---
description: Run RF-DETR object detection on images, video, and streams. Nano to 2XLarge models with 2.3-17.2 ms latency and up to 60.1 AP on COCO.
---
# Run an RF-DETR Object Detection Model
RF-DETR is a real-time transformer architecture for object detection, built on a DINOv2 vision transformer backbone. The base models are trained on the Microsoft COCO dataset and achieve state-of-the-art accuracy and latency trade-offs.
## Pre-trained Checkpoints
RF-DETR offers model sizes from Nano to 2XLarge, allowing trade-offs between accuracy, latency, and parameter count. All latency numbers were measured on an NVIDIA T4 using TensorRT, FP16, and batch size 1. Core models (Nano to Large) are licensed under Apache 2.0. XLarge and 2XLarge (marked with △) are provided by the [`rfdetr_plus`](https://github.com/roboflow/rf-detr-plus) extension (`pip install rfdetr[plus]`) under the Platform Model License 1.0 and require a Roboflow account.
| Size | RF-DETR package class | Inference package alias | COCO AP50 | COCO AP50:95 | 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]`
## Run on an Image
Perform inference on an image using either the `rfdetr` package or the `inference` package. To use a different model size, select the corresponding class or alias from the table above.
=== "rfdetr"
```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)
```
=== "inference"
```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)
```
!!! note "Using COCO classes vs. fine-tuned model classes"
`COCO_CLASSES` works for COCO-pretrained models (80 COCO classes, indexed 0-79).
For fine-tuned models, use `detections.data["class_name"]` instead — it resolves
class names from the checkpoint and works for both COCO and custom datasets.
For memory-constrained inference-only deployments with the `rfdetr` package, optimize the loaded model in place before
calling `predict()`. Pass `dtype="float16"` to halve weight memory in addition to clearing the base model reference.
This operation is irreversible — to restore the original model, create a new `RFDETR` instance:
```python
model.optimize_for_inference(compile=False, inplace=True, dtype="float16")
```
## Run on video, webcam, or RTSP stream
These examples use OpenCV for decoding and display. Replace ``, ``, and `` with your inputs. `` is usually `0` for the default camera.
=== "video"
```python
import cv2
import supervision as sv
from rfdetr import RFDETRMedium
from rfdetr.assets.coco_classes import COCO_CLASSES
model = RFDETRMedium()
video_capture = cv2.VideoCapture("")
if not video_capture.isOpened():
raise RuntimeError("Failed to open video source: ")
while True:
success, frame_bgr = video_capture.read()
if not success:
break
frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
detections = model.predict(frame_rgb, threshold=0.5)
labels = [COCO_CLASSES[class_id] for class_id in detections.class_id]
annotated_frame = sv.BoxAnnotator().annotate(frame_bgr, detections)
annotated_frame = sv.LabelAnnotator().annotate(annotated_frame, detections, labels)
cv2.imshow("RF-DETR Video", annotated_frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
video_capture.release()
cv2.destroyAllWindows()
```
=== "webcam"
```python
import cv2
import supervision as sv
from rfdetr import RFDETRMedium
from rfdetr.assets.coco_classes import COCO_CLASSES
model = RFDETRMedium()
WEBCAM_INDEX = 0
video_capture = cv2.VideoCapture(WEBCAM_INDEX)
if not video_capture.isOpened():
raise RuntimeError(f"Failed to open webcam: {WEBCAM_INDEX}")
while True:
success, frame_bgr = video_capture.read()
if not success:
break
frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
detections = model.predict(frame_rgb, threshold=0.5)
labels = [COCO_CLASSES[class_id] for class_id in detections.class_id]
annotated_frame = sv.BoxAnnotator().annotate(frame_bgr, detections)
annotated_frame = sv.LabelAnnotator().annotate(annotated_frame, detections, labels)
cv2.imshow("RF-DETR Webcam", annotated_frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
video_capture.release()
cv2.destroyAllWindows()
```
=== "stream"
```python
import cv2
import supervision as sv
from rfdetr import RFDETRMedium
from rfdetr.assets.coco_classes import COCO_CLASSES
model = RFDETRMedium()
video_capture = cv2.VideoCapture("")
if not video_capture.isOpened():
raise RuntimeError("Failed to open RTSP stream: ")
while True:
success, frame_bgr = video_capture.read()
if not success:
break
frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
detections = model.predict(frame_rgb, threshold=0.5)
labels = [COCO_CLASSES[class_id] for class_id in detections.class_id]
annotated_frame = sv.BoxAnnotator().annotate(frame_bgr, detections)
annotated_frame = sv.LabelAnnotator().annotate(annotated_frame, detections, labels)
cv2.imshow("RF-DETR RTSP", annotated_frame)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
video_capture.release()
cv2.destroyAllWindows()
```