PyTorch implementation and pretrained models for DINO. For details, see Emerging Properties in Self-Supervised Vision Transformers.
[blogpost] [arXiv] [Yannic Kilcher's video]
You can choose to download only the weights of the pretrained backbone used for downstream tasks, or the full checkpoint which contains backbone and projection head weights for both student and teacher networks. We also provide the backbone in onnx format, as well as detailed arguments and training/evaluation logs. Note that DeiT-S and ViT-S names refer exactly to the same architecture.
| arch | params | k-nn | linear | download | |||||
|---|---|---|---|---|---|---|---|---|---|
| ViT-S/16 | 21M | 74.5% | 77.0% | backbone only | full ckpt | onnx | args | logs | eval logs |
| ViT-S/8 | 21M | 78.3% | 79.7% | backbone only | full ckpt | onnx | args | logs | eval logs |
| ViT-B/16 | 85M | 76.1% | 78.2% | backbone only | full ckpt | onnx | args | logs | eval logs |
| ViT-B/8 | 85M | 77.4% | 80.1% | backbone only | full ckpt | onnx | args | logs | eval logs |
| ResNet-50 | 23M | 67.5% | 75.3% | backbone only | full ckpt | onnx | args | logs | eval logs |
We also release XCiT models ([arXiv] [code]) trained with DINO:
| arch | params | k-nn | linear | download | ||||
|---|---|---|---|---|---|---|---|---|
| xcit_small_12_p16 | 26M | 76.0% | 77.8% | backbone only | full ckpt | args | logs | eval |
| xcit_small_12_p8 | 26M | 77.1% | 79.2% | backbone only | full ckpt | args | logs | eval |
| xcit_medium_24_p16 | 84M | 76.4% | 78.8% | backbone only | full ckpt | args | logs | eval |
| xcit_medium_24_p8 | 84M | 77.9% | 80.3% | backbone only | full ckpt | args | logs | eval |
import torch
vits16 = torch.hub.load('facebookresearch/dino:main', 'dino_vits16')
vits8 = torch.hub.load('facebookresearch/dino:main', 'dino_vits8')
vitb16 = torch.hub.load('facebookresearch/dino:main', 'dino_vitb16')
vitb8 = torch.hub.load('facebookresearch/dino:main', 'dino_vitb8')
xcit_small_12_p16 = torch.hub.load('facebookresearch/dino:main', 'dino_xcit_small_12_p16')
xcit_small_12_p8 = torch.hub.load('facebookresearch/dino:main', 'dino_xcit_small_12_p8')
xcit_medium_24_p16 = torch.hub.load('facebookresearch/dino:main', 'dino_xcit_medium_24_p16')
xcit_medium_24_p8 = torch.hub.load('facebookresearch/dino:main', 'dino_xcit_medium_24_p8')
resnet50 = torch.hub.load('facebookresearch/dino:main', 'dino_resnet50')Please install PyTorch and download the ImageNet dataset. This codebase has been developed with python version 3.6, PyTorch version 1.7.1, CUDA 11.0 and torchvision 0.8.2. The exact arguments to reproduce the models presented in our paper can be found in the args column of the pretrained models section. For a glimpse at the full documentation of DINO training please run:
python main_dino.py --help
Run DINO with ViT-small network on a single node with 8 GPUs for 100 epochs with the following command. Training time is 1.75 day and the resulting checkpoint should reach 69.3% on k-NN eval and 74.0% on linear eval. We provide training and linear evaluation logs (with batch size 256 at evaluation time) for this run to help reproducibility.
python -m torch.distributed.launch --nproc_per_node=8 main_dino.py --arch vit_small --data_path /path/to/imagenet/train --output_dir /path/to/saving_dir
We use Slurm and submitit (pip install submitit). To train on 2 nodes with 8 GPUs each (total 16 GPUs):
python run_with_submitit.py --nodes 2 --ngpus 8 --arch vit_small --data_path /path/to/imagenet/train --output_dir /path/to/saving_dir
DINO with ViT-base network.
python run_with_submitit.py --nodes 2 --ngpus 8 --use_volta32 --arch vit_base --data_path /path/to/imagenet/train --output_dir /path/to/saving_dir
You can improve the performance of the vanilla run by:
- training for more epochs:
--epochs 300, - increasing the teacher temperature:
--teacher_temp 0.07 --warmup_teacher_temp_epochs 30. - removing last layer normalization (only safe with
--arch vit_small):--norm_last_layer false,
Full command.
python run_with_submitit.py --arch vit_small --epochs 300 --teacher_temp 0.07 --warmup_teacher_temp_epochs 30 --norm_last_layer false --data_path /path/to/imagenet/train --output_dir /path/to/saving_dir
The resulting pretrained model should reach 73.3% on k-NN eval and 76.0% on linear eval. Training time is 2.6 days with 16 GPUs. We provide training and linear evaluation logs (with batch size 256 at evaluation time) for this run to help reproducibility.
This code also works for training DINO on convolutional networks, like ResNet-50 for example. We highly recommend to adapt some optimization arguments in this case. For example following is a command to train DINO on ResNet-50 on a single node with 8 GPUs for 100 epochs. We provide training logs and final checkpoint for this run.
python -m torch.distributed.launch --nproc_per_node=8 main_dino.py --arch resnet50 --optimizer sgd --lr 0.03 --weight_decay 1e-4 --weight_decay_end 1e-4 --global_crops_scale 0.14 1 --local_crops_scale 0.05 0.14 --data_path /path/to/imagenet/train --output_dir /path/to/saving_dir
You can look at the self-attention of the [CLS] token on the different heads of the last layer by running:
python visualize_attention.py
You can generate videos like the one on the blog post with video_generation.py.
example.mp4
Extract frames from input video and generate attention video:
python video_generation.py --pretrained_weights dino_deitsmall8_pretrain.pth \
--input_path input/video.mp4 \
--output_path output/ \
--fps 25
Use folder of frames already extracted and generate attention video:
python video_generation.py --pretrained_weights dino_deitsmall8_pretrain.pth \
--input_path output/frames/ \
--output_path output/ \
--resize 256 \
Only generate video from folder of attention maps images:
python video_generation.py --input_path output/attention \
--output_path output/ \
--video_only \
--video_format avi
To evaluate a simple k-NN classifier with a single GPU on a pre-trained model, run:
python -m torch.distributed.launch --nproc_per_node=1 eval_knn.py --data_path /path/to/imagenet
If you choose not to specify --pretrained_weights, then DINO reference weights are used by default. If you want instead to evaluate checkpoints from a run of your own, you can run for example:
python -m torch.distributed.launch --nproc_per_node=1 eval_knn.py --pretrained_weights /path/to/checkpoint.pth --checkpoint_key teacher --data_path /path/to/imagenet
To train a supervised linear classifier on frozen weights on a single node with 8 gpus, run:
python -m torch.distributed.launch --nproc_per_node=8 eval_linear.py --data_path /path/to/imagenet
We release the logs and weights from evaluating the different models:
| arch | top-1 ImageNet | linear evaluation | |
|---|---|---|---|
| ViT-S/16 | 77.0% | linear weights | logs |
| ViT-S/8 | 79.7% | linear weights | logs |
| ViT-B/16 | 78.2% | linear weights | logs |
| ViT-B/8 | 80.1% | linear weights | logs |
| xcit_small_12_p16 | 77.8% | linear weights | logs |
| xcit_small_12_p8 | 79.2% | linear weights | logs |
| xcit_medium_24_p16 | 78.8% | linear weights | logs |
| xcit_medium_24_p8 | 80.3% | linear weights | logs |
| ResNet-50 | 75.3% | linear weights | logs |
You can check the performance of the pretrained weights on ImageNet validation set by running the following command lines:
python eval_linear.py --evaluate --arch vit_small --patch_size 16 --data_path /path/to/imagenet/train
python eval_linear.py --evaluate --arch vit_small --patch_size 8 --data_path /path/to/imagenet/train
python eval_linear.py --evaluate --arch vit_base --patch_size 16 --n_last_blocks 1 --avgpool_patchtokens true --data_path /path/to/imagenet/train
python eval_linear.py --evaluate --arch vit_base --patch_size 8 --n_last_blocks 1 --avgpool_patchtokens true --data_path /path/to/imagenet/train
python eval_linear.py --evaluate --arch resnet50 --data_path /path/to/imagenet/train
Please verify that you're using pytorch version 1.7.1 since we are not able to reproduce the results with most recent pytorch 1.8.1 at the moment.
Step 1: Prepare DAVIS 2017 data
cd $HOME
git clone https://github.com/davisvideochallenge/davis-2017 && cd davis-2017
./data/get_davis.sh
Step 2: Video object segmentation
python eval_video_segmentation.py --data_path $HOME/davis-2017/DAVIS/ --output_dir /path/to/saving_dir
Step 3: Evaluate the obtained segmentation
git clone https://github.com/davisvideochallenge/davis2017-evaluation $HOME/davis2017-evaluation
python $HOME/davis2017-evaluation/evaluation_method.py --task semi-supervised --results_path /path/to/saving_dir --davis_path $HOME/davis-2017/DAVIS/
Step 1: Prepare revisited Oxford and Paris by following this repo.
Step 2: Image retrieval (if you do not specify weights with --pretrained_weights then by default DINO weights pretrained on Google Landmark v2 dataset will be used).
Paris:
python -m torch.distributed.launch --use_env --nproc_per_node=1 eval_image_retrieval.py --imsize 512 --multiscale 1 --data_path /path/to/revisited_paris_oxford/ --dataset rparis6k
Oxford:
python -m torch.distributed.launch --use_env --nproc_per_node=1 eval_image_retrieval.py --imsize 224 --multiscale 0 --data_path /path/to/revisited_paris_oxford/ --dataset roxford5k
Step 1: Prepare Copydays dataset.
Step 2 (opt): Prepare a set of image distractors and a set of images on which to learn the whitening operator. In our paper, we use 10k random images from YFCC100M as distractors and 20k random images from YFCC100M (different from the distractors) for computing the whitening operation.
Step 3: Run copy detection:
python -m torch.distributed.launch --use_env --nproc_per_node=1 eval_copy_detection.py --data_path /path/to/copydays/ --whitening_path /path/to/whitening_data/ --distractors_path /path/to/distractors/
We report result on the strong subset. For example in the stdout from the command above we get: eval on strong mAP=0.858.
This repository is released under the Apache 2.0 license as found in the LICENSE file.
If you find this repository useful, please consider giving a star ⭐ and citation 🦖:
@inproceedings{caron2021emerging,
title={Emerging Properties in Self-Supervised Vision Transformers},
author={Caron, Mathilde and Touvron, Hugo and Misra, Ishan and J\'egou, Herv\'e and Mairal, Julien and Bojanowski, Piotr and Joulin, Armand},
booktitle={Proceedings of the International Conference on Computer Vision (ICCV)},
year={2021}
}
Run swin small on phantom:
python -m torch.distributed.launch --nproc_per_node=4 main_dino.py --arch swin_small --data_path /home/chaimb/ILSVRC/Data/CLS-LOC/ --output_dir /home/almogdavid/workspace/dino/trained_models/swin_small_dino_loss --exp_name training_swin_small --batch_size_per_gpu 85
Run swin tiny on juliet
python3 -m torch.distributed.launch --nproc_per_node=8 main_dino.py --arch swin_tiny --data_path /mnt/mount/evgeniizh/ILSVRC/Data/CLS-LOC/ --output_dir /home/almogdavid/workspace/dino/trained_models/swin_small_dino_loss --exp_name training_swin_tiny --batch_size_per_gpu 48