Segmentation
The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientThe @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.
Set FAL_KEY as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"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/segment", {
input: {
image_url: "https://storage.googleapis.com/falserverless/example_inputs/moondream-3-preview/segmentation_in.png",
object: "mango"
},
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);The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_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.
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/segment", {
input: {
image_url: "https://storage.googleapis.com/falserverless/example_inputs/moondream-3-preview/segmentation_in.png",
object: "mango"
},
webhookUrl: "https://optional.webhook.url/for/results",
});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/segment", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
logs: true,
});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/segment", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);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.
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.
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.
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);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.
image_url string* requiredURL of the image to be processed
object string* requiredObject to be segmented in the image
Spatial references to guide the segmentation. By feeding in references you can help the segmentation process. Must be either list of Point object with x and y members, or list of arrays containing either 2 floats (x,y) or 4 floats (x1,y1,x2,y2). NOTE: You can also use the point endpoint to get points for the objects, and pass them in here.
Sampling settings for the segmentation model
preview booleanWhether to preview the output and return a binary mask of the image
{
"image_url": "https://storage.googleapis.com/falserverless/example_inputs/moondream-3-preview/segmentation_in.png",
"object": "mango",
"spatial_references": [
{
"x": 0.6402737047898338,
"y": 0.40762463343108507
}
],
"preview": true
}finish_reason string* requiredReason for finishing the output generation
Usage information for the request
Segmentation mask image. If no object detected or preview not requested, will be null.
path stringSVG path data representing the segmentation mask. If not detected, will be null.
Bounding box of the segmented object. If not detected, will be null.
{
"finish_reason": "stop",
"usage_info": {
"input_tokens": 737,
"prefill_time_ms": 54.45315001998097,
"decode_time_ms": 811.5944429300725,
"output_tokens": 23,
"ttft_ms": 91.87838807702065
},
"image": {
"width": 1024,
"content_type": "image/png",
"height": 1024,
"url": "https://storage.googleapis.com/falserverless/example_outputs/moondream-3-preview/segmentation_out.png",
"file_name": "segmentation_out.png"
},
"path": "M.657,.996C.610,.984,.529,.938,.447,.875C.411,.848,.363,.815,.341,.802C.188,.714,.093,.623,.038,.511C.011,.455,.000,.406,.000,.340C.000,.269,.006,.234,.025,.186C.051,.123,.092,.079,.161,.042C.240,-0.000,.355,-0.011,.433,.017C.462,.027,.535,.066,.571,.091C.701,.180,.807,.265,.850,.311C.911,.379,.962,.468,.984,.545C.995,.585,.997,.601,.997,.664C.997,.765,.984,.811,.935,.879C.878,.958,.796,1.001,.705,1.000C.685,.999,.664,.998,.657,.996z",
"bbox": {
"y_max": 0.53515625,
"x_min": 0.5390625,
"x_max": 0.732421875,
"y_min": 0.2998046875
}
}x float* requiredX coordinate of the point in normalized format (0 to 1)
y float* requiredY coordinate of the point in normalized format (0 to 1)
temperature floatSampling temperature to use. Higher values will make the output more random, while lower values will make it more focused and deterministic. Default value: 1
top_p floatNucleus sampling probability mass to use, between 0 and 1. Default value: 1
max_tokens integerMaximum number of tokens to generate.
input_tokens integer* requiredNumber of input tokens processed
output_tokens integer* requiredNumber of output tokens generated
prefill_time_ms float* requiredTime taken for prefill in milliseconds
decode_time_ms float* requiredTime taken for decoding in milliseconds
ttft_ms float* requiredTime to first token in milliseconds
url string* requiredThe URL where the file can be downloaded from.
content_type stringThe mime type of the file.
file_name stringThe name of the file. It will be auto-generated if not provided.
file_size integerThe size of the file in bytes.
width integerThe width of the image
height integerThe height of the image
x_min float* requiredLeft boundary of detection box in normalized format (0 to 1)
y_min float* requiredTop boundary of detection box in normalized format (0 to 1)
x_max float* requiredRight boundary of detection box in normalized format (0 to 1)
y_max float* requiredBottom boundary of detection box in normalized format (0 to 1)