CURE-OR: Challenging Unreal and Real Environment for Object Recognition

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-OR dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture, captured with multiple devices in different setups. The majority of images in the dataset were acquired with smartphones and tested with off-the-shelf applications to benchmark the recognition performance of devices and applications that are used in our daily lives. Please refer to our GitHub page for code, papers, and more information.