Automatic and Accurate Classification of Hotel Bathrooms from Images
  with Deep Learning
Hakan Temiz
Hotel bathrooms are one of the most important places in terms of customer
satisfaction, and where the most complaints are reported. To share their
experiences, guests rate hotels, comment, and share images of their positive or
negative ratings. An important part of the room images shared by guests is
related to bathrooms. Guests tend to prove their satisfaction or
dissatisfaction with the bathrooms with images in their comments. These
Positive or negative comments and visuals potentially affect the prospective
guests. In this study, two different versions of a deep learning algorithm were
designed to classify hotel bathrooms as satisfactory (good) or unsatisfactory
(bad, when any defects such as dirtiness, deficiencies, malfunctions were
present) by analyzing images. The best-performer between the two models was
determined as a result of a series of extensive experimental studies. The
models were trained for each of 144 combinations of 5 hyper-parameter sets with
a data set containing more than 11 thousand bathroom images, specially created
for this study. The "HotelBath" data set was shared also with the community
with this study. Four different image sizes were taken into consideration: 128,
256, 512 and 1024 pixels in both directions. The classification performances of
the models were measured with several metrics. Both algorithms showed very
attractive performances even with many combinations of hyper-parameters. They
can classify bathroom images with very high accuracy. Suh that the top
algorithm achieved an accuracy of 92.4% and an AUC (area under the curve) score
of 0.967. In addition, other metrics also proved the success...