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• Edit the file utils/params.py so that params['root'] points to the directory where you have the dataset TerrassaBuildings900 and where you would like to store all the intermediate files.
• Run utils/params.py. Notice that this will create directories as well, so make sure you did the previous step !
• Run build_database.py to generate text files with image IDs for both the training and validation sets.
• Run get_features.py to generate random features for both training and validation sets and store them independently in dictionaries.
• Run classify.py and rank.py to generate results
• Run eval_classification.py and eval_ranking.py to evaluate the results. For a better analysis of the results, take a look at notebooks/gdsa_s4.ipynb.