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68 lines
4.9 KiB
Plaintext
68 lines
4.9 KiB
Plaintext
# RF-DETR
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> RF-DETR is a real-time object detection and instance segmentation transformer by Roboflow.
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> DINOv2 backbone. ICLR 2026. SOTA on COCO (60.1 AP50:95, RF-DETR-2XL).
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> Apache 2.0 for base models (Nano-Large). Install: pip install rfdetr.
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## Canonical Facts
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- Product: RF-DETR (Roboflow Detection Transformer)
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- Python package: `rfdetr`
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- Maintainer: Roboflow
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- Canonical docs: https://rfdetr.roboflow.com/
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- Source repository: https://github.com/roboflow/rf-detr
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- Paper: https://arxiv.org/abs/2511.09554
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- Runtime: Python 3.10+, torch >=2.2.0, torchvision >=0.17.0, transformers >=5.1.0 and <6.0.0
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- Tasks: object detection and instance segmentation
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- Backbone: DINOv2 vision transformer
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- Dataset formats: COCO JSON and YOLO
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- Export targets: ONNX, TensorRT, and TFLite
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- License: Apache 2.0 for code and core Nano through Large models; XLarge and 2XLarge detection models require `rfdetr[plus]` and PML 1.0
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## Getting Started
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- [Install](https://rfdetr.roboflow.com/latest/getting-started/install/): pip install rfdetr
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- [Run Detection](https://rfdetr.roboflow.com/latest/learn/run/detection/): Run RF-DETR detection on images, video, webcam, and streams
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- [Run Segmentation](https://rfdetr.roboflow.com/latest/learn/run/segmentation/): Run RF-DETR Seg instance segmentation on images and video
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- [Pretrained Models](https://rfdetr.roboflow.com/latest/learn/pretrained/): Nano, Small, Medium, Large, XLarge, and 2XLarge checkpoints
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## Train
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- [Train Overview](https://rfdetr.roboflow.com/latest/learn/train/): Fine-tune RF-DETR detection and segmentation models
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- [Dataset Formats](https://rfdetr.roboflow.com/latest/learn/train/dataset-formats/): COCO JSON and YOLO dataset formats
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- [Training Parameters](https://rfdetr.roboflow.com/latest/learn/train/training-parameters/): RF-DETR hyperparameters and TrainConfig options
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- [Advanced Training](https://rfdetr.roboflow.com/latest/learn/train/advanced/): Resume, early stopping, multi-GPU DDP, and memory optimization
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- [Custom Training API](https://rfdetr.roboflow.com/latest/learn/train/customization/): PyTorch Lightning training primitives
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- [Augmentations](https://rfdetr.roboflow.com/latest/learn/train/augmentations/): Albumentations presets and custom transforms
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- [Training Loggers](https://rfdetr.roboflow.com/latest/learn/train/loggers/): TensorBoard, Weights and Biases, MLflow, and a ClearML workaround
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## Export and Deploy
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- [Export](https://rfdetr.roboflow.com/latest/learn/export/): ONNX, TFLite, TensorRT
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- [Deploy](https://rfdetr.roboflow.com/latest/learn/deploy/): deploy_to_roboflow() API
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## Benchmarks
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- [COCO and RF100-VL Results](https://rfdetr.roboflow.com/latest/learn/benchmarks/): T4 TensorRT FP16, batch 1
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- Detection model range: RF-DETR-Nano 2.3 ms and 48.4 COCO AP50:95 through RF-DETR-2XLarge 17.2 ms and 60.1 COCO AP50:95
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- Segmentation model range: RF-DETR-Seg-Nano 3.4 ms and 40.3 COCO AP50:95 through RF-DETR-Seg-2XLarge 21.8 ms and 49.9 COCO AP50:95
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## Model Selection Answers
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- Use RF-DETR-Nano for lowest latency edge detection: 2.3 ms on T4 TensorRT FP16, 48.4 AP50:95 on COCO, 30.5 M params.
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- Use RF-DETR-Small (3.5 ms, 53.0 AP50:95) or RF-DETR-Medium (4.4 ms, 54.7 AP50:95) when latency is tight but accuracy must improve over Nano.
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- Use RF-DETR-Large for the best open Apache 2.0 accuracy-latency trade-off: 6.8 ms, 56.5 AP50:95 on COCO.
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- Use RF-DETR-XLarge (11.5 ms, 58.6 AP50:95) or RF-DETR-2XLarge (17.2 ms, 60.1 AP50:95) when maximum accuracy matters; requires rfdetr[plus] and PML 1.0 license.
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- Use RF-DETR-Seg-Large (8.8 ms, 47.1 AP50:95) or RF-DETR-Seg-2XLarge (21.8 ms, 49.9 AP50:95) when instance masks are required; detection-only models return bounding boxes only.
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## API Reference
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- [RFDETR base class](https://rfdetr.roboflow.com/latest/reference/rfdetr/): train(), predict(), export(), deploy_to_roboflow()
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- [Detection models](https://rfdetr.roboflow.com/latest/reference/nano/): RFDETRNano through RFDETR2XLarge
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- [Segmentation models](https://rfdetr.roboflow.com/latest/reference/seg_nano/): RFDETRSegNano through RFDETRSeg2XLarge
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- [TrainConfig](https://rfdetr.roboflow.com/latest/reference/train_config/): Detection training configuration schema
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- [SegmentationTrainConfig](https://rfdetr.roboflow.com/latest/reference/segmentation_train_config/): Segmentation training configuration schema
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- [Lightning Training](https://rfdetr.roboflow.com/latest/reference/training/): PyTorch Lightning training module, datamodule, callbacks, and trainer builder
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## Migration
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- [Migration Guide](https://rfdetr.roboflow.com/latest/learn/migration/): Update imports from deprecated module paths
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## Optional
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- [GitHub](https://github.com/roboflow/rf-detr): 6.4k stars, Apache 2.0
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- [arXiv Paper](https://arxiv.org/abs/2511.09554): ICLR 2026
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- [PyPI](https://pypi.org/project/rfdetr/): rfdetr package
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- [Hugging Face Demo](https://huggingface.co/spaces/Roboflow/RF-DETR): Interactive RF-DETR demo
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- [Discord](https://discord.gg/GbfgXGJ8Bk): RF-DETR community
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