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Moondream3 Preview Detect

wavespeed-ai /

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
输入

就绪

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

$0.001每次运行·~1000 / $1

下一步:

示例查看全部

person

person

person

glasses

Ice sculpture

earring

Ferris wheel

clothes

dog

Red carpet

相关模型

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.
提示:本网站部分功能由第三方 AI 模型提供支持。

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. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Moondream3 Preview Detect below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/moondream3-preview/detect" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/moondream3-preview/detect";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/moondream3-preview/detect", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

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 result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. 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?

Median end-to-end generation time on WaveSpeedAI is around 14 seconds per request, based on recent successful runs. Queue time varies 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.

Moondream3 Preview Detect | AI Image Understanding API | WaveSpeedAI