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.
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 semantic 3D keypoint correspondances
on depth estimation, semantic segmentation, using linear layers
on unsupervised object discovery
on video segmentation tracking
on video classification, using a small 4-layer attentive probe
Downstream Use
While fine-tuning the models can yield some gains, it is recommended to keep this option as a last resort: the frozen features are expected to provide good performance out-of-the-box.
Bias, Risks, and Limitations
Compared to DINOv2 and SEERv2, DINOv3 delivers somewhat consistent performance across income categories on geographical fairness and diversity, although with a notable performance drop in the low-income bucket compared to the highest-income bucket.
DINOv3 also achieves relatively good scores across different regions, improving over its predecessor DINOv2. However, a relative difference is still observed between Europe and Africa.
Recommendations
Fine-tuning is expected to increase the biases in the features produced by the model as they will be tuned to the fine-tuning labels.
How to Get Started with the Model
The example below demonstrates how to obtain an image embedding with [Pipeline] or the [AutoModel] class.
Web dataset (LVD-1689M): a curated dataset of 1,689 millions of images extracted from a large data
pool of 17 billions web images collected from public posts on Instagram
Satellite dataset (SAT-493M): a dataset of 493 millions of 512x512 images sampled randomly from Maxar RGB ortho-rectified imagery at 0.6 meter resolution
Training Procedure
Training objective:
DINO self-distillation loss with multi-crop
iBOT masked-image modeling loss
KoLeo regularization on [CLS] tokens
Gram anchoring
Training regime: PyTorch FSDP2 (with bf16 and fp8 matrix multiplications)
Distillation:
Distillation follows the standard DINOv3 pretraining procedure, except the teacher is a frozen pretrained ViT-7B.
Evaluation
Results
The reader is referred to the associated paper for details on the evaluation protocols
Results for ViT backbones pretrained (or distilled) on web (LVD-1689M)
Global Tasks
Dense Tasks
Model
IN-ReaL
IN-R
Obj.Net
Ox.-H
ADE20k
NYU↓
DAVIS
NAVI
SPair
DINOv3 ViT-S/16
87.0
60.4
50.9
49.5
47.0
0.403
72.7
56.3
50.4
DINOv3 ViT-S+/16
88.0
68.8
54.6
50.0
48.8
0.399
75.5
57.1
55.2
DINOv3 ViT-B/16
89.3
76.7
64.1
58.5
51.8
0.373
77.2
58.8
57.2
DINOv3 ViT-L/16
90.2
88.1
74.8
63.1
54.9
0.352
79.9
62.3
61.3
DINOv3 ViT-H+/16
90.3
90.0
78.6
64.5
54.8
0.352
79.3
63.3
56.3
DINOv3 ViT-7B/16
90.4
91.1
91.1
72.8
55.9
0.309
79.7
64.4
58.7
Results for ConvNeXt backbones distilled on web (LVD-1689M)
Global Tasks
Dense Tasks
Model
IN-ReaL
IN-R
Obj.Net
ADE20k
NYU↓
@256px
@512px
@256px
@512px
@256px
@512px
DINOv3 ConvNeXt Tiny
86.6
87.7
73.7
74.1
52.6
58.7
42.7
0.448
DINOv3 ConvNeXt Small
87.9
88.7
73.7
74.1
52.6
58.7
44.8
0.432
DINOv3 ConvNeXt Base
88.5
89.2
77.2
78.2
56.2
61.3
46.3
0.420
DINOv3 ConvNeXt Large
88.9
89.4
81.3
82.4
59.3
65.2
47.8
0.403
Results for ViT backbones pretrained (or distilled) on satellite (SAT-493M)
(GEO-Bench) Classification
Model
m-BEnet
m-brick-kiln
m-eurosat
m-forestnet
m-pv4ger
m-so2sat
mean
DINOv3 ViT-L/16
73.0
96.5
94.1
60.6
96.0
57.4
79.6
DINOv3 ViT-7B/16
74.0
97.2
94.8
62.3
96.1
62.1
81.1
(GEO-Bench) Segmentation
Model
m-cashew
m-chesapeake
m-NeonTree
m-nz-cattle
m-pv4ger-seg
m-SA-crop
mean
DINOv3 ViT-L/16
94.2
75.6
61.8
83.7
95.2
36.8
74.5
DINOv3 ViT-7B/16
94.1
76.6
62.6
83.4
95.5
37.6
75.0
Environmental Impact
Hardware Type: Nvidia H100
Hours used: 61,440 hours for ViT-7B model training
@misc{simeoni2025dinov3,
title={{DINOv3}},
author={Sim{\'e}oni, Oriane and Vo, Huy V. and Seitzer, Maximilian and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun and Ramamonjisoa, Micha{\"e}l and Massa, Francisco and Haziza, Daniel and Wehrstedt, Luca and Wang, Jianyuan and Darcet, Timoth{\'e}e and Moutakanni, Th{\'e}o and Sentana, Leonel and Roberts, Claire and Vedaldi, Andrea and Tolan, Jamie and Brandt, John and Couprie, Camille and Mairal, Julien and J{\'e}gou, Herv{\'e} and Labatut, Patrick and Bojanowski, Piotr},
year={2025},
eprint={2508.10104},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2508.10104},
}
