Nirman
timm

timm/vit_base_patch16_dinov3_qkvb.lvd1689m

Model Type: Image Feature Encoder - Model Stats: - Params (M): 85.7 - GMACs: 23.6 - Activations (M): 34.1 - Image size: 256 x 256 - Original...

Model Documentation

Model card for vit_base_patch16_dinov3_qkvb.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.

Model Details

  • Model Type: Image Feature Encoder
  • Model Stats:
  • Params (M): 85.7
  • GMACs: 23.6
  • Activations (M): 34.1
  • Image size: 256 x 256
  • Original: https://github.com/facebookresearch/dinov3
  • License: DINOv3
  • Dataset: LVD-1689M
  • Papers:
  • DINOv3: https://arxiv.org/abs/2508.10104
  • An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
  • PyTorch Image Models: https://github.com/huggingface/pytorch-image-models


    • Model Usage

    Image Classification

    python
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_base_patch16_dinov3_qkvb.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

    python
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_base_patch16_dinov3_qkvb.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 1


    for o in output:
    
    print shape of each feature map in output
    
    
    e.g.:
    
    
     torch.Size([1, 768, 16, 16])
    
    
     torch.Size([1, 768, 16, 16])
    
    
     torch.Size([1, 768, 16, 16])


        print(o.shape)


    Image Embeddings

    python
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_base_patch16_dinov3_qkvb.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, 768) 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

    bibtex
@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}
}
}

    bibtex
@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}
}

    bibtex
@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}}
}

    Files & Weights

    FilenameSizeAction
    model.safetensors 0.32 GB
    pytorch_model.bin 0.32 GB