dinov3 vitl16 pretrain lvd1689m EU AI Act Compliance Profile

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 do

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