Sel-supervised learning based on Jittor

Getting Started

1. Install Jittor

pip install jittor
python -m jittor.test.test_example
# If your computer contains an Nvidia graphics card, check the cudnn acceleration library
python -m jittor.test.test_cudnn_op

For more information on how to install jittor, you can check here.

2. Install OpenMPI

sudo apt install openmpi-bin openmpi-common libopenmpi-dev

For more information on how to use MPI for Distributed Training, please refer to here.

Supported methods

Method	Documentation	Pytorch Link
SERE, TPAMI, 2023	documentation	https://github.com/MCG-NKU/SERE
HSSL, TPAMI, 2025	documentation	https://github.com/lzyhha/HSSL

Please refer to the above documentations of the specific methods for the training guidance.

Citation

If this work is helpful for your research, please consider citing the following entry:

@article{li2023sere,
  title={SERE: Exploring Feature Self-relation for Self-supervised Transformer},
  author={Zhong-Yu Li and Shanghua Gao and Ming-Ming Cheng},
  journal=TPAMI,
  year={2023}
}

@article{li2025hssl,
  title={Enhancing Representations through Heterogeneous Self-Supervised Learning}, 
  author={Li, Zhong-Yu and Yin, Bo-Wen and Liu, Yongxiang and Liu, Li and Cheng, Ming-Ming},
  journal=TPAMI,
  year={2025}
}

Acknowledgement

This repository is built using the DINO repository, the iBOT repository, and the MAE repository.