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SiT: Self-supervised vIsion Transformer

This repository contains the official PyTorch self-supervised pretraining, finetuning, and evaluation codes for SiT (Self-supervised image Transformer).

The finetuning strategy is adopted from Deit

Usage

• Create an environment

conda create -n SiT python=3.8

• Activate the environment and install the necessary packages

conda activate SiT

conda install pytorch torchvision torchaudio cudatoolkit=11.0 -c pytorch

pip install -r requirements.txt

Self-supervised pre-training

python -m torch.distributed.launch --nproc_per_node=4 --use_env main.py --batch_size 64 --epochs 801 --data-set 'ImageNet' --output_dir 'checkpoints/SSL/ImageNet'

Self-supervised pre-trained models using SiT can be downloaded from here

Notes:

1. assign the --dataset_location parameter to the location of the downloaded dataset
2. Set lmbda to high value when pretraining on small datasets, e.g. lmbda=5

If you use this code for a paper, please cite:

@article{atito2021sit,

  title={SiT: Self-supervised vIsion Transformer},

  author={Atito, Sara and Awais, Muhammad and Kittler, Josef},

  journal={arXiv preprint arXiv:2104.03602},

  year={2021}

}

License

This repository is released under the GNU General Public License.