Moondream3 Preview [Detect] | Vision

fal-ai/moondream3-preview/detect

Moondream 3 is a vision language model that brings frontier-level visual reasoning with native object detection, pointing, and OCR capabilities to real-world applications requiring fast, inexpensive inference at scale.

Inference

Commercial use

Schema

LLMs

Playground API

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

Run Detect

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Please check the migration guide for more information.

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/moondream3-preview/detect", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/example_inputs/moondream-3-preview/detect_in.jpg",
    prompt: "Speed limit"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.

3. Queue#

Long-running requests

For long-running requests, such as training jobs or models with slower inference times, it is recommended to check the Queue status and rely on Webhooks instead of blocking while waiting for the result.

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("fal-ai/moondream3-preview/detect", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/example_inputs/moondream-3-preview/detect_in.jpg",
    prompt: "Speed limit"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("fal-ai/moondream3-preview/detect", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("fal-ai/moondream3-preview/detect", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

image_url string* required

URL of the image to be processed

Max width: 7000px, Max height: 7000px, Timeout: 20.0s

prompt string* required

Object to be detected in the image

preview boolean

Whether to preview the output

{
  "image_url": "https://storage.googleapis.com/falserverless/example_inputs/moondream-3-preview/detect_in.jpg",
  "prompt": "Speed limit",
  "preview": true
}

Output#

finish_reason string* required

Reason for finishing the output generation

usage_info UsageInfo* required

Usage information for the request

objects list<Object>* required

List of detected objects with their bounding boxes

image ImageFile

Image with bounding boxes drawn around detected objects

{
  "finish_reason": "stop",
  "usage_info": {
    "output_tokens": 23,
    "decode_time_ms": 811.5944429300725,
    "input_tokens": 737,
    "ttft_ms": 91.87838807702065,
    "prefill_time_ms": 54.45315001998097
  },
  "objects": [
    {
      "y_min": 0.16308235274382246,
      "x_max": 0.8755747037932524,
      "x_min": 0.8174849247502471,
      "y_max": 0.3061258583998726
    },
    {
      "y_min": 0.0987853935125991,
      "x_max": 0.7155113776357592,
      "x_min": 0.6706078794512399,
      "y_max": 0.21011001215700012
    }
  ],
  "image": "https://storage.googleapis.com/falserverless/example_outputs/moondream-3-preview/detect_out.png"
}

Other types#

UsageInfo#

input_tokens integer* required

Number of input tokens processed

output_tokens integer* required

Number of output tokens generated

prefill_time_ms float* required

Time taken for prefill in milliseconds

decode_time_ms float* required

Time taken for decoding in milliseconds

ttft_ms float* required

Time to first token in milliseconds

ImageFile#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

file_data string

File data

width integer

The width of the image

height integer

The height of the image

Point#

x float* required

X coordinate of the point in normalized format (0 to 1)

y float* required

Y coordinate of the point in normalized format (0 to 1)

Object#

x_min float* required

Left boundary of detection box in normalized format (0 to 1)

y_min float* required

Top boundary of detection box in normalized format (0 to 1)

x_max float* required

Right boundary of detection box in normalized format (0 to 1)

y_max float* required

Bottom boundary of detection box in normalized format (0 to 1)

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

UsageInfo#

ImageFile#

Point#

Object#

Related Models

Moondream3 Preview [Detect] Vision

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

UsageInfo#

ImageFile#

Point#

Object#

Related Models