Model card for vit_small_plus_patch16_dinov3.lvd1689m
A DINOv3 ViT model image feature encoder. Distilled on LVD-1689M from the DINOv3 ViT-7B model.
Model Notes
The original model weights ended up with all QKV projection biases being zeroes. For timm, have disabled the QKV bias (qkv_bias=False) for the models and not loaded the zero weights. For some model sizes there are variants with qkvb in the name that have the bias enabled (qkv_bias=True), but zero, to match the behaviour of transformers and original models.
The original models keep RoPE periods as a persistent bfloat16 buffer. timm generates float32 periods at init. This results in some numerical differences, however the timm approach should be less problematic running on devices without bfloat16 support, and appears to work as well if not slightly better for fine-tuning. model.rope.periods = model.rope.periods.to(torch.bfloat16).to(torch.float32) will truncate the periods to bfloat16 and result in matching outputs.
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_small_plus_patch16_dinov3.lvd1689m', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
Feature Map Extraction
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_small_plus_patch16_dinov3.lvd1689m',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1for o in output:
# print shape of each feature map in output# e.g.:# torch.Size([1, 384, 16, 16])# torch.Size([1, 384, 16, 16])# torch.Size([1, 384, 16, 16])print(o.shape)
Image Embeddings
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_small_plus_patch16_dinov3.lvd1689m',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 261, 384) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
Model Comparison
See the associated paper for details on the evaluation protocols
Results for ViT backbones pretrained (or distilled) on web (LVD-1689M)
Model
IN-ReaL
IN-R
Obj.Net
Ox.-H
ADE20k
NYU↓
DAVIS
NAVI
SPair
Global Tasks
Dense Tasks
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)
Model
IN-ReaL @256px
IN-ReaL @512px
IN-R @256px
IN-R @512px
Obj.Net @256px
Obj.Net @512px
ADE20k
NYU↓
Global Tasks
Dense Tasks
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
Citation
@article{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 others},
journal={arXiv preprint arXiv:2508.10104},
year={2025}
}
}
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
