rfdetr 1.8.0

RF-DETR

RF-DETR: Real-Time SOTA Object Detection, Instance Segmentation, and Keypoint Detection

RF-DETR is a real-time transformer architecture for object detection, instance segmentation, and keypoint detection (preview) developed by Roboflow. Built on a DINOv2 vision transformer backbone, RF-DETR delivers state-of-the-art accuracy and latency trade-offs on Microsoft COCO and RF100-VL.

RF-DETR uses a DINOv2 vision transformer backbone and supports object detection, instance segmentation, and keypoint detection (preview) in a single, consistent API. The open-source rfdetr package and Apache-designated models are released under Apache 2.0, while Plus components (rfdetr_plus, including RF-DETR-XL/2XL detection models) are licensed under PML 1.0.

https://github.com/user-attachments/assets/add23fd1-266f-4538-8809-d7dd5767e8e6

Install

To install RF-DETR, install the rfdetr package in a Python>=3.10 environment with pip.

pip install rfdetr

Install from source

By installing RF-DETR from source, you can explore the most recent features and enhancements that have not yet been officially released. Please note that these updates are still in development and may not be as stable as the latest published release.

pip install https://github.com/roboflow/rf-detr/archive/refs/heads/develop.zip

Benchmarks

RF-DETR achieves state-of-the-art results in both object detection and instance segmentation, with benchmarks reported on Microsoft COCO and RF100-VL (RF100-VL for detection only). The charts and tables below compare RF-DETR against other top real-time models across accuracy and latency for detection and segmentation. All latency numbers were measured on an NVIDIA T4 using TensorRT, FP16, and batch size 1. For full benchmarking methodology and reproducibility details, see roboflow/sab.

Detection

See object detection benchmark numbers

Architecture	COCO AP50	COCO AP50:95	RF100VL AP50	RF100VL AP50:95	Latency (ms)	Params (M)	Resolution	License
RF-DETR-N	67.6	48.4	85.0	57.7	2.3	30.5	384x384	Apache 2.0
RF-DETR-S	72.1	53.0	86.7	60.2	3.5	32.1	512x512	Apache 2.0
RF-DETR-M	73.6	54.7	87.4	61.2	4.4	33.7	576x576	Apache 2.0
RF-DETR-L	75.1	56.5	88.2	62.2	6.8	33.9	704x704	Apache 2.0
RF-DETR-XL △	77.4	58.6	88.5	62.9	11.5	126.4	700x700	PML 1.0
RF-DETR-2XL △	78.5	60.1	89.0	63.2	17.2	126.9	880x880	PML 1.0
YOLO11-N	52.0	37.4	81.4	55.3	2.5	2.6	640x640	AGPL-3.0
YOLO11-S	59.7	44.4	82.3	56.2	3.2	9.4	640x640	AGPL-3.0
YOLO11-M	64.1	48.6	82.5	56.5	5.1	20.1	640x640	AGPL-3.0
YOLO11-L	64.9	49.9	82.2	56.5	6.5	25.3	640x640	AGPL-3.0
YOLO11-X	66.1	50.9	81.7	56.2	10.5	56.9	640x640	AGPL-3.0
YOLO26-N	55.8	40.3	76.7	52.0	1.7	2.6	640x640	AGPL-3.0
YOLO26-S	64.3	47.7	82.7	57.0	2.6	9.4	640x640	AGPL-3.0
YOLO26-M	69.7	52.5	84.4	58.7	4.4	20.1	640x640	AGPL-3.0
YOLO26-L	71.1	54.1	85.0	59.3	5.7	25.3	640x640	AGPL-3.0
YOLO26-X	74.0	56.9	85.6	60.0	9.6	56.9	640x640	AGPL-3.0
LW-DETR-T	60.7	42.9	84.7	57.1	1.9	12.1	640x640	Apache 2.0
LW-DETR-S	66.8	48.0	85.0	57.4	2.6	14.6	640x640	Apache 2.0
LW-DETR-M	72.0	52.6	86.8	59.8	4.4	28.2	640x640	Apache 2.0
LW-DETR-L	74.6	56.1	87.4	61.5	6.9	46.8	640x640	Apache 2.0
LW-DETR-X	76.9	58.3	87.9	62.1	13.0	118.0	640x640	Apache 2.0
D-FINE-N	60.2	42.7	84.4	58.2	2.1	3.8	640x640	Apache 2.0
D-FINE-S	67.6	50.6	85.3	60.3	3.5	10.2	640x640	Apache 2.0
D-FINE-M	72.6	55.0	85.5	60.6	5.4	19.2	640x640	Apache 2.0
D-FINE-L	74.9	57.2	86.4	61.6	7.5	31.0	640x640	Apache 2.0
D-FINE-X	76.8	59.3	86.9	62.2	11.5	62.0	640x640	Apache 2.0

Segmentation

See instance segmentation benchmark numbers

Architecture	COCO AP50	COCO AP50:95	Latency (ms)	Params (M)	Resolution	License
RF-DETR-Seg-N	63.0	40.3	3.4	33.6	312x312	Apache 2.0
RF-DETR-Seg-S	66.2	43.1	4.4	33.7	384x384	Apache 2.0
RF-DETR-Seg-M	68.4	45.3	5.9	35.7	432x432	Apache 2.0
RF-DETR-Seg-L	70.5	47.1	8.8	36.2	504x504	Apache 2.0
RF-DETR-Seg-XL	72.2	48.8	13.5	38.1	624x624	Apache 2.0
RF-DETR-Seg-2XL	73.1	49.9	21.8	38.6	768x768	Apache 2.0
YOLOv8-N-Seg	45.6	28.3	3.5	3.4	640x640	AGPL-3.0
YOLOv8-S-Seg	53.8	34.0	4.2	11.8	640x640	AGPL-3.0
YOLOv8-M-Seg	58.2	37.3	7.0	27.3	640x640	AGPL-3.0
YOLOv8-L-Seg	60.5	39.0	9.7	46.0	640x640	AGPL-3.0
YOLOv8-XL-Seg	61.3	39.5	14.0	71.8	640x640	AGPL-3.0
YOLOv11-N-Seg	47.8	30.0	3.6	2.9	640x640	AGPL-3.0
YOLOv11-S-Seg	55.4	35.0	4.6	10.1	640x640	AGPL-3.0
YOLOv11-M-Seg	60.0	38.5	6.9	22.4	640x640	AGPL-3.0
YOLOv11-L-Seg	61.5	39.5	8.3	27.6	640x640	AGPL-3.0
YOLOv11-XL-Seg	62.4	40.1	13.7	62.1	640x640	AGPL-3.0
YOLO26-N-Seg	54.3	34.7	2.31	2.7	640x640	AGPL-3.0
YOLO26-S-Seg	62.4	40.2	3.47	10.4	640x640	AGPL-3.0
YOLO26-M-Seg	67.8	44.0	6.32	23.6	640x640	AGPL-3.0
YOLO26-L-Seg	69.8	45.5	7.58	28.0	640x640	AGPL-3.0
YOLO26-X-Seg	71.6	46.8	12.92	62.8	640x640	AGPL-3.0

Keypoints

See keypoint detection benchmark numbers

Architecture	COCO AP50:95	Latency (ms)	License
RF-DETR Keypoint (Preview)	71.8	9.7	Apache 2.0
YOLO11-pose N	48.9	3.2	AGPL-3.0
YOLO11-pose S	57.5	3.4	AGPL-3.0
YOLO11-pose M	64.2	5.2	AGPL-3.0
YOLO11-pose L	65.2	6.6	AGPL-3.0
YOLO11-pose X	68.6	10.6	AGPL-3.0
YOLO26-pose N	55.9	1.9	AGPL-3.0
YOLO26-pose S	62.0	2.7	AGPL-3.0
YOLO26-pose M	68.0	4.6	AGPL-3.0
YOLO26-pose L	69.2	5.9	AGPL-3.0
YOLO26-pose X	71.0	9.8	AGPL-3.0

Keypoint benchmarks report AP_50:95 (OKS-based); this is the standard COCO keypoint comparison metric.

Run Models

Detection

RF-DETR provides multiple model sizes, ranging from Nano to 2XLarge. To use a different model size, replace the class name in the code snippet below with another class from the table.

import supervision as sv
from rfdetr import RFDETRMedium
from rfdetr.assets.coco_classes import COCO_CLASSES

model = RFDETRMedium()

detections = model.predict("https://media.roboflow.com/dog.jpg", threshold=0.5)

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

annotated_image = sv.BoxAnnotator().annotate(detections.metadata["source_image"], detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels)

Run RF-DETR with Inference

You can also run RF-DETR models using the Inference library. To switch model size, select the appropriate inference package alias from the table below.

import requests
import supervision as sv
from PIL import Image
from inference import get_model

model = get_model("rfdetr-medium")

image = Image.open(requests.get("https://media.roboflow.com/dog.jpg", stream=True).raw)
predictions = model.infer(image, confidence=0.5)[0]
detections = sv.Detections.from_inference(predictions)

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

Size	RF-DETR package class	Inference package alias	COCO AP50	COCO AP50:95	Latency (ms)	Params (M)	Resolution	License
N	RFDETRNano	rfdetr-nano	67.6	48.4	2.3	30.5	384x384	Apache 2.0
S	RFDETRSmall	rfdetr-small	72.1	53.0	3.5	32.1	512x512	Apache 2.0
M	RFDETRMedium	rfdetr-medium	73.6	54.7	4.4	33.7	576x576	Apache 2.0
L	RFDETRLarge	rfdetr-large	75.1	56.5	6.8	33.9	704x704	Apache 2.0
XL	RFDETRXLarge △	rfdetr-xlarge	77.4	58.6	11.5	126.4	700x700	PML 1.0
2XL	RFDETR2XLarge △	rfdetr-2xlarge	78.5	60.1	17.2	126.9	880x880	PML 1.0

△ Requires the rfdetr_plus extension: pip install rfdetr[plus]. See License for details.

Segmentation

RF-DETR supports instance segmentation with model sizes from Nano to 2XLarge. To use a different model size, replace the class name in the code snippet below with another class from the table.

import supervision as sv
from rfdetr import RFDETRSegMedium
from rfdetr.assets.coco_classes import COCO_CLASSES

model = RFDETRSegMedium()

detections = model.predict("https://media.roboflow.com/dog.jpg", threshold=0.5)

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

annotated_image = sv.MaskAnnotator().annotate(detections.metadata["source_image"], detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels)

Run RF-DETR-Seg with Inference

You can also run RF-DETR-Seg models using the Inference library. To switch model size, select the appropriate inference package alias from the table below.

import requests
import supervision as sv
from PIL import Image
from inference import get_model

model = get_model("rfdetr-seg-medium")

image = Image.open(requests.get("https://media.roboflow.com/dog.jpg", stream=True).raw)
predictions = model.infer(image, confidence=0.5)[0]
detections = sv.Detections.from_inference(predictions)

annotated_image = sv.MaskAnnotator().annotate(image, detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections)

Size	RF-DETR package class	Inference package alias	COCO AP50	COCO AP50:95	Latency (ms)	Params (M)	Resolution	License
N	RFDETRSegNano	rfdetr-seg-nano	63.0	40.3	3.4	33.6	312x312	Apache 2.0
S	RFDETRSegSmall	rfdetr-seg-small	66.2	43.1	4.4	33.7	384x384	Apache 2.0
M	RFDETRSegMedium	rfdetr-seg-medium	68.4	45.3	5.9	35.7	432x432	Apache 2.0
L	RFDETRSegLarge	rfdetr-seg-large	70.5	47.1	8.8	36.2	504x504	Apache 2.0
XL	RFDETRSegXLarge	rfdetr-seg-xlarge	72.2	48.8	13.5	38.1	624x624	Apache 2.0
2XL	RFDETRSeg2XLarge	rfdetr-seg-2xlarge	73.1	49.9	21.8	38.6	768x768	Apache 2.0

Keypoints

RF-DETR supports keypoint detection (preview) with RFDETRKeypointPreview, pretrained on COCO person keypoints.

from rfdetr import RFDETRKeypointPreview

model = RFDETRKeypointPreview()
key_points = model.predict("image.jpg", threshold=0.5)

Size	RF-DETR package class	COCO AP50:95	Latency (ms)	Params (M)	Resolution	License
Keypoint (Preview)	RFDETRKeypointPreview	71.8	9.7	126.4	576x576	Apache 2.0

Train Models

RF-DETR supports training for object detection, instance segmentation, and keypoint detection (preview). You can train models in Google Colab or directly on the Roboflow platform. Below you will find a step-by-step video fine-tuning tutorial.

Documentation

Visit our documentation website to learn more about how to use RF-DETR.

License

Licensing is split by component:

• The open-source rfdetr package and Apache-designated model weights are licensed under Apache License 2.0. See LICENSE.
• Plus components, including the rfdetr_plus extension and RF-DETR-XL / RF-DETR-2XL detection models, are licensed under PML 1.0.

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.