Roboflow Docs

DINOv3

Run DINOv3 classification models trained on Roboflow via the Serverless Cloud API

Use Meta's DINOv3 self-supervised vision model for classification through the Serverless Cloud API. DINOv3 produces strong general-purpose visual features that can be adapted to your dataset by training a linear probe classifier on Roboflow.

DINOv3 API

1

Get your API Key

Create a Roboflow account, find your key on the Roboflow API settings page and make it available to your shell:

export ROBOFLOW_API_KEY="your-key-here"
2

Install the dependencies

Install the Inference SDK:

pip install -U inference-sdk supervision
3

Run the model

Call your trained DINOv3 classifier by its {workspace}/{model-slug} ID (see Versions, Trainings, and Models).

import os
import supervision as sv
from inference_sdk import InferenceHTTPClient

image = sv.load_image_from_url("https://media.roboflow.com/notebooks/examples/dog.jpeg")

client = InferenceHTTPClient(
    api_url="https://serverless.roboflow.com",
    api_key=os.environ["ROBOFLOW_API_KEY"],
)
# No pretrained aliases: train your own model and replace "your-project/1" with your model ID.
results = client.infer(image, model_id="your-project/1")
print(results)

Set api_url to match your deployment target:

  • https://serverless.roboflow.com for the Serverless Cloud API.
  • http://localhost:9001 for a local Inference server.
  • Your Dedicated Deployment URL for a private endpoint.

For self-hosted deployment and additional details, see the Inference documentation.