As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (CURE-OR) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In CURE

Dataset Files

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Documentation: 
[1] Dogancan Temel, Jinsol Lee, Ghassan AlRegib, "CURE-OR: Challenging Unreal and Real Environment for Object Recognition", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/h4fr-h268. Accessed: Jan. 22, 2025.
@data{h4fr-h268-19,
doi = {10.21227/h4fr-h268},
url = {http://dx.doi.org/10.21227/h4fr-h268},
author = {Dogancan Temel; Jinsol Lee; Ghassan AlRegib },
publisher = {IEEE Dataport},
title = {CURE-OR: Challenging Unreal and Real Environment for Object Recognition},
year = {2019} }
TY - DATA
T1 - CURE-OR: Challenging Unreal and Real Environment for Object Recognition
AU - Dogancan Temel; Jinsol Lee; Ghassan AlRegib
PY - 2019
PB - IEEE Dataport
UR - 10.21227/h4fr-h268
ER -
Dogancan Temel, Jinsol Lee, Ghassan AlRegib. (2019). CURE-OR: Challenging Unreal and Real Environment for Object Recognition. IEEE Dataport. http://dx.doi.org/10.21227/h4fr-h268
Dogancan Temel, Jinsol Lee, Ghassan AlRegib, 2019. CURE-OR: Challenging Unreal and Real Environment for Object Recognition. Available at: http://dx.doi.org/10.21227/h4fr-h268.
Dogancan Temel, Jinsol Lee, Ghassan AlRegib. (2019). "CURE-OR: Challenging Unreal and Real Environment for Object Recognition." Web.
1. Dogancan Temel, Jinsol Lee, Ghassan AlRegib. CURE-OR: Challenging Unreal and Real Environment for Object Recognition [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/h4fr-h268
Dogancan Temel, Jinsol Lee, Ghassan AlRegib. "CURE-OR: Challenging Unreal and Real Environment for Object Recognition." doi: 10.21227/h4fr-h268