chore: import upstream snapshot with attribution

docs/learn/deploy.md

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+---
+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.