PVPower Implementation

This repository supplements the paper "Deep Learning-based Pipeline for Module Power Prediction from EL Measurements" (arXiv) and can be used in order to verify our experiments. You are free to use this in your own experiments. However, please make sure to cite us correctly.

Training

In order to use this, you need to download the dataset, which is publicly available under doi:10.26165/JUELICH-DATA/TVWUUP. Please make sure to place it in the data subfolder, such that the data.csv is available as data/data.csv.

We provide a common entrypoint train.py that can be used to conduct most of the experiments reported in the paper. The default hyperparameters are set such that they correspond to the ones reported in the paper. Hence, you can for example train the first fold of the crossvalidation by issuing:

python train.py --cv_fold_id=0 --jobname=cv_fold_0

Please refer to the documentation for further options:

python train.py --help

Inference

We include our trained models as well and you may directly use them for inference on new data. Given, you've some sample images in my_data, you may run the model trained in the first fold of the crossvalidation as follows:

python inference.py --checkpoint_path=models_release/crossval/model_fold_0.ckpt --data_path=my_data --target_path=my_data/results

Or you can use the model that calculates the per-cell power loss using class activation maps like this:

python inference.py --checkpoint_path=models_release/cam/model.ckpt --data_path=my_data --target_path=my_data/results

For more options, please refer to

python inference.py --help

References

In case you use this code for your own scientific work, please cite the following arXiv preprint of our paper:

Hoffmann, M., Buerhop-Lutz, C., Reeb, L., Pickel, T., Winkler, T., Doll, B., Wurfl, T., Peters, I.M., Brabec, C., Maier, A., & Christlein, V. (2020). Deep Learning-based Pipeline for Module Power Prediction from EL Measurements. (arXiv)

BibTeX:

@misc{hoffmann2020deep,
   title={Deep Learning-based Pipeline for Module Power Prediction from EL Measurements}, 
   author={Mathis Hoffmann and Claudia Buerhop-Lutz and Luca Reeb and Tobias Pickel and Thilo Winkler and Bernd Doll and Tobias Würfl and Ian Marius Peters and Christoph Brabec and Andreas Maier and Vincent Christlein},
   year={2020},
   eprint={2009.14712},
   archivePrefix={arXiv},
   primaryClass={cs.CV}
}