roboflow--rf-detr/docs/learn/deploy.md

description

description
Deploy fine-tuned RF-DETR detection and segmentation models to Roboflow for cloud inference, edge hardware, and multi-step vision workflows.

Deploy a Trained RF-DETR Model

!!! tip "Key Takeaways"

- Deploy fine-tuned RF-DETR models to Roboflow with a single `deploy_to_roboflow()` call
- Supports both detection and segmentation model deployment
- Run deployed models via Roboflow Inference on cloud, edge hardware, or NVIDIA Jetson
- Model weights are cached locally after the first inference run for fast subsequent predictions

You can deploy a fine-tuned RF-DETR model to Roboflow.

Deploying to Roboflow allows you to create multi-step computer vision applications that run both in the cloud and your own hardware.

To deploy your model to Roboflow, run:

=== "Object Detection"

```python
from rfdetr import RFDETRNano

x = RFDETRNano(pretrain_weights="<path/to/pretrain/weights/dir>")
x.deploy_to_roboflow(
    workspace="<your-workspace>",
    project_id="<your-project-id>",
    version=1,
    api_key="<YOUR_API_KEY>",
)
```

=== "Image Segmentation"

```python
from rfdetr import RFDETRSegMedium

x = RFDETRSegMedium(pretrain_weights="<path/to/pretrain/weights/dir>")
x.deploy_to_roboflow(
    workspace="<your-workspace>",
    project_id="<your-project-id>",
    version=1,
    api_key="<YOUR_API_KEY>",
)
```

Above, set your Roboflow Workspace ID, the ID of the project to which you want to upload your model, and your Roboflow API key.

• Learn how to find your Workspace and Project ID.
• Learn how to find your API key.

You can then run your model with Roboflow Inference:

=== "Object Detection"

```python
import supervision as sv
from inference import get_model
from PIL import Image
from io import BytesIO
import requests

url = "https://media.roboflow.com/dog.jpeg"
image = Image.open(BytesIO(requests.get(url).content))

model = get_model("rfdetr-large")  # replace with your Roboflow model ID

predictions = model.infer(image, confidence=0.5)[0]

detections = sv.Detections.from_inference(predictions)

labels = [prediction.class_name for prediction in predictions.predictions]

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)
```

=== "Image Segmentation"

```python
import supervision as sv
from inference import get_model
from PIL import Image
from io import BytesIO
import requests

url = "https://media.roboflow.com/dog.jpeg"
image = Image.open(BytesIO(requests.get(url).content))

model = get_model("rfdetr-seg-small")  # replace with your Roboflow model ID

predictions = model.infer(image, confidence=0.5)[0]

detections = sv.Detections.from_inference(predictions)

labels = [prediction.class_name for prediction in predictions.predictions]

annotated_image = image.copy()
annotated_image = sv.MaskAnnotator().annotate(annotated_image, detections)
annotated_image = sv.LabelAnnotator().annotate(annotated_image, detections, labels)

sv.plot_image(annotated_image)
```

Above, replace rfdetr-large with the your Roboflow model ID. You can find this ID from the "Models" list in your Roboflow dashboard:

When you first run this model, your model weights will be cached for local use with Inference.

You will then see the results from your fine-tuned model.