| --- |
| 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). |
|
|
