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Moondream3 Preview Detect

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Moondream3 Detect: Precise object bounding boxes in images for accurate computer vision localization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-text
Input

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preview
If set to true, the function will wait for the result before returning the response. This property is only available through the API.

Inattivo

{
  "objects": [
    {
      "x_max": 0.6881352663040161,
      "x_min": 0.1556147336959839,
      "y_max": 0.9551899135112762,
      "y_min": 0.26160696148872375
    }
  ]
}

$0.001per esecuzione·~1000 / $1

Successivo:

EsempiVedi tutto

person

person

person

glasses

Ice sculpture

earring

Ferris wheel

clothes

dog

Red carpet

Modelli correlati

README

Moondream 3 — Object Detection

Moondream 3 Detect is a powerful vision-language model for identifying and localizing objects within images. It uses natural language input to detect specific items and returns their bounding box coordinates with high precision — ideal for visual search, annotation, and AI-assisted labeling.

✨ Key Features

  • Natural Language Object Queries Simply describe what you want to detect — e.g., “person,” “car,” “dog,” “chair.”

  • Accurate Bounding Boxes Returns precise x_min, y_min, x_max, y_max coordinates for each detected instance.

  • Multi-Object Detection Supports multiple instances of the same category in one image.

  • Fast and Lightweight Optimized for real-time or batch detection workflows with low latency.

⚙️ Example Usage

🔹 Detect Cars

{
 "image": "https://example.com/photo.jpg",
 "prompt": "car"
}

🔹 Detect People

{
 "image": "https://example.com/photo.jpg",
 "prompt": "person"
}

🔹 Detect Any Object

{
 "image": "https://example.com/photo.jpg",
 "prompt": "bicycle"
}

📦 Output Format

Bounding boxes are returned in normalized coordinates (range 0–1):

{
 "objects": [
 {
 "x_min": 0.1556,
 "x_max": 0.6881,
 "y_min": 0.2610,
 "y_max": 0.9551
 }
 ]
}

where

  • (x_min, y_min) = top-left corner
  • (x_max, y_max) = bottom-right corner

If multiple objects are detected, all boxes appear in the "objects" array.

💡 Best Practices

  • Use specific, clear object names for best accuracy.
  • For small or distant objects, higher-resolution images improve detection.
  • Supported formats: JPEG, PNG, WebP
  • Maximum image size: 10 MB

💰 Pricing

  • $0.001 per request
  • Contact WaveSpeedAI for bulk or enterprise pricing options.
Accessibilità:Questo sito web utilizza modelli di intelligenza artificiale forniti da terze parti.

Moondream3 Preview Detect API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/moondream3-preview/detect with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Moondream3 Preview Detect below.

HTTP example
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/moondream3-preview/detect" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image": "https://example.com/your-input.jpg",
    "enable_sync_mode": false
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].
Node.js example
// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("wavespeed-ai/moondream3-preview/detect", {
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://example.com/your-input.jpg",
        "enable_sync_mode": false
});

console.log(result.outputs[0]); // → URL of the generated output
Python example
# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "wavespeed-ai/moondream3-preview/detect",
    {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image": "https://example.com/your-input.jpg",
    "enable_sync_mode": false
}
)

print(output["outputs"][0])  # → URL of the generated output

Moondream3 Preview Detect API — Frequently asked questions

What is the Moondream3 Preview Detect API?

Moondream3 Preview Detect is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. Moondream3 Detect: Precise object bounding boxes in images for accurate computer vision localization. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Moondream3 Preview Detect API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/moondream3-preview-detect.

How much does Moondream3 Preview Detect cost per run?

Moondream3 Preview Detect starts at $0.001 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Moondream3 Preview Detect accept?

Key inputs: `prompt`, `image`, `enable_sync_mode`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/moondream3-preview-detect.

How long does Moondream3 Preview Detect take to generate?

Average end-to-end generation time on WaveSpeedAI is around 2 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Moondream3 Preview Detect outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.