# OwlV2

OwlV2 is Google's open-vocabulary object detector. You provide one or more example bounding boxes on a reference image, and OwlV2 detects similar objects in target images without any training.

{% hint style="info" %}
OwlV2 is not available on the Serverless Hosted API. Run it on a [Dedicated Deployment](/deploy/dedicated-deployments.md) or [self-hosted Inference](https://inference.roboflow.com/).
{% endhint %}

## Code sample

Install dependencies:

```bash
pip install requests supervision opencv-python
```

The sample below uses a single example box on the input image as the prompt and detects matching objects in the same image. In practice you typically pass a separate reference image. Set `URL` to your Dedicated Deployment URL or a local Inference server. Pass your [Roboflow API Key](https://app.roboflow.com/settings/api) via the `API_KEY` environment variable.

```python
import base64
import os
import urllib.request

import cv2
import numpy as np
import requests
import supervision as sv

URL = "https://your-deployment.roboflow.cloud"
IMAGE_URL = "https://media.roboflow.com/notebooks/examples/dog.jpeg"
IMAGE_PATH = "dog.jpeg"
OUTPUT_PATH = "dog_annotated.png"

urllib.request.urlretrieve(IMAGE_URL, IMAGE_PATH)
image = cv2.imread(IMAGE_PATH)
_, buffer = cv2.imencode(".jpg", image)
image_base64 = base64.b64encode(buffer).decode("utf-8")

response = requests.post(
    f"{URL}/owlv2/infer",
    json={
        "api_key": os.getenv("API_KEY"),
        "image": {"type": "base64", "value": image_base64},
        "training_data": [{
            "image": {"type": "base64", "value": image_base64},
            "boxes": [{"x": 360, "y": 800, "w": 500, "h": 500, "cls": "dog"}],
        }],
        "confidence": 0.99,
    },
)
preds = response.json()["predictions"]

xyxys = [
    [p["x"] - p["width"] / 2, p["y"] - p["height"] / 2,
     p["x"] + p["width"] / 2, p["y"] + p["height"] / 2]
    for p in preds
]
detections = sv.Detections(
    xyxy=np.array(xyxys, dtype=float),
    class_id=np.array([p.get("class_id", 0) for p in preds]),
    confidence=np.array([p["confidence"] for p in preds], dtype=float),
    data={"class_name": np.array([p["class"] for p in preds])},
)
labels = [f"{p['class']} {p['confidence']:.2f}" for p in preds]
annotated = sv.BoxAnnotator().annotate(scene=image.copy(), detections=detections)
annotated = sv.LabelAnnotator().annotate(scene=annotated, detections=detections, labels=labels)
cv2.imwrite(OUTPUT_PATH, annotated)
```

<figure><img src="/files/4LaMNEytsEDeGwc2WB8Q" alt=""><figcaption></figcaption></figure>

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

* `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 %}

OwlV2 confidences are typically very high (above 0.99). Tune the `confidence` parameter accordingly.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.roboflow.com/deploy/supported-models/owlv2.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.