aimv2-large-patch14-native

Name: aimv2-large-patch14-native
Author: apple

by apple

Multimodal autoregressive vision encoder outperforming CLIP and SigLIP on understanding tasks

459dl/month

16likes

309Mparams

HuggingFace Run it on your own data, free

Identifiers

Model ID

apple/aimv2-large-patch14-native

Feature URI

mixpeek://image_extractor@v1/apple_aimv2_large_v1

Overview

AIMv2 Large is Apple's 309M-parameter vision encoder pre-trained with a multimodal autoregressive objective that pairs the encoder with a decoder autoregressively generating raw image patches and text tokens. Unlike contrastive models such as CLIP, AIMv2 captures fine-grained visual features through its generative pre-training, outperforming both CLIP and SigLIP on multimodal understanding benchmarks.

On Mixpeek, AIMv2 provides high-quality visual feature extraction for downstream tasks like classification, grounding, and retrieval. Its native resolution variant accepts variable-size images without resizing artifacts, making it particularly effective for document images, satellite imagery, and other content where resolution matters.

Architecture

Vision Transformer with 24 layers, 1024-dim hidden size, 8 attention heads, patch size 14. 309M parameters. Pre-trained with multimodal autoregressive objective using a paired text decoder. Native resolution variant supports variable input sizes without fixed resizing.

Mixpeek SDK Integration

// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so AIMv2-large-patch14-native 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.

Capabilities

Outperforms CLIP and SigLIP on multimodal understanding benchmarks
Native resolution input without resizing artifacts
1024-dimensional feature representations
Strong transfer to localization, grounding, and classification
Outperforms DINOv2 on open-vocabulary detection

Use Cases on Mixpeek

High-fidelity visual feature extraction for document images and satellite imagery at native resolution

Visual search backbone replacing CLIP for higher accuracy on understanding tasks

Open-vocabulary object detection and referring expression grounding on video frames

Benchmarks

Dataset	Metric	Score	Source
ImageNet-1k (frozen trunk, 3B variant)	Top-1 Accuracy	89.5%	Fini et al., 2024: arXiv 2411.14402
Multimodal understanding (avg)	Score	Outperforms CLIP ViT-L & SigLIP	Fini et al., 2024: arXiv 2411.14402