Roboflow Docs

Moondream2

Run the Moondream2 open-vocabulary detection API on a Dedicated Deployment or self-hosted Inference

Moondream2 is a compact vision-language model. In Roboflow Inference, it is exposed as an open-vocabulary object detector: pass a class name as the prompt and receive bounding boxes for matching regions.

Moondream2 is not available on the Serverless Cloud API. Run it on a Dedicated Deployment or self-hosted Inference.

Moondream2 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 and supervision:

pip install -U inference-sdk supervision opencv-python
3

Run the model

Set api_url to your Dedicated Deployment URL or a local Inference server.

import os
import cv2
import numpy as np
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://your-deployment.roboflow.cloud",
    api_key=os.environ["ROBOFLOW_API_KEY"],
)
result = client.infer_lmm(
    image,
    model_id="moondream2",
    prompt="dog",
)

preds = result["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.get("confidence", 1.0) for p in preds], dtype=float),
    data={"class_name": np.array([p["class"] for p in preds])},
)
labels = [f"{p['class']} {p.get('confidence', 1.0):.2f}" for p in preds]
annotated = sv.BoxAnnotator().annotate(image.copy(), detections)
annotated = sv.LabelAnnotator().annotate(annotated, detections, labels=labels)
cv2.imwrite("dog_annotated.png", annotated)

Moondream2 inference speed

Latency measured with Roboflow Inference on 1x NVIDIA L4, batch size 1, captioning one image. Moondream2 cannot fix its output length, so latency varies with the response.

AliasLatency (ms)
moondream21669

Set api_url to match your deployment target:

Run Moondream2 with self-hosted Inference

Moondream2 can also be loaded directly with the inference package. Beyond detection, the model supports image captioning, point-prompt detection, and visual question answering.

1

Install the package

pip install "inference[transformers]"

Use inference-gpu[transformers] on a GPU machine.

2

Run the model

from PIL import Image

from inference.models.moondream2.moondream2 import Moondream2

model = Moondream2(api_key="YOUR_API_KEY")

image = Image.open("dog.jpeg")
result = model.query(image, "How many dogs are in this image?")

print(result)

Execution modes in Workflows

When used in a Workflow, Moondream2 runs in one of two modes:

  • Local execution: the model runs on your Inference server (GPU recommended).
  • Remote execution: the model is invoked over HTTP on a remote Inference server through the infer_lmm() client method.