# YOLOv12

We support YOLOv12 object detection inferencing via our [Serverless Hosted API](/deploy/serverless-hosted-api-v2.md). YOLOv12 is supported in five sizes (`n`, `s`, `m`, `l`, `x`) for the object detection task.

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

YOLOv12 input size is set when you train your model on Roboflow (typical values: 640x640 or 1280x1280).

{% hint style="info" %}
YOLOv12 does not ship with pretrained COCO aliases on the Serverless Hosted API. To use YOLOv12, train your own model on Roboflow and call it using your model URL in the form `your-project/version`.
{% endhint %}

## Code sample

Install the [Inference SDK](https://inference.roboflow.com/inference_helpers/inference_sdk/) and [supervision](https://supervision.roboflow.com/) for decoding and visualizing predictions:

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

Run detection against your trained YOLOv12 model, then decode the response with `sv.Detections.from_inference`, draw bounding boxes and labels, and save the annotated image. Pass your [Roboflow API Key](https://app.roboflow.com/settings/api) via the `API_KEY` environment variable, and replace the `model_id` with your own `project/version`.

```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)

image = cv2.imread(image_path)

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.getenv("API_KEY"),
)
result = client.infer(image, model_id="your-project/1")

detections = sv.Detections.from_inference(result)

labels = [
    f"{class_name} {confidence:.2f}"
    for class_name, confidence
    in zip(detections.data.get("class_name", []), detections.confidence)
]

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, labels=labels)

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

{% 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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