-DETR API -- you import the models from rfdetr_plus and keep using the rfdetr utilities:

import requests
import supervision as sv
from PIL import Image
from rfdetr_plus import RFDETRXLarge
from rfdetr.util.coco_classes import COCO_CLASSES

model = RFDETRXLarge(accept_platform_model_license=True)

image = Image.open(requests.get("https://media.roboflow.com/dog.jpg", stream=True).raw)
detections = model.predict(image, threshold=0.5)

labels = [f"{COCO_CLASSES[class_id]}" for class_id in detections.class_id]

annotated_image = sv.BoxAnnotator().annotate(image, detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels)

Train Models

RF-DETR+ models support fine-tuning with the same training API as core RF-DETR. You can train on your own dataset or use datasets from Roboflow Universe.

from rfdetr_plus import RFDETRXLarge

model = RFDETRXLarge(accept_platform_model_license=True)
model.train(dataset_dir="path/to/dataset", epochs=50, lr=1e-4)

Documentation

Visit the RF-DETR documentation website to learn more about training, export, deployment, and the full model lineup.

License

RF-DETR+ code and model checkpoints are licensed under the Platform Model License 1.0 (PML-1.0). See LICENSE for details. These models require a Roboflow account to run and fine-tune.

The core RF-DETR models (Nano through Large) are available under the Apache License 2.0 in the rfdetr package.

Acknowledgements

Our work is built upon LW-DETR, DINOv2, and Deformable DETR. Thanks to their authors for their excellent work!

Citation

If you find our work helpful for your research, please consider citing the following BibTeX entry.

@misc{rf-detr,
    title={RF-DETR: Neural Architecture Search for Real-Time Detection Transformers},
    author={Isaac Robinson and Peter Robicheaux and Matvei Popov and Deva Ramanan and Neehar Peri},
    year={2025},
    eprint={2511.09554},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2511.09554},
}

Contribute

We welcome and appreciate all contributions! If you notice any issues or bugs, have questions, or would like to suggest new features, please open an issue or pull request. By sharing your ideas and improvements, you help make RF-DETR better for everyone.