dinov3-vitl16-pretrain-lvd1689m
by facebook
High-traffic DINOv3 ViT-L checkpoint for dense visual features
facebook/dinov3-vitl16-pretrain-lvd1689mmixpeek://image_extractor@v1/facebook_dinov3_vitl_lvd1689m_v1Overview
DINOv3 is Meta's self-supervised vision foundation model family for dense, reusable visual features. The ViT-L LVD-1689M checkpoint is one of the most downloaded DINOv3 checkpoints on HuggingFace and is a practical alternative to the larger ViT-7B model.
On Mixpeek, DINOv3 ViT-L is a strong visual embedding backbone for image collections, video keyframes, satellite imagery, and fine-grained visual similarity tasks where label-free feature quality matters.
Architecture
Vision Transformer Large with 16x16 patches, distilled from the DINOv3 ViT-7B teacher and pretrained on the LVD-1689M web image dataset. Exposed through the Transformers image-feature-extraction pipeline.
Mixpeek SDK Integration
// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so dinov3-vitl16-pretrain-lvd1689m 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
- Dense image feature extraction without task labels
- Strong transfer across classification, segmentation, and retrieval tasks
- Practical ViT-L size compared with the larger ViT-7B checkpoint
- Works with the Transformers image-feature-extraction pipeline
Use Cases on Mixpeek
Performance
Model is gated on HuggingFace and requires license acceptance
Common Pipeline Companions
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Specification
Research Paper
DINOv3
arxiv.orgBuild a pipeline with dinov3-vitl16-pretrain-lvd1689m
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