https://github.com/roboflow/rf-detr_plus
https://github.com/roboflow/rf-detr_plus
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/roboflow/rf-detr_plus
- Owner: roboflow
- License: other
- Created: 2026-02-04T03:24:04.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2026-02-09T18:22:54.000Z (5 months ago)
- Last Synced: 2026-02-09T18:38:02.162Z (5 months ago)
- Language: Python
- Size: 44.9 KB
- Stars: 2
- Watchers: 0
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
# RF-DETR+: Large-Scale Detection Models for RF-DETR
[](https://badge.fury.io/py/rfdetr-plus)
[](https://pypistats.org/packages/rfdetr-plus)
[](https://badge.fury.io/py/rfdetr-plus)
[](https://github.com/roboflow/rf-detr-plus/blob/main/LICENSE)
[](https://discord.gg/GbfgXGJ8Bk)
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](https://github.com/roboflow/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](https://cocodataset.org/#home) and [RF100-VL](https://github.com/roboflow/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**](https://www.python.org/) environment with `pip`. This will also install [`rfdetr`](https://github.com/roboflow/rf-detr) as a dependency, which provides the core APIs and model definitions.
```bash
pip install rfdetr-plus
```
Install from source
```bash
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`](https://github.com/roboflow/rf-detr) / Apache 2.0 |
| S | `RFDETRSmall` | 72.1 | 53.0 | 86.7 | 60.2 | 3.5 | 32.1 | 512x512 | [`rfdetr`](https://github.com/roboflow/rf-detr) / Apache 2.0 |
| M | `RFDETRMedium` | 73.6 | 54.7 | 87.4 | 61.2 | 4.4 | 33.7 | 576x576 | [`rfdetr`](https://github.com/roboflow/rf-detr) / Apache 2.0 |
| L | `RFDETRLarge` | 75.1 | 56.5 | 88.2 | 62.2 | 6.8 | 33.9 | 704x704 | [`rfdetr`](https://github.com/roboflow/rf-detr) / Apache 2.0 |
| ⭐ XL | `RFDETRXLarge` | 77.4 | 58.6 | 88.5 | 62.9 | 11.5 | 126.4 | 700x700 | `rfdetr_plus` / [PML 1.0](LICENSE) |
| ⭐ 2XL | `RFDETR2XLarge` | 78.5 | 60.1 | 89.0 | 63.2 | 17.2 | 126.9 | 880x880 | `rfdetr_plus` / [PML 1.0](LICENSE) |
## Run Models
Install RF-DETR+ to use XL and 2XL models alongside the core RF-DETR lineup:
```bash
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:
```python
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](https://universe.roboflow.com/).
```python
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](https://rfdetr.roboflow.com) 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`](LICENSE) for details. These models require a [Roboflow](https://roboflow.com) 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`](https://github.com/roboflow/rf-detr) package.
## Acknowledgements
Our work is built upon [LW-DETR](https://arxiv.org/pdf/2406.03459), [DINOv2](https://arxiv.org/pdf/2304.07193), and [Deformable DETR](https://arxiv.org/pdf/2010.04159). 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](https://github.com/roboflow/rf-detr-plus/issues/new) or pull request. By sharing your ideas and improvements, you help make RF-DETR better for everyone.
