pytorch-be-your-own-teacher

An pytorch implementation of paper 'Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation', https://arxiv.org/abs/1905.08094

Introduction

We provide code of training ResNet18 and ResNet50 with multiple breanches on CIFAR100 dataset.

Dependencies:

Ubuntu 18.04.5 LTS
Python 3.6.9
PyTorch 1.7.1
torchvision 0.8.2
numpy 1.19.2
tensorboardX 2.1

Note: this is my machine environment, and the other version of software may also works.

Train an ResNet on CIFAR-100:

# resnet18
python train.py train multi_resnet18_kd \
                             --data-dir /PATH/TO/CIFAR100 

# resnet50
python train.py train multi_resnet18_kd \
                             --data-dir /PATH/TO/CIFAR100

Load an ResNet on CIFAR-100 and test it:

# resnet18
python train.py test multi_resnet18_kd \
                             --data-dir /PATH/TO/CIFAR100 \
                             --resume /PATH/TO/checkpoint

# resnet50
python train.py test multi_resnet50_kd \
                             --data-dir /PATH/TO/CIFAR100 \
                             --resume /PATH/TO/checkpoint

Result on Resnet18

As the original paper does not tell us the hyper-parameters, I just use the follow setting. If you find better hyper-parmeters, you could tell me in the issues. Moreover, we do not know the side-branch architecture details. So the accuracy is lower than the original paper. I will fine-tune the hyper-parameters.

21.01.12) At the same settings, with different library version, the performance of last classifier is better than paper result. However, hyperparameter searching is still needed.

alpha = 0.1
temperature = 3
beta = 1e-6

Method	Classifier 1/4	Classifier 2/4	Classifier 3/4	Classifier 4/4
Original	67.85	74.57	78.23	78.64
Ours	66.570	74.690	78.040	78.730

Result on Resnet50

alpha = 0.1
temperature = 3
beta = 1e-6

Method	Classifier 1/4	Classifier 2/4	Classifier 3/4	Classifier 4/4
Original	68.23	74.21	75.23	80.56
Ours	71.840	77.750	80.140	80.640