See the author's git repository here: https://github.com/Rohit102497/StokesVertexBeam

Taxonomy of hybridly polarized Stokes vortex beams

Paper Link: https://opg.optica.org/oe/fulltext.cfm?uri=oe-32-5-7404&id=546969

Citation

Please consider citing the below paper, if you are using the code provided in this repository.

@article{arora2024taxonomy,
  title={Taxonomy of hybridly polarized Stokes vortex beams},
  author={Arora, Gauri and Butola, Ankit and Rajput, Ruchi and Agarwal, Rohit and Agarwal, Krishna and Horsch, Alexander and Prasad, Dilip K and Senthilkumaran, Paramasivam},
  journal={Optics Express},
  volume={32},
  number={5},
  pages={7404--7416},
  year={2024},
  publisher={Optica Publishing Group}
}

Overview

This repository contains implementation codes of all the deep learning models used in the above paper.

Design

In our paper, we use three below-written experimental designs. For more details, please refer to the paper.

1. Design 1 - Training with only simulation data: We trained and validated all five deep-learning models using simulated data and tested on the experimental data.
2. Design 2 - 10-fold strategy on experimental data: We employed a 10-fold strategy to train a ResNet-18 model.
3. Design 3 - Mix Training: We train the deep-learning models on mix simulated and experimental data for generalized and robust training.

Datasets

The data that support the findings of this study are available from the corresponding author upon reasonable request. All the links below are private. Upon request, we can provide you access to this link.

For information on the datasets, please refer to the paper.

The datasets needs to be stored in the data folder. Download the data from the below links and save inside the data folder.

1. Design 1: https://figshare.com/s/ecb99ffe720944d031e4
2. Design 2: https://figshare.com/s/a21f0f14372a634619c4
3. Design 3: https://figshare.com/s/b50f38c5612477c5af95

Dependencies

Running the code

To run the models, change the control parameters accordingly in the main.py file and run

python Code/main.py --design 1

Control Parameters

• --design: The type of experiment design.
  choices = [1, 2, 3]
  default=3
• --model_name: Model to employ
  choices=["resnet", "alexnet", "vgg", "squeezenet", "densenet", "inception"]
  default="densenet"
• --use_pretrained: Use Pretrained model on ImageNet
  default="True"
• --feature_extract: Flag for feature extracting. When False, we finetune the whole model otherwise we only update the reshaped layer params
  default="False"
• --batch_size: Batch size for training (change depending on how much memory you have).
• --num_epochs: Number of epochs to train for.
• --lr: Learning rate of the model.
• --momentum: momentum of the model.
• --pretrain_simulated: If design is 2, then this is a must variable. It is true in case the model need to be pretrain on simulated data otherwise False.

Note: If design = 2, then make sure to assign pretrain_simulated variable

Results

The results are saved in the results folder in .data format. Please read them to get your prediction results.

AnalyseResult folder contains codes to analyse results. Provide correct result paths to run the code.