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_v1Overview
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
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Specification
Research Paper
DINOv3
arxiv.orgBuild a pipeline with dinov3-large
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