A report (report.pdf) is added to this github page in which each step is clearly explained and reviewed.

In this study, a Catalan Architecture image classification model was developed using both deep learning and non-deep learning methods. The multiclass classification problem involves the eight most frequently occuring styles in Catalunya. The deep learning approach consideres fine tuning a convolutional neural network. The non-deep learning approach encompasses extracting features from the images and training traditional machine learning models on the extracted features. The results showed that the deep learning methods outperformed the non-deep learning ones. However, the final model shows room of improvement, with as main limiting factors the data quality and computational costs.

1. On a Google Drive Account, create an empty directory in which all the Colab Files should be placed. At the top of each notebook, the 'project_dir' is requested in which you should specifiy the location to this Directory.
2. From here on all chunks of all notebooks should be ran in the order that is specified (in numerical order by fe. running '1_1_... ' before '1_2_...'). Each of the notebooks has the necessary instructions included in between chunks. The main functions and possible relevant details are listed for each of the notebooks below:
   • '1_0_data_scraper.ipynb' is used the necessary empty data folder and then scrape and download in images from http://invarquit.cultura.gencat.cat/Cerca/. Note that this will take a long while to download.
   • '1_1_train_test_split.ipynb' is to create the necessary file structure used later for model training (keras requires specific formats in order to use 'flow_from_directory'-functions.
   • '2_1_feature_extraction_cnn.ipynb' is used to extract features using pre-trained convolutional neural networks (cnns). The necessary file structure to save the extracted features in is created too.
   • '2_2_feature_extraction_no_cnn.ipynb' is similar to the previous notebook, except for using a non-deep-learning method called SIFT.
   • '3_1_SVM_training.ipynb' consider all methods used for feature_extr + SVM training.
   • '3_2_MLP_training.ipynb' considers all methods used for feature_extr + MLP training.
   • '3_3_TF_training.ipynb' considers training of the Transfer Learning Model including the performance evaluation on the test set of the best model.
   • '4_1_training_performance_reports.ipynb' is a function and routine to quickly summarize the performance of all model training of Neural Network based methods (so MLPs or TF)