SAM 3

An open detector from Meta that puts a box around every object you name in a photo. facebook/sam3

Get started Run in your cloud Hugging Face

Accuracy

COCO, average precision · not measured
Time per request

82.2 ms

per photo
Serverless price

$0.25

per 1,000 images

Find objects more accurately than GPT‑6.1 Sol, 982x faster

0.62

Objects boxed correctly

82.2 ms

Time per photo

$0.25

Cost per 1,000 photos

See the comparison on Detect

Call it

from urllib.request import urlopen
from sie_sdk import SIEClient
client = SIEClient(api_key="API keysk-sie-…")
image_bytes = urlopen(imagejunction.jpgbrowse).read()
image = {"data": image_bytes, "format": "jpeg"}
result = client.extract(
    "modelfacebook/sam3",
    {"images": [image]},
    labels=[
        "labelcar",
        "labelbus",
        "labeltruck",
    ],
)
for obj in result["objects"]:
    print(obj["label"], obj["score"], obj["bbox"])

import { SIEClient } from '@superlinked/sie-sdk';

const client = new SIEClient(undefined, { apiKey: 'sk-sie-…' });

const source = await fetch("https://huggingface.co/datasets/superlinked/sie-task-evidence/resolve/0031c63f8da106f93dc6537d38f03139b7a0bb44/detect-api-input/2026-10-14/junction.jpg");
if (!source.ok) throw new Error('Could not load the original photo.');
const image = new Uint8Array(await source.arrayBuffer());
const result = await client.extract(
  'facebook/sam3',
  { images: [image] },
  { labels: ["car","bus","truck"] },
);
console.log(result.objects);

curl --fail --silent --show-error --location --output 'junction.jpg' 'https://huggingface.co/datasets/superlinked/sie-task-evidence/resolve/0031c63f8da106f93dc6537d38f03139b7a0bb44/detect-api-input/2026-10-14/junction.jpg' || exit 1
images_bytes=$(base64 < 'junction.jpg' | tr -d '\n')
curl https://api.superlinked.com/v1/extract/facebook%2Fsam3 \
  -H "Authorization: Bearer sk-sie-…" \
  -H "Content-Type: application/json" \
  -d "{\"items\":[{\"images\":[{\"data\":\"$images_bytes\",\"format\":\"jpeg\"}]}],\"params\":{\"labels\":[\"car\",\"bus\",\"truck\"]}}"

Build the "Detect" capability into my app using the Superlinked Inference Engine (SIE).

Context
- SIE is an OpenAI-style inference API. Python SDK: `from sie_sdk import SIEClient`; TypeScript: `@superlinked/sie-sdk`.
- Base URL: https://api.superlinked.com (or my regional endpoint). Auth: Bearer key from env `SIE_API_KEY` (never hard-code it).
- Model: facebook/sam3 (SIE primitive: /extract). Keep the model id configurable.

Task
- Input: an uploaded image plus the object labels to find.
- Behaviour: return the named objects with confidence scores and bounding boxes
- Call the selected SIE primitive once per request and map the response into your domain type.

Deliverables
- A typed client wrapper, an application-level function for this task, error handling for timeouts/empty input, and unit tests with a stubbed client.
- Wire it into my existing stack (ask me which framework if unclear) and add a short usage example.

Run

Next to the other detectors

Next to the other detectors
Model	COCO, average precision	Time per request	Serverless price	Size
SAM 3 facebook	Not measured	82.2 ms per photo	$0.25 per 1,000 images	860M
grounding dino base IDEA-Research	0.486	Not timed	$0.31 per 1,000 images	233M
owlv2 base patch16 ensemble google	0.434	Not timed	$0.233 per 1,000 images	155M
owlv2 large patch14 ensemble google	0.428	Not timed	Run in your cloud	438M
grounding dino tiny IDEA-Research	0.403	Not timed	Run in your cloud	172M

All 8 detectors

Facts

Size: 860M parameters
License: other
Reads: Image
Endpoint: extract

Evaluation

Accuracy: Run on SIE against the public dataset named in each figure
Time: SIE timed on a GPU server with the client alongside, one request at a time
Prices: Serverless list prices
Sources: Task page runs and their inputs

Run SAM 3 on Serverless or in your own cloud

SIE Serverless

We host the models; you call one API.

No idle GPUs, pay for what you use
Fits your stack: SDK, API, CLI, MCP
Self-host the same open-source engine
SOC 2 Type 2, US or EU data residency
Create an account to try Serverless

Get started

SIE Cluster

Kubernetes in your own cloud, backed by our engineers.

Terraform modules for your cloud
Apache-2.0, same engine as Serverless
GPU workers scale to zero between jobs
Separate worker pools for each tenant

Agent prompt Deploy SIE to our AWS account with the superlinked/sie/aws Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Google Cloud project with the superlinked/sie/google Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Azure subscription with the superlinked/sie/azure Terraform module. Docs: superlinked.com/docs/deployment.

Deploy SIE to our Alibaba Cloud account with the superlinked/sie/alicloud Terraform module. Docs: superlinked.com/docs/deployment.

Talk to us Deploy guide

SIE Local

The open-source server on your own machine.

NVIDIA GPU
One container, no cluster
Process your data fully offline
Same SDK and model IDs as Serverless

docker run --gpus all -p 8080:8080 -e HF_TOKEN ghcr.io/superlinked/sie-server:latest-cuda12-transformers5

Quickstart

Find objects more accurately than GPT‑6.1 Sol, 982x faster

Call it

Next to the other detectors

Facts

Run SAM 3 on Serverless or in your own cloud