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 a…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy