dino-vitl16-pretrain-lvd

About

What dino-vitl16-pretrain-lvd is

Model Card for DINOv3 DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings, without fine-tuning. DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks, significantly surpassing previous self- and weakly-supervised foundation models. Model Details These are Vision Transformer and ConvNeXt models trained following the method described in the DINOv3 paper. 12 models are provided: - 10 models pretrained on web data (LVD-1689M dataset) - 1 ViT-7B trained from scratch, - 5 ViT-S/S+/B/L/H+ models distilled from the ViT-7B, - 4 ConvNeXt-{T/S/B/L} models distilled from the ViT-7B, - 2 models pretrained on satellite data (SAT-493M dataset) - 1 ViT-7B trained from scratch - 1 ViT-L distilled from the ViT-7B Each Transformer-based model takes an image as input and returns a class token, patch tokens (and register tokens). These models follow a ViT architecture, with a patch size of 16. For a 224x224 image, this results in 1 class token + 4 register tokens + 196 patch tokens = 201 tokens (for DINOv2 with registers this resulted in 1 + 4 + 256 = 261 tokens). The models can accept larger images provided the image shapes are multiples of the patch size (16). If this condition is not verified, the model will crop to the closest smaller multiple of the patch size. Model Description - Developed by: Meta AI - Model type: Vision Transformer, ConvNeXt - License: DINOv3 License Model Sources - Repository: https://github.com/facebookresearch/dinov3 - Paper: https://arxiv.org/abs/2508.10104 Uses The models are vision backbones providing multi-purpose features for downstream tasks. Direct Use The models can be used without fine-tuning, with downstream classifiers as simple as linear layers, to obtain competitive results: - on image classification, using k-NN classifiers on the class token - on image classification, with logistic regression classifiers applied on the class token - on image classification, with a linear layer applied on the class token and the average of the patch tokens - on image retrieval using nearest neighbors - on geometric and sem

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys dino-vitl16-pretrain-lvd for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (dino-vitl16-pretrain-lvd below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"dino-vitl16-pretrain-lvd","input":"text to embed"}'

Details

Languages, data & research

Papers

Licence

Commercial licence needed

Sources

Weights & code

Explore

More embedding models