bellymonster / weighted-soft-label-distillation

Rethinking soft labels for knowledge distillation: a bias-variance tradeoff perspective

Accepted by ICLR 2021

This is the offical PyTorch implementation of paper Rethinking soft labels for knowledge distillation: a bias-variance tradeoff perspective.

Requirements

• Python >= 3.6
• PyTorch >= 1.0.1

ImageNet Training

The code is used for training Imagenet. Our pre-trained teacher models are Pytorch official models. By default, we pack the ImageNet data as the lmdb file for faster IO. The lmdb files can be made as follows.

1. Generate the list of the image data. python dataset/mk_img_list.py --image_path 'the path of your image data' --output_path 'the path to output the list file'

2. Use the image list obtained above to make the lmdb file. python dataset/img2lmdb.py --image_path 'the path of your image data' --list_path 'the path of your image list' --output_path 'the path to output the lmdb file' --split 'split folder (train/val)'

• train_with_distillation.py: train the model with our distillation method
• imagenet_train_cfg.py: all dataset and hyperparameter settings
• knowledge_distiller.py: our weighted soft label distillation loss

Results

ImageNet

• ResNet 18

Network	Method	mIOU
ResNet 34	Teacher	73.31
ResNet 18	Original	69.75
ResNet 18	Proposed	72.04

• MobileNetV1

Network	Method	mIOU
ResNet 50	Teacher	76.16
MobileNetV1	Original	68.87
MobileNetV1	Proposed	71.52

Acknowledgments

In this code we refer to the following implementations: Overhaul and DenseNAS. Great thanks to them.

Reference

If you find this repo useful, please consider citing:

@inproceedings{zhou2021wsl,
  title={Rethinking soft labels for knowledge distillation: a bias-variance tradeoff perspective},
  author={Helong, Zhou and Liangchen, Song and Jiajie, Chen and Ye, Zhou and Guoli, Wang and Junsong, Yuan and Qian Zhang},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year={2021}
}