dinov3-large
by facebook
Next-generation self-supervised vision model with Gram anchoring and 6.7B scaling
facebook/dinov3-largemixpeek://image_extractor@v1/facebook_dinov3_large_v1Deploy dinov3-large
Single-tenantMixpeek has no managed extractor for this model. On a single-tenant deployment you upload the weights and a custom plugin serves them next to the rest of your pipeline.
Overview
DINOv3 is Meta AI's successor to DINOv2, introducing Gram anchoring to solve dense feature degradation during long training schedules. It scales up to 6.7B parameters (ViT-7B) and trains on 1.7 billion web images plus 493M satellite images, making it the most versatile vision foundation model available.
On Mixpeek, DINOv3 delivers state-of-the-art visual features for tasks ranging from classification and segmentation to satellite/aerial imagery analysis, all without fine-tuning.
Architecture
Vision Transformer with patch size 16. Scales from ViT-S (21M) to ViT-7B (6.7B params). Introduces Gram anchoring to stabilize dense features during extended training. Also distills into ConvNeXt backbones. Supports flexible resolution and post-hoc text alignment.
Mixpeek SDK Integration
// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so dinov3-large 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
- Gram anchoring for stable dense feature training
- Scales up to 6.7B parameters (ViT-7B)
- Trained on 1.7B web + 493M satellite images
- ViT and ConvNeXt backbone variants
- Multi-domain: natural images and satellite/aerial imagery
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| ImageNet (linear probe) | Top-1 Accuracy | 83.1% | DINOv3 model card |
Performance
Common Pipeline Companions
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Specification
Research Paper
DINOv3
arxiv.orgBuild a pipeline with dinov3-large
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