OKDPH
This repository contains the code for CVPR2023 OKDPH: Generalization Matters: Loss Minima Flattening via Parameter Hybridization for Efficient Online Knowledge Distillation.
Data
3 datasets were used in the paper:
- CIFAR-10
- CIFAR-100
- ImageNet: Downloadable from https://image-net.org/download.php
For downloaded data sets please place them in the 'dataset' folder.
dataset:
-- cifar-10-batches-py
-- cifar-100-python
Requirements
- PyTorch 1.0 or higher
- Python 3.6
Run
cd src
bash OKDPH.sh
For the case of four students:
cd src
python OKDPH.py --omega 0.8 --beta 0.8 --gamma 0.5 --interval 1_epoch \
--model_names resnet32 resnet32 resnet32 resnet32 \
--transes hflip cutout augment auto_aug base \
--log 21_cifar10_okdph_4stu_1ep
Please refer to the bash files for more running commands.
Baselines
cd src
bash baseline.sh