This repository provides supplementary material for the following publications (would appreciate a citation of our work if you find the tool useful)
M. Barker, M. Willans, D-S. Pham, A. Krishna, M. Hackett. Explainable Detection of Microplastics Using Transformer Neural Networks, in Proceedings of the Australasian Joint Conference on Artificial Intelligence (AJCAI), Perth December 2022.
Available in this repository:
- Reflectance micro-FTIR spectral data for standard and marine polymers
- Python code that implements the model
This code runs using pytorch-gpu which you can download from pytorch.org/.
Assuming pytorch is installed you can run the program with:
python3 run.py
All of the variables that control the models hyperparameters are in run.py. I last tested this code on a GTX 1080ti, so you may be able to change certain hyperparameters.
Currently the dataloaders load the micro-FTIR datasets that contain polyethylene and polypropylene in the no_fp directory. There is however an alternative option to train the model with filter paper samples. This can be done by using the csv files in the with_fp directory. Otherwise, you can extract the entire datasets in the marine_polymers.csv and standard_polymers.csv in the data directory.
You will need to change the global variable LABEL_DICT if you wish to use the filter paper samples. This can be done by changing:
LABEL_DICT = {'PP': 0,'PE':1}to
LABEL_DICT = {'PP': 0,'PE':1,'FP':2}
