# Self-Supervised Vision Transformers with DINO
PyTorch implementation and pretrained models for DINO. For details, see **Emerging Properties in Self-Supervised Vision Transformers**.
[[`blogpost`](https://ai.facebook.com/blog/dino-paws-computer-vision-with-self-supervised-transformers-and-10x-more-efficient-training)] [[`arXiv`](https://arxiv.org/abs/2104.14294)] [[`Yannic Kilcher's video`](https://www.youtube.com/watch?v=h3ij3F3cPIk)]
## Pretrained models
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.
We also release XCiT models ([[`arXiv`](https://arxiv.org/abs/2106.09681)] [[`code`](https://github.com/facebookresearch/xcit)]) trained with DINO:
### Pretrained models on PyTorch Hub
```python
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')
```
## Training
### Documentation
Please install [PyTorch](https://pytorch.org/) and download the [ImageNet](https://imagenet.stanford.edu/) 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](https://github.com/facebookresearch/dino#pretrained-models). For a glimpse at the full documentation of DINO training please run:
```
python main_dino.py --help
```
### Vanilla DINO training :sauropod:
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](https://dl.fbaipublicfiles.com/dino/example_runs_logs/dino_vanilla_deitsmall16_log.txt) and [linear evaluation](https://dl.fbaipublicfiles.com/dino/example_runs_logs/dino_vanilla_deitsmall16_eval.txt) 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
```
### Multi-node training
We use Slurm and [submitit](https://github.com/facebookincubator/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
```
### Boosting DINO performance :t-rex:
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](https://dl.fbaipublicfiles.com/dino/example_runs_logs/dino_boost_deitsmall16_log.txt) and [linear evaluation](https://dl.fbaipublicfiles.com/dino/example_runs_logs/dino_boost_deitsmall16_eval.txt) logs (with batch size 256 at evaluation time) for this run to help reproducibility.
### ResNet-50 and other convnets trainings
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](https://dl.fbaipublicfiles.com/dino/example_runs_logs/dino_rn50_log.txt) logs for this run.
```
python -m torch.distributed.launch --nproc_per_node=8 main_dino.py --arch resnet50 --optimizer sgd --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
```
## Self-attention visualization
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
```
## Self-attention video generation
You can generate videos like the one on the blog post with `video_generation.py`.
https://user-images.githubusercontent.com/46140458/116817761-47885e80-ab68-11eb-9975-d61d5a919e13.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
```
## Evaluation: k-NN classification on ImageNet
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
```
## Evaluation: Linear classification on 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:
## Evaluation: DAVIS 2017 Video object segmentation
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/
```
## Evaluation: Image Retrieval on revisited Oxford and Paris
Step 1: Prepare revisited Oxford and Paris by following [this repo](https://github.com/filipradenovic/revisitop).
Step 2: Image retrieval (if you do not specify weights with `--pretrained_weights` then by default [DINO weights pretrained on Google Landmark v2 dataset](https://dl.fbaipublicfiles.com/dino/dino_vitsmall16_googlelandmark_pretrain/dino_vitsmall16_googlelandmark_pretrain.pth) 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
```
## Evaluation: Copy detection on Copydays
Step 1: Prepare [Copydays dataset](https://lear.inrialpes.fr/~jegou/data.php#copydays).
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`.
## License
This repository is released under the Apache 2.0 license as found in the [LICENSE](LICENSE) file.
## Citation
If you find this repository useful, please consider giving a star :star: and citation :t-rex::
```
@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}
}
```
