Knowledge Distillation with the Reused Teacher Classifier (CVPR-2022) https://arxiv.org/abs/2203.14001

This repository aims to provide a compact and easy-to-use implementation of several representative knowledge distillation approaches on standard image classification tasks (e.g., CIFAR100, ImageNet).

• Generally, these KD approaches include a classification loss, a logit-level distillation loss, and an additional feature distillation loss. For fair comparison and ease of tuning, we fix the hyper-parameters for the first two loss terms as one throughout all experiments. (--cls 1 --div 1)

• The following approaches are currently supported by this toolbox, covering vanilla KD, feature-map distillation/feature-embedding distillation, instance-level distillation/pairwise-level distillation:

  • Vanilla KD, FitNet [ICLR-2015], AT [ICLR-2017], SP [CVPR-2019], VID [CVPR-2019]
  • CRD [ICLR-2020], SRRL [ICLR-2021], SemCKD [AAAI-2021]
  • KR [CVPR-2021]
  • SimKD [CVPR-2022]

• This toolbox is built on a open-source benchmark and our previous repository. The implementation of more KD approaches can be found there.

• Computing Infrastructure:

  • We use one NVIDIA GeForce RTX 2080Ti GPU for CIFAR-100 experiments. The PyTorch version is 1.0. We use four NVIDIA A40 GPUs for ImageNet experiments. The PyTorch version is 1.10.
  • As for ImageNet, we use DALI for data loading and pre-processing.

• The current codes have been reorganized and we have not tested them thoroughly. If you have any questions, please contact us without hesitation.

• Please put the CIFAR-100 and ImageNet dataset in the ../data/.

# CIFAR-100
python train_teacher.py --batch_size 64 --epochs 240 --dataset cifar100 --model resnet32x4 --learning_rate 0.05 --lr_decay_epochs 150,180,210 --weight_decay 5e-4 --trial 0 --gpu_id 0

# ImageNet
python train_teacher.py --batch_size 256 --epochs 120 --dataset imagenet --model ResNet18 --learning_rate 0.1 --lr_decay_epochs 30,60,90 --weight_decay 1e-4 --num_workers 32 --gpu_id 0,1,2,3 --dist-url tcp:https://127.0.0.1:23333 --multiprocessing-distributed --dali gpu --trial 0 

The pretrained teacher models used in our paper are provided in this link [GoogleDrive].

# CIFAR-100
python train_student.py --path_t ./save/teachers/models/resnet32x4_vanilla/resnet32x4_best.pth --distill simkd --model_s resnet8x4 -c 0 -d 0 -b 1 --trial 0

# ImageNet
python train_student.py --path-t './save/teachers/models/ResNet50_vanilla/ResNet50_best.pth' --batch_size 256 --epochs 120 --dataset imagenet --model_s ResNet18 --distill simkd -c 0 -d 0 -b 1 --learning_rate 0.1 --lr_decay_epochs 30,60,90 --weight_decay 1e-4 --num_workers 32 --gpu_id 0,1,2,3 --dist-url tcp:https://127.0.0.1:23444 --multiprocessing-distributed --dali gpu --trial 0 

More scripts are provided in ./scripts

If you find this repository useful, please consider citing the following paper:

@inproceedings{chen2022simkd,
  title={Knowledge Distillation with the Reused Teacher Classifier},
  author={Chen, Defang and Mei, Jian-Ping and Zhang, Hailin and Wang, Can and Feng, Yan and Chen, Chun},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11933--11942},
  year={2022}
}

@inproceedings{chen2021cross,
  author    = {Defang Chen and Jian{-}Ping Mei and Yuan Zhang and Can Wang and Zhe Wang and Yan Feng and Chun Chen},
  title     = {Cross-Layer Distillation with Semantic Calibration},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  pages     = {7028--7036},
  year      = {2021},
}