# YOLO11

We support YOLO11 inferencing via our [Serverless Hosted API](/deploy/serverless-hosted-api-v2.md). YOLO11 is available in two task variants pretrained on COCO:

* Object detection (640 and 1280 input sizes)
* Instance segmentation (640 input size)

For self-hosted deployment, see [Roboflow Inference](https://inference.roboflow.com/).

## Code samples

Install the dependencies:

```bash
pip install inference-sdk supervision
```

Each sample below runs inference through the Serverless Hosted API, decodes the response with [`supervision`](https://supervision.roboflow.com/), and writes an annotated image to disk. Pass your [Roboflow API Key](https://app.roboflow.com/settings/api) via the `API_KEY` environment variable.

### Object detection

Run `yolov11n-640` on a sample image and annotate boxes and labels.

```python
import os
import urllib.request

import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/cars-highway.png"
image_path = "cars-highway.png"
urllib.request.urlretrieve(image_url, image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)

image = cv2.imread(image_path)
results = client.infer(image, model_id="yolov11n-640")
detections = sv.Detections.from_inference(results)

box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()

annotated = box_annotator.annotate(scene=image.copy(), detections=detections)
annotated = label_annotator.annotate(scene=annotated, detections=detections)

cv2.imwrite("cars-highway-annotated.png", annotated)
```

<figure><img src="/files/sH0gQbIJicJg5R27FckA" alt=""><figcaption></figcaption></figure>

### Instance segmentation

Run `yolov11n-seg-640` on a sample image and annotate masks and labels.

```python
import os
import urllib.request

import cv2
import supervision as sv
from inference_sdk import InferenceHTTPClient

image_url = "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/bicycle.png"
image_path = "bicycle.png"
urllib.request.urlretrieve(image_url, image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)

image = cv2.imread(image_path)
results = client.infer(image, model_id="yolov11n-seg-640")
detections = sv.Detections.from_inference(results)

mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER_OF_MASS)

annotated = mask_annotator.annotate(scene=image.copy(), detections=detections)
annotated = label_annotator.annotate(scene=annotated, detections=detections)

cv2.imwrite("bicycle-annotated.png", annotated)
```

<figure><img src="/files/cvRZiIbY8xoyTnZ7DIQ5" alt=""><figcaption></figcaption></figure>

## Default COCO aliases

You can pass any of the following aliases as the `model_id`. The `inference-sdk` resolves each alias to a pretrained Roboflow Universe model.

| Task         | Alias              |
| ------------ | ------------------ |
| Detection    | `yolov11n-640`     |
| Detection    | `yolov11s-640`     |
| Detection    | `yolov11m-640`     |
| Detection    | `yolov11l-640`     |
| Detection    | `yolov11x-640`     |
| Detection    | `yolov11n-1280`    |
| Detection    | `yolov11s-1280`    |
| Detection    | `yolov11m-1280`    |
| Detection    | `yolov11l-1280`    |
| Detection    | `yolov11x-1280`    |
| Segmentation | `yolov11n-seg-640` |
| Segmentation | `yolov11s-seg-640` |
| Segmentation | `yolov11m-seg-640` |
| Segmentation | `yolov11l-seg-640` |
| Segmentation | `yolov11x-seg-640` |

The `yolo11*` prefix variants resolve to the same models.

{% hint style="info" %}
Set `api_url` to match your deployment target:

* `https://serverless.roboflow.com` for the Serverless Hosted API.
* `http://localhost:9001` for a local [Inference](https://inference.roboflow.com/) server.
* Your [Dedicated Deployment](/deploy/dedicated-deployments.md) URL for a private endpoint.
  {% endhint %}


---

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```
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