RF-DETR: Real-Time SOTA Detection and Segmentation

RF-DETR is a real-time transformer architecture for object detection and instance segmentation 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 both detection and instance segmentation 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.

bash
pip install rfdetr

<details> <summary>Install from source</summary>

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.

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

</details>

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. 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

<img alt="rf_detr_1-4_latency_accuracy_object_detection" src="https://storage.googleapis.com/com-roboflow-marketing/rf-detr/rf_detr_1-4_latency_accuracy_object_detection.png" /> <details> <summary>See object detection benchmark numbers</summary>

Architecture	COCO AP<sub>50</sub>	COCO AP<sub>50:95</sub>	RF100VL AP<sub>50</sub>	RF100VL AP<sub>50:95</sub>	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

</details>

Segmentation

<img alt="rf_detr_1-4_latency_accuracy_instance_segmentation" src="https://storage.googleapis.com/com-roboflow-marketing/rf-detr/rf_detr_1-4_latency_accuracy_instance_segmentation.png" /> <details> <summary>See instance segmentation benchmark numbers</summary>

Architecture	COCO AP<sub>50</sub>	COCO AP<sub>50:95</sub>	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

</details>

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.

python
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)

<details> <summary>Run RF-DETR with Inference</summary>

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.

python
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)

</details>

Size	RF-DETR package class	Inference package alias	COCO AP<sub>50</sub>	COCO AP<sub>50:95</sub>	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.

python
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)

<details> <summary>Run RF-DETR-Seg with Inference</summary>

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.

python
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)

</details>

Size	RF-DETR package class	Inference package alias	COCO AP<sub>50</sub>	COCO AP<sub>50:95</sub>	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

Train Models

RF-DETR supports training for both object detection and instance segmentation. 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.

bibtex
@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.