siglip2-so400m-patch16-naflex

Name: siglip2-so400m-patch16-naflex
Author: google

by google

SigLIP 2 at native aspect ratio and variable resolution

377Kdl/month

82likes

1.1Bparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

google/siglip2-so400m-patch16-naflex

Feature URI

Overview

SigLIP 2 replaces CLIP's softmax contrastive loss with a pairwise sigmoid loss, which removes the need for a global normalization across the batch and trains stably at large scale. The NaFlex variant adds the property that matters most for real documents and photographs: it handles native aspect ratios and variable sequence lengths instead of forcing every input into a fixed square.

Squashing a 16:9 video frame or a portrait page into a square distorts the spatial relationships a retrieval model is trying to encode. NaFlex avoids that, which is why it tends to do better on documents, screenshots and wide video frames than fixed-resolution encoders.

At 1.14B parameters this is a large encoder, so it usually belongs in a reranking or high-precision stage rather than running over every frame.

Architecture

SigLIP 2 so400m (shape-optimized 400M-class vision tower) with 16x16 patches and NaFlex support for native aspect ratio and variable sequence length. Trained with a pairwise sigmoid contrastive loss. 1,135,670,962 parameters total per the model card.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so siglip2-so400m-patch16-naflex runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
  "https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
  {
    method: "POST",
    headers: {
      Authorization: "Bearer API_KEY",
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      collection_id: "col_your_collection",
      documents: [
        {
          document_id: "asset-00412",
          // The vector name has to match a vector index on the collection.
          vectors: { "image-embedding": yourVector },
          payload: { source_key: "archive/2026/asset-00412" },
        },
      ],
    }),
  },
);

// Managed alternative, if this exact model is not the requirement:
// image_extractor@v1 runs google/siglip-base-patch16-224
// (768-d) over a bucket, with no inference of your own.