  With the emergence of autonomous indoor robots, the computer vision task of
indoor scene recognition has gained the spotlight. Indoor scene recognition is
a challenging problem in computer vision that relies on local and global
features in a scene. This study aims to compare the performance of five machine
learning algorithms on the task of indoor scene classification to identify the
pros and cons of each classifier. It also provides a comparison of low latency
feature extractors versus enormous feature extractors to understand the
performance effects. Finally, a simple MnasNet based indoor classification
system is proposed, which can achieve 72% accuracy at 23 ms latency.
