# YOLO-World

YOLO-World is an open-vocabulary object detection model that detects objects from arbitrary text class names without training. We support YOLO-World inferencing via our [Serverless Hosted API](/deploy/serverless-hosted-api-v2.md).

For more details on running YOLO-World, see the [Inference docs](https://inference.roboflow.com/).

## Code sample

Call the `/yolo_world/infer` endpoint directly with `curl`:

```bash
curl --location 'https://serverless.roboflow.com/yolo_world/infer' \
  --header 'Content-Type: application/json' \
  --data '{
    "api_key": "YOUR_API_KEY",
    "image": {"type": "url", "value": "https://storage.googleapis.com/com-roboflow-marketing/notebooks/examples/cars-highway.png"},
    "text": ["car", "truck"],
    "yolo_world_version_id": "v2-s",
    "confidence": 0.05
  }'
```

The same call through the SDK. Install it and [supervision](https://supervision.roboflow.com/):

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

Run YOLO-World with custom class names, then decode and visualize the predictions with supervision. Pass [Roboflow's API Key](https://app.roboflow.com/settings/api) via the `API_KEY` env variable.

```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"),
)

results = client.infer_from_yolo_world(
    inference_input=image_path,
    class_names=["car", "truck"],
    model_version="v2-s",
    confidence=0.05,
)

detections = sv.Detections.from_inference(results[0])

image = cv2.imread(image_path)
labels = [
    f"{name} {conf:.2f}"
    for name, conf in zip(detections.data["class_name"], detections.confidence)
]
annotated = sv.BoxAnnotator().annotate(scene=image.copy(), detections=detections)
annotated = sv.LabelAnnotator().annotate(scene=annotated, detections=detections, labels=labels)
cv2.imwrite("annotated.png", annotated)
```

<figure><img src="/files/VzSiFyFTGQC6sGlYMR4I" alt=""><figcaption></figcaption></figure>

The `class_names` argument accepts any list of class names. Available `model_version` values: `v2-s`, `v2-m`, `v2-l`, `v2-x`, `s`, `m`, `l`, `x`.

{% 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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