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Data Brief. 2018 Jan 3;17:71-75. doi: 10.1016/j.dib.2017.12.047. eCollection 2018 Apr.

MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection.

Author information

1
Department of Electrical Engineering, University of Wisconsin-Milwaukee, WI, USA.
2
Biomedical Informatics Research Center, Marshfield Clinic Research Institute, WI, USA.

Abstract

A fully-labeled image dataset provides a unique resource for reproducible research inquiries and data analyses in several computational fields, such as computer vision, machine learning and deep learning machine intelligence. With the present contribution, a large-scale fully-labeled image dataset is provided, and made publicly and freely available to the research community. The current dataset entitled MCIndoor20000 includes more than 20,000 digital images from three different indoor object categories, including doors, stairs, and hospital signs. To make a comprehensive dataset addressing current challenges that exist in indoor objects modeling, we cover a multiple set of variations in images, such as rotation, intra-class variation plus various noise models. The current dataset is freely and publicly available at https://github.com/bircatmcri/MCIndoor20000.

KEYWORDS:

Deep learning; Image classification; Image dataset; Indoor objects; Large-scale dataset; Supervised learning

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