dinov3-vitb16-pretrain-lvd1689m
by facebook
Self-supervised visual features with no text tower, distilled to 86M parameters
facebook/dinov3-vitb16-pretrain-lvd1689mOverview
DINOv3 learns image representations from images alone. There is no paired caption anywhere in training, which is the whole point: the features come from the visual structure of the data rather than from what someone happened to write underneath it.
That produces a different tool from CLIP or SigLIP. A contrastive image-text encoder is what you want when the query is words. A self-supervised encoder is what you want when the query is another image, because it was never asked to collapse visual detail into whatever a caption could describe. For deduplication, near-duplicate detection, visual clustering and image-to-image retrieval, that distinction usually shows up as better separation between things that look almost alike.
This is the ViT-B/16 checkpoint at 85.7M parameters, distilled from the 7B ViT-7B/16 teacher trained on LVD-1689M. It is the size most people can actually afford to run over a whole archive.
Architecture
Vision transformer, patch size 16, DINOv3ViTModel with 85,660,416 parameters. Self-supervised training on the LVD-1689M dataset, distilled from facebook/dinov3-vit7b16-pretrain-lvd1689m. Image-feature-extraction only: there is no text encoder, so it cannot answer a text query on its own.
Key Capabilities
- •Dense visual features for image-to-image retrieval
- •Near-duplicate and near-miss detection without labels
- •Frame-level features for clustering an unlabelled archive
- •A backbone for downstream heads trained on your own labels
Use Cases on Mixpeek
- •Deduplicating an image or video library where filenames and hashes disagree
- •Reverse image search over object storage, where the query is a picture
- •Clustering footage into groups before anyone has written a taxonomy
- •Reranking candidates from a text-first encoder using purely visual similarity
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Use dinov3-vitb16-pretrain-lvd1689m on Mixpeek
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Open StudioHow It Runs on Mixpeek
On Mixpeek, dinov3-vitb16-pretrain-lvd1689m runs as a managed extractor inside a processing pipeline. Point a bucket of image feature extraction data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.
Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.
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