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Detect objects, people, or other entities in images with precise bounding boxes and confidence scores. Ideal for inventory management, quality control, security applications, and automated visual inspection.
Object detection example showing detected objects with bounding boxes

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Persons

Persons detection example showing detected people with bounding boxes

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Faces

Faces detection example showing detected faces with bounding boxes

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Usage Example

For best results, we recommend using the Structured Outputs API to get responses in a structured and validated data format.
The following examples can also be used for face or person detection. The response schema is identical to the object detection example.
from vlmrun.client import VLMRun

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>", base_url="https://agent.vlm.run/v1")

# Execute object detection
response = client.agent.completions.create(
    model="vlmrun-orion-1:auto",
    messages=[
        {"role": "user", "content": [
            {"type": "text", "text": "Detect all objects in this image"},
            {"type": "image_url", "image_url": {"url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/image.object-detection/donuts.png", "detail": "auto"}}
        ]},
    ],
)

print(response.choices[0].message.content)

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FAQ

Orion supports open-world (aka open-set) detection. This means it is not limited to a predefined list of categories; it can detect, locate, and ground virtually any object described in natural language, including rare items, specific parts of objects, and complex visual relations.
The bounding boxes come in the format of normalized xywh, where x and y are the top-left corner of the bounding box, and w and h are the width and height of the bounding box. All values are between 0 and 1, and normalized by the image size. x and w are normalized by the image width, and y and h are normalized by the image height.
The confidence score is a value between 0 and 1 that indicates the confidence of the detection.