An embarrassingly simple comparison of machine learning algorithms for indoor scene classification

2021-09-25 · Bhanuka Manesha Samarasekara Vitharana Gamage

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.

📄 PDF Abstract BibTeX arXiv:2109.12261

Code (1)

bhanukaManesha/embarrassingly-simple-classifier-comparison 공식 구현 pytorch

Tasks

BIG-bench Machine Learning Classification Scene Classification Scene Recognition

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