MCIndoor20000 is a fully-labeled image dataset for indoor object detection. The dataset is freely and publicly available for any academic, educational, and research purposes. It includes more than 20,000 digital images from three different indoor object categories, i.e. doors, stairs, and hospital signs. To make a comprehensive dataset regarding current challenges in indoor objects modeling, the dataset covers a multiple set of variations in images, such as rotation, intra-class variation plus various noise models.

Related publications:

  • Fereshteh S.Bashiri, Eric LaRose, Peggy Peissig, Ahmad P. Tafti, "MCIndoor20000: A fully-labeled image dataset to advance indoor objects detection", Data in Brief, Volume 17, Pages 71-75, April 2018.