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# DINOv2: Learning Robust Visual Features without Supervision **[Meta AI Research, FAIR](https://ai.facebook.com/research/)** Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Patrick Labatut, Armand Joulin, Piotr Bojanowski [[`Paper`](https://arxiv.org/abs/2304.07193)] [[`Blog`](https://ai.facebook.com/blog/dino-v2-computer-vision-self-supervised-learning/)] [[`Demo`](https://dinov2.metademolab.com)] [[`BibTeX`](#citing-dinov2)] PyTorch implementation and pretrained models for DINOv2. For details, see the paper: **DINOv2: Learning Robust Visual Features without Supervision**. DINOv2 models produce high-performance visual features that can be directly employed with classifiers as simple as linear layers on a variety of computer vision tasks; these visual features are robust and perform well across domains without any requirement for fine-tuning. The models were pretrained on a dataset of 142 M images without using any labels or annotations. https://user-images.githubusercontent.com/60359573/230078733-5faffa19-e6ce-4c55-9200-62dd76f8236a.mp4 <div align="center"> Visualization of the three first principal components of the patch features of all frames, mapped to RGB values. </div> ## Pretrained models <table> <tr> <th>model</th> <th># of<br />params</th> <th>ImageNet<br />k-NN</th> <th>ImageNet<br />linear</th> <th>download</th> </tr> <tr> <td>ViT-S/14 distilled</td> <td align="right">21 M</td> <td align="right">79.0%</td> <td align="right">81.1%</td> <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_pretrain.pth">backbone only</a></td> </tr> <tr> <td>ViT-B/14 distilled</td> <td align="right">86 M</td> <td align="right">82.1%</td> <td align="right">84.5%</td> <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_pretrain.pth">backbone only</a></td> </tr> <tr> <td>ViT-L/14 distilled</td> <td align="right">300 M</td> <td align="right">83.5%</td> <td align="right">86.3%</td> <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_pretrain.pth">backbone only</a></td> </tr> <tr> <td>ViT-g/14</td> <td align="right">1,100 M</td> <td align="right">83.5%</td> <td align="right">86.5%</td> <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_pretrain.pth">backbone only</a></td> </tr> </table> ### Pretrained models via PyTorch Hub Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install the PyTorch and torchvision dependencies (these are the only required dependencies). Installing both PyTorch and torchvision with CUDA support is strongly recommended. The corresponding model card can be found in the [[`MODEL_CARD.md`](MODEL_CARD.md)] file. ```python import torch dinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14') dinov2_vitb14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14') dinov2_vitl14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14') dinov2_vitg14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14') ``` ## Installation The training and evaluation code requires PyTorch 2.0 and xFormers 0.0.18 as well as a number of other 3rd party packages. To setup all the required dependencies for training and evaluation, please follow the instructions below: *conda* **(Recommended)** - Create and activate a `dinov2` conda environment using the provided environment definition: ```shell conda env create -f conda.yaml conda activate dinov2 ``` *pip* - Use the provided `requirements.txt` to install the dependencies: ```shell pip install -r requirements.txt ``` ## Data preparation Expected contents for the ImageNet-1k data folder: - `<root>/test/ILSVRC2012_test_00000001.JPEG` - `<root>/test/[..]` - `<root>/test/ILSVRC2012_test_00100000.JPEG` - `<root>/train/n01440764/n01440764_10026.JPEG` - `<root>/train/[...]` - `<root>/train/n15075141/n15075141_9993.JPEG` - `<root>/val/n01440764/ILSVRC2012_val_00000293.JPEG` - `<root>/val/[...]` - `<root>/val/n15075141/ILSVRC2012_val_00049174.JPEG` - `<root>/labels.txt` For ImageNet-22k, please adapt the Dataset object accordingly. ## Training ### Fast setup: training DINOv2 ViT-L/16 on ImageNet-1k Run DINOv2 on 4 A100-80GB nodes (32 GPUs) in a SLURM cluster environment with submitit. ```shell python dinov2/run/train/train.py \ --nodes 4 \ --config-file dinov2/configs/train/vitl16_short.yaml \ --output-dir <PATH/TO/OUTPUT/DIR> \ train.dataset_path=ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> ``` Training time is approximately 1 day and the resulting checkpoint should reach 81.6% on k-NN eval and 82.9% on linear eval. The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation. ### Long setup: training DINOv2 ViT-L/14 on ImageNet-22k Run on 12 A100-80GB nodes (96 GPUs) in a SLURM cluster environment with submitit. ``` python dinov2/run/train/train.py \ --nodes 12 \ --config-file dinov2/configs/train/vitl14.yaml \ --output-dir <PATH/TO/OUTPUT/DIR> \ train.dataset_path=ImageNet22k:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> ``` Training time is approximately 3.3 days and the resulting checkpoint should reach 82.0% on k-NN eval and 84.5% on linear eval. The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation. ## Evaluation The training code regularly saves the teacher weights. In order to evaluate the model, run the following evaluation on a single node: ### k-NN classification on ImageNet-1k ``` python dinov2/run/eval/knn.py \ --config-file <PATH/TO/OUTPUT/DIR>/config.yaml \ --pretrained-weights <PATH/TO/OUTPUT/DIR>/eval/training_24999/teacher_checkpoint.pth \ --output-dir <PATH/TO/OUTPUT/DIR>/eval/training_24999/knn \ --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \ --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> ``` ### Logistic regression classification on ImageNet-1k ``` python dinov2/run/eval/log_regression.py \ --config-file <PATH/TO/OUTPUT/DIR>/config.yaml \ --pretrained-weights <PATH/TO/OUTPUT/DIR>/eval/training_24999/teacher_checkpoint.pth \ --output-dir <PATH/TO/OUTPUT/DIR>/eval/training_24999/logreg \ --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \ --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> ``` ### Linear classification with data augmentation on ImageNet-1k ``` python dinov2/run/eval/linear.py \ --config-file <PATH/TO/OUTPUT/DIR>/config.yaml \ --pretrained-weights <PATH/TO/OUTPUT/DIR>/eval/training_24999/teacher_checkpoint.pth \ --output-dir <PATH/TO/OUTPUT/DIR>/eval/training_24999/linear \ --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \ --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> ``` We release the weights from evaluating the different models: <table> <tr> <th>model</th> <th>ImageNet<br />top-1</th> <th>linear evaluation</th> </tr> <tr> <td>ViT-S/14 distilled</td> <td align="right">81.1%</td> <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_linear_head.pth">linear head weights</a></td> </tr> <tr> <td>ViT-B/14 distilled</td> <td align="right">84.5%</td> <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_linear_head.pth">linear head weights</a></td> </tr> <tr> <td>ViT-L/14 distilled</td> <td align="right">86.3%</td> <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_linear_head.pth">linear head weights</a></td> </tr> <tr> <td>ViT-g/14</td> <td align="righ