--- description: RF-DETR is a real-time transformer for object detection, instance segmentation, and keypoint detection (preview) by Roboflow. DINOv2 backbone, SOTA on COCO (60.1 AP50:95). Apache 2.0. hide: - navigation --- # 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 achieves state-of-the-art accuracy–latency trade-offs: RF-DETR-L reaches 56.5 AP50:95 on COCO at 6.8 ms (NVIDIA T4, TensorRT FP16), and RF-DETR-2XL achieves 60.1 AP50:95 — the first real-time model to exceed 60 AP on COCO. Accepted at [ICLR 2026](https://arxiv.org/abs/2511.09554). RF-DETR uses a DINOv2 vision transformer backbone and supports object detection, instance segmentation, and keypoint detection (preview) in a single, consistent API. Core models (Nano through Large) and all code are released under the Apache 2.0 license; XL and 2XLarge detection models require `rfdetr[plus]` and are provided under PML 1.0. Developed by Isaac Robinson, Peter Robicheaux, Matvei Popov, Deva Ramanan (CMU), and Neehar Peri (CMU) at [Roboflow](https://roboflow.com). If you use RF-DETR in your research, please cite: ```bibtex @inproceedings{robinson2026rfdetr, title = {RF-DETR: Real-Time Detection Transformer}, author = {Robinson, Isaac and Robicheaux, Peter and Popov, Matvei and Ramanan, Deva and Peri, Neehar}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2026}, url = {https://arxiv.org/abs/2511.09554} } ``` ## Install You can install and use `rfdetr` in a [**Python>=3.10**](https://www.python.org/) environment. For detailed installation instructions, including installing from source, and setting up a local development environment, check out our [install](getting-started/install.md) page. !!! example "Installation"

=== "pip" ```bash pip install rfdetr ``` === "uv" ```bash uv pip install rfdetr ``` For uv projects: ```bash uv add rfdetr ``` ## Quickstart

- **Run Detection Models** --- Load and run pre-trained RF-DETR detection models. [:octicons-arrow-right-24: Tutorial](learn/run/detection.md) - **Run Segmentation Models** --- Load and run pre-trained RF-DETR-Seg segmentation models. [:octicons-arrow-right-24: Tutorial](learn/run/segmentation.md) - **Train Models** --- Learn how to fine-tune RF-DETR models for detection and segmentation. [:octicons-arrow-right-24: Tutorial](learn/train/index.md)

## Tutorials

- **Train RF-DETR on a Custom Dataset. Video** --- ![Train RF-DETR on a Custom Dataset](https://i.ytimg.com/vi/-OvpdLAElFA/maxresdefault.jpg){ width="1280" height="720" loading="lazy" } End to end walkthrough of training RF-DETR on a custom dataset. [:octicons-arrow-right-24: Watch the video](https://www.youtube.com/watch?v=-OvpdLAElFA) - **Deploy RF-DETR to NVIDIA Jetson. Article** --- ![Deploy RF-DETR to NVIDIA Jetson](https://blog.roboflow.com/content/images/size/w1000/format/webp/2025/06/inst-3-.png){ width="1000" height="563" loading="lazy" } Instructions for deploying RF-DETR on NVIDIA Jetson with Roboflow Inference. [:octicons-arrow-right-24: Read the tutorial](https://blog.roboflow.com/how-to-deploy-rf-detr-to-an-nvidia-jetson/) - **Train and Deploy RF-DETR with Roboflow** --- ![Train and Deploy RF-DETR with Roboflow](https://blog.roboflow.com/content/images/size/w1000/format/webp/2025/03/img-blog-nycerebro-2.png){ width="1000" height="563" loading="lazy" } Cloud training and hardware deployment workflow using Roboflow. [:octicons-arrow-right-24: Read the tutorial](https://blog.roboflow.com/train-and-deploy-rf-detr-models-with-roboflow/)

## Benchmarks RF-DETR achieves the best accuracy–latency trade-off among real-time object detection and instance segmentation models. It also provides keypoint detection (preview) on COCO person keypoints. For detailed benchmark tables and methodology, check out our [benchmarks](learn/benchmarks.md) page. ### Detection Pareto front — detection accuracy vs latency: RF-DETR-2XL achieves 78.5 COCO AP50 (60.1 AP50:95) at 17.2 ms; RF-DETR-L achieves 75.1 AP50 at 6.8 ms, outperforming YOLO11x at comparable latency

Pareto front — detection accuracy vs latency: RF-DETR-2XL achieves 78.5 COCO AP50 (60.1 AP50:95) at 17.2 ms; RF-DETR-L achieves 75.1 AP50 at 6.8 ms, outperforming YOLO11x at comparable latency

| Architecture | COCO AP₅₀ | COCO AP_50:95 | RF100VL AP₅₀ | RF100VL AP_50:95 | Latency (ms) | Params (M) | Resolution | | ------------ | -------------------- | ----------------------- | ----------------------- | -------------------------- | ------------ | ---------- | ---------- | | RF-DETR-N | 67.6 | 48.4 | 85.0 | 57.7 | 2.3 | 30.5 | 384×384 | | RF-DETR-S | 72.1 | 53.0 | 86.7 | 60.2 | 3.5 | 32.1 | 512×512 | | RF-DETR-M | 73.6 | 54.7 | 87.4 | 61.2 | 4.4 | 33.7 | 576×576 | | RF-DETR-L | 75.1 | 56.5 | 88.2 | 62.2 | 6.8 | 33.9 | 704×704 | | RF-DETR-XL | 77.4 | 58.6 | 88.5 | 62.9 | 11.5 | 126.4 | 700×700 | | RF-DETR-2XL | 78.5 | 60.1 | 89.0 | 63.2 | 17.2 | 126.9 | 880×880 | ### Segmentation Pareto front — segmentation accuracy vs latency: RF-DETR-Seg-2XL achieves 73.1 COCO AP50 (49.9 AP50:95) at 21.8 ms; RF-DETR-Seg-L achieves 70.5 AP50 at 8.8 ms

Pareto front — segmentation accuracy vs latency: RF-DETR-Seg-2XL achieves 73.1 COCO AP50 (49.9 AP50:95) at 21.8 ms; RF-DETR-Seg-L achieves 70.5 AP50 at 8.8 ms

| Architecture | COCO AP₅₀ | COCO AP_50:95 | Latency (ms) | Params (M) | Resolution | | --------------- | -------------------- | ----------------------- | ------------ | ---------- | ---------- | | RF-DETR-Seg-N | 63.0 | 40.3 | 3.4 | 33.6 | 312×312 | | RF-DETR-Seg-S | 66.2 | 43.1 | 4.4 | 33.7 | 384×384 | | RF-DETR-Seg-M | 68.4 | 45.3 | 5.9 | 35.7 | 432×432 | | RF-DETR-Seg-L | 70.5 | 47.1 | 8.8 | 36.2 | 504×504 | | RF-DETR-Seg-XL | 72.2 | 48.8 | 13.5 | 38.1 | 624×624 | | RF-DETR-Seg-2XL | 73.1 | 49.9 | 21.8 | 38.6 | 768×768 | ### Keypoints RF-DETR Keypoint mAP vs latency chart comparing against YOLO26-pose and YOLO11-pose on MS COCO

RF-DETR Keypoint mAP vs latency chart comparing against YOLO26-pose and YOLO11-pose on MS COCO

| Architecture | COCO AP_50:95 | Latency (ms) | Params (M) | Resolution | | -------------------------- | ----------------------- | ------------ | ---------- | ---------- | | RF-DETR Keypoint (Preview) | 71.8 | 9.7 | 126.4 | 576×576 | > Keypoint benchmarks report AP_50:95 (OKS-based); this is the standard COCO keypoint comparison metric. > For the full competitor comparison (YOLO11-pose, YOLO26-pose), see the [Benchmarks](learn/benchmarks.md#keypoints) page. ## Frequently Asked Questions **What is RF-DETR?** RF-DETR (Roboflow Detection Transformer) is a real-time object detection and instance segmentation model from Roboflow, accepted at ICLR 2026. It uses a DINOv2 vision transformer backbone and achieves state-of-the-art accuracy–latency trade-offs on COCO (60.1 AP50:95 for RF-DETR-2XL) and RF100-VL. **How does RF-DETR compare to YOLOv11?** RF-DETR-L achieves 56.5 AP50:95 on COCO at 6.8 ms latency on an NVIDIA T4, outperforming YOLOv11x (50.9 AP) at lower latency. The DINOv2 backbone gives RF-DETR stronger performance on domain-shift benchmarks such as RF100-VL. **What GPU is required to train RF-DETR?** A CUDA-capable GPU with at least 8 GB VRAM (e.g., NVIDIA RTX 3060, T4, A10) is recommended for fine-tuning. Smaller models (RF-DETR-N and RF-DETR-S) can fit in 6 GB VRAM with reduced batch size. CPU inference is supported for evaluation. **Which dataset formats does RF-DETR support?** RF-DETR supports COCO JSON and YOLO-format datasets (with `dataset_file: "yolo"`). Roboflow datasets export directly to both formats. Detection and segmentation datasets use the same format — the model variant determines the task. **Can RF-DETR run in real time?** Yes. RF-DETR-N runs at 2.3 ms per frame on a T4 GPU (TensorRT FP16, batch 1), and RF-DETR-L at 6.8 ms — both well within real-time thresholds. ONNX and TFLite exports are available for edge deployment. **What is the difference between RF-DETR detection and segmentation models?** Detection models (e.g., `RFDETRLarge`) output bounding boxes. Segmentation models (e.g., `RFDETRSegLarge`) additionally output instance masks. Both share the same backbone and training API; segmentation adds a mask head and requires COCO-format segmentation annotations. **Does RF-DETR support keypoint detection?** RF-DETR Keypoint (Preview) detects 17 body keypoints per person on COCO, achieving 71.8 AP50:95 at 9.7 ms on NVIDIA T4. It is available in the `rfdetr` package as `RFDETRKeypointPreview`. See [Run Keypoint Models](learn/run/keypoints.md) for usage. **Is RF-DETR open source?** Yes. Core models (Nano through Large) and all training/inference code are released under the Apache 2.0 license. XLarge and 2XLarge models require the `rfdetr[plus]` package (PML 1.0 license). **How do I fine-tune RF-DETR on a custom dataset?** Instantiate a model and call `model.train(...)` with your dataset directory in COCO JSON or YOLO format. Example: `model = RFDETRLarge(); model.train(dataset_dir='./dataset', epochs=50, batch_size=4)`. The model downloads pretrained weights automatically and saves best checkpoints automatically (use `resume=` to continue from one). **How do I export RF-DETR to ONNX or TensorRT?** Call `model.export(format="onnx")` after training or loading a checkpoint. ONNX export works on CPU and produces a single `.onnx` file compatible with ONNX Runtime and OpenCV DNN. For TensorRT deployment, first export to ONNX and then convert the `.onnx` model with TensorRT tooling or helpers such as `trtexec`; this requires TensorRT and a CUDA GPU. **Which RF-DETR model size should I use?** RF-DETR-Nano (2.3 ms, 67.6 AP50 on COCO) is best for edge and real-time applications. RF-DETR-Large (6.8 ms, 56.5 AP50:95) offers the best accuracy–latency trade-off for server deployment. RF-DETR-2XLarge (17.2 ms, 60.1 AP50:95) maximizes accuracy when latency allows. > **Checkpoint note:** Current `RFDETRLarge` defaults to `rf-detr-large-2026.pth`. The older `rf-detr-large.pth` checkpoint is a legacy Large release kept for backward compatibility and has been superseded by the current release.