RF-DETR is a real-time, transformer-based object detection model architecture developed by Roboflow and released under the Apache 2.0 license.
RF-DETR is the first real-time model to exceed 60 AP on the Microsoft COCO benchmark alongside competitive performance at base sizes. It also achieves state-of-the-art performance on RF100-VL, an object detection benchmark that measures model domain adaptability to real world problems. RF-DETR is comparable speed to current real-time objection models.
RF-DETR is small enough to run on the edge, making it an ideal model for deployments that need both strong accuracy and real-time performance.
We validated the performance of RF-DETR on both Microsoft COCO and the RF100-VL benchmarks.
RF100-VL benchmark results Model paramsThe "Total Latency" reported here is measured on a T4 GPU using TensorRT10 FP16 (ms/img) and was introduced by LW-DETR. Unlike transformer-based models, YOLO models perform Non-Maximum Suppression (NMS) after generating predictions to refine bounding box candidates. While NMS boosts accuracy, it also slightly reduces speed due to the additional computation required, which varies with the number of objects in an image. Notably, many YOLO benchmarks include NMS in accuracy measurements but exclude it from speed metrics. By contrast, our benchmarking—following LW-DETR’s approach—factors in NMS latency to provide a uniform measure of the total time needed to obtain a final result across all models on the same hardware.
D-FINE’s fine-tuning capability is currently unavailable, making its domain adaptability performance inaccessible. The authors caution that “if your categories are very simple, it might lead to overfitting and suboptimal performance.” Furthermore, several open issues (#108, #146, #169, #214) currently prevent successful fine-tuning. We have opened an additional issue in hopes of ultimately benchmarking D-FINE with RF100-VL.
2025/03/20: We release RF-DETR real-time object detection model. Code and checkpoint for RF-DETR-large and RF-DETR-base are available.Pip install the rfdetr package in a Python>=3.9 environment.
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 git+https://github.com/roboflow/rf-detr.git
RF-DETR comes out of the box with checkpoints pre-trained on the Microsoft COCO dataset.
import io
import requests
import supervision as sv
from PIL import Image
from rfdetr import RFDETRBase
from rfdetr.util.coco_classes import COCO_CLASSES
model = RFDETRBase()
url = "https://media.roboflow.com/notebooks/examples/dog-2.jpeg"
image = Image.open(io.BytesIO(requests.get(url).content))
detections = model.predict(image, threshold=0.5)
labels = [
f"{COCO_CLASSES[class_id]} {confidence:.2f}"
for class_id, confidence
in zip(detections.class_id, detections.confidence)
]
annotated_image = image.copy()
annotated_image = sv.BoxAnnotator().annotate(annotated_image, detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels)
sv.plot_image(annotated_image) Video inference
import supervision as sv
from rfdetr import RFDETRBase
from rfdetr.util.coco_classes import COCO_CLASSES
model = RFDETRBase()
def callback(frame, index):
detections = model.predict(frame, threshold=0.5)
labels = [
f"{COCO_CLASSES[class_id]} {confidence:.2f}"
for class_id, confidence
in zip(detections.class_id, detections.confidence)
]
annotated_frame = frame.copy()
annotated_frame = sv.BoxAnnotator().annotate(annotated_frame, detections)
annotated_frame = sv.LabelAnnotator().annotate(annotated_frame, detections, labels)
return annotated_frame
process_video(
source_path=<SOURCE_VIDEO_PATH>,
target_path=<TARGET_VIDEO_PATH>,
callback=callback
) Webcam inference
import cv2
import supervision as sv
from rfdetr import RFDETRBase
from rfdetr.util.coco_classes import COCO_CLASSES
model = RFDETRBase()
cap = cv2.VideoCapture(0)
while True:
success, frame = cap.read()
if not success:
break
detections = model.predict(frame, threshold=0.5)
labels = [
f"{COCO_CLASSES[class_id]} {confidence:.2f}"
for class_id, confidence
in zip(detections.class_id, detections.confidence)
]
annotated_frame = frame.copy()
annotated_frame = sv.BoxAnnotator().annotate(annotated_frame, detections)
annotated_frame = sv.LabelAnnotator().annotate(annotated_frame, detections, labels)
cv2.imshow("Webcam", annotated_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
RF-DETR is available in two variants: RF-DETR-B 29M RFDETRBase and RF-DETR-L 128M RFDETRLarge. The corresponding COCO pretrained checkpoints are automatically loaded when you initialize either class.
Both model variants support configurable input resolutions. A higher resolution usually improves prediction quality by capturing more detail, though it can slow down inference. You can adjust the resolution by passing the resolution argument when initializing the model. resolution value must be divisible by 56.
model = RFDETRBase(resolution=560)
RF-DETR expects the dataset to be in COCO format. Divide your dataset into three subdirectories: train, valid, and test. Each subdirectory should contain its own _annotations.coco.json file that holds the annotations for that particular split, along with the corresponding image files. Below is an example of the directory structure:
dataset/
├── train/
│ ├── _annotations.coco.json
│ ├── image1.jpg
│ ├── image2.jpg
│ └── ... (other image files)
├── valid/
│ ├── _annotations.coco.json
│ ├── image1.jpg
│ ├── image2.jpg
│ └── ... (other image files)
└── test/
├── _annotations.coco.json
├── image1.jpg
├── image2.jpg
└── ... (other image files)
Roboflow allows you to create object detection datasets from scratch or convert existing datasets from formats like YOLO, and then export them in COCO JSON format for training. You can also explore Roboflow Universe to find pre-labeled datasets for a range of use cases.
You can fine-tune RF-DETR from pre-trained COCO checkpoints. By default, the RF-DETR-B checkpoint will be used. To get started quickly, please refer to our fine-tuning Google Colab notebook.
from rfdetr import RFDETRBase model = RFDETRBase() model.train(dataset_dir=<DATASET_PATH>, epochs=10, batch_size=4, grad_accum_steps=4, lr=1e-4, output_dir=<OUTPUT_PATH>)
Different GPUs have different amounts of VRAM (video memory), which limits how much data they can handle at once during training. To make training work well on any machine, you can adjust two settings: batch_size and grad_accum_steps. These control how many samples are processed at a time. The key is to keep their product equal to 16 — that’s our recommended total batch size. For example, on powerful GPUs like the A100, set batch_size=16 and grad_accum_steps=1. On smaller GPUs like the T4, use batch_size=4 and grad_accum_steps=4. We use a method called gradient accumulation, which lets the model simulate training with a larger batch size by gradually collecting updates before adjusting the weights.
You can fine-tune RF-DETR on multiple GPUs using PyTorch’s Distributed Data Parallel (DDP). Create a main.py script that initializes your model and calls .train() as usual than run it in terminal.
python -m torch.distributed.launch \
--nproc_per_node=8 \
--use_env \
main.py
Replace 8 in the --nproc_per_node argument with the number of GPUs you want to use. This approach creates one training process per GPU and splits the workload automatically. Note that your effective batch size is multiplied by the number of GPUs, so you may need to adjust your batch_size and grad_accum_steps to maintain the same overall batch size.
During training, two model checkpoints (the regular weights and an EMA-based set of weights) will be saved in the specified output directory. The EMA (Exponential Moving Average) file is a smoothed version of the model’s weights over time, often yielding better stability and generalization.
TensorBoard is a powerful toolkit that helps you visualize and track training metrics. With TensorBoard set up, you can train your model and keep an eye on the logs to monitor performance, compare experiments, and optimize model training.
Launch TensorBoardTo use TensorBoard locally, navigate to your project directory and run:
tensorboard --logdir <OUTPUT_DIR>
Then open http://localhost:6006/ in your browser to view your logs.
To use TensorBoard in Google Colab run:
%load_ext tensorboard %tensorboard --logdir <OUTPUT_DIR>
This will start a TensorBoard session in the Google Colab environment.
from rfdetr import RFDETRBase model = RFDETRBase(pretrain_weights=<CHECKPOINT_PATH>) detections = model.predict(<IMAGE_PATH>)
RF-DETR supports exporting models to the ONNX format, which enables interoperability with various inference frameworks and can improve deployment efficiency. To export your model, simply initialize it and call the .export() method.
from rfdetr import RFDETRBase model = RFDETRBase() model.export()
This command saves the ONNX model to the output directory.
Both the code and the weights pretrained on the COCO dataset are released under the Apache 2.0 license.
Our work is built upon LW-DETR, DINOv2, and Deformable DETR. Thanks to their authors for their excellent work!
If you find our work helpful for your research, please consider citing the following BibTeX entry.
@software{rf-detr,
author = {Robinson, Isaac and Robicheaux, Peter and Popov, Matvei},
license = {Apache-2.0},
title = {RF-DETR},
howpublished = {\url{https://github.com/roboflow/rf-detr}},
year = {2025},
note = {SOTA Real-Time Object Detection Model}
}
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.
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