DINOv3

// Overview

DINOv3 is a family of vision foundation models from Meta AI Research (FAIR). It is trained with self-supervised learning, so it learns from large amounts of unlabeled images instead of hand-labeled datasets. The result is a backbone that turns any image into general-purpose feature vectors you can reuse across many downstream tasks.

It is meant for computer vision developers and researchers who need strong image representations without training a model from scratch. The released backbones range from a 21M-parameter ViT-S/16 up to a 6.7B-parameter ViT-7B/16, plus ConvNeXt variants, so you can trade off accuracy against compute. The same frozen features can drive image classification, semantic segmentation, monocular depth estimation, retrieval, and dense matching.

As a multimodal/computer-vision tool, DINOv3 sits at the feature-extraction layer of a vision pipeline. You load a pretrained backbone, run images through it to get patch-level or global features, and attach a lightweight head (often a linear probe) for your specific task. The models are also available through the Hugging Face Hub, Transformers, and timm.

// What it does

• Self-supervised training on the LVD-1689M web image dataset, so no labels are needed to learn the features
• High-quality dense, patch-level features usable for segmentation, depth, and dense correspondence, not just whole-image classification
• Multiple backbone sizes: ViT-S/S+/B/L/H+ distilled models, a 6.7B-parameter ViT-7B/16, and ConvNeXt variants
• Loadable directly through torch.hub.load() from a local clone, pointing to downloaded weight URLs or files
• Also supported by Hugging Face Transformers (>=4.56.0) and PyTorch Image Models / timm (>=1.0.20)
• Reference code and configs for linear segmentation (ADE20K) and linear depth estimation (NYUv2-Depth)

// Getting started

Set up the environment, request the pretrained weights from Meta, then load a backbone and extract features from an image. Note that the reference repo targets a Linux environment with PyTorch >= 2.7.1.

micromamba env create -f conda.yaml
micromamba activate dinov3

import torch

REPO_DIR = "<PATH/TO/DINOV3/REPO>"

# Load DINOv3 ViT backbone
dinov3_vitl16 = torch.hub.load(
    REPO_DIR,
    'dinov3_vitl16',
    source='local',
    weights="<CHECKPOINT/URL/OR/PATH>",
)

# Extract features from an image
from torchvision.transforms import v2
from PIL import Image

img = Image.open("image.jpg")
transform = v2.Compose([
    v2.ToImage(),
    v2.Resize((256, 256), antialias=True),
    v2.ToDtype(torch.float32, scale=True),
    v2.Normalize(mean=(0.485, 0.456, 0.406),
                 std=(0.229, 0.224, 0.225)),
])

with torch.inference_mode():
    features = dinov3_vitl16(transform(img)[None])

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Extract reusable image embeddings as input to a custom classifier or downstream model without training a vision backbone yourself
• Build semantic segmentation or monocular depth estimation by adding a linear probe on top of frozen DINOv3 features
• Power image retrieval and similarity search using the global feature vectors
• Use dense patch features for correspondence and matching, including specialized domains like satellite or microscopy imagery via the metadata-guided recipes

// How DINOv3 compares

DINOv3 alongside other open-source vision & understanding tools AI/TLDR tracks, ranked by GitHub stars.