2.3. Soft-story Building Classifier

The Soft-story Building Classifier is validated here.

The trained classifier is tested on a ground truth dataset that can be downloaded here. Accuracy is 83.8%. Precision is 83.8%. Recall is 83.8%. F1 is 83.8%.

Run the following python script to test on this dataset.

# download the testing dataset

import wget
import zipfile
with zipfile.ZipFile('softstory-buildings-val.zip', 'r') as zip_ref:

# prepare the image lists

import shutil
import os
import pandas as pd
from glob import glob

softstoryList = glob('softstory-buildings-val/softstory/*.png')
othersList = glob('softstory-buildings-val/others/*.png')

# define the paths of images in a list

# import the module
from brails.modules import SoftstoryClassifier

# initialize the classifier
model = SoftstoryClassifier()

# use the model to predict
predictions = model.predict(imgs)

prediction = predictions['prediction'].values.tolist()
label = ['softstory']*len(softstoryList) + ['others']*len(othersList)

# Plot results
class_names = ['softstory','others']
from brails.utils.plotUtils import plot_confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score,accuracy_score

print(' Accuracy is   : {}, Random guess is 0.5'.format(accuracy_score(prediction,label)))
cnf_matrix = confusion_matrix(prediction,label)
plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion matrix',normalize=True,xlabel='Labels',ylabel='Predictions')

The confusion matrix tested on this dataset is shown in Fig. 2.3.1.

Confusion matrix soft-story

Fig. 2.3.1 Confusion matrix - Soft-story building classifier