marqo fashionSigLIP
An open image and text embedding model, for finding photos with words. Marqo/marqo-fashionSigLIP
- Accuracy
0.838
Flickr30k image to text, nDCG@10
- Time per request
Not timed one request at a time
- Serverless price
Call it
from sie_sdk import SIEClientclient = SIEClient("http://localhost:8080")query = "querya red leather handbag"images = [ imageblack-camera.pngbrowse, imageblue-running-sneaker.pngbrowse, imagegreen-backpack.pngbrowse, imagered-leather-handbag.pngbrowse,]# Encode the text query + each catalog image in one batch.items = [{"text": query}] + [{"images": [f]} for f in images]vecs = client.encode("modelMarqo/marqo-fashionSigLIP", items)print(vecs)import { readFile } from 'node:fs/promises';
import { SIEClient } from '@superlinked/sie-sdk';
const client = new SIEClient('http://localhost:8080');
const query = "a red leather handbag";
const imagePaths = [
"black-camera.png",
"blue-running-sneaker.png",
"green-backpack.png",
"red-leather-handbag.png",
];
const images = await Promise.all(imagePaths.map((path) => readFile(path)));
// Encode the text query + each catalog image in one batch.
const items = [{ text: query }, ...images.map((image) => ({ images: [image] }))];
const vecs = await client.encode('Marqo/marqo-fashionSigLIP', items);
console.log(vecs);image_0=$(base64 < 'black-camera.png' | tr -d '\n')
image_1=$(base64 < 'blue-running-sneaker.png' | tr -d '\n')
image_2=$(base64 < 'green-backpack.png' | tr -d '\n')
image_3=$(base64 < 'red-leather-handbag.png' | tr -d '\n')
curl http://localhost:8080/v1/encode/Marqo%2Fmarqo-fashionSigLIP \
-H "Content-Type: application/json" \
-d "{\"items\":[{\"text\":\"a red leather handbag\"},{\"images\":[{\"data\":\"$image_0\",\"format\":\"png\"}]},{\"images\":[{\"data\":\"$image_1\",\"format\":\"png\"}]},{\"images\":[{\"data\":\"$image_2\",\"format\":\"png\"}]},{\"images\":[{\"data\":\"$image_3\",\"format\":\"png\"}]}],\"params\":{\"output_types\":[\"dense\"]}}"Build the "Image search" 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: http://localhost:8080, my own SIE server, which takes no API key.
- Model: Marqo/marqo-fashionSigLIP (SIE primitive: /encode). Keep the model id configurable.
Task
- Input: a text query plus one or more candidate images.
- Behaviour: return native dense vectors for the query and candidate images
- Encode the text query and candidate images together in one client.encode call, preserving the original image bytes. Return the native dense vectors without application scoring or ranking.
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. Edit the code and run it live Sign up to run this example.
Get started no credit card required
Next to the other image and text models
| Model | Flickr30k image to text, nDCG@10 | Time per request | Serverless price | Size |
|---|---|---|---|---|
| marqo fashionSigLIP Marqo | 0.838 | Not timed | Run in your cloud | 203M |
| SigLIP so400m google | 0.900 | Not timed | $0.031 per 1M tokens $0.0233 per 1,000 images | 878M |
| CLIP ViT H 14 laion2B s32B b79K laion | 0.862 | Not timed | Run in your cloud | 986M |
| siglip so400m patch14 224 google | 0.838 | Not timed | Run in your cloud | 877M |
| siglip2 base patch16 224 google | 0.816 | Not timed | $0.0231 per 1M tokens $0.0143 per 1,000 images | 375M |
Measured on public datasets
| Dataset | nDCG@10 |
|---|---|
| Flickr30k | 0.838 |
Facts
- Size
- 203M parameters
- License
- Apache-2.0
- Reads
- Text (up to 64 tokens), image
- Endpoint
-
encode
Evaluation
- Accuracy
- Run on SIE against the public dataset named in each figure
- Prices
- Serverless list prices, effective 10 Nov 2026
Run marqo fashionSigLIP in your own cloud, or start on Serverless
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
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. SIE Local
The open-source server on your own machine.
- Runs on your 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 ghcr.io/superlinked/sie-server:latest-cuda12-default