Detection

from vlmrun.client import VLMRun

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# 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)

from vlmrun.client import VLMRun
from pydantic import BaseModel, Field

# Define the response schema
class Detection(BaseModel):
  label: str = Field(..., description="Name of the detected object")
  xywh: tuple[float, float, float, float] = Field(..., description="Bounding box (x, y, width, height)")

class Detections(BaseModel):
  detections: list[Detection] = Field(..., description="List of detections")

# Initialize the VLMRun client
client = VLMRun(api_key="<VLMRUN_API_KEY>")

# Execute object detection with structured output
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"}}
        ]},
    ],
    response_format={"type": "json_schema", "schema": Detections.model_json_schema()},
)

# Validate the response
result = Detections.model_validate_json(response.choices[0].message.content)
# >>> Detections(detections=[Detection(label="donut", xywh=(...)), ...])

import { VlmRun } from "vlmrun";

const client = new VlmRun({
  apiKey: "<VLMRUN_API_KEY>",
  baseURL: "https://api.vlm.run/v1"
});

const response = await 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" } }
      ]
    }
  ]
});

console.log(response.choices[0].message.content);

import { VlmRun } from "vlmrun";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

// Define the response schema with Zod
const DetectionsSchema = z.object({
  detections: z.array(z.object({
    label: z.string().describe("Name of the detected object"),
    xywh: z.array(z.number()).describe("Bounding box (x, y, width, height)")
  })).describe("List of detections")
});

// Initialize the VLMRun client
const client = new VlmRun({
  apiKey: "<VLMRUN_API_KEY>",
  baseURL: "https://api.vlm.run/v1"
});

// Execute object detection with structured output
const response = await 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" } }
      ]
    }
  ],
  response_format: {
    type: "json_schema",
    schema: zodToJsonSchema(DetectionsSchema)
  }
});

const result = DetectionsSchema.parse(JSON.parse(response.choices[0].message.content));