Semi-PKD: Semi-supervised Pseudoknowledge Distillation for saliency prediction

This paper has been published to ICT Express.

Paper: ICT Express

This offical implementation of Semi-PKD (Semi-Supervised Pseudoknowledge Distillation) from Semi-PKD: Semi-supervised Pseudoknowledge Distillation for saliency prediction by Chakkrit Termritthikun.

Overview

This repository contains the source code for Semi-PKD, which accompanies the research paper titled Semi-PKD: Semi-supervised Pseudoknowledge Distillation for saliency prediction. The purpose of this repository is to provide transparency and reproducibility of the research results presented in the paper.

This code is based on the implementation of EML-NET-Saliency, SimpleNet, MSI-Net, and EEEA-Net.

Prerequisite for server

• Tested on Ubuntu OS version 22.04 LTS
• Tested on Python 3.11.8
• Tested on CUDA 12.3
• Tested on PyTorch 2.2.1 and TorchVision 0.17.1
• Tested on NVIDIA RTX 4090 24 GB

Cloning source code

git clone https://github.com/chakkritte/Semi-PKD/
cd Semi-PKD
mkdir data

The dataset folder structure:

Semi-PKD
|__ data
    |_ salicon
      |_ fixations
      |_ saliency
      |_ stimuli
    |_ mit1003
      |_ fixations
      |_ saliency
      |_ stimuli
    |_ cat2000
      |_ fixations
      |_ saliency
      |_ stimuli
    |_ pascals
      |_ fixations
      |_ saliency
      |_ stimuli
    |_ osie
      |_ fixations
      |_ saliency
      |_ stimuli

Creating new environments

conda create -n semipkd python=3.11.8
conda activate semipkd
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia

Install Requirements

pip install -r requirements.txt --no-cache-dir

Citation

If you use Semi-PKD or any part of this research, please cite our paper:

@article{TERMRITTHIKUN2024,
  title = {Semi-PKD: Semi-supervised Pseudoknowledge Distillation for saliency prediction},
  journal = {ICT Express},
  year = {2024},
  issn = {2405-9595},
  doi = {https://doi.org/10.1016/j.icte.2024.11.004},
  author = {Chakkrit Termritthikun and Ayaz Umer and Suwichaya Suwanwimolkul and Ivan Lee},
}

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.