Knowledge Condensation Distillation (ECCV 2022)(Link)

A Pytorch Implementation of ''Knowledge Condensation Distillation'' (Continuously being organized).

Introduction

In this project, we use Ubuntu 16.04.5, Python 3.7, Pytorch 1.9.1 and CUDA 10.2.

Running

Teacher Training

An example of teacher training is:

python train_teacher.py --model resnet32x4

Student Training

Fetch the pretrained teacher models by:

sh scripts/fetch_pretrained_teachers.sh

which will download and save the models to save/models

An example of student training by the proposed KCD is:

python super_train_student.py --epochs 240 --path_t ./save/models/resnet32x4_vanilla/ckpt_epoch_240.pth \
--distill kd --model_s resnet8x4 -r 0.1 -a 0.9 -b 0 --trial 1 --warmup 0 --purification 40 --threshold 0.7 --version v3

All the commands can be found in folder scripts.

Citation

If you find this repository useful, please consider citing our paper, thanks.

@article{li2022knowledge,
  title={Knowledge Condensation Distillation},
  author={Li, Chenxin and Lin, Mingbao and Ding, Zhiyuan and Lin, Nie and Zhuang, Yihong and Huang, Yue and Ding, Xinghao and Cao, Liujuan},
  journal={arXiv preprint arXiv:2207.05409},
  year={2022}
}

Acknowledgement

Some of our implementation is borrowed from CRD