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---
language:
- en
tags:
- dino
- dinov3
- arxiv:2508.10104
license: other
license_name: dinov3-license
license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license
base_model: dinov3-vitl16-pretrain-lvd1689m
pipeline_tag: image-feature-extraction
library_name: transformers
---
## License and commercial use

This model redistributes DINO Materials under the [DINOv3 License Agreement](LICENSE.md). Commercial use is permitted provided you comply with that agreement and with applicable export and trade control laws. Full terms: [LICENSE.md](LICENSE.md), [TERMS_OF_USE.md](TERMS_OF_USE.md).

# DINOv3 ViT-L/16

> Original model by Meta AI: [facebookresearch/dinov3](https://github.com/facebookresearch/dinov3)

Vision backbone for dense visual features (ViT-L, patch 16). Built with DINOv3.


# Model Card

This repository hosts **DINOv3 ViT-L/16** pretrained on LVD-1689M: a Vision Transformer (ViT-L, patch size 16) distilled from the DINOv3 ViT-7B teacher. It produces dense visual features suitable for classification, retrieval, segmentation, and other vision tasks without fine-tuning.

## Model Details

This model takes an image as input and returns a class token, patch tokens, and register tokens. For a 224×224 image: 1 class token + 4 register tokens + 196 patch tokens = 201 tokens. Inputs can be larger provided dimensions are multiples of 16; otherwise the image is cropped to the nearest smaller multiple.

### Model Description

- **Original model:** Meta AI (DINOv3)
- **Model type:** Vision Transformer (ViT-L/16)
- **License:** [DINOv3 License](LICENSE.md)

### Model Sources

- **Repository:** [https://github.com/facebookresearch/dinov3](https://github.com/facebookresearch/dinov3)
- **Paper:** [https://arxiv.org/abs/2508.10104](https://arxiv.org/abs/2508.10104)

## Uses

This model is a vision backbone providing multi-purpose features for downstream tasks.

### Direct Use

The model 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

Fine-tuning can yield additional gains but is optional; frozen features are typically strong 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.

## Evaluation

Representative results for **DINOv3 ViT-L/16** (LVD-1689M) from the paper:

| Model | IN-ReaL | IN-R | Obj.Net | Ox.-H | ADE20k | NYU↓ | DAVIS | NAVI | SPair |
|-------|---------|------|---------|-------|--------|------|-------|------|-------|
| DINOv3 ViT-L/16 | 90.2 | 88.1 | 74.8 | 63.1 | 54.9 | 0.352 | 79.9 | 62.3 | 61.3 |

See the [paper](https://arxiv.org/abs/2508.10104) for evaluation protocols and full benchmarks.

## Technical Specifications

- **Architecture:** ViT-L (300M parameters), patch size 16, embedding dimension 1024, 4 register tokens, 16 heads, MLP FFN, RoPE

## More Information

More on DINOv3: [blog](https://ai.meta.com/blog/dinov3-self-supervised-vision-model/), [project page](https://ai.meta.com/dinov3/).

## Citation

**BibTeX**

```
@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},
}
```

DINOv3 by Meta. Use subject to the [DINOv3 License](LICENSE.md).