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

---
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.](https://docs.roboflow.com/developer/authentication/workspace-and-project-ids)
- [Learn how to find your API key.](https://docs.roboflow.com/developer/authentication/find-your-roboflow-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:

![](https://media.roboflow.com/rfdetr/models-list.png)

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.