# Dino v3

Use Meta's [DINOv3](https://github.com/facebookresearch/dinov3) self-supervised vision model for classification through the [Serverless Hosted API](/deploy/serverless-hosted-api-v2.md). DINOv3 produces strong general-purpose visual features that can be adapted to your dataset by training a linear probe classifier on Roboflow.

There are no public DINOv3 aliases. Train your own DINOv3 classifier on your dataset and call it by your `{model_id}/{version}`.

## Code sample

Install the [Inference SDK](https://inference.roboflow.com/):

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

Pass [your Roboflow API Key](https://app.roboflow.com/settings/api) via the `API_KEY` env variable, and replace `your-project/1` with your own model ID and version.

```python
import os
import urllib.request
from inference_sdk import InferenceHTTPClient

image_url = "https://media.roboflow.com/notebooks/examples/dog.jpeg"
image_path = "dog.jpeg"
urllib.request.urlretrieve(image_url, image_path)

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

{% 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 %}

For self-hosted deployment and additional details, see the [Inference documentation](https://inference.roboflow.com/).


---

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