:new: [2026-03-10] :fire: The Canopy Height Maps v2 (CHMv2) model and inference code are now available (more details on downloading the model weights and using the code here). The model weights are also available in Hugging Face Hub and supported by the Hugging Face Transformers library. Building on our original high-resolution canopy height maps released in 2024, CHMv2 delivers substantial improvements in accuracy, detail, and global consistency by leveraging DINOv3.

[2025-11-20] Distillation code and configurations for ConvNeXt backbones are now released!

[2025-10-13] Semantic segmentation (ADE20K) and monocular depth estimation (NYUv2-Depth) linear probing code are now released!

[2025-09-17] DINOv3 backbones are now supported by the PyTorch Image Models / timm library starting with version 1.0.20

[2025-08-29] DINOv3 backbones are supported by released versions of the Hugging Face Transformers library starting with version 4.56.0

[2025-08-14] DINOv3 backbones are now available in Hugging Face Hub and supported by the development version of the Hugging Face Transformers library

DINOv3 🦖🦖🦖

Meta AI Research, FAIR

Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, <br/> Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, <br/> Francisco Massa, Daniel Haziza, Luca Wehrstedt, Jianyuan Wang, <br/> Timothée Darcet, Théo Moutakanni, Leonel Sentana, Claire Roberts, <br/> Andrea Vedaldi, Jamie Tolan, John Brandt, Camille Couprie, <br/> Julien Mairal, Hervé Jégou, Patrick Labatut, Piotr Bojanowski

[ :scroll: Paper] [ :newspaper: Blog] [ :globe_with_meridians: Website] [ :book: BibTeX]

Reference PyTorch implementation and models for DINOv3. For details, see the DINOv3 paper.

Overview

<i></em><b>High-resolution dense features.</b><br/>We visualize the cosine similarity maps obtained with DINOv3 output features<br/> between the patches marked with a red cross and all other patches.</i>

</div> <br/>

An extended family of versatile vision foundation models producing high-quality dense features and achieving outstanding performance on various vision tasks including outperforming the specialized state of the art across a broad range of settings, without fine-tuning

Pretrained models

:information_source: Please follow the link provided below to get access to all the model weights: once accepted, an e-mail will be sent with the complete list of URLs pointing to all the available model weights (both backbones and adapters). These URLs can then be used to either:

download the model or adapter weights to a local filesystem and point torch.hub.load() to these local weights via the weights or backbone_weights parameters, or
directly invoke torch.hub.load() to download and load a backbone or an adapter from its URL via also the weights or backbone_weights parameters.

See the example code snippets below.

:warning: Please use wget instead of a web browser to download the weights.

ViT models pretrained on web dataset (LVD-1689M):

<table style="margin: auto"> <thead> <tr> <th>Model</th> <th>Parameters</th> <th>Pretraining<br/>Dataset</th> <th>Download</th> </tr> </thead> <tbody> <tr> <td>ViT-S/16 distilled </td> <td align="right">21M</td> <td align="center">LVD-1689M</td> <td align="center"><a href="https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/">[link]</a></td> </tr> <tr> <td>ViT-S+/16 distilled</td> <td align="right">29M</td> <td align="center">LVD-1689M</td> <td align="center"><a href="https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/">[link]</a></td> </tr> <tr> <td>ViT-B/16 distilled</td> <td align="right">86M</td> <td align="center">LVD-1689M</td> <td align="center"><a href="https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/">[link]</a></td> </tr> <tr> <td>ViT-L/16 distilled</td> <td align="right">300M</td> <td align="center">LVD-1689M</td> <td align="center"><a href="https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/">[link]</a></td> </tr> <tr> <td>ViT-H+/16 distilled</td> <td align="right">840M</td> <td align="center">LVD-1689M</td> <td align="center"><a href="https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/">[link]</a></td> </tr> <tr> <td>ViT-7B/16</td> <td align="right">6,716M</td> <td align="center">LVD-1689M</td> <td align="center"><a href="https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/">[link]</a></td> </tr> </tbody> </table>

ConvNeXt models pretrained on web dataset (LVD-1689M):

<table style="margin: auto"> <thead> <tr> <th>Model</th> <th>Parameters</th> <th>Pretraining<br/>Dataset</th> <th>Download</th> </tr> </thead> <tbody> <tr> <td>ConvNeXt Tiny</td> <td align="right">29M</td> <td align="center">LVD-1689M</td> <td align="center"><a href="https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/">[link]</a></td> </tr> <tr> <td>ConvNeXt Small</td> <td align="right">50M</td> <td align="center">LVD-1689M</td> <td align="center"><a href="https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/">[link]</a></td> </tr> <tr> <td>ConvNeXt Base</td> <td align="right">89M</td> <td align="center">LVD-1689M</td> <td align="center"><a href="https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/">[link]</a></td> </tr> <tr> <td>ConvNeXt Large</td> <td align="right">198M</td> <td align="center">LVD-1689M</td> <td align="center"><a href="https://ai.meta.com/resources/models-and-libraries/dinov3-downloads/">[link]</a></td> </tr> </tbody> </table>

ViT models pretrained on satellite dataset (SAT-493M):

Pretrained backbones (via PyTorch Hub)

Please follow the instructions here to install PyTorch (the only required dependency for loading the model). Installing PyTorch with CUDA support is strongly recommended.

import torch

REPO_DIR = <PATH/TO/A/LOCAL/DIRECTORY/WHERE/THE/DINOV3/REPO/WAS/CLONED>

# DINOv3 ViT models pretrained on web images
dinov3_vits16 = torch.hub.load(REPO_DIR, 'dinov3_vits16', source='local', weights=<CHECKPOINT/URL/OR/PATH>)
dinov3_vits16plus = torch.hub.load(REPO_DIR, 'dinov3_vits16plus', source='local', weights=<CHECKPOINT/URL/OR/PATH>)
dinov3_vitb16 = torch.hub.load(REPO_DIR, 'dinov3_vitb16', source='local', weights=<CHECKPOINT/URL/OR/PATH>)
dinov3_vitl16 = torch.hub.load(REPO_DIR, 'dinov3_vitl16', source='local', weights=<CHECKPOINT/URL/OR/PATH>)
dinov3_vith16plus = torch.hub.load(REPO_DIR, 'dinov3_vith16plus', source='local', weights=<CHECKPOINT/URL/OR/PATH>)
dinov3_vit7b16 = torch.hub.load(REPO_DIR, 'dinov3_vit7b16', source='local', weights=<CHECKPOINT/URL/OR/PATH>)

# DINOv3 ConvNeXt models pretrained on web images
dinov3_convnext_tiny = torch.hub.load(REPO_DIR, 'dinov3_convnext_tiny', source='local', weights=<CHECKPOINT/URL/OR/PATH>)
dinov3_convnext_small = torch.hub.load(REPO_DIR, 'dinov3_convnext_small', source='local', weights=<CHECKPOINT/URL/OR/PATH>)
dinov3_convnext_base = torch.hub.load(REPO_DIR, 'dinov3_convnext_base', source='local', weights=<CHECKPOINT/URL/OR/PATH>)
dinov3_convnext_large = torch.hub.load(REPO_DIR, 'dinov3_convnext_large', source='local', weights=<CHECKPOINT/URL/OR/PATH>)

# DINOv3 ViT models pretrained on satellite imagery
dinov3_vitl16 = torch.hub.load(REPO_DIR, 'dinov3_vitl16', source='local', weights=<CHECKPOINT/URL/OR/PATH>)
dinov3_vi

Dinov3

Install / Use

README

DINOv3 🦖🦖🦖

Overview

Pretrained models

Pretrained backbones (via PyTorch Hub)