This project was developed for the course of Artificial Neural Networks and Deep Learning for the MSc. in Mathematical Engineering at Politecnico di Milano, A.Y. 2022/2023.
.
├── README.md
├── misc
│ ├── accuracy_results.csv
│ ├── confusion.pdf
│ ├── ensemble_vgg16-resnet50.png
│ ├── single_augmentation.jpg
│ ├── supernet.pdf
│ └── ypred.npy
├── models
├── notebooks
│ ├── final_model.ipynb
│ ├── keras_tuner.ipynb
│ ├── metrics.ipynb
│ ├── supernet_choice.ipynb
│ └── supernet_histories
├── report
│ ├── bibliography.bib
│ ├── report.pdf
│ └── report.tex
├── requirements.txt
└── training_data_final
final_model.ipynb
is the main file that performs training and fine-tuning, and saves the models. Its decisions are partially based on the results fromsupernet_choice.ipynb
andkeras_tuner.ipynb
. Finally, these models are evaluated inmetrics.ipynb
.keras_tuner.ipynb
provides the implementation of the Keras tuner code to tune the hyperparameters of the models.metrics.ipynb
contains the code to evaluate the models and provide accuracies and F1-scores.supernet_choice.ipynb
performs a pilot run of transfer learning testing different pretrained models.accuracy_results.csv
contains a table of the accuracies of some of the models that we trained during the competition, it's included for the sake of completeness and as appendix in case the report explanations wouldn't be clear enough.
- Paolo Botta (@ploki99)
- Teo Bucci (@teobucci)
- Silvia Caresana (@silviacaresana)
Check out the final report.pdf
.