This paper has been accepted to IEEE Transactions on Industrial Informatics (IEEE TII).
Paper: IEEE Transactions on Industrial Informatics
This offical implementation of PKD (Pseudo Knowledge Distillation) from On-device Saliency Prediction based on Pseudo Knowledge Distillation by Chakkrit Termritthikun.
This code is based on the implementation of EML-NET-Saliency, SimpleNet, MSI-Net, and EEEA-Net.
- Tested on Ubuntu OS version 20.04.4 LTS
- Tested on Python 3.6.13
- Tested on CUDA 11.6
- Tested on PyTorch 1.10.2 and TorchVision 0.11.3
- Tested on NVIDIA V100 32 GB (four cards)
git clone https://github.com/chakkritte/PKD/
cd PKD
mkdir data
PKD
|__ data
|_ salicon
|_ fixations
|_ saliency
|_ stimuli
|_ mit1003
|_ fixations
|_ saliency
|_ stimuli
|_ cat2000
|_ fixations
|_ saliency
|_ stimuli
|_ pascals
|_ fixations
|_ saliency
|_ stimuli
|_ osie
|_ fixations
|_ saliency
|_ stimuli
conda create -n pkd python=3.6.13
conda activate pkd
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt --no-cache-dir
python main.py --student eeeac2 --teacher ofa595 --dataset salicon --model_val_path model_salicon.pt
python main.py --student eeeac2 --teacher ofa595 --dataset mit1003 --model_val_path model_mit1003.pt
python main.py --student eeeac2 --teacher ofa595 --dataset cat2000 --model_val_path model_cat2000.pt
python main.py --student eeeac2 --teacher ofa595 --dataset pascals --model_val_path model_pascals.pt
python main.py --student eeeac2 --teacher ofa595 --dataset osie --model_val_path model_osie.pt
If you use PKD or any part of this research, please cite our paper:
@ARTICLE{9720080,
author={Umer, Ayaz and Termritthikun, Chakkrit and Qiu, Tie and Leong, Philip Heng Wai and Lee, Ivan},
journal={IEEE Transactions on Industrial Informatics},
title={On-device Saliency Prediction based on Pseudo Knowledge Distillation},
year={2022},
volume={},
number={},
pages={1-1},
doi={10.1109/TII.2022.3153365}}
Apache-2.0 License